diff --git a/IDs_mapping.csv b/IDs_mapping.csv
deleted file mode 100644
index 2d752d3..0000000
--- a/IDs_mapping.csv
+++ /dev/null
@@ -1,68 +0,0 @@
-admission_type_id,description
-1,Emergency
-2,Urgent
-3,Elective
-4,Newborn
-5,Not Available
-6,NULL
-7,Trauma Center
-8,Not Mapped
-,
-discharge_disposition_id,description
-1,Discharged to home
-2,Discharged/transferred to another short term hospital
-3,Discharged/transferred to SNF
-4,Discharged/transferred to ICF
-5,Discharged/transferred to another type of inpatient care institution
-6,Discharged/transferred to home with home health service
-7,Left AMA
-8,Discharged/transferred to home under care of Home IV provider
-9,Admitted as an inpatient to this hospital
-10,Neonate discharged to another hospital for neonatal aftercare
-11,Expired
-12,Still patient or expected to return for outpatient services
-13,Hospice / home
-14,Hospice / medical facility
-15,Discharged/transferred within this institution to Medicare approved swing bed
-16,Discharged/transferred/referred another institution for outpatient services
-17,Discharged/transferred/referred to this institution for outpatient services
-18,NULL
-19,"Expired at home. Medicaid only, hospice."
-20,"Expired in a medical facility. Medicaid only, hospice."
-21,"Expired, place unknown. Medicaid only, hospice."
-22,Discharged/transferred to another rehab fac including rehab units of a hospital .
-23,Discharged/transferred to a long term care hospital.
-24,Discharged/transferred to a nursing facility certified under Medicaid but not certified under Medicare.
-25,Not Mapped
-26,Unknown/Invalid
-30,Discharged/transferred to another Type of Health Care Institution not Defined Elsewhere
-27,Discharged/transferred to a federal health care facility.
-28,Discharged/transferred/referred to a psychiatric hospital of psychiatric distinct part unit of a hospital
-29,Discharged/transferred to a Critical Access Hospital (CAH).
-,
-admission_source_id,description
-1, Physician Referral
-2,Clinic Referral
-3,HMO Referral
-4,Transfer from a hospital
-5, Transfer from a Skilled Nursing Facility (SNF)
-6, Transfer from another health care facility
-7, Emergency Room
-8, Court/Law Enforcement
-9, Not Available
-10, Transfer from critial access hospital
-11,Normal Delivery
-12, Premature Delivery
-13, Sick Baby
-14, Extramural Birth
-15,Not Available
-17,NULL
-18, Transfer From Another Home Health Agency
-19,Readmission to Same Home Health Agency
-20, Not Mapped
-21,Unknown/Invalid
-22, Transfer from hospital inpt/same fac reslt in a sep claim
-23, Born inside this hospital
-24, Born outside this hospital
-25, Transfer from Ambulatory Surgery Center
-26,Transfer from Hospice
diff --git a/PointClickCare_LightGBM.ipynb b/PointClickCare_LightGBM.ipynb
index 4d86256..9c29629 100644
--- a/PointClickCare_LightGBM.ipynb
+++ b/PointClickCare_LightGBM.ipynb
@@ -5,8 +5,7 @@
     "colab": {
       "name": "PointClickCare_LightGBM.ipynb",
       "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true
+      "collapsed_sections": []
     },
     "kernelspec": {
       "name": "python3",
@@ -35,7 +34,7 @@
         "from sklearn.model_selection import train_test_split\n",
         "from sklearn.preprocessing import LabelBinarizer\n"
       ],
-      "execution_count": null,
+      "execution_count": 87,
       "outputs": []
     },
     {
@@ -47,7 +46,7 @@
         "# from google.colab import drive\n",
         "# drive.mount('/content/drive')   "
       ],
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": []
     },
     {
@@ -59,7 +58,7 @@
         "# data = pd.read_csv(\"/content/drive/My Drive/PointClickCare/diabetic_data.csv\")\n",
         "data = pd.read_csv(\"/content/sample_data/diabetic_data.csv\")"
       ],
-      "execution_count": null,
+      "execution_count": 88,
       "outputs": []
     },
     {
@@ -70,12 +69,12 @@
           "height": 326
         },
         "id": "w4mzkRYDDSu2",
-        "outputId": "bd6b062d-5877-4295-e9f7-d9cce2d3430e"
+        "outputId": "10765356-d04b-452f-a7e9-06f19085a8f4"
       },
       "source": [
         "data.head()"
       ],
-      "execution_count": null,
+      "execution_count": 89,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -435,7 +434,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 127
+          "execution_count": 89
         }
       ]
     },
@@ -456,7 +455,7 @@
           "height": 1000
         },
         "id": "0icyqWZp8PXm",
-        "outputId": "b33ca7c6-574c-4e40-c4c3-5a057d7592f3"
+        "outputId": "814749fc-4b97-4d6a-bfef-4527e7611a39"
       },
       "source": [
         "import matplotlib\n",
@@ -468,12 +467,12 @@
         "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
         "plt.show()"
       ],
-      "execution_count": null,
+      "execution_count": 34,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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353s0/foxmv7FvDrLDe3eUV88dlut1y9sFacvf3e7Zvxxqkb37qJXZix2c43db9n6LUo5nKF5b/1V0++/TU+/+a86y91x1RjN+eeL+uZ/n9bGn/Zo2XrH+PLHqTco4bVnlPDaM7pp3AiN7HepJ6t/Vs62z7y3cqv6xTfXvPuuUr/45npv1TZJ0mfrd6pDbJQSpo3XxzeN0t8WrldJebnCggL1wsQBmnX3RL1z/XC9sGCdck+UeGx/z1S53a5nF67X29cO1qw7r9ScHSl1nHeSHTHcXeN066UX6JUlmyVJwQEBenBQd/3uirpv1Pnb+H5KuG20Em4b7Rc381e0lXsmafrYvpo+r+7z6NPz1urpsX017x5nW0l2tpVV29Qvvpnm3TtJ/eKbVbQVyXGcX128Uf3bV79+ur1vV704cYD7dqoeeLoPLd19SNvTj+qbqeP1+W1j9OHq7cov9p0+5IjV1uvta6/QrKljNWd7HX1mi7PP3D2hWp/ZcyRHc3cc0Kw7x+qda6/QM9+vU7ndrnYxDZVw+xgl3D5GX906WqFBgRreubUkaU1KhhJ3H1LC7WM0a+o43e5jN1FWtSw5TSnH8zTv7gmafuVlmj6/9ryaJD09f62evrKv5t09QSnH87Q8+bAk6b3V29WvbZzm3T1R/drGVbSVRqEhemLkpbX2PdBm0++HXazvpo3X5zeP0mcbdtf6f+Ft7hpXWkVF6OObRmrmtPG6Z2AP/cUZIwcH2PTBjSOUMHW8vrlznFYkp2lzapZndvYXcNc56NWJ/ZVw+5X69o4rdbyoWPN3HvLI/vy+Qcy+AAAgAElEQVRS5Xa7nl2wTm9fN1Szpo07xbiy13Ec7pmoW/tcoFeWbJLkHFe2p2jW1HF657qhemaBY1yRpMk92uudU9zgdTi3QCv3H1bzhg3cu3P1xF3HSJI+WbdTHZo09Oj+1AdiuNrcNZ9ye58LNPvOMfr61pHakHpEy5znLV/mjnjlpE/W7VKHmMqHFtktS0/MXqNXJvbXt3eOVYuG4ZqZtM8zO+oCd8Unp9pubESo/nPzKCXcMVaf3zJK763arsy8yvn+73ceVINg33levDvG19PFaL5+DnJn//no18OVcPuYagmZ/hTvA2frfP59zNPXhrszs/Xlpt364vYxSpg6Tkv2OH5n8SXemIcLCQxQwtRxSpg6zicTMiXmEXB6no7fOjVppL9PHqRLWzf1+L6eKXf1nzv7dasYO34z9CL1adO0IiFTkj758adq10i+wtPzTbQVh0taN61oL1UTMiXfbSsAANQnv07KNMbcZoz5xxl+Zo4x5ozuhDfG3GOMueXMavez2+ttjBlbX9s7zXe1MMZ8dYr3lhhjvHM3WbN46fgRKeeoZC+X9dMGmQ7VgzHToaesbY6bXaxdG2XaXOB4I6aZdGCn4++F+VJxkdSsjePfh/dLBdVvgvcniTsPalKP9jLGqFerWOWdKFFWXvUEhay8QuUXl6pXq1gZYzSpR3st2nlQktShSZTanUMBbNLhY2oTHaHWUREKDgjQmC5tlLgnrVqZxD2puqpbvCRp1AWttPpAhizLUte4aDWNCJMkdWzSUCfKylVSVi5J6tUiRrHO9851e5avVOGx496uhsclZWarTcMGat2wgYIDbBrToZkS92dUK5O4P0NXdXY8zWhU+zitTj0qy7JkJBWVlavMbldxebmCAmwK96EfCn+x5vHS8Uwp54hj3N3+o0ynGk82zjkqZaVKllX78yk7pZJij1TV3RJ3HdSkns6xtmWs8k6U1j3WlpSqV0vnWNuzvRbtOjnWNqpzrO3arLGaRjp+TO0YG1Vt3PE3iZt3alK/Xo5j1L6V8gqLlZVT+weLXu1bKbZRZJ3b+Pu3SzR1VH+FBJ0D/adFe0f/yXb2n21rZS6o8cTenKNS5qHa/cdeLpWXOf4eGOhYMfMcwVjrkJR+TG2iTsYrNo25oHUd8UqarurmeBrpqM4ttfpAZu14JaZ6vJKwdX/Fips2YxTdwD+eensqiTsPaFKvDs44t6nyik8V55aoV6umjrG3Vwct2nlAkjSgQ8uKJ833ahWrdD9N4k38KUWTendyHIfWcT8T75eoV+s4x3Ho3UmLduyXJEWEBleUKyopk1HtMWVO0l6N7d7RrfvhDu6K/f2VO68NF+48oJZREerYxL8eEkAMd3qJSbs16bLujmPUrqXyioqVlZNfq1yvdi0V63xaaVV9O7dVWHCQJKlnfAtlZPvv/MpJiWs3atLQATLGqPcFHZVbUKjMY9nVyoSFhKhvT8fNf8FBgeravq3Sjx6rta3Zy9Zo7OC+Hql3fTjbPpO465Cu6uFYBf2qKuOLkVRQXCrLslRYWqpGYSEKtNkUH9NQ8Y0dN/Y3jWygmPBQHSs84bkdPkNJh4+pTVRk5XnnwjZKdN7sdlLi7jRd1T1eUvXzToPgQF3SKlYhgQFeqHn9S9x1UJN6tKvSVkqUlV+jreQ7zzkn20qPdhVtInHXQV3V09lWqrQhSfp03U6NvKCNYmo8LOLyds19Pv73dB/acyRHl7aOU6DNpgbBQercNKoiwdMXOPpMRPU+s7t6wlfi7kO6qns7SdKoLq21OiVdlmUpcfchjbmwjYIDA9QqKkJtoiKUdLj6OLs6JUNtoiLUslG4JOnzjbs1tV9XBTv7mS8nxyTuTtWk7if7UBPHtU5+UbUyWflFyi8uU6+WTRxtpXs7LXIev8TdNdqK8/WY8FD1aB6jQFv1mD82IqxipavwkCC1j2lYLenBF7hrXLmoVWzFCiC9WjRRhvO60BijcGcMU2a3q6zcLtVxreQr3HUOigg5eQwslZbbffgIOCQdPlr9GrBr27rHlR4nx5U2Wp2SUTmudG1bOa5ERyjp8FFJ0qVtmqpRlWvnqv66aIN+e8VFdV5L+yJ3HaP03EIt3ZumKT07eHyfzhYxXG3umE8JCwpU3zaOG0qDAwLUNS5aGT52rqmLu+KV9NxCLU1O05Re7Su2k11UrKAAW8U10OXxzbSgShzsbe6KT0613eCAgIq4rbTcLrsqfycpKCnVxz/+pLv7d/fErrvEHePr6WI0Xz8HuTver8mf4n3gbJ3Pv495+tpw79Ec9WzZRGFBgQq02dSnTVMtdB5HX+GNeTh/wDwCTsfT8ZvjN0X/eJCRO8eVk+Zs26+xXeMr/p2eW6Cle9I0pbfv3ZPg6fkm2srP8+W2AgBAffLrpMxfwrKssZZlZZ++ZLXPvGVZVt2P0v9lekvySFKmZVlplmVd44nvOiORjWTlVUkUyz8uRTaqVUYny1h2qaRICguXslJlOvSQjE1qFCPFtZYioz1XdzfKzCtUs4bhFf+Oa9ig1o9cGXmFiousWibcpZsuUrPzdfW7s3TLv+Zp3YGM05b3BRn5RWoWWfm0yGaRYcqs8YNRRn6RmjmfKBlosykyOEjZRdWfIr9g1yF1bRpVMamPc19GQbGaVUm8bRYRqsyC4lOWcbSdQGWfKNWo9s0UFhigIf9arOH/Xqrbe7VT1Clu4vArkdHVx9287HNm7DxTdY+1NcaWvCLFVRl/4iIbnNENbgt+OqCuzRr77biTmZ2nZtGVk0Zx0ZHKyHb9KZLbDxxW+vEcDenR2R3V87yGUbJyq/yAnHtcijyDRJaG0bLd9ZRsD78ka+VcKf+MQlGfxVjr4IhXqhyHU8UrkVWOQ0gd8cruVHVtGq3gwICKVaVe/2GbpnyyUI/MWqUjBb6b1OCKWmNvZHjdcW6NMnWNvd9s3O13K/ydlJlXoGYNKxOh4hqGKyO3oFqZjNyC6sehYbgy8yrLvLbwRw175TN9l7RHDw67pNpni0rKtHzPIY2s8qOIvyD2r85d14YFJaV6f+VW3Te47pVBfBkx3OnViuGiIpVRx4M1XPHNqs0a1NX/btKuKePocTVr0rji382aRCvz6KkfXJSbX6DFP27S5T27Vns9NfOIDmVmqV+Prqf4pO852z5ztKBIsc73mkSE6WiB47M3XtpFyUdzNOT/vtakd77TEyMvla3Gg0e2pB5RaXm52kTX/QAXX1A7hmugzJrHJ7+w+nmnjhiuLk/OXavJH83Xmyu3yarroUc+JjO/qI44rY620rBBtTInz9NHC04oNsLZVsLDdNQZt2bkFWrhzoO6/hL/vC70dB/qEhetFclpKiot0/HCE1qbkuFTN1pm5FX2B8nZZ+q87qnaZ4KVXVTibGPVj1PNuGbOjhSNvbBtxb/3H8/T+oNZ+tW/FuiWzxZW3PziizLzCqvFsXXtnyNuC6tWprKtnKh4gGCT8NCKPuSK1Ox87cg8rp4tmpzNLtQ7d40rVX29ea8GdWhR8e9yu12T35utga99pf7tmqtXS986JlW58xw07b9LNegfMxQeHKhRF7Sq34rXs4y8IjWrcj3TrI7YvWqZyuNQfIp+V/0Y1rRo1yE1jQhTlzj/mRN31zF6cdF6PTb0oloxnD8ghqvN3fMpuSdKtGRvmvq1jXPTHtQfd8UrLy7aoMeu6F2tz0SHhajMbtdWZ4yyYOdBn4rd3BWf/Nx2D+cW6Kr352jYGzM0tW/Xigdgvb5si27r00VhPjTX4u5zUM0YzR/OQe7qP8ZIU/+7WNd8NE//3bSnoow/xfvA2Tqffx/z9LVhp9gorT+YqezCYhWVlmnZ3jQd9qHzs+SdebiSsnJd+8EcXf/RPC3c6TsPkaiKeQScjqfjN3/i7v5TVFqm5clpGtmlTcVrL36/Xo8Nu0g2H5xW8PR8kz9xZ1vZlJqlye99p7s+T9TurMp74ny5rQAAUJ98OinTGDPDGLPeGLPNGHOX87XbjTG7jDFrJQ2oUvYjY8ybxpjVxphkY8wVxpgPjDE7jDEfVSm33xjTxBgTboyZbYzZbIzZaoz5lfP9F40x240xW4wxLztfe8oY85jz772d37HFGJNgjIl2vr7EGPNXY8xaZ/0GnWKfgiU9LelXxphNxphfGWN2G2Nine/bjDF7jDGxzn16yxizzrnN8c4yAcaYl4wxPzrrcffPHMN4Y8xW59/DjDGfO49JgqQ6lwo0xtzl/M51767eVlcRr7KSVsnKy5bt5t/LNnSKlLbPkbSJU4qNCNOiB6fom2kT9IeRffT7hGXKLz79D5Dngt1HcvTq0i16apR3FoWF/0nKzJHNGC25eagW3DhYH23ep4M+NlEL37Y7K1uvJm7QU2P7ebsqXmG3W/rrVwv0+2tGebsqviP3uOzvPCX7P56Q6dlfCvePp6S5E2NtdbuP5OjVZUl6auTFkqRyu6X0/CL1bhGjr28eod7NY/TS0i1erqVveGv5ZgXYbJrQo/3pC5+jHhnRR4m/vUHje3TUp2u2V3tvya4UXdw6TlF++BTc+kDsf3pvLNukW/p2rXgCMCqd7zFcVd/+uFVbD6brjuH+sypkfSgrL9djr7ylm8aPUOtmTau9N2f5Go3uf6kCAnx6KtVtjDEyzhuQVySnqUtctJY+PEXfTB2nZ+evrTbHlJVXqMe//UHPTejvlzf6n62/je+nmXdcqX//epjWH8rSt9v2e7tKHlW1rbzw/Tr9dph/JnzUN1f60ID2LTSoQ0vd8NE8PTZjuXq1bKKA8+ROhZLyci3ek6rRXVpXvFZut5Rzolif3zxSj11xkR6d+YNfJcj8Usa4vnZSQUmpHk5Yrj8Ov6RihcRzUdX+c9Ka/en6ZvMe/XboxRWvBdhsSpg6TosfvFpJaUe1O/PceCDWmXr3uiFaev8klZTbteZAprer4zOKSsv0zqptenBQT29XxeuW7ElV4wah6tas8ekLn0fO9xjuVPMpZXa7HvtutW66uJNaR0Wc4tPntiV7UtU4PKRWnzHG6JWJA/Ri4kb96l/zFR4cJNs5Gru5Gp80bxiuGXeO1by7J2jm1mQdKSjSjozjOpidrxEXtD79Bs4RNWO08/0c9O8bR+jr267U29deof9s2K11Bx3xyfka7wNn43z/fcyVa8MOTRppar9umvr5It31eaK6NI0+p+dWXJ2HW/jAZH15x1i9NGmAXly4TgeO/7IHOfoL5hFwps6XOTZX1NV/luw+pItbxSrKuerskt2H1Dg8VN2ax3ijivARVdtK12aNtfD+yUqYOl43XnqBHvxqqSTaCgDg/BLo7Qqcxh2WZR0zxoRJ+tEYM1vSdEmXSMqRtFjSxirloyVdLmmipG/lSNqc6vxsb8uyNlUpe6WkNMuyxkmSMaaRMSZG0mRJXSzLsowxdS2B9C9JD1qWtdQY87Skv0h6xPleoGVZlxljxjpfH1Hzw5ZllRhj/izpUsuyHnB+dxdJN0p6zfmZzZZlZTmDlnhJl0nqIGmxMaajpFsk5ViW1ccYEyLpB2PMAsuy9p3meN4rqdCyrAuNMT0lbairkGVZ70h6R5LKX37APTOfeTkykdGq2HhEtJSXU6uMIqMdq0kZmxQcJhU5VoixlnxT8Vnbrx+Vjvvvj8ufrftJX27cJUnq0byJ0quslJORW1jtaevSySewVC1TUPGkyVMJDgyoeLJpt+Yxah0dqf1Hc9Xdx5/uExcRpvQqT6pJzytS04iw2mVyHU+pKbPblVdSqqiwYGf5Qj004we9MLav2kSfnz8Wnq/iwkOUXuXpnen5J9Q0PKTOMs0iQp1tp0xRoUGaveewBrVpoqAAm2LCQnRRs2htzcxR64Y/3898Xt7x6uNuZFTlasTngc/W7dSXG3dLknq0iKljrK0xtkSGVXtCZUZe4WnHWklKzy3QQ18t0QsTB/j0qjB1+WzJj/ryB0do0KNtC6Ufz614L+N4nuKiXNufguJi7U7L1K2vfixJOpKbr/vf/Fxv3Hu9urdtcZpP+6jcbJmGjSv7T8Nox2qzZyo/W1ZWmtSmk7RjfX3W0CsYax0c8UqV43CqeCWvqDJeKa4Rr3y7Si+M6aM2zpubosKCFRYYoJGdHE+7Hd25lb7eut8zO1SPPvtxh77c4IxzW9SIc/MK6o5za5SpOvYmbNqtpbsO6YNbRtf6McCXfbZmm77c8JMkqUeLWKXn5le8V3NVTKn26pmOeL96GUka37Oj7vl0XrXVMuck7dXYHv65sh2xv2euDbekHtGCHSl6ZdE65Z0okTFGIYEBurHPhfW4J/WHGO70Plu2Xl+udEx39WjTvHoMl52nuEZntj8rf9qnd+av1McP36jgIF+fNqzbp7MX6qvvHT/4de/YTulHKlc8Tz9yXE1j6l6Z4i9vfKS2zeN068TRtd6bu3yN/nT3ze6pcD2qzz4TEx6mrLxCxUY2UFZeoRo7E/4TNu/V1P7dZIxR28YN1SoqQslHctWzZRPlF5foni8W6+EreqtXy1h37+5ZqR3DFappzeMT0aD6eadKDHfK7TqPX3hIkMZd2FZJh49pUvd29b8DZ+mzdTv1pXOVklptJa+g7raSW1itzMnzdEx4qLLyCxUb0UBZ+YVq3MBxTbDt8FH9dsYKSdLxwmIt25uqAJvNp2/I9nYfumdgD90zsIck6XczlqttY995oE9cZINqqz+l5xWe4rrHscKOo8+UKCosWE2dMdxJjlWZKs/Py5MPq2tcYzUJr7ryWZhGdm4tY4x6toiRzRgdLyquOI7e9tn6Xfpys7MPNY+pFsfW3D+p9lPVq7eVUGXlFyk2IkxZ+UVqHH76fSwtt+uRhOUa3y1eI32kT3liXJGknZnH9ec5q/X2r4YpqkH1OQhJahgarMvaxml5cpo6Na3rJz7vc9c56KSQwAAN69hCibtT1T++Wb3WvT7FRYYpvcr1THodsfvJMpXjSqmiwkLUNLJBHf2uzufQSpIOHs9Xak6+Jn8wt6L8lI/m6YtbRlesBOeL3HGMEnenavGeQ1q2N03F5eUqKC7V72et1N8m9PfYfp2N8z2Gq4s751P+Mn+d2kZH6JZL/WPlc3fEK4l7Dmnx7lQt23u4Vp/p3bKJ/n2j43aQH/Yd1v5jufImT8QndY8t1bfbNLKBOjaJ0vqDWTpeWKyt6cc04p8zVW7ZdbSgWLd+ulAf31jrNhqPctc5qK4YzV/OQe6K9+OqtKnhnVtpS9pRXdq6qc/H+8DZOp9/H/P2teGU3h01pXdHSdL/LtlYbcUzb/H2PNzJ9tY6OlKXtYnTjvRjPvE7iLfbykn+MI8Az8Zv/sBT/UeS5mxP0dhu8RX/3nAoS4t3H9KyvakqLnNeI81cob9NGljfu/mLeHK+yR94oq1EhFTOuQzp2FLPzF+r44UnfL6tAABQn3z98e4PGWM2S1otqbWkmyUtsSwry7KsEklf1Cg/y3I8Pi1JUoZlWUmWZdklbZMjubGqJEkjnatbDrIsK0eORM8Tkt43xlwtqdpyPcaYRpKiLMta6nzpY0mDqxT5xvnf9XV838/5QI5ES0m6Q9KHVd77r2VZdsuydktKltRF0ihJtxhjNklaIylGUicXvmewpH9LkmVZWyR5b7md9BQpOlZqFCPZAmS6XCxrb/XqWHuTZLo5VmUwnS+SddAxYaXAICnIGci17SLZ7dLRdE/Wvl7dcGkXJUybqIRpEzX8gjaamZQsy7K0+VCWIkODFFvjoiA2soEiQoK0+VCWLMvSzKRkDTvNReGxghMqtztWEz14PE8px3PVygcmWE6ne/PGSjmer0PZ+SopL9fcnw5oaMfqCT1DO7TQDOcTaxfsPKS+bZrKGKPcEyW69+vlenRwT13cyreTT1H/ujdtpJScQh3KLVRJuV1z96ZraHz1lU6GxjfVjF1pkqQFyRnq2yJGxhg1jwjV6lTHTbuFpWXanJmt9n56Y381h1Okxk0rx92ufWTtOX9WXbvh0guUMG28EqaN1/DOrTVzi3OsTc1SZMgpxtrgIG1OdY61W5I1rPPPj7W5J0p07xeL9ejQi3Vx66Y/W9YX3XBFHyU8ebcSnrxbw3tdoJmrNzuOUfIhRYaFKNbFG/ojw0K18uXfaeFzD2vhcw+rV7tW/p2QKTlW5W4cJ0U1cfSfbpfJ2rXp9J+THA+YCHQ+TS+0gUzrjn4dt1TFWOvQvVm0UrLzdSinwHEcdh7U0A7Nq5UZ2qG5ZmxLkSQt2JVaPV5J+EGPDuqhi1tWxivGGF3RobnWHsySJK0+kKkOMb4fu9V0Q58LlXD3JCXcPckR527e64xzMxUZEnyKODdYmw9lOsbezXs17II2kqTlew7p/ZVb9cb1wxXmZ0lCN/TtpoR7pyjh3ikafmG8Zm7a7TgOBzMUGfozx+FghuM4bNqtYV3aSpL2H618kE3iT/vVvknlj4N5J0r0Y0p6RVl/Q+zvmWvDf986RgsfvEYLH7xGN1/WVXcN6OGzCZkSMZwrbhh8iRIev1MJj9+p4T07a+barY5jtC9VkaEhim3k+vl1+8F0Tf9inv5x1zWKqSMZ3F/cOG6EEl57RgmvPaPh/S7WzMWO1RY27dyjyPAwNW1c+8aK1/79tfIKC/XHqTfUei/5UJpyCgrUu0tHT1T/rNRnnxnauZVmJCVLkmYkJWtY51aSpOaNwrV6vyOePZJfpH1Hc9U6OkIl5eV68KulmtSzvUZf6PvnIsd5J6/yvLPjgIZ2bFmtzNCOLTTD+WAMx3kn7mdvfCuz23W8sFiS42aOpXvT1LFJI7ftw9m44dILlDB1nBKmjtPwzq00M2lflbYSrNiIGm0lwnnOOdlWkvZVtpVOrTRji7OtVGlD398/WQudf0Z3aaM/jb7MpxMyJe/2oXK7XdnO9rMz47h2Zh7XgPbVryu8qe4+06pamaGdWmrGVsdzIxf8dLCizwzt2EpzdxxQSVm5DmXnK+V4nno0r1xtas72FI2tMW4M69RKaw9kSJL2H8tVabld0WG1b5zzlhsu6ayEO8Yq4Y6xGt6plWZuPdmHjjjaSo0b2GMjwhQREqjNqUccbWXrPg1zPoRnaMcabaVTq1rfV5VlWfrTnNVqH9NIt13mO3GcJ8aVtJwCPfT1Ur04cYDiYyqTlo8VnFDuCceqzSdKy7Ry32G1j/GdpOaa3HEOKigpVZbzwVlldruW7j2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2uauNDM58+OIrBrSv2lfl7+NNwpyJJMyZ6PETMsF9/ZOTu7d39U3+v6mDaRUR4rETMsF990q/ts1d98MTk/rzhyUV+Tu7bzx/mjLQ/Ym7TpKOniE58xLLH5jJ45MHXj6uLNnA/MkDWf7ATJIzL7niyoDYaBbdN52F906nbeNw3lhn91+WOhw89MlaHp04gMX33cw7d413DSD0BO54tnG593RYFvOWbOL5qQP5dM5EWoYFs2iPvdBEdmEx81du5eUZQ1k8ZyJ/njaozvOiNiqXa+eOfCn3r22HiW0SXuW95q/cxjOT+pMwezwT49vy2oZ9eBp35cnTq7czqH0LlsydxCd3j6O9c7HSu/t2YeHdE0iYPZ6hsS35mwfmSTl31ldujGnmKofKJ2Sm5eTz7taDLJg9nk9/Mtnu295/0u3p/Drq+jlHZEgg//7ROBLmTuKD2eN5c+Ne0nPy6yy9V5J03I6By386mcfH3cTjK2o+DwWYv2IL88f1ZflPJ5N8MYd1x88B8Oam/fRrE8Xyn06hX5soV15c7X1fTNpF75iKZyLB/vZku/KflmFBjO7oOYO14duXlVcbk/CP748k4e4JHj0h011jMqZ3b8/rtSwi8sK0Qa57YnSnGI+7J8q5I198vLz47Ygb+GzuJD744Rje33HE9Z53941n4Y/tfBnaIZq/rd9b45pEGoq6rtcC7D13gezCYvcm7DpLOppC8oVslt9/M49P6s/jSzbWetz8pZuYP2kAy++/meQL2aw7avetDGjfkkX3TmPhPVNp2ySMN760n7XGNWvEgrmTSfjpVF6/fTSPfbbR9fzMU3hKXQXgtQ37aBwcwLKfTmbx3In0aV319bqgPgT5Nuq6f7Jvpb///QejCPD1YWD7mou31yd35Ul4oD/zRvdmdt+qCw0cSc9iwc4j/Gf2eBLmTGTNUbvv3FPUdX1/QLvmLJozgYU/nkDbxqG8sdFz+1bKuWPcSlpuAe9+dYwFPxjOp3eOsvtRDjWcsfsiDZWX0Y9+vns/nspznto6GWN+ZIz569c8Z6kxJuJrnnOPMebOr3d1DY8xpqUx5qPLvLbGGNO7rq8JYE9qJq0jQoiJCMHP24vxnWJIPHq2yjGJR88yras9SH9Mx2g2nUrHsizioxrRLMTeBaVDkzAKS8soLrUnvZSv9lvqsCgpc+DB370a9py7QOtG5Xnizfj4NiQeqVoxTTxyhmnd2wEwpnNrNiWnYVkWiUfOMD6+DX4+3rSKCKF1oxD2nLsAQGp2PmuPnWVGj4azm0NliYdOM7V7e4wx9GwVSU5hMRnVHvJl5OSTW1RCz1aRGGOY2r09qw/Zg7Rjm0bQrpZOyv8aLdrCxXS4dB4cZVj7t2Liqk4u5NIFyEgBy6p5fvIhKC6qk0t1pz3nMqt+fzq3riWmpDCta1sAxnRqxaZT9vcnyM+HG1tFXnFlnpOZOWTmF3Jjq5or2/+3OLpuA/mZF+v7MupE4uHTTO3hjCvRkeQUltQeV4pL6BntjCs92rP68Gnn+WeY1t3eXWdapXhjgLyiEizLIr+khPBAf48aIHcle9Iu0joimJjwYLtcjosm8XhqlWMSj6cyrYv9sHhMhxZsOn0eyxlXVh07R3R4EB2qTQQqczgoLC2j1PnfZg1sokNlibsPMbVvd/u+adeKnPxCMi7V7Fjs2a4VkeE1J0SFVNoppqCoGBpULaWqxAMnmfq9TnZetG5OTmERGdl5VY7JyM4jt6iYnq2b29+h73Vi9YGTV3zf9UdP07F5Ezo7dxGJCArAuwF8h9xVr23I9qRl0Tq8ekxJq3JM4vE0pnWuFFPOVMSU2pzOzqdNRDCNnd+l/jFN+fzYOfclwg3seyWYmAhnvnRqRWK1NCQeO8e0eHtS4Zi4lmw6lYFlWQT6+rjKlKKyMoyxY0hOUQnbUi4wo5t9f/l5ezWIlRcrSzxwkqm9OtoxJSbKjik51WJKTp5d34+JsmNKr46svspgyEA/X9fE7qKSMoyHx9095yrHEm/Gd2lde3uwW3l7MIZNyakV7cEurSvagxEh7DmXWeXcTclptI4IITrc3sHtg6+OMKdfPH7OOrCnT0a08ye0av4crbrYQ+KRs0zr1hb4+vX9hmpPujPehgXZcaVDCxJPplc5JvFkOtM62ZPCxsQ2Z1PKhRrxdsmRs4x3rmIa6OtN32h7BV0/by/iI8NIyyusg9S4x+qN25g6agjGGHp16Uh2Xh7pF2q2e0KC7d0YSsvKKCktdcWM2NataB/Tsk6v+XpwR0w5diGbHi2auMqkPjHNWOVsI53MzKF3jL3L8YC2zVl5+DQNQeKhZKb27OAsg5pduc8pppldBvXswOqDyQCEVCpzC0pKMZWKmvc272d0fFuaOHdy9mSJh08ztXu7Sm3kYjJyq+VDrjMfytvI3du52sKJh08zrYezjVyp7Qzw3rZDjO7UukEtMFKbuuifXLL3BOM9eEdVcN+9Euzn66rfFhRX/S71b9eCYD/P2Q3yahIPnmJqD2dcaXWVuNLKGVd6dGD1QXsRo4Gx0a56f89WkaQ668Xrj6XQMaoxnZ2r3Htae9kdzzYu955ZBUX4envRtrE9qax/u+asPGTn35L9JxndKYaWzjqvp9RxVS7Xzl1toNTsfNYeP8uMnlV3RzcGcotLAMgpKiYyxPPKaHfkSU5RMdtOZzDDGX/9vL1d/QaVd1AtKCn16FazO+srl1PmsKr2bXvYPVPXzzn8vL1dfQglpQ4cV+jHq2uJR1KY2q38/mhKTlExGbkFVY7JyC0gt6iUntFN7bzo1o7Vzu+XXUZVygvX3y//vvtSM7mQX8iAtrUPyD6ZmU1mfhE3OuOxp7hSWfnp3hPc+s5ypr+9lEeXb6kyKajclcYkNBTuGpPRu3Uzwq/QL2tZFisOnmJCvGft3lbOHfkSGRLo2rEt2N+X9k3CXJO5a5RBnlwIiVxFXddryxwOnluzk98M61U3CbxOEg+dYmrP2Ir2ctHl2svFFe3lnrGsPnSZ9nK2fW6V52elZR4ZTzyprpKw+zhz+9k7xnkZQ6N66LNTH4J8G/XRP1lu5cFTDI5tSaCvZ/VVuitPmgQH0L1lU3yqzX44duESPaKbVvRJtW7GqkOn8BR1Xd8f2K5FRfnUsimpHrJ40ZW4Y9wKVO9HKaVZiOKqiIh8d3jOU9tvwbKsCZZlZX3Nc161LOuf7rqm68EY861r8JZlnbUsa+b1uJ7rKS23gOahFQ+vmocGkl6tw6HyMT5eXoT6+5JVUHWlr5VHUohv1sj1EAhg7kfrGPzKYoL9fBjTseHsapeWU0Dz0GDXv5uHBtVYYbTyMRV5UkR6Tj7NQ4Ncx0WFBpGWY+fnn1Zv5zfDv4eXJ/Y8XYP0nHyah1XkS1RYEGk18iWfqNDKxwRf0+qsKVm53PzGYu7853K2nUq76vEeKbQRVk6lAaU5WRDaqP6up57Y8aLiO3DZmBJmH+Pj5UWoX82YcjlLD55iXKeYKg1JabhqjyvV7pecAqKqxdXyuHIhr4BI52tNQwK5kGef+4PenTl+4RJD/+9jpr7+GfNG924wsTctt5DmlQaVNA8JID2v+neo4hj7O+RDVmExecWlvLX9KPfd1KnK8VEhgcy+oQMj//45Q99cSYi/DwPb1P1Ki9dLelYOzRtV7Dod1SiMtKyvt9rb+2u3MfYPf+X5hNXMmzX2el9inUnPzqN5eIjr31FhIaRVm5SZlp1HVHil71l4MOmVjnl/016mvfgfHv74Cy4V2IsDJJ/PwhjD3L9/xoy/LuCtpK/cnJLrw5312odXbGP6Pz/nlY37rzhh0dOk5RXQPLSig7XWmJJX6DrGVS4X2oMjU7LzufmDJO78ZAPbztqd3K3Dgzh5MY+U7HxKHQ5WH08ltVo+e7q03MKq90pIIOm5hdWOqajTuO4V52rHu85lMvmdVUz912oeHdkLHy8vzlzKo3GgPw+v3MHN7ybyyOc7yC8prbtEXQfpOXk0D69al0/Lrlbfz84nKqxaTMmpFlNeWsDDn6xxxRSAXafTmPzih0z96wIenTrYo3ffTcvJd9VVwdkerDWWVL4//MgqKCa9Uj0XytuDVfNw6YFkJnSpGPR18mIO209ncOs/V3Ln+6s8fgBdzVgbRHr1+ltuftX6fi2xtjYPL9vC9H+s4JUN+xpUrAVnLK00UKB5cADpedXjSiHNQyrHWx9XvC23/Ng5JnaoOZA0u6iENSfT6deqiRuuvm6knc+kRWTF4jrNmzYh7UJmrcf+eN6TDLx1LsGBgYwd3K+uLtEt3BFT4pqGs/1MBlkFRRSUlJJ0/CznnPG6Q9NwVh+xJ0qvOHi6QTx0BkjPrqVtWFu9Nqxan1Olcuovq7cx4oUP+Gz3UR4YfoPrnFUHk7mtd8PYYdX+f14pjaHBtbeRq9wXwa576kJeIZEhzjZycCAXnHEoLSefVYdOc9uNHWt8ZnFpGbe8vZTb/rGcVYc8c7JQZe7snyy3fP8JJnr4pEx33SsAqw6dYuKrn3LPh1/wxMT+7kyGW6Xn5Nes29Z2r1TOo8vcK5/sPMLgDvbzjeQL2RgDc99dwYzXFvHW+t1uSsE3445nG5d7z0aB/pQ6HOx11l9XHjzlKndOZuaQXVjMXe+tYubfl7Foz3G3pfnrULlcO3e1gf60ege/GdarRr/s/HE3cc+CtQx/eSGf7jvJ3H5Vd3rwBO7IkzNZeTQO8ufhpZu5+e/LeGTZZvKLK/oN/pK0ixF/W8Rn+5N5YHB3N6fwm3NnGbQzJYPpb37GTz5I5EhGlvPcIGb3jWfkXxMY+n8fE+Lvy8D2nrVYS3085ziXnce0NxYz4qWPmdO/G80qvXd9qr0sqa2uFljlmIq8KHRN1G4aHOC6Py73vg7L4pnVO/gfZ/2/Nkv3JzOuS2uPe654ubLy2PlLLD+QzLt3jCHh7gl4G8Nn+07WOP9KYxKMgTn/+YKZf1/GhzuP1kl6vgl3jcm4mu2nM2gSHOCaFOJp3J0vKVm5HEi/SI+WFX00f1m7ixEvL+SzfSd5YHC1RadFGpC6rte+v+MIwztEe+QiI1dSo+4Sern2ctVjam0vf3WEwR2iXf/edSaDya8sZOqri3h0Yn+PWzjbU+oq5burvrRuFzP+voxfJqzjfF7dP2tVH4J8G/XZP7ls/0kmxre9nsm5LtyZJ7WJi4xg++l0svKdfVLHKvqkPEF91fcBPtl9jMEe1ndQG3eMW4kKCWT2jR0Y+eZyhr6+zO5HaRNVd4kSERGpZ3XeCjXGLDTGbDfG7DPG/MT5t9nGmMPGmC3AwErH/sMY84oxZpMx5rgxZpgx5m1jzAFjzD8qHXfSGNPUGBNsjFlijNlljNlrjLnV+fqfjDH7jTG7jTHPOf/2mDHmN87fezk/Y7cxJsEY08j59zXGmP9njNnivL7BV0hXV+dxO53vE+f8+4POa9lrjPml829tjTF7K537G2PMY5U+8y/GmG3AL4wxfYwxG5xp2mKMCTXGeBtjnjXGbHV+1k+vcF2uzzLGBBpjPnDmXwJQaw+NMeYnxphtxphtb3jwYPgj5y/xQtIeHhtdtRPhjZmDWXvPJIrLHGw+lX6Zs78b1hxNoXFQAF2dKxBKhciQQFY/MINP5k7modF9+G1CErlF1zZBT757lh48zcQurev7MsQDGWNcD9W/PH6WzlGNWPuLGXwyZyJPrNjynYgrL28+xJ292tfYueJSYTGJx1P5/K5RrPnxGApKyvj0oOcPtnWn24f2ZsX8+3lw+kheW7auvi+n3tzWtysrfn07n9w/i8jQIJ5ZugGwdzrfkXyOZ2aN5N2fTGPV/hNsPHbmKu/236G2eu0zE/qy6K4xvHvbMLannOfT/Z6zuqA7RQb7s/qukXxy2xAeGhTPb1d+RW5xCeEBfvxhWHceXLGDH368gZZhQQ1m4vv10rNFYxbfNYoPvz+MN7Ycpqi0jDKHxf70LG7t0Y5P7hhBoI8Pb249XN+XWqdu6xvPige/zyc/m2nHlGUbXa/1jIli8c9n8eE9N/PG2q8oamATVq+X4rIyvjiawljn7rRgr1R5qbCID344mt8M+x4PLlrf4CYkXg/PTOrHorvH8e73R7D9TAaf1jLg8L/drrQsAny8iWtSdafvUoeD33y+kzu6tyEmzDMG2rrbW089zLp/v0ZxSQmbdu69+gnfMbFNw5nTtwtz/vMFP/lwDZ2bNcLbWRY/MaEvH3x1hJn/WE5ecQm+HjbwyZ1+ObI3iQ/exqQeHXhvywEAnl6+iV+P6oOX13errgJV28hPf76NX4+ofaG4VfdPZ8HdE3h26kD+tGobpy5+vQVv/tvsSskgwNeHuGbfnYXWKt8rAKM6tWbJPVP468yhvJi0qx6vzDO8mrQTby/D5O6xgF0u7ziVxjM3D+Xduyey6mAyG4+freerrB/GGJ6fOpA/rd7Bre8sJ9jf1xVnyhwO9qVm8sotw3jj1uG8smEvJzOz6/mK3UPlcu3WHE2hcbB/rc/E/rntEK/eMpQvfjaN6d3b8/8Sd9TDFda9MoeD/akXufV7Hfhk9ngCfX14c9N+1+u/HNKTxPumMim+De9tP1KPV1p3KpdB8c0bs+pn00mYM4kf9O7EAx+tBeBSQRGJR07z+X3TWPPzGRSUlPLp3v/eQdrX+pyjRVgwC+dOZvl901i0+xjnG9iCadfCGHPVXWP/veMwQ2JbVplEU93SA8keOWD7cmXlpuRU9qVdZJZzp8xNyamczsr9Wu/97h2j+Xj2eF6bNZx/bz/Mtu/4OI3qlhw4WWXBtO+SvOISfpGwjv8deWOVHTJ/ObQniT+bxqSubXlv+3erT1vkai5Xr03PyWfFwVP8oJYFsL4rXl23C28vLyZ3r9hBtGerSBbfO40P50zijS/3UFT63/ss6NvUVcocDlJz8ukVHcnHs8fTK7opzyZ67rjQ60F9CHIlX6d/MiM3n8PpWR63WM/1Vj1PahPbNJw5/boy54PV/OSDRLtP6jv4LKS6Vzfstcunrm3r+1LcrrZxK/YYwXN8fvdY1swdb48RPPDdGOMkIiICUB97qd9tWVamMSYQ2GqMWQI8DtwIXAK+ACq3+BoB/YEpwKfYkzbnOM/tZVnWzkrHjgPOWpY1EcAYE26MaQJMBzpblmUZYyJquaZ/Ag9YlrXWGDMfeBT4pfM1H8uybjLGTHD+fdRl0nUP8H+WZb1njPEDvI0xNwKzgb6AATYbY9YCFy/zHuX8LMvq7Xyfg8CtlmVtNcaEAQXAj4FLlmX1Mcb4A+uNMSstyzpxlfe9F8i3LKuLMaYHUOsTR8uyXgdeByh7/WG3jMqMCgkktdIqIqk5BTSrtopX+THNQ4ModTjIKSohItDPeXw+P/90I0+P70PriBCq8/fxZkRsSxKPnWVA24ax4kZUaCCplXZ7Sc3Jr7HCaPkxzcMq54k/zUKDqqx2XL6CVuKRFL44eoakY2cpKisjr6iE3y7ewDOTB9RZur6J97cdZMFXdsd79xZNSa20S0Fadn6VVV2hfFWaysfkXXV1Vj8fb9dOVF1bNCGmUSgnL2TTrdLqjA1CzkVMaCNcX9TQCMi5Woj572PHi4rvwGVjSnZ+RUwprogpV3IwPYsyh0OTmxu497cdYsFX9qCS7i2b1BJXqt0voYFVVidMqxSTmwQHkpGTT2RoEBk5+TQOsnceSth1jDkDumKMoU3jMFpFhHD8fDY9oj0/rkSFBFTZcS41t5BmwdW/Q/YxzUMDnd+hUiIC/NiddpGVR8/y/Pr95BSVYIzB39ubJkH+RIcF0TjIH4DRsS3Yee4iUypNCPF076/dxoL1drW0e5sWpF6s6IRPu5hNVETo5U69ogk3dmX+v5ddl2usK+9v2suCrfaAre6tmpF6qWJARlp2bpVVS8G5G8ilSt+zS3k0cx7TNKSijL6lTxfu/edSAJqHB9O7bQsaOe+9IR1bs/9sBv1jPXvnc3fVa8vjUrCfLxM7t2ZPaiZTuzaMQRtRwYGk5lSspFdrTAkOIDXH3oHXVS4H+GKMwS/QWUdrFkFMmL1DZreoCIa3i2J4O7tu/+HeZLwbWN9+VEhA1Xslt4BmIQHVjrHrNK5YW1RCREDV+kpskzCC/Lw5cj6bqNBAokID6dnCrqeMiWvJm9s8fwDL+5v2smDbQQC6R0eSeqlqXT6q2oPi6juXpV3Ko1loLTGldxfu/VfN+BrbrBFBfr4cSb9It+jI65qW6yUqNIjU7Mr12fzLxJL8Su3BYiIC/WjmrOeWs9uDFfmy7vg54qMa0zS46q6+ozvaO8H3aNkEL2O4WFDkqtd4mpqxNp9m1etvIUFV6/tFV6/vl+dTsL8vE7u0Yc+5TKZ28+xdyiqLCg4gtdKKtal5hTQLrh5XAkjNrRxvS4kIqBgAt+zoOSZ0qPkQ+dG1e2kTEcydPRtOfpR779PlLFi2GoDuHWM5l3He9Vrq+QtENbl8287fz4+R/fuweuNWBt7YcHdpcFdMmdEzlhk97QlCf167y7VKbvsmYbx563AATmZmk+TBE4Xe37KfBdsPAdA9upY+p9rqtdnV+pxqGXw9qXss97y3ggeG38C+s+f59UdfAHAxv5CkI6fx9jKM6tLWDSn6Zt7fdogFzt1rarSRc/JqbyNXuS/yXPdUk+AAMnLziQwJIiM339UG3HfuAr9e+CUAF/OLSDqWgreXF6M6xbjuqZhGodzUOooDqZm0bvTN2lfuUhf9k+WW7TvBBA/dJbMu7pXKereO4kzWRi7mF9LIQ+sl1b2/ZT8LdjjvlZZNa9Zta7tXKudRtXslYecR1h45zdt3jncNgmoeFkzvNs1deTKkQwz7z12gv4cMBHPHsw37fWp/z17Rkbx7x2gA1p84x8nMHOdnBBEe6E+Qnw9Bfj70jmnGwfSset+RSuVy7dyRL4lHz/DFkRSSjp2r8kzsdyNv4FB6Fj2dz3/Gd2nNTz5cUyfp/DrckSflP+VpH9Mphjc3Hajx2ZO6tuWeBWs9arfMuiiDQvwr2oxDO0TzxxVbuJhfyObkNKIjQmjsbF+N7tSanWfOM6Vbe+qTpzznaBYaRAfn7ihj62mS2fvbD7Ngl/P+aNGklrKktrpaQZVjKvIigIzcAiJDAsnILXD9f6+9jApiZ8p5tp/J4N87jpBfUkpJWRlBfr48OKwXAAfTLlLmsDzmuWLlvBrXuQ0D29UsKy0LpnZr50pDuVWHTvPy+j0A/HF83yuW21GV8nNkx1bsPneB3q2b1UUSvxZ31VuupNThYNWhMyz40bjrl5DrzF35UlLm4JcJ65jUtS2jO9X+fHBSfFvuWbBGu2VKg1WX9dqJ8W1Izspl3GufAVBYUsrY1xaz4qeT6yaxX9P7Ww9UbS/XqM/V1l6uekyN9vLhM7x959haJw3FRkYQ5OfDkfSseh/75Yl1lV8N7Umgr7crHo/t3JqPd9f9wiPqQ5CvyxP6J5fvP8WoTjH4envGwld1nSfVzejVgRm9OgDw5zVfVdldsr7VR30/Yfdx1h5N4e3vj7zqpFZP4I5xK2ey84kOC64YI9ihJTvPZjJFm6CIiMh3RH3UEn9ujNkFbAJigB8CayzLyrAsqxj4T7XjF1v2dg17gDTLsvZYluUA9gFtqx27Bxjt3N1ysGVZl7AnehYCbxljbgaq7EVujAkHIizLWuv80zvAkEqHfOL87/ZaPq+yjcA8Y8xDQBvLsgqAQUCCZVl5lmXlOt/rsrttVlKeB52Ac5ZlbQWwLCvbsqxSYAxwpzFmJ7AZaALEXcP7DgHedb7XbmD3NZzjFt2aNyI5K5czl/IoLnOw7NBphse2qHLM8NgWLNyXDMDKwyn0bd0MYwzZhcXcm7CeBwd354ZKD4DyikvJcE4oKXU4WHv8HO0ae9Zgnivp1qIJyZk5nMnKpbisjGX7kxneIbrKMcM7tGLhHnvu7cqDp+jbJgpjDMM7RLNsfzLFpWWcycolOTOH7i2a8OCwXnzxs+msum8qz08ZSN82UR4/IRPg9t6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nTucXvtA0rt/VigoJljFGj8xcqVnbDqh/o4rxUOjnjGUHNaqpQY3sB2Zbjp7Sg7NWqWm1aLk8Hr28eJOm3dRZVcJD9MrSzfpw3W490C75QtkICG6PR3+Y/a3G39zDrjf/naXuybVL1ps12+16c+9gzdiwS68u+F7jhnbWtiNZmrlht6bdNUCHs0/rlx/P04y7B9ptyS09FRkSbLclH3yl1IY11apWNQ1Mqa8/D7pWkvTE50s0efV23Xx14JaR22P0h3mrNf76Tnb5TJiv7okJ529rR/fxtrXrNW7ANUquGq1JI7spyOFQZvYZDftgrro1jNfR3Dx98N12Tbu9l8KCnHr0ixWasXmfhqXU8+OZ/gyWQ44R98n9j2elE0flfOI1udculzL2FqXZu13u9EelgjxZna+TY+id8vznz4WHHQNuk9m+zg+Zv3TKam7lrWUbFRsRqpmj+8ljjLJOV5zFWOiXz8/tMfrDovUaP+gaxUWF6aZPF6t7/Rol++WN+xQdGqRZv+imGVsP6NWlmzWub2u7X56zWn/s1crul8/Y/bLL49HLX2/QtJtT7T5oySZ9uHaXHrim0Q/kJHCUVf0ZN7CDokK9Y5VpyzRryz71b1JxXrKlXGxlMVYJcjj0ZI+r1Sw+Vjl5Bbrhv1+qY4MEJVWrrGsbxOvRbq0U5HDo1fnf6Z2l6/V499Z+LIGLK4t7wyCHQ092aa5mNWKUk1+gGz5aoI51q5f4zkDm9nj0h69WavxN3e3r5r3Z6p5U65zrxtsH3TNIMzbs9vZBnezrZuMeTftlf/u6mThfM8YMUIOq0Uq787rC7+/2z6nq2Shw68752NfKWo0f2sG+Viam29dK8T5o/V5FhwVr1qie9r3h4o0ad529OEKdypFKu6Vrqe9965utig0P1czbetjjlTMVabxy6cf7Z++Z/3tjZ1UJD/XXqf0o5Xlf2KJmVY2dvkzv3tJD9WOj9fqiNZq6dqeGtwrswO+yGMNJ0lsrtyk2PEQzb+3mrT8F/jrFH62s2pQ7r05U+9rV7OfLaUu1aNchpdaPK5UuUNl90CqNH9HVrk/vz1H3xHPvg3ba5TKmv30ftHCNxg3uaN8HdW6urUeytO3IycL0J07n6S8L1ujTUb0UGxGmZ2Ys19Ldh9SxXuCWC+1KaYxrL2zR9gPafeyUvrx3iNYcOKLffrlCE++4rlS63325Qr/r314ta1bTPRPnK33HAaUm1tL4pevVoX68xlzbXO8sWafxS9fr8R5Xq0P9ePVIri3LsrT58HE99lm6pt87WO3rxyvNG/xw4nSe+r05VZ0q0LwC8KNZDjmG3SX327+Tso7J+fAf5d7wrXRoX1Ga/Tvl/utTUkG+rI595BhwmzwfvOa/PF8iZXFvuOPoSU1avV0TR/VRsNOhuz9ZoK5JtVSvSqUfyIl/lfdzIElad/CoTlaAe8Py7oMk6Zlpi3VPpxa6tkGCcvIL5LACe7G0K11Z1B+nw6FhLRrq1jaN9PQXpYPrRrVrotHtK25Q2aLv1mn3wcP68vXfa/XWnfrd2x9q4h+fKZVu9OA+at+8sfILXBr929e0aNU6pV7dXJJ03bVt9dxdt5R31suOZckxdLTc41+Uso7K+cDLdl98eH9RmhNH5P7kn3KkDir50SatpVoN5P7bk5IzWM57npd78/dS3ulyPomfj3ecAAC49CpY9ETFZoz5148NyPSKkfSrYt9zoCIHZErS2oPHVDemkurERCnE6dR1Tetq3rb9JdLM23pAQ5vXlyT1aVxby/YckjFGESFBalO7eoXeveJ81mYcU92YKG+ZOHRd4zqat+1AiTTzth3QUO8Lon0a1dKyPYdljFGzuCqqERUuSUqqGq0zLrfyXW6FBwepvXf19RCnQ81qVNGhUxXnRoDr5OLWHj6hutERqhMdYV83ifGat+tQiTTzdh3SUO8L630axmnZ/qMyxsiSdNrllsvjUZ7brWCnQ5Ehl0GsfkJ96fhhKeuIvQPMhm9kJZdcCU5ZR6XM/ZIxpT+/e7OUn1cuWS0P8zbu0pDWjWVZllrVjdepM3nKPJlTIk3myRxl5+WrVd14WZalIa0ba+7GXT/4vYu37VWj+KpqkmBP5sZEhMkZgKvd/pCglBZy7dsjz4F9kqtAebNnKjS1R6l0kfc8pNz/jZcpdl2EtO8k17Ytcm3dLEkyWVmSx1NueS9LVoPGMocPSkcyJLdLnm8WytGqY4k0ZvOawnri2bFJqlKt9Pe06SKz7pvLoj45GqfIc3CvTMZ+yeWSa+FXCup4zosquUX1ygoLK9a+GFlh4ZLDKYWESQUFMjkl6+Dlalv6EuUeO+7vbJS5tYeOq27lKNWpHGn3xY1qa96OjBJp5u3I0NBm9qqDfZJratneTBnvNTJn+wHVio5QUtWih4SWZRX2yS6PRy6PR6pAz4HKYgwXHhyk9t5J3RCnU83iqujQqdxyOZ9LZe2hE6pbObLoWkmupXk7zhm37Tikod6X8fskJWjZviMyxmjxnkw1qhqtJtXsifCY8JDCnZSiQoIlSS6PUYHHI6sCPTT8OWPZ4qZvO6jrvDtYGCMZGeW63DLGKLvApRreHb8D2dqDR1W3SlRRvWlWT/O27iuRZt7WfRraooEkqU+Tulq2264387bu03XN6ikkyKnaMVGqWyVKaw8e9bYlZ6+Pkm1J18RasixLlmWpRUJVZQR4fbLvlyNVJ8ZbfxrX1rztJRc4mLf9YMm2do/d1oYHBxW+aJvndpeoI26P0RnvPdEZl0s1ov6fvfsOrKrI+z/+mZtOEhIIJCEhlCSAdKRIB+kIIqLo7tpQFJe17OOz67Oru8/a22N7fivr2tf1WXXVVSMi0rtCpEqXTqgplFRIu/f8/jiXdOSu5ib34vvFP8m955zMHOacmTlnvjO+X1bOq11HWSeOSyez7DbcxpUyPaqvVG3t2SqV2W0z6+AumegqbbikFKlptKydmxoy1fXOG89WJClt28GKlfEcxlTMjuwPqJfrtjU7Vxwcn1AAACAASURBVG2imigpyl0HpbbS0gM16qADWbr6EntF1LEp8Uo/6q6XD59Qx5jIyno51K6XLcuuhyrqoNIyv6iDzvHW9RMRUqWt4nT5U7NWEuflHG+0VVpGhKlLvD0oIzwkSMkxTZXtvpcMbt+qov7umdDC59sqknf6hi3DQ9Ul1p6NPjw4SMnNI5VdWNxAOfrx7DooonodVFe56Xau3CQpPSOzstx0blNZbqIjtPX4qWr7pmdkqU10hBKjwuVPtmadttu2FWUloXZZOZBZWQeltlL6kZxa/aCa0nYe0oy+qZLc7RU/CUSUvNfe9zcN2S/MPVuioACH2jW32zMD28dr4a5D8nXeaMNJUtrOIxUrbtrXj2+uYlcXb9xTwoIC1b+13V+03y9HKcuP6h/JXQdVvZ4uaVNHG+6oru7aTpJn/aDDuUVq2yxCzZvYbfyBbeO0aHf1a9TXcF+pjXbt+S3dfViTu7e33ykntlRBcalyCqunN6fwjApLytQzsaX9Trl7ey3Zdbhi/6t7JEuSru6RrCW77c/Dg4Mq6uezpeWqq6pe+N0hDU1JUFjQRTBWATifNqmyTmZKp7Lt57fffi3TtV+1Tax926UyO4DOytgjE1V7pUB/5I2+4b6T+erRKqaiP9AvKVaLdx+u68/7jIZ+D+R0ufT8sk2630cnA6iqoeugvTm5crosDXIvahEeHEQd5OO8cf1IUt82sYry0VXMf6yl6zZr8uUDZIxRr47Jyj9zVtmnq0++GRYSrP7dOkmSgoMC1SW5jTJPXsRjfJJSZZ3MctfFTrk2r5bpUr0u1ukcKfOQZNUYAxjbWtaBnfbYwLISWZkZMp16Nlza6xFjnAAAqH9+Ez1hjGlnjNlpjHnDGLPdGLPQGBNmjFlujOnr3qaFMeag++dbjTGfGWMWGWMOGmPuMcb8xhizyRiTbow57xQM7mP+rzFmvftv9jPGfGqM2WOMeaLKdjcZY9YaY741xrxmjAlwf36bMWa3MWatpMFVtn/EGHO/++dUY8xiY8xmY8xGY0yKMSbCGLPE/ftWY8xk967PSEpx/53n3Odim/s4ocaYt93bbzLGjKiS/0+NMfPd6X7W/XmAMebvxpht7n3+s/7+lzyXVXhW8ZFhFb/HRzZRdo1gwazCM4p3r9gX6HAoMiRIuR6sRPDHeWs15e8L9Mrq7Rd8Se1Lap+TMGUX1jwnlduc75ws3HNUXWKbKbjGi6L84lIt339cA9xBmv6AcnJhWUUlio+oco4iQpVdVHLebQIdDkUGByq3uExjk+MVFhig4f+3TKPeXaHberZX9MXwoCGymayCKg8ICnKlyGaNl55Glp1fpPioytWm4ppGKKtGUGZWfpHiqgxeiosKV3aVbd5P36arX/pQf/xkmfLO2uUr40SujDGa8fYXuvYv/9JbK/1v8HZAyzi5siofKriyM+VoWf0eGdipsxxx8Sr9emX1fdu0lSxLUS+9rmb/97Ga3Dy9QdLcIKJjpFM5Fb9auSekZud/4eMYMk7WtvW1P+83XK61y72RwgbniGkpV07l4B7XiSyZmJa1tguadJ3C/5amkNt/reJXnpckla9aIqv4rMLfn6eIf8xR6SfvSYX5tfaF/6rVXokIrd2GKzpbvS4OCVRucamKSsv11vo9ussd5FGV02VpyntLNeSNeRrUJlY94/1nBjlvtuEkd7t27zEN8OFZ6OuSVXRW8ZGVgRl2u61mWSmu2MZutwUpt7hMGblFMkaaMfsbXfvhSr21cW+1/WbM/kZD/7ZI4UGBGpviP7OJ/5i2bFXz9x3XxA52voMCHHpoaFdd/dFXGv6P5dp3ulDXugce+rKsgrOKj6xsj9nXzZnzblN53ZQou+CM4iMrV7uPi2xSMRmP0+XSlL99qSEvfapB7eLVM6H6RAplTpc+335AQ3x8FvqswuIa99qwWgEJ9r2nxn3FPQP05uOnNOmdxZr8jyV6eFQvBTociosI0219UjXqzfka/vo8RYQEabCf3VeqMlEx9ovCc06f+N5BO46BY+XascG9s1HANXfIlfaWl1Ppfd54tnJuJvFZX2/Xtf9YrPvmrNGJIv8ZkEy9XLesomLFR1Stl8PqqIMqt6mzXp6zVtd+9JXe2rRPkrsOGt5VV3+wSsPfWWrXQZ39Z/U2bz6bnPHxKg19ZY7CgwM1tqPv18tVcV5s3mqrnHM0t1A7s0+rR0LtSZ8+3bJPQ5MT6jM7XuGtvuE5R/OLtDM7Tz3i/ec5Z1ZBZf0iuctNnddP1TooWLlnS5VdeLbavna5qV7mvtyZoQmd/W+Vc7t+qVlWarZti6vfV4Ir27ZH88/omn+u0C2ffK31R+1BhPkldh9pVvouXfvBCt03b71OnPGfidK80d6X7LFfd3z6taa+t0wfbTnQMJn5ERqyX9gsLETlLpe2uQeiLvzukE8HCp3jjTZcxfWzdreu/egr3bdgo39dP164p1SVX1Km5QeyNCCpdh3ty6reM6TvacNV7QcFf38/qE2zCB08VaCjeUUqd7m0ZM9Rn79uuK/URrv2/Oz2V5X3xZHhtfKXVXBWcdXaaOEV19bJomK1jLC/axEeppNVnp0s3nVIE1/9XDM/WqYnJlafCFaS5u04qIld2tVndgCfY6KaS7knKj/IPWl/dh6O/iPl+s7/xmDUxRt9ww4torThSI5yz5bobFm5Vu4/puP5vlfvVNXQ74He37BbI1Jbq2WVtqKvaug66OCpAkWGBuvXH6/QNW/N1XNLNsh5kUzCfrHydhuuLu9v2K2r3/pSf5ybrjw/WHG2pqyTuYqPqaxn4ptHK/t7Ai7zi85o2fotGtij8vnkwvSNmvybx/Qfz7+m4ydOnXdff2HXxVX6vXknZaI8fNZ6PEOOjr2koGCpSaRMclcpyr/6yecwxgkAgPrnN0GZbh0kvWxZVldJuZKuvcD23SRdI6mfpCclnbEs61JJayTdcoF9Sy3L6ivpVUmzJd3tPt6txpgYY0xnST+TNNiyrF6SnJJuNMa0kvSo7GDMIZK6nOf477nz0lPSIEnHJRVLmmJZVm9JIyS9YOzpeh6QtM+yrF6WZf1XjePcLcmyLKu7pF9IescYc+5tVC93GrtL+pkxJsn9WaJlWd3c+7xdM2HGmDvdAanr31ix8QKnybc8e+UAzZ4+Xu/+YqQ2HMnR59sPNnaSGtSeE3l6ceVWPTKmd7XPy10u3T/3G910aaqSoiPOs/dPx0+9nJyzNTtPDmO0/OYRWnjjMP198wEd9vGHlGh4P+/fVQt+e4M+ved6tYxsome/XC3JXslhY8ZxPXv9KL1759VavOOA1uzz7RmB/23GKOK+36vwz8/W/i4gUEG9eiv/T7/T6Rk3KeTy0QrqN6D2dhc503+kTNsOci38uPoXUc1lEtvJOjfQ/yeibM6/VDR9ikremqWQX9iBugGdukoul4puvEJF0yYr+NobZeITGzml8BUvf/Odbrk0tc6VqgMcRmk3jtSy28dpa9Zp7TlBMK/kbtfOWaOb+nT4SbVry12WNh47pWfHXqp3rxmsxfsyteZw5cv7Nyb314rbRqvU6dI3R058z5EuPpuzchUaGKAOze2ZGMucLn2w45A+mTpYK26+XJ2aR+qNTfsbOZWNJ8DhUNr0CVp299Xaevyk9uTkVvv+8YXr1DcpVn2T/Gfynh+iZ6vmmjNttD76xeV6Y+1ulZQ7lVdcqqX7j2vR9HFaPuMKnS1z6vOdvreCgzeYfpfLtEmVteQT+/ehE+Xavr76i8ifsJrPVpwuS5mFZ9UrIUaf3DxavVrF6LkVWxo5lb7hJ10vHz+tZ0f30rtTBmrx/iytOXLCroO2HdIn1w/Wimkj1Smmqd7YuK+xk9ugzvds8o2pQ7Vi5pV2W+VQdiOlrvFwXr5fUWmZ/iNtlR4c1adiBdFzXl29TQEOhya5V7G6WH1f31CSikrL9R9z1+rB4d1rnaOfqlKnU8v2HtW4S/wn+L0+tAwP0ZJbR+vTXwzX74d21e8WblRhaZmcLpcyC4vVq1UzffLz4eoV30zPfbW9sZPbYOpq70vSuz8bpk9uHKnXpgzSPzfv1/qfWH/5nLr6hcYYvTB5sJ5ZslE/e2e+wkOC5PDjVUY9cb42nNNlKbOoWL3im+mT64eoV1y0nlu9s7GT2yDOd085p9zl0v3zN+imnu2V5GerEntDVGiwHhrTR7+Zs0Y3v79MCVHhF/11cz7cV+pGu7aSMaba6tWjO7XR3JlX6S9Th+ullZurbZtTeEa7s3M12IcDVoGGZnoPlWmdImv57MZOis9KaRGlO/p31h0fLtOdHy3XJbHNFPATq3fOqatezi44owW7DuvGvh0bO3kNzpM6yOlyacPhbP3XqN766LYrdCS3UJ9t+em+S0RtP+/dQQtmTtKn069Qy4gwPbvEv8ZQ/7vKnU7d/79v6qYJI5QUZ09Qf3nfHlryylOa/eJDGtSjsx78y98bN5GNzNqzRa7vNingrsfluOHXsg7tqb2a5k8AY5wA32L4x7+f4D9f5W9BmQcsy/rW/fMGSe0usP0yy7IKLMvKkZQnaY77860e7Pt5lW23W5Z13LKsEkn7JSVJGiWpj6R1xphv3b8nS+ovabllWTmWZZVK+rDmgY0xkbIDI9MkybKsYsuyzsietPUpY8wWSYslJUq60DTzQyS96z7Od5IyJJ3rUS+xLCvPsqxiSTsktXWnP9kYM8sYM15SrZaPZVmvW5bV17KsvjOG9675db2IiwhTZpUZZzILzig2MqzGNk2U6Q4QK3e5VFBSpuiw71/FL849q014SJAmdm6rrcf9Z4aW2ufkrGIjap6Tym1qnpPMgjP69edr9PQV/dSmxkC4hxduVNtmkbqlTwcv56J+UU4uLC48RJlVZqrJLCxWbHjIebcpd7lUUFqu6NAgzd17XEPbtFBQgEMxYSG6NL6ZtmXnNWj6vaLgtEzVlTEjo6WC88/0dDF6P32bpsz6SFNmfaSWkU2UmVdY8V1WfqHimlZ/eR7XNFxZeZUrY2blFSnWvU2LiCYKcDjkcBhd16+zth6xVwuMjwpX33at1Cw8TGHBQRrWsY12HMuRP3HmZMkRF1/xuyM2Xq6cykGQpkm4AlM6qNkr7yjms0UK6tZTUc+/rMDOXeXKzlTppvWy8nKlkmKVfL1SQZ3ONw+Dn8k9KTWvXAXSRLeQTtceoG86X6qACT+X8+VHpPLqK5Y5+gyVtWm15HR6O7UNwnUyR46WlU0yR4s4WSfPX97LVyxU4KDLJUmBI8arfIN9Lqy803Ju36yADp29nWQ0oFrtlcLi2m248LDqdXFJuaJDg7Ul87Re+GqbRv9tgf6xaZ9eX7db722u/sKnaUiwLmvdQqsysuQvvNWGk6SHF6y327V9O9VvohtAXHiYMgsqZ2a12201y0poxTZ2u61M0aFBio8IVd+EGDULC1ZYUICGtYvVjpzq7baQwACNTI7X0gOZ8hc/pi17zry9xzUhtXKVx+9OFkiS2kQ1kTFG41PitSnT99uCcZFhyiyobI/Z102T825Ted2EKDaySbWVB7IKziiuxjXXNDRYl7WJ06r9xys+e/mrrTp1pkS/H+Wdfn99iosIrXGvPavYKquj2NuEVZyHivMTWv2+khLTVE2CA7TnRL7WHMpRYtNwNW8SoqAAh8akJujbY/7bN7TyTkrNqqzk3ayF/VkNplNPOcb9TM7XHpfKy+3P2l8ix7CJCnj0LTmmTJe5bKQcV01rqKTXK288W4kOC1ZYYIDGdLAn1hjXsbV2ZFcPcPZl1Mt1iwsPVWZh1Xr5bB11UOU2tevl5pX1ctuW2pGTr+/cL5jbRIXbdVBqK7+og87x5rNJyd1WSUnQ0n3HvJiL+sd5sXmrrVLmdOm+tFW6sms7jelUPbgubct+rdh7VM9eNajaYDpf5a2+YZnTpfvmrtWVnZI0JtW/BqrHRVbWL5K73NR5/VStg0oVHRas2Iiwavva5aayzK3af1xd4pqrRbjvr/hRk12/1CwrNdu2odXvK6V22zY4IKDi/tI1NlpJUeE6eLpI0aHu9kqK3Tcal5pQq8/oy7zR3j+3jyTFNAnRqNQEbfHxermh+4W9Elvq3ZvG6MNp49U3KVbtmjf1VtbqjTfacNGhQfb1k2y/KxiX0ko7cvxn4KA37innPLx0i9pGR+iWXskNkJP6VfWeIX1PG65qP6j0wv2gEakJ+vCm0frnTaPUvnmk2jWLrP/E1yPuK7XRrq3u/fW7NOXNuZry5ly1jAhTZn6V98UFRbX+z+Miw5RVrY1WVHFtxYSHKqfQ/i6n8IyaN6l+f5akvm3idCS3UKfPVN7L5+84pNGdkhQU4G/D14B/j5V3SoqusqJWdIz9WQ2mQ3c5Rl0r59vPSM7yBkyh93irb3htzxR9fOt4/ePG0WoaGuyT9U5VDVkv78w6rYzTBRr/6hyN/utsFZeVa9yrn8uXNGYdFN+0iS6JbaakZpEKdDg0qmOSdmT67/uhnwJvXz81tQgPs8fKGaPreqZo63H/mNT0vXnLNOX+xzXl/sfVslmUMk9WluvMU7mKjal7VciHX31XbVvFatqVoys+axYZoeAg+5381FFDtH1/hncT3wDsujim8oOoGFl5nj8rspalyfnn38v15pP2Bzm+/Uz/fBjjBABA/fO3p1olVX52SgqUVK7KfIR+z/auKr+73Pt68req7ld1XyPpHffqlb0sy+pkWdYjnmTie9woqaWkPu7VN7NUO0//jlrny7Ks05J6SlouaaakN3/E8X+wbq2aK+N0gY7kFqrU6dS8nYc0IrX6qlEjUhP02baDkqSFu46of5u4731IXe5y6fQZO8tlTpdW7Dum1BZRXstDfesW30wZuYU6klekUqdL83Yd1oiUVtW2GZHSSp9ttzs4C3cfVf82sTLGKL+4VL9K+1q/GdpdvRNbVNvnz19tU2FpmR4c0bPB8lJfKCcX1i02Shl5Z3Qk/4xdbvZlakS76qvcjGgXq892253Ahfuz1D8hRsYYtYoIVfpRu/N9pqxcm7NzldzsIljZ4niG1DxWioqRHAEyXfrJ2vvTWsXkhgHdlHbv9Uq793qN6txeszftkmVZ2nwoU5EhIWpZIyizZdNwRYQEa/OhTFmWpdmbdmlk53aSpJwqDz4X7zigDnH2w4nBHdpod+YpnS0tU7nTpXUHjym1ZfMGy2N9KN+xTYFJbeVISJQCgxQy9gqVrFpW8b1VVKgTYwfr5NVjdPLqMSrbtll599+t8p3bVZr+tQJTOkohoVJAgIJ791P5gb2NmJv6Yx3cJRObIMXESQGBcvQbLtfm9OobJaUo4KZ7Vf7yI1JB7QFe5rLL5Vq3vEHS2xBcu3bIkdBGJi5BCgxU4PAxKk9fWW0bk1D5kj3gsiFyHbVX2rKyMxXYs5/9RUioHJd0k+vIwYZKOhpAt7jo6m243Uc0Ijm+2jYjkuP12Q67TCzcc0z9k1rIGKN3rxuqxdPHafH0cbr50hTd2a+jbuyZrFNnSpRfUipJKi53avWhHCX7+ACfqrzRhpOkP6/aqsKSMj046lJvJd2rusVFKSOvqLLdtueoRrSvPgfPiPZx+uy7w5KkhXuPq39ru6wMbtNSu0/l62yZU+Uul9YdPaXU5hEqKi1XTlHlQMMVB7PU3o/acz+mLStJLsvS/H2Z1YIy48JDtO90kU6dta+h1UdO+kUbt1urGGWcqnLd7Mio47pprc+2HpAkLfzukPq3ta+bEamJmrcjQ6XlTh3JLVTGqQJ1bxWjU2eKlV/svpeUlWv1wUwlx9iDEj7evFdfHziu568a5BerFnSLb6aM01X7y0c0IrlGfzm5VY17bUsZY3Qkr0jlLnum0qP5Z7T/VKESo5qoVWSYNh8/pbNl5bIsS+mHspXc3H/utbVk7JZpWaUN13uYrC3fVN+mdbIcP7/HDsgsrGzDud55Xs6Hpsv58O1ypf1N1tqlcn3+TgNnoH5449mKMUaXp7TS2sP2pBzph7KVEuM/ZYV6uW52HVSlXt57vHa93C5Wn313RJK0cF+m+ifaddDgpJbafbKgsl4+dkqpzSIUFxGqfacLdeqs/dxp9eETflEHneON66eotFw5VV7cr9h/XO397F7LebF5o61iWZb+9GW6kmOidOtl1ScvWrX/mN76ZodenjpcYUEXepXjG7zRN7QsS39avEnJzSN0a+/UxsjWj1J3HdS62jYjOiTqs23nys3hijpoRGprzdt5qLLcnC5Q91aVzx6/3JGhCZ3bNmh+6otdVop0JM9dB+0+phHta5SV9nGVdVCVvuGpsyVyuixJ0uG8ImXkFqm1e0Kay9vHae0Re5Bg+pETSvHx+0pV3mjvnykrV5F7xb8zZeVanZGtDi18e5B2Q/cLT7qfJ5SWO/Vm+g79rJfv32e80YYzxujydrFae9R9/Rw9qZTmftSG88I9RZL+vOY7+/3ysK4Nm6F6YtdBhZXX03eHNKLG5AYjUhL02faDks71g2Iv2A86d93kFZfqn5v2aWoP3w5Y5b5SG+3a6m7o20lpd0xU2h0TNapja83eesB+p3w0R5EhwWoZUT3YoWVEE0WEBGnz0Rz7nfLWAxrZ0X4/NqJD64pVxj7bsr/i84xTBbIs+16zI/OkSsudig6rDJaZu+OgJnRp1wC5BRrZ4b0yLVrZY1kCAuXoNVjW9nXVt0loL8e1v7QDMgv9Z5KIC/FW3/BcvXMsv0iLdx/WxC6+3UdsyHp5eGqiVt17jRbfNVmL75qs0KBALZh5VYPn+fs0Zh3UrVWMCkpKdcpdhtIzMpXix2MHfwq8cf18n5wqE98s3n1EHVr6R/m48YoRSnv+T0p7/k8adVkvzV6eLsuy9O3u/YpsEqbYZrXz8f/++ZkKzpzVg7ddX+3z7NOV7xGXrt+s5MRWNXf1P0f2ycTE25PcBgTI0XOQrJ3rPdvXGKmJ+1lBfBuZVm1l7fHP8aiMcQIAoP6Zcx0PX2eMaSfpC8uyurl/v19ShKTWkjZYlvWKMeY+SfdZltXOGHOrpL6WZd3j3v6g+/cTNb+r428tl3S/ZVnrjTGXu3++sup3ks5Imi1psGVZ2caY5pIiJZVKSpfUW/YqlEslbbYs6x5jzCOSCi3Let4Yky7pGcuyPjPGhEgKkDRDUqplWfcaY0a4920vqUDSRsuy2tY8F8aY30jqalnW7caYjpIWyV4p8xc18v+FpOclbZNUallWvjGmm6R33QGgdXK+9ZDXCsiKfcf0zNJNclmWpnRP1syBXTRr1VZ1jW+ukR0SVVLu1O/npmtnVq6iQ4P1/FUDleSeTXz0q3NUWFquMqdLTUOC9Mb1w5XQNFy3/HOpyl0uOV2WBraL0+9H9FKAo55jj7246teK/cf1zPLNcrksTenWTjMHdNasr7era1wzjUxNsM/JvLXame0+JxP7Kyk6Qq+m79Qb33ynNlUGer05dajKnC6NfP1LJTePrJhZ8MZeqZrao339Jz4goP6PKT8uJ5KU3zAzUa/IyNEzq3fa56hTa83sk6JZ6/aoa8sojWwXa5+jpVu080SBokOC9PyYnkpq2kRFZeX647Kt2ne6SJbsfW/v5YWyUVNJyYW3+bGSu8kx+jrJOGRtWS1rzTyZoZNkHc+Q9m6R4tvKcc1MKbSJ5CyTCvPleusxSZLjxt9KMfFSUIh0tkiuef+QDuzwbnpTL/HaoS3L0hNzVumrPYcVGhSoJ68ZoW6t7WCHKbM+Utq99kOVbUey9YdPlqqk3KmhHdroj5OGyBij3/9rib47fkJGUmKzSD0yeXhFUOfn3+7WGys2ykga1qmt7h8/sF7TfvLZ1+v1eHUJHjRMEb95QMbh0Nk5aTrz9msKv/Mele3crtIqAZqSFP3K31X40nMq37ldkhQyfpLCb50hWZZKVq9U0awXvJ7eZr3aeP1vSJLp1k8BP/ul5HDI9fVCub78QI6rbpaVsUfW5nQF/OfTMontJPfsndapHHvFTEmKiVPg715Q+QM3Sw3Qziw+1DAz0gX0G6TQX/5GcgSobOHnKv3gbQXf/Es59+yUM32lQmb+VgGXXiaVl8sqzFfJX5+TK2O/FBqm0N8+JEcbe3BG2aI5Kvv4Xa+n978W7PL637iQ29//mzpePkQRLWKUn5WtOQ8/pdV/+0ejpefll3/ltWOvOJCpZ1ZuteviLm0187JOmrVmp7rGRWtkciu7Ll6wQTtz8hQdGqTnr+inpKjqAfJ/Sd+pJkGBmt6ng3bl5OnBRRvlcllyydL4Dom6q7+X6oqQ2jOF1of6bsNFhARp5Ctz7HZtoN3uvPHSVE3tmVL/iT9TdOFtfqAVB7P0zKod7rKSpJl9O2jWN7vUNTZKI9vH2+dl0bfaeSLPbreN611RVj7fdURvbNgrI6NhbVvq/sFddOJMie76Yq1KnS65LOmyxBg9MLSLAr3Rti33zozMP7QtK0lrj57Ui9/s1gfXVG+DfLD9kN7dmqFAh1FCZJieGtG91goq9SK6fiekWLHvqJ5ZvNE+Fz2SNXNQN81auUVdWzXXyA6t7XMxZ7V2Zp1WdFiwnp88pOK6eXX1NqVt2a8Ah9EDo/poWEqCdmWf1oNfpMtlWXJZlsZf0kZ3DekuSer+P/9UQlS4mgTbg8HGdEyq+O5HKzl74W1+gBUHMvXM8i1yWdKUrm01s38nzVq9w+4vp7jvtfPXa2d2nn1fmdBPSdHh+nzHIb2xbrcCAxxyGOlX/S/RaPdAzFmrd2r+7iMKcBh1bhmtx8dcquDA+u/bWtu21fsx62K69JVj6gzJOORKXyRrwUdyTLxR1qE9srauleOeJ2QS2kr59qyv1ukcuV57vPox+o+SadNBrn+96t209vDexFH1/WwlpkmojuYX6YEv16mgpEzNmgTryXH9lNC0yfek4gfyk2crDVovFxbU/zHdVmRk65mvdtj3lUtaa2bfT8IlcAAAIABJREFUVM1au9uug9rH2edlyWbtdK+g9PyYS5XkHqT++a6jemPjPhkjDWsTq/sH2W21D7Zl6N0tBxXocNh10Kge3qmDwrxQ/lT/149lSXelfe1uq1i6LKmlHhjR0zttFS/y6/MSVH/lr77bKhsOZ+vm9xarY8tonYt/uG94Tw1PSdS4Vz9XmdOlKPdqVT0TWuiR8ZfVT0bOnrnwNj9QffcNNxw9qZs/XqWOMU0rz9GgLhpeI9imXoR6Z8XJFfuO6ZklGyvroEFdNWvVFncd5C43X6ypLDdXDa5SbrYrbau73IzsrWEpdhvuTGm5Rr0yWwtnTlJkiBfuseecKfTaoe2+4Xb7vtIlSTP7ddSs9O/UNTZaI5PP9Q032WUlJFjPj7f7hgv3HtOsb3Yp0GG3be/p36ki+Opo/hk9sGiT3V4JC9GTo3sqIbKe64tA7wWT1Hd7/3BukX49x55ortxlaeIlSZrZ30ure4fU3/XTkP3C55Zu0op9R+WyLP380g66pV89PnvK997K8t5owx0tOKsHFn+rgpJyNQsL1pMjeyjhAiun/CDeau/X8z0ls/CsRr69WMnNIirfL/dop6ldvRDo4KX6R3K34ZZuss9L9/Z2P+irbeoa30wjU8/1g76pbMNNGlDZD3rti+r9oOuGKbVFlO6fs0bfuVcivmtgF03o7KV3NgH1d7+9aO4r9eiiadfWc//Bsiw9sWCdvtp/zH6nfOVAdXMHLEx5c67S7pgoSdp2/KT+MGe1/U45JUF/HNtPxhjlninRf6at0vH8IiVEhevFKUMVHRaiN9ds1+yt+xXocCg0KED3j+ytPkn2u+qjuYW68f8WaOm919TrxHEB0/5Ub8fCxav8/qkN/jfNJZfKMfk2+/ntuqWylnwqx7ifyTq8T9aO9XLc+ZBMqzaVz29zT8j19v80XPq6dPPasb3RN7zpvcXKPVuiIIdDvxt5qQa280JfWZLq8f7UkPVyVX1e+Egbfnt9rc9/sIugDlp94LieXbxBlqSu8c31yIT+Cq6H9jp1UHX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vxvVXu6Yh6tnSmnz8wIBOWvqDW/TFHSO1N+OyNqZ69/OxO/orAX5++kXcQH01b5o+uWeiPt57svKcb67bp8dG9lXC3Hj9eHRfvbluf51r8jbuaINOXszTZ4dS9OkPblXCPXFan5yp1NyChkjed1bZHj06Uy/FD9NLK3bUe9zLy3fo5anDtOJRR3t02mqPuraK0J/mjKkzthLROFj/c+dYLXp4ul6fPkLPLNpS32kb1MbkdKVezteKR6brpck366WVu+o97uWVO/Xy5KFa8ch0pV7O16Zkq//1wfYjGtahtVY8MkPDOrSubIu/7ryNAvyVMDdeCXPj6wRkHsq4pLziUjel9vq5q+8iWXXYW+v2aURn3wiaquCuPHlwWG8lPDRVCQ9N1U/H3qQhMZE+E5ApMWZbwV33R7uIMP39RxO0aN40zR/VVy848vdkdq4+239Snz4wRQkPTdX6U9bfwZvdyG3QtWw8labUS3la8ePb9NK04Xpp6bZ6j3t52Xa9PG2EVvz4NqVeytMmR9DPiM5ttejRWVo4f6Y6tmiqBZutcYaukc302bzpSnhkpt6/e4Je/Gpbjedpb+Ou8jOsY1Rl/frKtOF6fmlV/fTA0F56Y8ZI9yfORdxZ17Zv1qTyHbOvBGRK7qtTukZG6LMH45Uwb5revytOLy7f7tXlR/J8G9SpRXhl2fp87hQFB/prXPf2nkmskzxdr2w4dV5HMnP05UNT9cn9U/S3HUdUUOJdfX1PjyPMHdpLCx+0noXGxEbrf7YcqnNN3sBd90p4SCM9O2GwHhhac2HLrPxC/XPXMX32wBQtfni6bHZTy46kuDWNgLtZ49gH9d7MYVryozgtO5GmU5dqjdceOWuNwd033hrH3lJrHHvjYY3uUPN94OsbkjSqQ6SW3hOnL+++VZ195H2Qp+sVb382dEf7I0mz+3bW+/UsrvL88h166tYBWvTgVI3r1l4f7jhS5xhv4Ol8eWvWqMoxygnd22tCN+/qu+HreXqcNiQgQK9PH6ElD0/X+3fF6fXVu716HLs6d42z+Bp3zSmVpNUnzqtxkO/ti2XNd9qr9+aM1pK5k7Ts6Nl68uSMNUdwXrw1n2fDQUmOPBnVRz+/tV+N40ODApVw/8TKf22bhmpC13YeSxMAAA2NoEwvZBjGi4ZhPG0YxsuGYYx34XknGIaxxzCMJMf/xrnq3N/F2uPnNLNvZxmGof7tWim/uFQX8gtrHHMhv1AFJWXq366VDMPQzL6dteb4OUlSl5YR6tQi/Gt/x9JDZzSldye3pcGVkjJyFBPRRO0jwhTk768pPWO01vGQU2HtyXTN6tNRkjSxezttP5sl0zTVOChAg9q1qnfVwIpVrsrtpspsdhluT8n1W3vinGb27WTdG9GOe6Og1r1R4Lg3oivujU6V98baE+c0q19nSdKsfp215oT186WHz2hC9/ZqG27tltLCsXOZYRgKDarIJ7vKbXbJy3PKneUn8fhZRUeEKbZlhNvT4SqUnypWXoTVzIuTNYNJ1548r1l9rLpxYo/22p6aKdM0tfbkeU3pGaOgAH+1iwhTTESYkjJyanx3e2qWYiLCFO0oR5/sO6mHhvVSkCP/KsqVt0rKzlVMeKjaN22sIH8/TYlto7Up2TWOWZuSrVndrQluE7tEaXvaJZmmqS3nLqpbiybq4ZisHhEcJH8/Q+fyCtUhPFTNHZMEh7drodXJvhV8mJR1WTERoWofHmrlS7e2WpucWeOYtWcyNauHNWAyMbaNtp+/INM0r3nOkMAADW1nBRoG+fupV2S4sgqK3ZcIN1h7NEUzb+pu1bUxUcovLtGFvKs1jrmQd1UFJaXqHxNl1bU3ddeaoylO/46lB09qSl/v3VEoKeOSYppVq1N6dai/TulbUafEaHtqVlWd0qtDVZ3SLExJGZfqtrt2e2WzO6ZLtAzDkGEY6tumhTJrtW3ewtN1bZeW4erkWKXfFyRlVs8fP03p3l5rT6XXOGbtqXTN6m0FKk/sFq3tZ7NlmqZ6tW6myLAQSVJsi6YqLreptNxm1Skx1ktoq05ppqz8Is8m7DpZ+RKq9hGOurZ7O609XTOwdO3pDM3qZe04MLFrW20/a9W1IYEBCnDs1l1is8kwqnolmflF2nAmS7c7+jm+wNP9/VZhjStXSQ1tFKjOLcKVXeBb98+1nNq0VYU5lxv6MtwuKeuyYsLDqvVV2tXtqyRn1iw/56r6Komn0xXdtLFiW9ScrGKVn0zd3sd7d8r5OtfTt61u6cl0TXHsfBgS6K+h0VZ5CfL3U69WTZV11bv7cO7or7QKC6nc3c+qN5oq29EvMQxrxVdJKigpU2STEA+m9rtxRxt0Oidf/aKaV34+pF1LJZ6s2d57u7Unzmlmv87V2qOy+sdWSqu1R9XaFrPjygAAIABJREFUHauPVndspVdU88pdT2JbRVT2Z7zJ2pNpmtmnoi1uqfySUl2o1TZeKChSQUm5+ke3tNLep5PWOMqWVaYcbXHfztV+/s3nrc1mt+uP6/bp6bHeu2Kyu/oukvR/u49rQvcYtWjs3eMptbkzTyosO5yieB9Zeb4CY7YWd90fN7VrVbl7Qf+2LZWVZ53z9KUr6hfdsqpNiolU4vGz8mY3cht0LWuPn9XM/l0c5SfSakPqLT+l6t8u0sqT/l20xvG3HtklurLP0r9dK2U67o8afZlymwwvH+R3V/kJDQqs7McVlZbXyIfhndoo1IcmEXri/bKvcVed8nXjUd7K021QddtTMhXTrImiw8PcmcRvzdP1yqmLVzQ4JlIBfn5qHBSgbpHNtOm0dy2u5+lxhOq7khWVlXttW+Sue6VFaLD6tm2pAL+6CbfZTRWX21Rut6u43Fb5bgDwVXXeuXeNrn8cu6cVBDYxto22n7tYbRw7Q9HhjRVbLegyv6RMu9NzdHtva9wuyN9PTb10t8PaPF2vePuzoTvaH0kaHBOp8OCgOr8v5XJ+5YK/IzpFadXxumMv3sDT+VLBNE2tPHZW8b188/3QjcrT47QdWzRVx+bWPI3IJo3VIjRYOYXe/c6sgrvGWXyNu+aUXi0t0993Hdcjw3vV+czbJWXk1Kx3e8TUM58nTbN6d5TkfJ5USMnJV05hsQa1887NCQAAcAeCMr2YaZrPm6aZ6MJTXpQ03TTNvpLuk/SRC8/9rWXnFyqqaWjl/2/dtLGyanX8s/IL1bpJ9WNCKwevnbHiyBlN9ZGgzKyCIkVVm8gX1aSxsmtNNM8qKFRUU+uFeoCfn5o0ClRu0TevvjPv3xs0+s8LFRoUoIndvX8FkuyCopr3RpPQOpPus/KL1NqRFxXHVEysvnS1WK3CrM9ahobokmMCaUpOvvKKS3XfP1dpzofLtCgpufL7Nrtdsz9YqlH/9blGdGqj/tHe/VDgrvJztbRMf916SI/dUv8KP96K8lMlK78qnZIjLwpq50WRoppUz4sg5RaVOspe9XJV975adjRV8T2rBiVTLudrz7kL+o9/rNK9HydWDnB6q6yrxYqqFjgaFRqs7FqTzLMKihUVZh0T4OenJkEByi0uU2ruVRmGNO+rXbr9sy366z6rDokJD1VKboHS8gpVbrdrzZksZfpYoEfW1WJFVXvRGRUWrOyCevLFUc6sfAlUrmMFuLS8Qt32rw2694st2p1W9x7IKynT+jNZGtbeu+vW2rLzriqq2gSK1k3DlFUrKDMr76pah1era8NDlV3tmI+3H9KsP32q575YpytFJXV+x4qk05ra33uDMrPyixRVrS2x6tfa7U3VMVX1a4nVVjWpXadYZcNmt2v2h8s06k9fakTHKPWvtVNomc2uxYfPaJSX7hbj6brW19Rtl0OukT/V6pR62uVVJ9PUK7JZZeB/hbziUq1PztCwGN/YObRC9XpUkqLCQuqpa2vfN1V17YGMHE3/e6JmfrRGL4wbUPkS5I31B/X06N6qZ06L12qI/n6FtNwCHc3KUb+2LVyeLrhPnXolLLhuvXK1qLI/Y5WfAOUWl+pqabn+uvukHhvao85539iYpKdH9fGp8lPd9fRtq1txOkNTY+u2uXklZVqfkq1h7by7vLirv1IhLbdAR7Mvq5+jv/LMuEH6w7p9intnof6wdp+eHDPAXUlzGXe0QV1bNNGetIvKLSpRUVm5NqZkKsPHnoPqH1uppz2qdY98m7HJVcfOqldU8zr9mYZW/71f37hSSI1jKtJutcXWZy1Dgyvb4q87b2m5TXf87wrd9Y+VSqw22eXjPSc0NrZd5fm8kbv6Lln5hUo8fk53Derm7iS4nLvypEJRWbk2JadrQo8YdyXBLRiztbj7/pCkLw6c1ugu1qISXVtFaM+5bOUWOtqk0+nK8PKJYjdyG3QtdfKkSWj95afWMfXlyZf7TtbYVfbA+Qua/peFmvnuIr0wdXjl87Q3cmf5STx+VlPfXaz5/16nV6b6zi6Qtbnz/XJaboFuW7BE9/5jhXaf9Z2FF91ZpxxIu6Dp7y3WzPe/0guTh3p1+ZE83wZVt+xIqlcuKOHpeqVHZDNtTs5QUVm5LhcWa2dqljLza75XaWieHkeQpP/acEBx7yzUV4dT9MTomrvKeAtPlJ/qWjdprAeG9tK4PydozH9/obBGgRrZuW7ZAnyJNRZbaxz7au33Y8U1x7GDqo1j7zmlx27uXuP483mFah4SpOcS9+u2j9frN4n7VVhW7va0uIKn6xVvfzZ0d/tTW2zL8MqF1FYeO+u1iyF7Ol8q7Dl3QS1CgysD7uAbGnKc9mD6RZXZ7Ipp5hu7FbtznMWXuGtO6dubDun+Id0VEug7i1xVqP4+UPqa+TzV8yTIuXm2krTs2FlN7t7eJxZ2AgDAVbx71PwGYhjGc4ZhnDAMY7Ok7o6f/a9hGHMc//28YRi7DMM4ZBjG+4ajx2IYxhDDMA4ahrHfMIw/GIZx6Fq/wzTNfaZpVixpcVhSiGEYjeq5locNw9htGMbuBet2ujytnnIg7YKCAwPUNbJZQ19Kg1tw5xhteHymSm127Tib/c1f+B6p2G1LsgJADmfm6C93xmnBXXH6y+YkpVzKkyT5+/kp4aGpWvfEbUpKv6ST2bkNedkN5p2N+3Xv0F6VO5jhxi4/tZXabFp3Kk2TerSv/JnNbupKcYk+uWeCnr71Jj21aMvX7p7oy8pNU3szLuv34/rrn7OGKfFMlradv6jwRoF6/pbeemr1ft2zcIfaNg2R3w00sNAqtJHW3D9eX/5gjH45urd+sWqvCkqrJvqX2+16esUe/ah/J7WvFrx4I7hraG+t/Nnd+vLHd6pVk8b6/bKtNT4/cC7L6qu09u5AB3fw9/NTwtx4rXt8lpIyLunkhZrt7m9X7dLg9pGVq3feSOqra29EJy9e0Vsbk/TihIE1fl5ut+vppTv0o5ti1T7Cu1add7f+bZpryX3j9e8f3KoFO0+opNym9ckZat64kXq3vnGfeZzt70vWZPaffLlRvxo/WGGNrr06Lr5f3tlxTPfeFFtn55f1yZlqHtJIvVt7/25T7nQgK1fBAf7qWmsX0XK7XU+v3q8f9e2g9tVeXt9orpaW6ScJm/SrcYMqd7b4ZN9JPRM3UGsfn6Vfjhuo3yzb3sBX6X71tUFdWjTVQ0O66aEvt+rhhK3q0SpC/jfQc5AzTl7I1Vtr9+rF+GENfSluZRiGnPnLJz42U5/dP1l/mDFSbyTu1dnL+crOL9TK4+f0w8G+F5T4XVXvu7y+erd+FnfTDTWGUJ/qeVJh/cnzGtiulSJC6ry+uCHdyGO29d0fO1Iy9eWBU/rZWOt5sUvLcD00rLce+mSNHv5krXpENpO/r6664SI3ShtUn3c3HZC/n5+mO3Z0lqwdHZY8Okv/fmiaFmxOUkm5b0xiv161y8/47jFaOn+G/jxnjP608UADXpl3ahUWojVP3K4v503XLycM0S8SNqqgxLkJh99n/aNbackjM/TvufFasPWQSnxk911XcKYNqlBqs2ndyfOa5GMLSnxbztQrIzu31egubXX331fq6YWb1T+65Q31rFjfOIIkPTmmv9Y+PkvTenfU/+050YBX6Bn1lZ/arhSVaO3Jc1r92Cyt/8/bVVRWrsWH6i6yB9wo3tlxXPcO6FxnHNtmN3Uk+4r+o29HfXn3rQoJ9NcHu0810FU2HGfqFZ4Na3olfqg+2XtSc/62XFdLyxXo5YtreNrSoyk+vUgyrt+3Gae9UFCoZxZv0avTht9wY7n1jbPc6I5mXda53AKN7+b9m3s0hGXHzmlqz+/3szEAALX53jIN30OGYQySdJekAbL+Jnsl7al12J9N03zZcfxHkqZJWiLpb5Lmmaa5zTCMN77Fr71d0l7TNOts2WSa5vuS3pck20evuTSy5uPdx/TZPmuQuW+blsqstpNUVl5hjRU6pYoVjKofc1WRTZybDLj88BnF+8gumZLUOixEmdVWYcnML1Rkk5BaxzRWZp61ylO53a78kjJFhDg3mbhRgL/iYttq7ck0jegY5dJrd4WPdx/XZ/utgcO+bVvUvDfyr9ZYmV+SWjcJUVa1Fc2y8q8q0rGaXIvQYF0oKFSrsMa6UFCo5o0bOb7TWOEhjdQ4KECNgwI0OCZSx7Ivq2OLqlWvmgYH6eYOrbUpOV1dI71rcq4nys/BtItadTRVb67ZrfziUhmGoUYB/vrhkJ4uTInr3ejlp7rWTax0VsjML6wsG5XHhIUo07HLm5UXpYoICVJkWEiN71qrSVfdM5uSM9SrdXO1DK25+9mEbtbqRv3atpCfYehyUYmaNw6WN2odGqzMaisoZl4tVmRozWttHRasTMcKleV2u/JLyxURHKio0GANbtNczRz3zS0xrXTkQp6Gt2upsR1ba2zH1pKkfx8563MvmFuHBtfY3TOzoFiRYfXkS35RtXwpU0RwkAzDUFCIteJ+78gItQ8PVcrlq+rjCHB4Ye1BdYgI070DfGOA7uPth/TZriOSpL7tIpV5paDys6y8ghqrxEnWKutZV6rVtVeuKtJxTMuwqvJzx5CeevQfy2p8d/nBU4rv5727ZEpWe1t9FWurfq3d3ljHVNUpZYoIaaTIJo1rrLhZe6cdydHuxrTWpuQMdW1l3TPvbE5STmGJ/nTbzW5M2fXxdF3ra+q2y0XXyJ+ietvlzPxC/efibXp9yhDF1Aq8fGHVXnVo1kT3Durq/oS4WEU9WiGzoKieutZx3zQJqcqX4Jr9lS4tmqpxkL9OXszT3vQcrUvO0MaULJWU23S1tFy/WL5bv58y2CNp+jYaur9fZrPryS82alrvjj634xLqqVcKiuvWK6EhynSsdmqVn3JFBAfpYOZlrTqZpjc3H1J+SVnlM05WQZHWncnQxg8zVWKzW+VnxW79frL3lZ9ruZ6+bYXlpzIUH1t3F4IXNhxSh4hQ3dvf+8dV3NVfKbPZ9WTCJqve6F61WMKiQ2f07PhBkqTJPWL0/PId7kyeS7ijDeoT1Uy39+mo2/t0lCT9v82Ha6w27K0+3n1cn+07Kame9iivbn+1dZOQGqtHZ9Vzf9UnM++q/vPz9Xp9xkivWTn74z0n9NkBR1vcpkU9935940pFNY6pSLvVFhepVViILhQUqbmj7qm/TDWuPJ8ktY8I080xkTqadVnBAf5KvZyvye8ukSQVl5Vr0ruLtXL+DFcn/1vzRN/lcMYl/WzhZknS5cISbTydJn8/P43v7p0LtHgiTyosO5Kq+N4d3ZQS12LM1uKp++N49mU9v2y73vuPOEVU+/ntA2J1+wBrjOX/rd9XY7V3b3Ejt0HX8vGuo/psr6P8tG1Zz31TT/mpdUz1PEnYf1IbTpzXh/dOqnfCdpdWEWocFKCT2bnqU233sobmyfpVkgbHtNb53G26XFisZl46jl+bJ+raoAD/yp1le7dpofbNmijlUp5X3SvVeapOqdClZXi18uNdiww2dBskSZtOp6tXVHO19JLd3xu6Xpk/sq/mj+wrSfr5ws3q4GU7UHl6HKG6ab06av5n671mt0xP3yvVbUvJVHREWOXz5ITuMdp//qJm9PGN94lAfayx2Frj2KG1348F1xzHLnWMY2dd1qpT6Xpzy5GqcWx/f02MbaPWYcHqH2Utzjkxtq0+2HPSo+n6NhqyXpG8+9nQ3e/da+vcIlwf3BUnSUrJydPG02kuTI3reDpfJGtRysTj5/XZ/ZNdlxC4TUOP0xaUlGr+p+v0kzED1D+6lVvTer08Pc7iC9wxp/RA+iUdyszR+HeXyGY3damwRPf9a63+/oM4t6XDlSreB1a45nye6nlS6tw822PZubLZ7eod1dzl1w0AgDdjCRzvMFpSgmmahaZp5klaXM8xYw3D2GEYRpKkOEm9DcOIkNTENM1tjmM+duaXGYbRW9LvJD3igmv/Vu4e3EMJ82YoYd4Mjeseo0VJyTJNUwfOX1CT4EC1qtXxb9WkscIaBerA+QsyTVOLkpIV58SkFLtpasXRFJ8KyuzTprlSL+frfG6BSm02LT96VmNjo2scMza2rRYeSpEkrTp+XkNjWn/tA8/V0jJdcAz4ldvt2nA6Q51aeNdLjwp3D+6uhIemKuGhqRrXrZ0WJZ2x7o20C2rSKEitwmrdG2GOeyOt4t44o7hu1r0xtms7LTxoraK48GBy5c/jurXX3nMXVG63q6isXAfTLqpLi3DlXC1WXrG12m1xWbm2nslQZy/MJ0+Un3/eN0WJT8xR4hNzdM/NvfTwyL5eNbnnWm708lNd/XlRc2WmsV2jtfDQGUnSqmPnKvNibGw7LT96VqXlNp3PLVDq5Xz1bVP1kLzsSGqdleLiurbTzrNZkqyB3DKbXc28eBeDPpHhSs29qvN5hSq12bX8VIbGdqy5E9/YjpFaeNwakF51OlNDo1vIMAyNjGmlEzn5Kiqzqdxu1670HMU2t4KFLhVaaxxcKSnTvw6d1Zye3jmB8lr6tI6w8uWKI19OpGtsp5oByGM7tdbCY+clSatOZWhou5YyDEM5RSWy2a01HM5duarU3KtqF27VR/+97ZgKSsv0q1t6ezZB1+HuYX2U8MSdSnjiTo3r2UmL9h236tqzmWrSqJFa1QrKbNU0VGGNgnTgbKZV1+47rrieHSVJF6oNXiYeOVNjR0y73dSKpNOK7+fdgWV92rRQak61OuVIaj31azstTKqoU85qaIeKOiVay4+kVtUpOfnq26aFcgprtbspmZXt7ucHTmnLmQz9ccYIr15h0NN1ra/pE9VMqbkFOn/lqlWnHD+nsV3a1DhmbJc2Wng4VZK06kSahsZEyjAM5RWX6tGELXpqdF8NjK456e2/Nx+y6pSx/T2WFlfqE9VMqZer58t5je1cK186t9HCI2clSatOpmto+1YyDEPnr1xVud0uSUrLK1RyToGiwxvrqVG9tW7eFCU+OElvxg/R0PYtvTIgU2rY/r5pmvrN0m3q3DJc9w/t5dmEwyWsvkq18nPivMZ2rtVX6RxVq/xYfZV/3jFaiXMnKXHuJN1zUxc9PKSbfti/s54a2VvrHpysxLmT9OaUwRrarqVPBWRK19e3lRxjJ6czFN+1Zl303ztOqKCkXL8a6f3PgpJ7+iumaeo3y7arc4tw3X9zzXyIDAvRrrPZkqTtqVnq4OXBDpJ72iCp6jkoPa9QiafSNbW7968KfPfg7kqYN00J86ZpXLf2WnQwuVp7dI2xlaBq7VG1duda8opL9ein6/TU2IEa6EU7v989qJsS5sYrYW68xnVtp0WHKtrii1baa710bxUWorBGATqQdtFK+6EziutqlS2rTDna4qRkxXW1/vZxsdH1nvdKcalKHbsqXS4s1t60C+rSMlxjYqO16YnblPjYTCU+NlPBgQFeEZApeabvsvrx2Up0/JvUI0a/mXSz1wZkSp7JE0nKLy7VrrNZiuvqvXlRHWO2Fk/cH+lXruo/v9igN2aMrLHQoiRdcixUkX7lqhKPndNUL3w3dCO3Qddy95CeSnhkphIemWmVnwOnHeUn27pv6i0/QTpwPtvKkwOnFdfdWnRn06n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\n",
             "text/plain": [
               "<Figure size 5040x5040 with 2 Axes>"
             ]
@@ -493,7 +492,7 @@
           "height": 328
         },
         "id": "WecHbHDEG6J9",
-        "outputId": "6c66249a-6e4a-4775-faed-6314dcb9793e"
+        "outputId": "fb9b4453-a67d-410f-f346-f17ee5c419fb"
       },
       "source": [
         "df_age = data[data.readmitted != 'NO'].groupby('age').count()[['readmitted']].reset_index()\n",
@@ -501,7 +500,7 @@
         "plt.title('Age vs. Readmission Count')\n",
         "plt.show()"
       ],
-      "execution_count": null,
+      "execution_count": 85,
       "outputs": [
         {
           "output_type": "display_data",
@@ -526,7 +525,7 @@
           "height": 368
         },
         "id": "dhWiU5o5J7Bq",
-        "outputId": "261b7242-ce3d-46ee-9119-8912f0755d42"
+        "outputId": "2cc5dd90-2c8d-49f7-9831-b19622c870ba"
       },
       "source": [
         "df_race = data[data.readmitted != 'NO'].groupby('race').count()[['readmitted']].reset_index()\n",
@@ -534,7 +533,7 @@
         "plt.title('Race vs. Readmission Count')\n",
         "plt.show()"
       ],
-      "execution_count": null,
+      "execution_count": 81,
       "outputs": [
         {
           "output_type": "display_data",
@@ -559,7 +558,7 @@
           "height": 299
         },
         "id": "ItBC8iteLyV9",
-        "outputId": "35ae35e9-c271-4ab5-eb7b-baa7e77aff04"
+        "outputId": "12485124-8962-475e-9255-74c997bdf51b"
       },
       "source": [
         "# Number of inpatient visits = Number of inpatient visits of the patient in the year preceding the encounter\n",
@@ -568,7 +567,7 @@
         "plt.title('Number Inpatient vs. Readmission Count')\n",
         "plt.show()"
       ],
-      "execution_count": null,
+      "execution_count": 86,
       "outputs": [
         {
           "output_type": "display_data",
@@ -597,80 +596,13 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "j0yXB2GnEMpc",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "c544f134-35b9-4a52-a289-fcbf6463c48a"
+        "id": "j0yXB2GnEMpc"
       },
       "source": [
         "data.isin(['?']).sum(axis=0)"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "encounter_id                    0\n",
-              "patient_nbr                     0\n",
-              "race                         2273\n",
-              "gender                          0\n",
-              "age                             0\n",
-              "weight                      98569\n",
-              "admission_type_id               0\n",
-              "discharge_disposition_id        0\n",
-              "admission_source_id             0\n",
-              "time_in_hospital                0\n",
-              "payer_code                  40256\n",
-              "medical_specialty           49949\n",
-              "num_lab_procedures              0\n",
-              "num_procedures                  0\n",
-              "num_medications                 0\n",
-              "number_outpatient               0\n",
-              "number_emergency                0\n",
-              "number_inpatient                0\n",
-              "diag_1                         21\n",
-              "diag_2                        358\n",
-              "diag_3                       1423\n",
-              "number_diagnoses                0\n",
-              "max_glu_serum                   0\n",
-              "A1Cresult                       0\n",
-              "metformin                       0\n",
-              "repaglinide                     0\n",
-              "nateglinide                     0\n",
-              "chlorpropamide                  0\n",
-              "glimepiride                     0\n",
-              "acetohexamide                   0\n",
-              "glipizide                       0\n",
-              "glyburide                       0\n",
-              "tolbutamide                     0\n",
-              "pioglitazone                    0\n",
-              "rosiglitazone                   0\n",
-              "acarbose                        0\n",
-              "miglitol                        0\n",
-              "troglitazone                    0\n",
-              "tolazamide                      0\n",
-              "examide                         0\n",
-              "citoglipton                     0\n",
-              "insulin                         0\n",
-              "glyburide-metformin             0\n",
-              "glipizide-metformin             0\n",
-              "glimepiride-pioglitazone        0\n",
-              "metformin-rosiglitazone         0\n",
-              "metformin-pioglitazone          0\n",
-              "change                          0\n",
-              "diabetesMed                     0\n",
-              "readmitted                      0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 41
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -683,81 +615,19 @@
         "for col_name in data:\n",
         "  data = data.loc[data[col_name] != '?'] "
       ],
-      "execution_count": null,
+      "execution_count": 91,
       "outputs": []
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "6SDi5yHZPmEN",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9e053c22-53d3-4a0a-92f5-f1712df66a13"
+        "id": "6SDi5yHZPmEN"
       },
       "source": [
         "data.isin(['?']).sum(axis=0)"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "race                        0\n",
-              "gender                      0\n",
-              "age                         0\n",
-              "admission_type_id           0\n",
-              "discharge_disposition_id    0\n",
-              "admission_source_id         0\n",
-              "time_in_hospital            0\n",
-              "num_lab_procedures          0\n",
-              "num_procedures              0\n",
-              "num_medications             0\n",
-              "number_outpatient           0\n",
-              "number_emergency            0\n",
-              "number_inpatient            0\n",
-              "diag_1                      0\n",
-              "diag_2                      0\n",
-              "diag_3                      0\n",
-              "number_diagnoses            0\n",
-              "max_glu_serum               0\n",
-              "A1Cresult                   0\n",
-              "metformin                   0\n",
-              "repaglinide                 0\n",
-              "nateglinide                 0\n",
-              "chlorpropamide              0\n",
-              "glimepiride                 0\n",
-              "acetohexamide               0\n",
-              "glipizide                   0\n",
-              "glyburide                   0\n",
-              "tolbutamide                 0\n",
-              "pioglitazone                0\n",
-              "rosiglitazone               0\n",
-              "acarbose                    0\n",
-              "miglitol                    0\n",
-              "troglitazone                0\n",
-              "tolazamide                  0\n",
-              "examide                     0\n",
-              "citoglipton                 0\n",
-              "insulin                     0\n",
-              "glyburide-metformin         0\n",
-              "glipizide-metformin         0\n",
-              "glimepiride-pioglitazone    0\n",
-              "metformin-rosiglitazone     0\n",
-              "metformin-pioglitazone      0\n",
-              "change                      0\n",
-              "diabetesMed                 0\n",
-              "readmitted                  0\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 43
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -766,13 +636,13 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "DPc6kqQbP58A",
-        "outputId": "9a405bf3-7a53-49a3-f9cc-af464a4628dd"
+        "outputId": "d22465a9-f28e-49bc-ec18-92eff77ac602"
       },
       "source": [
         "count_row = data.shape[0]\n",
         "print(count_row)"
       ],
-      "execution_count": null,
+      "execution_count": 93,
       "outputs": [
         {
           "output_type": "stream",
@@ -786,17 +656,30 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "edEOZHd8QfK_",
+        "id": "edEOZHd8QfK_"
+      },
+      "source": [
+        "data.head()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 326
         },
-        "outputId": "4153c466-2cef-4408-a5a3-322d0da1eebc"
+        "id": "7UI2L_J-uhto",
+        "outputId": "6a05843c-d4cb-4fdd-cb14-a6a68f194bbd"
       },
       "source": [
+        "# convert readmitted data to be 0 for NO and 1 for <30 or > 30\n",
+        "data[\"readmitted\"].replace({\"NO\": 0, \">30\": 1, \"<30\": 1}, inplace=True)\n",
         "data.head()"
       ],
-      "execution_count": null,
+      "execution_count": 94,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -914,7 +797,7 @@
               "      <td>No</td>\n",
               "      <td>Ch</td>\n",
               "      <td>Yes</td>\n",
-              "      <td>&gt;30</td>\n",
+              "      <td>1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
@@ -962,7 +845,7 @@
               "      <td>No</td>\n",
               "      <td>No</td>\n",
               "      <td>Yes</td>\n",
-              "      <td>NO</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
@@ -1010,7 +893,7 @@
               "      <td>No</td>\n",
               "      <td>Ch</td>\n",
               "      <td>Yes</td>\n",
-              "      <td>NO</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
@@ -1058,7 +941,7 @@
               "      <td>No</td>\n",
               "      <td>Ch</td>\n",
               "      <td>Yes</td>\n",
-              "      <td>NO</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>5</th>\n",
@@ -1106,7 +989,7 @@
               "      <td>No</td>\n",
               "      <td>No</td>\n",
               "      <td>Yes</td>\n",
-              "      <td>&gt;30</td>\n",
+              "      <td>1</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1114,11 +997,11 @@
             ],
             "text/plain": [
               "              race  gender      age  ...  change  diabetesMed  readmitted\n",
-              "1        Caucasian  Female  [10-20)  ...      Ch          Yes         >30\n",
-              "2  AfricanAmerican  Female  [20-30)  ...      No          Yes          NO\n",
-              "3        Caucasian    Male  [30-40)  ...      Ch          Yes          NO\n",
-              "4        Caucasian    Male  [40-50)  ...      Ch          Yes          NO\n",
-              "5        Caucasian    Male  [50-60)  ...      No          Yes         >30\n",
+              "1        Caucasian  Female  [10-20)  ...      Ch          Yes           1\n",
+              "2  AfricanAmerican  Female  [20-30)  ...      No          Yes           0\n",
+              "3        Caucasian    Male  [30-40)  ...      Ch          Yes           0\n",
+              "4        Caucasian    Male  [40-50)  ...      Ch          Yes           0\n",
+              "5        Caucasian    Male  [50-60)  ...      No          Yes           1\n",
               "\n",
               "[5 rows x 45 columns]"
             ]
@@ -1126,7 +1009,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 45
+          "execution_count": 94
         }
       ]
     },
@@ -1134,425 +1017,76 @@
       "cell_type": "code",
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 326
+          "base_uri": "https://localhost:8080/"
         },
-        "id": "7UI2L_J-uhto",
-        "outputId": "ad0840d5-b604-4b1a-cf41-459917ac5dd1"
+        "id": "2ZTojEkcy65Z",
+        "outputId": "5040ba51-15de-40f0-a6c8-6e3b0c59fb3d"
       },
       "source": [
-        "# convert readmitted data to be 0 for NO and 1 for <30 or > 30\n",
-        "data[\"readmitted\"].replace({\"NO\": 0, \">30\": 1, \"<30\": 1}, inplace=True)\n",
-        "data.head()"
+        "# Convert non numerical data to numerical data\n",
+        "def handle_non_numerical_data(df):\n",
+        "    columns = df.columns.values\n",
+        "    for column in columns:\n",
+        "        text_digit_vals = {}\n",
+        "        def convert_to_int(val):\n",
+        "            return text_digit_vals[val]\n",
+        "\n",
+        "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
+        "            column_contents = df[column].values.tolist()\n",
+        "            unique_elements = set(column_contents)\n",
+        "            x = 0\n",
+        "            for unique in unique_elements:\n",
+        "                if unique not in text_digit_vals:\n",
+        "                    text_digit_vals[unique] = x\n",
+        "                    x+=1\n",
+        "\n",
+        "            df[column] = list(map(convert_to_int, df[column]))\n",
+        "\n",
+        "    return df\n",
+        "data = handle_non_numerical_data(data)\n",
+        "print(data.head())"
       ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>race</th>\n",
-              "      <th>gender</th>\n",
-              "      <th>age</th>\n",
-              "      <th>admission_type_id</th>\n",
-              "      <th>discharge_disposition_id</th>\n",
-              "      <th>admission_source_id</th>\n",
-              "      <th>time_in_hospital</th>\n",
-              "      <th>num_lab_procedures</th>\n",
-              "      <th>num_procedures</th>\n",
-              "      <th>num_medications</th>\n",
-              "      <th>number_outpatient</th>\n",
-              "      <th>number_emergency</th>\n",
-              "      <th>number_inpatient</th>\n",
-              "      <th>diag_1</th>\n",
-              "      <th>diag_2</th>\n",
-              "      <th>diag_3</th>\n",
-              "      <th>number_diagnoses</th>\n",
-              "      <th>max_glu_serum</th>\n",
-              "      <th>A1Cresult</th>\n",
-              "      <th>metformin</th>\n",
-              "      <th>repaglinide</th>\n",
-              "      <th>nateglinide</th>\n",
-              "      <th>chlorpropamide</th>\n",
-              "      <th>glimepiride</th>\n",
-              "      <th>acetohexamide</th>\n",
-              "      <th>glipizide</th>\n",
-              "      <th>glyburide</th>\n",
-              "      <th>tolbutamide</th>\n",
-              "      <th>pioglitazone</th>\n",
-              "      <th>rosiglitazone</th>\n",
-              "      <th>acarbose</th>\n",
-              "      <th>miglitol</th>\n",
-              "      <th>troglitazone</th>\n",
-              "      <th>tolazamide</th>\n",
-              "      <th>examide</th>\n",
-              "      <th>citoglipton</th>\n",
-              "      <th>insulin</th>\n",
-              "      <th>glyburide-metformin</th>\n",
-              "      <th>glipizide-metformin</th>\n",
-              "      <th>glimepiride-pioglitazone</th>\n",
-              "      <th>metformin-rosiglitazone</th>\n",
-              "      <th>metformin-pioglitazone</th>\n",
-              "      <th>change</th>\n",
-              "      <th>diabetesMed</th>\n",
-              "      <th>readmitted</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Caucasian</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>[10-20)</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>7</td>\n",
-              "      <td>3</td>\n",
-              "      <td>59</td>\n",
-              "      <td>0</td>\n",
-              "      <td>18</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>276</td>\n",
-              "      <td>250.01</td>\n",
-              "      <td>255</td>\n",
-              "      <td>9</td>\n",
-              "      <td>None</td>\n",
-              "      <td>None</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Up</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Ch</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>AfricanAmerican</td>\n",
-              "      <td>Female</td>\n",
-              "      <td>[20-30)</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>7</td>\n",
-              "      <td>2</td>\n",
-              "      <td>11</td>\n",
-              "      <td>5</td>\n",
-              "      <td>13</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>648</td>\n",
-              "      <td>250</td>\n",
-              "      <td>V27</td>\n",
-              "      <td>6</td>\n",
-              "      <td>None</td>\n",
-              "      <td>None</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Steady</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Caucasian</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>[30-40)</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>7</td>\n",
-              "      <td>2</td>\n",
-              "      <td>44</td>\n",
-              "      <td>1</td>\n",
-              "      <td>16</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>8</td>\n",
-              "      <td>250.43</td>\n",
-              "      <td>403</td>\n",
-              "      <td>7</td>\n",
-              "      <td>None</td>\n",
-              "      <td>None</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Up</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Ch</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Caucasian</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>[40-50)</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>7</td>\n",
-              "      <td>1</td>\n",
-              "      <td>51</td>\n",
-              "      <td>0</td>\n",
-              "      <td>8</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>197</td>\n",
-              "      <td>157</td>\n",
-              "      <td>250</td>\n",
-              "      <td>5</td>\n",
-              "      <td>None</td>\n",
-              "      <td>None</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Steady</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Steady</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Ch</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>Caucasian</td>\n",
-              "      <td>Male</td>\n",
-              "      <td>[50-60)</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>3</td>\n",
-              "      <td>31</td>\n",
-              "      <td>6</td>\n",
-              "      <td>16</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>414</td>\n",
-              "      <td>411</td>\n",
-              "      <td>250</td>\n",
-              "      <td>9</td>\n",
-              "      <td>None</td>\n",
-              "      <td>None</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Steady</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>No</td>\n",
-              "      <td>Yes</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "              race  gender      age  ...  change  diabetesMed  readmitted\n",
-              "1        Caucasian  Female  [10-20)  ...      Ch          Yes           1\n",
-              "2  AfricanAmerican  Female  [20-30)  ...      No          Yes           0\n",
-              "3        Caucasian    Male  [30-40)  ...      Ch          Yes           0\n",
-              "4        Caucasian    Male  [40-50)  ...      Ch          Yes           0\n",
-              "5        Caucasian    Male  [50-60)  ...      No          Yes           1\n",
-              "\n",
-              "[5 rows x 45 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 46
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2ZTojEkcy65Z",
-        "outputId": "6768b971-0b8e-43e6-d51d-23faf77083c6"
-      },
-      "source": [
-        "# Convert non numerical data to numerical data\n",
-        "def handle_non_numerical_data(df):\n",
-        "    columns = df.columns.values\n",
-        "    for column in columns:\n",
-        "        text_digit_vals = {}\n",
-        "        def convert_to_int(val):\n",
-        "            return text_digit_vals[val]\n",
-        "\n",
-        "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
-        "            column_contents = df[column].values.tolist()\n",
-        "            unique_elements = set(column_contents)\n",
-        "            x = 0\n",
-        "            for unique in unique_elements:\n",
-        "                if unique not in text_digit_vals:\n",
-        "                    text_digit_vals[unique] = x\n",
-        "                    x+=1\n",
-        "\n",
-        "            df[column] = list(map(convert_to_int, df[column]))\n",
-        "\n",
-        "    return df\n",
-        "data = handle_non_numerical_data(data)\n",
-        "print(data.head())"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "   race  gender  age  ...  change  diabetesMed  readmitted\n",
-            "1     1       0    9  ...       1            1           1\n",
-            "2     2       0    3  ...       0            1           0\n",
-            "3     1       2    8  ...       1            1           0\n",
-            "4     1       2    0  ...       1            1           0\n",
-            "5     1       2    5  ...       0            1           1\n",
-            "\n",
-            "[5 rows x 45 columns]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sZBQ_sM-OUB3"
-      },
-      "source": [
-        "# Check that data is balance"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "_DL-Rraf42wF",
-        "outputId": "46f24858-ac8b-473a-d69c-3e46bef414cd"
-      },
-      "source": [
-        "# Check the distribution of values for readmitted to see if the data is balanced\n",
-        "data['readmitted'].value_counts()"
-      ],
-      "execution_count": null,
+      "execution_count": 95,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "   race  gender  age  ...  change  diabetesMed  readmitted\n",
+            "1     2       2    9  ...       0            1           1\n",
+            "2     1       2    3  ...       1            1           0\n",
+            "3     2       0    4  ...       0            1           0\n",
+            "4     2       0    5  ...       0            1           0\n",
+            "5     2       0    7  ...       1            1           1\n",
+            "\n",
+            "[5 rows x 45 columns]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sZBQ_sM-OUB3"
+      },
+      "source": [
+        "# Check that data is balance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_DL-Rraf42wF",
+        "outputId": "46dedd51-a755-4d0e-bd24-f8329b21000e"
+      },
+      "source": [
+        "# Check the distribution of values for readmitted to see if the data is balanced\n",
+        "data['readmitted'].value_counts()"
+      ],
+      "execution_count": 96,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1566,7 +1100,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 48
+          "execution_count": 96
         }
       ]
     },
@@ -1586,7 +1120,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "0iY93tOmRVwn",
-        "outputId": "2d3a3fc9-d4d8-484f-a203-5f6c97116ce4"
+        "outputId": "a7864e4e-ddbb-44c3-96f3-76805c83418c"
       },
       "source": [
         "#extracting predictor values & outcome values\n",
@@ -1595,18 +1129,18 @@
         "print(x)\n",
         "print(y)"
       ],
-      "execution_count": null,
+      "execution_count": 97,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "[[1 0 9 ... 1 1 1]\n",
-            " [2 0 3 ... 1 0 1]\n",
-            " [1 2 8 ... 1 1 1]\n",
+            "[[2 2 9 ... 0 0 1]\n",
+            " [1 2 3 ... 0 1 1]\n",
+            " [2 0 4 ... 0 0 1]\n",
             " ...\n",
-            " [1 2 7 ... 1 1 1]\n",
-            " [1 0 4 ... 1 1 1]\n",
-            " [1 2 7 ... 1 0 0]]\n",
+            " [2 0 8 ... 0 0 1]\n",
+            " [2 2 0 ... 0 0 1]\n",
+            " [2 0 8 ... 0 1 0]]\n",
             "[[1]\n",
             " [0]\n",
             " [0]\n",
@@ -1628,7 +1162,7 @@
         "#create training and validation sets\n",
         "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)\n"
       ],
-      "execution_count": null,
+      "execution_count": 98,
       "outputs": []
     },
     {
@@ -1643,7 +1177,7 @@
         "x_train = sc.fit_transform(x_train)\n",
         "x_test = sc.transform(x_test)"
       ],
-      "execution_count": null,
+      "execution_count": 99,
       "outputs": []
     },
     {
@@ -1656,7 +1190,7 @@
         "train_data = lightgbm.Dataset(x_train, label=y_train)\n",
         "test_data = lightgbm.Dataset(x_test, label=y_test)"
       ],
-      "execution_count": null,
+      "execution_count": 100,
       "outputs": []
     },
     {
@@ -1686,7 +1220,7 @@
         "\n",
         "clf = lightgbm.train(params, train_data, 100)"
       ],
-      "execution_count": null,
+      "execution_count": 101,
       "outputs": []
     },
     {
@@ -1705,7 +1239,7 @@
         "    else:  \n",
         "       y_pred[i]=0\n"
       ],
-      "execution_count": null,
+      "execution_count": 102,
       "outputs": []
     },
     {
@@ -1715,7 +1249,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "5HnuJ8XCwe0l",
-        "outputId": "0ddf4153-3471-407b-fe3d-1ae0c731cda7"
+        "outputId": "23665836-6a27-4e96-f838-d5d1cf79adfa"
       },
       "source": [
         "# Confusion matrix\n",
@@ -1728,19 +1262,19 @@
         "cm\n",
         "accuracy"
       ],
-      "execution_count": null,
+      "execution_count": 103,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "0.5819939626335972"
+              "0.581789997552419"
             ]
           },
           "metadata": {
             "tags": []
           },
-          "execution_count": 55
+          "execution_count": 103
         }
       ]
     },
@@ -1761,44 +1295,14 @@
       "source": [
         "import pickle\n",
         "# open a file, where you ant to store the data\n",
-        "file = open('diabetes_readmission_model_2.pkl', 'wb')\n",
+        "file = open('diabetes_readmission_model.pkl', 'wb')\n",
         "\n",
         "# dump information to that file\n",
         "pickle.dump(clf, file)"
       ],
-      "execution_count": null,
+      "execution_count": 104,
       "outputs": []
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 197
-        },
-        "id": "vDK4K7bR5Hsk",
-        "outputId": "a040a29e-aad8-4c38-8ce9-6146ee108ac4"
-      },
-      "source": [
-        "loaded_model = pickle.load(open('diabetes_readmission_model_2.pkl', 'rb'))\n",
-        "# result = loaded_model.score(x_test, y_test)\n",
-        "print(loaded_model.dump_model())"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "EOFError",
-          "evalue": "ignored",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mEOFError\u001b[0m                                  Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-57-29bfcd8958b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mloaded_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'diabetes_readmission_model_2.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# result = loaded_model.score(x_test, y_test)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaded_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mEOFError\u001b[0m: Ran out of input"
-          ]
-        }
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1811,7 +1315,12 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "8Tdn6GKM1URC"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "8Tdn6GKM1URC",
+        "outputId": "ca1c21a7-d008-48f8-d807-dc06b7f97b5e"
       },
       "source": [
         "import sklearn\n",
@@ -1819,19 +1328,56 @@
         "y_pred=y_pred.ravel()\n",
         "sklearn.utils.multiclass.type_of_target(y_pred)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'binary'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 88
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "XQP61FTqWGzx"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "XQP61FTqWGzx",
+        "outputId": "701f0e04-a076-4d56-d5b2-7a67eb8e1613"
       },
       "source": [
         "data['readmitted'].value_counts(dropna=False).to_string()\n"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'NO     52338\\n>30    34649\\n<30    11066'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 50
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -1847,19 +1393,233 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "q92cb7TTDul9"
+        "id": "q92cb7TTDul9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "outputId": "feda4ea6-06b8-4948-bbaa-0c24cda987f9"
       },
       "source": [
         "numerical_summary = data.describe()\n",
         "numerical_summary.transpose()\n"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>encounter_id</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>1.652016e+08</td>\n",
+              "      <td>1.026403e+08</td>\n",
+              "      <td>12522.0</td>\n",
+              "      <td>84961194.0</td>\n",
+              "      <td>152388987.0</td>\n",
+              "      <td>2.302709e+08</td>\n",
+              "      <td>443867222.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>patient_nbr</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>5.433040e+07</td>\n",
+              "      <td>3.869636e+07</td>\n",
+              "      <td>135.0</td>\n",
+              "      <td>23413221.0</td>\n",
+              "      <td>45505143.0</td>\n",
+              "      <td>8.754595e+07</td>\n",
+              "      <td>189502619.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>admission_type_id</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>2.024006e+00</td>\n",
+              "      <td>1.445403e+00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.000000e+00</td>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>discharge_disposition_id</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>3.715642e+00</td>\n",
+              "      <td>5.280166e+00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>4.000000e+00</td>\n",
+              "      <td>28.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>admission_source_id</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>5.754437e+00</td>\n",
+              "      <td>4.064081e+00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>7.000000e+00</td>\n",
+              "      <td>25.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>time_in_hospital</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>4.395987e+00</td>\n",
+              "      <td>2.985108e+00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.000000e+00</td>\n",
+              "      <td>14.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>num_lab_procedures</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>4.309564e+01</td>\n",
+              "      <td>1.967436e+01</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>5.700000e+01</td>\n",
+              "      <td>132.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>num_procedures</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>1.339730e+00</td>\n",
+              "      <td>1.705807e+00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.000000e+00</td>\n",
+              "      <td>6.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>num_medications</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>1.602184e+01</td>\n",
+              "      <td>8.127566e+00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>2.000000e+01</td>\n",
+              "      <td>81.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>number_outpatient</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>3.693572e-01</td>\n",
+              "      <td>1.267265e+00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000e+00</td>\n",
+              "      <td>42.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>number_emergency</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>1.978362e-01</td>\n",
+              "      <td>9.304723e-01</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000e+00</td>\n",
+              "      <td>76.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>number_inpatient</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>6.355659e-01</td>\n",
+              "      <td>1.262863e+00</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.000000e+00</td>\n",
+              "      <td>21.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>number_diagnoses</th>\n",
+              "      <td>101766.0</td>\n",
+              "      <td>7.422607e+00</td>\n",
+              "      <td>1.933600e+00</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>9.000000e+00</td>\n",
+              "      <td>16.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                             count          mean  ...           75%          max\n",
+              "encounter_id              101766.0  1.652016e+08  ...  2.302709e+08  443867222.0\n",
+              "patient_nbr               101766.0  5.433040e+07  ...  8.754595e+07  189502619.0\n",
+              "admission_type_id         101766.0  2.024006e+00  ...  3.000000e+00          8.0\n",
+              "discharge_disposition_id  101766.0  3.715642e+00  ...  4.000000e+00         28.0\n",
+              "admission_source_id       101766.0  5.754437e+00  ...  7.000000e+00         25.0\n",
+              "time_in_hospital          101766.0  4.395987e+00  ...  6.000000e+00         14.0\n",
+              "num_lab_procedures        101766.0  4.309564e+01  ...  5.700000e+01        132.0\n",
+              "num_procedures            101766.0  1.339730e+00  ...  2.000000e+00          6.0\n",
+              "num_medications           101766.0  1.602184e+01  ...  2.000000e+01         81.0\n",
+              "number_outpatient         101766.0  3.693572e-01  ...  0.000000e+00         42.0\n",
+              "number_emergency          101766.0  1.978362e-01  ...  0.000000e+00         76.0\n",
+              "number_inpatient          101766.0  6.355659e-01  ...  1.000000e+00         21.0\n",
+              "number_diagnoses          101766.0  7.422607e+00  ...  9.000000e+00         16.0\n",
+              "\n",
+              "[13 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "AOJdOEZeNtQO"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AOJdOEZeNtQO",
+        "outputId": "9ff04cda-3290-4c09-ce20-05e1bbb30aa6"
       },
       "source": [
         "numerical_columns = list(numerical_summary.columns)\n",
@@ -1867,7 +1627,15 @@
         "print(cat_columns)"
       ],
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "['max_glu_serum', 'insulin', 'metformin', 'diabetesMed', 'age', 'metformin-pioglitazone', 'readmitted', 'diag_1', 'weight', 'rosiglitazone', 'metformin-rosiglitazone', 'A1Cresult', 'glipizide-metformin', 'change', 'diag_3', 'repaglinide', 'nateglinide', 'examide', 'glimepiride', 'acarbose', 'glimepiride-pioglitazone', 'tolazamide', 'pioglitazone', 'tolbutamide', 'troglitazone', 'race', 'chlorpropamide', 'miglitol', 'payer_code', 'citoglipton', 'gender', 'acetohexamide', 'glyburide', 'diag_2', 'medical_specialty', 'glyburide-metformin', 'glipizide']\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "code",
diff --git a/app.py b/app.py
index a8bffa1..ed48a55 100644
--- a/app.py
+++ b/app.py
@@ -1,31 +1,36 @@
 # import required packages
-from flask import Flask, request
-from flask_cors import CORS  # comment this on deployment
+from flask import Flask, render_template, request
+import requests
 import pickle
-import pandas as pd
+import numpy as np
+from flask_cors import CORS #comment this on deployment
+import sklearn
+from sklearn.preprocessing import StandardScaler
 
 # create a Flask object
 app = Flask(__name__, static_url_path='', static_folder='frontend/build')
-CORS(app)  # comment this on deployment
+CORS(app) #comment this on deployment
 
 # load the ml model which we have saved earlier in .pkl format - rn I'm using Maria's model
 model = pickle.load(open('diabetes_readmission_model.pkl', 'rb'))
 
+@app.route('/', methods=['GET'])
+# create a function Home that will return index.html(which contains html form)
+def Home():
+    return render_template('index.html')
+
+# creating object for StandardScaler
+standard_to = StandardScaler()
 
 # this gets called in readmissionform
-@app.route('/prediction/', methods=['POST'])
-def predict():
+@app.route('/predict_model', methods=['POST'])
+def add_movie():
     data = request.get_json()
 
-    race_map = {'Caucasian': 1, 'African American': 2, 'Asian': 3, 'Hispanic': 4, 'Other': 5}
-    gender_map = {'Female': 1, 'Male': 2, 'Other': 3}
-    age_map = {'0-10': 1, '10-20': 2, '30-40': 3, '40-50': 4, '60-70': 5, '70-80': 6, '80-90': 7, '90-100': 8, '100': 9}
-
     # all the values from the cleaned data set see: diabetes_readmission_model
-
-    race = race_map[data['race']]
-    gender = gender_map[data['gender']]
-    age = age_map[data['age']]
+    race = data['Race']
+    gender = data['Gender']
+    age = data['Age']
     admission_type_id = data['admission_type_id']
     discharge_disposition_id = data['discharge_disposition_id']
     admission_source_id = data['admission_source_id']
@@ -60,33 +65,83 @@ def predict():
     examide = data['examide']
     citoglipton = data['citoglipton']
     insulin = data['insulin']
-    glyburide_metformin = 1
-    glipizide_metformin = 1
-    glimepiride_pioglitazone = 1
-    metformin_rosiglitazone = 1
-    metformin_pioglitazone = 1
-    change = 1
-    diabetes_med = 1
+    glyburide_metformin = data['glyburide_metformin']
+    glipizide_metformin = data['glipizide_metformin']
+    glimepiride_pioglitazone = data['glimepiride_pioglitazone']
+    metformin_rosiglitazone = data['metformin_rosiglitazone']
+    metformin_pioglitazone = data['metformin_pioglitazone']
+    change = data['change']
+    diabetesMed = data['diabetesMed']
 
     prediction = model.predict([[race, gender, age, admission_type_id, discharge_disposition_id, admission_source_id,
-                                 time_in_hospital, num_lab_procedures, num_procedures, num_medications,
-                                 number_outpatient,
+                                 time_in_hospital, num_lab_procedures, num_procedures, num_medications, number_outpatient,
                                  number_emergency, number_inpatient, diag_1, diag_2, diag_3, number_diagnosis,
-                                 max_glu_serum, a1cresult, metformin, repaglinide, nateglinide, chlorpropamide,
-                                 glimepiride,
+                                 max_glu_serum, a1cresult, metformin, repaglinide, nateglinide, chlorpropamide, glimepiride,
                                  acetohexamide, glipizide, glyburide, tolbutamide, pioglitazone, rosiglitazone,
                                  acarbose, miglitol, troglitazone, tolazamide, examide, citoglipton, insulin,
-                                 glyburide_metformin, glipizide_metformin, glimepiride_pioglitazone,
-                                 metformin_rosiglitazone,
-                                 metformin_pioglitazone, change, diabetes_med]])
+                                 glyburide_metformin, glipizide_metformin, glimepiride_pioglitazone, metformin_rosiglitazone,
+                                 metformin_pioglitazone, change, diabetesMed]])
+
+    return 'Done', 201
+
+# Use this as an example of how to call the ML models ------------------------- delete after we are done with predict_model
+@app.route("/predict", methods=['POST'])
+# define the predict function which is going to predict the results from ml model based on the given values through html form
+def predict():
+    Fuel_Type_Diesel = 0
+    if request.method == 'POST':
+
+        # Use request.form to get the data from html form through post method.
+        # these all are nothing but features of our dataset(ml model)
+        Year = int(request.form['Year'])
+        Year = 2020 - Year
+        Present_Price = float(request.form['Present_Price'])
+        Kms_Driven = int(request.form['Kms_Driven'])
+        Kms_Driven2 = np.log(Kms_Driven)
+        Owner = int(request.form['Owner'])
+        Fuel_Type_Petrol = request.form['Fuel_Type_Petrol']
+
+        # Fuel_Type(feature) is categorised into petrol, diesel, cng, therefore we have done one-hot encoding on it while building model
+        if (Fuel_Type_Petrol == 'Petrol'):
+            Fuel_Type_Petrol = 1
+            Fuel_Type_Diesel = 0
+        elif (Fuel_Type_Petrol == 'Diesel'):
+            Fuel_Type_Petrol = 0
+            Fuel_Type_Diesel = 1
+        else:
+            Fuel_Type_Petrol = 0
+            Fuel_Type_Diesel = 0
+
+        # Seller_type(feature) is categorised into indivividual and dealer,therefore we have done one-hot encoding on it while building model
+        Seller_Type_Individual = request.form['Seller_Type_Individual']
+        if (Seller_Type_Individual == 'Individual'):
+            Seller_Type_Individual = 1
+        else:
+            Seller_Type_Individual = 0
+
+        # Transmission mannual(feature) is categorised into mannual and automatic,therefore we have done one-hot encoding on it while building model
+        Transmission_Mannual = request.form['Transmission_Mannual']
+        if (Transmission_Mannual == 'Mannual'):
+            Transmission_Mannual = 1
+        else:
+            Transmission_Mannual = 0
+        prediction = model.predict([[Present_Price, Kms_Driven2, Owner, Year, Fuel_Type_Diesel, Fuel_Type_Petrol,
+                                     Seller_Type_Individual, Transmission_Mannual]])
+        output = round(prediction[0], 2)
+
+        # condition for invalid values
+        if output < 0:
+            return render_template('index.html', prediction_text="Sorry you cannot sell this car")
 
-    readmission_chance = prediction[0]
-    response = {'result': readmission_chance}
-    print(response)
-    return response, 201
+        # condition for prediction when values are valid
+        else:
+            return render_template('index.html', prediction_text="You Can Sell the Car at {} lakhs".format(output))
 
+    # html form to be displayed on screen when no values are inserted; without any output or prediction
+    else:
+        return render_template('index.html')
 
 if __name__ == "__main__":
     # run method starts our web service
     # Debug : as soon as I save anything in my structure, server should start again
-    app.run(debug=True, port=9999)
+    app.run(debug=True)
diff --git a/diabetes_readmission_model.pkl b/diabetes_readmission_model.pkl
index f75feba..51c8097 100644
Binary files a/diabetes_readmission_model.pkl and b/diabetes_readmission_model.pkl differ
diff --git a/frontend/src/Graphs.js b/frontend/src/Graphs.js
index 0569efe..4f3584d 100644
--- a/frontend/src/Graphs.js
+++ b/frontend/src/Graphs.js
@@ -1,138 +1,150 @@
 import { useState, useEffect } from "react";
-import { LineChart, Line, CartesianGrid, XAxis, YAxis } from "recharts";
-import * as d3 from "d3";
-import diabetic_data from "./diabetic_data.csv";
+import { LineChart, Line, CartesianGrid, XAxis, YAxis, BarChart, Bar, Tooltip, Legend } from 'recharts';
+import * as d3 from 'd3';
+import diabetic_data from './diabetic_data.csv';
+import age_avg from './age_avg.csv';
+import race_avg from './race_avg.csv';
 
-const age_readmitted_data = {};
-const race_readmitted_data = {};
-const inpatient_readmitted_data = {};
+const age_readmitted_data = {}
+const race_readmitted_data = {}
+const inpatient_readmitted_data = {}
+const age_avg_readmitted_data = []
+const race_avg_readmitted_data = []
 
-function Graphs() {
-  const [ageData, setAgeData] = useState([]);
-  const [raceData, setRaceData] = useState([]);
-  const [inpatientData, setInpatientData] = useState([]);
-
-  function age_graph_data() {
-    const temp_data = [];
-    for (const age in age_readmitted_data) {
-      temp_data.push({
-        age: age,
-        readmission_number: age_readmitted_data[age],
-      });
+function Graphs() { 
+    const [ageData, setAgeData] = useState([]);
+    const [raceData, setRaceData] = useState([]);
+    const [inpatientData, setInpatientData] = useState([]);
+    const [ageAvgData, setAgeAvgData] = useState([]);
+    const [raceAvgData, setRaceAvgData] = useState([]);
+    
+    function age_graph_data(){
+        const temp_data = []
+        for (const age in age_readmitted_data){
+            temp_data.push({'age': age, 'readmission_number': age_readmitted_data[age]})
+        }
+        setAgeData(temp_data)
     }
-    setAgeData(temp_data);
-  }
 
-  function race_graph_data() {
-    const temp_data = [];
-    for (const race in race_readmitted_data) {
-      temp_data.push({
-        race: race,
-        readmission_number: race_readmitted_data[race],
-      });
+    function race_graph_data(){
+        const temp_data = []
+        for (const race in race_readmitted_data){
+            temp_data.push({'race': race, 'readmission_number': race_readmitted_data[race]})
+        }
+        setRaceData(temp_data)
     }
-    setRaceData(temp_data);
-  }
 
-  function inpatient_graph_data() {
-    const temp_data = [];
-    for (const inpatient in inpatient_readmitted_data) {
-      temp_data.push({
-        inpatient: inpatient,
-        readmission_number: inpatient_readmitted_data[inpatient],
-      });
+    function inpatient_graph_data(){
+        const temp_data = []
+        for (const inpatient in inpatient_readmitted_data){
+            temp_data.push({'inpatient': inpatient, 'readmission_number': inpatient_readmitted_data[inpatient]})
+        }
+        setInpatientData(temp_data)
     }
-    setInpatientData(temp_data);
-  }
 
-  useEffect(() => {
-    d3.csv(diabetic_data, function (diabetic_data) {
-      if (age_readmitted_data[diabetic_data["age"]]) {
-        if (diabetic_data["readmitted"] !== "NO") {
-          age_readmitted_data[diabetic_data["age"]] += 1;
-        }
-      } else {
-        age_readmitted_data[diabetic_data["age"]] = 1;
-      }
+    useEffect(() => {
+         d3.csv(diabetic_data, function(diabetic_data) { 
+                if (age_readmitted_data[diabetic_data["age"]]){
+                    if (diabetic_data["readmitted"] !== "NO"){
+                        age_readmitted_data[diabetic_data["age"]] += 1
+                    }
+                } else {
+                    age_readmitted_data[diabetic_data["age"]] = 1
+                }
 
-      if (race_readmitted_data[diabetic_data["race"]]) {
-        if (diabetic_data["readmitted"] !== "NO") {
-          race_readmitted_data[diabetic_data["race"]] += 1;
-        }
-      } else {
-        race_readmitted_data[diabetic_data["race"]] = 1;
-      }
+                if (race_readmitted_data[diabetic_data["race"]]){
+                    if (diabetic_data["readmitted"] !== "NO"){
+                        race_readmitted_data[diabetic_data["race"]] += 1
+                    }
+                } else {
+                    race_readmitted_data[diabetic_data["race"]] = 1
+                }
 
-      if (inpatient_readmitted_data[diabetic_data["number_inpatient"]]) {
-        if (diabetic_data["readmitted"] !== "NO") {
-          inpatient_readmitted_data[diabetic_data["number_inpatient"]] += 1;
-        }
-      } else {
-        inpatient_readmitted_data[diabetic_data["number_inpatient"]] = 1;
-      }
-    }).then(() => {
-      age_graph_data();
-      race_graph_data();
-      inpatient_graph_data();
-    });
-  }, []);
+                if (inpatient_readmitted_data[diabetic_data["number_inpatient"]]){
+                    if (diabetic_data["readmitted"] !== "NO"){
+                        inpatient_readmitted_data[diabetic_data["number_inpatient"]] += 1
+                    }
+                } else {
+                    inpatient_readmitted_data[diabetic_data["number_inpatient"]] = 1
+                }
+            }).then(() => {
+                age_graph_data()
+                race_graph_data()
+                inpatient_graph_data()
+            });
 
-  return (
-    <>
-      {result === "" ? null : (
-        <Row>
-          <Col className="result-container">
-            <h5 id="result">{result}</h5>
-          </Col>
-        </Row>
-      )}
-      {ageData && (
-        <>
-          <h1>Age vs. Readmission Count</h1>
-          <LineChart width={600} height={300} data={ageData}>
-            <Line
-              type="monotone"
-              dataKey="readmission_number"
-              stroke="#8884d8"
-            />
-            <CartesianGrid stroke="#ccc" />
-            <XAxis dataKey="age" />
-            <YAxis />
-          </LineChart>
-        </>
-      )}
-      {raceData && (
-        <>
-          <h1>Race vs. Readmission Count</h1>
-          <LineChart width={600} height={300} data={raceData}>
-            <Line
-              type="monotone"
-              dataKey="readmission_number"
-              stroke="#8884d8"
-            />
-            <CartesianGrid stroke="#ccc" />
-            <XAxis dataKey="race" />
-            <YAxis />
-          </LineChart>
-        </>
-      )}
-      {inpatientData && (
-        <>
-          <h1>Number Inpatient vs. Readmission Count</h1>
-          <LineChart width={600} height={300} data={inpatientData}>
-            <Line
-              type="monotone"
-              dataKey="readmission_number"
-              stroke="#8884d8"
-            />
-            <CartesianGrid stroke="#ccc" />
-            <XAxis dataKey="inpatient" />
-            <YAxis />
-          </LineChart>
-        </>
-      )}
-    </>
-  );
-}
+        d3.csv(age_avg, function(age_avg) {
+            age_avg_readmitted_data.push({"age": age_avg["age"], "readmission_number": parseFloat(age_avg["avg(prediction)"]) })
+        }).then(() => setAgeAvgData(age_avg_readmitted_data));
 
-export default Graphs;
+        d3.csv(race_avg, function(race_avg) {
+            race_avg_readmitted_data.push({"race": race_avg["race"], "readmission_number": parseFloat(race_avg["avg(prediction)"]) })
+        }).then(() => setRaceAvgData(race_avg_readmitted_data));
+    }, [])
+    
+    return (
+        <>
+        {ageData && 
+            <>
+                <h1>Age vs. Readmission Count</h1>
+                <LineChart width={600} height={300} data={ageData}>
+                    <Line type="monotone" dataKey="readmission_number" stroke="#8884d8" />
+                    <CartesianGrid stroke="#ccc" />
+                    <XAxis dataKey="age" />
+                    <YAxis />
+                </LineChart>
+            </>
+        }
+        {raceData && 
+            <>
+            <h1>Race vs. Readmission Count</h1>
+                <LineChart width={600} height={300} data={raceData}>
+                    <Line type="monotone" dataKey="readmission_number" stroke="#8884d8" />
+                    <CartesianGrid stroke="#ccc" />
+                    <XAxis dataKey="race" />
+                    <YAxis />
+                </LineChart>
+            </>
+        }
+        {inpatientData && 
+            <>
+            <h1>Number Inpatient vs. Readmission Count</h1>
+                <LineChart width={600} height={300} data={inpatientData}>
+                    <Line type="monotone" dataKey="readmission_number" stroke="#8884d8" />
+                    <CartesianGrid stroke="#ccc" />
+                    <XAxis dataKey="inpatient" />
+                    <YAxis />
+                </LineChart>
+            </>
+        }
+        {ageAvgData &&
+            <>
+                <h1>Age vs. Predicted Readmission Average</h1>
+                <BarChart width={600} height={300} data={ageAvgData}>
+                    <CartesianGrid stroke="#ccc" />
+                    <XAxis dataKey="age" />
+                    <YAxis />
+                    <Tooltip />
+                    <Legend />
+                    <Bar dataKey="readmission_number" fill="#8884d8" name="avg" />
+                </BarChart>
+            </>
+        }
+        {raceAvgData &&
+            <>
+                <h1>Race vs. Predicted Readmission Average</h1>
+                <BarChart width={600} height={300} data={raceAvgData}>
+                    <CartesianGrid stroke="#ccc" />
+                    <XAxis dataKey="race" />
+                    <YAxis />
+                    <Tooltip />
+                    <Legend />
+                    <Bar dataKey="readmission_number" fill="#8884d8" name="avg" />
+                </BarChart>
+            </>
+        }
+      </>
+    );
+  }
+  
+  export default Graphs;
\ No newline at end of file
diff --git a/frontend/src/Login.js b/frontend/src/Login.js
index f35dc1f..ab9f2ed 100644
--- a/frontend/src/Login.js
+++ b/frontend/src/Login.js
@@ -15,10 +15,15 @@ import {
 import { ArrowBackIcon, ViewIcon, ViewOffIcon } from "@chakra-ui/icons";
 import { ButtonPrimary } from "./components/Button";
 import { Card, Container } from "./components/Layout";
-import { Body2, Headline4, HeadlineVarient, Link } from "./components/Text";
+import {
+  Body2,
+  Headline4,
+  HeadlineVarient,
+  Link,
+} from "./components/Text";
 import theme from "./Theme";
 import { Formik, Form, Field } from "formik";
-import history from "./history";
+import history from './history';
 
 const emailRegex = new RegExp(
   "^[\\w.\\-]{1,100}@[\\w.\\-]{1,100}\\.[A-Za-z]{2,4}$"
@@ -147,14 +152,15 @@ const LoginPage = () => {
                         marginTop="24px"
                         isLoading={props.isSubmitting}
                         type="submit"
-                        onClick={() => history.push("/ReadmissionForm")}
+                        onClick={() => history.push('/ReadmissionForm')}
                       >
                         Log in
                       </ButtonPrimary>
                     </Form>
                   )}
                 </Formik>
-                <Body2 marginTop="36px"></Body2>
+                <Body2 marginTop="36px">
+                </Body2>
               </Card>
             </Flex>
           </Flex>
@@ -164,4 +170,4 @@ const LoginPage = () => {
   );
 };
 
-export default LoginPage;
+export default LoginPage;
\ No newline at end of file
diff --git a/frontend/src/ReadmissionForm.js b/frontend/src/ReadmissionForm.js
index 8fbbc14..4467799 100644
--- a/frontend/src/ReadmissionForm.js
+++ b/frontend/src/ReadmissionForm.js
@@ -14,43 +14,11 @@ class ReadmissionForm extends Component {
     this.state = {
       isLoading: false,
       formData: {
-        race: "Caucasian",
-        gender: "Female",
-        age: "0-10",
-        admission_type_id: 1,
-        discharge_disposition_id: 1,
-        admission_source_id: 1,
-        time_in_hospital: 1,
-        num_lab_procedures: 1,
-        num_procedures: 1,
-        num_medications: 1,
-        number_outpatient: 1,
-        number_emergency: 1,
-        number_inpatient: 1,
-        diag_1: 1,
-        diag_2: 1,
-        diag_3: 1,
-        number_diagnosis: 1,
-        max_glu_serum: 1,
-        a1cresult: 1,
-        metformin: 1,
-        repaglinide: 1,
-        nateglinide: 1,
-        chlorpropamide: 1,
-        glimepiride: 1,
-        acetohexamide: 1,
-        glipizide: 1,
-        glyburide: 1,
-        tolbutamide: 1,
-        pioglitazone: 1,
-        rosiglitazone: 1,
-        acarbose: 1,
-        miglitol: 1,
-        troglitazone: 1,
-        tolazamide: 1,
-        examide: 1,
-        citoglipton: 1,
-        insulin: 1,
+        textfield1: "",
+        textfield2: "",
+        select1: 1,
+        select2: 1,
+        select3: 1,
       },
       result: "",
     };
@@ -69,7 +37,7 @@ class ReadmissionForm extends Component {
   handlePredictClick = (event) => {
     const formData = this.state.formData;
     this.setState({ isLoading: true });
-    fetch("http://127.0.0.1:9999/prediction/", {
+    fetch("http://127.0.0.1:5000/prediction/", {
       headers: {
         Accept: "application/json",
         "Content-Type": "application/json",
@@ -94,6 +62,7 @@ class ReadmissionForm extends Component {
     const isLoading = this.state.isLoading;
     const formData = this.state.formData;
     const result = this.state.result;
+
     return (
       <Container>
         <div>
@@ -103,32 +72,24 @@ class ReadmissionForm extends Component {
           <Form>
             <Form.Row>
               <Form.Group as={Col}>
-                <Form.Label>Race</Form.Label>
+                <Form.Label>Text Field 1</Form.Label>
                 <Form.Control
-                  as="select"
-                  value={formData.race}
-                  name="race"
+                  type="text"
+                  placeholder="Name"
+                  name="textfield1"
+                  value={formData.textfield1}
                   onChange={this.handleChange}
-                >
-                  <option>Caucasian</option>
-                  <option>African American</option>
-                  <option>Asian</option>
-                  <option>Hispanic</option>
-                  <option>Other</option>
-                </Form.Control>
+                />
               </Form.Group>
               <Form.Group as={Col}>
-                <Form.Label>Gender</Form.Label>
+                <Form.Label>Text Field 2</Form.Label>
                 <Form.Control
-                  as="select"
-                  value={formData.gender}
-                  name="gender"
+                  type="text"
+                  placeholder="Patient number"
+                  name="textfield2"
+                  value={formData.textfield2}
                   onChange={this.handleChange}
-                >
-                  <option>Female</option>
-                  <option>Male</option>
-                  <option>Other</option>
-                </Form.Control>
+                />
               </Form.Group>
             </Form.Row>
             <Form.Row>
@@ -136,8 +97,8 @@ class ReadmissionForm extends Component {
                 <Form.Label>Age</Form.Label>
                 <Form.Control
                   as="select"
-                  value={formData.age}
-                  name="age"
+                  value={formData.select1}
+                  name="select1"
                   onChange={this.handleChange}
                 >
                   <option>0-10</option>
@@ -152,196 +113,32 @@ class ReadmissionForm extends Component {
                 </Form.Control>
               </Form.Group>
               <Form.Group as={Col}>
-                <Form.Label>Admission Type</Form.Label>
-                <Form.Control
-                  as="select"
-                  value={formData.admission_type_id}
-                  name="admission_type_id"
-                  onChange={this.handleChange}
-                >
-                  <option>1</option>
-                  <option>2</option>
-                  <option>3</option>
-                  <option>4</option>
-                  <option>6</option>
-                </Form.Control>
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Discharge Disposition</Form.Label>
+                <Form.Label>A1c Test Result</Form.Label>
                 <Form.Control
                   as="select"
-                  value={formData.discharge_disposition_id}
-                  name="discharge_disposition_id"
+                  value={formData.select2}
+                  name="select2"
                   onChange={this.handleChange}
                 >
-                  <option>1</option>
-                  <option>2</option>
-                  <option>3</option>
-                  <option>4</option>
-                  <option>6</option>
+                  <option>None</option>
+                  <option> Greater than 8 </option>
+                  <option> Greater than 7</option>
+                  <option>Normal</option>
                 </Form.Control>
               </Form.Group>
               <Form.Group as={Col}>
-                <Form.Label>Admission Source</Form.Label>
-                <Form.Control
-                  as="select"
-                  value={formData.admission_source_id}
-                  name="admission_source_id"
-                  onChange={this.handleChange}
-                >
-                  <option>1</option>
-                  <option>2</option>
-                  <option>3</option>
-                  <option>4</option>
-                  <option>6</option>
-                </Form.Control>
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Time in hospital</Form.Label>
-                <Form.Control
-                  as="select"
-                  value={formData.time_in_hospital}
-                  name="time_in_hospital"
-                  onChange={this.handleChange}
-                >
-                  <option>1</option>
-                  <option>2</option>
-                  <option>3</option>
-                  <option>4</option>
-                  <option>6</option>
-                </Form.Control>
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Number of lab procedures</Form.Label>
-                <Form.Control
-                  type="text"
-                  value={formData.num_lab_procedures}
-                  name="num_lab_procedures"
-                  onChange={this.handleChange}
-                />
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Number of procedures</Form.Label>
-                <Form.Control
-                  type="text"
-                  value={formData.num_procedures}
-                  name="num_procedures"
-                  onChange={this.handleChange}
-                />
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Number of medications</Form.Label>
-                <Form.Control
-                  type="text"
-                  value={formData.num_medications}
-                  name="num_medications"
-                  onChange={this.handleChange}
-                />
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Number of outpatient visits</Form.Label>
-                <Form.Control
-                  type="text"
-                  value={formData.number_outpatient}
-                  name="number_outpatient"
-                  onChange={this.handleChange}
-                />
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Number of emergency visits</Form.Label>
-                <Form.Control
-                  type="text"
-                  value={formData.number_emergency}
-                  name="number_emergency"
-                  onChange={this.handleChange}
-                />
-              </Form.Group>
-              <Form.Group as={Col}>
-                <Form.Label>Number of inpatient visits</Form.Label>
-                <Form.Control
-                  type="text"
-                  value={formData.number_inpatient}
-                  name="number_inpatient"
-                  onChange={this.handleChange}
-                />
-                <Form.Group as={Col}>
-                  <Form.Label>Primary Diagnosis</Form.Label>
-                  <Form.Control
-                    as="select"
-                    value={formData.diag_1}
-                    name="diag_1"
-                    onChange={this.handleChange}
-                  >
-                    <option>1</option>
-                    <option>2</option>
-                    <option>3</option>
-                    <option>4</option>
-                    <option>6</option>
-                  </Form.Control>
-                  <Form.Label>Secondary Diagnosis</Form.Label>
-                  <Form.Control
-                    as="select"
-                    value={formData.diag_2}
-                    name="diag_2"
-                    onChange={this.handleChange}
-                  >
-                    <option>1</option>
-                    <option>2</option>
-                    <option>3</option>
-                    <option>4</option>
-                    <option>6</option>
-                  </Form.Control>
-                </Form.Group>
-                <Form.Label>Tertiary Diagnosis</Form.Label>
+                <Form.Label>Race</Form.Label>
                 <Form.Control
                   as="select"
-                  value={formData.diag_3}
-                  name="diag_3"
-                  onChange={this.handleChange}
-                >
-                  <option>1</option>
-                  <option>2</option>
-                  <option>3</option>
-                  <option>4</option>
-                  <option>6</option>
-                </Form.Control>
-                <Form.Label>Total Diagnosis</Form.Label>
-                <Form.Control
-                  type="text"
-                  value={formData.number_diagnosis}
-                  name="number_diagnosis"
-                  placeholder="number of diagnosis"
-                  onChange={this.handleChange}
-                />
-              </Form.Group>
-              <Form.Group>
-                <Form.Label>
-                  Prescribed medications (select multiple)
-                </Form.Label>
-                <Form.Control
-                  name="metformin"
+                  value={formData.select3}
+                  name="select3"
                   onChange={this.handleChange}
-                  as="select"
-                  multiple
                 >
-                  <option>metformin</option>
-                  <option>repaglinide</option>
-                  <option>nateglinide</option>
-                  <option>nateglinide</option>
-                  <option>chlorpropamide</option>
-                  <option>glimepiride</option>
-                  <option>acetohexamide</option>
-                  <option>glipizide</option>
-                  <option>glyburide</option>
-                  <option>tolbutamide</option>
-                  <option>pioglitazone</option>
-                  <option>rosiglitazone</option>
-                  <option>acarbose</option>
-                  <option>miglitol</option>
-                  <option>troglitazone</option>
-                  <option>tolazamide</option>
-                  <option>examide</option>
-                  <option>citoglipton</option>
+                  <option> Caucasian</option>
+                  <option> Asian</option>
+                  <option> African American</option>
+                  <option> Hispanic</option>
+                  <option> Other</option>
                 </Form.Control>
               </Form.Group>
             </Form.Row>
@@ -368,6 +165,13 @@ class ReadmissionForm extends Component {
               </Col>
             </Row>
           </Form>
+          {result === "" ? null : (
+            <Row>
+              <Col className="result-container">
+                <h5 id="result">{result}</h5>
+              </Col>
+            </Row>
+          )}
         </div>
       </Container>
     );
diff --git a/frontend/src/age_avg.csv b/frontend/src/age_avg.csv
new file mode 100644
index 0000000..ce4a650
--- /dev/null
+++ b/frontend/src/age_avg.csv
@@ -0,0 +1,11 @@
+age,avg(prediction)
+[70-80),0.4701274290401155
+[90-100),0.4393574276918335
+[40-50),0.4537352812660922
+[10-20),0.40137526350750785
+[20-30),0.46058455206254817
+[30-40),0.4325848045939877
+[0-10),0.30586247241086745
+[80-90),0.4633664703681194
+[50-60),0.45188039006242475
+[60-70),0.4633848263902463
diff --git a/frontend/src/history.js b/frontend/src/history.js
index df29f89..9a980d3 100644
--- a/frontend/src/history.js
+++ b/frontend/src/history.js
@@ -1,3 +1,3 @@
-import { createBrowserHistory as history } from "history";
+import { createBrowserHistory as history} from 'history';
 
 export default history();
diff --git a/frontend/src/race_avg.csv b/frontend/src/race_avg.csv
new file mode 100644
index 0000000..b256b64
--- /dev/null
+++ b/frontend/src/race_avg.csv
@@ -0,0 +1,7 @@
+race,avg(prediction)
+Caucasian,0.4664002326633795
+Other,0.4283481266561604
+AfricanAmerican,0.4558919661625747
+Hispanic,0.42637748279701565
+Asian,0.38953353563569904
+?,0.34392652760527476
diff --git a/linear_regression.ipynb b/linear_regression.ipynb
deleted file mode 100644
index fd8dd8f..0000000
--- a/linear_regression.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","source":["## Attach the notebook to the cluster and run all commands in the notebook\n\n1. Return to this notebook. \n1. In the notebook menu bar, select **<img src=\"http://docs.databricks.com/_static/images/notebooks/detached.png\"/></a> > Quickstart**.\n1. When the cluster changes from <img src=\"http://docs.databricks.com/_static/images/clusters/cluster-starting.png\"/></a> to <img src=\"http://docs.databricks.com/_static/images/clusters/cluster-running.png\"/></a>, click **<img src=\"http://docs.databricks.com/_static/images/notebooks/run-all.png\"/></a> Run All**."],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9f17d6a6-b705-4f6b-817b-e06c720ec433"}}},{"cell_type":"code","source":["%sql\nDROP TABLE IF EXISTS diabetes;\n\nCREATE TABLE diabetes\nUSING csv\nOPTIONS (path \"/FileStore/tables/diabetic_data.csv\", header \"true\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"3faf3302-c0fc-4162-b4de-0d3e15f458b3"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr></tr></thead><tbody></tbody></table></div>"]}}],"execution_count":0},{"cell_type":"code","source":["# %sql\n# SELECT * FROM diabetes EXCEPT (select * from diabetes where time_in_hospital rlike '^(([0-9]*)|(([0-9]*)\\.([0-9]*)))$')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5dcf58c2-a5e5-4c66-8b1d-d535f44a4bf7"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["%sql\nDESCRIBE TABLE diabetes;"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"46d62fb8-1cc8-4949-b3d2-0831f365dc57"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[["encounter_id","string",null],["patient_nbr","string",null],["race","string",null],["gender","string",null],["age","string",null],["weight","string",null],["admission_type_id","string",null],["discharge_disposition_id","string",null],["admission_source_id","string",null],["time_in_hospital","string",null],["payer_code","string",null],["medical_specialty","string",null],["num_lab_procedures","string",null],["num_procedures","string",null],["num_medications","string",null],["number_outpatient","string",null],["number_emergency","string",null],["number_inpatient","string",null],["diag_1","string",null],["diag_2","string",null],["diag_3","string",null],["number_diagnoses","string",null],["max_glu_serum","string",null],["A1Cresult","string",null],["metformin","string",null],["repaglinide","string",null],["nateglinide","string",null],["chlorpropamide","string",null],["glimepiride","string",null],["acetohexamide","string",null],["glipizide","string",null],["glyburide","string",null],["tolbutamide","string",null],["pioglitazone","string",null],["rosiglitazone","string",null],["acarbose","string",null],["miglitol","string",null],["troglitazone","string",null],["tolazamide","string",null],["examide","string",null],["citoglipton","string",null],["insulin","string",null],["glyburide-metformin","string",null],["glipizide-metformin","string",null],["glimepiride-pioglitazone","string",null],["metformin-rosiglitazone","string",null],["metformin-pioglitazone","string",null],["change","string",null],["diabetesMed","string",null],["readmitted","string",null]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"col_name","type":"\"string\"","metadata":"{\"comment\":\"name of the column\"}"},{"name":"data_type","type":"\"string\"","metadata":"{\"comment\":\"data type of the column\"}"},{"name":"comment","type":"\"string\"","metadata":"{\"comment\":\"comment of the column\"}"}],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>col_name</th><th>data_type</th><th>comment</th></tr></thead><tbody><tr><td>encounter_id</td><td>string</td><td>null</td></tr><tr><td>patient_nbr</td><td>string</td><td>null</td></tr><tr><td>race</td><td>string</td><td>null</td></tr><tr><td>gender</td><td>string</td><td>null</td></tr><tr><td>age</td><td>string</td><td>null</td></tr><tr><td>weight</td><td>string</td><td>null</td></tr><tr><td>admission_type_id</td><td>string</td><td>null</td></tr><tr><td>discharge_disposition_id</td><td>string</td><td>null</td></tr><tr><td>admission_source_id</td><td>string</td><td>null</td></tr><tr><td>time_in_hospital</td><td>string</td><td>null</td></tr><tr><td>payer_code</td><td>string</td><td>null</td></tr><tr><td>medical_specialty</td><td>string</td><td>null</td></tr><tr><td>num_lab_procedures</td><td>string</td><td>null</td></tr><tr><td>num_procedures</td><td>string</td><td>null</td></tr><tr><td>num_medications</td><td>string</td><td>null</td></tr><tr><td>number_outpatient</td><td>string</td><td>null</td></tr><tr><td>number_emergency</td><td>string</td><td>null</td></tr><tr><td>number_inpatient</td><td>string</td><td>null</td></tr><tr><td>diag_1</td><td>string</td><td>null</td></tr><tr><td>diag_2</td><td>string</td><td>null</td></tr><tr><td>diag_3</td><td>string</td><td>null</td></tr><tr><td>number_diagnoses</td><td>string</td><td>null</td></tr><tr><td>max_glu_serum</td><td>string</td><td>null</td></tr><tr><td>A1Cresult</td><td>string</td><td>null</td></tr><tr><td>metformin</td><td>string</td><td>null</td></tr><tr><td>repaglinide</td><td>string</td><td>null</td></tr><tr><td>nateglinide</td><td>string</td><td>null</td></tr><tr><td>chlorpropamide</td><td>string</td><td>null</td></tr><tr><td>glimepiride</td><td>string</td><td>null</td></tr><tr><td>acetohexamide</td><td>string</td><td>null</td></tr><tr><td>glipizide</td><td>string</td><td>null</td></tr><tr><td>glyburide</td><td>string</td><td>null</td></tr><tr><td>tolbutamide</td><td>string</td><td>null</td></tr><tr><td>pioglitazone</td><td>string</td><td>null</td></tr><tr><td>rosiglitazone</td><td>string</td><td>null</td></tr><tr><td>acarbose</td><td>string</td><td>null</td></tr><tr><td>miglitol</td><td>string</td><td>null</td></tr><tr><td>troglitazone</td><td>string</td><td>null</td></tr><tr><td>tolazamide</td><td>string</td><td>null</td></tr><tr><td>examide</td><td>string</td><td>null</td></tr><tr><td>citoglipton</td><td>string</td><td>null</td></tr><tr><td>insulin</td><td>string</td><td>null</td></tr><tr><td>glyburide-metformin</td><td>string</td><td>null</td></tr><tr><td>glipizide-metformin</td><td>string</td><td>null</td></tr><tr><td>glimepiride-pioglitazone</td><td>string</td><td>null</td></tr><tr><td>metformin-rosiglitazone</td><td>string</td><td>null</td></tr><tr><td>metformin-pioglitazone</td><td>string</td><td>null</td></tr><tr><td>change</td><td>string</td><td>null</td></tr><tr><td>diabetesMed</td><td>string</td><td>null</td></tr><tr><td>readmitted</td><td>string</td><td>null</td></tr></tbody></table></div>"]}}],"execution_count":0},{"cell_type":"code","source":["from pyspark.sql.types import IntegerType\nfrom pyspark.sql.functions import when\n\ndataset = spark.read.csv(\"/FileStore/tables/diabetic_data.csv\", header=\"true\")\ncolsToConvert = [\"time_in_hospital\", \"num_procedures\", \"num_lab_procedures\", \"num_medications\", \"number_outpatient\", \"number_inpatient\", \"number_emergency\", \"number_diagnoses\", \"A1Cresult\", \"max_glu_serum\"]\nfor col in colsToConvert:\n  dataset = dataset.withColumn(col, dataset[col].cast(IntegerType()))\ndataset = dataset.withColumn(\"readmitted\", when(dataset[\"readmitted\"] == \"NO\", 0).otherwise(1))\ndataset = dataset.withColumn(\"A1Cresult\", when(dataset[\"A1Cresult\"] == \">8\", 3).when(dataset[\"A1Cresult\"] == \">7\", 2).when(dataset[\"A1Cresult\"] == \"Norm\", 1).otherwise(0))\ndataset = dataset.withColumn(\"max_glu_serum\", when(dataset[\"max_glu_serum\"] == \">300\", 3).when(dataset[\"max_glu_serum\"] == \">200\", 2).when(dataset[\"max_glu_serum\"] == \"Norm\", 1).otherwise(0))\n\ndisplay(dataset)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"119819f5-0086-4cf3-9b09-4ddf186998a1"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":true,"datasetInfos":[],"data":[["2278392","8222157","Caucasian","Female","[0-10)","?","6","25","1",1,"?","Pediatrics-Endocrinology",41,0,1,0,0,0,"250.83","?","?",1,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No",0],["149190","55629189","Caucasian","Female","[10-20)","?","1","1","7",3,"?","?",59,0,18,0,0,0,"276","250.01","255",9,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Up","No","No","No","No","No","Ch","Yes",1],["64410","86047875","AfricanAmerican","Female","[20-30)","?","1","1","7",2,"?","?",11,5,13,2,0,1,"648","250","V27",6,0,0,"No","No","No","No","No","No","Steady","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Yes",0],["500364","82442376","Caucasian","Male","[30-40)","?","1","1","7",2,"?","?",44,1,16,0,0,0,"8","250.43","403",7,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Up","No","No","No","No","No","Ch","Yes",0],["16680","42519267","Caucasian","Male","[40-50)","?","1","1","7",1,"?","?",51,0,8,0,0,0,"197","157","250",5,0,0,"No","No","No","No","No","No","Steady","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","Ch","Yes",0],["35754","82637451","Caucasian","Male","[50-60)","?","2","1","2",3,"?","?",31,6,16,0,0,0,"414","411","250",9,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","No","Yes",1],["55842","84259809","Caucasian","Male","[60-70)","?","3","1","2",4,"?","?",70,1,21,0,0,0,"414","411","V45",7,0,0,"Steady","No","No","No","Steady","No","No","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","Ch","Yes",0],["63768","114882984","Caucasian","Male","[70-80)","?","1","1","7",5,"?","?",73,0,12,0,0,0,"428","492","250",8,0,0,"No","No","No","No","No","No","No","Steady","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Yes",1],["12522","48330783","Caucasian","Female","[80-90)","?","2","1","4",13,"?","?",68,2,28,0,0,0,"398","427","38",8,0,0,"No","No","No","No","No","No","Steady","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","Ch","Yes",0],["15738","63555939","Caucasian","Female","[90-100)","?","3","3","4",12,"?","InternalMedicine",33,3,18,0,0,0,"434","198","486",8,0,0,"No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","No","Steady","No","No","No","No","No","Ch","Yes",0],["28236","89869032","AfricanAmerican","Female","[40-50)","?","1","1","7",9,"?","?",47,2,17,0,0,0,"250.7","403","996",9,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","No","Yes",1],["36900","77391171","AfricanAmerican","Male","[60-70)","?","2","1","4",7,"?","?",62,0,11,0,0,0,"157","288","197",7,0,0,"No","No","No","No","No","No","No","Up","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","Ch","Yes",1],["40926","85504905","Caucasian","Female","[40-50)","?","1","3","7",7,"?","Family/GeneralPractice",60,0,15,0,1,0,"428","250.43","250.6",8,0,0,"Steady","Up","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Down","No","No","No","No","No","Ch","Yes",1],["42570","77586282","Caucasian","Male","[80-90)","?","1","6","7",10,"?","Family/GeneralPractice",55,1,31,0,0,0,"428","411","427",8,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","No","Yes",0],["62256","49726791","AfricanAmerican","Female","[60-70)","?","3","1","2",1,"?","?",49,5,2,0,0,0,"518","998","627",8,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","No","Yes",1],["73578","86328819","AfricanAmerican","Male","[60-70)","?","1","3","7",12,"?","?",75,5,13,0,0,0,"999","507","996",9,0,0,"No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Up","No","No","No","No","No","Ch","Yes",0],["77076","92519352","AfricanAmerican","Male","[50-60)","?","1","1","7",4,"?","?",45,4,17,0,0,0,"410","411","414",8,0,0,"No","No","No","No","No","No","Steady","No","No","No","No","No","No","No","No","No","No","Steady","No","No","No","No","No","Ch","Yes",1],["84222","108662661","Caucasian","Female","[50-60)","?","1","1","7",3,"?","Cardiology",29,0,11,0,0,0,"682","174","250",3,0,0,"No","No","No","No","No","No","No","Steady","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","No","Yes",0],["89682","107389323","AfricanAmerican","Male","[70-80)","?","1","1","7",5,"?","?",35,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white'><tr><th>encounter_id</th><th>patient_nbr</th><th>race</th><th>gender</th><th>age</th><th>weight</th><th>admission_type_id</th><th>discharge_disposition_id</th><th>admission_source_id</th><th>time_in_hospital</th><th>payer_code</th><th>medical_specialty</th><th>num_lab_procedures</th><th>num_procedures</th><th>num_medications</th><th>number_outpatient</th><th>number_emergency</th><th>number_inpatient</th><th>diag_1</th><th>diag_2</th><th>diag_3</th><th>number_diagnoses</th><th>max_glu_serum</th><th>A1Cresult</th><th>metformin</th><th>repaglinide</th><th>nateglinide</th><th>chlorpropamide</th><th>glimepiride</th><th>acetohexamide</th><th>glipizide</th><th>glyburide</th><th>tolbutamide</th><th>pioglitazone</th><th>rosiglitazone</th><th>acarbose</th><th>miglitol</th><th>troglitazone</th><th>tolazamide</th><th>examide</th><th>citoglipton</th><th>insulin</th><th>glyburide-metformin</th><th>glipizide-metformin</th><th>glimepiride-pioglitazone</th><th>metformin-rosiglitazone</th><th>metformin-pioglitazone</th><th>change</th><th>diabetesMed</th><th>readmitted</th></tr></thead><tbody><tr><td>2278392</td><td>8222157</td><td>Caucasian</td><td>Female</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Pediatrics-Endocrinology</td><td>41</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>250.83</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>149190</td><td>55629189</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>59</td><td>0</td><td>18</td><td>0</td><td>0</td><td>0</td><td>276</td><td>250.01</td><td>255</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>64410</td><td>86047875</td><td>AfricanAmerican</td><td>Female</td><td>[20-30)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>11</td><td>5</td><td>13</td><td>2</td><td>0</td><td>1</td><td>648</td><td>250</td><td>V27</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>500364</td><td>82442376</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>44</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>8</td><td>250.43</td><td>403</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>16680</td><td>42519267</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>51</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>197</td><td>157</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>35754</td><td>82637451</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>31</td><td>6</td><td>16</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>55842</td><td>84259809</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>70</td><td>1</td><td>21</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>V45</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>63768</td><td>114882984</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>5</td><td>?</td><td>?</td><td>73</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>428</td><td>492</td><td>250</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>12522</td><td>48330783</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>13</td><td>?</td><td>?</td><td>68</td><td>2</td><td>28</td><td>0</td><td>0</td><td>0</td><td>398</td><td>427</td><td>38</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>15738</td><td>63555939</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>3</td><td>3</td><td>4</td><td>12</td><td>?</td><td>InternalMedicine</td><td>33</td><td>3</td><td>18</td><td>0</td><td>0</td><td>0</td><td>434</td><td>198</td><td>486</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>28236</td><td>89869032</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>9</td><td>?</td><td>?</td><td>47</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>403</td><td>996</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>36900</td><td>77391171</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>7</td><td>?</td><td>?</td><td>62</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>157</td><td>288</td><td>197</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>40926</td><td>85504905</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>7</td><td>?</td><td>Family/GeneralPractice</td><td>60</td><td>0</td><td>15</td><td>0</td><td>1</td><td>0</td><td>428</td><td>250.43</td><td>250.6</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>42570</td><td>77586282</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>10</td><td>?</td><td>Family/GeneralPractice</td><td>55</td><td>1</td><td>31</td><td>0</td><td>0</td><td>0</td><td>428</td><td>411</td><td>427</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>62256</td><td>49726791</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>49</td><td>5</td><td>2</td><td>0</td><td>0</td><td>0</td><td>518</td><td>998</td><td>627</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>73578</td><td>86328819</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>12</td><td>?</td><td>?</td><td>75</td><td>5</td><td>13</td><td>0</td><td>0</td><td>0</td><td>999</td><td>507</td><td>996</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>77076</td><td>92519352</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>45</td><td>4</td><td>17</td><td>0</td><td>0</td><td>0</td><td>410</td><td>411</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>84222</td><td>108662661</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>29</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>682</td><td>174</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>89682</td><td>107389323</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>5</td><td>?</td><td>?</td><td>35</td><td>5</td><td>23</td><td>0</td><td>0</td><td>0</td><td>402</td><td>425</td><td>416</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>148530</td><td>69422211</td><td>?</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>6</td><td>2</td><td>6</td><td>?</td><td>?</td><td>42</td><td>2</td><td>23</td><td>0</td><td>0</td><td>0</td><td>737</td><td>427</td><td>714</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>150006</td><td>22864131</td><td>?</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>66</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>410</td><td>427</td><td>428</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>150048</td><td>21239181</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>36</td><td>2</td><td>11</td><td>0</td><td>0</td><td>0</td><td>572</td><td>456</td><td>427</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>182796</td><td>63000108</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>47</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>410</td><td>401</td><td>582</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>183930</td><td>107400762</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>2</td><td>6</td><td>1</td><td>11</td><td>?</td><td>?</td><td>42</td><td>2</td><td>19</td><td>0</td><td>0</td><td>0</td><td>V57</td><td>715</td><td>V43</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>216156</td><td>62718876</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>19</td><td>4</td><td>18</td><td>0</td><td>0</td><td>0</td><td>189</td><td>496</td><td>427</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>221634</td><td>21861756</td><td>Other</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>33</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>786</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>236316</td><td>40523301</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>6</td><td>?</td><td>Cardiology</td><td>64</td><td>3</td><td>18</td><td>0</td><td>0</td><td>0</td><td>427</td><td>428</td><td>414</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>248916</td><td>115196778</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>1</td><td>2</td><td>?</td><td>Surgery-General</td><td>25</td><td>2</td><td>11</td><td>0</td><td>0</td><td>0</td><td>996</td><td>585</td><td>250.01</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>250872</td><td>41606064</td><td>Caucasian</td><td>Male</td><td>[20-30)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>10</td><td>?</td><td>?</td><td>53</td><td>0</td><td>20</td><td>0</td><td>0</td><td>0</td><td>277</td><td>250.02</td><td>263</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>252822</td><td>18196434</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>5</td><td>?</td><td>Cardiology</td><td>52</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>428</td><td>410</td><td>414</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>253380</td><td>56480238</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>?</td><td>87</td><td>0</td><td>18</td><td>0</td><td>0</td><td>0</td><td>584</td><td>496</td><td>250.42</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>253722</td><td>96664626</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>5</td><td>7</td><td>1</td><td>?</td><td>?</td><td>53</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>462</td><td>250.01</td><td>276</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>260166</td><td>80845353</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>Family/GeneralPractice</td><td>27</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>996</td><td>999</td><td>250.01</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>293058</td><td>114715242</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>6</td><td>2</td><td>5</td><td>?</td><td>?</td><td>37</td><td>0</td><td>18</td><td>0</td><td>0</td><td>0</td><td>473</td><td>996</td><td>482</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>293118</td><td>3327282</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>11</td><td>2</td><td>5</td><td>?</td><td>?</td><td>46</td><td>2</td><td>20</td><td>0</td><td>0</td><td>0</td><td>428</td><td>585</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>325848</td><td>63023292</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>41</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>411</td><td>250.01</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>325866</td><td>98427861</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>33</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>174</td><td>135</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>326028</td><td>112002975</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>33</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>486</td><td>244</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>358776</td><td>101002446</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>7</td><td>?</td><td>?</td><td>47</td><td>2</td><td>22</td><td>0</td><td>0</td><td>0</td><td>998</td><td>41</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>377268</td><td>104672268</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>44</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>996</td><td>403</td><td>250.41</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>383430</td><td>80588529</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>1</td><td>?</td><td>Cardiology</td><td>28</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250.01</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>419304</td><td>99715041</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>7</td><td>?</td><td>?</td><td>36</td><td>2</td><td>9</td><td>0</td><td>0</td><td>0</td><td>511</td><td>571</td><td>585</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>421194</td><td>96435585</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>13</td><td>?</td><td>?</td><td>48</td><td>2</td><td>18</td><td>0</td><td>0</td><td>1</td><td>V57</td><td>276</td><td>781</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>449142</td><td>66274866</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>59</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>428</td><td>496</td><td>278</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>450210</td><td>80177094</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>11</td><td>7</td><td>7</td><td>?</td><td>?</td><td>72</td><td>1</td><td>27</td><td>0</td><td>0</td><td>0</td><td>432</td><td>997</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>464994</td><td>106936875</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>10</td><td>3</td><td>8</td><td>0</td><td>0</td><td>0</td><td>626</td><td>250.01</td><td>998</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>486156</td><td>86240259</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>5</td><td>4</td><td>9</td><td>?</td><td>InternalMedicine</td><td>25</td><td>3</td><td>16</td><td>0</td><td>0</td><td>2</td><td>428</td><td>427</td><td>250.01</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>498030</td><td>51838164</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>3</td><td>4</td><td>9</td><td>?</td><td>?</td><td>2</td><td>0</td><td>12</td><td>0</td><td>0</td><td>1</td><td>295</td><td>599</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>537834</td><td>90097839</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>65</td><td>5</td><td>19</td><td>0</td><td>0</td><td>0</td><td>414</td><td>424</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>544194</td><td>34997814</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>11</td><td>?</td><td>?</td><td>67</td><td>2</td><td>25</td><td>0</td><td>0</td><td>0</td><td>428</td><td>491</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>550098</td><td>21820806</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>40</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>196</td><td>198</td><td>197</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>584136</td><td>72200358</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>5</td><td>?</td><td>?</td><td>44</td><td>5</td><td>13</td><td>0</td><td>0</td><td>0</td><td>998</td><td>553</td><td>568</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>590346</td><td>37746639</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>8</td><td>?</td><td>?</td><td>55</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>707</td><td>682</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>591996</td><td>58497651</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>53</td><td>0</td><td>11</td><td>1</td><td>0</td><td>0</td><td>786</td><td>427</td><td>278</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>604188</td><td>113848434</td><td>?</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>54</td><td>4</td><td>4</td><td>0</td><td>0</td><td>0</td><td>618</td><td>997</td><td>618</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>623880</td><td>17928900</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>58</td><td>3</td><td>14</td><td>0</td><td>0</td><td>0</td><td>182</td><td>286</td><td>250.02</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>630342</td><td>93232917</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>3</td><td>6</td><td>2</td><td>9</td><td>?</td><td>?</td><td>37</td><td>2</td><td>19</td><td>0</td><td>0</td><td>1</td><td>250.7</td><td>440</td><td>403</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>654918</td><td>3146373</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>36</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>845</td><td>493</td><td>305</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>669954</td><td>56714769</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>7</td><td>?</td><td>?</td><td>58</td><td>1</td><td>21</td><td>0</td><td>0</td><td>0</td><td>423</td><td>242</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>676416</td><td>71778564</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>6</td><td>4</td><td>7</td><td>?</td><td>?</td><td>2</td><td>1</td><td>8</td><td>0</td><td>0</td><td>1</td><td>808</td><td>70</td><td>250.01</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>676422</td><td>63754317</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>48</td><td>2</td><td>15</td><td>0</td><td>1</td><td>0</td><td>250.4</td><td>V45</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>679722</td><td>94994802</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>4</td><td>?</td><td>Cardiology</td><td>45</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>428</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>682494</td><td>64729746</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>3</td><td>?</td><td>?</td><td>64</td><td>0</td><td>15</td><td>0</td><td>0</td><td>1</td><td>428</td><td>250.03</td><td>707</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>685086</td><td>24370299</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>4</td><td>?</td><td>?</td><td>57</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>722</td><td>357</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>712350</td><td>110537262</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>11</td><td>7</td><td>4</td><td>?</td><td>?</td><td>58</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>403</td><td>V45</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>713268</td><td>58231314</td><td>?</td><td>Female</td><td>[90-100)</td><td>?</td><td>2</td><td>3</td><td>2</td><td>8</td><td>?</td><td>?</td><td>43</td><td>3</td><td>18</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>440</td><td>715</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>715086</td><td>3376278</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>1</td><td>5</td><td>7</td><td>1</td><td>?</td><td>?</td><td>51</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>250.11</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>792402</td><td>83775519</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>25</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>410</td><td>424</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>796152</td><td>23043240</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>32</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>414</td><td>424</td><td>424</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>833424</td><td>74447460</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>55</td><td>2</td><td>7</td><td>0</td><td>0</td><td>0</td><td>784</td><td>511</td><td>518</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>870294</td><td>95075649</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>7</td><td>?</td><td>?</td><td>75</td><td>2</td><td>22</td><td>0</td><td>0</td><td>0</td><td>707</td><td>428</td><td>403</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>881016</td><td>55152216</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>12</td><td>?</td><td>?</td><td>83</td><td>2</td><td>32</td><td>0</td><td>0</td><td>0</td><td>189</td><td>403</td><td>V45</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>922080</td><td>22655295</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>4</td><td>?</td><td>?</td><td>31</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>440</td><td>250.03</td><td>582</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>927786</td><td>60679647</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>1</td><td>14</td><td>?</td><td>Surgery-General</td><td>28</td><td>3</td><td>28</td><td>0</td><td>0</td><td>0</td><td>151</td><td>196</td><td>553</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>955884</td><td>93196251</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>5</td><td>?</td><td>Cardiology</td><td>34</td><td>0</td><td>17</td><td>0</td><td>0</td><td>1</td><td>414</td><td>411</td><td>276</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>961464</td><td>105366150</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>Cardiology</td><td>51</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>964548</td><td>106469577</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>58</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>786</td><td>396</td><td>414</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1050294</td><td>100113975</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>3</td><td>1</td><td>3</td><td>?</td><td>Orthopedics</td><td>57</td><td>1</td><td>21</td><td>0</td><td>0</td><td>0</td><td>715</td><td>250.01</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1069716</td><td>34806888</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>11</td><td>4</td><td>2</td><td>?</td><td>?</td><td>35</td><td>1</td><td>10</td><td>1</td><td>0</td><td>0</td><td>997</td><td>427</td><td>403</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>1070256</td><td>23043240</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>3</td><td>?</td><td>?</td><td>41</td><td>4</td><td>20</td><td>0</td><td>0</td><td>1</td><td>414</td><td>411</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1072554</td><td>114039603</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>29</td><td>1</td><td>7</td><td>0</td><td>0</td><td>0</td><td>427</td><td>424</td><td>250</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>1077924</td><td>21820806</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>3</td><td>?</td><td>?</td><td>52</td><td>0</td><td>13</td><td>0</td><td>0</td><td>2</td><td>198</td><td>197</td><td>197</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>1079592</td><td>101707335</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>7</td><td>7</td><td>5</td><td>?</td><td>?</td><td>58</td><td>0</td><td>16</td><td>1</td><td>0</td><td>1</td><td>998</td><td>250.01</td><td>424</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1110420</td><td>96190083</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>59</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>564</td><td>414</td><td>794</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>1115640</td><td>76197258</td><td>Other</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>5</td><td>?</td><td>?</td><td>64</td><td>4</td><td>14</td><td>0</td><td>0</td><td>0</td><td>414</td><td>428</td><td>585</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1119858</td><td>68661747</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>39</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>428</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1136472</td><td>64494171</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>69</td><td>5</td><td>25</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250.52</td><td>411</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1139226</td><td>73921680</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>3</td><td>2</td><td>3</td><td>?</td><td>?</td><td>10</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>812</td><td>496</td><td>250</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1157454</td><td>57857994</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>7</td><td>?</td><td>?</td><td>69</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>250.02</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1157574</td><td>91181331</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>38</td><td>1</td><td>17</td><td>0</td><td>0</td><td>1</td><td>250.7</td><td>440</td><td>V42</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>1161024</td><td>20830941</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>5</td><td>?</td><td>Cardiology</td><td>36</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>410</td><td>424</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1185942</td><td>54746082</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>6</td><td>2</td><td>8</td><td>?</td><td>?</td><td>47</td><td>3</td><td>21</td><td>0</td><td>0</td><td>1</td><td>398</td><td>276</td><td>427</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1186980</td><td>91370916</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>27</td><td>0</td><td>12</td><td>0</td><td>0</td><td>1</td><td>996</td><td>250</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1193868</td><td>101334321</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>34</td><td>1</td><td>6</td><td>0</td><td>0</td><td>0</td><td>997</td><td>577</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1194678</td><td>22997880</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>6</td><td>?</td><td>?</td><td>65</td><td>3</td><td>18</td><td>0</td><td>0</td><td>0</td><td>403</td><td>535</td><td>531</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1195056</td><td>79549353</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>19</td><td>2</td><td>8</td><td>0</td><td>0</td><td>0</td><td>996</td><td>413</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1195548</td><td>106343838</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>64</td><td>0</td><td>21</td><td>0</td><td>0</td><td>1</td><td>38</td><td>428</td><td>427</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1212006</td><td>84981816</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>55</td><td>4</td><td>23</td><td>0</td><td>0</td><td>1</td><td>414</td><td>411</td><td>511</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>1257282</td><td>84488562</td><td>Other</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Surgery-General</td><td>53</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>590</td><td>250.01</td><td>401</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>1260216</td><td>92117574</td><td>?</td><td>Male</td><td>[30-40)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>6</td><td>?</td><td>?</td><td>72</td><td>2</td><td>16</td><td>0</td><td>0</td><td>1</td><td>556</td><td>285</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1260312</td><td>38688588</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>11</td><td>7</td><td>9</td><td>?</td><td>?</td><td>56</td><td>2</td><td>30</td><td>0</td><td>0</td><td>0</td><td>410</td><td>491</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1260894</td><td>91530936</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>11</td><td>2</td><td>7</td><td>?</td><td>?</td><td>73</td><td>0</td><td>23</td><td>0</td><td>0</td><td>0</td><td>486</td><td>511</td><td>250.02</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1262736</td><td>50253120</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>3</td><td>?</td><td>?</td><td>22</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>786</td><td>413</td><td>414</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1270524</td><td>67897251</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>1</td><td>?</td><td>?</td><td>59</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>411</td><td>401</td><td>490</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1274802</td><td>71003736</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>31</td><td>2</td><td>11</td><td>0</td><td>0</td><td>0</td><td>578</td><td>53</td><td>562</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>1414158</td><td>48925980</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>34</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>707</td><td>780</td><td>250.8</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>1445010</td><td>23807808</td><td>Other</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>9</td><td>?</td><td>?</td><td>96</td><td>3</td><td>20</td><td>0</td><td>0</td><td>0</td><td>250.32</td><td>518</td><td>482</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>1455252</td><td>96440301</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>34</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>427</td><td>428</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>1517730</td><td>105882957</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>35</td><td>1</td><td>26</td><td>0</td><td>0</td><td>0</td><td>433</td><td>414</td><td>V45</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1668252</td><td>76159881</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>46</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1676676</td><td>72405540</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>5</td><td>?</td><td>?</td><td>72</td><td>5</td><td>22</td><td>0</td><td>0</td><td>0</td><td>250.4</td><td>403</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1686690</td><td>10777860</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>39</td><td>2</td><td>21</td><td>0</td><td>0</td><td>0</td><td>V58</td><td>150</td><td>496</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1759458</td><td>48131838</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>1</td><td>3</td><td>?</td><td>?</td><td>33</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>569</td><td>566</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1802280</td><td>49407813</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>39</td><td>2</td><td>13</td><td>0</td><td>0</td><td>0</td><td>185</td><td>414</td><td>V45</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1810752</td><td>6915888</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>14</td><td>?</td><td>Cardiology</td><td>78</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>434</td><td>250.6</td><td>250.7</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>1880598</td><td>10430154</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Gastroenterology</td><td>48</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>536</td><td>401</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>1881372</td><td>3115566</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>4</td><td>?</td><td>?</td><td>65</td><td>2</td><td>19</td><td>0</td><td>0</td><td>0</td><td>428</td><td>410</td><td>518</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>1968528</td><td>720936</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>10</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>56</td><td>2</td><td>24</td><td>0</td><td>0</td><td>0</td><td>440</td><td>413</td><td>250.52</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2040360</td><td>661014</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>Family/GeneralPractice</td><td>61</td><td>4</td><td>25</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>411</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2087382</td><td>15856002</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>11</td><td>4</td><td>2</td><td>?</td><td>?</td><td>70</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>434</td><td>507</td><td>784</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2087892</td><td>29754351</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>5</td><td>?</td><td>?</td><td>58</td><td>6</td><td>25</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>491</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2088630</td><td>1719279</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>Family/GeneralPractice</td><td>68</td><td>0</td><td>25</td><td>0</td><td>0</td><td>0</td><td>428</td><td>867</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2091690</td><td>7919802</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>47</td><td>2</td><td>13</td><td>0</td><td>0</td><td>0</td><td>578</td><td>285</td><td>401</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2092362</td><td>5041602</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>Nephrology</td><td>88</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>276</td><td>581</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2092848</td><td>6500556</td><td>Other</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Nephrology</td><td>42</td><td>2</td><td>22</td><td>0</td><td>0</td><td>0</td><td>250.4</td><td>403</td><td>420</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2095932</td><td>276606</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>62</td><td>0</td><td>21</td><td>0</td><td>0</td><td>0</td><td>250.32</td><td>403</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2135022</td><td>815391</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Family/GeneralPractice</td><td>58</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>255</td><td>486</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2139660</td><td>18030078</td><td>?</td><td>Male</td><td>[30-40)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>3</td><td>?</td><td>?</td><td>44</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>410</td><td>8</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2140146</td><td>53612379</td><td>AfricanAmerican</td><td>Female</td><td>[20-30)</td><td>?</td><td>2</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>56</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>250.13</td><td>V15</td><td>724</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2223336</td><td>558360</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>9</td><td>?</td><td>Orthopedics-Reconstructive</td><td>60</td><td>5</td><td>17</td><td>0</td><td>0</td><td>0</td><td>997</td><td>8</td><td>730</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2226870</td><td>58471101</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>6</td><td>?</td><td>?</td><td>66</td><td>0</td><td>15</td><td>1</td><td>0</td><td>2</td><td>599</td><td>276</td><td>789</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2228292</td><td>6836652</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Gastroenterology</td><td>34</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>558</td><td>250.02</td><td>131</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2262036</td><td>427482</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>72</td><td>0</td><td>22</td><td>0</td><td>0</td><td>0</td><td>599</td><td>788</td><td>250.82</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2265486</td><td>44279478</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>6</td><td>?</td><td>?</td><td>55</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>574</td><td>250.02</td><td>276</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2267052</td><td>7472070</td><td>AfricanAmerican</td><td>Female</td><td>[90-100)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Surgery-General</td><td>35</td><td>2</td><td>11</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>440</td><td>707</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2292606</td><td>53848278</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>13</td><td>?</td><td>?</td><td>75</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>491</td><td>250.03</td><td>999</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2294130</td><td>5332491</td><td>Other</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>InternalMedicine</td><td>60</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>402</td><td>250.01</td><td>424</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2298006</td><td>2519748</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>47</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>486</td><td>250</td><td>427</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2300958</td><td>5287950</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>69</td><td>5</td><td>33</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2302170</td><td>2024739</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Nephrology</td><td>65</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>584</td><td>276</td><td>424</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2305260</td><td>2780676</td><td>AfricanAmerican</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>44</td><td>1</td><td>4</td><td>0</td><td>0</td><td>0</td><td>682</td><td>250.03</td><td>41</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2305632</td><td>3635847</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>7</td><td>?</td><td>Cardiology</td><td>43</td><td>5</td><td>20</td><td>0</td><td>0</td><td>0</td><td>410</td><td>250.01</td><td>493</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2309268</td><td>77475465</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>9</td><td>?</td><td>?</td><td>42</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>682</td><td>428</td><td>250.03</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2309376</td><td>41606064</td><td>Caucasian</td><td>Male</td><td>[20-30)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>35</td><td>0</td><td>12</td><td>0</td><td>0</td><td>1</td><td>277</td><td>250.02</td><td>753</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2311404</td><td>95586993</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>6</td><td>3</td><td>7</td><td>11</td><td>?</td><td>?</td><td>70</td><td>1</td><td>23</td><td>0</td><td>0</td><td>0</td><td>560</td><td>997</td><td>276</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2313120</td><td>100035</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>57</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>244</td><td>250.02</td><td>401</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2356308</td><td>608841</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>50</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>401</td><td>276</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2363592</td><td>1059561</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>1</td><td>6</td><td>8</td><td>0</td><td>0</td><td>0</td><td>414</td><td>340</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2371176</td><td>966042</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>18</td><td>3</td><td>27</td><td>0</td><td>0</td><td>0</td><td>574</td><td>250.01</td><td>496</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2373468</td><td>1741662</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>61</td><td>2</td><td>39</td><td>0</td><td>0</td><td>0</td><td>577</td><td>574</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2398146</td><td>8147493</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>31</td><td>2</td><td>8</td><td>0</td><td>0</td><td>0</td><td>414</td><td>496</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2422806</td><td>3377124</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>14</td><td>?</td><td>InternalMedicine</td><td>41</td><td>1</td><td>18</td><td>0</td><td>0</td><td>0</td><td>730</td><td>998</td><td>250</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2429610</td><td>90460242</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>7</td><td>?</td><td>Nephrology</td><td>55</td><td>1</td><td>16</td><td>0</td><td>0</td><td>1</td><td>250.6</td><td>581</td><td>786</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2430276</td><td>69318126</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>7</td><td>?</td><td>?</td><td>82</td><td>2</td><td>21</td><td>0</td><td>0</td><td>0</td><td>427</td><td>228</td><td>529</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2459892</td><td>5074974</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>37</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>997</td><td>403</td><td>263</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2466036</td><td>4311585</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>10</td><td>?</td><td>Cardiology</td><td>46</td><td>6</td><td>23</td><td>0</td><td>0</td><td>0</td><td>414</td><td>276</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2473188</td><td>981198</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Nephrology</td><td>47</td><td>3</td><td>24</td><td>0</td><td>0</td><td>0</td><td>250.4</td><td>403</td><td>276</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2473668</td><td>720369</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>34</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>786</td><td>530</td><td>401</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2474778</td><td>77574096</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>34</td><td>2</td><td>24</td><td>0</td><td>0</td><td>1</td><td>188</td><td>493</td><td>401</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2476920</td><td>4279149</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Orthopedics-Reconstructive</td><td>54</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>824</td><td>250.82</td><td>E888</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2478690</td><td>99378891</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>Cardiology</td><td>37</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>250.8</td><td>786</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2478978</td><td>4136517</td><td>Other</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>48</td><td>2</td><td>22</td><td>0</td><td>0</td><td>0</td><td>414</td><td>511</td><td>518</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2521974</td><td>80499960</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>3</td><td>7</td><td>5</td><td>?</td><td>?</td><td>47</td><td>1</td><td>6</td><td>0</td><td>0</td><td>0</td><td>332</td><td>294</td><td>425</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2523660</td><td>8354835</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Gastroenterology</td><td>34</td><td>2</td><td>8</td><td>0</td><td>0</td><td>0</td><td>562</td><td>285</td><td>250.02</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2524134</td><td>81644544</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>6</td><td>?</td><td>?</td><td>49</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>427</td><td>427</td><td>595</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2524314</td><td>384939</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Psychiatry</td><td>46</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>291</td><td>493</td><td>303</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2527092</td><td>708741</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Emergency/Trauma</td><td>70</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>276</td><td>250.6</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2530254</td><td>101686554</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>18</td><td>0</td><td>15</td><td>5</td><td>1</td><td>0</td><td>296</td><td>427</td><td>250.02</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2532486</td><td>2038905</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Surgery-General</td><td>38</td><td>2</td><td>14</td><td>0</td><td>0</td><td>0</td><td>562</td><td>567</td><td>560</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2538204</td><td>8348193</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Surgery-General</td><td>34</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>785</td><td>711</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2548314</td><td>48452823</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>3</td><td>?</td><td>?</td><td>52</td><td>1</td><td>7</td><td>0</td><td>0</td><td>0</td><td>578</td><td>250.01</td><td>428</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2548842</td><td>3168720</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>7</td><td>?</td><td>Pulmonology</td><td>42</td><td>3</td><td>10</td><td>0</td><td>0</td><td>0</td><td>510</td><td>512</td><td>8</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2549268</td><td>88614963</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>2</td><td>4</td><td>6</td><td>?</td><td>Cardiology</td><td>31</td><td>2</td><td>19</td><td>0</td><td>0</td><td>3</td><td>428</td><td>427</td><td>V45</td><td>6</td><td>0</td><td>0</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2549826</td><td>688347</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>58</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>401</td><td>305</td><td>492</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2551236</td><td>1784367</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>InternalMedicine</td><td>39</td><td>2</td><td>6</td><td>0</td><td>0</td><td>0</td><td>263</td><td>276</td><td>332</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2552952</td><td>86240259</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>11</td><td>?</td><td>InternalMedicine</td><td>44</td><td>0</td><td>19</td><td>0</td><td>1</td><td>6</td><td>276</td><td>8</td><td>496</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2553282</td><td>2604141</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>67</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>428</td><td>424</td><td>425</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2562210</td><td>8005509</td><td>?</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Cardiology</td><td>61</td><td>5</td><td>32</td><td>0</td><td>0</td><td>0</td><td>414</td><td>V15</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2563734</td><td>5039316</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>45</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>434</td><td>427</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2565846</td><td>83864538</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>29</td><td>2</td><td>15</td><td>0</td><td>0</td><td>1</td><td>414</td><td>411</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2567256</td><td>826470</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>13</td><td>?</td><td>Psychiatry</td><td>58</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>295</td><td>305</td><td>794</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2569692</td><td>112394736</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>52</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>486</td><td>276</td><td>401</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2569794</td><td>794313</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Psychiatry</td><td>45</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>295</td><td>250.01</td><td>296</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2570172</td><td>357300</td><td>?</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Emergency/Trauma</td><td>33</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>786</td><td>729</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2577756</td><td>105388416</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>47</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>402</td><td>250.02</td><td>424</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2584908</td><td>1174005</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>12</td><td>?</td><td>Surgery-General</td><td>44</td><td>3</td><td>19</td><td>0</td><td>0</td><td>0</td><td>997</td><td>585</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2586528</td><td>1427400</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>48</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>250.32</td><td>707</td><td>250.82</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2587620</td><td>6656679</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>3</td><td>?</td><td>InternalMedicine</td><td>54</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>438</td><td>780</td><td>438</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2588250</td><td>2741004</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>2</td><td>?</td><td>Cardiology</td><td>46</td><td>3</td><td>16</td><td>0</td><td>0</td><td>0</td><td>511</td><td>425</td><td>496</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2594658</td><td>5281155</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Psychiatry</td><td>45</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>295</td><td>250.02</td><td>789</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2594700</td><td>3163068</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>61</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>427</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2595384</td><td>3151539</td><td>AfricanAmerican</td><td>Male</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Gastroenterology</td><td>70</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>70</td><td>276</td><td>250.01</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2595612</td><td>89193870</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>53</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>401</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2599620</td><td>2498787</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-Neuro</td><td>31</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250</td><td>V43</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2600796</td><td>1451637</td><td>Caucasian</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>66</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>493</td><td>250</td><td>?</td><td>2</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2601576</td><td>279009</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Orthopedics-Reconstructive</td><td>31</td><td>2</td><td>16</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250.51</td><td>362</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2602860</td><td>511965</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Surgery-General</td><td>57</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>560</td><td>403</td><td>250.4</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2629110</td><td>2283327</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Obsterics&Gynecology-GynecologicOnco</td><td>60</td><td>5</td><td>27</td><td>0</td><td>0</td><td>0</td><td>182</td><td>280</td><td>250.02</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2638410</td><td>6225435</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>ObstetricsandGynecology</td><td>65</td><td>3</td><td>25</td><td>0</td><td>0</td><td>0</td><td>642</td><td>648</td><td>654</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2652750</td><td>100605564</td><td>Caucasian</td><td>Male</td><td>[20-30)</td><td>?</td><td>2</td><td>6</td><td>2</td><td>10</td><td>?</td><td>?</td><td>46</td><td>0</td><td>30</td><td>0</td><td>1</td><td>1</td><td>997</td><td>560</td><td>V42</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2655870</td><td>716805</td><td>Asian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Cardiology</td><td>44</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>786</td><td>493</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2660244</td><td>80156115</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>51</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>491</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2663004</td><td>780975</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>46</td><td>5</td><td>12</td><td>0</td><td>0</td><td>0</td><td>410</td><td>414</td><td>401</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2664138</td><td>8432703</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>45</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>491</td><td>250.02</td><td>244</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2671290</td><td>3492477</td><td>AfricanAmerican</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>10</td><td>?</td><td>Pediatrics</td><td>1</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>V15</td><td>V70</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2674350</td><td>3845322</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>48</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>486</td><td>276</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2674482</td><td>2596824</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>52</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>786</td><td>424</td><td>250.02</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2677470</td><td>4943709</td><td>?</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>33</td><td>4</td><td>19</td><td>0</td><td>0</td><td>0</td><td>625</td><td>618</td><td>618</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2680044</td><td>564732</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>66</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>571</td><td>70</td><td>599</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2706618</td><td>1121121</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>12</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>51</td><td>3</td><td>27</td><td>0</td><td>0</td><td>0</td><td>997</td><td>428</td><td>411</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2724570</td><td>87806799</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>49</td><td>1</td><td>33</td><td>0</td><td>0</td><td>0</td><td>738</td><td>428</td><td>737</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2730936</td><td>83363985</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>5</td><td>2</td><td>1</td><td>?</td><td>?</td><td>9</td><td>3</td><td>20</td><td>0</td><td>0</td><td>2</td><td>593</td><td>401</td><td>428</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2732148</td><td>52897581</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>19</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>618</td><td>250.03</td><td>625</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2732424</td><td>6539481</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Nephrology</td><td>63</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>428</td><td>581</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2735352</td><td>3053970</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>46</td><td>5</td><td>15</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250.01</td><td>996</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2735898</td><td>95326821</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>24</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>250.01</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2735964</td><td>2359485</td><td>Caucasian</td><td>Female</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Pediatrics-Endocrinology</td><td>34</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2736744</td><td>42470721</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>51</td><td>0</td><td>18</td><td>0</td><td>0</td><td>0</td><td>786</td><td>414</td><td>V45</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2736798</td><td>93479463</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>22</td><td>5</td><td>20</td><td>0</td><td>0</td><td>1</td><td>410</td><td>414</td><td>V45</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2738358</td><td>5014656</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>36</td><td>3</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>414</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2745456</td><td>634689</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Family/GeneralPractice</td><td>73</td><td>2</td><td>19</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250.6</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2789766</td><td>2499588</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Cardiology</td><td>66</td><td>6</td><td>26</td><td>0</td><td>0</td><td>0</td><td>414</td><td>413</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2790720</td><td>48573</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>InternalMedicine</td><td>35</td><td>2</td><td>9</td><td>0</td><td>0</td><td>0</td><td>682</td><td>444</td><td>681</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2791716</td><td>105589584</td><td>Hispanic</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>6</td><td>?</td><td>?</td><td>57</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>276</td><td>276</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2792280</td><td>3306681</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>54</td><td>5</td><td>4</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250.6</td><td>250.51</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2798190</td><td>1160226</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Nephrology</td><td>47</td><td>6</td><td>14</td><td>0</td><td>0</td><td>0</td><td>250.42</td><td>38</td><td>404</td><td>9</td><td>0</td><td>0</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2799012</td><td>114304932</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>34</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>401</td><td>V10</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2802432</td><td>8320608</td><td>Other</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Surgery-General</td><td>33</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>807</td><td>250.02</td><td>810</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2806932</td><td>492012</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>53</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>V10</td><td>244</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2808240</td><td>2160585</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Gastroenterology</td><td>38</td><td>5</td><td>8</td><td>0</td><td>0</td><td>0</td><td>456</td><td>578</td><td>280</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2811000</td><td>91305</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>41</td><td>5</td><td>10</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>440</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2812446</td><td>924174</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>73</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.43</td><td>581</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2817642</td><td>107102214</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>48</td><td>1</td><td>10</td><td>0</td><td>0</td><td>1</td><td>446</td><td>277</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2824680</td><td>499995</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>61</td><td>1</td><td>25</td><td>0</td><td>0</td><td>0</td><td>518</td><td>491</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2830938</td><td>284400</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>36</td><td>4</td><td>19</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>707</td><td>785</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2837574</td><td>63956250</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>58</td><td>0</td><td>8</td><td>0</td><td>0</td><td>1</td><td>428</td><td>424</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2842320</td><td>4030317</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Gastroenterology</td><td>50</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>572</td><td>571</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2843922</td><td>580788</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>Cardiology</td><td>69</td><td>6</td><td>25</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2852430</td><td>2341521</td><td>?</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Hematology/Oncology</td><td>67</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>276</td><td>157</td><td>588</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2855388</td><td>3094443</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>InternalMedicine</td><td>52</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>575</td><td>997</td><td>250</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2862930</td><td>108387207</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>6</td><td>?</td><td>InternalMedicine</td><td>64</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>250.41</td><td>707</td><td>403</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>2865816</td><td>8450352</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>34</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>496</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2866344</td><td>6531597</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Orthopedics-Reconstructive</td><td>47</td><td>2</td><td>20</td><td>0</td><td>0</td><td>0</td><td>820</td><td>285</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2867850</td><td>567018</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>58</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>599</td><td>276</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2868276</td><td>32409162</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>33</td><td>5</td><td>17</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250</td><td>244</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2869680</td><td>794772</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>71</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>515</td><td>428</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2874540</td><td>7926723</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>48</td><td>1</td><td>14</td><td>2</td><td>1</td><td>0</td><td>780</td><td>781</td><td>401</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2881794</td><td>87652710</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>5</td><td>?</td><td>?</td><td>51</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>560</td><td>250.42</td><td>581</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>2896332</td><td>3782196</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>14</td><td>?</td><td>Psychiatry</td><td>44</td><td>2</td><td>12</td><td>0</td><td>0</td><td>0</td><td>296</td><td>250</td><td>401</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2913624</td><td>5073354</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Pediatrics-Endocrinology</td><td>36</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>278</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2922318</td><td>49453713</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>63</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>250.22</td><td>780</td><td>599</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2922522</td><td>109286217</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>6</td><td>4</td><td>11</td><td>?</td><td>Cardiology</td><td>2</td><td>2</td><td>12</td><td>0</td><td>0</td><td>1</td><td>38</td><td>599</td><td>250.01</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2922720</td><td>369684</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Cardiology</td><td>35</td><td>2</td><td>15</td><td>0</td><td>0</td><td>0</td><td>427</td><td>425</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2926554</td><td>86209290</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>7</td><td>?</td><td>?</td><td>37</td><td>2</td><td>9</td><td>0</td><td>0</td><td>1</td><td>V57</td><td>157</td><td>250.02</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2927544</td><td>1119429</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>57</td><td>5</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>427</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2928240</td><td>958194</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>InternalMedicine</td><td>57</td><td>5</td><td>22</td><td>0</td><td>0</td><td>0</td><td>562</td><td>560</td><td>569</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2929944</td><td>1729764</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>11</td><td>4</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>272</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2930886</td><td>1004859</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>77</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>428</td><td>511</td><td>997</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2935362</td><td>5592591</td><td>Other</td><td>Male</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Gastroenterology</td><td>65</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>577</td><td>276</td><td>250</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2938614</td><td>5776821</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Nephrology</td><td>50</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>780</td><td>585</td><td>250.43</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2939256</td><td>3762756</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Nephrology</td><td>35</td><td>2</td><td>3</td><td>0</td><td>0</td><td>0</td><td>996</td><td>707</td><td>250.4</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2941296</td><td>3436695</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>Family/GeneralPractice</td><td>68</td><td>1</td><td>21</td><td>0</td><td>0</td><td>0</td><td>682</td><td>599</td><td>250.02</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2941434</td><td>105017130</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>5</td><td>?</td><td>?</td><td>34</td><td>5</td><td>28</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2948334</td><td>3425292</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>36</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>250.8</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2949804</td><td>22827528</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>3</td><td>?</td><td>?</td><td>37</td><td>4</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>428</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2951364</td><td>15849</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>46</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>491</td><td>276</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>2952726</td><td>25434</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>Cardiology</td><td>60</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>428</td><td>276</td><td>8</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2955354</td><td>277065</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>36</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>780</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2957580</td><td>4845384</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Otolaryngology</td><td>55</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>995</td><td>250.6</td><td>918</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2967810</td><td>1660293</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>Family/GeneralPractice</td><td>68</td><td>0</td><td>19</td><td>0</td><td>0</td><td>0</td><td>572</td><td>571</td><td>584</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>2968386</td><td>8568180</td><td>Caucasian</td><td>Female</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pediatrics-Endocrinology</td><td>45</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>276</td><td>54</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>2970666</td><td>1159830</td><td>Other</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Surgery-Colon&Rectal</td><td>62</td><td>2</td><td>21</td><td>0</td><td>0</td><td>0</td><td>235</td><td>571</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>2972052</td><td>1011627</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>36</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>786</td><td>250.01</td><td>272</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3001770</td><td>15528213</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>69</td><td>2</td><td>25</td><td>0</td><td>0</td><td>0</td><td>196</td><td>V10</td><td>493</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3005748</td><td>419832</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>71</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>250.8</td><td>401</td><td>414</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3006750</td><td>1217313</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>57</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>786</td><td>426</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3030000</td><td>795222</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>54</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>428</td><td>584</td><td>250.41</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3039162</td><td>539910</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pediatrics-Endocrinology</td><td>50</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>250.82</td><td>276</td><td>788</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3043188</td><td>481788</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Orthopedics-Reconstructive</td><td>57</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250.01</td><td>428</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3043686</td><td>4624767</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Cardiology</td><td>59</td><td>6</td><td>29</td><td>0</td><td>0</td><td>0</td><td>414</td><td>285</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3043728</td><td>541134</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>14</td><td>?</td><td>InternalMedicine</td><td>71</td><td>3</td><td>33</td><td>0</td><td>0</td><td>0</td><td>574</td><td>496</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3044688</td><td>4049325</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Cardiology</td><td>81</td><td>6</td><td>24</td><td>0</td><td>0</td><td>0</td><td>410</td><td>276</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3045732</td><td>8317971</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Orthopedics-Reconstructive</td><td>10</td><td>3</td><td>17</td><td>0</td><td>0</td><td>0</td><td>721</td><td>496</td><td>250.01</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3048198</td><td>3454722</td><td>Other</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>67</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>462</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3048516</td><td>929223</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>35</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>491</td><td>402</td><td>416</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3051324</td><td>42867</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>12</td><td>?</td><td>?</td><td>76</td><td>6</td><td>31</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>426</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3052140</td><td>3323655</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Orthopedics-Reconstructive</td><td>44</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>824</td><td>250.01</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3075540</td><td>1421838</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>InternalMedicine</td><td>39</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250.01</td><td>722</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3077586</td><td>430092</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>48</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>491</td><td>276</td><td>250.92</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3083802</td><td>1802943</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>44</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>787</td><td>250.6</td><td>276</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3086520</td><td>95564241</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>8</td><td>?</td><td>?</td><td>35</td><td>2</td><td>8</td><td>0</td><td>0</td><td>1</td><td>V57</td><td>153</td><td>250.02</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3092106</td><td>103892157</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>7</td><td>?</td><td>?</td><td>66</td><td>3</td><td>19</td><td>0</td><td>0</td><td>0</td><td>556</td><td>560</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3105624</td><td>6609888</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>59</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3105702</td><td>493038</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>56</td><td>3</td><td>19</td><td>0</td><td>0</td><td>0</td><td>786</td><td>427</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3108096</td><td>5832918</td><td>Caucasian</td><td>Female</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Pediatrics-CriticalCare</td><td>54</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3113952</td><td>23393484</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>11</td><td>7</td><td>4</td><td>?</td><td>?</td><td>73</td><td>0</td><td>19</td><td>0</td><td>0</td><td>0</td><td>162</td><td>511</td><td>196</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3115770</td><td>82844478</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>48</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>496</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3118128</td><td>78246</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>9</td><td>?</td><td>Orthopedics-Reconstructive</td><td>59</td><td>6</td><td>22</td><td>0</td><td>0</td><td>0</td><td>724</td><td>285</td><td>998</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3118806</td><td>995562</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Orthopedics-Reconstructive</td><td>31</td><td>4</td><td>16</td><td>0</td><td>0</td><td>0</td><td>724</td><td>272</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3119718</td><td>818874</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>58</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>491</td><td>428</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3120912</td><td>108453753</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>59</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>401</td><td>780</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3148422</td><td>63285453</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>3</td><td>7</td><td>8</td><td>?</td><td>?</td><td>56</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>724</td><td>733</td><td>996</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3148872</td><td>2486844</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>57</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>282</td><td>34</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3152880</td><td>638910</td><td>Caucasian</td><td>Male</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>56</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>250.83</td><td>276</td><td>461</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3156462</td><td>81353088</td><td>AfricanAmerican</td><td>Male</td><td>[20-30)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>90</td><td>0</td><td>15</td><td>7</td><td>0</td><td>3</td><td>514</td><td>250.43</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3170268</td><td>2579337</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>InternalMedicine</td><td>66</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>427</td><td>428</td><td>486</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3171708</td><td>5120595</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>72</td><td>1</td><td>26</td><td>0</td><td>0</td><td>1</td><td>574</td><td>276</td><td>535</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3174918</td><td>5332491</td><td>Other</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>58</td><td>1</td><td>11</td><td>0</td><td>0</td><td>1</td><td>428</td><td>511</td><td>584</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3175806</td><td>22856067</td><td>Other</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>33</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3183870</td><td>60789438</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>7</td><td>?</td><td>?</td><td>58</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>996</td><td>250.02</td><td>276</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3184194</td><td>371538</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>13</td><td>?</td><td>InternalMedicine</td><td>57</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>428</td><td>276</td><td>V45</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3185130</td><td>2579427</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>46</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>427</td><td>250.03</td><td>787</td><td>8</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3185160</td><td>1190160</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Cardiology</td><td>40</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>428</td><td>427</td><td>V45</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3185682</td><td>1300581</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>InternalMedicine</td><td>31</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>682</td><td>881</td><td>891</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3185730</td><td>669393</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>49</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>401</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3186846</td><td>8566578</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Surgery-Neuro</td><td>31</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>722</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3187926</td><td>476343</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Surgery-General</td><td>54</td><td>2</td><td>12</td><td>0</td><td>0</td><td>0</td><td>V55</td><td>998</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3242274</td><td>69921</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>55</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>281</td><td>276</td><td>332</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3247932</td><td>12447</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Endocrinology</td><td>70</td><td>2</td><td>20</td><td>0</td><td>0</td><td>0</td><td>486</td><td>428</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3251226</td><td>3719655</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Hematology/Oncology</td><td>69</td><td>2</td><td>11</td><td>0</td><td>0</td><td>0</td><td>486</td><td>203</td><td>284</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3251232</td><td>3260223</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>63</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>577</td><td>250.02</td><td>496</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3251994</td><td>969633</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>34</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>414</td><td>V45</td><td>401</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3252690</td><td>1251378</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Surgery-General</td><td>42</td><td>2</td><td>10</td><td>0</td><td>0</td><td>0</td><td>574</td><td>574</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3253836</td><td>8649234</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>12</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>93</td><td>4</td><td>31</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3277446</td><td>87963039</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>44</td><td>0</td><td>16</td><td>0</td><td>0</td><td>1</td><td>998</td><td>V45</td><td>414</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3291906</td><td>2906667</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>13</td><td>?</td><td>Cardiology</td><td>78</td><td>6</td><td>24</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.41</td><td>599</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3299892</td><td>1423485</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>1</td><td>?</td><td>?</td><td>35</td><td>3</td><td>7</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>272</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3301014</td><td>2940129</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Nephrology</td><td>34</td><td>1</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.33</td><td>403</td><td>V45</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3302454</td><td>94582188</td><td>Other</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>5</td><td>4</td><td>10</td><td>?</td><td>Family/GeneralPractice</td><td>31</td><td>4</td><td>16</td><td>0</td><td>0</td><td>1</td><td>722</td><td>428</td><td>496</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3314976</td><td>76903839</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>77</td><td>4</td><td>29</td><td>0</td><td>0</td><td>0</td><td>410</td><td>426</td><td>458</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3315732</td><td>997209</td><td>Other</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>50</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>428</td><td>403</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3320148</td><td>5075091</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>ObstetricsandGynecology</td><td>76</td><td>2</td><td>30</td><td>0</td><td>0</td><td>0</td><td>642</td><td>648</td><td>648</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3325074</td><td>720936</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>37</td><td>0</td><td>13</td><td>0</td><td>0</td><td>1</td><td>786</td><td>414</td><td>401</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3328212</td><td>554472</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>3</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>786</td><td>250.6</td><td>250.7</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3328332</td><td>4582233</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>45</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>434</td><td>780</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3330822</td><td>567018</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>5</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>51</td><td>1</td><td>8</td><td>0</td><td>0</td><td>1</td><td>530</td><td>250.6</td><td>599</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3331848</td><td>442341</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>12</td><td>?</td><td>InternalMedicine</td><td>103</td><td>4</td><td>61</td><td>0</td><td>0</td><td>0</td><td>466</td><td>428</td><td>250.03</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3334290</td><td>3629394</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>12</td><td>?</td><td>Nephrology</td><td>67</td><td>1</td><td>24</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>440</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3335544</td><td>634689</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Family/GeneralPractice</td><td>61</td><td>1</td><td>20</td><td>0</td><td>0</td><td>1</td><td>530</td><td>250.13</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3339828</td><td>6550191</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Surgery-Neuro</td><td>48</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>722</td><td>729</td><td>?</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3339876</td><td>2518758</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Psychiatry</td><td>10</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>296</td><td>250.01</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3358950</td><td>71235666</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>43</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>435</td><td>250</td><td>414</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3360276</td><td>5288760</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>5</td><td>3</td><td>?</td><td>Surgery-General</td><td>43</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>730</td><td>440</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3373998</td><td>99333783</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>Cardiology</td><td>60</td><td>1</td><td>25</td><td>0</td><td>0</td><td>1</td><td>427</td><td>403</td><td>250.01</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3388536</td><td>3596877</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>46</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>250.12</td><td>276</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3393054</td><td>1552221</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>7</td><td>?</td><td>Psychiatry</td><td>1</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>296</td><td>293</td><td>780</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3413016</td><td>1211985</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>InternalMedicine</td><td>33</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250.01</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3413064</td><td>581508</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>41</td><td>2</td><td>15</td><td>0</td><td>0</td><td>0</td><td>434</td><td>427</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3413148</td><td>2283327</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Obsterics&Gynecology-GynecologicOnco</td><td>34</td><td>0</td><td>10</td><td>0</td><td>0</td><td>1</td><td>998</td><td>250.02</td><td>182</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3413556</td><td>4798962</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Cardiology</td><td>78</td><td>5</td><td>27</td><td>0</td><td>0</td><td>0</td><td>414</td><td>413</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3413892</td><td>5161536</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>37</td><td>5</td><td>14</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3414078</td><td>804132</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>39</td><td>3</td><td>19</td><td>0</td><td>0</td><td>0</td><td>562</td><td>285</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3414192</td><td>1697409</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>46</td><td>5</td><td>7</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3414750</td><td>1306215</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>62</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>245</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3417978</td><td>2525661</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pulmonology</td><td>63</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>590</td><td>578</td><td>41</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3418590</td><td>4338342</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Orthopedics-Reconstructive</td><td>56</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>715</td><td>999</td><td>285</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3419166</td><td>2923146</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>47</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>440</td><td>250</td><td>593</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3460704</td><td>491760</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>13</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>V53</td><td>427</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3465762</td><td>362610</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Cardiology</td><td>41</td><td>5</td><td>18</td><td>0</td><td>0</td><td>0</td><td>428</td><td>427</td><td>426</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3471966</td><td>2486277</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>31</td><td>5</td><td>6</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3478770</td><td>113023935</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>45</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>780</td><td>414</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3479946</td><td>3656088</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>8</td><td>?</td><td>Family/GeneralPractice</td><td>77</td><td>4</td><td>40</td><td>0</td><td>0</td><td>0</td><td>414</td><td>491</td><td>276</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3482724</td><td>4382343</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>34</td><td>6</td><td>11</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250</td><td>278</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3483090</td><td>5877477</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>11</td><td>?</td><td>Cardiology</td><td>34</td><td>6</td><td>20</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>707</td><td>785</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3487032</td><td>980262</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Urology</td><td>11</td><td>2</td><td>16</td><td>0</td><td>0</td><td>0</td><td>593</td><td>401</td><td>414</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3489684</td><td>567279</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Family/GeneralPractice</td><td>56</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>820</td><td>413</td><td>997</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3507642</td><td>53461359</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>4</td><td>?</td><td>?</td><td>46</td><td>0</td><td>23</td><td>0</td><td>0</td><td>0</td><td>789</td><td>198</td><td>197</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3511230</td><td>4190778</td><td>Caucasian</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>ObstetricsandGynecology</td><td>63</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.03</td><td>278</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3530574</td><td>47881611</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>33</td><td>4</td><td>8</td><td>0</td><td>0</td><td>0</td><td>786</td><td>403</td><td>413</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3547464</td><td>4910139</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>13</td><td>?</td><td>InternalMedicine</td><td>70</td><td>3</td><td>15</td><td>0</td><td>0</td><td>0</td><td>780</td><td>250.41</td><td>250.7</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3557748</td><td>1582326</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>48</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>566</td><td>250.12</td><td>278</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3560988</td><td>1070181</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>61</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>822</td><td>250.01</td><td>496</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3563736</td><td>3292920</td><td>Caucasian</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>31</td><td>4</td><td>14</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.01</td><td>664</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3564084</td><td>6325848</td><td>Hispanic</td><td>Male</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>71</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>250.11</td><td>558</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3565002</td><td>1383318</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>46</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>486</td><td>496</td><td>250.01</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3590394</td><td>20295</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>12</td><td>?</td><td>Surgery-General</td><td>80</td><td>6</td><td>26</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>785</td><td>682</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3595830</td><td>90341199</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>41</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>780</td><td>787</td><td>250</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3598680</td><td>338976</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Surgery-Neuro</td><td>51</td><td>1</td><td>31</td><td>0</td><td>0</td><td>0</td><td>191</td><td>428</td><td>493</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3599982</td><td>2789793</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>58</td><td>0</td><td>26</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>998</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3600198</td><td>4937643</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Nephrology</td><td>43</td><td>2</td><td>18</td><td>0</td><td>0</td><td>0</td><td>557</td><td>403</td><td>564</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3607776</td><td>694908</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Surgery-Neuro</td><td>47</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>432</td><td>342</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3616560</td><td>25831539</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>4</td><td>?</td><td>?</td><td>66</td><td>2</td><td>14</td><td>0</td><td>0</td><td>0</td><td>574</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3616710</td><td>73884501</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>12</td><td>?</td><td>Cardiology</td><td>60</td><td>0</td><td>21</td><td>0</td><td>0</td><td>0</td><td>491</td><td>427</td><td>401</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3625566</td><td>7906419</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Surgery-Neuro</td><td>47</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250.01</td><td>201</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3628932</td><td>1660932</td><td>Asian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>46</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>577</td><td>250.01</td><td>272</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3629472</td><td>34609950</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>4</td><td>?</td><td>?</td><td>53</td><td>6</td><td>18</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3634938</td><td>81931626</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>56</td><td>6</td><td>18</td><td>0</td><td>0</td><td>0</td><td>410</td><td>414</td><td>V45</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3673536</td><td>1090098</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>72</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>38</td><td>599</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3677814</td><td>5505885</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>43</td><td>2</td><td>5</td><td>0</td><td>0</td><td>0</td><td>435</td><td>573</td><td>356</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3684690</td><td>8420310</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Cardiology</td><td>34</td><td>3</td><td>7</td><td>0</td><td>0</td><td>0</td><td>414</td><td>413</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3686592</td><td>68029074</td><td>Hispanic</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>59</td><td>1</td><td>3</td><td>0</td><td>0</td><td>0</td><td>435</td><td>276</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3686640</td><td>654273</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Pulmonology</td><td>46</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3692532</td><td>3201255</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Pediatrics-CriticalCare</td><td>75</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>626</td><td>V15</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3693342</td><td>70110</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>77</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>428</td><td>599</td><td>413</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3699204</td><td>4029444</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Psychiatry</td><td>51</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>296</td><td>303</td><td>276</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3699894</td><td>8879148</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>65</td><td>5</td><td>28</td><td>0</td><td>0</td><td>0</td><td>410</td><td>427</td><td>427</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3706656</td><td>39393621</td><td>Caucasian</td><td>Female</td><td>[20-30)</td><td>?</td><td>2</td><td>11</td><td>6</td><td>7</td><td>?</td><td>?</td><td>62</td><td>0</td><td>25</td><td>0</td><td>0</td><td>1</td><td>277</td><td>428</td><td>263</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3711330</td><td>5990040</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Orthopedics-Reconstructive</td><td>45</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>820</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3712704</td><td>463932</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Endocrinology</td><td>58</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>733</td><td>250.53</td><td>250.43</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3712746</td><td>61873722</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>80</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>428</td><td>305</td><td>424</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3712782</td><td>303957</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>42</td><td>4</td><td>21</td><td>0</td><td>0</td><td>0</td><td>410</td><td>458</td><td>V15</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3714534</td><td>4269735</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Surgery-General</td><td>31</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>440</td><td>414</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3725712</td><td>516384</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Nephrology</td><td>85</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>276</td><td>584</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3753906</td><td>550674</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Nephrology</td><td>90</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>403</td><td>710</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3759678</td><td>54746082</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>46</td><td>5</td><td>22</td><td>0</td><td>0</td><td>2</td><td>162</td><td>427</td><td>496</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3769290</td><td>1495962</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>45</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>427</td><td>250.02</td><td>401</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3773286</td><td>5985369</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>10</td><td>?</td><td>Orthopedics-Reconstructive</td><td>63</td><td>3</td><td>24</td><td>0</td><td>0</td><td>0</td><td>996</td><td>415</td><td>292</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3775698</td><td>29173437</td><td>?</td><td>Female</td><td>[80-90)</td><td>?</td><td>3</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>36</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>414</td><td>427</td><td>250.02</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3777042</td><td>305793</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>InternalMedicine</td><td>44</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>401</td><td>491</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3780702</td><td>1598274</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>16</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>786</td><td>V42</td><td>401</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3781500</td><td>28088019</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>36</td><td>1</td><td>9</td><td>0</td><td>0</td><td>5</td><td>198</td><td>284</td><td>196</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3782454</td><td>78047901</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>8</td><td>?</td><td>?</td><td>76</td><td>6</td><td>46</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>491</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3783138</td><td>3667590</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Orthopedics-Reconstructive</td><td>50</td><td>2</td><td>16</td><td>0</td><td>0</td><td>0</td><td>820</td><td>285</td><td>250.01</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3783156</td><td>1162701</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Surgery-General</td><td>47</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>562</td><td>569</td><td>401</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3783912</td><td>100533195</td><td>Asian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>8</td><td>?</td><td>?</td><td>60</td><td>2</td><td>18</td><td>0</td><td>0</td><td>0</td><td>491</td><td>518</td><td>250.03</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3786414</td><td>502200</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>64</td><td>2</td><td>26</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3787566</td><td>693747</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Orthopedics-Reconstructive</td><td>10</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>722</td><td>414</td><td>272</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3828228</td><td>2798721</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Psychiatry-Child/Adolescent</td><td>52</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>296</td><td>250.02</td><td>401</td><td>3</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3830268</td><td>1748268</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>38</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>435</td><td>401</td><td>496</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3830322</td><td>24696</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>InternalMedicine</td><td>68</td><td>1</td><td>20</td><td>0</td><td>0</td><td>0</td><td>428</td><td>496</td><td>416</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3833994</td><td>486162</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>InternalMedicine</td><td>58</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>428</td><td>414</td><td>782</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3844308</td><td>412038</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>15</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>428</td><td>414</td><td>715</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3851616</td><td>5840127</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-General</td><td>39</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>276</td><td>403</td><td>996</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3861774</td><td>4856382</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>51</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.02</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3861894</td><td>8946090</td><td>AfricanAmerican</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>47</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>434</td><td>729</td><td>784</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3863238</td><td>109797327</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>56</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>759</td><td>473</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3863700</td><td>2524077</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>3</td><td>1</td><td>3</td><td>?</td><td>Orthopedics-Reconstructive</td><td>57</td><td>1</td><td>24</td><td>0</td><td>0</td><td>0</td><td>715</td><td>250</td><td>401</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3880440</td><td>602604</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>48</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>455</td><td>250.01</td><td>455</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3894402</td><td>8147232</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>34</td><td>6</td><td>12</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3894828</td><td>96165</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>Nephrology</td><td>78</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>38</td><td>403</td><td>250.01</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3896568</td><td>48573</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>34</td><td>0</td><td>7</td><td>0</td><td>0</td><td>1</td><td>440</td><td>682</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3902532</td><td>4893183</td><td>AfricanAmerican</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>63</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3914808</td><td>3678201</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>70</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>112</td><td>428</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3916122</td><td>75338109</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>2</td><td>?</td><td>Pulmonology</td><td>46</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>562</td><td>250</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3917364</td><td>3280554</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Cardiology</td><td>41</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>427</td><td>425</td><td>427</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3921864</td><td>15051789</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>7</td><td>?</td><td>?</td><td>32</td><td>2</td><td>28</td><td>0</td><td>0</td><td>0</td><td>711</td><td>41</td><td>714</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>3923562</td><td>68310432</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>Cardiology</td><td>53</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>560</td><td>276</td><td>564</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3930396</td><td>55426896</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>7</td><td>?</td><td>?</td><td>37</td><td>1</td><td>28</td><td>0</td><td>0</td><td>1</td><td>577</td><td>401</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>3936438</td><td>732303</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>7</td><td>?</td><td>Orthopedics-Reconstructive</td><td>12</td><td>3</td><td>28</td><td>0</td><td>0</td><td>0</td><td>722</td><td>292</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3936462</td><td>3306321</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>72</td><td>3</td><td>23</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3936522</td><td>78770403</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>55</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>250.8</td><td>427</td><td>E932</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3937374</td><td>6414021</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Family/GeneralPractice</td><td>71</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3973368</td><td>55890810</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>60</td><td>1</td><td>31</td><td>0</td><td>0</td><td>0</td><td>185</td><td>998</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3974694</td><td>40158</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Pediatrics-CriticalCare</td><td>41</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3977208</td><td>34568172</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>3</td><td>1</td><td>3</td><td>?</td><td>?</td><td>56</td><td>2</td><td>26</td><td>0</td><td>0</td><td>0</td><td>440</td><td>496</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>3982836</td><td>8778510</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>34</td><td>6</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250.01</td><td>411</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>3983004</td><td>45144</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>13</td><td>?</td><td>Pediatrics-Pulmonology</td><td>56</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>482</td><td>277</td><td>250.03</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3986274</td><td>352377</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>55</td><td>0</td><td>18</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>707</td><td>357</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>3988380</td><td>3186981</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>30</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>434</td><td>435</td><td>780</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>3996996</td><td>22757895</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>3</td><td>?</td><td>?</td><td>52</td><td>2</td><td>14</td><td>0</td><td>0</td><td>0</td><td>410</td><td>496</td><td>780</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4027362</td><td>55479555</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>11</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>78</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>434</td><td>427</td><td>348</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4039038</td><td>470754</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Surgery-General</td><td>63</td><td>1</td><td>18</td><td>0</td><td>0</td><td>0</td><td>575</td><td>998</td><td>780</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4057452</td><td>1101294</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>70</td><td>2</td><td>9</td><td>0</td><td>0</td><td>0</td><td>780</td><td>426</td><td>496</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4063908</td><td>3467412</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>35</td><td>3</td><td>6</td><td>0</td><td>0</td><td>0</td><td>410</td><td>250.6</td><td>250.41</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4064748</td><td>9025047</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Cardiology</td><td>47</td><td>5</td><td>10</td><td>0</td><td>0</td><td>0</td><td>410</td><td>401</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4065138</td><td>9029196</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Pediatrics-Endocrinology</td><td>50</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4066842</td><td>70269183</td><td>Hispanic</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>6</td><td>?</td><td>?</td><td>48</td><td>1</td><td>18</td><td>0</td><td>0</td><td>1</td><td>434</td><td>428</td><td>250.6</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4075428</td><td>5047470</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>47</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>202</td><td>512</td><td>401</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4080900</td><td>686988</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>46</td><td>6</td><td>23</td><td>0</td><td>0</td><td>0</td><td>786</td><td>428</td><td>531</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4084140</td><td>28766817</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>4</td><td>?</td><td>?</td><td>69</td><td>6</td><td>19</td><td>0</td><td>0</td><td>0</td><td>410</td><td>250.03</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4084524</td><td>76959585</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>10</td><td>?</td><td>?</td><td>72</td><td>1</td><td>19</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>276</td><td>294</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4084572</td><td>4571388</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>36</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>493</td><td>250.02</td><td>401</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4086876</td><td>2892654</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>48</td><td>0</td><td>6</td><td>0</td><td>0</td><td>2</td><td>250.13</td><td>276</td><td>250.23</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4088250</td><td>1422432</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Endocrinology</td><td>46</td><td>5</td><td>15</td><td>0</td><td>0</td><td>0</td><td>250.83</td><td>996</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4089042</td><td>8039988</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>9</td><td>?</td><td>Cardiology</td><td>70</td><td>6</td><td>33</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>285</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4092882</td><td>111555</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>61</td><td>3</td><td>19</td><td>0</td><td>0</td><td>0</td><td>280</td><td>428</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4099092</td><td>5193639</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>10</td><td>?</td><td>?</td><td>71</td><td>6</td><td>41</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>997</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4107000</td><td>1502829</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>66</td><td>4</td><td>16</td><td>0</td><td>0</td><td>0</td><td>410</td><td>414</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4113636</td><td>522342</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>34</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.01</td><td>278</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4116180</td><td>1957212</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>20</td><td>8</td><td>?</td><td>?</td><td>24</td><td>4</td><td>31</td><td>0</td><td>0</td><td>0</td><td>189</td><td>401</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4129332</td><td>442782</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Nephrology</td><td>63</td><td>3</td><td>15</td><td>0</td><td>0</td><td>0</td><td>996</td><td>599</td><td>788</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4134588</td><td>6288831</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>48</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>276</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4136232</td><td>1606311</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Endocrinology</td><td>44</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>562</td><td>280</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4139334</td><td>1791090</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Pediatrics</td><td>23</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>682</td><td>682</td><td>250.03</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4140282</td><td>7443135</td><td>Caucasian</td><td>Female</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pediatrics</td><td>11</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>493</td><td>250.03</td><td>714</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4150626</td><td>89177013</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>50</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>428</td><td>427</td><td>459</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4166928</td><td>90460242</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>3</td><td>?</td><td>?</td><td>63</td><td>1</td><td>18</td><td>0</td><td>0</td><td>2</td><td>414</td><td>599</td><td>403</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4169322</td><td>100510452</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>5</td><td>?</td><td>?</td><td>64</td><td>2</td><td>33</td><td>0</td><td>0</td><td>0</td><td>553</td><td>496</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4171422</td><td>4765626</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>42</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>560</td><td>276</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4171512</td><td>435366</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Urology</td><td>12</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>997</td><td>780</td><td>E878</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4174734</td><td>707769</td><td>?</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Pediatrics</td><td>25</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>225</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4174992</td><td>73154043</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>37</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>599</td><td>290</td><td>437</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4175052</td><td>4729023</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Orthopedics-Reconstructive</td><td>54</td><td>2</td><td>20</td><td>0</td><td>0</td><td>0</td><td>715</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4193160</td><td>3778047</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Surgery-General</td><td>39</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>789</td><td>401</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4206732</td><td>2464146</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Gastroenterology</td><td>56</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>577</td><td>250.6</td><td>733</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4208082</td><td>333252</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>7</td><td>?</td><td>Surgery-Colon&Rectal</td><td>49</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>154</td><td>196</td><td>507</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4209828</td><td>2425617</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>56</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>560</td><td>535</td><td>714</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4211154</td><td>86797872</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>10</td><td>7</td><td>9</td><td>?</td><td>?</td><td>72</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>434</td><td>428</td><td>250.01</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4212192</td><td>4225995</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-General</td><td>32</td><td>2</td><td>21</td><td>0</td><td>0</td><td>0</td><td>682</td><td>707</td><td>250.7</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4218042</td><td>8922573</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Cardiology</td><td>70</td><td>6</td><td>13</td><td>0</td><td>0</td><td>0</td><td>410</td><td>250</td><td>272</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4226292</td><td>60832998</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>8</td><td>?</td><td>?</td><td>1</td><td>2</td><td>11</td><td>0</td><td>0</td><td>1</td><td>V57</td><td>250.13</td><td>486</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4227894</td><td>4065975</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>33</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.93</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4232790</td><td>83120292</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>37</td><td>1</td><td>20</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4234932</td><td>688842</td><td>AfricanAmerican</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>49</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.02</td><td>525</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4242192</td><td>8035524</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>ObstetricsandGynecology</td><td>46</td><td>3</td><td>16</td><td>0</td><td>0</td><td>0</td><td>648</td><td>642</td><td>250.53</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4243080</td><td>719487</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>10</td><td>?</td><td>InternalMedicine</td><td>51</td><td>2</td><td>14</td><td>0</td><td>0</td><td>0</td><td>599</td><td>403</td><td>996</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4243416</td><td>647181</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>36</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>435</td><td>427</td><td>437</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4248912</td><td>687978</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>53</td><td>1</td><td>20</td><td>0</td><td>0</td><td>0</td><td>250.22</td><td>428</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4251816</td><td>6884595</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pediatrics-Endocrinology</td><td>52</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>536</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4253874</td><td>1426698</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>7</td><td>?</td><td>?</td><td>49</td><td>6</td><td>21</td><td>0</td><td>0</td><td>0</td><td>410</td><td>398</td><td>397</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4255176</td><td>2139525</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>InternalMedicine</td><td>54</td><td>3</td><td>16</td><td>0</td><td>0</td><td>0</td><td>571</td><td>571</td><td>572</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4255452</td><td>99109602</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>10</td><td>?</td><td>?</td><td>59</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>332</td><td>276</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4255566</td><td>27936</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>Surgery-General</td><td>34</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>441</td><td>444</td><td>805</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4259334</td><td>4696065</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Orthopedics-Reconstructive</td><td>50</td><td>1</td><td>18</td><td>0</td><td>0</td><td>0</td><td>715</td><td>496</td><td>401</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4259742</td><td>4582233</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>57</td><td>1</td><td>15</td><td>0</td><td>0</td><td>1</td><td>599</td><td>276</td><td>250.51</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4262964</td><td>8308170</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>3</td><td>?</td><td>Cardiology</td><td>44</td><td>6</td><td>12</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250.02</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4296672</td><td>104789817</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>6</td><td>1</td><td>13</td><td>?</td><td>?</td><td>37</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>276</td><td>250.02</td><td>437</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4313136</td><td>83898765</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>5</td><td>4</td><td>1</td><td>?</td><td>?</td><td>35</td><td>1</td><td>13</td><td>0</td><td>0</td><td>1</td><td>414</td><td>411</td><td>250.01</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4318740</td><td>482958</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>38</td><td>5</td><td>8</td><td>0</td><td>0</td><td>0</td><td>414</td><td>272</td><td>305</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4321050</td><td>6167682</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>1</td><td>?</td><td>Cardiology</td><td>17</td><td>5</td><td>9</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>272</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4322028</td><td>4887171</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Psychiatry</td><td>41</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>295</td><td>319</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4329624</td><td>99941634</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>4</td><td>7</td><td>?</td><td>?</td><td>78</td><td>3</td><td>36</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4332414</td><td>55861758</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>75</td><td>5</td><td>20</td><td>0</td><td>0</td><td>0</td><td>202</td><td>203</td><td>453</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4337124</td><td>9124884</td><td>Other</td><td>Male</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Pediatrics-Endocrinology</td><td>50</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>276</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4337322</td><td>8946090</td><td>AfricanAmerican</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Neurology</td><td>41</td><td>0</td><td>5</td><td>0</td><td>0</td><td>1</td><td>434</td><td>342</td><td>784</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4342122</td><td>5973228</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>44</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>250.81</td><td>401</td><td>244</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4342398</td><td>1147545</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>14</td><td>?</td><td>Family/GeneralPractice</td><td>71</td><td>1</td><td>32</td><td>0</td><td>0</td><td>0</td><td>486</td><td>428</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4342554</td><td>21943170</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>12</td><td>?</td><td>?</td><td>69</td><td>0</td><td>19</td><td>0</td><td>0</td><td>0</td><td>349</td><td>250.02</td><td>331</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4342662</td><td>1355697</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Surgery-Neuro</td><td>48</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>?</td><td>780</td><td>997</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4342920</td><td>5997636</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Orthopedics-Reconstructive</td><td>52</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>715</td><td>276</td><td>736</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4355634</td><td>2568312</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Cardiology</td><td>59</td><td>5</td><td>17</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250.01</td><td>428</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4361868</td><td>3489048</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>Family/GeneralPractice</td><td>56</td><td>5</td><td>24</td><td>0</td><td>0</td><td>0</td><td>557</td><td>70</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4373364</td><td>886266</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>46</td><td>5</td><td>14</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>401</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4375530</td><td>78398298</td><td>Hispanic</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>3</td><td>1</td><td>9</td><td>?</td><td>InternalMedicine</td><td>47</td><td>3</td><td>31</td><td>0</td><td>0</td><td>0</td><td>996</td><td>711</td><td>402</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4403682</td><td>6035463</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>35</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>962</td><td>303</td><td>250.8</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4404288</td><td>1734102</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>27</td><td>2</td><td>5</td><td>0</td><td>0</td><td>0</td><td>996</td><td>E878</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4404996</td><td>3344895</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Urology</td><td>30</td><td>4</td><td>11</td><td>0</td><td>0</td><td>0</td><td>592</td><td>250.01</td><td>591</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4407876</td><td>112077396</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>4</td><td>?</td><td>Pulmonology</td><td>60</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>410</td><td>446</td><td>414</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4409652</td><td>2029716</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Nephrology</td><td>44</td><td>2</td><td>15</td><td>0</td><td>0</td><td>0</td><td>707</td><td>41</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4413204</td><td>645309</td><td>Hispanic</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>6</td><td>?</td><td>Pulmonology</td><td>75</td><td>2</td><td>19</td><td>0</td><td>0</td><td>0</td><td>507</td><td>255</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4433586</td><td>1591542</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>62</td><td>5</td><td>14</td><td>0</td><td>0</td><td>0</td><td>599</td><td>280</td><td>535</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4439622</td><td>289908</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Cardiology</td><td>52</td><td>0</td><td>19</td><td>0</td><td>0</td><td>0</td><td>250.8</td><td>403</td><td>425</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4440984</td><td>1159938</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>61</td><td>5</td><td>10</td><td>0</td><td>0</td><td>0</td><td>414</td><td>428</td><td>427</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4442922</td><td>76024827</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>38</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>386</td><td>428</td><td>427</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4469928</td><td>3252960</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>73</td><td>5</td><td>14</td><td>0</td><td>0</td><td>0</td><td>575</td><td>427</td><td>576</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4470960</td><td>4161582</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>Surgery-General</td><td>32</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>276</td><td>V44</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4475958</td><td>1524294</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>37</td><td>1</td><td>5</td><td>0</td><td>0</td><td>0</td><td>682</td><td>242</td><td>715</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4476318</td><td>300825</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>60</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>682</td><td>38</td><td>250</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4477266</td><td>105143886</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>5</td><td>?</td><td>?</td><td>39</td><td>4</td><td>20</td><td>1</td><td>0</td><td>1</td><td>414</td><td>428</td><td>411</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4477536</td><td>952029</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Anesthesiology-Pediatric</td><td>43</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>465</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4478058</td><td>62473113</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>43</td><td>3</td><td>21</td><td>0</td><td>0</td><td>0</td><td>156</td><td>196</td><td>533</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4478760</td><td>1719279</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>47</td><td>0</td><td>24</td><td>0</td><td>0</td><td>1</td><td>428</td><td>682</td><td>584</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4486032</td><td>2241153</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>60</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>401</td><td>?</td><td>2</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4487532</td><td>6564861</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>10</td><td>?</td><td>?</td><td>37</td><td>3</td><td>28</td><td>0</td><td>0</td><td>0</td><td>200</td><td>788</td><td>401</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4488132</td><td>497439</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Cardiology</td><td>50</td><td>6</td><td>20</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>703</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4489938</td><td>331920</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Surgery-General</td><td>53</td><td>0</td><td>4</td><td>0</td><td>0</td><td>1</td><td>728</td><td>250.7</td><td>787</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4493220</td><td>92948409</td><td>?</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>52</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>348</td><td>413</td><td>401</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4493466</td><td>4118256</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pulmonology</td><td>57</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>599</td><td>250.02</td><td>41</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4494078</td><td>29952900</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>6</td><td>2</td><td>?</td><td>?</td><td>61</td><td>4</td><td>13</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>414</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4495386</td><td>293967</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>42</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>410</td><td>414</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4497462</td><td>3628350</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>66</td><td>3</td><td>13</td><td>0</td><td>0</td><td>0</td><td>786</td><td>401</td><td>250.02</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4497762</td><td>8891883</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Emergency/Trauma</td><td>42</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>434</td><td>427</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4497834</td><td>5521167</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Gastroenterology</td><td>30</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>558</td><td>250</td><td>349</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4504644</td><td>2579661</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>36</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>428</td><td>424</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4507116</td><td>3538080</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Nephrology</td><td>54</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>584</td><td>276</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4507902</td><td>9037764</td><td>Caucasian</td><td>Male</td><td>[90-100)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>45</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>459</td><td>250</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4525302</td><td>4173138</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Pediatrics-CriticalCare</td><td>21</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>V15</td><td>315</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4526874</td><td>4065939</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>ObstetricsandGynecology</td><td>22</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.01</td><td>658</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4537050</td><td>7986087</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Cardiology</td><td>79</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>250.6</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4540344</td><td>568098</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>48</td><td>4</td><td>15</td><td>0</td><td>0</td><td>0</td><td>414</td><td>414</td><td>V45</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4560078</td><td>3602925</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>34</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>414</td><td>427</td><td>414</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4573914</td><td>4309191</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>32</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>724</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4575066</td><td>1601532</td><td>AfricanAmerican</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pediatrics-CriticalCare</td><td>65</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4576464</td><td>2477079</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Obsterics&Gynecology-GynecologicOnco</td><td>49</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>618</td><td>998</td><td>250.02</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4582062</td><td>76155741</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>6</td><td>?</td><td>?</td><td>26</td><td>6</td><td>24</td><td>0</td><td>0</td><td>0</td><td>410</td><td>403</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4619742</td><td>1748268</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>37</td><td>2</td><td>13</td><td>0</td><td>0</td><td>1</td><td>435</td><td>414</td><td>331</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4622622</td><td>52605</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>22</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>427</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4629828</td><td>1753524</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>25</td><td>2</td><td>24</td><td>0</td><td>0</td><td>0</td><td>185</td><td>998</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4649478</td><td>50846004</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>8</td><td>?</td><td>?</td><td>37</td><td>1</td><td>9</td><td>0</td><td>0</td><td>1</td><td>996</td><td>403</td><td>608</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4654188</td><td>113423850</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>2</td><td>?</td><td>?</td><td>60</td><td>4</td><td>15</td><td>0</td><td>1</td><td>0</td><td>427</td><td>250.51</td><td>285</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4655094</td><td>106062624</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>7</td><td>?</td><td>Cardiology</td><td>37</td><td>1</td><td>29</td><td>0</td><td>0</td><td>2</td><td>428</td><td>250.6</td><td>599</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4657218</td><td>764316</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Cardiology</td><td>36</td><td>2</td><td>23</td><td>0</td><td>0</td><td>0</td><td>426</td><td>414</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4657536</td><td>108477</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>Nephrology</td><td>70</td><td>3</td><td>24</td><td>0</td><td>0</td><td>0</td><td>996</td><td>38</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4659948</td><td>4974165</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>66</td><td>4</td><td>22</td><td>0</td><td>0</td><td>1</td><td>569</td><td>403</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4663212</td><td>910170</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Surgery-General</td><td>47</td><td>0</td><td>20</td><td>0</td><td>0</td><td>0</td><td>38</td><td>682</td><td>427</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4664574</td><td>2743047</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>Cardiology</td><td>77</td><td>6</td><td>32</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4664652</td><td>581508</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>69</td><td>0</td><td>12</td><td>0</td><td>0</td><td>1</td><td>434</td><td>427</td><td>401</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4672434</td><td>9083610</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Endocrinology</td><td>77</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>578</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4679262</td><td>1431603</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Cardiology</td><td>49</td><td>3</td><td>20</td><td>0</td><td>0</td><td>0</td><td>410</td><td>496</td><td>272</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4680186</td><td>1516860</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>44</td><td>4</td><td>15</td><td>0</td><td>0</td><td>0</td><td>414</td><td>427</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4682388</td><td>707769</td><td>?</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Pediatrics</td><td>5</td><td>0</td><td>4</td><td>0</td><td>0</td><td>1</td><td>388</td><td>784</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4728330</td><td>26055648</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>8</td><td>?</td><td>Cardiology</td><td>46</td><td>3</td><td>20</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>280</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4729674</td><td>1285902</td><td>AfricanAmerican</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-General</td><td>34</td><td>2</td><td>9</td><td>0</td><td>0</td><td>0</td><td>574</td><td>574</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4730190</td><td>407358</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>54</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>486</td><td>496</td><td>716</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4731546</td><td>20733057</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>?</td><td>73</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>593</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4734798</td><td>112367394</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>4</td><td>?</td><td>?</td><td>87</td><td>0</td><td>12</td><td>1</td><td>0</td><td>1</td><td>584</td><td>403</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4736166</td><td>1125135</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>43</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>577</td><td>250.01</td><td>786</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4736214</td><td>1324764</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Psychiatry</td><td>59</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>296</td><td>300</td><td>278</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4765758</td><td>7526574</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>12</td><td>?</td><td>Radiology</td><td>36</td><td>4</td><td>16</td><td>0</td><td>0</td><td>0</td><td>593</td><td>403</td><td>284</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4772286</td><td>6169482</td><td>Caucasian</td><td>Female</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pediatrics-Endocrinology</td><td>51</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>276</td><td>382</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4798818</td><td>5521167</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Gastroenterology</td><td>56</td><td>2</td><td>10</td><td>0</td><td>0</td><td>1</td><td>789</td><td>787</td><td>250.6</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4801326</td><td>767061</td><td>AfricanAmerican</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>33</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>996</td><td>403</td><td>250.41</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4804620</td><td>111516057</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>3</td><td>?</td><td>?</td><td>55</td><td>1</td><td>16</td><td>0</td><td>0</td><td>1</td><td>428</td><td>250.01</td><td>300</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4804968</td><td>114960726</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>61</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>276</td><td>780</td><td>250.03</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4809204</td><td>6290973</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>12</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>386</td><td>496</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4834632</td><td>348984</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>41</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>682</td><td>403</td><td>V42</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4840590</td><td>104561757</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>3</td><td>?</td><td>?</td><td>11</td><td>1</td><td>18</td><td>0</td><td>0</td><td>1</td><td>682</td><td>427</td><td>496</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4840836</td><td>1647585</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Psychiatry</td><td>30</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>296</td><td>250.01</td><td>?</td><td>2</td><td>0</td><td>0</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4843464</td><td>6458139</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>1</td><td>6</td><td>5</td><td>7</td><td>?</td><td>InternalMedicine</td><td>34</td><td>0</td><td>17</td><td>0</td><td>0</td><td>1</td><td>507</td><td>496</td><td>428</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4854336</td><td>23031225</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>6</td><td>20</td><td>4</td><td>?</td><td>?</td><td>36</td><td>4</td><td>23</td><td>0</td><td>0</td><td>0</td><td>682</td><td>496</td><td>250</td><td>9</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4860744</td><td>109389519</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>36</td><td>1</td><td>25</td><td>0</td><td>0</td><td>0</td><td>607</td><td>V10</td><td>272</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4860756</td><td>74952</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>48</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>599</td><td>788</td><td>41</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4862514</td><td>294273</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Surgery-General</td><td>38</td><td>1</td><td>14</td><td>0</td><td>0</td><td>0</td><td>789</td><td>562</td><td>282</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4863114</td><td>97389531</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>3</td><td>6</td><td>4</td><td>13</td><td>?</td><td>Cardiology</td><td>25</td><td>2</td><td>9</td><td>0</td><td>0</td><td>1</td><td>707</td><td>250.01</td><td>707</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4865082</td><td>5498046</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Surgery-General</td><td>43</td><td>5</td><td>10</td><td>0</td><td>0</td><td>0</td><td>996</td><td>403</td><td>276</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4865826</td><td>757530</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Endocrinology</td><td>46</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>337</td><td>250</td><td>272</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4869474</td><td>111501</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>42</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>486</td><td>581</td><td>492</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4878348</td><td>24880779</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>20</td><td>13</td><td>?</td><td>?</td><td>47</td><td>1</td><td>25</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4878900</td><td>4201875</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Cardiology</td><td>38</td><td>0</td><td>20</td><td>0</td><td>0</td><td>0</td><td>996</td><td>427</td><td>496</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4879860</td><td>73311894</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>7</td><td>?</td><td>?</td><td>50</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>434</td><td>162</td><td>196</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4882110</td><td>1784367</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>42</td><td>0</td><td>6</td><td>0</td><td>0</td><td>1</td><td>466</td><td>707</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4884108</td><td>5609844</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>33</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>786</td><td>402</td><td>438</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4888146</td><td>113730309</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>42</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>996</td><td>599</td><td>599</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4888266</td><td>8589357</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>95</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>82</td><td>287</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4888752</td><td>3053970</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Orthopedics-Reconstructive</td><td>81</td><td>2</td><td>17</td><td>0</td><td>0</td><td>1</td><td>531</td><td>280</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4890552</td><td>355986</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>36</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4892538</td><td>677412</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>35</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>786</td><td>250.01</td><td>401</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4893408</td><td>9327402</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>12</td><td>2</td><td>8</td><td>0</td><td>0</td><td>0</td><td>414</td><td>410</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4893582</td><td>5152140</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>60</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>599</td><td>584</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>4897554</td><td>693576</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-General</td><td>49</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>530</td><td>287</td><td>571</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4902108</td><td>5150925</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>44</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>786</td><td>496</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4919700</td><td>511047</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Emergency/Trauma</td><td>43</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>424</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4922826</td><td>1734102</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>34</td><td>3</td><td>19</td><td>0</td><td>0</td><td>1</td><td>440</td><td>447</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4936380</td><td>533412</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>97</td><td>1</td><td>23</td><td>0</td><td>0</td><td>0</td><td>250.12</td><td>780</td><td>571</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4939896</td><td>1690902</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>13</td><td>?</td><td>Surgery-General</td><td>45</td><td>2</td><td>21</td><td>0</td><td>0</td><td>0</td><td>707</td><td>682</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4959690</td><td>2526498</td><td>AfricanAmerican</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>65</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>276</td><td>425</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4960230</td><td>23065767</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>8</td><td>?</td><td>?</td><td>52</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>571</td><td>789</td><td>511</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4960926</td><td>467208</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>10</td><td>?</td><td>Gastroenterology</td><td>61</td><td>1</td><td>27</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>790</td><td>536</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>4965444</td><td>97125948</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>1</td><td>8</td><td>?</td><td>Family/GeneralPractice</td><td>46</td><td>3</td><td>10</td><td>0</td><td>0</td><td>2</td><td>584</td><td>276</td><td>276</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4969548</td><td>3862251</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>37</td><td>3</td><td>9</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>4971204</td><td>13041</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>InternalMedicine</td><td>67</td><td>3</td><td>26</td><td>0</td><td>0</td><td>0</td><td>812</td><td>285</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4973010</td><td>734211</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-Neuro</td><td>57</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>225</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>4973118</td><td>1332981</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>48</td><td>2</td><td>11</td><td>0</td><td>0</td><td>0</td><td>440</td><td>250</td><td>401</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>4977414</td><td>79956981</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>4</td><td>?</td><td>?</td><td>84</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>428</td><td>707</td><td>250.42</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>4980942</td><td>9213129</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>14</td><td>5</td><td>10</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5038050</td><td>539685</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>Cardiology</td><td>49</td><td>3</td><td>5</td><td>0</td><td>0</td><td>0</td><td>411</td><td>250</td><td>272</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5041998</td><td>1206027</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>14</td><td>?</td><td>Cardiology</td><td>61</td><td>6</td><td>34</td><td>0</td><td>0</td><td>0</td><td>414</td><td>997</td><td>427</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5048562</td><td>45979002</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>11</td><td>?</td><td>?</td><td>70</td><td>6</td><td>29</td><td>0</td><td>0</td><td>1</td><td>427</td><td>428</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5049342</td><td>4780035</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>75</td><td>2</td><td>7</td><td>0</td><td>0</td><td>0</td><td>596</td><td>591</td><td>585</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5050146</td><td>4156794</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>4</td><td>2</td><td>?</td><td>Cardiology</td><td>10</td><td>2</td><td>12</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5051634</td><td>435366</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Urology</td><td>49</td><td>0</td><td>22</td><td>0</td><td>0</td><td>1</td><td>997</td><td>560</td><td>596</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5051868</td><td>5990040</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Orthopedics-Reconstructive</td><td>39</td><td>2</td><td>17</td><td>0</td><td>0</td><td>1</td><td>996</td><td>250.02</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5052870</td><td>6453342</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>50</td><td>1</td><td>6</td><td>0</td><td>0</td><td>0</td><td>780</td><td>571</td><td>287</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5053842</td><td>1041291</td><td>?</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>42</td><td>1</td><td>5</td><td>0</td><td>0</td><td>0</td><td>784</td><td>414</td><td>250</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5056230</td><td>30800925</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>52</td><td>0</td><td>10</td><td>0</td><td>0</td><td>5</td><td>288</td><td>200</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5074566</td><td>22829274</td><td>?</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>20</td><td>2</td><td>?</td><td>?</td><td>33</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5096592</td><td>491643</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>41</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>786</td><td>428</td><td>997</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5116878</td><td>105345450</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>19</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>998</td><td>998</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5121912</td><td>6261003</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Orthopedics-Reconstructive</td><td>31</td><td>2</td><td>12</td><td>0</td><td>0</td><td>0</td><td>824</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5122188</td><td>6490224</td><td>Asian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Hematology/Oncology</td><td>45</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>V58</td><td>154</td><td>197</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5122290</td><td>4598280</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>54</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>428</td><td>401</td><td>780</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5123070</td><td>1197414</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>50</td><td>3</td><td>25</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250</td><td>401</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5123124</td><td>2535174</td><td>?</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Cardiology</td><td>60</td><td>4</td><td>25</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250</td><td>272</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5123250</td><td>89400861</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>2</td><td>11</td><td>20</td><td>1</td><td>?</td><td>?</td><td>68</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>584</td><td>403</td><td>250.01</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5130642</td><td>1719279</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>67</td><td>0</td><td>22</td><td>0</td><td>0</td><td>2</td><td>428</td><td>707</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5165052</td><td>2470770</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>81</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>466</td><td>276</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5165772</td><td>313236</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>37</td><td>3</td><td>5</td><td>0</td><td>0</td><td>0</td><td>786</td><td>414</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5184162</td><td>51738111</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>6</td><td>2</td><td>6</td><td>?</td><td>?</td><td>74</td><td>5</td><td>41</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>997</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5188536</td><td>70317027</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>6</td><td>2</td><td>3</td><td>?</td><td>?</td><td>49</td><td>1</td><td>27</td><td>0</td><td>0</td><td>0</td><td>997</td><td>250.02</td><td>599</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5189298</td><td>7783875</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Urology</td><td>64</td><td>2</td><td>21</td><td>0</td><td>0</td><td>0</td><td>599</td><td>276</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5197476</td><td>87840972</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>27</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>198</td><td>162</td><td>197</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5197890</td><td>3008313</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Surgery-General</td><td>48</td><td>6</td><td>16</td><td>0</td><td>0</td><td>0</td><td>730</td><td>785</td><td>250.42</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5204424</td><td>9107163</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Orthopedics-Reconstructive</td><td>44</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>722</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5210952</td><td>4244877</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>41</td><td>2</td><td>15</td><td>0</td><td>0</td><td>0</td><td>656</td><td>648</td><td>644</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5231520</td><td>64511352</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>5</td><td>7</td><td>1</td><td>?</td><td>?</td><td>43</td><td>2</td><td>8</td><td>0</td><td>0</td><td>1</td><td>435</td><td>440</td><td>250.8</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5235234</td><td>9395991</td><td>?</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>ObstetricsandGynecology</td><td>56</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.02</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5240286</td><td>3240630</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>79</td><td>1</td><td>21</td><td>0</td><td>0</td><td>0</td><td>250.12</td><td>276</td><td>578</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5240640</td><td>7490601</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>34</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>530</td><td>425</td><td>428</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5243178</td><td>838854</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>62</td><td>3</td><td>13</td><td>0</td><td>0</td><td>0</td><td>531</td><td>250.82</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5249430</td><td>5702373</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Cardiology</td><td>74</td><td>6</td><td>35</td><td>0</td><td>0</td><td>0</td><td>414</td><td>285</td><td>276</td><td>8</td><td>0</td><td>0</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5253192</td><td>64564002</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>5</td><td>?</td><td>Cardiology</td><td>60</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>434</td><td>780</td><td>427</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5253882</td><td>69973740</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>7</td><td>?</td><td>?</td><td>54</td><td>5</td><td>13</td><td>0</td><td>0</td><td>0</td><td>787</td><td>198</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5255370</td><td>61462863</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>64</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>250.22</td><td>491</td><td>599</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5264082</td><td>67518999</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>?</td><td>48</td><td>4</td><td>16</td><td>0</td><td>1</td><td>0</td><td>996</td><td>38</td><td>585</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5266074</td><td>38724426</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>5</td><td>?</td><td>?</td><td>45</td><td>2</td><td>16</td><td>0</td><td>0</td><td>0</td><td>682</td><td>V42</td><td>250.4</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5266116</td><td>14515371</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>43</td><td>3</td><td>26</td><td>0</td><td>0</td><td>4</td><td>530</td><td>403</td><td>428</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5266218</td><td>5620536</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Pulmonology</td><td>47</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>491</td><td>428</td><td>276</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5267052</td><td>2408922</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>46</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>433</td><td>292</td><td>780</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5272992</td><td>36686808</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>4</td><td>11</td><td>?</td><td>?</td><td>23</td><td>2</td><td>8</td><td>0</td><td>0</td><td>1</td><td>428</td><td>496</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5277336</td><td>830205</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>47</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>786</td><td>250.02</td><td>272</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5279394</td><td>400500</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Family/GeneralPractice</td><td>79</td><td>1</td><td>18</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5280666</td><td>5565897</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Pulmonology</td><td>43</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>491</td><td>518</td><td>401</td><td>4</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5281974</td><td>633906</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>61</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>578</td><td>250.02</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5282856</td><td>352377</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>39</td><td>0</td><td>16</td><td>0</td><td>0</td><td>1</td><td>786</td><td>250.52</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5283786</td><td>5287950</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>36</td><td>0</td><td>12</td><td>0</td><td>0</td><td>1</td><td>786</td><td>996</td><td>682</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5287668</td><td>6127722</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Psychiatry</td><td>41</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>296</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5289156</td><td>2071701</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>76</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.12</td><td>276</td><td>V11</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5289846</td><td>13320</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>58</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>276</td><td>787</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5290392</td><td>463230</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>56</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>276</td><td>250.02</td><td>558</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5292372</td><td>3397761</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>52</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>466</td><td>428</td><td>414</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5293386</td><td>21689424</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>53</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>486</td><td>414</td><td>V45</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5293506</td><td>1723572</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>79</td><td>1</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>135</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5293998</td><td>1697436</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>84</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>573</td><td>577</td><td>E932</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5294886</td><td>73286937</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>50</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>577</td><td>571</td><td>250.01</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5296632</td><td>508824</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Surgery-Neuro</td><td>51</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>722</td><td>401</td><td>250</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5297256</td><td>6656679</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>46</td><td>0</td><td>6</td><td>0</td><td>0</td><td>1</td><td>786</td><td>780</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5319174</td><td>87847776</td><td>Other</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>6</td><td>7</td><td>11</td><td>?</td><td>?</td><td>71</td><td>1</td><td>20</td><td>0</td><td>0</td><td>0</td><td>820</td><td>250.02</td><td>E885</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5323656</td><td>92840139</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>4</td><td>?</td><td>?</td><td>44</td><td>0</td><td>14</td><td>0</td><td>0</td><td>1</td><td>428</td><td>427</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5334888</td><td>102364812</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>4</td><td>?</td><td>?</td><td>63</td><td>2</td><td>19</td><td>0</td><td>0</td><td>0</td><td>427</td><td>428</td><td>496</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5335140</td><td>59859657</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>44</td><td>4</td><td>14</td><td>0</td><td>0</td><td>0</td><td>414</td><td>427</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5349312</td><td>6897267</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Cardiology</td><td>65</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>410</td><td>581</td><td>250.4</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5351838</td><td>7440732</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>10</td><td>2</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5352888</td><td>9295110</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>49</td><td>4</td><td>8</td><td>0</td><td>0</td><td>0</td><td>410</td><td>414</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5357160</td><td>23179356</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>62</td><td>0</td><td>14</td><td>0</td><td>1</td><td>3</td><td>571</td><td>303</td><td>571</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5357616</td><td>4747986</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Surgery-General</td><td>31</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>440</td><td>401</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5358312</td><td>2600469</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>2</td><td>4</td><td>11</td><td>?</td><td>?</td><td>75</td><td>2</td><td>33</td><td>0</td><td>0</td><td>0</td><td>197</td><td>486</td><td>162</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5395752</td><td>8760915</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>48</td><td>2</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>427</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5414448</td><td>95439834</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>28</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>996</td><td>250.41</td><td>403</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5424786</td><td>3217491</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>50</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>276</td><td>599</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5427300</td><td>47412873</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>2</td><td>?</td><td>?</td><td>62</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>491</td><td>162</td><td>198</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5427474</td><td>104752755</td><td>Caucasian</td><td>Female</td><td>[20-30)</td><td>?</td><td>2</td><td>6</td><td>2</td><td>10</td><td>?</td><td>?</td><td>74</td><td>3</td><td>21</td><td>0</td><td>0</td><td>1</td><td>571</td><td>425</td><td>585</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5433990</td><td>352377</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Psychiatry</td><td>53</td><td>2</td><td>22</td><td>0</td><td>0</td><td>2</td><td>780</td><td>304</td><td>250.51</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5458920</td><td>3105513</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>34</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>427</td><td>272</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5459490</td><td>3000240</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>13</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>492</td><td>440</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5480742</td><td>348120</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Cardiology</td><td>68</td><td>5</td><td>32</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250.02</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5488950</td><td>3198834</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Psychiatry</td><td>50</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>296</td><td>250.02</td><td>401</td><td>5</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5491998</td><td>65392893</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>51</td><td>4</td><td>23</td><td>0</td><td>0</td><td>0</td><td>198</td><td>117</td><td>198</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5496270</td><td>105366150</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>6</td><td>?</td><td>Family/GeneralPractice</td><td>54</td><td>0</td><td>17</td><td>0</td><td>0</td><td>2</td><td>427</td><td>411</td><td>599</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5503818</td><td>74020698</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>19</td><td>4</td><td>19</td><td>0</td><td>0</td><td>0</td><td>220</td><td>154</td><td>218</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5505486</td><td>439470</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>54</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>433</td><td>401</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5514390</td><td>66338397</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>2</td><td>?</td><td>?</td><td>55</td><td>4</td><td>8</td><td>0</td><td>0</td><td>0</td><td>435</td><td>401</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5516124</td><td>22783734</td><td>?</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>5</td><td>?</td><td>?</td><td>66</td><td>0</td><td>18</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>486</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5524668</td><td>3457683</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>53</td><td>6</td><td>16</td><td>0</td><td>0</td><td>0</td><td>410</td><td>250</td><td>305</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5562150</td><td>13590</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Cardiology</td><td>34</td><td>5</td><td>17</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>428</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5563794</td><td>4472064</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>20</td><td>6</td><td>?</td><td>?</td><td>19</td><td>4</td><td>22</td><td>0</td><td>0</td><td>0</td><td>516</td><td>496</td><td>303</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5576172</td><td>9416952</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>34</td><td>2</td><td>5</td><td>0</td><td>0</td><td>0</td><td>414</td><td>786</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5583924</td><td>3381201</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Surgery-General</td><td>31</td><td>1</td><td>5</td><td>0</td><td>0</td><td>0</td><td>566</td><td>428</td><td>41</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5593122</td><td>83268891</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>1</td><td>?</td><td>?</td><td>39</td><td>1</td><td>5</td><td>0</td><td>0</td><td>0</td><td>210</td><td>250</td><td>401</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5593794</td><td>2020491</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>20</td><td>5</td><td>?</td><td>?</td><td>39</td><td>6</td><td>50</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>412</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5600406</td><td>7308234</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>52</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>599</td><td>707</td><td>250.02</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5650074</td><td>106706241</td><td>Other</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>7</td><td>?</td><td>?</td><td>57</td><td>1</td><td>20</td><td>0</td><td>0</td><td>0</td><td>410</td><td>518</td><td>511</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5651706</td><td>317934</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>51</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>562</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5652390</td><td>27926721</td><td>?</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>1</td><td>?</td><td>?</td><td>29</td><td>3</td><td>13</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5654628</td><td>81353088</td><td>AfricanAmerican</td><td>Male</td><td>[20-30)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>?</td><td>81</td><td>2</td><td>27</td><td>9</td><td>0</td><td>5</td><td>486</td><td>428</td><td>250.42</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5655186</td><td>67825269</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>3</td><td>?</td><td>?</td><td>51</td><td>0</td><td>11</td><td>0</td><td>1</td><td>2</td><td>250.6</td><td>276</td><td>571</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5657328</td><td>8127882</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>6</td><td>1</td><td>6</td><td>?</td><td>?</td><td>28</td><td>2</td><td>16</td><td>1</td><td>0</td><td>1</td><td>V57</td><td>250.6</td><td>276</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5661192</td><td>111353067</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>4</td><td>?</td><td>?</td><td>46</td><td>2</td><td>24</td><td>0</td><td>0</td><td>1</td><td>410</td><td>250.02</td><td>599</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5670816</td><td>875979</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>63</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.12</td><td>276</td><td>599</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5681436</td><td>362610</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>59</td><td>0</td><td>15</td><td>0</td><td>0</td><td>1</td><td>428</td><td>425</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5682786</td><td>80019189</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>4</td><td>?</td><td>?</td><td>37</td><td>4</td><td>16</td><td>0</td><td>0</td><td>1</td><td>410</td><td>424</td><td>414</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5683404</td><td>1941948</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>1</td><td>?</td><td>?</td><td>57</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>922</td><td>847</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5683578</td><td>901332</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>34</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>808</td><td>852</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5683854</td><td>56222487</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>12</td><td>?</td><td>?</td><td>50</td><td>2</td><td>31</td><td>0</td><td>0</td><td>0</td><td>997</td><td>599</td><td>V42</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5691738</td><td>2407023</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>2</td><td>?</td><td>?</td><td>29</td><td>2</td><td>11</td><td>0</td><td>0</td><td>0</td><td>410</td><td>996</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5693046</td><td>42499845</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>5</td><td>?</td><td>?</td><td>77</td><td>5</td><td>15</td><td>3</td><td>0</td><td>0</td><td>410</td><td>398</td><td>402</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5694618</td><td>98992197</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>53</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>410</td><td>398</td><td>396</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5697012</td><td>35259498</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>46</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>562</td><td>560</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5698884</td><td>1896381</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>3</td><td>20</td><td>4</td><td>?</td><td>?</td><td>37</td><td>2</td><td>22</td><td>0</td><td>0</td><td>1</td><td>996</td><td>996</td><td>V14</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5719230</td><td>389862</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Cardiology</td><td>40</td><td>4</td><td>23</td><td>0</td><td>0</td><td>0</td><td>786</td><td>427</td><td>272</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5733036</td><td>4103964</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>10</td><td>?</td><td>InternalMedicine</td><td>57</td><td>0</td><td>20</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.83</td><td>458</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5744958</td><td>5184918</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>10</td><td>?</td><td>Pulmonology</td><td>53</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>496</td><td>280</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5754180</td><td>8325099</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Cardiology</td><td>36</td><td>3</td><td>9</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5755800</td><td>45703377</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>11</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>50</td><td>2</td><td>11</td><td>0</td><td>0</td><td>1</td><td>434</td><td>518</td><td>427</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5790000</td><td>1054494</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>8</td><td>?</td><td>Cardiology</td><td>65</td><td>3</td><td>23</td><td>0</td><td>0</td><td>0</td><td>434</td><td>733</td><td>784</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5805234</td><td>1665054</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Surgery-General</td><td>48</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>682</td><td>496</td><td>305</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5828766</td><td>45655695</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>1</td><td>?</td><td>?</td><td>47</td><td>0</td><td>10</td><td>0</td><td>1</td><td>0</td><td>572</td><td>287</td><td>570</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5835240</td><td>76433292</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>1</td><td>?</td><td>?</td><td>19</td><td>2</td><td>18</td><td>1</td><td>0</td><td>0</td><td>996</td><td>403</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5836536</td><td>69023016</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>14</td><td>?</td><td>?</td><td>78</td><td>6</td><td>43</td><td>0</td><td>0</td><td>0</td><td>414</td><td>401</td><td>428</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5848968</td><td>3051027</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>22</td><td>2</td><td>19</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.03</td><td>654</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5893320</td><td>55111707</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>39</td><td>3</td><td>27</td><td>0</td><td>0</td><td>1</td><td>197</td><td>196</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5897310</td><td>61162254</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>6</td><td>20</td><td>8</td><td>?</td><td>?</td><td>61</td><td>1</td><td>20</td><td>0</td><td>0</td><td>0</td><td>440</td><td>401</td><td>250</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5905512</td><td>4951944</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>5</td><td>4</td><td>7</td><td>?</td><td>?</td><td>38</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>434</td><td>729</td><td>433</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5912364</td><td>1061811</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>53</td><td>3</td><td>21</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.6</td><td>780</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>5914314</td><td>64052667</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>40</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>286</td><td>428</td><td>E934</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>5915502</td><td>84595104</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>9</td><td>?</td><td>?</td><td>31</td><td>4</td><td>20</td><td>0</td><td>0</td><td>0</td><td>996</td><td>573</td><td>255</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5916612</td><td>28982313</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>1</td><td>1</td><td>20</td><td>4</td><td>?</td><td>?</td><td>52</td><td>2</td><td>9</td><td>0</td><td>0</td><td>0</td><td>518</td><td>250.11</td><td>244</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5929704</td><td>90056529</td><td>Asian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>23</td><td>6</td><td>23</td><td>0</td><td>0</td><td>0</td><td>885</td><td>816</td><td>882</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5960106</td><td>65418831</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>2</td><td>?</td><td>?</td><td>42</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>786</td><td>414</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5970528</td><td>91228698</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>1</td><td>?</td><td>?</td><td>29</td><td>3</td><td>14</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>278</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>5981844</td><td>4204035</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Orthopedics-Reconstructive</td><td>46</td><td>1</td><td>20</td><td>0</td><td>0</td><td>0</td><td>958</td><td>250.6</td><td>41</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>5983116</td><td>3521574</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Surgery-General</td><td>33</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>682</td><td>707</td><td>459</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>5993358</td><td>60354486</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>1</td><td>2</td><td>14</td><td>?</td><td>?</td><td>43</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>296</td><td>250</td><td>564</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>5993400</td><td>5118912</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Pulmonology</td><td>70</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>428</td><td>162</td><td>496</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6000072</td><td>80845353</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>54</td><td>0</td><td>13</td><td>0</td><td>0</td><td>3</td><td>414</td><td>250.01</td><td>411</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6011832</td><td>96830199</td><td>AfricanAmerican</td><td>Female</td><td>[20-30)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>11</td><td>1</td><td>13</td><td>0</td><td>0</td><td>1</td><td>661</td><td>648</td><td>V27</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6044778</td><td>96901416</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>6</td><td>1</td><td>8</td><td>?</td><td>?</td><td>29</td><td>2</td><td>13</td><td>0</td><td>0</td><td>0</td><td>V57</td><td>428</td><td>682</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6065928</td><td>105956433</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>67</td><td>0</td><td>10</td><td>0</td><td>1</td><td>0</td><td>648</td><td>642</td><td>648</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6068238</td><td>115196778</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>44</td><td>1</td><td>16</td><td>0</td><td>0</td><td>3</td><td>427</td><td>585</td><td>250.41</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6077826</td><td>37044999</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>6</td><td>?</td><td>?</td><td>74</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>276</td><td>428</td><td>425</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6079362</td><td>4420413</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Cardiology</td><td>52</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6087666</td><td>91292337</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>57</td><td>1</td><td>11</td><td>0</td><td>0</td><td>2</td><td>969</td><td>428</td><td>458</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6090396</td><td>471951</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>9</td><td>?</td><td>InternalMedicine</td><td>73</td><td>3</td><td>18</td><td>0</td><td>0</td><td>0</td><td>38</td><td>575</td><td>38</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6094266</td><td>68360337</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>5</td><td>?</td><td>?</td><td>55</td><td>6</td><td>23</td><td>0</td><td>0</td><td>0</td><td>410</td><td>416</td><td>397</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6094530</td><td>106656858</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>12</td><td>?</td><td>?</td><td>59</td><td>1</td><td>46</td><td>0</td><td>0</td><td>0</td><td>824</td><td>V42</td><td>250.02</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6100236</td><td>5027454</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Surgery-General</td><td>51</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>433</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6100254</td><td>8605278</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>9</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>69</td><td>2</td><td>26</td><td>0</td><td>0</td><td>0</td><td>196</td><td>427</td><td>427</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6131094</td><td>2970225</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>InternalMedicine</td><td>49</td><td>3</td><td>20</td><td>0</td><td>0</td><td>0</td><td>433</td><td>486</td><td>998</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6138192</td><td>339651</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>43</td><td>1</td><td>18</td><td>0</td><td>0</td><td>0</td><td>486</td><td>250.01</td><td>786</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6174762</td><td>93432348</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>57</td><td>6</td><td>15</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>997</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6176124</td><td>76802157</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>67</td><td>3</td><td>16</td><td>0</td><td>0</td><td>0</td><td>518</td><td>491</td><td>300</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6176166</td><td>4667985</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>61</td><td>4</td><td>29</td><td>0</td><td>0</td><td>0</td><td>414</td><td>285</td><td>599</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6178680</td><td>206946</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>11</td><td>7</td><td>1</td><td>?</td><td>?</td><td>74</td><td>0</td><td>8</td><td>0</td><td>0</td><td>1</td><td>410</td><td>428</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6179820</td><td>113836635</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>45</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>998</td><td>567</td><td>250.01</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6181560</td><td>3682530</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Endocrinology</td><td>78</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>288</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6194106</td><td>6000192</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>46</td><td>2</td><td>24</td><td>0</td><td>0</td><td>0</td><td>530</td><td>788</td><td>493</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6197640</td><td>89515035</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>3</td><td>5</td><td>2</td><td>7</td><td>?</td><td>?</td><td>43</td><td>3</td><td>14</td><td>0</td><td>0</td><td>1</td><td>202</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6217362</td><td>109693890</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>1</td><td>?</td><td>Pulmonology</td><td>34</td><td>1</td><td>5</td><td>0</td><td>0</td><td>0</td><td>434</td><td>250</td><td>401</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6248682</td><td>4146147</td><td>AfricanAmerican</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Pediatrics-Endocrinology</td><td>10</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>250.93</td><td>V15</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6291540</td><td>96662457</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>7</td><td>?</td><td>Pulmonology</td><td>45</td><td>0</td><td>19</td><td>0</td><td>0</td><td>0</td><td>562</td><td>250.6</td><td>357</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6299184</td><td>1295469</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>Family/GeneralPractice</td><td>63</td><td>0</td><td>19</td><td>0</td><td>0</td><td>1</td><td>997</td><td>428</td><td>250.01</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6318930</td><td>27274527</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>3</td><td>?</td><td>?</td><td>41</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>227</td><td>250</td><td>493</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6319422</td><td>21575907</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>54</td><td>3</td><td>15</td><td>0</td><td>0</td><td>0</td><td>410</td><td>412</td><td>458</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6320778</td><td>62915535</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>2</td><td>?</td><td>?</td><td>62</td><td>2</td><td>20</td><td>0</td><td>0</td><td>1</td><td>996</td><td>250.41</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6325398</td><td>4937643</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>Nephrology</td><td>48</td><td>4</td><td>17</td><td>0</td><td>0</td><td>1</td><td>112</td><td>403</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6337620</td><td>77025807</td><td>Asian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>8</td><td>?</td><td>Pulmonology</td><td>59</td><td>3</td><td>27</td><td>0</td><td>0</td><td>2</td><td>404</td><td>584</td><td>491</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6355014</td><td>4963419</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Family/GeneralPractice</td><td>51</td><td>3</td><td>16</td><td>0</td><td>0</td><td>0</td><td>434</td><td>424</td><td>564</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6355440</td><td>9128817</td><td>Caucasian</td><td>Male</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Pediatrics-CriticalCare</td><td>61</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>276</td><td>785</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6375564</td><td>798147</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>14</td><td>?</td><td>InternalMedicine</td><td>76</td><td>4</td><td>28</td><td>0</td><td>0</td><td>0</td><td>427</td><td>428</td><td>711</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6380298</td><td>493911</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>37</td><td>3</td><td>9</td><td>0</td><td>0</td><td>0</td><td>780</td><td>427</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6382680</td><td>780975</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>44</td><td>4</td><td>20</td><td>0</td><td>0</td><td>1</td><td>414</td><td>411</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6384432</td><td>2150901</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>5</td><td>?</td><td>?</td><td>40</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>577</td><td>491</td><td>577</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6384966</td><td>7646184</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>59</td><td>5</td><td>16</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>424</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6387198</td><td>4235580</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>12</td><td>?</td><td>Cardiology</td><td>75</td><td>6</td><td>33</td><td>0</td><td>0</td><td>0</td><td>414</td><td>424</td><td>492</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6392556</td><td>64603215</td><td>?</td><td>Male</td><td>[40-50)</td><td>?</td><td>2</td><td>6</td><td>2</td><td>11</td><td>?</td><td>?</td><td>27</td><td>6</td><td>23</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>785</td><td>585</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6395592</td><td>86572602</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>52</td><td>1</td><td>8</td><td>0</td><td>0</td><td>1</td><td>780</td><td>441</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6400152</td><td>93121191</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>3</td><td>?</td><td>?</td><td>38</td><td>2</td><td>24</td><td>0</td><td>0</td><td>0</td><td>996</td><td>250.7</td><td>443</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6402138</td><td>109862649</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>8</td><td>?</td><td>?</td><td>74</td><td>5</td><td>42</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6409200</td><td>108580194</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>6</td><td>?</td><td>Surgery-General</td><td>53</td><td>0</td><td>26</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>250.7</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6409620</td><td>81255195</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>9</td><td>?</td><td>?</td><td>56</td><td>1</td><td>7</td><td>0</td><td>0</td><td>0</td><td>577</td><td>250.02</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6447234</td><td>68622138</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>3</td><td>1</td><td>1</td><td>2</td><td>?</td><td>Surgery-General</td><td>34</td><td>2</td><td>18</td><td>0</td><td>0</td><td>0</td><td>996</td><td>250.43</td><td>403</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6463974</td><td>5020047</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>14</td><td>?</td><td>Cardiology</td><td>77</td><td>6</td><td>37</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>496</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6465714</td><td>1211985</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>InternalMedicine</td><td>36</td><td>6</td><td>18</td><td>0</td><td>0</td><td>1</td><td>250.7</td><td>440</td><td>401</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6478860</td><td>7596648</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>50</td><td>2</td><td>22</td><td>0</td><td>0</td><td>0</td><td>428</td><td>427</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6479016</td><td>20676798</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>7</td><td>?</td><td>?</td><td>105</td><td>3</td><td>16</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.02</td><td>276</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6479334</td><td>99530388</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>7</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>33</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.02</td><td>496</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6479826</td><td>2699316</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>46</td><td>4</td><td>11</td><td>0</td><td>0</td><td>0</td><td>428</td><td>411</td><td>424</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6487182</td><td>6228999</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>33</td><td>2</td><td>16</td><td>0</td><td>0</td><td>0</td><td>721</td><td>707</td><td>729</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6489306</td><td>1917261</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Orthopedics-Reconstructive</td><td>44</td><td>3</td><td>13</td><td>0</td><td>0</td><td>0</td><td>823</td><td>998</td><td>836</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6490980</td><td>9900837</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>63</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>434</td><td>250.02</td><td>272</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6492654</td><td>5287950</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>40</td><td>4</td><td>14</td><td>0</td><td>0</td><td>2</td><td>786</td><td>414</td><td>401</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6494982</td><td>76825836</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>9</td><td>?</td><td>?</td><td>103</td><td>1</td><td>18</td><td>0</td><td>0</td><td>0</td><td>428</td><td>581</td><td>250.6</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6495960</td><td>58136508</td><td>Caucasian</td><td>Female</td><td>[90-100)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>9</td><td>?</td><td>?</td><td>86</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>466</td><td>515</td><td>518</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6504906</td><td>1734102</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>36</td><td>4</td><td>22</td><td>0</td><td>0</td><td>2</td><td>996</td><td>305</td><td>E878</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6548964</td><td>93520359</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>6</td><td>?</td><td>?</td><td>44</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>491</td><td>428</td><td>250.01</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6564180</td><td>9196353</td><td>Caucasian</td><td>Female</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Pediatrics-Endocrinology</td><td>47</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>372</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6571380</td><td>73088208</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>5</td><td>?</td><td>?</td><td>44</td><td>5</td><td>28</td><td>0</td><td>0</td><td>0</td><td>530</td><td>250.02</td><td>998</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6572046</td><td>41275089</td><td>?</td><td>Male</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>2</td><td>4</td><td>?</td><td>?</td><td>33</td><td>1</td><td>24</td><td>0</td><td>0</td><td>0</td><td>715</td><td>733</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6577986</td><td>2656431</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>Family/GeneralPractice</td><td>47</td><td>3</td><td>18</td><td>0</td><td>0</td><td>0</td><td>730</td><td>707</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6579774</td><td>66656601</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>3</td><td>2</td><td>10</td><td>?</td><td>?</td><td>66</td><td>3</td><td>34</td><td>0</td><td>0</td><td>0</td><td>191</td><td>492</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6581646</td><td>3611637</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Nephrology</td><td>45</td><td>3</td><td>10</td><td>0</td><td>0</td><td>1</td><td>996</td><td>38</td><td>996</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6582546</td><td>63754317</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>4</td><td>?</td><td>?</td><td>42</td><td>1</td><td>11</td><td>0</td><td>1</td><td>2</td><td>250.6</td><td>403</td><td>357</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6584874</td><td>9394128</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>71</td><td>3</td><td>25</td><td>0</td><td>0</td><td>0</td><td>414</td><td>518</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6590442</td><td>2486844</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>8</td><td>?</td><td>InternalMedicine</td><td>51</td><td>0</td><td>16</td><td>0</td><td>0</td><td>1</td><td>282</td><td>250.02</td><td>780</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6608874</td><td>85118040</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>56</td><td>1</td><td>15</td><td>1</td><td>0</td><td>0</td><td>428</td><td>493</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6620760</td><td>704430</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>52</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>276</td><td>428</td><td>250.02</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6636342</td><td>55632276</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>2</td><td>?</td><td>?</td><td>46</td><td>4</td><td>21</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6646260</td><td>2568312</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Cardiology</td><td>53</td><td>4</td><td>26</td><td>0</td><td>0</td><td>1</td><td>786</td><td>428</td><td>403</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6651384</td><td>106312131</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>37</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>786</td><td>414</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6656496</td><td>4837077</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>66</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>276</td><td>584</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6657678</td><td>54452511</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>4</td><td>?</td><td>?</td><td>61</td><td>3</td><td>19</td><td>0</td><td>0</td><td>1</td><td>486</td><td>585</td><td>250.02</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6697722</td><td>1550520</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Gastroenterology</td><td>43</td><td>2</td><td>9</td><td>0</td><td>0</td><td>0</td><td>532</td><td>285</td><td>303</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6709620</td><td>4910841</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Cardiology</td><td>51</td><td>2</td><td>12</td><td>0</td><td>0</td><td>0</td><td>428</td><td>426</td><td>401</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6720732</td><td>56464254</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>10</td><td>?</td><td>Family/GeneralPractice</td><td>68</td><td>4</td><td>16</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.6</td><td>584</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6721254</td><td>9968661</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Cardiology</td><td>42</td><td>5</td><td>17</td><td>0</td><td>0</td><td>0</td><td>428</td><td>496</td><td>794</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6722676</td><td>103360833</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>11</td><td>?</td><td>?</td><td>78</td><td>6</td><td>51</td><td>0</td><td>0</td><td>0</td><td>416</td><td>276</td><td>424</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6725226</td><td>2026161</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>54</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>346</td><td>599</td><td>250.02</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6735816</td><td>1532520</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Psychiatry</td><td>17</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>296</td><td>305</td><td>300</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6736032</td><td>9264663</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>Family/GeneralPractice</td><td>70</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>428</td><td>427</td><td>707</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6736968</td><td>4867677</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Obsterics&Gynecology-GynecologicOnco</td><td>49</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>182</td><td>250.01</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6737004</td><td>9343692</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>7</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>49</td><td>1</td><td>23</td><td>0</td><td>0</td><td>0</td><td>162</td><td>512</td><td>496</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6740700</td><td>8208234</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>12</td><td>?</td><td>Family/GeneralPractice</td><td>77</td><td>2</td><td>21</td><td>0</td><td>0</td><td>0</td><td>515</td><td>482</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6797352</td><td>5058702</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>46</td><td>5</td><td>11</td><td>0</td><td>0</td><td>0</td><td>414</td><td>414</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6800616</td><td>726057</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>6</td><td>?</td><td>InternalMedicine</td><td>41</td><td>1</td><td>3</td><td>0</td><td>0</td><td>0</td><td>434</td><td>250.01</td><td>427</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6804738</td><td>641007</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>1</td><td>11</td><td>?</td><td>Psychiatry</td><td>58</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>296</td><td>300</td><td>V43</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6805482</td><td>894942</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>43</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>786</td><td>250.03</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6812988</td><td>5984469</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>63</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>428</td><td>276</td><td>295</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6815154</td><td>100955538</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>57</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>414</td><td>412</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6819072</td><td>111999186</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>1</td><td>?</td><td>?</td><td>59</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>535</td><td>276</td><td>427</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6820692</td><td>669393</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>47</td><td>0</td><td>7</td><td>0</td><td>0</td><td>1</td><td>428</td><td>401</td><td>250.01</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6822684</td><td>54485127</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>8</td><td>?</td><td>?</td><td>61</td><td>4</td><td>28</td><td>0</td><td>0</td><td>2</td><td>250.41</td><td>403</td><td>250.6</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6833718</td><td>1501533</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>34</td><td>6</td><td>9</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>250.01</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6842886</td><td>4865508</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>11</td><td>3</td><td>8</td><td>0</td><td>0</td><td>0</td><td>410</td><td>414</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6867720</td><td>77574114</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>4</td><td>?</td><td>?</td><td>55</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>428</td><td>496</td><td>295</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6867804</td><td>689355</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>8</td><td>?</td><td>Cardiology</td><td>65</td><td>6</td><td>30</td><td>0</td><td>0</td><td>0</td><td>414</td><td>285</td><td>250</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6874512</td><td>3480381</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>57</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>428</td><td>403</td><td>424</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6879588</td><td>463302</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>36</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>453</td><td>414</td><td>443</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6884346</td><td>3778047</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>57</td><td>0</td><td>11</td><td>0</td><td>0</td><td>1</td><td>535</td><td>276</td><td>535</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6889836</td><td>9595107</td><td>AfricanAmerican</td><td>Male</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Pulmonology</td><td>61</td><td>1</td><td>20</td><td>0</td><td>0</td><td>1</td><td>250.13</td><td>585</td><td>780</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6892062</td><td>443916</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>11</td><td>?</td><td>Psychiatry</td><td>11</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>295</td><td>496</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6893976</td><td>3179286</td><td>AfricanAmerican</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Family/GeneralPractice</td><td>70</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>786</td><td>112</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6894558</td><td>1686141</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>InternalMedicine</td><td>50</td><td>6</td><td>25</td><td>0</td><td>0</td><td>0</td><td>574</td><td>250.01</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6895560</td><td>2836125</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>47</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>434</td><td>250.02</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6896538</td><td>1076481</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>62</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>250</td><td>401</td><td>278</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6896670</td><td>59648958</td><td>AfricanAmerican</td><td>Female</td><td>[20-30)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>59</td><td>0</td><td>6</td><td>1</td><td>2</td><td>1</td><td>648</td><td>250.03</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6896718</td><td>311418</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>5</td><td>?</td><td>InternalMedicine</td><td>63</td><td>0</td><td>25</td><td>0</td><td>0</td><td>0</td><td>402</td><td>518</td><td>491</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6897186</td><td>5033565</td><td>Caucasian</td><td>Female</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>44</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>435</td><td>427</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6897456</td><td>2724075</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>59</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>595</td><td>599</td><td>403</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6897696</td><td>3488049</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Psychiatry</td><td>61</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>295</td><td>382</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6899208</td><td>10045584</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Gastroenterology</td><td>44</td><td>6</td><td>15</td><td>0</td><td>0</td><td>0</td><td>571</td><td>456</td><td>250.01</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6899730</td><td>838854</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>40</td><td>0</td><td>11</td><td>0</td><td>0</td><td>1</td><td>250.82</td><td>276</td><td>428</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6900738</td><td>1360332</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Gastroenterology</td><td>34</td><td>1</td><td>11</td><td>0</td><td>0</td><td>0</td><td>211</td><td>V45</td><td>V45</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6902166</td><td>92152944</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>3</td><td>?</td><td>?</td><td>58</td><td>3</td><td>23</td><td>0</td><td>0</td><td>0</td><td>410</td><td>398</td><td>397</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6902646</td><td>1411479</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Psychiatry</td><td>45</td><td>1</td><td>7</td><td>0</td><td>0</td><td>0</td><td>303</td><td>250.01</td><td>799</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6904998</td><td>1883097</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>2</td><td>3</td><td>20</td><td>5</td><td>?</td><td>?</td><td>51</td><td>2</td><td>26</td><td>0</td><td>0</td><td>0</td><td>820</td><td>285</td><td>425</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6905298</td><td>4908897</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>3</td><td>1</td><td>1</td><td>8</td><td>?</td><td>Cardiology</td><td>62</td><td>6</td><td>30</td><td>0</td><td>0</td><td>0</td><td>410</td><td>250</td><td>281</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6907074</td><td>109816038</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>61</td><td>0</td><td>4</td><td>0</td><td>0</td><td>1</td><td>250.8</td><td>276</td><td>197</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6907530</td><td>4327020</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>51</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>578</td><td>303</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6907938</td><td>404289</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>11</td><td>?</td><td>InternalMedicine</td><td>65</td><td>3</td><td>29</td><td>0</td><td>0</td><td>0</td><td>574</td><td>780</td><td>250.01</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6911658</td><td>2231748</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>78</td><td>3</td><td>10</td><td>0</td><td>0</td><td>1</td><td>428</td><td>424</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6932958</td><td>4914693</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>77</td><td>1</td><td>23</td><td>0</td><td>0</td><td>0</td><td>577</td><td>577</td><td>304</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6934086</td><td>102190059</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>4</td><td>?</td><td>?</td><td>60</td><td>0</td><td>11</td><td>0</td><td>0</td><td>1</td><td>599</td><td>790</td><td>41</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6936978</td><td>369684</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>7</td><td>?</td><td>Cardiology</td><td>49</td><td>0</td><td>20</td><td>0</td><td>0</td><td>1</td><td>428</td><td>427</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6946248</td><td>79024365</td><td>AfricanAmerican</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>3</td><td>?</td><td>Gastroenterology</td><td>36</td><td>2</td><td>4</td><td>0</td><td>0</td><td>0</td><td>286</td><td>403</td><td>707</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6952410</td><td>23076369</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Surgery-General</td><td>48</td><td>4</td><td>19</td><td>0</td><td>0</td><td>1</td><td>996</td><td>403</td><td>153</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6971064</td><td>97976430</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>3</td><td>1</td><td>20</td><td>1</td><td>?</td><td>?</td><td>14</td><td>4</td><td>10</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>410</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>6974160</td><td>2096937</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>41</td><td>3</td><td>17</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.01</td><td>658</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6981924</td><td>9923850</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Pediatrics-Endocrinology</td><td>48</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>250.01</td><td>V65</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6984210</td><td>5702373</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>51</td><td>1</td><td>13</td><td>0</td><td>0</td><td>1</td><td>535</td><td>560</td><td>250.02</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6988968</td><td>85853835</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>2</td><td>6</td><td>?</td><td>?</td><td>26</td><td>2</td><td>10</td><td>0</td><td>0</td><td>0</td><td>440</td><td>V42</td><td>250</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>6989070</td><td>5875713</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>45</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>250.02</td><td>276</td><td>276</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>6991644</td><td>2791395</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Orthopedics-Reconstructive</td><td>48</td><td>2</td><td>15</td><td>0</td><td>0</td><td>0</td><td>715</td><td>599</td><td>285</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>6992808</td><td>789876</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>76</td><td>1</td><td>6</td><td>0</td><td>0</td><td>0</td><td>535</td><td>250.02</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>6993210</td><td>52728858</td><td>?</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>41</td><td>4</td><td>11</td><td>0</td><td>0</td><td>0</td><td>435</td><td>413</td><td>250.02</td><td>9</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>6993492</td><td>5833440</td><td>Caucasian</td><td>Male</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>10</td><td>?</td><td>Surgery-Neuro</td><td>77</td><td>1</td><td>25</td><td>0</td><td>0</td><td>0</td><td>852</td><td>427</td><td>276</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7028472</td><td>3184785</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Cardiology</td><td>34</td><td>5</td><td>9</td><td>0</td><td>0</td><td>0</td><td>414</td><td>V45</td><td>V45</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7046418</td><td>2742849</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>InternalMedicine</td><td>45</td><td>0</td><td>15</td><td>0</td><td>0</td><td>0</td><td>428</td><td>250.01</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7055958</td><td>5745888</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>10</td><td>?</td><td>Nephrology</td><td>49</td><td>0</td><td>19</td><td>0</td><td>0</td><td>0</td><td>584</td><td>710</td><td>403</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>7062456</td><td>6414021</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>13</td><td>?</td><td>InternalMedicine</td><td>93</td><td>1</td><td>24</td><td>0</td><td>0</td><td>1</td><td>428</td><td>425</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7062552</td><td>328887</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>51</td><td>0</td><td>14</td><td>0</td><td>0</td><td>1</td><td>786</td><td>425</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7063266</td><td>8509725</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>2</td><td>7</td><td>1</td><td>?</td><td>?</td><td>59</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>577</td><td>250.03</td><td>780</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>7065768</td><td>3357999</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>ObstetricsandGynecology</td><td>38</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>218</td><td>250</td><td>616</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7067946</td><td>9961560</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>Cardiology</td><td>69</td><td>1</td><td>23</td><td>0</td><td>0</td><td>0</td><td>410</td><td>428</td><td>799</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7089882</td><td>58871601</td><td>Other</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>1</td><td>1</td><td>7</td><td>?</td><td>InternalMedicine</td><td>55</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>486</td><td>491</td><td>401</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7100040</td><td>1395522</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>71</td><td>0</td><td>21</td><td>0</td><td>0</td><td>0</td><td>486</td><td>496</td><td>428</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7104774</td><td>94784535</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>6</td><td>1</td><td>6</td><td>?</td><td>Family/GeneralPractice</td><td>51</td><td>3</td><td>23</td><td>0</td><td>0</td><td>1</td><td>466</td><td>425</td><td>427</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7111506</td><td>338247</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>12</td><td>?</td><td>Pulmonology</td><td>95</td><td>3</td><td>19</td><td>0</td><td>0</td><td>0</td><td>428</td><td>424</td><td>496</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7112478</td><td>284400</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-General</td><td>35</td><td>1</td><td>9</td><td>0</td><td>0</td><td>1</td><td>682</td><td>707</td><td>250.82</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7125354</td><td>9402138</td><td>?</td><td>Male</td><td>[30-40)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>41</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>782</td><td>250.6</td><td>357</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7134888</td><td>41299119</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>31</td><td>0</td><td>12</td><td>0</td><td>0</td><td>1</td><td>780</td><td>428</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7135560</td><td>791460</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Surgery-General</td><td>53</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>540</td><td>250.01</td><td>401</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7136664</td><td>4250106</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>7</td><td>?</td><td>InternalMedicine</td><td>47</td><td>2</td><td>9</td><td>0</td><td>0</td><td>0</td><td>996</td><td>998</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7138326</td><td>581499</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>1</td><td>6</td><td>?</td><td>Obsterics&Gynecology-GynecologicOnco</td><td>77</td><td>2</td><td>19</td><td>0</td><td>0</td><td>0</td><td>571</td><td>572</td><td>789</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>7138524</td><td>9765999</td><td>Other</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>Urology</td><td>12</td><td>1</td><td>13</td><td>0</td><td>0</td><td>0</td><td>189</td><td>401</td><td>250</td><td>3</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7178106</td><td>4688325</td><td>Caucasian</td><td>Male</td><td>[10-20)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>2</td><td>?</td><td>Pediatrics-CriticalCare</td><td>40</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7178910</td><td>9998433</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>5</td><td>?</td><td>Psychiatry</td><td>53</td><td>0</td><td>18</td><td>0</td><td>0</td><td>0</td><td>296</td><td>571</td><td>V42</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7188852</td><td>97797942</td><td>Other</td><td>Male</td><td>[60-70)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>1</td><td>?</td><td>?</td><td>33</td><td>3</td><td>17</td><td>0</td><td>0</td><td>0</td><td>414</td><td>424</td><td>401</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7189884</td><td>1681974</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>7</td><td>?</td><td>Endocrinology</td><td>83</td><td>2</td><td>17</td><td>0</td><td>0</td><td>0</td><td>250.82</td><td>428</td><td>599</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7197084</td><td>10001988</td><td>Caucasian</td><td>Male</td><td>[0-10)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Pediatrics-Endocrinology</td><td>47</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>276</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7201710</td><td>7422984</td><td>AfricanAmerican</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>2</td><td>1</td><td>6</td><td>?</td><td>Cardiology</td><td>61</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>433</td><td>428</td><td>403</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7205370</td><td>10152693</td><td>Asian</td><td>Male</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>1</td><td>8</td><td>?</td><td>Cardiology</td><td>49</td><td>1</td><td>23</td><td>0</td><td>0</td><td>0</td><td>414</td><td>428</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7212504</td><td>81353088</td><td>AfricanAmerican</td><td>Male</td><td>[20-30)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>?</td><td>46</td><td>0</td><td>11</td><td>9</td><td>0</td><td>6</td><td>428</td><td>250.83</td><td>425</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7212516</td><td>5128785</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>6</td><td>?</td><td>Family/GeneralPractice</td><td>49</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>433</td><td>424</td><td>729</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7214772</td><td>1427859</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>ObstetricsandGynecology</td><td>45</td><td>2</td><td>13</td><td>0</td><td>0</td><td>0</td><td>618</td><td>401</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7216542</td><td>3538080</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Nephrology</td><td>59</td><td>1</td><td>15</td><td>0</td><td>0</td><td>1</td><td>250.13</td><td>584</td><td>403</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7216812</td><td>1660293</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>36</td><td>0</td><td>14</td><td>0</td><td>0</td><td>1</td><td>571</td><td>572</td><td>250.02</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7217022</td><td>1126458</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>Cardiology</td><td>35</td><td>4</td><td>27</td><td>0</td><td>0</td><td>0</td><td>786</td><td>710</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7226970</td><td>448839</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>7</td><td>?</td><td>InternalMedicine</td><td>40</td><td>1</td><td>13</td><td>0</td><td>0</td><td>1</td><td>276</td><td>250.82</td><td>250.6</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7257798</td><td>315684</td><td>AfricanAmerican</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Cardiology</td><td>35</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>427</td><td>401</td><td>250</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7261392</td><td>5105286</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>34</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>250.8</td><td>707</td><td>428</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7269804</td><td>10187370</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>2</td><td>1</td><td>4</td><td>?</td><td>Pulmonology</td><td>48</td><td>3</td><td>11</td><td>0</td><td>0</td><td>0</td><td>562</td><td>285</td><td>518</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7272702</td><td>4668507</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>3</td><td>?</td><td>Endocrinology</td><td>59</td><td>0</td><td>9</td><td>0</td><td>0</td><td>0</td><td>789</td><td>496</td><td>304</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7274742</td><td>1529712</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>2</td><td>6</td><td>4</td><td>5</td><td>?</td><td>?</td><td>24</td><td>0</td><td>12</td><td>0</td><td>0</td><td>1</td><td>721</td><td>427</td><td>250</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7274928</td><td>114194385</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>5</td><td>2</td><td>1</td><td>?</td><td>?</td><td>33</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>457</td><td>401</td><td>250</td><td>5</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7284348</td><td>5287950</td><td>Caucasian</td><td>Female</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>60</td><td>0</td><td>10</td><td>0</td><td>0</td><td>3</td><td>786</td><td>414</td><td>250</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7285104</td><td>1598733</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>9</td><td>?</td><td>InternalMedicine</td><td>63</td><td>3</td><td>24</td><td>0</td><td>0</td><td>0</td><td>558</td><td>276</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>7286064</td><td>398358</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>InternalMedicine</td><td>44</td><td>0</td><td>17</td><td>0</td><td>0</td><td>0</td><td>276</td><td>250.6</td><td>250.83</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7287294</td><td>1215324</td><td>Caucasian</td><td>Female</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>56</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>493</td><td>250.02</td><td>401</td><td>5</td><td>0</td><td>0</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7291986</td><td>3362949</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Cardiology</td><td>34</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>780</td><td>V45</td><td>414</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7292178</td><td>60974532</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>4</td><td>?</td><td>?</td><td>28</td><td>3</td><td>23</td><td>3</td><td>0</td><td>1</td><td>711</td><td>280</td><td>401</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7292958</td><td>772065</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Cardiology</td><td>45</td><td>3</td><td>18</td><td>0</td><td>0</td><td>0</td><td>285</td><td>403</td><td>578</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7296180</td><td>1411479</td><td>Caucasian</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>2</td><td>?</td><td>Psychiatry</td><td>46</td><td>0</td><td>5</td><td>0</td><td>0</td><td>1</td><td>303</td><td>250.01</td><td>296</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7296540</td><td>56993832</td><td>Hispanic</td><td>Female</td><td>[50-60)</td><td>?</td><td>6</td><td>1</td><td>7</td><td>2</td><td>?</td><td>?</td><td>66</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>401</td><td>272</td><td>3</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7298202</td><td>3030309</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>1</td><td>1</td><td>5</td><td>?</td><td>Urology</td><td>48</td><td>0</td><td>19</td><td>0</td><td>0</td><td>0</td><td>38</td><td>788</td><td>601</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Up</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7301244</td><td>99756</td><td>AfricanAmerican</td><td>Female</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>InternalMedicine</td><td>33</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>786</td><td>250</td><td>?</td><td>2</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7301982</td><td>65013930</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>2</td><td>1</td><td>4</td><td>5</td><td>?</td><td>?</td><td>44</td><td>1</td><td>12</td><td>0</td><td>0</td><td>0</td><td>414</td><td>276</td><td>287</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7302378</td><td>36413037</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>8</td><td>?</td><td>?</td><td>30</td><td>4</td><td>26</td><td>0</td><td>0</td><td>0</td><td>996</td><td>283</td><td>250.01</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7302828</td><td>318501</td><td>AfricanAmerican</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>55</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>276</td><td>599</td><td>250.03</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7306530</td><td>639675</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Family/GeneralPractice</td><td>64</td><td>0</td><td>11</td><td>0</td><td>0</td><td>0</td><td>786</td><td>303</td><td>291</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7331322</td><td>6325848</td><td>Hispanic</td><td>Male</td><td>[20-30)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>4</td><td>?</td><td>InternalMedicine</td><td>75</td><td>1</td><td>14</td><td>0</td><td>0</td><td>1</td><td>250.13</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7352712</td><td>3039678</td><td>Caucasian</td><td>Female</td><td>[10-20)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Pediatrics-Endocrinology</td><td>62</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.03</td><td>78</td><td>75</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7357404</td><td>50124213</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>5</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>48</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>434</td><td>250.01</td><td>425</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7359630</td><td>308727</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Family/GeneralPractice</td><td>54</td><td>1</td><td>8</td><td>0</td><td>0</td><td>0</td><td>511</td><td>401</td><td>512</td><td>8</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7366248</td><td>5288760</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>4</td><td>5</td><td>5</td><td>?</td><td>InternalMedicine</td><td>49</td><td>1</td><td>8</td><td>0</td><td>0</td><td>1</td><td>428</td><td>250.81</td><td>707</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7368432</td><td>5014656</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Family/GeneralPractice</td><td>45</td><td>2</td><td>8</td><td>0</td><td>0</td><td>1</td><td>434</td><td>401</td><td>414</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7370142</td><td>3306690</td><td>Caucasian</td><td>Female</td><td>[20-30)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>4</td><td>?</td><td>ObstetricsandGynecology</td><td>22</td><td>1</td><td>16</td><td>0</td><td>0</td><td>0</td><td>648</td><td>250.01</td><td>660</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7371168</td><td>3759597</td><td>Caucasian</td><td>Female</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>4</td><td>?</td><td>Family/GeneralPractice</td><td>71</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>486</td><td>599</td><td>571</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7371972</td><td>1777680</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>InternalMedicine</td><td>32</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>786</td><td>427</td><td>426</td><td>5</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7372068</td><td>1697436</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>InternalMedicine</td><td>49</td><td>1</td><td>11</td><td>0</td><td>0</td><td>1</td><td>574</td><td>576</td><td>427</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>7372182</td><td>1455480</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>6</td><td>25</td><td>7</td><td>3</td><td>?</td><td>Cardiology</td><td>49</td><td>3</td><td>13</td><td>0</td><td>0</td><td>0</td><td>401</td><td>425</td><td>250.01</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7372386</td><td>113286348</td><td>Caucasian</td><td>Female</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>11</td><td>?</td><td>Psychiatry</td><td>46</td><td>0</td><td>11</td><td>0</td><td>0</td><td>2</td><td>296</td><td>250.83</td><td>250.6</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7373118</td><td>296622</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>2</td><td>?</td><td>Surgery-Cardiovascular/Thoracic</td><td>47</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>433</td><td>427</td><td>414</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7373364</td><td>1553643</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>1</td><td>?</td><td>Surgery-General</td><td>46</td><td>1</td><td>7</td><td>0</td><td>0</td><td>0</td><td>250.7</td><td>440</td><td>250.5</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7384422</td><td>3546306</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>1</td><td>3</td><td>?</td><td>InternalMedicine</td><td>70</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>428</td><td>425</td><td>491</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7400064</td><td>106677</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>6</td><td>25</td><td>1</td><td>3</td><td>?</td><td>Cardiology</td><td>46</td><td>0</td><td>13</td><td>0</td><td>0</td><td>0</td><td>427</td><td>276</td><td>276</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7408302</td><td>6270498</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>7</td><td>?</td><td>Cardiology</td><td>70</td><td>6</td><td>24</td><td>0</td><td>0</td><td>0</td><td>414</td><td>250</td><td>598</td><td>4</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7414854</td><td>11605212</td><td>Caucasian</td><td>Male</td><td>[30-40)</td><td>?</td><td>2</td><td>1</td><td>20</td><td>3</td><td>?</td><td>?</td><td>34</td><td>2</td><td>7</td><td>0</td><td>0</td><td>0</td><td>250.6</td><td>536</td><td>337</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7424202</td><td>56056428</td><td>AfricanAmerican</td><td>Male</td><td>[40-50)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>6</td><td>?</td><td>?</td><td>50</td><td>2</td><td>10</td><td>0</td><td>0</td><td>0</td><td>511</td><td>250</td><td>401</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr><tr><td>7425954</td><td>2486826</td><td>AfricanAmerican</td><td>Female</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>7</td><td>?</td><td>InternalMedicine</td><td>59</td><td>2</td><td>18</td><td>0</td><td>0</td><td>0</td><td>593</td><td>591</td><td>250.6</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7431366</td><td>760932</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>3</td><td>1</td><td>1</td><td>6</td><td>?</td><td>Urology</td><td>12</td><td>3</td><td>12</td><td>0</td><td>0</td><td>0</td><td>188</td><td>599</td><td>250</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>7432206</td><td>1234845</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>3</td><td>1</td><td>1</td><td>1</td><td>?</td><td>Surgery-Neuro</td><td>10</td><td>1</td><td>15</td><td>0</td><td>0</td><td>0</td><td>722</td><td>250</td><td>401</td><td>4</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7432662</td><td>48573</td><td>AfricanAmerican</td><td>Female</td><td>[80-90)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>2</td><td>?</td><td>InternalMedicine</td><td>34</td><td>0</td><td>7</td><td>0</td><td>0</td><td>2</td><td>250.7</td><td>440</td><td>682</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>1</td></tr><tr><td>7432920</td><td>94626414</td><td>AfricanAmerican</td><td>Female</td><td>[60-70)</td><td>?</td><td>1</td><td>1</td><td>7</td><td>1</td><td>?</td><td>?</td><td>57</td><td>1</td><td>17</td><td>1</td><td>1</td><td>1</td><td>427</td><td>425</td><td>397</td><td>9</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7485582</td><td>4993056</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>11</td><td>1</td><td>1</td><td>?</td><td>Surgery-Neuro</td><td>52</td><td>0</td><td>4</td><td>0</td><td>0</td><td>0</td><td>431</td><td>432</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>0</td></tr><tr><td>7487646</td><td>5505885</td><td>AfricanAmerican</td><td>Female</td><td>[30-40)</td><td>?</td><td>1</td><td>1</td><td>1</td><td>5</td><td>?</td><td>InternalMedicine</td><td>56</td><td>1</td><td>12</td><td>0</td><td>0</td><td>1</td><td>340</td><td>244</td><td>722</td><td>7</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7494870</td><td>114767910</td><td>AfricanAmerican</td><td>Male</td><td>[50-60)</td><td>?</td><td>1</td><td>5</td><td>7</td><td>1</td><td>?</td><td>?</td><td>52</td><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>250.8</td><td>292</td><td>250.4</td><td>6</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7497384</td><td>3616155</td><td>Caucasian</td><td>Male</td><td>[70-80)</td><td>?</td><td>1</td><td>3</td><td>7</td><td>13</td><td>?</td><td>InternalMedicine</td><td>48</td><td>1</td><td>19</td><td>0</td><td>0</td><td>1</td><td>38</td><td>788</td><td>250.03</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>1</td></tr><tr><td>7500936</td><td>1070622</td><td>Caucasian</td><td>Male</td><td>[0-10)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>1</td><td>?</td><td>Pediatrics-CriticalCare</td><td>36</td><td>0</td><td>8</td><td>0</td><td>0</td><td>0</td><td>250.13</td><td>?</td><td>?</td><td>1</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>0</td></tr><tr><td>7501614</td><td>23165379</td><td>Caucasian</td><td>Male</td><td>[60-70)</td><td>?</td><td>1</td><td>6</td><td>7</td><td>11</td><td>?</td><td>Cardiology</td><td>63</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>414</td><td>411</td><td>427</td><td>8</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7502424</td><td>4792239</td><td>Caucasian</td><td>Female</td><td>[70-80)</td><td>?</td><td>1</td><td>2</td><td>7</td><td>12</td><td>?</td><td>Cardiology</td><td>67</td><td>5</td><td>24</td><td>0</td><td>0</td><td>0</td><td>410</td><td>285</td><td>414</td><td>7</td><td>0</td><td>0</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Down</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>1</td></tr><tr><td>7555146</td><td>2766582</td><td>Caucasian</td><td>Male</td><td>[50-60)</td><td>?</td><td>2</td><td>1</td><td>1</td><td>9</td><td>?</td><td>Cardiology</td><td>73</td><td>6</td><td>26</td><td>0</td><td>0</td><td>0</td><td>414</td><td>997</td><td>427</td><td>6</td><td>0</td><td>0</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Steady</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Ch</td><td>Yes</td><td>0</td></tr></tbody></table>Showing the first 1000 rows.</div>"]}}],"execution_count":0},{"cell_type":"code","source":["trainDF, testDF = dataset.randomSplit([0.8, 0.2], seed=42)\nprint(trainDF.cache().count())\nprint(testDF.count())"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"90497652-bb43-4992-b45f-a25a380fab6f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">81565\n20201\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">81565\n20201\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["from pyspark.ml.feature import StringIndexer, OneHotEncoder\n \ncategoricalCols = [\"race\", \"gender\", \"discharge_disposition_id\", \"admission_source_id\", \"diabetesMed\", \"metformin\", 'repaglinide', \"diag_1\", \"diag_2\", \"diag_3\", 'nateglinide', 'chlorpropamide', 'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone', 'tolazamide', 'insulin', 'glyburide-metformin', 'glipizide-metformin', 'glimepiride-pioglitazone', 'metformin-rosiglitazone', 'metformin-pioglitazone', 'change', 'admission_type_id']\n # no examide or citoglipton bc IllegalArgumentException: requirement failed: The input column examideIndex should have at least two distinct values.\n\n# The following two lines are estimators. They return functions that we will later apply to transform the dataset.\nstringIndexer = StringIndexer(inputCols=categoricalCols, outputCols=[x + \"Index\" for x in categoricalCols]) \nstringIndexer.setHandleInvalid(\"keep\")\nencoder = OneHotEncoder(inputCols=stringIndexer.getOutputCols(), outputCols=[x + \"OHE\" for x in categoricalCols]) \n \n# The label column (\"income\") is also a string value - it has two possible values, \"<=50K\" and \">50K\". \n# Convert it to a numeric value using StringIndexer.\n#labelToIndex = StringIndexer(inputCol=\"income\", outputCol=\"label\")\nageIndexer = StringIndexer(inputCol=\"age\", outputCol=\"ageIndex\") \n\n# stringIndexerModel = stringIndexer.fit(dataset)\n# display(stringIndexerModel.transform(dataset))\nlabelToIndex = StringIndexer(inputCol=\"readmitted\", outputCol=\"label\")\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"8cb5bfc9-1b28-4287-a1ad-12eba429e7bd"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["from pyspark.ml.feature import VectorAssembler\n \n# This includes both the numeric columns and the one-hot encoded binary vector columns in our dataset.\nnumericCols = [\"time_in_hospital\", \"num_procedures\", \"num_lab_procedures\", \"num_medications\", \"number_outpatient\", \"number_inpatient\", \"number_emergency\", \"number_diagnoses\", \"A1Cresult\", \"max_glu_serum\"]\n\n\nassemblerInputs = [c + \"OHE\" for c in categoricalCols] + numericCols + [\"ageIndex\"]\n\nvecAssembler = VectorAssembler(inputCols=assemblerInputs, outputCol=\"features\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9ffee985-6b7c-443a-a6a5-285d53ad034a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["from pyspark.ml.regression import LinearRegression\nlr = LinearRegression(featuresCol=\"features\", labelCol=\"label\", regParam=0.3, maxIter=10)\n\n\n# https://spark.apache.org/docs/latest/ml-classification-regression.html#logistic-regression"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"23cd9e17-dd79-4e08-9852-c184cc260f2c"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["from pyspark.ml import Pipeline\n \n# Define the pipeline based on the stages created in previous steps.\npipeline = Pipeline(stages=[stringIndexer, encoder, ageIndexer, labelToIndex, vecAssembler, lr])\n \n# Define the pipeline model.\npipelineModel = pipeline.fit(trainDF)\n\nlrModel = pipelineModel.stages[-1]\n# Print the coefficients and intercept for linear regression\nprint(\"Coefficients: %s\" % str(lrModel.coefficients))\nprint(\"Intercept: %s\" % str(lrModel.intercept))\n\n# Summarize the model over the training set and print out some metrics\ntrainingSummary = lrModel.summary\nprint(\"numIterations: %d\" % trainingSummary.totalIterations)\nprint(\"objectiveHistory: %s\" % str(trainingSummary.objectiveHistory))\nprint(\"RMSE: %f\" % trainingSummary.rootMeanSquaredError)\n\ntrainingSummary.residuals.show()\n# Apply the pipeline model to the test dataset.\npredDF = pipelineModel.transform(testDF)\n\n# https://stackoverflow.com/questions/44773758/how-to-conditionally-replace-value-in-a-column-based-on-evaluation-of-expression"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fcc0abac-11f8-4684-b738-4859e9b9cf36"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Coefficients: 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0.4211618808902852\nnumIterations: 1\nobjectiveHistory: [0.0]\nRMSE: 0.465586\n+--------------------+\n|           residuals|\n+--------------------+\n|  0.5148265210350758|\n| -0.5720596939351361|\n| -0.4969417179081904|\n|  0.4680195087827972|\n|  0.6399527094425326|\n| -0.3668881631662366|\n|  0.4442110157023905|\n| -0.5971390646332071|\n|  0.6286369819762785|\n|  0.6798327659264154|\n| -0.3917806466520356|\n| -0.4489480579924207|\n|-0.29256850410803265|\n|  0.5184935505618591|\n| 0.37761437838362233|\n|  0.5541680868982132|\n| -0.4606453989038623|\n|  0.4531425882463149|\n|-0.19288485684994855|\n| -0.5443485654120556|\n+--------------------+\nonly showing top 20 rows\n\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source 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0.4211618808902852\nnumIterations: 1\nobjectiveHistory: [0.0]\nRMSE: 0.465586\n+--------------------+\n           residuals|\n+--------------------+\n  0.5148265210350758|\n -0.5720596939351361|\n -0.4969417179081904|\n  0.4680195087827972|\n  0.6399527094425326|\n -0.3668881631662366|\n  0.4442110157023905|\n -0.5971390646332071|\n  0.6286369819762785|\n  0.6798327659264154|\n -0.3917806466520356|\n -0.4489480579924207|\n-0.29256850410803265|\n  0.5184935505618591|\n 0.37761437838362233|\n  0.5541680868982132|\n -0.4606453989038623|\n  0.4531425882463149|\n-0.19288485684994855|\n -0.5443485654120556|\n+--------------------+\nonly showing top 20 rows\n\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["#display(predDF.select(\"features\", \"label\", \"prediction\"))\n#display(pipelineModel.stages[-1], predDF.drop(\"prediction\", \"rawPrediction\", \"probability\"), \"ROC\")\n# probability is only for logistic regression \nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\nmcEvaluator = MulticlassClassificationEvaluator(metricName=\"accuracy\")\n\nmcEvaluator.evaluate(predDF)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"38fb3a13-8988-4e17-a684-77373587e809"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[224]: 0.0</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[224]: 0.0</div>"]}}],"execution_count":0},{"cell_type":"code","source":["#test_result = lrModel.evaluate(predDF)\n#print(\"Root Mean Squared Error (RMSE) on test data = %g\" % test_result.rootMeanSquaredError)\n#predDF.select(\"prediction\",\"features\").show(5)\n#display(predDF)\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\n# https://docs.databricks.com/notebooks/visualizations/matplotlib.html\ndf_age = dataset[dataset.readmitted != 0].select(\"age\", \"readmitted\").groupby('age').count().orderBy(\"age\")\n#display(df_age.toPandas())\n# result = df.sort(['A', 'B'], ascending=[1, 0])\n#df_age = dataset[dataset.readmitted != 0].groupby('age').count()\ndf_age.toPandas().plot(x=\"age\", y=\"count\", kind=\"bar\")\nplt.title('Age vs. Readmission Count')\nplt.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"bcd7d2f1-b23c-461c-ae06-c95c77ddfe5f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/e34ba7a7-f5c0-4cec-8b4b-e0a2a0039a05.png","removedWidgets":[],"addedWidgets":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"}}],"execution_count":0},{"cell_type":"code","source":["df_race = dataset[dataset.readmitted != 0].select(\"race\", \"readmitted\").groupby('race').count()\ndf_race.toPandas().plot(x=\"race\", y=\"count\", kind=\"bar\")\nplt.title('Age vs. Readmission Count')\nplt.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1742b283-9f9c-4d70-9d8f-0eaec9864312"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"/plots/03e3d990-65cb-4f6e-b736-293bd123277e.png","removedWidgets":[],"addedWidgets":{},"type":"image","arguments":{}}},"output_type":"display_data","data":{"image/png":"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"}}],"execution_count":0},{"cell_type":"code","source":["import pyspark.sql.functions as functions\npredDF = predDF.withColumn(\"accurate\", functions.when((predDF[\"label\"] == 1) &( predDF[\"prediction\"] >= 0.5 ), 1).when((predDF[\"label\"] == 0) & (predDF[\"prediction\"] < 0.5) , 1).otherwise(0))\ndisplay(predDF.select(\"prediction\", \"label\", \"accurate\"))\npredDF = predDF.withColumn(\"accurate\", predDF[\"accurate\"].cast(IntegerType()))\ndisplay(predDF.groupBy('accurate').count())"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"32bb1625-dba0-4b81-990c-ed70e5c4e423"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":true,"datasetInfos":[],"data":[[0.5245787384678087,1.0,1],[0.4036495578034703,0.0,1],[0.4221794548320073,0.0,1],[0.440093870174005,1.0,0],[0.14847369360300183,0.0,1],[0.44402904327275233,0.0,1],[0.4541308994387101,1.0,0],[0.5415704353093801,1.0,1],[0.5734394788137035,1.0,1],[0.4142779305919548,0.0,1],[0.5680280196311073,1.0,1],[0.4863573402121205,1.0,0],[0.4186419321463618,0.0,1],[0.3701367049519617,0.0,1],[0.5006284616599892,1.0,1],[0.5086100009358969,0.0,0],[0.39809257690328925,1.0,0],[0.34303686577744014,0.0,1],[0.3221644091465484,0.0,1],[0.6706709274667562,1.0,1],[0.81712941425347,0.0,0],[0.6425476639934113,1.0,1],[0.6611709589971916,1.0,1],[0.48201231865536176,0.0,1],[0.19354461776541973,0.0,1],[0.4661527274443737,0.0,1],[0.36602065172063847,0.0,1],[0.5260444082116673,0.0,0],[0.47130418021166565,0.0,1],[0.6684028988032773,0.0,0],[0.46575463063642397,1.0,0],[0.4734750869854394,1.0,0],[0.24546611623740633,0.0,1],[0.5963728861895128,1.0,1],[0.36214595628801627,0.0,1],[0.43554330781697054,0.0,1],[0.7508240238440811,1.0,1],[0.6140069189990598,0.0,0],[0.4353601976560748,0.0,1],[0.5320843586701322,0.0,0],[0.43974552834091324,0.0,1],[0.5672048585156625,1.0,1],[0.42472636213601306,0.0,1],[0.403901143168027,0.0,1],[0.4126698613546109,1.0,0],[0.35049377338570836,0.0,1],[0.4471420385985248,1.0,0],[0.45392253369350116,0.0,1],[0.49659904712511427,1.0,0],[0.2920062241706114,0.0,1],[0.6269419695218632,1.0,1],[0.5924762159772262,1.0,1],[0.38387194090280785,0.0,1],[0.544896324527,1.0,1],[0.5543757826214424,0.0,0],[0.48459568081536064,1.0,0],[0.6544141277441397,1.0,1],[0.47190532944229546,1.0,0],[0.5988277115100541,1.0,1],[0.5205338407601277,0.0,0],[0.4809870743552845,1.0,0],[0.6976539046027439,1.0,1],[0.5366276680059419,0.0,0],[0.4179413830729079,0.0,1],[0.518408876054554,1.0,1],[0.4549280838592917,0.0,1],[0.38824944001956996,0.0,1],[0.44790450363026374,1.0,0],[0.5103467335235394,1.0,1],[0.5582598350534396,0.0,0],[0.14790720957476122,0.0,1],[0.41795482803520323,1.0,0],[0.3521410589888431,0.0,1],[0.3414427533403659,0.0,1],[0.35537125966726113,0.0,1],[0.34208869157025484,0.0,1],[0.5365585242078064,0.0,0],[0.3981408881313028,0.0,1],[0.42964223390092426,0.0,1],[0.48376197941916954,0.0,1],[0.46617417058691696,0.0,1],[0.4590901417903206,0.0,1],[0.478455742110203,1.0,0],[0.3914518261623003,1.0,0],[0.5430731121009122,1.0,1],[0.4335022046003033,0.0,1],[0.4968490848062805,1.0,0],[0.48491845491406815,1.0,0],[0.5700796466376015,1.0,1],[0.4700073380840228,0.0,1],[0.6385828498658466,1.0,1],[0.6000367654351746,1.0,1],[0.4948157747419151,1.0,0],[0.427207425265447,0.0,1],[0.5127768208225771,1.0,1],[0.30032333213688567,0.0,1],[0.4838566567412701,1.0,0],[0.5803283524927318,1.0,1],[0.37621764996053497,1.0,0],[0.4688335990969216,0.0,1],[0.5841431082428183,0.0,0],[0.5800103144671575,1.0,1],[0.6425956086545164,1.0,1],[0.7633236122889556,1.0,1],[0.41442749244159954,1.0,0],[0.44217240367228516,1.0,0],[0.5593469111245888,0.0,0],[0.3618470284010989,1.0,0],[0.43576249185762495,1.0,0],[0.5299805620869207,1.0,1],[0.63680200434927,0.0,0],[0.26564135550629503,0.0,1],[0.6724733000699494,1.0,1],[0.2752588937515807,0.0,1],[0.40324401001889093,0.0,1],[0.4549047654650197,1.0,0],[0.47322961980942646,1.0,0],[0.4868594029896923,1.0,0],[0.25770440927831906,0.0,1],[0.5653683410061996,0.0,0],[0.5149729196172783,0.0,0],[0.36546099510290186,1.0,0],[0.5235351602392234,1.0,1],[0.5435803878456685,1.0,1],[0.31308114215662863,0.0,1],[0.6217205335934164,1.0,1],[0.5592620108657627,1.0,1],[0.501375502905639,1.0,1],[0.4935184228435917,1.0,0],[0.36846698140123507,0.0,1],[0.5980645630206526,1.0,1],[0.35275451355590265,0.0,1],[0.3518135783107344,0.0,1],[0.35536517741867,0.0,1],[0.6152280358604781,1.0,1],[0.5061729620113005,1.0,1],[0.32076644808516325,0.0,1],[0.47414341282687666,0.0,1],[0.22788627630311356,0.0,1],[0.48321962323726736,1.0,0],[0.6011902381258387,0.0,0],[0.47724221153706153,1.0,0],[0.6101271920446456,1.0,1],[0.45268165291192236,0.0,1],[0.5266699628791917,0.0,0],[0.4398644126744173,1.0,0],[0.6307326007860615,1.0,1],[0.4999254050502073,0.0,1],[0.3232218163345544,0.0,1],[0.4399308289108828,0.0,1],[0.39727592990180605,1.0,0],[0.39933330998985067,0.0,1],[0.3432806755599075,1.0,0],[0.5324820426671799,1.0,1],[0.3101086447500446,1.0,0],[0.5771198310222512,0.0,0],[0.32548845840942736,0.0,1],[0.5070262453202887,0.0,0],[0.34532319791117544,0.0,1],[0.6069058913062213,1.0,1],[0.6746836258980797,0.0,0],[0.18218205189808553,0.0,1],[0.19841314650060604,1.0,0],[0.5541091873318726,1.0,1],[0.4332644450812855,1.0,0],[0.5344268329096328,0.0,0],[0.5229651517860827,1.0,1],[0.4096786287168959,0.0,1],[0.4217187810991531,0.0,1],[0.5864445326462764,1.0,1],[0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white'><tr><th>prediction</th><th>label</th><th>accurate</th></tr></thead><tbody><tr><td>0.5245787384678087</td><td>1.0</td><td>1</td></tr><tr><td>0.4036495578034703</td><td>0.0</td><td>1</td></tr><tr><td>0.4221794548320073</td><td>0.0</td><td>1</td></tr><tr><td>0.440093870174005</td><td>1.0</td><td>0</td></tr><tr><td>0.14847369360300183</td><td>0.0</td><td>1</td></tr><tr><td>0.44402904327275233</td><td>0.0</td><td>1</td></tr><tr><td>0.4541308994387101</td><td>1.0</td><td>0</td></tr><tr><td>0.5415704353093801</td><td>1.0</td><td>1</td></tr><tr><td>0.5734394788137035</td><td>1.0</td><td>1</td></tr><tr><td>0.4142779305919548</td><td>0.0</td><td>1</td></tr><tr><td>0.5680280196311073</td><td>1.0</td><td>1</td></tr><tr><td>0.4863573402121205</td><td>1.0</td><td>0</td></tr><tr><td>0.4186419321463618</td><td>0.0</td><td>1</td></tr><tr><td>0.3701367049519617</td><td>0.0</td><td>1</td></tr><tr><td>0.5006284616599892</td><td>1.0</td><td>1</td></tr><tr><td>0.5086100009358969</td><td>0.0</td><td>0</td></tr><tr><td>0.39809257690328925</td><td>1.0</td><td>0</td></tr><tr><td>0.34303686577744014</td><td>0.0</td><td>1</td></tr><tr><td>0.3221644091465484</td><td>0.0</td><td>1</td></tr><tr><td>0.6706709274667562</td><td>1.0</td><td>1</td></tr><tr><td>0.81712941425347</td><td>0.0</td><td>0</td></tr><tr><td>0.6425476639934113</td><td>1.0</td><td>1</td></tr><tr><td>0.6611709589971916</td><td>1.0</td><td>1</td></tr><tr><td>0.48201231865536176</td><td>0.0</td><td>1</td></tr><tr><td>0.19354461776541973</td><td>0.0</td><td>1</td></tr><tr><td>0.4661527274443737</td><td>0.0</td><td>1</td></tr><tr><td>0.36602065172063847</td><td>0.0</td><td>1</td></tr><tr><td>0.5260444082116673</td><td>0.0</td><td>0</td></tr><tr><td>0.47130418021166565</td><td>0.0</td><td>1</td></tr><tr><td>0.6684028988032773</td><td>0.0</td><td>0</td></tr><tr><td>0.46575463063642397</td><td>1.0</td><td>0</td></tr><tr><td>0.4734750869854394</td><td>1.0</td><td>0</td></tr><tr><td>0.24546611623740633</td><td>0.0</td><td>1</td></tr><tr><td>0.5963728861895128</td><td>1.0</td><td>1</td></tr><tr><td>0.36214595628801627</td><td>0.0</td><td>1</td></tr><tr><td>0.43554330781697054</td><td>0.0</td><td>1</td></tr><tr><td>0.7508240238440811</td><td>1.0</td><td>1</td></tr><tr><td>0.6140069189990598</td><td>0.0</td><td>0</td></tr><tr><td>0.4353601976560748</td><td>0.0</td><td>1</td></tr><tr><td>0.5320843586701322</td><td>0.0</td><td>0</td></tr><tr><td>0.43974552834091324</td><td>0.0</td><td>1</td></tr><tr><td>0.5672048585156625</td><td>1.0</td><td>1</td></tr><tr><td>0.42472636213601306</td><td>0.0</td><td>1</td></tr><tr><td>0.403901143168027</td><td>0.0</td><td>1</td></tr><tr><td>0.4126698613546109</td><td>1.0</td><td>0</td></tr><tr><td>0.35049377338570836</td><td>0.0</td><td>1</td></tr><tr><td>0.4471420385985248</td><td>1.0</td><td>0</td></tr><tr><td>0.45392253369350116</td><td>0.0</td><td>1</td></tr><tr><td>0.49659904712511427</td><td>1.0</td><td>0</td></tr><tr><td>0.2920062241706114</td><td>0.0</td><td>1</td></tr><tr><td>0.6269419695218632</td><td>1.0</td><td>1</td></tr><tr><td>0.5924762159772262</td><td>1.0</td><td>1</td></tr><tr><td>0.38387194090280785</td><td>0.0</td><td>1</td></tr><tr><td>0.544896324527</td><td>1.0</td><td>1</td></tr><tr><td>0.5543757826214424</td><td>0.0</td><td>0</td></tr><tr><td>0.48459568081536064</td><td>1.0</td><td>0</td></tr><tr><td>0.6544141277441397</td><td>1.0</td><td>1</td></tr><tr><td>0.47190532944229546</td><td>1.0</td><td>0</td></tr><tr><td>0.5988277115100541</td><td>1.0</td><td>1</td></tr><tr><td>0.5205338407601277</td><td>0.0</td><td>0</td></tr><tr><td>0.4809870743552845</td><td>1.0</td><td>0</td></tr><tr><td>0.6976539046027439</td><td>1.0</td><td>1</td></tr><tr><td>0.5366276680059419</td><td>0.0</td><td>0</td></tr><tr><td>0.4179413830729079</td><td>0.0</td><td>1</td></tr><tr><td>0.518408876054554</td><td>1.0</td><td>1</td></tr><tr><td>0.4549280838592917</td><td>0.0</td><td>1</td></tr><tr><td>0.38824944001956996</td><td>0.0</td><td>1</td></tr><tr><td>0.44790450363026374</td><td>1.0</td><td>0</td></tr><tr><td>0.5103467335235394</td><td>1.0</td><td>1</td></tr><tr><td>0.5582598350534396</td><td>0.0</td><td>0</td></tr><tr><td>0.14790720957476122</td><td>0.0</td><td>1</td></tr><tr><td>0.41795482803520323</td><td>1.0</td><td>0</td></tr><tr><td>0.3521410589888431</td><td>0.0</td><td>1</td></tr><tr><td>0.3414427533403659</td><td>0.0</td><td>1</td></tr><tr><td>0.35537125966726113</td><td>0.0</td><td>1</td></tr><tr><td>0.34208869157025484</td><td>0.0</td><td>1</td></tr><tr><td>0.5365585242078064</td><td>0.0</td><td>0</td></tr><tr><td>0.3981408881313028</td><td>0.0</td><td>1</td></tr><tr><td>0.42964223390092426</td><td>0.0</td><td>1</td></tr><tr><td>0.48376197941916954</td><td>0.0</td><td>1</td></tr><tr><td>0.46617417058691696</td><td>0.0</td><td>1</td></tr><tr><td>0.4590901417903206</td><td>0.0</td><td>1</td></tr><tr><td>0.478455742110203</td><td>1.0</td><td>0</td></tr><tr><td>0.3914518261623003</td><td>1.0</td><td>0</td></tr><tr><td>0.5430731121009122</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the first 1000 rows.</div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[1,12703],[0,7498]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"accurate","type":"\"integer\"","metadata":"{}"},{"name":"count","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>accurate</th><th>count</th></tr></thead><tbody><tr><td>1</td><td>12703</td></tr><tr><td>0</td><td>7498</td></tr></tbody></table></div>"]}}],"execution_count":0},{"cell_type":"code","source":["import pyspark.sql.functions as functions\npredDF = predDF.withColumn(\"accurate\", functions.when((predDF[\"label\"] == 1) &( predDF[\"prediction\"] >= 0.5 ), 1).when((predDF[\"label\"] == 0) & (predDF[\"prediction\"] < 0.5) , 1).otherwise(0))\n\ndisplay(predDF.select(\"features\", \"prediction\", \"readmitted\", \"accurate\"))\n\npredDF = predDF.withColumn(\"accurate\", predDF[\"accurate\"].cast(IntegerType()))\n\n# # >>> df.select(when(df.age == 2, df.age + 1).alias(\"age\")).collect()\n# #[Row(age=3), Row(age=None)]\ndisplay(predDF.select(functions.sum(\"accurate\"), 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own\"},{\"idx\":2251,\"name\":\"acetohexamideOHE_No\"},{\"idx\":2252,\"name\":\"acetohexamideOHE_Steady\"},{\"idx\":2253,\"name\":\"glipizideOHE_No\"},{\"idx\":2254,\"name\":\"glipizideOHE_Steady\"},{\"idx\":2255,\"name\":\"glipizideOHE_Up\"},{\"idx\":2256,\"name\":\"glipizideOHE_Down\"},{\"idx\":2257,\"name\":\"glyburideOHE_No\"},{\"idx\":2258,\"name\":\"glyburideOHE_Steady\"},{\"idx\":2259,\"name\":\"glyburideOHE_Up\"},{\"idx\":2260,\"name\":\"glyburideOHE_Down\"},{\"idx\":2261,\"name\":\"tolbutamideOHE_No\"},{\"idx\":2262,\"name\":\"tolbutamideOHE_Steady\"},{\"idx\":2263,\"name\":\"pioglitazoneOHE_No\"},{\"idx\":2264,\"name\":\"pioglitazoneOHE_Steady\"},{\"idx\":2265,\"name\":\"pioglitazoneOHE_Up\"},{\"idx\":2266,\"name\":\"pioglitazoneOHE_Down\"},{\"idx\":2267,\"name\":\"rosiglitazoneOHE_No\"},{\"idx\":2268,\"name\":\"rosiglitazoneOHE_Steady\"},{\"idx\":2269,\"name\":\"rosiglitazoneOHE_Up\"},{\"idx\":2270,\"name\":\"rosiglitazoneOHE_Down\"},{\"idx\":2271,\"name\":\"acarboseOHE_No\"},{\"idx\":2272,\"name\":\"acarboseOHE_Steady\"},{\"idx\":2273,\"name\":\"acarboseOHE_Up\"},{\"idx\":2274,\"name\":\"acarboseOHE_Down\"},{\"idx\":2275,\"name\":\"miglitolOHE_No\"},{\"idx\":2276,\"name\":\"miglitolOHE_Steady\"},{\"idx\":2277,\"name\":\"miglitolOHE_Down\"},{\"idx\":2278,\"name\":\"miglitolOHE_Up\"},{\"idx\":2279,\"name\":\"troglitazoneOHE_No\"},{\"idx\":2280,\"name\":\"troglitazoneOHE_Steady\"},{\"idx\":2281,\"name\":\"tolazamideOHE_No\"},{\"idx\":2282,\"name\":\"tolazamideOHE_Steady\"},{\"idx\":2283,\"name\":\"insulinOHE_No\"},{\"idx\":2284,\"name\":\"insulinOHE_Steady\"},{\"idx\":2285,\"name\":\"insulinOHE_Down\"},{\"idx\":2286,\"name\":\"insulinOHE_Up\"},{\"idx\":2287,\"name\":\"glyburide-metforminOHE_No\"},{\"idx\":2288,\"name\":\"glyburide-metforminOHE_Steady\"},{\"idx\":2289,\"name\":\"glyburide-metforminOHE_Up\"},{\"idx\":2290,\"name\":\"glyburide-metforminOHE_Down\"},{\"idx\":2291,\"name\":\"glipizide-metforminOHE_No\"},{\"idx\":2292,\"name\":\"glipizide-metforminOHE_Steady\"},{\"idx\":2293,\"name\":\"glimepiride-pioglitazoneOHE_No\"},{\"idx\":2294,\"name\":\"glimepiride-pioglitazoneOHE_Steady\"},{\"idx\":2295,\"name\":\"metformin-rosiglitazoneOHE_No\"},{\"idx\":2296,\"name\":\"metformin-rosiglitazoneOHE_Steady\"},{\"idx\":2297,\"name\":\"metformin-pioglitazoneOHE_No\"},{\"idx\":2298,\"name\":\"metformin-pioglitazoneOHE_Steady\"},{\"idx\":2299,\"name\":\"changeOHE_No\"},{\"idx\":2300,\"name\":\"changeOHE_Ch\"},{\"idx\":2301,\"name\":\"admission_type_idOHE_1\"},{\"idx\":2302,\"name\":\"admission_type_idOHE_3\"},{\"idx\":2303,\"name\":\"admission_type_idOHE_2\"},{\"idx\":2304,\"name\":\"admission_type_idOHE_6\"},{\"idx\":2305,\"name\":\"admission_type_idOHE_5\"},{\"idx\":2306,\"name\":\"admission_type_idOHE_8\"},{\"idx\":2307,\"name\":\"admission_type_idOHE_7\"},{\"idx\":2308,\"name\":\"admission_type_idOHE_4\"}]},\"num_attrs\":2320}}"},{"name":"prediction","type":"\"double\"","metadata":"{\"ml_attr\":{}}"},{"name":"readmitted","type":"\"integer\"","metadata":"{}"},{"name":"accurate","type":"\"integer\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>features</th><th>prediction</th><th>readmitted</th><th>accurate</th></tr></thead><tbody><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 57.0, 21.0, 1.0, 9.0, 2.0))</td><td>0.5245787384678087</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 38, 52, 54, 58, 186, 813, 1592, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 71.0, 18.0, 9.0, 4.0))</td><td>0.4036495578034703</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 90, 860, 1550, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 68.0, 8.0, 3.0, 4.0))</td><td>0.4221794548320073</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 146, 888, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 43.0, 16.0, 4.0, 1.0))</td><td>0.440093870174005</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 214, 802, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 13.0, 5.0, 5.0))</td><td>0.14847369360300183</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 97, 853, 1753, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 54.0, 3.0, 2.0, 4.0, 2.0))</td><td>0.44402904327275233</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 158, 757, 1526, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 57.0, 23.0, 9.0, 3.0))</td><td>0.4541308994387101</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 62, 766, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 57.0, 13.0, 9.0))</td><td>0.5415704353093801</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 760, 1497, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 59.0, 19.0, 1.0, 7.0))</td><td>0.5734394788137035</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 64, 763, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 47.0, 10.0, 4.0, 3.0))</td><td>0.4142779305919548</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 19, 35, 52, 54, 58, 65, 766, 1878, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 94.0, 15.0, 1.0, 2.0, 9.0, 2.0))</td><td>0.5680280196311073</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 36, 53, 54, 58, 116, 758, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 50.0, 21.0, 1.0, 9.0, 4.0))</td><td>0.4863573402121205</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 762, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 13.0, 17.0, 7.0))</td><td>0.4186419321463618</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 134, 984, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 72.0, 13.0, 8.0))</td><td>0.3701367049519617</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 64, 767, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 61.0, 16.0, 1.0, 9.0, 4.0))</td><td>0.5006284616599892</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 96, 769, 1708, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 3.0, 60.0, 21.0, 7.0, 3.0))</td><td>0.5086100009358969</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 65, 791, 1493, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 5.0, 8.0, 7.0, 2.0))</td><td>0.39809257690328925</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 224, 826, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 4.0, 3.0, 8.0))</td><td>0.34303686577744014</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 85, 857, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 56.0, 7.0, 4.0, 2.0))</td><td>0.3221644091465484</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 41, 52, 54, 58, 145, 767, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 33.0, 11.0, 6.0, 8.0, 1.0))</td><td>0.6706709274667562</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 32, 35, 53, 54, 58, 82, 758, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 77.0, 17.0, 1.0, 5.0, 6.0))</td><td>0.81712941425347</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 54, 58, 181, 864, 1618, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 36.0, 12.0, 5.0, 1.0, 9.0, 4.0))</td><td>0.6425476639934113</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 73, 771, 1600, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 1.0, 42.0, 25.0, 3.0, 9.0, 4.0))</td><td>0.6611709589971916</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 90, 1107, 1856, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 9.0, 3.0, 8.0))</td><td>0.48201231865536176</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 340, 802, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 6.0, 4.0, 5.0))</td><td>0.19354461776541973</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 69, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 25.0, 26.0, 6.0, 2.0))</td><td>0.4661527274443737</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 767, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 61.0, 21.0, 6.0, 4.0))</td><td>0.36602065172063847</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 67, 772, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 51.0, 13.0, 9.0))</td><td>0.5260444082116673</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 38, 52, 55, 58, 70, 764, 1704, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 9.0, 16.0, 1.0, 1.0, 6.0, 3.0))</td><td>0.47130418021166565</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 187, 797, 1481, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 61.0, 32.0, 1.0, 6.0))</td><td>0.6684028988032773</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 165, 787, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 34.0, 12.0, 1.0, 5.0, 1.0))</td><td>0.46575463063642397</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 878, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 48.0, 14.0, 1.0, 8.0, 1.0))</td><td>0.4734750869854394</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 148, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 51.0, 3.0, 1.0, 8.0))</td><td>0.24546611623740633</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 62, 756, 1545, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 3.0, 46.0, 25.0, 1.0, 7.0))</td><td>0.5963728861895128</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 112, 753, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 48.0, 5.0, 3.0, 2.0))</td><td>0.36214595628801627</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 102, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 7.0, 3.0, 5.0, 2.0))</td><td>0.43554330781697054</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 72, 1150, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2313, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 2.0, 60.0, 18.0, 1.0, 1.0, 9.0))</td><td>0.7508240238440811</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 43, 52, 55, 58, 76, 785, 1639, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 20.0, 18.0, 9.0, 1.0))</td><td>0.6140069189990598</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 91, 797, 1571, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 67.0, 17.0, 5.0, 6.0))</td><td>0.4353601976560748</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 54, 58, 80, 830, 1622, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 62.0, 15.0, 8.0, 4.0))</td><td>0.5320843586701322</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 55, 58, 176, 777, 1643, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 40.0, 29.0, 9.0, 2.0))</td><td>0.43974552834091324</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 72, 758, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 67.0, 9.0, 3.0, 7.0, 3.0))</td><td>0.5672048585156625</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 67, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 38.0, 10.0, 2.0, 5.0))</td><td>0.42472636213601306</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 88, 853, 1481, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 3.0, 6.0, 2.0))</td><td>0.403901143168027</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 108, 836, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 66.0, 15.0, 1.0, 9.0, 6.0))</td><td>0.4126698613546109</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 40, 53, 54, 58, 77, 767, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 39.0, 7.0, 6.0, 4.0))</td><td>0.35049377338570836</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 38, 52, 54, 58, 62, 772, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 69.0, 16.0, 8.0, 1.0))</td><td>0.4471420385985248</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 40, 52, 54, 58, 74, 870, 1494, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 41.0, 14.0, 2.0, 5.0, 4.0))</td><td>0.45392253369350116</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 60.0, 20.0, 6.0, 3.0))</td><td>0.49659904712511427</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 90, 753, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 60.0, 14.0, 3.0, 8.0))</td><td>0.2920062241706114</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 70, 754, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 55.0, 19.0, 2.0, 6.0, 2.0))</td><td>0.6269419695218632</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 62, 753, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 69.0, 20.0, 8.0))</td><td>0.5924762159772262</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 108, 756, 1650, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 44.0, 23.0, 9.0))</td><td>0.38387194090280785</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 63, 942, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 71.0, 15.0, 8.0, 2.0))</td><td>0.544896324527</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 91, 764, 1520, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 49.0, 37.0, 9.0, 1.0))</td><td>0.5543757826214424</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 91, 785, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 5.0, 42.0, 23.0, 1.0, 9.0, 1.0))</td><td>0.48459568081536064</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 62, 772, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 60.0, 19.0, 2.0, 8.0, 3.0))</td><td>0.6544141277441397</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 762, 1688, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 33.0, 21.0, 9.0))</td><td>0.47190532944229546</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 43, 52, 56, 58, 197, 781, 1617, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 31.0, 25.0, 9.0, 3.0))</td><td>0.5988277115100541</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1522, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 55.0, 23.0, 1.0, 9.0))</td><td>0.5205338407601277</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 53, 54, 58, 73, 787, 1640, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 42.0, 12.0, 2.0, 9.0, 1.0))</td><td>0.4809870743552845</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 103, 753, 1501, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 69.0, 20.0, 4.0, 8.0))</td><td>0.6976539046027439</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 58, 219, 824, 1565, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 68.0, 10.0, 6.0, 3.0))</td><td>0.5366276680059419</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 65, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 32.0, 17.0, 9.0, 2.0))</td><td>0.4179413830729079</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 62, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 50.0, 19.0, 8.0, 3.0))</td><td>0.518408876054554</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 105, 772, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 62.0, 13.0, 6.0, 1.0))</td><td>0.4549280838592917</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 141, 805, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 49.0, 4.0, 4.0))</td><td>0.38824944001956996</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 63, 761, 1486, 2239, 2243, 2247, 2251, 2254, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 37.0, 17.0, 5.0, 2.0))</td><td>0.44790450363026374</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 137, 755, 1563, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 43.0, 18.0, 1.0, 7.0, 2.0))</td><td>0.5103467335235394</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 290, 767, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 42.0, 13.0, 3.0, 8.0, 5.0))</td><td>0.5582598350534396</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 56, 58, 163, 755, 1553, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 6.0, 24.0, 17.0, 4.0, 1.0))</td><td>0.14790720957476122</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 72, 756, 1704, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 5.0, 49.0, 15.0, 7.0, 3.0))</td><td>0.41795482803520323</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 54, 58, 120, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 53.0, 6.0, 3.0, 2.0))</td><td>0.3521410589888431</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 159, 808, 1499, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 12.0, 10.0, 5.0, 1.0))</td><td>0.3414427533403659</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 65, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 36.0, 11.0, 6.0, 4.0))</td><td>0.35537125966726113</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 55, 58, 64, 776, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 22.0, 17.0, 8.0, 5.0))</td><td>0.34208869157025484</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 67, 754, 1495, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 44.0, 25.0, 8.0, 1.0))</td><td>0.5365585242078064</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 53, 54, 58, 63, 762, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 66.0, 27.0, 7.0, 3.0))</td><td>0.3981408881313028</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 90, 861, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 7.0, 2.0, 2.0, 8.0))</td><td>0.42964223390092426</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 109, 797, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 58.0, 17.0, 5.0, 3.0))</td><td>0.48376197941916954</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 137, 757, 1671, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 59.0, 12.0, 7.0, 1.0))</td><td>0.46617417058691696</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 63, 768, 1522, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 72.0, 29.0, 9.0, 2.0))</td><td>0.4590901417903206</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 72, 754, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 42.0, 21.0, 1.0, 4.0, 4.0))</td><td>0.478455742110203</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 64, 761, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 37.0, 10.0, 8.0, 2.0))</td><td>0.3914518261623003</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 79, 777, 1675, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 12.0, 7.0, 1.0, 5.0, 1.0))</td><td>0.5430731121009122</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 89, 866, 1567, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 36.0, 5.0, 5.0))</td><td>0.4335022046003033</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 66, 759, 1595, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 62.0, 10.0, 9.0))</td><td>0.4968490848062805</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 62, 783, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 65.0, 14.0, 7.0))</td><td>0.48491845491406815</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 842, 1632, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 57.0, 16.0, 1.0, 7.0, 2.0))</td><td>0.5700796466376015</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 71, 792, 1667, 2239, 2243, 2249, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 60.0, 5.0, 2.0, 6.0))</td><td>0.4700073380840228</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 43, 52, 54, 58, 68, 755, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 51.0, 19.0, 9.0, 3.0))</td><td>0.6385828498658466</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 72, 760, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 52.0, 8.0, 1.0, 9.0, 4.0))</td><td>0.6000367654351746</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 37, 53, 54, 58, 73, 876, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 56.0, 28.0, 8.0, 2.0))</td><td>0.4948157747419151</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 87, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 47.0, 6.0, 5.0))</td><td>0.427207425265447</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 71, 886, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 38.0, 7.0, 5.0, 1.0))</td><td>0.5127768208225771</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 95, 769, 1607, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 4.0, 51.0, 19.0, 5.0, 2.0))</td><td>0.30032333213688567</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 224, 771, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 58.0, 12.0, 5.0, 4.0))</td><td>0.4838566567412701</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 62, 756, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 49.0, 9.0, 8.0))</td><td>0.5803283524927318</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 775, 1508, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 46.0, 12.0, 6.0, 4.0))</td><td>0.37621764996053497</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 62, 758, 1508, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 7.0, 8.0, 4.0))</td><td>0.4688335990969216</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 72, 758, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 46.0, 14.0, 9.0, 1.0))</td><td>0.5841431082428183</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 72, 836, 1536, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2266, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 66.0, 16.0, 9.0, 1.0))</td><td>0.5800103144671575</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 55, 58, 62, 758, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 71.0, 15.0, 2.0, 9.0, 3.0))</td><td>0.6425956086545164</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 80, 760, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 44.0, 10.0, 5.0, 5.0, 2.0))</td><td>0.7633236122889556</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 97, 759, 1496, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 58.0, 8.0, 8.0, 2.0))</td><td>0.41442749244159954</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 53, 54, 58, 87, 767, 1616, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 71.0, 8.0, 4.0, 1.0))</td><td>0.44217240367228516</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 62, 772, 1511, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 57.0, 14.0, 1.0, 9.0, 3.0))</td><td>0.5593469111245888</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 86, 826, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 59.0, 23.0, 1.0, 7.0, 3.0))</td><td>0.3618470284010989</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 62, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 35.0, 7.0, 8.0, 3.0))</td><td>0.43576249185762495</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 40, 52, 54, 58, 91, 764, 1639, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 48.0, 8.0, 9.0))</td><td>0.5299805620869207</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 142, 753, 1674, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 34.0, 9.0, 1.0, 6.0, 3.0))</td><td>0.63680200434927</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 36, 53, 54, 58, 69, 774, 1949, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 53.0, 16.0, 4.0, 1.0))</td><td>0.26564135550629503</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 13, 35, 53, 54, 58, 73, 786, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 60.0, 15.0, 4.0, 9.0, 1.0))</td><td>0.6724733000699494</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 148, 753, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 41.0, 5.0, 2.0, 9.0))</td><td>0.2752588937515807</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 753, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 39.0, 14.0, 4.0, 1.0))</td><td>0.40324401001889093</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 84, 760, 1616, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 61.0, 11.0, 8.0, 4.0))</td><td>0.4549047654650197</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 196, 1148, 1557, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 80.0, 15.0, 8.0, 2.0))</td><td>0.47322961980942646</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 62, 758, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 57.0, 7.0, 6.0, 2.0))</td><td>0.4868594029896923</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 163, 756, 1665, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 47.0, 11.0, 9.0, 1.0))</td><td>0.25770440927831906</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 74, 766, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 66.0, 5.0, 1.0, 8.0, 3.0))</td><td>0.5653683410061996</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 146, 982, 1518, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 34.0, 12.0, 6.0, 1.0))</td><td>0.5149729196172783</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(5, 6, 9, 37, 52, 54, 58, 66, 755, 1561, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 40.0, 15.0, 1.0, 4.0))</td><td>0.36546099510290186</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 70, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 36.0, 8.0, 4.0, 3.0))</td><td>0.5235351602392234</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 136, 753, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 53.0, 18.0, 7.0, 1.0))</td><td>0.5435803878456685</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 34.0, 11.0, 4.0, 4.0))</td><td>0.31308114215662863</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 137, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 70.0, 14.0, 3.0, 6.0))</td><td>0.6217205335934164</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 418, 759, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 76.0, 20.0, 6.0, 5.0))</td><td>0.5592620108657627</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 89, 976, 1498, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 61.0, 20.0, 9.0, 1.0))</td><td>0.501375502905639</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 19, 35, 52, 54, 58, 90, 779, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 33.0, 5.0, 4.0, 2.0, 8.0))</td><td>0.4935184228435917</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 65, 763, 1540, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 84.0, 36.0, 9.0, 2.0))</td><td>0.36846698140123507</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 85, 758, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 72.0, 12.0, 2.0, 8.0))</td><td>0.5980645630206526</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 71, 857, 1482, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 37.0, 10.0, 5.0))</td><td>0.35275451355590265</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 646, 767, 1990, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 60.0, 17.0, 9.0, 5.0))</td><td>0.3518135783107344</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 11, 40, 52, 54, 58, 469, 756, 1625, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 42.0, 23.0, 8.0))</td><td>0.35536517741867</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 222, 777, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 52.0, 14.0, 2.0, 9.0, 2.0))</td><td>0.6152280358604781</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 116, 808, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 41.0, 6.0, 6.0))</td><td>0.5061729620113005</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 325, 1029, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 10.0, 1.0, 2.0, 8.0))</td><td>0.32076644808516325</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 64, 911, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 63.0, 18.0, 1.0, 7.0))</td><td>0.47414341282687666</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 52, 54, 58, 65, 765, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 46.0, 18.0, 9.0))</td><td>0.22788627630311356</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 65, 772, 1486, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 56.0, 25.0, 7.0, 3.0))</td><td>0.48321962323726736</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 94, 782, 1578, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 31.0, 12.0, 7.0, 7.0, 2.0))</td><td>0.6011902381258387</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 768, 1482, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 50.0, 14.0, 6.0, 4.0))</td><td>0.47724221153706153</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 263, 805, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 62.0, 17.0, 9.0, 3.0))</td><td>0.6101271920446456</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 38, 52, 54, 58, 62, 772, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 35.0, 13.0, 8.0))</td><td>0.45268165291192236</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 65, 760, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 39.0, 16.0, 8.0, 2.0))</td><td>0.5266699628791917</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 52, 54, 58, 71, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 65.0, 13.0, 1.0, 4.0, 2.0))</td><td>0.4398644126744173</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 80, 765, 1618, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 55.0, 9.0, 1.0, 9.0, 5.0))</td><td>0.6307326007860615</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 74, 763, 1617, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 52.0, 11.0, 8.0))</td><td>0.4999254050502073</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 86, 782, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 41.0, 10.0, 6.0, 5.0))</td><td>0.3232218163345544</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 102, 839, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 45.0, 11.0, 7.0, 2.0))</td><td>0.4399308289108828</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 70, 774, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 55.0, 11.0, 1.0, 9.0, 4.0))</td><td>0.39727592990180605</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 508, 770, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 81.0, 25.0, 9.0, 4.0))</td><td>0.39933330998985067</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 164, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 10.0, 1.0, 6.0, 3.0))</td><td>0.3432806755599075</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 773, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 55.0, 8.0, 1.0, 9.0))</td><td>0.5324820426671799</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 224, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 43.0, 4.0, 1.0, 8.0))</td><td>0.3101086447500446</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 224, 817, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 51.0, 15.0, 9.0, 2.0))</td><td>0.5771198310222512</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 63, 762, 1568, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 44.0, 15.0, 9.0))</td><td>0.32548845840942736</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 55, 58, 67, 754, 1555, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 25.0, 13.0, 2.0, 9.0, 1.0))</td><td>0.5070262453202887</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 95, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 4.0, 38.0, 19.0, 1.0, 3.0))</td><td>0.34532319791117544</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 55, 58, 66, 768, 1505, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 82.0, 18.0, 7.0))</td><td>0.6069058913062213</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 73, 803, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 12.0, 17.0, 2.0, 1.0, 9.0, 4.0))</td><td>0.6746836258980797</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 53, 54, 58, 77, 786, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 81.0, 16.0, 9.0))</td><td>0.18218205189808553</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 55, 58, 234, 1030, 1719, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 47.0, 25.0, 6.0, 5.0))</td><td>0.19841314650060604</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 109, 790, 1524, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2313, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 9.0, 1.0, 9.0))</td><td>0.5541091873318726</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 72, 755, 1608, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 50.0, 9.0, 7.0))</td><td>0.4332644450812855</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 55, 58, 83, 763, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 46.0, 16.0, 1.0, 9.0, 3.0))</td><td>0.5344268329096328</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 142, 864, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 44.0, 9.0, 1.0, 6.0, 5.0))</td><td>0.5229651517860827</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 85, 753, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 63.0, 16.0, 9.0))</td><td>0.4096786287168959</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 231, 758, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 29.0, 12.0, 6.0, 4.0))</td><td>0.4217187810991531</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 57, 58, 432, 771, 1500, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 13.0, 2.0, 9.0, 4.0))</td><td>0.5864445326462764</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 38, 52, 54, 58, 87, 754, 1643, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2308, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 73.0, 12.0, 9.0))</td><td>0.4266124645890414</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 66, 754, 1558, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 69.0, 9.0, 6.0, 2.0))</td><td>0.3918082078723777</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 63, 762, 1487, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 28.0, 27.0, 1.0, 8.0, 4.0))</td><td>0.4199157372076954</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 117, 753, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 59.0, 11.0, 1.0, 9.0, 2.0))</td><td>0.5129805363035437</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 63, 762, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 59.0, 16.0, 7.0))</td><td>0.39753897130595583</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 753, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 39.0, 19.0, 1.0, 9.0))</td><td>0.5899666021545047</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 463, 790, 1721, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 46.0, 7.0, 9.0, 2.0))</td><td>0.4208726721169085</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 87, 754, 1551, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 4.0, 37.0, 20.0, 5.0, 6.0))</td><td>0.7342140992805581</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 88, 757, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 50.0, 10.0, 4.0, 2.0))</td><td>0.33944765783191166</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 55, 58, 65, 756, 1486, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 72.0, 40.0, 9.0))</td><td>0.36377466666801483</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 55, 58, 67, 775, 1495, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 62.0, 25.0, 7.0))</td><td>0.5081907492242459</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 90, 753, 1617, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 81.0, 9.0, 7.0, 7.0))</td><td>0.4468668452933392</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 234, 966, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 56.0, 10.0, 7.0, 2.0))</td><td>0.13370047882627917</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 40, 53, 54, 58, 159, 808, 1525, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 24.0, 19.0, 5.0))</td><td>0.19156240832267724</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 247, 753, 1487, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2270, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 57.0, 17.0, 4.0, 9.0))</td><td>0.7567976684618596</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 96, 754, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 67.0, 21.0, 9.0, 3.0))</td><td>0.4912077095664331</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 757, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 41.0, 21.0, 9.0))</td><td>0.35746517270600964</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 54, 58, 63, 762, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 30.0, 17.0, 9.0))</td><td>0.4144440055401242</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 62, 756, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 78.0, 21.0, 1.0, 7.0, 1.0))</td><td>0.6227020203678988</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 62, 995, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 44.0, 11.0, 9.0, 2.0))</td><td>0.5144117883830663</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 127, 790, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 45.0, 24.0, 2.0, 9.0, 2.0))</td><td>0.5120992777163353</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 38, 52, 54, 59, 65, 963, 1629, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 25.0, 10.0, 8.0))</td><td>0.2875759836465803</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 94, 782, 1535, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 4.0, 74.0, 22.0, 1.0, 9.0, 4.0))</td><td>0.3805171200943057</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 134, 769, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 38.0, 10.0, 9.0))</td><td>0.5465751278340616</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 138, 847, 1502, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 47.0, 14.0, 1.0, 4.0, 7.0, 5.0))</td><td>0.7030539551932753</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 88, 767, 1626, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 40.0, 17.0, 9.0, 4.0))</td><td>0.4947051364620505</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 94, 798, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 45.0, 13.0, 1.0, 6.0))</td><td>0.5022090585618695</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 56, 58, 65, 756, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 6.0, 72.0, 38.0, 7.0))</td><td>0.4672628232728568</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 95, 796, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 61.0, 33.0, 9.0, 2.0))</td><td>0.3977958887535845</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 66, 832, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 77.0, 12.0, 2.0, 9.0, 3.0))</td><td>0.6502809501363315</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 850, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 61.0, 9.0, 7.0, 2.0))</td><td>0.47601831523598975</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 129, 999, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 51.0, 25.0, 7.0, 5.0))</td><td>0.2817030272610388</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 55, 58, 80, 959, 1536, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 38.0, 12.0, 7.0, 4.0))</td><td>0.6539771890149468</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 775, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 34.0, 23.0, 9.0, 1.0))</td><td>0.39675954605993685</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 114, 775, 1639, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 31.0, 13.0, 1.0, 9.0, 1.0))</td><td>0.6647188357151879</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 63, 762, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 34.0, 18.0, 9.0))</td><td>0.41097728516193305</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 53.0, 8.0, 5.0, 2.0))</td><td>0.36942287899876436</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 89, 887, 1751, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 67.0, 25.0, 6.0, 3.0))</td><td>0.41218603899314904</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 68, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 57.0, 8.0, 2.0, 6.0, 3.0))</td><td>0.6049405667762622</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 224, 1002, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 44.0, 12.0, 7.0, 2.0))</td><td>0.6737324707845268</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 54, 58, 75, 822, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 3.0, 97.0, 42.0, 9.0))</td><td>0.29295997517402556</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 35, 52, 54, 58, 83, 757, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 56.0, 10.0, 5.0, 4.0))</td><td>0.37776616555723025</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 83, 850, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 31.0, 22.0, 8.0, 4.0))</td><td>0.502393027044729</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 145, 760, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 61.0, 27.0, 1.0, 9.0, 1.0))</td><td>0.5304042906797728</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 13, 35, 52, 54, 58, 63, 762, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 37.0, 15.0, 2.0, 8.0, 2.0))</td><td>0.5447202284059359</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 62, 757, 1553, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 70.0, 9.0, 9.0, 3.0))</td><td>0.39173366779542074</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 135, 977, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 63.0, 17.0, 7.0, 3.0))</td><td>0.5458692446008375</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 70, 763, 2014, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 57.0, 19.0, 6.0))</td><td>0.872052976650384</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 55, 58, 185, 961, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 77.0, 26.0, 9.0, 4.0))</td><td>0.6211555384668935</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 67, 754, 1545, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 52.0, 12.0, 9.0))</td><td>0.4636785323236695</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 67, 763, 1484, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 63.0, 16.0, 1.0, 4.0, 1.0))</td><td>0.5246416820745112</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 55, 58, 137, 761, 1517, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2265, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 5.0, 83.0, 50.0, 9.0, 2.0))</td><td>0.5321540823507169</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 226, 757, 1527, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 21.0, 9.0, 4.0, 1.0, 6.0, 4.0))</td><td>0.3918424284913748</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 55, 58, 71, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 13.0, 8.0, 1.0))</td><td>0.4762053760043591</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 67, 756, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 47.0, 6.0, 1.0, 5.0))</td><td>0.3018730095338384</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 130, 754, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 67.0, 9.0, 4.0, 1.0))</td><td>0.3786359060867602</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 67, 754, 1499, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 59.0, 15.0, 9.0, 1.0))</td><td>0.5277001038345119</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 134, 761, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 68.0, 22.0, 9.0))</td><td>0.44020108040620376</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 75, 763, 1564, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 48.0, 7.0, 6.0))</td><td>0.39120420912795706</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 362, 802, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 31.0, 25.0, 5.0, 7.0))</td><td>0.17450241753873055</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 189, 758, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 37.0, 9.0, 3.0, 7.0, 6.0))</td><td>0.6624334758198387</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 383, 774, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 82.0, 17.0, 9.0, 3.0))</td><td>0.35430521645131074</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 53, 54, 58, 121, 767, 1751, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 53.0, 5.0, 3.0))</td><td>0.49187083864440145</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 148, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 34.0, 4.0, 1.0, 8.0))</td><td>0.24216374787331296</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 106, 764, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 72.0, 3.0, 7.0, 4.0))</td><td>0.46367793182119554</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 37, 52, 54, 58, 77, 753, 1534, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 75.0, 17.0, 7.0))</td><td>0.5048891709614276</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 246, 755, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 31.0, 6.0, 7.0, 3.0))</td><td>0.43877959377496084</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 65, 810, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 69.0, 14.0, 1.0, 9.0, 3.0))</td><td>0.45874127717352314</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 394, 767, 1806, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 45.0, 12.0, 1.0, 3.0, 7.0))</td><td>0.21633686736799745</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 281, 756, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 47.0, 11.0, 6.0))</td><td>0.6115685266276386</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 37, 52, 55, 58, 83, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 37.0, 12.0, 7.0, 5.0))</td><td>0.5080436747719996</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 59, 79, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 25.0, 13.0, 2.0, 7.0, 3.0))</td><td>0.5495640510758446</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 69, 755, 1491, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 51.0, 23.0, 4.0, 1.0))</td><td>0.33780287275489207</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 776, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 45.0, 21.0, 6.0, 1.0))</td><td>0.3771421284038631</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 36, 53, 54, 58, 109, 1012, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 3.0, 62.0, 11.0, 1.0, 9.0))</td><td>0.3265956227096105</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 86, 870, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 23.0, 15.0, 8.0))</td><td>0.447998963636947</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 174, 950, 1747, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 37.0, 14.0, 6.0, 4.0))</td><td>0.15574843209702366</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 762, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 37.0, 15.0, 8.0, 2.0))</td><td>0.43090056626607764</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 245, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 5.0, 1.0, 9.0))</td><td>0.21897776361115848</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 56, 58, 94, 771, 1592, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 34.0, 10.0, 2.0, 6.0, 4.0))</td><td>0.5591905206232232</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 52, 54, 58, 91, 785, 1573, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 37.0, 30.0, 4.0, 4.0))</td><td>0.4230113110052798</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 35, 52, 54, 58, 91, 797, 1576, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 68.0, 22.0, 9.0, 3.0))</td><td>0.19426752097641392</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 779, 1487, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 6.0, 83.0, 43.0, 1.0, 1.0, 9.0, 2.0))</td><td>0.5423759757734892</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 39.0, 4.0, 3.0, 2.0))</td><td>0.24047988186371033</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 43, 52, 54, 58, 62, 772, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 38.0, 14.0, 1.0, 9.0))</td><td>0.7053195534916803</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 19, 35, 52, 54, 58, 99, 899, 1522, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 77.0, 17.0, 6.0, 8.0))</td><td>0.40282081701138034</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 93, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 47.0, 20.0, 2.0))</td><td>0.3301572650506578</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 4.0, 2.0))</td><td>0.3330458142312754</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 791, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 6.0, 43.0, 10.0, 7.0, 1.0))</td><td>0.39599567416089504</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 588, 759, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 10.0, 13.0, 4.0, 4.0))</td><td>0.22483418861052715</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 139, 788, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 75.0, 16.0, 4.0, 9.0, 1.0))</td><td>0.7257924829379823</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 65, 775, 1523, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 68.0, 20.0, 7.0, 2.0))</td><td>0.3750977865151304</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 76, 768, 1841, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 28.0, 19.0, 1.0, 9.0))</td><td>0.6285207664575017</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 118, 756, 1780, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 35.0, 8.0, 4.0, 2.0))</td><td>0.4126500836236669</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 64, 812, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 40.0, 16.0, 4.0))</td><td>0.34392684342847435</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 40, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 51.0, 38.0, 6.0, 2.0))</td><td>0.3000433020671918</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 52, 54, 58, 274, 755, 1492, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 50.0, 22.0, 2.0, 1.0))</td><td>0.398151649206073</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 55, 58, 64, 761, 1544, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 39.0, 13.0, 2.0, 1.0, 7.0, 2.0))</td><td>0.6981442348922935</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 403, 793, 1553, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 53.0, 18.0, 7.0, 1.0))</td><td>0.2152271765482395</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 35, 52, 54, 58, 74, 755, 1858, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 53.0, 4.0, 9.0, 6.0))</td><td>0.43343927663411896</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 73, 763, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 32.0, 14.0, 9.0, 1.0))</td><td>0.516935403682127</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 784, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 13.0, 7.0, 6.0, 1.0))</td><td>0.438917226514848</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 109, 793, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 46.0, 12.0, 5.0, 2.0))</td><td>0.2878620666905499</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 97, 959, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 33.0, 6.0, 6.0, 4.0))</td><td>0.43244814501107615</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 37, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 5.0, 67.0, 46.0, 7.0, 2.0))</td><td>0.3230696720386945</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 62, 932, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 61.0, 25.0, 2.0, 1.0, 9.0, 3.0))</td><td>0.7958974345340968</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 832, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 68.0, 29.0, 9.0, 2.0))</td><td>0.5417185450875092</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 63, 762, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 37.0, 13.0, 1.0, 5.0, 2.0))</td><td>0.4327022303964118</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 791, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 35.0, 9.0, 5.0))</td><td>0.3928706557175412</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 107, 788, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 44.0, 11.0, 9.0, 1.0))</td><td>0.5323942046667338</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 86, 804, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 61.0, 10.0, 1.0, 4.0, 1.0))</td><td>0.34984170135182746</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 104, 1300, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 72.0, 17.0, 9.0, 2.0))</td><td>0.31775535580814945</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 59, 62, 768, 1529, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 65.0, 20.0, 2.0, 9.0))</td><td>0.6846150569265013</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 819, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 54.0, 14.0, 9.0, 3.0))</td><td>0.6573656424568456</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 106, 811, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 64.0, 14.0, 3.0, 7.0, 4.0))</td><td>0.7639929841214372</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 766, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 65.0, 12.0, 9.0))</td><td>0.44772712837151846</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 210, 792, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 51.0, 16.0, 6.0))</td><td>0.4442046309598451</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 43.0, 16.0, 2.0, 8.0))</td><td>0.5824009037122329</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 53, 54, 58, 66, 874, 1565, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 56.0, 19.0, 7.0))</td><td>0.47746864376266757</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 63, 776, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 34.0, 14.0, 6.0, 1.0))</td><td>0.3898789965399498</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 68, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 11.0, 2.0, 5.0, 6.0))</td><td>0.5539915568763099</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 87, 793, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 61.0, 9.0, 5.0, 2.0))</td><td>0.32098752948138876</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 54, 58, 80, 759, 1604, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 2.0, 55.0, 26.0, 1.0, 9.0))</td><td>0.5610382253169652</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 375, 802, 1710, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 15.0, 8.0, 5.0))</td><td>0.058465155944159186</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 129, 767, 1709, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 12.0, 1.0, 3.0, 7.0))</td><td>0.2705370684037682</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 66, 756, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 53.0, 6.0, 7.0))</td><td>0.43883947189697353</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 130, 756, 1484, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 22.0, 21.0, 7.0, 1.0))</td><td>0.44013583461346567</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 59, 63, 776, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 76.0, 29.0, 7.0, 1.0))</td><td>0.46766687636504417</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 130, 767, 1491, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 40.0, 11.0, 6.0, 1.0))</td><td>0.48391359717229365</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 37, 52, 54, 58, 137, 761, 1517, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 84.0, 45.0, 6.0))</td><td>0.5397781394329231</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 46.0, 13.0, 6.0, 1.0))</td><td>0.35710815990412365</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 64, 789, 1481, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 69.0, 18.0, 7.0, 4.0))</td><td>0.3920886689347733</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 57.0, 13.0, 3.0, 7.0))</td><td>0.6274118743531233</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 63, 763, 1482, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 69.0, 26.0, 7.0, 1.0))</td><td>0.3649530703528291</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 66, 763, 2165, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 61.0, 7.0, 7.0, 3.0))</td><td>0.1300851664708081</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 79, 816, 1587, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 51.0, 12.0, 9.0))</td><td>0.4825416806890145</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 92, 772, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 17.0, 13.0, 7.0, 3.0))</td><td>0.4487622318405795</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 154, 755, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 31.0, 12.0, 3.0, 2.0))</td><td>0.28394765278759326</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 67, 756, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 73.0, 8.0, 5.0, 6.0))</td><td>0.3995305889717539</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 55, 58, 224, 771, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 57.0, 14.0, 8.0, 1.0))</td><td>0.5259306200498667</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 98, 835, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 73.0, 7.0, 2.0, 7.0))</td><td>0.3970556282584116</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 89, 777, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 70.0, 9.0, 7.0, 2.0))</td><td>0.5057829134696442</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 84, 811, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 13.0, 5.0, 1.0))</td><td>0.51295528335694</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 40, 52, 55, 58, 229, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 19.0, 18.0, 5.0, 1.0))</td><td>0.17641780275414376</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 65, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 28.0, 23.0, 8.0))</td><td>0.4463248411654499</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 15.0, 1.0, 7.0, 5.0))</td><td>0.45115726897327624</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 63, 762, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 52.0, 13.0, 8.0, 1.0))</td><td>0.47210090708488467</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 66, 910, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 52.0, 10.0, 5.0, 4.0))</td><td>0.3350596679597668</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 66, 763, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 22.0, 14.0, 3.0, 2.0))</td><td>0.43695878993005066</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 135, 753, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 23.0, 4.0, 6.0, 3.0))</td><td>0.38172822659230715</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 113, 753, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 43.0, 7.0, 7.0, 2.0))</td><td>0.4382978198183998</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 53, 54, 58, 68, 767, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 58.0, 7.0, 5.0))</td><td>0.5219764539877956</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 36, 52, 54, 58, 63, 782, 1606, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 55.0, 14.0, 7.0, 1.0))</td><td>0.4157047072511968</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 94, 771, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 57.0, 14.0, 5.0, 2.0))</td><td>0.464213854951009</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 83, 973, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 62.0, 22.0, 1.0, 6.0, 4.0))</td><td>0.48615232142830234</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 11, 36, 52, 54, 58, 92, 764, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 55.0, 16.0, 7.0, 2.0))</td><td>0.5169670506154702</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 53, 54, 58, 66, 768, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 16.0, 19.0, 6.0))</td><td>0.4470895864905031</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 174, 1166, 1512, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 34.0, 11.0, 5.0, 4.0))</td><td>0.16651576070830687</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 210, 764, 1744, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 32.0, 23.0, 9.0))</td><td>0.5583641450083392</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 783, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 6.0, 1.0, 4.0))</td><td>0.37787193540074127</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 149, 769, 1543, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 23.0, 12.0, 6.0, 3.0))</td><td>0.32016517149808926</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 74, 826, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 40.0, 5.0, 4.0, 2.0))</td><td>0.4720377438758725</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 77, 845, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 4.0, 85.0, 29.0, 1.0, 9.0))</td><td>0.46650867629287657</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 74, 765, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 53.0, 9.0, 1.0, 8.0, 5.0))</td><td>0.5728855152963284</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 127, 825, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 39.0, 12.0, 4.0))</td><td>0.3353470865890682</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 214, 767, 1873, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 22.0, 16.0, 6.0, 7.0))</td><td>0.12342736354346712</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 49.0, 8.0, 1.0, 4.0, 2.0))</td><td>0.2962284927205031</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 72, 832, 1555, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 37.0, 11.0, 1.0, 8.0, 3.0))</td><td>0.5420132191485286</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 40, 52, 54, 58, 295, 805, 1531, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 56.0, 8.0, 1.0, 2.0, 8.0, 4.0))</td><td>0.5232718247164447</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 71, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 10.0, 6.0, 2.0))</td><td>0.46121672504808175</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 760, 1515, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 43.0, 12.0, 7.0, 1.0))</td><td>0.5865610979769991</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 380, 761, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 21.0, 12.0, 6.0, 1.0))</td><td>0.44128468585409814</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 55, 58, 65, 759, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 81.0, 49.0, 7.0, 4.0))</td><td>0.30961681569352995</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 41, 52, 54, 58, 337, 839, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 89.0, 15.0, 7.0, 3.0))</td><td>0.43585321186735543</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 65, 761, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 47.0, 9.0, 1.0, 5.0))</td><td>0.3609042091178215</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 83, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 39.0, 12.0, 4.0, 2.0))</td><td>0.40374560191334996</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 57, 58, 88, 755, 1482, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 53.0, 10.0, 4.0, 4.0))</td><td>0.352649047589406</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 57, 58, 63, 769, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 63.0, 25.0, 6.0))</td><td>0.3894581944887644</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 62, 1114, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2280, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 68.0, 25.0, 9.0, 4.0))</td><td>0.6110784630911062</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 67, 755, 1514, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 40.0, 8.0, 3.0, 2.0))</td><td>0.4002625030117353</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 35, 52, 54, 59, 390, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 62.0, 21.0, 9.0, 5.0))</td><td>0.4083007943427424</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 62, 1002, 1527, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 22.0, 8.0, 7.0, 2.0))</td><td>0.5724829531710824</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 55, 58, 81, 768, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 23.0, 19.0, 8.0))</td><td>0.4677200092676684</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 18, 35, 52, 54, 58, 63, 753, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 78.0, 30.0, 8.0))</td><td>0.46450204934165673</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 95, 767, 1607, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 48.0, 19.0, 4.0, 4.0))</td><td>0.3111111948321448</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 89, 767, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 68.0, 13.0, 3.0, 9.0))</td><td>0.6487159971756394</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 48.0, 12.0, 1.0, 6.0))</td><td>0.3961836378653525</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 55, 58, 69, 763, 1492, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 17.0, 20.0, 2.0, 4.0))</td><td>0.3367125959495544</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 70, 798, 1633, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 50.0, 21.0, 9.0, 2.0))</td><td>0.4420967960979181</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 10, 37, 53, 54, 58, 99, 753, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 29.0, 13.0, 7.0, 3.0))</td><td>0.2571632629921756</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 66, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 42.0, 4.0, 3.0, 2.0))</td><td>0.3389757981397037</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 53, 54, 58, 65, 765, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 25.0, 16.0, 8.0))</td><td>0.1445576117817376</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 78, 753, 1651, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 4.0, 78.0, 17.0, 1.0, 9.0))</td><td>0.4455980777897968</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 80, 799, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 63.0, 15.0, 2.0, 8.0, 4.0))</td><td>0.5730955960369005</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 10, 35, 53, 54, 58, 99, 961, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 64.0, 8.0, 9.0, 3.0))</td><td>0.45550577161337996</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 131, 770, 1765, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 4.0, 43.0, 17.0, 4.0, 9.0))</td><td>0.592956126326804</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 123, 774, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 1.0, 72.0, 15.0, 3.0, 9.0, 3.0))</td><td>0.5843809076764386</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 65.0, 33.0, 5.0, 1.0))</td><td>0.31865554606736124</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 197, 798, 1803, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 48.0, 7.0, 1.0, 6.0, 5.0))</td><td>0.516923487562073</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 53, 54, 58, 388, 987, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 4.0, 76.0, 24.0, 1.0, 9.0, 5.0))</td><td>0.3957470894175252</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 39, 52, 56, 58, 82, 935, 1497, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 64.0, 24.0, 1.0, 7.0, 3.0))</td><td>0.3880262849372963</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 147, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 24.0, 11.0, 3.0, 2.0))</td><td>0.2970037958772549</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 39.0, 9.0, 3.0, 2.0))</td><td>0.3086758239353416</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 102, 767, 1803, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 31.0, 8.0, 4.0, 9.0))</td><td>0.7443324665748963</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 95, 864, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 4.0, 20.0, 17.0, 8.0, 2.0))</td><td>0.4328996306135816</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 56, 58, 66, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 65.0, 37.0, 9.0, 5.0))</td><td>0.49327738015345335</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 335, 761, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 52.0, 31.0, 8.0))</td><td>0.42568291332430636</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 62, 824, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 92.0, 22.0, 2.0, 9.0, 1.0))</td><td>0.6728332622005713</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 95, 767, 1540, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 55.0, 18.0, 9.0))</td><td>0.4069588938274318</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 55, 58, 93, 754, 1525, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 56.0, 32.0, 6.0))</td><td>0.5150075716547744</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 52, 54, 58, 66, 1208, 1522, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 74.0, 23.0, 3.0, 9.0, 1.0))</td><td>0.05684286750600648</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 65, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 45.0, 18.0, 6.0, 4.0))</td><td>0.2870476999858153</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 287, 758, 1609, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 30.0, 15.0, 4.0, 9.0, 4.0))</td><td>0.6317638606590832</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 76, 754, 1488, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 38.0, 9.0, 9.0))</td><td>0.6020934374192533</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 77, 798, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 24.0, 16.0, 6.0))</td><td>0.4310870874502372</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 758, 1529, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 52.0, 13.0, 9.0))</td><td>0.5773723013899492</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 66, 769, 1546, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 51.0, 18.0, 9.0))</td><td>0.4561220390060513</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 24, 35, 52, 55, 58, 70, 771, 1894, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 52.0, 9.0, 7.0, 2.0))</td><td>0.5383403480617419</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 40, 52, 54, 58, 100, 759, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 45.0, 11.0, 1.0, 9.0))</td><td>0.475295863521345</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 186, 773, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 7.0, 8.0, 3.0))</td><td>0.4279630658073857</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 77, 753, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 40.0, 13.0, 5.0, 3.0))</td><td>0.3931532351491857</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 149, 754, 1497, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 58.0, 14.0, 9.0, 6.0))</td><td>0.44967040049806045</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 66, 825, 1485, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 82.0, 28.0, 9.0))</td><td>0.4994707439785534</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 67, 754, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 39.0, 12.0, 9.0, 1.0))</td><td>0.5014521170651254</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 196, 1148, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 54.0, 4.0, 7.0, 6.0))</td><td>0.39077288574791175</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 55, 58, 64, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 33.0, 7.0, 3.0, 2.0))</td><td>0.3055301093264299</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 52, 54, 58, 64, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 13.0, 6.0))</td><td>0.4358113050183041</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 70, 754, 1499, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 1.0, 45.0, 18.0, 9.0, 3.0))</td><td>0.5493849693302909</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 107, 781, 1619, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 22.0, 23.0, 8.0))</td><td>0.4453802681068516</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 104, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 47.0, 11.0, 4.0))</td><td>0.39100396760088746</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 11, 35, 52, 54, 58, 90, 754, 1528, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 66.0, 25.0, 7.0, 2.0))</td><td>0.5325028539881009</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 85, 819, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 50.0, 4.0, 9.0, 1.0))</td><td>0.5634012311382048</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 36, 53, 54, 58, 183, 758, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 29.0, 8.0, 6.0))</td><td>0.0038502207111076325</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 53.0, 15.0, 5.0, 1.0))</td><td>0.37593279379276345</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 58, 194, 878, 1522, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 6.0, 75.0, 28.0, 9.0, 3.0))</td><td>0.5419285829607133</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 791, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 42.0, 8.0, 5.0))</td><td>0.29528318188995617</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 73, 763, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 46.0, 21.0, 6.0, 8.0, 3.0))</td><td>0.7441716646699414</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 54, 58, 80, 764, 1554, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 42.0, 8.0, 2.0, 9.0, 3.0))</td><td>0.6291788025272267</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 98, 795, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 66.0, 10.0, 6.0, 1.0))</td><td>0.5151519280354544</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 64, 776, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 69.0, 6.0, 5.0, 1.0))</td><td>0.443660756352065</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 54, 58, 65, 754, 1643, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 62.0, 17.0, 1.0, 9.0, 1.0))</td><td>0.2955279114522038</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 36, 52, 55, 59, 142, 828, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2272, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 25.0, 16.0, 9.0))</td><td>0.645511980207143</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 62, 760, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 64.0, 11.0, 9.0))</td><td>0.5566166625470893</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 148, 1063, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 7.0, 2.0, 4.0))</td><td>0.141570421743417</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 53, 54, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 49.0, 10.0, 7.0, 3.0))</td><td>0.3617343525172901</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 64, 755, 1557, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 11.0, 4.0, 4.0))</td><td>0.3753752575962695</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 55, 58, 148, 754, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 37.0, 11.0, 4.0, 1.0))</td><td>0.38119212349051745</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 38, 53, 54, 58, 183, 758, 1796, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 10.0, 6.0, 1.0, 6.0))</td><td>-0.07085899234720677</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 79, 767, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 35.0, 9.0, 1.0, 2.0, 1.0))</td><td>0.5086274088290877</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 99, 753, 1628, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 25.0, 10.0, 8.0))</td><td>0.36105153676240864</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 41, 52, 54, 58, 111, 796, 1526, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 56.0, 35.0, 7.0, 1.0))</td><td>0.45729077266231466</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 414, 1530, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 19.0, 8.0, 8.0))</td><td>0.5946828710397873</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 304, 770, 1484, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 49.0, 11.0, 6.0, 3.0))</td><td>0.4632054651930009</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 96, 793, 1526, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 56.0, 17.0, 1.0, 9.0, 6.0))</td><td>0.5178581003214455</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 53, 54, 58, 62, 895, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 57.0, 21.0, 5.0))</td><td>0.5496912966651062</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 13, 35, 52, 54, 58, 105, 759, 1521, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 95.0, 26.0, 7.0))</td><td>0.47466911616385127</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 55, 58, 129, 827, 1991, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 14.0, 4.0, 7.0))</td><td>0.1757985344762691</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 75, 898, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 25.0, 17.0, 8.0, 2.0))</td><td>0.39135269364887604</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 754, 1488, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 66.0, 14.0, 9.0, 1.0))</td><td>0.5665452315540727</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 106, 755, 1585, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 17.0, 14.0, 1.0, 6.0, 2.0))</td><td>0.5182972664877341</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 139, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 62.0, 8.0, 7.0, 2.0))</td><td>0.5060924445162001</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 74, 758, 1641, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 45.0, 17.0, 9.0))</td><td>0.5209387935355109</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 113, 811, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2272, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 26.0, 16.0, 3.0, 8.0, 2.0))</td><td>0.6832149927675923</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 62, 760, 1651, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 45.0, 5.0, 5.0, 1.0))</td><td>0.5509121916391337</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 162, 766, 1753, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 58.0, 7.0, 1.0, 3.0, 5.0))</td><td>0.5398762014674534</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 93, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 12.0, 3.0))</td><td>0.2961833007075563</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 67, 772, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 53.0, 14.0, 9.0))</td><td>0.599025072141327</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 52, 54, 58, 128, 902, 1740, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 26.0, 21.0, 1.0, 8.0, 1.0))</td><td>0.09119721795510422</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(5, 7, 9, 43, 52, 54, 58, 263, 758, 1880, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 39.0, 27.0, 1.0, 9.0, 1.0))</td><td>0.7585314227982445</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 555, 834, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2266, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 27.0, 23.0, 8.0, 1.0))</td><td>0.4791682245047027</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 68, 818, 1743, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 75.0, 16.0, 5.0))</td><td>0.6939479448531908</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 54, 58, 69, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 19.0, 16.0, 3.0))</td><td>0.34726134892392196</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 326, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 1.0, 21.0, 2.0, 2.0))</td><td>0.3492546131495866</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 16, 36, 53, 54, 58, 138, 845, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 60.0, 18.0, 3.0, 1.0))</td><td>0.3319444377309123</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 149, 763, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 22.0, 12.0, 6.0, 3.0))</td><td>0.389083435798545</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 68, 779, 1626, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 63.0, 18.0, 4.0, 1.0, 4.0, 4.0))</td><td>0.6548041695855658</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 160, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2270, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 47.0, 10.0, 4.0, 3.0))</td><td>0.3215627619223204</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 200, 919, 1564, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 54.0, 17.0, 7.0, 2.0))</td><td>0.5067893800391566</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 101, 830, 1871, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 45.0, 18.0, 6.0, 2.0))</td><td>0.635402638507777</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 143, 778, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 57.0, 7.0, 4.0, 5.0))</td><td>0.2799282505014868</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 89, 799, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 70.0, 23.0, 5.0, 6.0))</td><td>0.4383577877668074</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 77, 753, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 55.0, 13.0, 1.0, 6.0))</td><td>0.4606678418194047</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 66, 759, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 42.0, 9.0, 9.0, 6.0))</td><td>0.41394080621332663</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 309, 989, 1806, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 36.0, 20.0, 9.0))</td><td>0.20189759554221606</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 38, 52, 54, 58, 104, 753, 1612, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 71.0, 16.0, 6.0, 4.0))</td><td>0.4126263032699512</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 55, 58, 63, 755, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 56.0, 32.0, 4.0, 1.0))</td><td>0.3420238993131625</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 53, 54, 58, 68, 756, 1548, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 61.0, 12.0, 9.0, 3.0))</td><td>0.5097418997786998</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 46.0, 10.0, 2.0, 5.0, 1.0))</td><td>0.46652575236901495</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 40, 52, 54, 58, 87, 753, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 8.0, 5.0, 1.0))</td><td>0.3954271123422852</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 46.0, 9.0, 4.0, 1.0))</td><td>0.3565297364149951</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 39, 52, 54, 58, 145, 794, 1503, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 9.0, 4.0, 5.0))</td><td>0.2905588541171152</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 81, 773, 1568, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 87.0, 35.0, 9.0, 1.0))</td><td>0.3409474221872153</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 78, 753, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 2.0, 102.0, 34.0, 7.0, 5.0))</td><td>0.47283336494407696</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 97, 754, 1646, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 46.0, 17.0, 9.0, 1.0))</td><td>0.5058842115994928</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 94, 753, 1532, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 57.0, 6.0, 1.0, 9.0, 2.0))</td><td>0.44369464506430945</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 54, 58, 62, 777, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 5.0, 69.0, 21.0, 2.0, 9.0))</td><td>0.7266002556266093</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 54, 58, 75, 758, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 36.0, 9.0, 5.0, 6.0))</td><td>0.13466962337323768</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 77, 885, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 27.0, 16.0, 1.0, 8.0))</td><td>0.6471660097959321</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 63, 777, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 30.0, 12.0, 8.0, 2.0))</td><td>0.4666274661526697</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 78, 754, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 91.0, 11.0, 1.0, 6.0, 4.0))</td><td>0.5065136503392239</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 55, 58, 65, 833, 1588, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 72.0, 11.0, 6.0, 3.0))</td><td>0.49110107644902995</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 53, 54, 58, 65, 761, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 48.0, 14.0, 3.0, 5.0))</td><td>0.24235606825012912</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 66, 754, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 66.0, 10.0, 9.0, 4.0))</td><td>0.503027790149992</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 54, 58, 73, 910, 1578, 2239, 2243, 2247, 2251, 2254, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 47.0, 17.0, 8.0, 5.0))</td><td>0.3876717471153184</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 24.0, 14.0, 4.0, 1.0))</td><td>0.27730970175803643</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 62, 768, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 28.0, 30.0, 7.0, 1.0))</td><td>0.6058060060369346</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 83, 868, 1690, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 45.0, 13.0, 8.0, 1.0))</td><td>0.5629367664865254</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 52, 54, 58, 85, 778, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 33.0, 7.0, 7.0, 1.0))</td><td>0.4168517062597744</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 74, 766, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 32.0, 7.0, 8.0))</td><td>0.4103758183483085</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 113, 775, 1496, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 51.0, 14.0, 9.0))</td><td>0.4606336202562616</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 1002, 1712, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2265, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 58.0, 17.0, 8.0, 4.0))</td><td>0.7891706513102545</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 756, 1494, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 46.0, 13.0, 8.0, 1.0))</td><td>0.4332330520397724</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 65, 932, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 45.0, 16.0, 3.0, 9.0, 2.0))</td><td>0.6622191927807854</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 42, 52, 54, 58, 174, 1041, 1719, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 2.0, 18.0, 4.0, 2.0))</td><td>0.31322564713820766</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 66, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 10.0, 6.0))</td><td>0.4605725697469614</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 63, 982, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 48.0, 40.0, 6.0, 1.0))</td><td>0.35292008901980043</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 70, 754, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 49.0, 25.0, 6.0))</td><td>0.5272149998026563</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 83, 773, 1562, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 18.0, 30.0, 5.0, 5.0))</td><td>0.5064506582404033</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 215, 867, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 34.0, 9.0, 6.0))</td><td>0.4842793773160605</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 765, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 53.0, 8.0, 1.0, 5.0))</td><td>0.5684399498923773</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 57, 58, 64, 1176, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 64.0, 14.0, 7.0))</td><td>0.42151011733548865</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 54, 58, 94, 754, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 1.0, 37.0, 20.0, 9.0, 1.0))</td><td>0.45468484513774016</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 68, 792, 1494, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 64.0, 14.0, 5.0, 1.0))</td><td>0.569888063608629</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 161, 780, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 3.0, 2.0))</td><td>0.3015862923395183</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 255, 1178, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 63.0, 9.0, 6.0, 1.0))</td><td>0.5812332501679669</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 88, 777, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 39.0, 10.0, 9.0, 5.0))</td><td>0.49218049020848653</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 64, 773, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 62.0, 21.0, 8.0))</td><td>0.49790169734601525</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 67, 759, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 76.0, 12.0, 9.0, 3.0))</td><td>0.503008142017501</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 62, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 51.0, 19.0, 9.0))</td><td>0.5275968826473193</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 56, 58, 80, 771, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 26.0, 10.0, 1.0, 1.0, 9.0))</td><td>0.6723782935753818</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 53, 54, 58, 68, 786, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 88.0, 12.0, 5.0, 2.0))</td><td>0.21492191556882984</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 67, 834, 1489, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 27.0, 19.0, 6.0))</td><td>0.48775649909948937</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 85, 755, 1590, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 44.0, 15.0, 8.0, 2.0))</td><td>0.4488221582905743</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 37, 52, 54, 58, 105, 761, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 27.0, 13.0, 8.0))</td><td>0.4804179858750661</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 96, 840, 1493, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 37.0, 2.0, 4.0, 1.0))</td><td>0.47548056425291024</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 62, 757, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 57.0, 12.0, 5.0, 1.0))</td><td>0.44781984794185603</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 92, 775, 1595, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 10.0, 5.0, 6.0, 1.0))</td><td>0.45940620724182857</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 62, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 32.0, 18.0, 9.0, 2.0))</td><td>0.4936141963526422</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 71.0, 12.0, 5.0, 2.0))</td><td>0.4488790158611313</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 97, 753, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 61.0, 7.0, 3.0, 5.0))</td><td>0.4111118146174487</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 75, 759, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 55.0, 18.0, 8.0, 3.0))</td><td>0.4739819325925695</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 85, 842, 1500, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 40.0, 8.0, 8.0, 2.0))</td><td>0.578791101578059</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 146, 790, 1706, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 5.0, 59.0, 35.0, 1.0, 1.0, 8.0, 7.0))</td><td>0.5730244213088527</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 267, 759, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 70.0, 13.0, 9.0, 1.0))</td><td>0.44384897955668356</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 16, 35, 52, 54, 58, 80, 754, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 74.0, 20.0, 1.0, 9.0, 1.0))</td><td>0.5889852982401871</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 161, 780, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 26.0, 9.0, 8.0, 3.0))</td><td>0.41526816231047764</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 137, 775, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 11.0, 6.0))</td><td>0.5085929032418037</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 66, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 38.0, 13.0, 7.0))</td><td>0.4860639562995077</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 19.0, 6.0))</td><td>0.3366660609338564</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 191, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 38.0, 14.0, 1.0, 9.0, 4.0))</td><td>0.4798200661226778</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 84, 811, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 67.0, 14.0, 3.0, 9.0, 4.0))</td><td>0.6223318749853471</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 66, 778, 1574, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 20.0, 22.0, 1.0, 8.0, 6.0))</td><td>0.4469233825232206</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 55, 58, 128, 897, 1542, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 40.0, 13.0, 1.0, 7.0, 1.0))</td><td>0.4441297928123564</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 62, 772, 1677, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 54.0, 9.0, 4.0, 1.0))</td><td>0.44079379310575934</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 38.0, 18.0, 2.0, 6.0))</td><td>0.44926541621002786</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 178, 1511, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 5.0, 58.0, 24.0, 9.0))</td><td>0.4169259766736698</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 68, 753, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2269, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 24.0, 26.0, 1.0, 8.0, 1.0))</td><td>0.5190266396360106</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 274, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 52.0, 16.0, 1.0, 2.0, 6.0, 1.0))</td><td>0.5900105173008461</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 93, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 42.0, 4.0, 6.0))</td><td>0.3884000934033597</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 118, 799, 1534, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 77.0, 33.0, 8.0, 2.0))</td><td>0.40036040144247825</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 73, 815, 1591, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 62.0, 26.0, 1.0, 9.0, 1.0, 9.0, 7.0))</td><td>0.8932523121341697</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 773, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 67.0, 16.0, 9.0, 2.0))</td><td>0.49652221586036716</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 41, 52, 54, 58, 92, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 49.0, 22.0, 7.0))</td><td>0.491437128578763</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 63, 762, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 11.0, 4.0, 1.0))</td><td>0.44266063795920996</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 66, 801, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 43.0, 13.0, 9.0))</td><td>0.4006174706384096</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 91, 764, 1520, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 17.0, 9.0, 4.0))</td><td>0.43742153769307035</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 170, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 43.0, 7.0, 8.0, 2.0))</td><td>0.3982701117425878</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 53, 54, 58, 99, 754, 1532, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 20.0, 13.0, 8.0))</td><td>0.11460723300575287</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 36, 52, 54, 58, 133, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 47.0, 24.0, 7.0, 1.0))</td><td>0.5601738140332215</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 93, 758, 1549, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 65.0, 23.0, 8.0))</td><td>0.5201766749523712</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 756, 1485, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 48.0, 41.0, 9.0, 1.0))</td><td>0.4847876769599403</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 55, 58, 167, 857, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 43.0, 25.0, 4.0, 2.0))</td><td>0.26082714818259295</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 17, 36, 53, 54, 58, 63, 784, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 48.0, 22.0, 8.0, 2.0))</td><td>0.3532819761931362</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 74, 815, 1562, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 30.0, 7.0, 8.0, 2.0))</td><td>0.39593178934551415</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 81, 768, 1767, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 27.0, 16.0, 6.0))</td><td>0.45018861388714276</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 35, 52, 54, 58, 102, 754, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 75.0, 24.0, 1.0, 9.0, 3.0))</td><td>0.5697002774124691</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 91, 771, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 67.0, 18.0, 1.0, 9.0, 1.0))</td><td>0.596720796848351</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 368, 851, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 23.0, 9.0, 8.0))</td><td>0.5108205625750125</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 65, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 69.0, 19.0, 7.0, 2.0))</td><td>0.41813068042992485</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 90, 1073, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 47.0, 7.0, 5.0))</td><td>0.46416170043519955</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 55, 58, 89, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 53.0, 10.0, 1.0, 9.0))</td><td>0.49418993123648236</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 92, 765, 1579, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 51.0, 22.0, 6.0, 1.0))</td><td>0.4603462729577086</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 644, 999, 1611, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 46.0, 4.0, 6.0, 5.0))</td><td>0.07430249688387464</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 65, 761, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 23.0, 13.0, 7.0))</td><td>0.3088569455500879</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 77, 773, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 47.0, 10.0, 1.0, 6.0, 6.0))</td><td>0.3811102820422535</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 52, 54, 58, 69, 769, 1512, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 2.0, 20.0, 9.0, 1.0))</td><td>0.4231415227128722</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 53, 54, 58, 330, 931, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 34.0, 9.0, 9.0))</td><td>0.23714041346725243</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 9.0, 5.0))</td><td>0.27844896774495925</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 10, 36, 52, 54, 58, 211, 756, 1511, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 43.0, 20.0, 2.0, 9.0))</td><td>0.5502500745151035</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 239, 801, 1692, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 63.0, 10.0, 5.0, 2.0))</td><td>0.4316277264619956</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 62, 772, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 40.0, 12.0, 9.0))</td><td>0.4351796791693063</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 54, 58, 112, 753, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 48.0, 9.0, 9.0, 2.0))</td><td>0.4713157478809139</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 53, 54, 58, 99, 774, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 19.0, 7.0, 8.0, 3.0))</td><td>-0.005079381354845591</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 810, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 25.0, 21.0, 9.0, 1.0))</td><td>0.4874047685252522</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 137, 777, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 10.0, 1.0, 5.0, 5.0))</td><td>0.4509949908618473</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 63, 755, 1568, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 13.0, 7.0, 3.0, 2.0))</td><td>0.144642734610205</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 96, 781, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 76.0, 25.0, 9.0, 1.0))</td><td>0.5006141203003888</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 119, 916, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 59.0, 14.0, 5.0))</td><td>0.3673291370229643</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 24, 36, 53, 54, 58, 70, 850, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 39.0, 12.0, 4.0, 4.0))</td><td>0.3390337326604843</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 36, 52, 54, 58, 63, 774, 1522, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 64.0, 22.0, 8.0, 1.0))</td><td>0.3411083726753153</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 36, 52, 54, 58, 69, 757, 1481, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 19.0, 30.0, 5.0, 2.0))</td><td>0.3979841047164755</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 142, 755, 1563, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 2.0, 15.0, 8.0, 2.0))</td><td>0.45991436240350075</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 58, 67, 756, 1685, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 49.0, 17.0, 7.0, 3.0))</td><td>0.49019388436680167</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 108, 774, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 62.0, 4.0, 9.0, 2.0))</td><td>0.3871313676216672</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 74, 827, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 46.0, 4.0, 6.0, 3.0))</td><td>0.47509498079832596</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 35, 52, 55, 58, 63, 754, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 62.0, 12.0, 6.0, 1.0))</td><td>0.3797618647275127</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 42, 52, 54, 58, 91, 797, 1489, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 35.0, 26.0, 9.0, 3.0))</td><td>0.6368869072620977</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 35, 53, 54, 58, 64, 761, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 5.0, 7.0, 3.0))</td><td>0.3333706126625667</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 67, 754, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 16.0, 5.0, 1.0))</td><td>0.44052024944675866</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 36, 52, 55, 58, 116, 783, 1484, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 51.0, 25.0, 9.0))</td><td>0.4883266266131713</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 77, 941, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 69.0, 7.0, 9.0))</td><td>0.42866716385009207</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 62, 788, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 26.0, 12.0, 7.0, 1.0))</td><td>0.684669701061638</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 13, 38, 53, 54, 58, 79, 864, 1554, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 1.0, 47.0, 10.0, 1.0, 9.0, 2.0))</td><td>0.4234082655785796</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 67, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 33.0, 8.0, 4.0, 1.0))</td><td>0.3509915678111103</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 158, 759, 1506, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 64.0, 11.0, 7.0, 1.0))</td><td>0.5244632615075748</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 38, 52, 54, 59, 62, 756, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 31.0, 19.0, 3.0, 6.0))</td><td>0.552015878404113</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 53, 54, 58, 118, 779, 1579, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 58.0, 5.0, 6.0, 2.0))</td><td>0.4314049982720582</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 95, 774, 1607, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 32.0, 19.0, 8.0, 1.0))</td><td>0.2938255218240011</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 110, 790, 1614, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 63.0, 12.0, 8.0, 1.0))</td><td>0.3703235222774195</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 62, 772, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 59.0, 9.0, 3.0, 7.0))</td><td>0.7213703154146927</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 81, 768, 1511, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 24.0, 20.0, 8.0, 3.0))</td><td>0.4543533097791921</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 68, 754, 1700, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 47.0, 23.0, 9.0))</td><td>0.47132883033351863</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 55, 58, 75, 819, 1632, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 21.0, 20.0, 1.0, 8.0))</td><td>0.550446722471569</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 77, 798, 1740, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 47.0, 13.0, 1.0, 7.0))</td><td>0.5413261185148517</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 165, 915, 1513, 2239, 2243, 2247, 2251, 2253, 2258, 2262, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 72.0, 27.0, 7.0, 3.0))</td><td>0.4151886099398936</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 63, 762, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 8.0, 4.0, 2.0))</td><td>0.47397688160219714</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 53, 54, 58, 107, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 32.0, 8.0, 4.0, 4.0))</td><td>0.3620239078816743</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 226, 870, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 35.0, 18.0, 1.0, 9.0))</td><td>0.49153871747122135</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 80, 764, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 5.0, 45.0, 17.0, 3.0, 9.0, 2.0))</td><td>0.7317131966932886</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 99, 759, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 23.0, 20.0, 1.0, 8.0, 3.0))</td><td>0.4462808597045983</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 190, 820, 1684, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 22.0, 19.0, 6.0, 4.0))</td><td>0.5105910261865086</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 65, 763, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 36.0, 6.0, 4.0, 2.0))</td><td>0.3639578585841284</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 17, 36, 53, 54, 58, 106, 779, 1608, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 58.0, 3.0, 6.0, 4.0))</td><td>0.4067907980902132</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 53, 54, 58, 99, 1014, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 19.0, 11.0, 4.0, 3.0))</td><td>0.3665227318263936</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 64, 812, 1527, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 38.0, 6.0, 6.0, 2.0))</td><td>0.32825809266887646</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 53, 54, 58, 187, 754, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 7.0, 5.0))</td><td>0.5746787209135704</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 59, 62, 760, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 44.0, 14.0, 1.0, 9.0))</td><td>0.6191405581068921</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 62, 781, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 11.0, 7.0, 2.0))</td><td>0.5016910077684852</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 36, 52, 54, 58, 63, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 63.0, 17.0, 4.0, 2.0))</td><td>0.29454636480385815</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 93, 760, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 43.0, 8.0, 1.0, 7.0, 3.0))</td><td>0.5270511991678457</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 42, 52, 55, 58, 150, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 11.0, 13.0, 3.0, 3.0))</td><td>0.3552542523929211</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 150, 772, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 9.0, 9.0, 7.0))</td><td>0.4269682176502088</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 105, 763, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 47.0, 14.0, 9.0, 1.0))</td><td>0.4015133395079627</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 52, 54, 58, 62, 775, 1481, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 28.0, 11.0, 5.0, 3.0))</td><td>0.48549147009713534</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 64, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 39.0, 5.0, 2.0))</td><td>0.324124493479792</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 108, 773, 1487, 2239, 2243, 2247, 2251, 2256, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 26.0, 16.0, 1.0, 8.0, 3.0))</td><td>0.4678628555502993</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 55, 58, 92, 817, 1571, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 26.0, 21.0, 6.0, 1.0))</td><td>0.48196793022597306</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 76, 889, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 24.0, 9.0, 1.0, 6.0))</td><td>0.5875546054803815</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 56, 58, 433, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 51.0, 18.0, 4.0, 4.0))</td><td>0.22795369778560773</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 73, 786, 1763, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 26.0, 23.0, 1.0, 8.0, 1.0))</td><td>0.5327994243551198</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 53, 54, 58, 106, 763, 1542, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 45.0, 6.0, 4.0))</td><td>0.5213873642752693</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 17, 35, 52, 54, 58, 291, 753, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 70.0, 8.0, 3.0, 7.0))</td><td>0.4808811291171772</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 97, 757, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 53.0, 4.0, 2.0, 4.0))</td><td>0.33506780624611887</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 761, 1525, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 37.0, 13.0, 9.0))</td><td>0.43560616790667506</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 130, 847, 1544, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 13.0, 1.0, 8.0, 4.0))</td><td>0.5273876670901224</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 17, 36, 52, 55, 58, 95, 755, 1558, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 31.0, 11.0, 3.0, 1.0))</td><td>0.30561483794268307</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 52, 55, 58, 83, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 66.0, 6.0, 2.0, 7.0))</td><td>0.4237773491170048</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 758, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 45.0, 11.0, 8.0, 2.0))</td><td>0.559812526046173</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 110, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 42.0, 27.0, 2.0, 3.0))</td><td>0.25318105613416975</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 73, 979, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 27.0, 16.0, 8.0))</td><td>0.4116290103123988</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 35, 52, 54, 58, 71, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 35.0, 13.0, 2.0, 9.0, 3.0))</td><td>0.6014641470875457</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 40, 53, 54, 58, 91, 797, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 5.0, 45.0, 28.0, 9.0, 2.0))</td><td>0.4754338384183618</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 36, 52, 54, 58, 100, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 36.0, 12.0, 8.0))</td><td>0.364275039214928</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 70, 771, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 50.0, 9.0, 4.0, 8.0))</td><td>0.3872770373788801</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 73, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 46.0, 18.0, 2.0, 6.0))</td><td>0.5632883450051287</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 474, 779, 1510, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 4.0, 4.0, 5.0))</td><td>0.26533471016902144</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 4.0, 46.0, 43.0, 3.0, 2.0))</td><td>0.384781273347365</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 136, 773, 1914, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 89.0, 27.0, 2.0, 7.0, 3.0))</td><td>0.7031063485286051</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 71, 777, 1581, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 25.0, 4.0, 1.0, 6.0, 1.0))</td><td>0.4499090971756395</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 287, 755, 1564, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 10.0, 6.0, 6.0, 1.0))</td><td>0.3760578435883072</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 222, 761, 1525, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 59.0, 15.0, 8.0))</td><td>0.5350103894767942</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 35, 53, 54, 58, 96, 754, 1601, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 63.0, 12.0, 9.0))</td><td>0.23104859697744992</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 46.0, 13.0, 8.0, 1.0))</td><td>0.4090797824611765</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 46.0, 20.0, 6.0, 2.0))</td><td>0.37465459691427205</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 231, 754, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 18.0, 14.0, 8.0))</td><td>0.4069436712168092</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 55, 58, 74, 786, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 16.0, 13.0, 1.0, 8.0))</td><td>0.4552700314940642</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 63, 755, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 23.0, 8.0, 3.0))</td><td>0.29028874332071886</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 75, 860, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 60.0, 10.0, 7.0, 1.0))</td><td>0.4342243636001503</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 38.0, 20.0, 9.0))</td><td>0.49688581409920735</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 11.0, 6.0))</td><td>0.3857454034225889</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 53, 54, 58, 73, 814, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 23.0, 25.0, 5.0, 2.0))</td><td>0.36016731341395514</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 777, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 56.0, 15.0, 9.0, 4.0))</td><td>0.4521481125706622</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 82, 869, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2282, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 16.0, 21.0, 8.0, 3.0))</td><td>0.25871116990348764</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 62, 762, 1513, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 62.0, 27.0, 9.0))</td><td>0.5580797430527182</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 42, 52, 55, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 61.0, 35.0, 5.0, 1.0))</td><td>0.42619282600490144</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 1.0, 6.0, 5.0, 1.0))</td><td>0.25438863705063613</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 221, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 36.0, 13.0, 5.0))</td><td>0.3058237561257835</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 54, 58, 69, 769, 1490, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 52.0, 15.0, 9.0))</td><td>0.45628083784601875</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 130, 765, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 59.0, 15.0, 1.0, 9.0))</td><td>0.54027264328348</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 151, 770, 1642, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 4.0, 1.0, 5.0, 5.0))</td><td>0.4532303008595634</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 73, 786, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 54.0, 7.0, 1.0, 9.0, 2.0))</td><td>0.507629113410816</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 35, 52, 55, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 48.0, 12.0, 2.0, 5.0))</td><td>0.5664654871530429</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 37, 52, 54, 58, 84, 763, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 24.0, 11.0, 1.0, 2.0, 7.0))</td><td>0.38300748561856135</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 92, 952, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 17.0, 21.0, 5.0, 1.0))</td><td>0.45806213505560334</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 288, 756, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 40.0, 25.0, 9.0))</td><td>0.29198116702186827</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 62, 756, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 58.0, 18.0, 1.0, 5.0))</td><td>0.5547832760065594</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 54, 58, 73, 1020, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 49.0, 21.0, 9.0, 2.0))</td><td>0.6367525721396594</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 65, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 4.0, 8.0, 4.0))</td><td>0.2638589913927377</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 35, 53, 54, 58, 66, 783, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 50.0, 13.0, 1.0, 9.0, 3.0))</td><td>0.21448364277033194</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 94, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 20.0, 8.0, 1.0, 4.0))</td><td>0.39386889406366843</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 55, 58, 69, 767, 1507, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 19.0, 30.0, 1.0, 7.0, 4.0))</td><td>0.47443791199440793</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 70, 864, 1554, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 49.0, 20.0, 8.0, 4.0))</td><td>0.5362300337955492</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 65, 761, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 24.0, 11.0, 1.0, 7.0, 1.0))</td><td>0.4561272278522751</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 75, 834, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 2.0, 53.0, 20.0, 1.0, 9.0, 1.0))</td><td>0.6416784200867793</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 24, 42, 52, 54, 58, 139, 898, 1696, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 4.0, 41.0, 11.0, 9.0, 3.0))</td><td>0.5190383972514416</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 52, 54, 58, 63, 754, 1486, 2239, 2243, 2249, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 63.0, 44.0, 9.0, 4.0))</td><td>0.45882603499856994</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 66, 758, 1877, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 43.0, 7.0, 6.0, 3.0))</td><td>0.415353328109322</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 57, 58, 683, 895, 1491, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 53.0, 22.0, 5.0, 1.0))</td><td>0.2712014366604007</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 53, 54, 58, 160, 813, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 48.0, 13.0, 8.0))</td><td>0.43580965470845345</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 175, 828, 1669, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 26.0, 12.0, 8.0))</td><td>0.40908436775367335</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 92, 864, 1535, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 25.0, 23.0, 8.0))</td><td>0.5937710023921263</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 53, 54, 58, 79, 754, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 45.0, 17.0, 2.0, 7.0))</td><td>0.4872026042526208</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 62, 772, 1667, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 45.0, 22.0, 9.0, 3.0))</td><td>0.6871701193736908</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 138, 932, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 26.0, 11.0, 8.0, 4.0))</td><td>0.5005004192898962</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 47.0, 10.0, 6.0, 1.0))</td><td>0.37459191310848694</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 63, 762, 1482, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 44.0, 20.0, 7.0, 4.0))</td><td>0.2815627872751817</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 68, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 31.0, 9.0, 4.0, 1.0))</td><td>0.3960317426756295</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 76, 862, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 17.0, 9.0, 1.0, 7.0, 1.0))</td><td>0.36418902827054356</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 334, 802, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 55.0, 13.0, 9.0, 5.0))</td><td>0.38727400642428267</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 38, 52, 55, 58, 63, 763, 1540, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 88.0, 53.0, 9.0, 2.0))</td><td>0.2989267592608578</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 232, 760, 1550, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 13.0, 9.0, 2.0))</td><td>0.5402603731487501</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 38.0, 12.0, 6.0, 1.0))</td><td>0.29175869772594903</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 64, 770, 1557, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 38.0, 18.0, 1.0, 6.0, 1.0))</td><td>0.4038818066761407</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 116, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 10.0, 6.0))</td><td>0.38919198037344505</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 55, 58, 69, 1033, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 50.0, 27.0, 1.0, 7.0, 1.0))</td><td>0.49330293748221055</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 53, 54, 58, 274, 857, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 10.0, 6.0, 1.0))</td><td>0.3388074444453294</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 62, 784, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 67.0, 10.0, 9.0, 1.0))</td><td>0.39659711186215163</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 38, 52, 55, 58, 65, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 85.0, 47.0, 6.0, 1.0))</td><td>0.29984162009645654</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 54, 58, 167, 810, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 8.0, 8.0, 6.0))</td><td>0.12432186491105157</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 40, 52, 54, 58, 144, 825, 1553, 2239, 2244, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 5.0, 25.0, 27.0, 6.0, 1.0))</td><td>0.30906333191663093</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 16, 35, 53, 54, 58, 64, 761, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 3.0, 6.0, 5.0))</td><td>0.40902961715988395</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 53, 54, 58, 65, 777, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 10.0, 9.0, 9.0, 1.0))</td><td>0.3227214426253103</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 56, 58, 63, 776, 1490, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 47.0, 8.0, 6.0, 3.0))</td><td>0.41302041105804327</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 257, 754, 1769, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 49.0, 33.0, 9.0, 1.0))</td><td>0.3364940188726825</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 38, 53, 54, 58, 63, 754, 1530, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 45.0, 8.0, 9.0, 1.0))</td><td>0.36415135469624343</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 56, 58, 64, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 9.0, 6.0, 1.0))</td><td>0.4127737658373679</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 40, 52, 54, 58, 163, 818, 1672, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 19.0, 16.0, 8.0))</td><td>0.322275543478831</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 74, 757, 1500, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 63.0, 9.0, 7.0))</td><td>0.5832277057723749</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 120, 758, 1585, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 48.0, 22.0, 1.0, 7.0, 3.0))</td><td>0.42942613555350495</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 767, 1563, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 48.0, 11.0, 3.0, 3.0, 1.0))</td><td>0.5508004564375808</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 65, 761, 1493, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 22.0, 20.0, 1.0, 8.0))</td><td>0.38223866816809937</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 83, 755, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 46.0, 9.0, 1.0, 7.0, 2.0))</td><td>0.500447827143102</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 63, 762, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 36.0, 13.0, 6.0, 1.0))</td><td>0.5007817670597317</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 53, 54, 58, 74, 834, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 25.0, 11.0, 1.0, 1.0, 5.0))</td><td>0.4985563197014522</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 87, 963, 1623, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 40.0, 9.0, 8.0, 3.0))</td><td>0.3377048586681894</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 55, 58, 264, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 21.0, 14.0, 1.0, 9.0))</td><td>0.6008151028063984</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 78, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 77.0, 18.0, 9.0, 4.0))</td><td>0.4656029444870715</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 95, 777, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 73.0, 19.0, 9.0, 2.0))</td><td>0.5059222479139088</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 57, 58, 69, 799, 1884, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 50.0, 16.0, 6.0))</td><td>0.28739084638154844</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 52, 55, 58, 80, 771, 1495, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2272, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 45.0, 18.0, 1.0, 9.0, 2.0))</td><td>0.6653707363893253</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 62, 761, 1491, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 21.0, 12.0, 1.0, 5.0, 1.0))</td><td>0.46044490029236496</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 36, 53, 54, 58, 73, 760, 1579, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 29.0, 23.0, 7.0))</td><td>0.4695997359820767</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 64, 808, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 64.0, 17.0, 9.0))</td><td>0.5218684126815426</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 38, 52, 54, 58, 100, 815, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 37.0, 17.0, 5.0, 2.0))</td><td>0.2846389956297924</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 36, 53, 54, 58, 144, 1012, 2013, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 55.0, 20.0, 7.0, 1.0))</td><td>0.031687958289136786</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 156, 779, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 26.0, 7.0, 1.0, 5.0, 2.0))</td><td>0.3573085589138351</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 57, 58, 84, 754, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 42.0, 11.0, 1.0, 8.0, 5.0))</td><td>0.5124833714480392</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 35, 52, 54, 58, 144, 770, 1836, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 37.0, 13.0, 5.0))</td><td>-0.037242752587392514</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 67, 755, 1497, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 35.0, 2.0, 5.0))</td><td>0.39245051842999074</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 35, 53, 54, 58, 62, 759, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 42.0, 19.0, 2.0, 9.0))</td><td>0.32618642825580896</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 67, 756, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2313, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 21.0, 9.0, 1.0, 8.0))</td><td>0.16556384462843282</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 73, 910, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 47.0, 29.0, 9.0, 5.0))</td><td>0.4564969279215122</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 74, 770, 1693, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 74.0, 18.0, 9.0, 4.0))</td><td>0.34675279423211586</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 753, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 10.0, 2.0, 9.0))</td><td>0.5219129105870304</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 55, 58, 64, 812, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 4.0, 19.0, 18.0, 3.0, 1.0, 9.0, 2.0))</td><td>0.6265254091908732</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 36, 53, 54, 58, 174, 781, 1535, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 39.0, 7.0, 9.0, 4.0))</td><td>0.24199386721782623</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 59, 62, 762, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 49.0, 16.0, 3.0, 8.0))</td><td>0.6976022748438535</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 70, 767, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 25.0, 16.0, 1.0, 8.0, 1.0))</td><td>0.44625794281157943</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 90, 753, 1590, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 65.0, 5.0, 5.0, 5.0))</td><td>0.4312046480165353</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 36, 52, 54, 58, 114, 817, 1520, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 3.0, 61.0, 22.0, 5.0))</td><td>0.537218629626863</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 772, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 48.0, 14.0, 1.0, 9.0))</td><td>0.5499916084135245</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 129, 2051, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 41.0, 10.0, 9.0, 5.0))</td><td>0.4252469337035876</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 140, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 49.0, 15.0, 3.0))</td><td>0.4059630838217397</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 36, 52, 54, 58, 63, 777, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 54.0, 4.0, 6.0, 2.0))</td><td>0.47243365323721054</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 53, 54, 58, 91, 797, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 6.0, 61.0, 20.0, 7.0, 1.0))</td><td>0.4326046183288627</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 158, 777, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 6.0, 49.0, 22.0, 1.0, 9.0, 1.0))</td><td>0.563686314228413</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 54, 58, 62, 760, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 23.0, 11.0, 9.0, 8.0))</td><td>0.9022512973675878</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 54, 58, 65, 864, 1563, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 39.0, 13.0, 9.0, 2.0))</td><td>0.4833048227042961</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 88, 1237, 1795, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 60.0, 9.0, 9.0, 4.0))</td><td>0.43208744006889854</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 22, 35, 53, 54, 58, 66, 864, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 51.0, 22.0, 9.0, 1.0))</td><td>0.3124888967766046</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 77, 760, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 50.0, 14.0, 9.0))</td><td>0.48591571626403873</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 81, 997, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 46.0, 14.0, 9.0, 1.0))</td><td>0.5087409637860709</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 63, 762, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 44.0, 47.0, 6.0, 2.0))</td><td>0.40505241606204073</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 342, 758, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 4.0, 21.0, 29.0, 1.0, 4.0, 1.0, 9.0))</td><td>0.7192263017853675</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 47.0, 41.0, 5.0, 2.0))</td><td>0.27770761472965205</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 62, 756, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 39.0, 7.0, 7.0, 1.0))</td><td>0.501866553649899</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 263, 855, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 26.0, 31.0, 1.0, 8.0))</td><td>0.5749994192547301</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 38, 52, 54, 58, 201, 755, 1545, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 53.0, 10.0, 1.0, 8.0, 1.0))</td><td>0.27675653833599945</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 58, 71, 825, 1485, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 81.0, 15.0, 1.0, 9.0))</td><td>0.5460479891362512</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 37, 53, 54, 58, 62, 784, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 21.0, 14.0, 1.0, 8.0))</td><td>0.49190133093653</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 59, 66, 758, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 64.0, 9.0, 9.0))</td><td>0.48647939005473123</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 72, 917, 1520, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 38.0, 9.0, 7.0))</td><td>0.4024332326605647</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 96, 754, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 38.0, 12.0, 1.0, 8.0, 1.0))</td><td>0.47226151325351756</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 95, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 11.0, 24.0, 4.0, 5.0))</td><td>0.25037186094636854</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 40, 53, 54, 58, 63, 762, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 53.0, 12.0, 7.0, 2.0))</td><td>0.3296633514295284</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 11, 40, 52, 55, 58, 304, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 67.0, 32.0, 5.0))</td><td>0.2955061359075314</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 64, 767, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 51.0, 20.0, 7.0, 2.0))</td><td>0.4762709733863837</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 53, 54, 58, 75, 759, 1612, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 79.0, 8.0, 9.0, 3.0))</td><td>0.14447430929265886</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 125, 801, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 51.0, 16.0, 7.0))</td><td>0.5077856413999889</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 88, 755, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 9.0, 4.0, 5.0))</td><td>0.3891704301140702</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 91, 764, 1520, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 36.0, 19.0, 7.0, 1.0))</td><td>0.5743322576870058</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 55, 58, 97, 753, 1485, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 11.0, 10.0, 8.0, 1.0))</td><td>0.48612478185893326</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 55, 58, 99, 754, 1485, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 56.0, 11.0, 8.0, 3.0))</td><td>0.13924914447007203</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 390, 757, 1565, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 43.0, 2.0, 4.0))</td><td>0.17628469235213223</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 99, 768, 1509, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 24.0, 16.0, 6.0))</td><td>0.45914823143726446</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 10, 40, 52, 54, 58, 126, 754, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 40.0, 35.0, 1.0, 9.0, 3.0))</td><td>0.4424033448814621</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 77, 764, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 67.0, 18.0, 1.0, 9.0))</td><td>0.5845693281460641</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 40, 52, 54, 58, 74, 932, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 55.0, 19.0, 1.0, 1.0, 9.0, 4.0))</td><td>0.5645012729889429</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 89, 872, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 11.0, 4.0))</td><td>0.4312989229349088</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 754, 1494, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 41.0, 12.0, 9.0, 4.0))</td><td>0.48757277855789893</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 216, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 61.0, 23.0, 3.0, 9.0, 1.0))</td><td>0.7067505976054085</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 55, 58, 137, 1052, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 7.0, 4.0))</td><td>0.39537639885360487</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 62, 758, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 24.0, 20.0, 9.0, 1.0))</td><td>0.6264739806499442</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 72, 767, 1523, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 74.0, 10.0, 7.0))</td><td>0.4235474801136093</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 97, 761, 1630, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 36.0, 4.0, 3.0, 6.0))</td><td>0.35821295547848875</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 81, 759, 1533, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 18.0, 14.0, 8.0, 3.0))</td><td>0.3896235530411808</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 176, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 47.0, 13.0, 9.0, 3.0))</td><td>0.4046368581897699</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 77, 769, 1551, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 65.0, 13.0, 8.0, 3.0))</td><td>0.4900054029248979</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 105, 758, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 34.0, 18.0, 8.0, 1.0))</td><td>0.4806973187485771</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 9.0, 1.0, 4.0, 2.0))</td><td>0.305127677821468</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 55, 58, 159, 757, 2003, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 16.0, 15.0, 4.0, 1.0))</td><td>0.08310159597072175</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 319, 756, 1567, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 25.0, 6.0, 5.0, 3.0))</td><td>0.41543633943394326</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 37, 52, 54, 58, 163, 978, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 64.0, 23.0, 4.0, 1.0))</td><td>0.29212796030609434</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 141, 780, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 44.0, 15.0, 1.0, 9.0, 2.0))</td><td>0.5207624808567776</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 82, 935, 1497, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 63.0, 19.0, 6.0, 3.0))</td><td>0.5380737578093815</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 94, 771, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 46.0, 13.0, 1.0, 9.0))</td><td>0.46143162663714576</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 99, 786, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 21.0, 13.0, 5.0, 6.0))</td><td>0.35672421495179957</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 98, 914, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 28.0, 26.0, 8.0, 1.0))</td><td>0.46346238755668095</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 87, 840, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 25.0, 13.0, 3.0))</td><td>0.3881625962407689</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 65, 774, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 6.0, 79.0, 42.0, 1.0, 9.0, 2.0))</td><td>0.4574688633566817</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 24, 35, 53, 54, 58, 76, 817, 1604, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 33.0, 7.0, 8.0, 2.0))</td><td>0.43516862678456986</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 106, 770, 1550, 2239, 2243, 2247, 2251, 2254, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 41.0, 12.0, 7.0, 4.0))</td><td>0.5118578332262529</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 76, 770, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 34.0, 7.0, 8.0, 3.0))</td><td>0.47333012535580843</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 39.0, 6.0, 4.0))</td><td>0.3738161389241558</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 64, 755, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 12.0, 1.0, 6.0, 1.0))</td><td>0.44812472062221503</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 69.0, 16.0, 7.0, 1.0))</td><td>0.5239176956421894</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 186, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 71.0, 15.0, 7.0))</td><td>0.5612664341989643</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 40, 52, 55, 58, 111, 822, 2028, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 56.0, 38.0, 9.0))</td><td>0.34851383082335763</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 187, 1013, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 1.0, 13.0, 4.0, 2.0))</td><td>0.49436222997073154</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 35.0, 20.0, 7.0, 2.0))</td><td>0.47090097532375724</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 106, 770, 1483, 2239, 2243, 2247, 2251, 2255, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 67.0, 10.0, 3.0, 6.0, 4.0))</td><td>0.6482835950476369</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 144, 808, 1490, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 8.0, 1.0, 9.0, 1.0))</td><td>0.4053856336086401</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 18, 35, 53, 54, 58, 74, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 56.0, 14.0, 1.0, 9.0, 3.0))</td><td>0.5408511254371731</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 57, 58, 76, 764, 1504, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 6.0, 91.0, 46.0, 9.0))</td><td>0.5935657258084162</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 148, 948, 1547, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 3.0, 87.0, 15.0, 9.0))</td><td>0.4243854572715264</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 70, 769, 2001, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 19.0, 11.0, 8.0))</td><td>0.27938138717281896</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 55, 58, 113, 818, 1496, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 63.0, 7.0, 2.0, 8.0, 2.0))</td><td>0.6547451549427594</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 55, 58, 113, 756, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 26.0, 15.0, 5.0))</td><td>0.4785161761580555</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 58, 63, 775, 1517, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 67.0, 52.0, 9.0, 2.0))</td><td>0.4202947178172482</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 754, 1645, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 61.0, 17.0, 9.0, 3.0))</td><td>0.4562860032055088</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 64, 755, 1486, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 49.0, 10.0, 6.0, 2.0))</td><td>0.3724045218287271</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 123, 776, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 57.0, 22.0, 9.0, 3.0))</td><td>0.4639088609914652</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 75, 763, 1495, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 51.0, 10.0, 4.0, 9.0, 2.0))</td><td>0.6449383223805425</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 55, 58, 156, 848, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 12.0, 16.0, 8.0))</td><td>0.3343233517199663</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 36, 52, 54, 58, 405, 848, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 8.0, 3.0, 2.0, 3.0))</td><td>0.5301839551789832</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 6.0, 5.0, 4.0))</td><td>0.3219200376389505</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 62, 758, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 56.0, 24.0, 5.0, 8.0))</td><td>0.8465312082141419</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 55, 58, 66, 768, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 56.0, 23.0, 9.0))</td><td>0.4649137755846237</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 63, 776, 1712, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 6.0, 42.0, 25.0, 9.0, 4.0))</td><td>0.5040836592379156</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 86, 767, 1516, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 58.0, 19.0, 9.0))</td><td>0.5151843991232065</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 62, 759, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 22.0, 15.0, 2.0, 7.0))</td><td>0.5390652449173093</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 132, 758, 1509, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 49.0, 16.0, 7.0, 1.0))</td><td>0.4392365279779221</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 198, 823, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 47.0, 29.0, 5.0, 2.0))</td><td>0.3459809222134437</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 62, 760, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 71.0, 13.0, 1.0, 9.0))</td><td>0.6732755830351487</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 94, 799, 1612, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 49.0, 19.0, 1.0, 9.0, 1.0))</td><td>0.5261437178016258</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 90, 753, 1562, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 80.0, 13.0, 4.0, 9.0))</td><td>0.31304824861735436</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 387, 772, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 50.0, 21.0, 1.0, 5.0, 7.0))</td><td>0.40875974780453767</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 663, 1556, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 10.0, 5.0, 1.0))</td><td>0.6984549130649476</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 62, 760, 1526, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 51.0, 8.0, 5.0, 4.0))</td><td>0.47626212634743126</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 62, 780, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 39.0, 11.0, 2.0, 6.0, 1.0))</td><td>0.5543205279525943</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 107, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 41.0, 14.0, 4.0))</td><td>0.37741901390378074</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 11, 38, 52, 54, 58, 62, 764, 1490, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 60.0, 18.0, 9.0, 1.0))</td><td>0.436575876477442</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 66, 755, 1512, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 76.0, 15.0, 5.0))</td><td>0.392209443689701</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 109, 770, 1843, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 53.0, 13.0, 9.0))</td><td>0.27658845715331143</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 58, 75, 759, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 2.0, 12.0, 1.0, 6.0))</td><td>0.42250592143910926</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 36, 52, 54, 58, 67, 772, 1484, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 35.0, 15.0, 9.0, 1.0))</td><td>0.5193172405559268</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 755, 1557, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 74.0, 14.0, 4.0, 2.0))</td><td>0.4679050896610716</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 55, 58, 63, 767, 1765, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 88.0, 46.0, 7.0, 1.0))</td><td>0.44795953705714014</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 91, 915, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 57.0, 12.0, 5.0, 2.0))</td><td>0.49081379131591885</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 16, 35, 52, 55, 58, 62, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 60.0, 14.0, 9.0, 3.0))</td><td>0.4574585093934569</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 35, 53, 54, 58, 73, 764, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 35.0, 3.0, 7.0, 1.0))</td><td>0.5464124804950423</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 34.0, 28.0, 9.0, 2.0))</td><td>0.3640007200660671</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 65, 754, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 56.0, 15.0, 9.0, 3.0))</td><td>0.4725337526997281</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 76, 760, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 60.0, 22.0, 1.0, 9.0, 2.0))</td><td>0.5092224265263113</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 73, 796, 1490, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 67.0, 14.0, 1.0, 9.0, 1.0))</td><td>0.504590367065239</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 45, 53, 54, 58, 88, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 13.0, 15.0, 4.0, 2.0))</td><td>0.32811934604927373</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 36, 53, 54, 58, 76, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 2.0, 1.0, 5.0))</td><td>0.32357811267553716</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 55, 58, 126, 767, 1496, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 24.0, 15.0, 4.0, 5.0))</td><td>0.32072616229587914</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 53, 54, 58, 68, 753, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 46.0, 6.0, 8.0, 3.0))</td><td>0.5195936956386993</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 114, 764, 1483, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 1.0, 81.0, 18.0, 1.0, 9.0, 3.0))</td><td>0.6034716314859581</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 52, 55, 58, 230, 777, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 55.0, 17.0, 9.0))</td><td>0.5374201362168604</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 36, 53, 54, 58, 91, 785, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 34.0, 22.0, 3.0, 9.0, 1.0))</td><td>0.5998401448419213</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 40, 52, 54, 58, 298, 756, 1600, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 41.0, 13.0, 1.0, 7.0, 2.0))</td><td>0.3637079897003609</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 37, 53, 54, 58, 63, 762, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 11.0, 8.0, 1.0))</td><td>0.40570651140881503</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 82, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 71.0, 14.0, 9.0))</td><td>0.3880790633505582</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 95, 763, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 8.0, 3.0, 3.0))</td><td>0.3016150997425576</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 65, 957, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 19.0, 10.0, 6.0, 3.0))</td><td>0.052079963452282996</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 95, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 49.0, 11.0, 3.0, 2.0))</td><td>0.26445119751582574</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 373, 777, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 45.0, 14.0, 4.0, 7.0))</td><td>0.4642955171811677</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 35, 53, 54, 58, 71, 774, 1627, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 4.0, 65.0, 26.0, 9.0, 1.0))</td><td>0.03747052817724689</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 74, 794, 1483, 2239, 2243, 2248, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 57.0, 15.0, 11.0, 1.0, 9.0, 1.0))</td><td>0.6475700709429233</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 63, 762, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 40.0, 12.0, 5.0, 2.0))</td><td>0.33680942817632764</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 81, 829, 1576, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2269, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 26.0, 40.0, 2.0, 8.0))</td><td>0.11381658953184509</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 16, 40, 52, 55, 58, 86, 804, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 2.0, 1.0, 6.0, 2.0))</td><td>0.4102667242614921</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 67, 754, 1502, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 61.0, 19.0, 7.0))</td><td>0.45748498961471734</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 216, 771, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2269, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 69.0, 20.0, 3.0, 1.0))</td><td>0.49832550184052665</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 77, 797, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 1.0, 75.0, 26.0, 2.0, 6.0))</td><td>0.6842430076127664</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 77, 753, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 59.0, 16.0, 1.0, 9.0, 3.0))</td><td>0.5587658633925876</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 81, 784, 1513, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 26.0, 33.0, 2.0, 8.0))</td><td>0.23684920533388779</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 117, 801, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 71.0, 12.0, 9.0, 1.0))</td><td>0.4751680275945012</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 63, 754, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 40.0, 23.0, 8.0, 5.0))</td><td>0.34881275566174047</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 296, 842, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 65.0, 15.0, 8.0))</td><td>0.5353390290954155</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 55, 58, 71, 754, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 20.0, 11.0, 7.0))</td><td>0.42796324152945475</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 763, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 62.0, 19.0, 9.0))</td><td>0.5810019571202357</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 92, 797, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 36.0, 21.0, 9.0, 1.0))</td><td>0.4180513235199662</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 16, 42, 52, 54, 58, 91, 797, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 38.0, 24.0, 4.0, 7.0, 2.0))</td><td>0.6743719449557839</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 180, 756, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 4.0, 68.0, 30.0, 7.0, 3.0))</td><td>0.510149552956042</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 90, 753, 1657, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 60.0, 15.0, 2.0, 9.0, 4.0))</td><td>0.539218491390401</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 35, 52, 54, 58, 272, 773, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 53.0, 16.0, 4.0, 8.0, 5.0, 7.0, 5.0))</td><td>1.0271698323939857</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 767, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 44.0, 6.0, 6.0, 2.0))</td><td>0.5475817760635884</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 85, 769, 1481, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 57.0, 11.0, 2.0, 6.0, 1.0))</td><td>0.5368034319645332</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 107, 753, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 61.0, 7.0, 3.0, 4.0))</td><td>0.37242013697543985</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 265, 754, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 40.0, 20.0, 9.0, 3.0))</td><td>0.396775454044478</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 39, 53, 54, 58, 118, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 47.0, 7.0, 4.0, 1.0))</td><td>0.25868245287897873</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 53, 54, 58, 110, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 40.0, 15.0, 3.0, 1.0))</td><td>0.3036297619419412</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 41, 53, 54, 58, 175, 809, 1892, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 23.0, 8.0, 2.0))</td><td>0.25036569732645886</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 95, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 64.0, 19.0, 8.0, 1.0))</td><td>0.3823363781345661</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 754, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 41.0, 6.0, 1.0, 6.0))</td><td>0.4071784508634328</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 36, 53, 54, 58, 168, 763, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 17.0, 9.0, 2.0))</td><td>0.12184957975416794</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 71, 756, 1524, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 28.0, 18.0, 6.0, 3.0))</td><td>0.4337638997470934</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 74, 896, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 23.0, 7.0, 2.0, 8.0, 1.0))</td><td>0.46154437788335234</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 67, 757, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 36.0, 7.0, 3.0, 5.0))</td><td>0.3289219602250157</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 77, 793, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 61.0, 12.0, 9.0, 3.0))</td><td>0.39717753193287153</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 46.0, 13.0, 4.0, 4.0))</td><td>0.2716219430948167</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 328, 760, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 5.0, 63.0, 18.0, 1.0, 9.0, 1.0))</td><td>0.5168172128422941</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 6.0, 45.0, 35.0, 3.0, 2.0))</td><td>0.3212924275095712</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 39, 52, 54, 58, 113, 764, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 14.0, 11.0, 9.0, 1.0))</td><td>0.4714068379093845</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 53, 54, 58, 72, 887, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 6.0, 1.0, 5.0))</td><td>0.38727030820558983</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 137, 900, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 7.0, 4.0, 2.0))</td><td>0.3961342687926411</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 105, 756, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 73.0, 13.0, 8.0, 2.0))</td><td>0.3942302898376439</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 63, 783, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 23.0, 9.0, 4.0, 2.0))</td><td>0.2234066169829491</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 84, 805, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 78.0, 17.0, 7.0, 1.0))</td><td>0.46556107236477295</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 784, 1540, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 3.0, 62.0, 14.0, 1.0, 9.0, 1.0))</td><td>0.5042428270585666</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 107, 753, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 58.0, 13.0, 2.0, 4.0, 4.0))</td><td>0.52219977653365</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 62, 997, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 51.0, 17.0, 1.0, 2.0, 6.0, 2.0))</td><td>0.7903552108477647</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 61.0, 29.0, 7.0))</td><td>0.3702891579021531</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 54, 58, 65, 754, 1545, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 6.0, 76.0, 31.0, 9.0, 4.0))</td><td>0.41497552403750726</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 55, 58, 116, 808, 1572, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 43.0, 20.0, 9.0, 1.0))</td><td>0.4795911204368749</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 75, 829, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 62.0, 12.0, 1.0, 1.0, 6.0, 2.0))</td><td>0.48426519951964936</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 62, 756, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 53.0, 10.0, 7.0))</td><td>0.5289444612581095</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 86, 763, 1715, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 47.0, 12.0, 5.0, 5.0))</td><td>0.2916640900486493</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 106, 758, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 72.0, 11.0, 9.0, 2.0))</td><td>0.49241834369482285</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 153, 889, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 44.0, 15.0, 3.0, 7.0))</td><td>0.5247602518812428</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 317, 753, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 61.0, 14.0, 9.0))</td><td>0.5243126490360481</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 105, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 1.0, 49.0, 22.0, 7.0, 2.0, 1.0, 9.0))</td><td>0.6777365483189957</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 92, 761, 1567, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 26.0, 19.0, 1.0, 6.0, 2.0))</td><td>0.5133589458959473</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 55, 58, 69, 1068, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 20.0, 24.0, 7.0))</td><td>0.4093535406518529</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 127, 782, 1516, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 25.0, 33.0, 2.0, 8.0))</td><td>0.5784690280884495</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 65, 753, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 47.0, 18.0, 4.0, 9.0))</td><td>0.5894326148184418</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 40, 52, 54, 58, 104, 755, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 23.0, 19.0, 2.0, 5.0, 4.0))</td><td>0.47039654501632805</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 518, 1474, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 16.0, 4.0, 2.0))</td><td>0.41412375827826087</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 53, 54, 58, 99, 759, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 57.0, 19.0, 9.0, 3.0))</td><td>0.40519807306787203</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 52, 55, 58, 125, 882, 1617, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 82.0, 20.0, 8.0, 2.0))</td><td>0.13722002334969502</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 79, 785, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 51.0, 9.0, 9.0, 1.0))</td><td>0.37602023323121536</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 41, 52, 55, 58, 92, 817, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 2.0, 45.0, 16.0, 7.0, 2.0))</td><td>0.44387057285153003</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 11, 38, 52, 54, 58, 79, 864, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 25.0, 10.0, 5.0, 9.0, 2.0))</td><td>0.7438434586485152</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 67, 810, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 8.0, 4.0))</td><td>0.3439339339842252</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 273, 753, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 27.0, 15.0, 8.0, 1.0))</td><td>0.48388415008911384</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 63, 762, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 47.0, 14.0, 1.0, 8.0, 2.0))</td><td>0.46019064828720413</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 62, 983, 1643, 2239, 2243, 2249, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 83.0, 25.0, 9.0))</td><td>0.45633377853942864</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 758, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 4.0, 57.0, 14.0, 6.0, 1.0))</td><td>0.44915072173224085</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 22, 36, 53, 54, 58, 91, 785, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 34.0, 20.0, 6.0, 4.0))</td><td>0.15122495825971694</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 18, 38, 52, 55, 58, 241, 1198, 1481, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 50.0, 21.0, 4.0, 2.0))</td><td>0.33029127328199775</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 84, 943, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 73.0, 22.0, 7.0))</td><td>0.41218778246964294</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 92, 760, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 52.0, 20.0, 2.0, 9.0))</td><td>0.5830881246441572</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 52, 54, 58, 64, 756, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 56.0, 19.0, 9.0))</td><td>0.5129118632445823</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 64, 795, 1593, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 67.0, 18.0, 9.0, 3.0))</td><td>0.4759861815453962</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 83, 794, 1645, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 42.0, 14.0, 1.0, 5.0, 2.0))</td><td>0.48082892562063995</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 94, 789, 1525, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 3.0, 21.0, 1.0, 9.0))</td><td>0.39758350639609513</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 94, 771, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 25.0, 14.0, 8.0, 5.0))</td><td>0.4812031793266297</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 108, 754, 1513, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 60.0, 19.0, 8.0, 1.0))</td><td>0.5009562961982895</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 118, 770, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 59.0, 5.0, 5.0, 2.0))</td><td>0.4863386249943671</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 24, 38, 52, 54, 58, 79, 782, 1535, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 52.0, 12.0, 1.0, 9.0, 4.0))</td><td>0.41016388479277605</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 155, 812, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 55.0, 11.0, 1.0, 9.0))</td><td>0.474459237708614</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 77, 767, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 60.0, 15.0, 9.0, 2.0))</td><td>0.47552918279118006</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 72, 773, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 78.0, 11.0, 8.0))</td><td>0.48900539702585927</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 65, 754, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 4.0, 57.0, 11.0, 7.0))</td><td>0.48427292690419943</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 659, 1345, 1556, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 35.0, 8.0, 1.0, 8.0))</td><td>0.15561898523917767</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 52, 55, 58, 69, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 41.0, 33.0, 1.0, 9.0, 1.0))</td><td>0.4214864268638875</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 57, 58, 63, 762, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 38.0, 19.0, 3.0, 2.0))</td><td>0.35828753004436065</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 108, 757, 1527, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 45.0, 34.0, 1.0, 1.0, 6.0, 2.0))</td><td>0.3698201782704784</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 149, 798, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 20.0, 10.0, 8.0))</td><td>0.41655478543574087</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 882, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 92.0, 18.0, 9.0, 4.0))</td><td>0.5454401693578246</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 90, 753, 2063, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 5.0, 3.0, 8.0))</td><td>0.33742409302411747</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 126, 806, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 27.0, 13.0, 2.0, 8.0, 3.0))</td><td>0.4539250539675345</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 63, 762, 1481, 2239, 2244, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2313, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 21.0, 9.0, 1.0, 7.0))</td><td>0.46209990706301185</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 327, 776, 1547, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 14.0, 8.0, 6.0))</td><td>0.4609640216752623</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 84, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 20.0, 12.0, 7.0, 4.0))</td><td>0.3610032064580636</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 86, 755, 1482, 2239, 2243, 2249, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 48.0, 15.0, 6.0, 1.0))</td><td>0.29756729762978806</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 84, 791, 1481, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2266, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 9.0, 7.0, 1.0))</td><td>0.48437257854059423</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 65, 764, 1565, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 56.0, 24.0, 9.0))</td><td>0.4989444446205215</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 767, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 16.0, 5.0))</td><td>0.40651226602978197</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 97, 780, 1885, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 11.0, 8.0, 2.0))</td><td>0.3697534346291355</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 53, 54, 58, 92, 760, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 49.0, 13.0, 2.0, 9.0, 1.0))</td><td>0.6117794480139507</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 19, 40, 52, 54, 58, 72, 820, 1528, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 44.0, 7.0, 9.0, 2.0))</td><td>0.37293115624232387</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 240, 770, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 35.0, 5.0, 9.0))</td><td>0.4637078738554014</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 106, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 41.0, 5.0, 4.0, 4.0))</td><td>0.3982274079181806</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 181, 1046, 1583, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 64.0, 15.0, 1.0, 9.0))</td><td>0.5208394995035719</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 18, 35, 52, 54, 58, 390, 753, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 68.0, 11.0, 9.0, 1.0))</td><td>0.3026216168331513</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 64, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 36.0, 13.0, 9.0, 3.0))</td><td>0.48717381670283566</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 52, 57, 58, 69, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 23.0, 23.0, 4.0, 1.0))</td><td>0.33957321970130117</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 15, 35, 52, 54, 58, 65, 797, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 70.0, 19.0, 9.0, 6.0))</td><td>0.08201935922197534</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 35, 52, 54, 58, 249, 1120, 1481, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 57.0, 13.0, 5.0, 5.0))</td><td>0.7704257630209403</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 219, 828, 1766, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 16.0, 17.0, 1.0, 1.0, 8.0, 2.0))</td><td>0.49440763250434894</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 64, 759, 1834, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 9.0, 1.0, 5.0, 2.0))</td><td>0.38788862196363877</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 772, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 40.0, 13.0, 1.0, 9.0, 1.0))</td><td>0.6874765843186574</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 762, 1487, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 66.0, 13.0, 3.0, 8.0, 1.0))</td><td>0.6747053660195084</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 108, 803, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 55.0, 5.0, 4.0, 1.0))</td><td>0.3607256581374703</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 763, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 86.0, 28.0, 4.0, 4.0))</td><td>0.35966979363607476</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 53, 54, 58, 65, 761, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 34.0, 12.0, 4.0, 2.0))</td><td>0.3072088565909671</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 78, 760, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 79.0, 28.0, 2.0, 8.0, 4.0))</td><td>0.5717644803118319</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 37, 52, 55, 58, 186, 839, 1631, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 17.0, 23.0, 1.0, 8.0, 2.0))</td><td>0.571803342486731</td><td>0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 249, 773, 1657, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 1.0, 61.0, 19.0, 1.0, 9.0, 4.0))</td><td>0.6003581500199596</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 38, 53, 54, 58, 65, 754, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 3.0, 41.0, 11.0, 9.0))</td><td>0.35982217343259815</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 65, 775, 1646, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 6.0, 80.0, 39.0, 9.0, 2.0))</td><td>0.362765486586329</td><td>0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 659, 820, 1893, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 43.0, 11.0, 2.0, 9.0))</td><td>0.46426137269946555</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 38, 52, 55, 58, 63, 762, 1540, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 37.0, 17.0, 9.0, 2.0))</td><td>0.2520510379480721</td><td>1</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 62, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 50.0, 16.0, 9.0))</td><td>0.543242346735894</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 10, 35, 52, 55, 58, 62, 764, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2313, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 52.0, 15.0, 1.0, 2.0, 9.0))</td><td>0.6308150461947708</td><td>1</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 37, 52, 54, 58, 64, 761, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 44.0, 24.0, 5.0))</td><td>0.373816686972977</td><td>1</td><td>0</td></tr></tbody></table>Showing the first 1000 rows.</div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[12703,20201]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"sum(accurate)","type":"\"long\"","metadata":"{}"},{"name":"count(accurate)","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>sum(accurate)</th><th>count(accurate)</th></tr></thead><tbody><tr><td>12703</td><td>20201</td></tr></tbody></table></div>"]}}],"execution_count":0},{"cell_type":"code","source":["mode_val = []\nfor i in [ 'race',\n 'gender',\n 'age',\n 'weight',\n 'admission_type_id',\n 'discharge_disposition_id',\n 'admission_source_id',\n 'time_in_hospital',\n 'payer_code',\n 'medical_specialty',\n 'num_lab_procedures',\n 'num_procedures',\n 'num_medications',\n 'number_outpatient',\n 'number_emergency',\n 'number_inpatient',\n 'diag_1',\n 'diag_2',\n 'diag_3',\n 'number_diagnoses',\n 'max_glu_serum',\n 'A1Cresult',\n 'metformin',\n 'repaglinide',\n 'nateglinide',\n 'chlorpropamide',\n 'glimepiride',\n 'acetohexamide',\n 'glipizide',\n 'glyburide',\n 'tolbutamide',\n 'pioglitazone',\n 'rosiglitazone',\n 'acarbose',\n 'miglitol',\n 'troglitazone',\n 'tolazamide',\n 'examide',\n 'citoglipton',\n 'insulin',\n 'glyburide-metformin',\n 'glipizide-metformin',\n 'glimepiride-pioglitazone',\n 'metformin-rosiglitazone',\n 'metformin-pioglitazone',\n 'change',\n 'diabetesMed']:\n  #cnts = dataset.groupBy(i).count()\n  mode = dataset.groupBy(i).count().orderBy(\"count\", ascending=False)\n  #mode2 = mode.withColumn(i,col(i).cast(\"double\"))\n  print((i, mode.first()[0], mode.first()[1]))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"61cd18d8-88b7-402c-9b6d-f8c50ed1ec26"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">(&#39;race&#39;, &#39;Caucasian&#39;, 76099)\n(&#39;gender&#39;, &#39;Female&#39;, 54708)\n(&#39;age&#39;, &#39;[70-80)&#39;, 26068)\n(&#39;weight&#39;, &#39;?&#39;, 98569)\n(&#39;admission_type_id&#39;, &#39;1&#39;, 53990)\n(&#39;discharge_disposition_id&#39;, &#39;1&#39;, 60234)\n(&#39;admission_source_id&#39;, &#39;7&#39;, 57494)\n(&#39;time_in_hospital&#39;, 3, 17756)\n(&#39;payer_code&#39;, &#39;?&#39;, 40256)\n(&#39;medical_specialty&#39;, &#39;?&#39;, 49949)\n(&#39;num_lab_procedures&#39;, 1, 3208)\n(&#39;num_procedures&#39;, 0, 46652)\n(&#39;num_medications&#39;, 13, 6086)\n(&#39;number_outpatient&#39;, 0, 85027)\n(&#39;number_emergency&#39;, 0, 90383)\n(&#39;number_inpatient&#39;, 0, 67630)\n(&#39;diag_1&#39;, &#39;428&#39;, 6862)\n(&#39;diag_2&#39;, &#39;276&#39;, 6752)\n(&#39;diag_3&#39;, &#39;250&#39;, 11555)\n(&#39;number_diagnoses&#39;, 9, 49474)\n(&#39;max_glu_serum&#39;, 0, 101766)\n(&#39;A1Cresult&#39;, 0, 101766)\n(&#39;metformin&#39;, &#39;No&#39;, 81778)\n(&#39;repaglinide&#39;, &#39;No&#39;, 100227)\n(&#39;nateglinide&#39;, &#39;No&#39;, 101063)\n(&#39;chlorpropamide&#39;, &#39;No&#39;, 101680)\n(&#39;glimepiride&#39;, &#39;No&#39;, 96575)\n(&#39;acetohexamide&#39;, &#39;No&#39;, 101765)\n(&#39;glipizide&#39;, &#39;No&#39;, 89080)\n(&#39;glyburide&#39;, &#39;No&#39;, 91116)\n(&#39;tolbutamide&#39;, &#39;No&#39;, 101743)\n(&#39;pioglitazone&#39;, &#39;No&#39;, 94438)\n(&#39;rosiglitazone&#39;, &#39;No&#39;, 95401)\n(&#39;acarbose&#39;, &#39;No&#39;, 101458)\n(&#39;miglitol&#39;, &#39;No&#39;, 101728)\n(&#39;troglitazone&#39;, &#39;No&#39;, 101763)\n(&#39;tolazamide&#39;, &#39;No&#39;, 101727)\n(&#39;examide&#39;, &#39;No&#39;, 101766)\n(&#39;citoglipton&#39;, &#39;No&#39;, 101766)\n(&#39;insulin&#39;, &#39;No&#39;, 47383)\n(&#39;glyburide-metformin&#39;, &#39;No&#39;, 101060)\n(&#39;glipizide-metformin&#39;, &#39;No&#39;, 101753)\n(&#39;glimepiride-pioglitazone&#39;, &#39;No&#39;, 101765)\n(&#39;metformin-rosiglitazone&#39;, &#39;No&#39;, 101764)\n(&#39;metformin-pioglitazone&#39;, &#39;No&#39;, 101765)\n(&#39;change&#39;, &#39;No&#39;, 54755)\n(&#39;diabetesMed&#39;, &#39;Yes&#39;, 78363)\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">(&#39;race&#39;, &#39;Caucasian&#39;, 76099)\n(&#39;gender&#39;, &#39;Female&#39;, 54708)\n(&#39;age&#39;, &#39;[70-80)&#39;, 26068)\n(&#39;weight&#39;, &#39;?&#39;, 98569)\n(&#39;admission_type_id&#39;, &#39;1&#39;, 53990)\n(&#39;discharge_disposition_id&#39;, &#39;1&#39;, 60234)\n(&#39;admission_source_id&#39;, &#39;7&#39;, 57494)\n(&#39;time_in_hospital&#39;, 3, 17756)\n(&#39;payer_code&#39;, &#39;?&#39;, 40256)\n(&#39;medical_specialty&#39;, &#39;?&#39;, 49949)\n(&#39;num_lab_procedures&#39;, 1, 3208)\n(&#39;num_procedures&#39;, 0, 46652)\n(&#39;num_medications&#39;, 13, 6086)\n(&#39;number_outpatient&#39;, 0, 85027)\n(&#39;number_emergency&#39;, 0, 90383)\n(&#39;number_inpatient&#39;, 0, 67630)\n(&#39;diag_1&#39;, &#39;428&#39;, 6862)\n(&#39;diag_2&#39;, &#39;276&#39;, 6752)\n(&#39;diag_3&#39;, &#39;250&#39;, 11555)\n(&#39;number_diagnoses&#39;, 9, 49474)\n(&#39;max_glu_serum&#39;, 0, 101766)\n(&#39;A1Cresult&#39;, 0, 101766)\n(&#39;metformin&#39;, &#39;No&#39;, 81778)\n(&#39;repaglinide&#39;, &#39;No&#39;, 100227)\n(&#39;nateglinide&#39;, &#39;No&#39;, 101063)\n(&#39;chlorpropamide&#39;, &#39;No&#39;, 101680)\n(&#39;glimepiride&#39;, &#39;No&#39;, 96575)\n(&#39;acetohexamide&#39;, &#39;No&#39;, 101765)\n(&#39;glipizide&#39;, &#39;No&#39;, 89080)\n(&#39;glyburide&#39;, &#39;No&#39;, 91116)\n(&#39;tolbutamide&#39;, &#39;No&#39;, 101743)\n(&#39;pioglitazone&#39;, &#39;No&#39;, 94438)\n(&#39;rosiglitazone&#39;, &#39;No&#39;, 95401)\n(&#39;acarbose&#39;, &#39;No&#39;, 101458)\n(&#39;miglitol&#39;, &#39;No&#39;, 101728)\n(&#39;troglitazone&#39;, &#39;No&#39;, 101763)\n(&#39;tolazamide&#39;, &#39;No&#39;, 101727)\n(&#39;examide&#39;, &#39;No&#39;, 101766)\n(&#39;citoglipton&#39;, &#39;No&#39;, 101766)\n(&#39;insulin&#39;, &#39;No&#39;, 47383)\n(&#39;glyburide-metformin&#39;, &#39;No&#39;, 101060)\n(&#39;glipizide-metformin&#39;, &#39;No&#39;, 101753)\n(&#39;glimepiride-pioglitazone&#39;, &#39;No&#39;, 101765)\n(&#39;metformin-rosiglitazone&#39;, &#39;No&#39;, 101764)\n(&#39;metformin-pioglitazone&#39;, &#39;No&#39;, 101765)\n(&#39;change&#39;, &#39;No&#39;, 54755)\n(&#39;diabetesMed&#39;, &#39;Yes&#39;, 78363)\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["\n# for i in numericCols:\n#   avg = dataset.select(functions.mean(dataset[i]))\n#   #mode2 = mode.withColumn(i,col(i).cast(\"double\"))\n#   print((i, avg.first()[0]))\nstringIndexerModel = ageIndexer.fit(dataset)\ndf2 = stringIndexerModel.transform(dataset)\nprint(df2.select(functions.mean(df2[\"ageIndex\"])).first()[0])\n#print(trainDF.select(functions.mean(trainDF[\"ageIndex\"])))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"609cd10c-6ec3-41cd-b3ee-93440f1567ca"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">1.980376550124796\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div 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g_3OHE_987\"},{\"idx\":2208,\"name\":\"diag_3OHE_989\"},{\"idx\":2209,\"name\":\"diag_3OHE_E815\"},{\"idx\":2210,\"name\":\"diag_3OHE_E817\"},{\"idx\":2211,\"name\":\"diag_3OHE_E822\"},{\"idx\":2212,\"name\":\"diag_3OHE_E825\"},{\"idx\":2213,\"name\":\"diag_3OHE_E826\"},{\"idx\":2214,\"name\":\"diag_3OHE_E828\"},{\"idx\":2215,\"name\":\"diag_3OHE_E852\"},{\"idx\":2216,\"name\":\"diag_3OHE_E853\"},{\"idx\":2217,\"name\":\"diag_3OHE_E854\"},{\"idx\":2218,\"name\":\"diag_3OHE_E861\"},{\"idx\":2219,\"name\":\"diag_3OHE_E864\"},{\"idx\":2220,\"name\":\"diag_3OHE_E865\"},{\"idx\":2221,\"name\":\"diag_3OHE_E883\"},{\"idx\":2222,\"name\":\"diag_3OHE_E892\"},{\"idx\":2223,\"name\":\"diag_3OHE_E894\"},{\"idx\":2224,\"name\":\"diag_3OHE_E900\"},{\"idx\":2225,\"name\":\"diag_3OHE_E901\"},{\"idx\":2226,\"name\":\"diag_3OHE_E904\"},{\"idx\":2227,\"name\":\"diag_3OHE_E915\"},{\"idx\":2228,\"name\":\"diag_3OHE_E924\"},{\"idx\":2229,\"name\":\"diag_3OHE_E946\"},{\"idx\":2230,\"name\":\"diag_3OHE_E949\"},{\"idx\":2231,\"name\":\"diag_3OHE_E955\"},{\"idx\":2232,\"name\":\"diag_3OHE_E965\"},{\"idx\":2233,\"name\":\"diag_3OHE_E987\"},{\"idx\":2234,\"name\":\"diag_3OHE_V01\"},{\"idx\":2235,\"name\":\"diag_3OHE_V07\"},{\"idx\":2236,\"name\":\"diag_3OHE_V11\"},{\"idx\":2237,\"name\":\"diag_3OHE_V22\"},{\"idx\":2238,\"name\":\"diag_3OHE_V60\"},{\"idx\":2239,\"name\":\"nateglinideOHE_No\"},{\"idx\":2240,\"name\":\"nateglinideOHE_Steady\"},{\"idx\":2241,\"name\":\"nateglinideOHE_Up\"},{\"idx\":2242,\"name\":\"nateglinideOHE_Down\"},{\"idx\":2243,\"name\":\"chlorpropamideOHE_No\"},{\"idx\":2244,\"name\":\"chlorpropamideOHE_Steady\"},{\"idx\":2245,\"name\":\"chlorpropamideOHE_Up\"},{\"idx\":2246,\"name\":\"chlorpropamideOHE_Down\"},{\"idx\":2247,\"name\":\"glimepirideOHE_No\"},{\"idx\":2248,\"name\":\"glimepirideOHE_Steady\"},{\"idx\":2249,\"name\":\"glimepirideOHE_Up\"},{\"idx\":2250,\"name\":\"glimepirideOHE_Down\"},{\"idx\":2251,\"name\":\"acetohexamideOHE_No\"},{\"idx\":2252,\"name\":\"acetohexamideOHE_Steady\"},{\"idx\":2253,\"name\":\"glipizideOHE_No\"},{\"idx\":2254,\"name\":\"glipizideOHE_Steady\"},{\"idx\":2255,\"name\":\"glipizideOHE_Up\"},{\"idx\":2256,\"name\":\"glipizideOHE_Down\"},{\"idx\":2257,\"name\":\"glyburideOHE_No\"},{\"idx\":2258,\"name\":\"glyburideOHE_Steady\"},{\"idx\":2259,\"name\":\"glyburideOHE_Up\"},{\"idx\":2260,\"name\":\"glyburideOHE_Down\"},{\"idx\":2261,\"name\":\"tolbutamideOHE_No\"},{\"idx\":2262,\"name\":\"tolbutamideOHE_Steady\"},{\"idx\":2263,\"name\":\"pioglitazoneOHE_No\"},{\"idx\":2264,\"name\":\"pioglitazoneOHE_Steady\"},{\"idx\":2265,\"name\":\"pioglitazoneOHE_Up\"},{\"idx\":2266,\"name\":\"pioglitazoneOHE_Down\"},{\"idx\":2267,\"name\":\"rosiglitazoneOHE_No\"},{\"idx\":2268,\"name\":\"rosiglitazoneOHE_Steady\"},{\"idx\":2269,\"name\":\"rosiglitazoneOHE_Up\"},{\"idx\":2270,\"name\":\"rosiglitazoneOHE_Down\"},{\"idx\":2271,\"name\":\"acarboseOHE_No\"},{\"idx\":2272,\"name\":\"acarboseOHE_Steady\"},{\"idx\":2273,\"name\":\"acarboseOHE_Up\"},{\"idx\":2274,\"name\":\"acarboseOHE_Down\"},{\"idx\":2275,\"name\":\"miglitolOHE_No\"},{\"idx\":2276,\"name\":\"miglitolOHE_Steady\"},{\"idx\":2277,\"name\":\"miglitolOHE_Down\"},{\"idx\":2278,\"name\":\"miglitolOHE_Up\"},{\"idx\":2279,\"name\":\"troglitazoneOHE_No\"},{\"idx\":2280,\"name\":\"troglitazoneOHE_Steady\"},{\"idx\":2281,\"name\":\"tolazamideOHE_No\"},{\"idx\":2282,\"name\":\"tolazamideOHE_Steady\"},{\"idx\":2283,\"name\":\"insulinOHE_No\"},{\"idx\":2284,\"name\":\"insulinOHE_Steady\"},{\"idx\":2285,\"name\":\"insulinOHE_Down\"},{\"idx\":2286,\"name\":\"insulinOHE_Up\"},{\"idx\":2287,\"name\":\"glyburide-metforminOHE_No\"},{\"idx\":2288,\"name\":\"glyburide-metforminOHE_Steady\"},{\"idx\":2289,\"name\":\"glyburide-metforminOHE_Up\"},{\"idx\":2290,\"name\":\"glyburide-metforminOHE_Down\"},{\"idx\":2291,\"name\":\"glipizide-metforminOHE_No\"},{\"idx\":2292,\"name\":\"glipizide-metforminOHE_Steady\"},{\"idx\":2293,\"name\":\"glimepiride-pioglitazoneOHE_No\"},{\"idx\":2294,\"name\":\"glimepiride-pioglitazoneOHE_Steady\"},{\"idx\":2295,\"name\":\"metformin-rosiglitazoneOHE_No\"},{\"idx\":2296,\"name\":\"metformin-rosiglitazoneOHE_Steady\"},{\"idx\":2297,\"name\":\"metformin-pioglitazoneOHE_No\"},{\"idx\":2298,\"name\":\"metformin-pioglitazoneOHE_Steady\"},{\"idx\":2299,\"name\":\"changeOHE_No\"},{\"idx\":2300,\"name\":\"changeOHE_Ch\"},{\"idx\":2301,\"name\":\"admission_type_idOHE_1\"},{\"idx\":2302,\"name\":\"admission_type_idOHE_3\"},{\"idx\":2303,\"name\":\"admission_type_idOHE_2\"},{\"idx\":2304,\"name\":\"admission_type_idOHE_6\"},{\"idx\":2305,\"name\":\"admission_type_idOHE_5\"},{\"idx\":2306,\"name\":\"admission_type_idOHE_8\"},{\"idx\":2307,\"name\":\"admission_type_idOHE_7\"},{\"idx\":2308,\"name\":\"admission_type_idOHE_4\"}]},\"num_attrs\":2320}}"},{"name":"prediction","type":"\"double\"","metadata":"{\"ml_attr\":{}}"},{"name":"label","type":"\"double\"","metadata":"{\"ml_attr\":{\"vals\":[\"0\",\"1\"],\"type\":\"nominal\",\"name\":\"label\"}}"},{"name":"accurate","type":"\"integer\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>features</th><th>prediction</th><th>label</th><th>accurate</th></tr></thead><tbody><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 57.0, 21.0, 1.0, 9.0, 2.0))</td><td>0.5245787384678087</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 38, 52, 54, 58, 186, 813, 1592, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 71.0, 18.0, 9.0, 4.0))</td><td>0.4036495578034703</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 90, 860, 1550, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 68.0, 8.0, 3.0, 4.0))</td><td>0.4221794548320073</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 146, 888, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 43.0, 16.0, 4.0, 1.0))</td><td>0.440093870174005</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 214, 802, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 13.0, 5.0, 5.0))</td><td>0.14847369360300183</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 97, 853, 1753, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 54.0, 3.0, 2.0, 4.0, 2.0))</td><td>0.44402904327275233</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 158, 757, 1526, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 57.0, 23.0, 9.0, 3.0))</td><td>0.4541308994387101</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 62, 766, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 57.0, 13.0, 9.0))</td><td>0.5415704353093801</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 760, 1497, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 59.0, 19.0, 1.0, 7.0))</td><td>0.5734394788137035</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 64, 763, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 47.0, 10.0, 4.0, 3.0))</td><td>0.4142779305919548</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 19, 35, 52, 54, 58, 65, 766, 1878, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 94.0, 15.0, 1.0, 2.0, 9.0, 2.0))</td><td>0.5680280196311073</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 36, 53, 54, 58, 116, 758, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 50.0, 21.0, 1.0, 9.0, 4.0))</td><td>0.4863573402121205</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 762, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 13.0, 17.0, 7.0))</td><td>0.4186419321463618</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 134, 984, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 72.0, 13.0, 8.0))</td><td>0.3701367049519617</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 64, 767, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 61.0, 16.0, 1.0, 9.0, 4.0))</td><td>0.5006284616599892</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 96, 769, 1708, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 3.0, 60.0, 21.0, 7.0, 3.0))</td><td>0.5086100009358969</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 65, 791, 1493, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 5.0, 8.0, 7.0, 2.0))</td><td>0.39809257690328925</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 224, 826, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 4.0, 3.0, 8.0))</td><td>0.34303686577744014</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 85, 857, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 56.0, 7.0, 4.0, 2.0))</td><td>0.3221644091465484</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 41, 52, 54, 58, 145, 767, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 33.0, 11.0, 6.0, 8.0, 1.0))</td><td>0.6706709274667562</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 32, 35, 53, 54, 58, 82, 758, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 77.0, 17.0, 1.0, 5.0, 6.0))</td><td>0.81712941425347</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 54, 58, 181, 864, 1618, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 36.0, 12.0, 5.0, 1.0, 9.0, 4.0))</td><td>0.6425476639934113</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 73, 771, 1600, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 1.0, 42.0, 25.0, 3.0, 9.0, 4.0))</td><td>0.6611709589971916</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 90, 1107, 1856, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 9.0, 3.0, 8.0))</td><td>0.48201231865536176</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 340, 802, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 6.0, 4.0, 5.0))</td><td>0.19354461776541973</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 69, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 25.0, 26.0, 6.0, 2.0))</td><td>0.4661527274443737</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 767, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 61.0, 21.0, 6.0, 4.0))</td><td>0.36602065172063847</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 67, 772, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 51.0, 13.0, 9.0))</td><td>0.5260444082116673</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 38, 52, 55, 58, 70, 764, 1704, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 9.0, 16.0, 1.0, 1.0, 6.0, 3.0))</td><td>0.47130418021166565</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 187, 797, 1481, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 61.0, 32.0, 1.0, 6.0))</td><td>0.6684028988032773</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 165, 787, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 34.0, 12.0, 1.0, 5.0, 1.0))</td><td>0.46575463063642397</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 878, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 48.0, 14.0, 1.0, 8.0, 1.0))</td><td>0.4734750869854394</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 148, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 51.0, 3.0, 1.0, 8.0))</td><td>0.24546611623740633</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 62, 756, 1545, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 3.0, 46.0, 25.0, 1.0, 7.0))</td><td>0.5963728861895128</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 112, 753, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 48.0, 5.0, 3.0, 2.0))</td><td>0.36214595628801627</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 102, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 7.0, 3.0, 5.0, 2.0))</td><td>0.43554330781697054</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 72, 1150, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2313, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 2.0, 60.0, 18.0, 1.0, 1.0, 9.0))</td><td>0.7508240238440811</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 43, 52, 55, 58, 76, 785, 1639, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 20.0, 18.0, 9.0, 1.0))</td><td>0.6140069189990598</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 91, 797, 1571, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 67.0, 17.0, 5.0, 6.0))</td><td>0.4353601976560748</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 54, 58, 80, 830, 1622, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 62.0, 15.0, 8.0, 4.0))</td><td>0.5320843586701322</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 55, 58, 176, 777, 1643, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 40.0, 29.0, 9.0, 2.0))</td><td>0.43974552834091324</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 72, 758, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 67.0, 9.0, 3.0, 7.0, 3.0))</td><td>0.5672048585156625</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 67, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 38.0, 10.0, 2.0, 5.0))</td><td>0.42472636213601306</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 88, 853, 1481, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 3.0, 6.0, 2.0))</td><td>0.403901143168027</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 108, 836, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 66.0, 15.0, 1.0, 9.0, 6.0))</td><td>0.4126698613546109</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 40, 53, 54, 58, 77, 767, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 39.0, 7.0, 6.0, 4.0))</td><td>0.35049377338570836</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 38, 52, 54, 58, 62, 772, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 69.0, 16.0, 8.0, 1.0))</td><td>0.4471420385985248</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 40, 52, 54, 58, 74, 870, 1494, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 41.0, 14.0, 2.0, 5.0, 4.0))</td><td>0.45392253369350116</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 60.0, 20.0, 6.0, 3.0))</td><td>0.49659904712511427</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 90, 753, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 60.0, 14.0, 3.0, 8.0))</td><td>0.2920062241706114</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 70, 754, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 55.0, 19.0, 2.0, 6.0, 2.0))</td><td>0.6269419695218632</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 62, 753, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 69.0, 20.0, 8.0))</td><td>0.5924762159772262</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 108, 756, 1650, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 44.0, 23.0, 9.0))</td><td>0.38387194090280785</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 63, 942, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 71.0, 15.0, 8.0, 2.0))</td><td>0.544896324527</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 91, 764, 1520, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 49.0, 37.0, 9.0, 1.0))</td><td>0.5543757826214424</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 91, 785, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 5.0, 42.0, 23.0, 1.0, 9.0, 1.0))</td><td>0.48459568081536064</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 62, 772, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 60.0, 19.0, 2.0, 8.0, 3.0))</td><td>0.6544141277441397</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 762, 1688, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 33.0, 21.0, 9.0))</td><td>0.47190532944229546</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 43, 52, 56, 58, 197, 781, 1617, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 31.0, 25.0, 9.0, 3.0))</td><td>0.5988277115100541</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1522, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 55.0, 23.0, 1.0, 9.0))</td><td>0.5205338407601277</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 53, 54, 58, 73, 787, 1640, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 42.0, 12.0, 2.0, 9.0, 1.0))</td><td>0.4809870743552845</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 103, 753, 1501, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 69.0, 20.0, 4.0, 8.0))</td><td>0.6976539046027439</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 58, 219, 824, 1565, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 68.0, 10.0, 6.0, 3.0))</td><td>0.5366276680059419</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 65, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 32.0, 17.0, 9.0, 2.0))</td><td>0.4179413830729079</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 62, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 50.0, 19.0, 8.0, 3.0))</td><td>0.518408876054554</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 105, 772, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 62.0, 13.0, 6.0, 1.0))</td><td>0.4549280838592917</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 141, 805, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 49.0, 4.0, 4.0))</td><td>0.38824944001956996</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 63, 761, 1486, 2239, 2243, 2247, 2251, 2254, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 37.0, 17.0, 5.0, 2.0))</td><td>0.44790450363026374</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 137, 755, 1563, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 43.0, 18.0, 1.0, 7.0, 2.0))</td><td>0.5103467335235394</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 290, 767, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 42.0, 13.0, 3.0, 8.0, 5.0))</td><td>0.5582598350534396</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 56, 58, 163, 755, 1553, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 6.0, 24.0, 17.0, 4.0, 1.0))</td><td>0.14790720957476122</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 72, 756, 1704, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 5.0, 49.0, 15.0, 7.0, 3.0))</td><td>0.41795482803520323</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 54, 58, 120, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 53.0, 6.0, 3.0, 2.0))</td><td>0.3521410589888431</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 159, 808, 1499, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 12.0, 10.0, 5.0, 1.0))</td><td>0.3414427533403659</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 65, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 36.0, 11.0, 6.0, 4.0))</td><td>0.35537125966726113</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 55, 58, 64, 776, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 22.0, 17.0, 8.0, 5.0))</td><td>0.34208869157025484</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 67, 754, 1495, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 44.0, 25.0, 8.0, 1.0))</td><td>0.5365585242078064</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 53, 54, 58, 63, 762, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 66.0, 27.0, 7.0, 3.0))</td><td>0.3981408881313028</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 90, 861, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 7.0, 2.0, 2.0, 8.0))</td><td>0.42964223390092426</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 109, 797, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 58.0, 17.0, 5.0, 3.0))</td><td>0.48376197941916954</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 137, 757, 1671, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 59.0, 12.0, 7.0, 1.0))</td><td>0.46617417058691696</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 63, 768, 1522, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 72.0, 29.0, 9.0, 2.0))</td><td>0.4590901417903206</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 72, 754, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 42.0, 21.0, 1.0, 4.0, 4.0))</td><td>0.478455742110203</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 64, 761, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 37.0, 10.0, 8.0, 2.0))</td><td>0.3914518261623003</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 79, 777, 1675, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 12.0, 7.0, 1.0, 5.0, 1.0))</td><td>0.5430731121009122</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 89, 866, 1567, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 36.0, 5.0, 5.0))</td><td>0.4335022046003033</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 66, 759, 1595, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 62.0, 10.0, 9.0))</td><td>0.4968490848062805</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 62, 783, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 65.0, 14.0, 7.0))</td><td>0.48491845491406815</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 842, 1632, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 57.0, 16.0, 1.0, 7.0, 2.0))</td><td>0.5700796466376015</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 71, 792, 1667, 2239, 2243, 2249, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 60.0, 5.0, 2.0, 6.0))</td><td>0.4700073380840228</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 43, 52, 54, 58, 68, 755, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 51.0, 19.0, 9.0, 3.0))</td><td>0.6385828498658466</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 72, 760, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 52.0, 8.0, 1.0, 9.0, 4.0))</td><td>0.6000367654351746</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 37, 53, 54, 58, 73, 876, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 56.0, 28.0, 8.0, 2.0))</td><td>0.4948157747419151</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 87, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 47.0, 6.0, 5.0))</td><td>0.427207425265447</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 71, 886, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 38.0, 7.0, 5.0, 1.0))</td><td>0.5127768208225771</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 95, 769, 1607, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 4.0, 51.0, 19.0, 5.0, 2.0))</td><td>0.30032333213688567</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 224, 771, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 58.0, 12.0, 5.0, 4.0))</td><td>0.4838566567412701</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 62, 756, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 49.0, 9.0, 8.0))</td><td>0.5803283524927318</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 775, 1508, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 46.0, 12.0, 6.0, 4.0))</td><td>0.37621764996053497</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 62, 758, 1508, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 7.0, 8.0, 4.0))</td><td>0.4688335990969216</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 72, 758, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 46.0, 14.0, 9.0, 1.0))</td><td>0.5841431082428183</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 72, 836, 1536, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2266, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 66.0, 16.0, 9.0, 1.0))</td><td>0.5800103144671575</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 55, 58, 62, 758, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 71.0, 15.0, 2.0, 9.0, 3.0))</td><td>0.6425956086545164</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 80, 760, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 44.0, 10.0, 5.0, 5.0, 2.0))</td><td>0.7633236122889556</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 97, 759, 1496, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 58.0, 8.0, 8.0, 2.0))</td><td>0.41442749244159954</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 53, 54, 58, 87, 767, 1616, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 71.0, 8.0, 4.0, 1.0))</td><td>0.44217240367228516</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 62, 772, 1511, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 57.0, 14.0, 1.0, 9.0, 3.0))</td><td>0.5593469111245888</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 86, 826, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 59.0, 23.0, 1.0, 7.0, 3.0))</td><td>0.3618470284010989</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 62, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 35.0, 7.0, 8.0, 3.0))</td><td>0.43576249185762495</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 40, 52, 54, 58, 91, 764, 1639, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 48.0, 8.0, 9.0))</td><td>0.5299805620869207</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 142, 753, 1674, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 34.0, 9.0, 1.0, 6.0, 3.0))</td><td>0.63680200434927</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 36, 53, 54, 58, 69, 774, 1949, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 53.0, 16.0, 4.0, 1.0))</td><td>0.26564135550629503</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 13, 35, 53, 54, 58, 73, 786, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 60.0, 15.0, 4.0, 9.0, 1.0))</td><td>0.6724733000699494</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 148, 753, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 41.0, 5.0, 2.0, 9.0))</td><td>0.2752588937515807</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 753, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 39.0, 14.0, 4.0, 1.0))</td><td>0.40324401001889093</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 84, 760, 1616, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 61.0, 11.0, 8.0, 4.0))</td><td>0.4549047654650197</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 196, 1148, 1557, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 80.0, 15.0, 8.0, 2.0))</td><td>0.47322961980942646</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 62, 758, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 57.0, 7.0, 6.0, 2.0))</td><td>0.4868594029896923</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 163, 756, 1665, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 47.0, 11.0, 9.0, 1.0))</td><td>0.25770440927831906</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 74, 766, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 66.0, 5.0, 1.0, 8.0, 3.0))</td><td>0.5653683410061996</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 146, 982, 1518, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 34.0, 12.0, 6.0, 1.0))</td><td>0.5149729196172783</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(5, 6, 9, 37, 52, 54, 58, 66, 755, 1561, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 40.0, 15.0, 1.0, 4.0))</td><td>0.36546099510290186</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 70, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 36.0, 8.0, 4.0, 3.0))</td><td>0.5235351602392234</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 136, 753, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 53.0, 18.0, 7.0, 1.0))</td><td>0.5435803878456685</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 34.0, 11.0, 4.0, 4.0))</td><td>0.31308114215662863</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 137, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 70.0, 14.0, 3.0, 6.0))</td><td>0.6217205335934164</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 418, 759, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 76.0, 20.0, 6.0, 5.0))</td><td>0.5592620108657627</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 89, 976, 1498, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 61.0, 20.0, 9.0, 1.0))</td><td>0.501375502905639</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 19, 35, 52, 54, 58, 90, 779, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 33.0, 5.0, 4.0, 2.0, 8.0))</td><td>0.4935184228435917</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 65, 763, 1540, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 84.0, 36.0, 9.0, 2.0))</td><td>0.36846698140123507</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 85, 758, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 72.0, 12.0, 2.0, 8.0))</td><td>0.5980645630206526</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 71, 857, 1482, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 37.0, 10.0, 5.0))</td><td>0.35275451355590265</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 646, 767, 1990, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 60.0, 17.0, 9.0, 5.0))</td><td>0.3518135783107344</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 11, 40, 52, 54, 58, 469, 756, 1625, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 42.0, 23.0, 8.0))</td><td>0.35536517741867</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 222, 777, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 52.0, 14.0, 2.0, 9.0, 2.0))</td><td>0.6152280358604781</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 116, 808, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 41.0, 6.0, 6.0))</td><td>0.5061729620113005</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 325, 1029, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 10.0, 1.0, 2.0, 8.0))</td><td>0.32076644808516325</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 64, 911, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 63.0, 18.0, 1.0, 7.0))</td><td>0.47414341282687666</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 52, 54, 58, 65, 765, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 46.0, 18.0, 9.0))</td><td>0.22788627630311356</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 65, 772, 1486, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 56.0, 25.0, 7.0, 3.0))</td><td>0.48321962323726736</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 94, 782, 1578, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 31.0, 12.0, 7.0, 7.0, 2.0))</td><td>0.6011902381258387</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 768, 1482, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 50.0, 14.0, 6.0, 4.0))</td><td>0.47724221153706153</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 263, 805, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 62.0, 17.0, 9.0, 3.0))</td><td>0.6101271920446456</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 38, 52, 54, 58, 62, 772, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 35.0, 13.0, 8.0))</td><td>0.45268165291192236</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 65, 760, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 39.0, 16.0, 8.0, 2.0))</td><td>0.5266699628791917</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 52, 54, 58, 71, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 65.0, 13.0, 1.0, 4.0, 2.0))</td><td>0.4398644126744173</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 80, 765, 1618, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 55.0, 9.0, 1.0, 9.0, 5.0))</td><td>0.6307326007860615</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 74, 763, 1617, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 52.0, 11.0, 8.0))</td><td>0.4999254050502073</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 86, 782, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 41.0, 10.0, 6.0, 5.0))</td><td>0.3232218163345544</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 102, 839, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 45.0, 11.0, 7.0, 2.0))</td><td>0.4399308289108828</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 70, 774, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 55.0, 11.0, 1.0, 9.0, 4.0))</td><td>0.39727592990180605</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 508, 770, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 81.0, 25.0, 9.0, 4.0))</td><td>0.39933330998985067</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 164, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 10.0, 1.0, 6.0, 3.0))</td><td>0.3432806755599075</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 773, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 55.0, 8.0, 1.0, 9.0))</td><td>0.5324820426671799</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 224, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 43.0, 4.0, 1.0, 8.0))</td><td>0.3101086447500446</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 224, 817, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 51.0, 15.0, 9.0, 2.0))</td><td>0.5771198310222512</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 63, 762, 1568, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 44.0, 15.0, 9.0))</td><td>0.32548845840942736</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 55, 58, 67, 754, 1555, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 25.0, 13.0, 2.0, 9.0, 1.0))</td><td>0.5070262453202887</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 95, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 4.0, 38.0, 19.0, 1.0, 3.0))</td><td>0.34532319791117544</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 55, 58, 66, 768, 1505, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 82.0, 18.0, 7.0))</td><td>0.6069058913062213</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 73, 803, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 12.0, 17.0, 2.0, 1.0, 9.0, 4.0))</td><td>0.6746836258980797</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 53, 54, 58, 77, 786, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 81.0, 16.0, 9.0))</td><td>0.18218205189808553</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 55, 58, 234, 1030, 1719, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 47.0, 25.0, 6.0, 5.0))</td><td>0.19841314650060604</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 109, 790, 1524, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2313, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 9.0, 1.0, 9.0))</td><td>0.5541091873318726</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 72, 755, 1608, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 50.0, 9.0, 7.0))</td><td>0.4332644450812855</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 55, 58, 83, 763, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 46.0, 16.0, 1.0, 9.0, 3.0))</td><td>0.5344268329096328</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 142, 864, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 44.0, 9.0, 1.0, 6.0, 5.0))</td><td>0.5229651517860827</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 85, 753, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 63.0, 16.0, 9.0))</td><td>0.4096786287168959</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 231, 758, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 29.0, 12.0, 6.0, 4.0))</td><td>0.4217187810991531</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 57, 58, 432, 771, 1500, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 13.0, 2.0, 9.0, 4.0))</td><td>0.5864445326462764</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 38, 52, 54, 58, 87, 754, 1643, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2308, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 73.0, 12.0, 9.0))</td><td>0.4266124645890414</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 66, 754, 1558, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 69.0, 9.0, 6.0, 2.0))</td><td>0.3918082078723777</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 63, 762, 1487, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 28.0, 27.0, 1.0, 8.0, 4.0))</td><td>0.4199157372076954</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 117, 753, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 59.0, 11.0, 1.0, 9.0, 2.0))</td><td>0.5129805363035437</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 63, 762, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 59.0, 16.0, 7.0))</td><td>0.39753897130595583</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 753, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 39.0, 19.0, 1.0, 9.0))</td><td>0.5899666021545047</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 463, 790, 1721, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 46.0, 7.0, 9.0, 2.0))</td><td>0.4208726721169085</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 87, 754, 1551, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 4.0, 37.0, 20.0, 5.0, 6.0))</td><td>0.7342140992805581</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 88, 757, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 50.0, 10.0, 4.0, 2.0))</td><td>0.33944765783191166</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 55, 58, 65, 756, 1486, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 72.0, 40.0, 9.0))</td><td>0.36377466666801483</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 55, 58, 67, 775, 1495, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 62.0, 25.0, 7.0))</td><td>0.5081907492242459</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 90, 753, 1617, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 81.0, 9.0, 7.0, 7.0))</td><td>0.4468668452933392</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 234, 966, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 56.0, 10.0, 7.0, 2.0))</td><td>0.13370047882627917</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 40, 53, 54, 58, 159, 808, 1525, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 24.0, 19.0, 5.0))</td><td>0.19156240832267724</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 247, 753, 1487, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2270, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 57.0, 17.0, 4.0, 9.0))</td><td>0.7567976684618596</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 96, 754, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 67.0, 21.0, 9.0, 3.0))</td><td>0.4912077095664331</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 757, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 41.0, 21.0, 9.0))</td><td>0.35746517270600964</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 54, 58, 63, 762, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 30.0, 17.0, 9.0))</td><td>0.4144440055401242</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 62, 756, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 78.0, 21.0, 1.0, 7.0, 1.0))</td><td>0.6227020203678988</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 62, 995, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 44.0, 11.0, 9.0, 2.0))</td><td>0.5144117883830663</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 127, 790, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 45.0, 24.0, 2.0, 9.0, 2.0))</td><td>0.5120992777163353</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 38, 52, 54, 59, 65, 963, 1629, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 25.0, 10.0, 8.0))</td><td>0.2875759836465803</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 94, 782, 1535, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 4.0, 74.0, 22.0, 1.0, 9.0, 4.0))</td><td>0.3805171200943057</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 134, 769, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 38.0, 10.0, 9.0))</td><td>0.5465751278340616</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 138, 847, 1502, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 47.0, 14.0, 1.0, 4.0, 7.0, 5.0))</td><td>0.7030539551932753</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 88, 767, 1626, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 40.0, 17.0, 9.0, 4.0))</td><td>0.4947051364620505</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 94, 798, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 45.0, 13.0, 1.0, 6.0))</td><td>0.5022090585618695</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 56, 58, 65, 756, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 6.0, 72.0, 38.0, 7.0))</td><td>0.4672628232728568</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 95, 796, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 61.0, 33.0, 9.0, 2.0))</td><td>0.3977958887535845</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 66, 832, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 77.0, 12.0, 2.0, 9.0, 3.0))</td><td>0.6502809501363315</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 850, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 61.0, 9.0, 7.0, 2.0))</td><td>0.47601831523598975</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 129, 999, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 51.0, 25.0, 7.0, 5.0))</td><td>0.2817030272610388</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 55, 58, 80, 959, 1536, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 38.0, 12.0, 7.0, 4.0))</td><td>0.6539771890149468</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 775, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 34.0, 23.0, 9.0, 1.0))</td><td>0.39675954605993685</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 114, 775, 1639, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 31.0, 13.0, 1.0, 9.0, 1.0))</td><td>0.6647188357151879</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 63, 762, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 34.0, 18.0, 9.0))</td><td>0.41097728516193305</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 53.0, 8.0, 5.0, 2.0))</td><td>0.36942287899876436</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 89, 887, 1751, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 67.0, 25.0, 6.0, 3.0))</td><td>0.41218603899314904</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 68, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 57.0, 8.0, 2.0, 6.0, 3.0))</td><td>0.6049405667762622</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 224, 1002, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 44.0, 12.0, 7.0, 2.0))</td><td>0.6737324707845268</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 54, 58, 75, 822, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 3.0, 97.0, 42.0, 9.0))</td><td>0.29295997517402556</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 35, 52, 54, 58, 83, 757, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 56.0, 10.0, 5.0, 4.0))</td><td>0.37776616555723025</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 83, 850, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 31.0, 22.0, 8.0, 4.0))</td><td>0.502393027044729</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 145, 760, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 61.0, 27.0, 1.0, 9.0, 1.0))</td><td>0.5304042906797728</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 13, 35, 52, 54, 58, 63, 762, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 37.0, 15.0, 2.0, 8.0, 2.0))</td><td>0.5447202284059359</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 62, 757, 1553, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 70.0, 9.0, 9.0, 3.0))</td><td>0.39173366779542074</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 135, 977, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 63.0, 17.0, 7.0, 3.0))</td><td>0.5458692446008375</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 70, 763, 2014, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 57.0, 19.0, 6.0))</td><td>0.872052976650384</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 55, 58, 185, 961, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 77.0, 26.0, 9.0, 4.0))</td><td>0.6211555384668935</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 67, 754, 1545, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 52.0, 12.0, 9.0))</td><td>0.4636785323236695</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 67, 763, 1484, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 63.0, 16.0, 1.0, 4.0, 1.0))</td><td>0.5246416820745112</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 55, 58, 137, 761, 1517, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2265, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 5.0, 83.0, 50.0, 9.0, 2.0))</td><td>0.5321540823507169</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 226, 757, 1527, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 21.0, 9.0, 4.0, 1.0, 6.0, 4.0))</td><td>0.3918424284913748</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 55, 58, 71, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 13.0, 8.0, 1.0))</td><td>0.4762053760043591</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 67, 756, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 47.0, 6.0, 1.0, 5.0))</td><td>0.3018730095338384</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 130, 754, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 67.0, 9.0, 4.0, 1.0))</td><td>0.3786359060867602</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 67, 754, 1499, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 59.0, 15.0, 9.0, 1.0))</td><td>0.5277001038345119</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 134, 761, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 68.0, 22.0, 9.0))</td><td>0.44020108040620376</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 75, 763, 1564, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 48.0, 7.0, 6.0))</td><td>0.39120420912795706</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 362, 802, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 31.0, 25.0, 5.0, 7.0))</td><td>0.17450241753873055</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 189, 758, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 37.0, 9.0, 3.0, 7.0, 6.0))</td><td>0.6624334758198387</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 383, 774, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 82.0, 17.0, 9.0, 3.0))</td><td>0.35430521645131074</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 53, 54, 58, 121, 767, 1751, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 53.0, 5.0, 3.0))</td><td>0.49187083864440145</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 148, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 34.0, 4.0, 1.0, 8.0))</td><td>0.24216374787331296</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 106, 764, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 72.0, 3.0, 7.0, 4.0))</td><td>0.46367793182119554</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 37, 52, 54, 58, 77, 753, 1534, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 75.0, 17.0, 7.0))</td><td>0.5048891709614276</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 246, 755, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 31.0, 6.0, 7.0, 3.0))</td><td>0.43877959377496084</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 65, 810, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 69.0, 14.0, 1.0, 9.0, 3.0))</td><td>0.45874127717352314</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 394, 767, 1806, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 45.0, 12.0, 1.0, 3.0, 7.0))</td><td>0.21633686736799745</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 281, 756, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 47.0, 11.0, 6.0))</td><td>0.6115685266276386</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 37, 52, 55, 58, 83, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 37.0, 12.0, 7.0, 5.0))</td><td>0.5080436747719996</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 59, 79, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 25.0, 13.0, 2.0, 7.0, 3.0))</td><td>0.5495640510758446</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 69, 755, 1491, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 51.0, 23.0, 4.0, 1.0))</td><td>0.33780287275489207</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 776, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 45.0, 21.0, 6.0, 1.0))</td><td>0.3771421284038631</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 36, 53, 54, 58, 109, 1012, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 3.0, 62.0, 11.0, 1.0, 9.0))</td><td>0.3265956227096105</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 86, 870, 1531, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 23.0, 15.0, 8.0))</td><td>0.447998963636947</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 174, 950, 1747, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 37.0, 14.0, 6.0, 4.0))</td><td>0.15574843209702366</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 762, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 37.0, 15.0, 8.0, 2.0))</td><td>0.43090056626607764</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 245, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 5.0, 1.0, 9.0))</td><td>0.21897776361115848</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 56, 58, 94, 771, 1592, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 34.0, 10.0, 2.0, 6.0, 4.0))</td><td>0.5591905206232232</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 52, 54, 58, 91, 785, 1573, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 37.0, 30.0, 4.0, 4.0))</td><td>0.4230113110052798</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 35, 52, 54, 58, 91, 797, 1576, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 68.0, 22.0, 9.0, 3.0))</td><td>0.19426752097641392</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 779, 1487, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 6.0, 83.0, 43.0, 1.0, 1.0, 9.0, 2.0))</td><td>0.5423759757734892</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 39.0, 4.0, 3.0, 2.0))</td><td>0.24047988186371033</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 43, 52, 54, 58, 62, 772, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 38.0, 14.0, 1.0, 9.0))</td><td>0.7053195534916803</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 19, 35, 52, 54, 58, 99, 899, 1522, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 77.0, 17.0, 6.0, 8.0))</td><td>0.40282081701138034</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 93, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 47.0, 20.0, 2.0))</td><td>0.3301572650506578</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 4.0, 2.0))</td><td>0.3330458142312754</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 791, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 6.0, 43.0, 10.0, 7.0, 1.0))</td><td>0.39599567416089504</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 588, 759, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 10.0, 13.0, 4.0, 4.0))</td><td>0.22483418861052715</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 139, 788, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 75.0, 16.0, 4.0, 9.0, 1.0))</td><td>0.7257924829379823</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 65, 775, 1523, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 68.0, 20.0, 7.0, 2.0))</td><td>0.3750977865151304</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 76, 768, 1841, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 28.0, 19.0, 1.0, 9.0))</td><td>0.6285207664575017</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 118, 756, 1780, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 35.0, 8.0, 4.0, 2.0))</td><td>0.4126500836236669</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 64, 812, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 40.0, 16.0, 4.0))</td><td>0.34392684342847435</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 40, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 51.0, 38.0, 6.0, 2.0))</td><td>0.3000433020671918</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 52, 54, 58, 274, 755, 1492, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 50.0, 22.0, 2.0, 1.0))</td><td>0.398151649206073</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 55, 58, 64, 761, 1544, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 39.0, 13.0, 2.0, 1.0, 7.0, 2.0))</td><td>0.6981442348922935</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 403, 793, 1553, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 53.0, 18.0, 7.0, 1.0))</td><td>0.2152271765482395</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 35, 52, 54, 58, 74, 755, 1858, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 53.0, 4.0, 9.0, 6.0))</td><td>0.43343927663411896</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 73, 763, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 32.0, 14.0, 9.0, 1.0))</td><td>0.516935403682127</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 784, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 13.0, 7.0, 6.0, 1.0))</td><td>0.438917226514848</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 109, 793, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 46.0, 12.0, 5.0, 2.0))</td><td>0.2878620666905499</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 97, 959, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 33.0, 6.0, 6.0, 4.0))</td><td>0.43244814501107615</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 37, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 5.0, 67.0, 46.0, 7.0, 2.0))</td><td>0.3230696720386945</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 62, 932, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 61.0, 25.0, 2.0, 1.0, 9.0, 3.0))</td><td>0.7958974345340968</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 832, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 68.0, 29.0, 9.0, 2.0))</td><td>0.5417185450875092</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 63, 762, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 37.0, 13.0, 1.0, 5.0, 2.0))</td><td>0.4327022303964118</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 63, 791, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 35.0, 9.0, 5.0))</td><td>0.3928706557175412</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 107, 788, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 44.0, 11.0, 9.0, 1.0))</td><td>0.5323942046667338</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 86, 804, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 61.0, 10.0, 1.0, 4.0, 1.0))</td><td>0.34984170135182746</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 104, 1300, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 72.0, 17.0, 9.0, 2.0))</td><td>0.31775535580814945</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 59, 62, 768, 1529, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 65.0, 20.0, 2.0, 9.0))</td><td>0.6846150569265013</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 819, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 54.0, 14.0, 9.0, 3.0))</td><td>0.6573656424568456</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 106, 811, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 64.0, 14.0, 3.0, 7.0, 4.0))</td><td>0.7639929841214372</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 766, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 65.0, 12.0, 9.0))</td><td>0.44772712837151846</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 210, 792, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 51.0, 16.0, 6.0))</td><td>0.4442046309598451</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 43.0, 16.0, 2.0, 8.0))</td><td>0.5824009037122329</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 53, 54, 58, 66, 874, 1565, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 56.0, 19.0, 7.0))</td><td>0.47746864376266757</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 63, 776, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 34.0, 14.0, 6.0, 1.0))</td><td>0.3898789965399498</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 68, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 11.0, 2.0, 5.0, 6.0))</td><td>0.5539915568763099</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 87, 793, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 61.0, 9.0, 5.0, 2.0))</td><td>0.32098752948138876</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 54, 58, 80, 759, 1604, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 2.0, 55.0, 26.0, 1.0, 9.0))</td><td>0.5610382253169652</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 375, 802, 1710, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 15.0, 8.0, 5.0))</td><td>0.058465155944159186</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 129, 767, 1709, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 12.0, 1.0, 3.0, 7.0))</td><td>0.2705370684037682</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 66, 756, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 53.0, 6.0, 7.0))</td><td>0.43883947189697353</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 130, 756, 1484, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 22.0, 21.0, 7.0, 1.0))</td><td>0.44013583461346567</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 59, 63, 776, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 76.0, 29.0, 7.0, 1.0))</td><td>0.46766687636504417</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 130, 767, 1491, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 40.0, 11.0, 6.0, 1.0))</td><td>0.48391359717229365</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 37, 52, 54, 58, 137, 761, 1517, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 84.0, 45.0, 6.0))</td><td>0.5397781394329231</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 46.0, 13.0, 6.0, 1.0))</td><td>0.35710815990412365</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 64, 789, 1481, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 69.0, 18.0, 7.0, 4.0))</td><td>0.3920886689347733</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 57.0, 13.0, 3.0, 7.0))</td><td>0.6274118743531233</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 63, 763, 1482, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 69.0, 26.0, 7.0, 1.0))</td><td>0.3649530703528291</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 66, 763, 2165, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 61.0, 7.0, 7.0, 3.0))</td><td>0.1300851664708081</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 79, 816, 1587, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 51.0, 12.0, 9.0))</td><td>0.4825416806890145</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 92, 772, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 17.0, 13.0, 7.0, 3.0))</td><td>0.4487622318405795</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 154, 755, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 31.0, 12.0, 3.0, 2.0))</td><td>0.28394765278759326</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 67, 756, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 73.0, 8.0, 5.0, 6.0))</td><td>0.3995305889717539</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 55, 58, 224, 771, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 57.0, 14.0, 8.0, 1.0))</td><td>0.5259306200498667</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 98, 835, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 73.0, 7.0, 2.0, 7.0))</td><td>0.3970556282584116</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 89, 777, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 70.0, 9.0, 7.0, 2.0))</td><td>0.5057829134696442</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 84, 811, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 13.0, 5.0, 1.0))</td><td>0.51295528335694</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 40, 52, 55, 58, 229, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 19.0, 18.0, 5.0, 1.0))</td><td>0.17641780275414376</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 65, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 28.0, 23.0, 8.0))</td><td>0.4463248411654499</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 15.0, 1.0, 7.0, 5.0))</td><td>0.45115726897327624</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 63, 762, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 52.0, 13.0, 8.0, 1.0))</td><td>0.47210090708488467</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 66, 910, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 52.0, 10.0, 5.0, 4.0))</td><td>0.3350596679597668</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 66, 763, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 22.0, 14.0, 3.0, 2.0))</td><td>0.43695878993005066</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 135, 753, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 23.0, 4.0, 6.0, 3.0))</td><td>0.38172822659230715</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 113, 753, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 43.0, 7.0, 7.0, 2.0))</td><td>0.4382978198183998</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 53, 54, 58, 68, 767, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 58.0, 7.0, 5.0))</td><td>0.5219764539877956</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 36, 52, 54, 58, 63, 782, 1606, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 55.0, 14.0, 7.0, 1.0))</td><td>0.4157047072511968</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 94, 771, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 57.0, 14.0, 5.0, 2.0))</td><td>0.464213854951009</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 83, 973, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 62.0, 22.0, 1.0, 6.0, 4.0))</td><td>0.48615232142830234</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 11, 36, 52, 54, 58, 92, 764, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 55.0, 16.0, 7.0, 2.0))</td><td>0.5169670506154702</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 53, 54, 58, 66, 768, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 16.0, 19.0, 6.0))</td><td>0.4470895864905031</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 174, 1166, 1512, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 34.0, 11.0, 5.0, 4.0))</td><td>0.16651576070830687</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 210, 764, 1744, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 32.0, 23.0, 9.0))</td><td>0.5583641450083392</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 783, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 6.0, 1.0, 4.0))</td><td>0.37787193540074127</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 149, 769, 1543, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 23.0, 12.0, 6.0, 3.0))</td><td>0.32016517149808926</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 74, 826, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 40.0, 5.0, 4.0, 2.0))</td><td>0.4720377438758725</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 77, 845, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 4.0, 85.0, 29.0, 1.0, 9.0))</td><td>0.46650867629287657</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 74, 765, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 53.0, 9.0, 1.0, 8.0, 5.0))</td><td>0.5728855152963284</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 127, 825, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 39.0, 12.0, 4.0))</td><td>0.3353470865890682</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 214, 767, 1873, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 22.0, 16.0, 6.0, 7.0))</td><td>0.12342736354346712</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 49.0, 8.0, 1.0, 4.0, 2.0))</td><td>0.2962284927205031</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 72, 832, 1555, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 37.0, 11.0, 1.0, 8.0, 3.0))</td><td>0.5420132191485286</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 40, 52, 54, 58, 295, 805, 1531, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 56.0, 8.0, 1.0, 2.0, 8.0, 4.0))</td><td>0.5232718247164447</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 71, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 10.0, 6.0, 2.0))</td><td>0.46121672504808175</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 760, 1515, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 43.0, 12.0, 7.0, 1.0))</td><td>0.5865610979769991</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 380, 761, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 21.0, 12.0, 6.0, 1.0))</td><td>0.44128468585409814</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 55, 58, 65, 759, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 81.0, 49.0, 7.0, 4.0))</td><td>0.30961681569352995</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 41, 52, 54, 58, 337, 839, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 89.0, 15.0, 7.0, 3.0))</td><td>0.43585321186735543</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 65, 761, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 47.0, 9.0, 1.0, 5.0))</td><td>0.3609042091178215</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 83, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 39.0, 12.0, 4.0, 2.0))</td><td>0.40374560191334996</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 57, 58, 88, 755, 1482, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 53.0, 10.0, 4.0, 4.0))</td><td>0.352649047589406</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 57, 58, 63, 769, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 63.0, 25.0, 6.0))</td><td>0.3894581944887644</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 62, 1114, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2280, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 68.0, 25.0, 9.0, 4.0))</td><td>0.6110784630911062</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 67, 755, 1514, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 40.0, 8.0, 3.0, 2.0))</td><td>0.4002625030117353</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 35, 52, 54, 59, 390, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 62.0, 21.0, 9.0, 5.0))</td><td>0.4083007943427424</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 62, 1002, 1527, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 22.0, 8.0, 7.0, 2.0))</td><td>0.5724829531710824</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 55, 58, 81, 768, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 23.0, 19.0, 8.0))</td><td>0.4677200092676684</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 18, 35, 52, 54, 58, 63, 753, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 78.0, 30.0, 8.0))</td><td>0.46450204934165673</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 95, 767, 1607, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 48.0, 19.0, 4.0, 4.0))</td><td>0.3111111948321448</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 89, 767, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 68.0, 13.0, 3.0, 9.0))</td><td>0.6487159971756394</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 48.0, 12.0, 1.0, 6.0))</td><td>0.3961836378653525</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 55, 58, 69, 763, 1492, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 17.0, 20.0, 2.0, 4.0))</td><td>0.3367125959495544</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 70, 798, 1633, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 50.0, 21.0, 9.0, 2.0))</td><td>0.4420967960979181</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 10, 37, 53, 54, 58, 99, 753, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 29.0, 13.0, 7.0, 3.0))</td><td>0.2571632629921756</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 66, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 42.0, 4.0, 3.0, 2.0))</td><td>0.3389757981397037</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 53, 54, 58, 65, 765, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 25.0, 16.0, 8.0))</td><td>0.1445576117817376</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 78, 753, 1651, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 4.0, 78.0, 17.0, 1.0, 9.0))</td><td>0.4455980777897968</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 80, 799, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 63.0, 15.0, 2.0, 8.0, 4.0))</td><td>0.5730955960369005</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 10, 35, 53, 54, 58, 99, 961, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 64.0, 8.0, 9.0, 3.0))</td><td>0.45550577161337996</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 131, 770, 1765, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 4.0, 43.0, 17.0, 4.0, 9.0))</td><td>0.592956126326804</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 123, 774, 1513, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 1.0, 72.0, 15.0, 3.0, 9.0, 3.0))</td><td>0.5843809076764386</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 65.0, 33.0, 5.0, 1.0))</td><td>0.31865554606736124</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 197, 798, 1803, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 48.0, 7.0, 1.0, 6.0, 5.0))</td><td>0.516923487562073</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 53, 54, 58, 388, 987, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 4.0, 76.0, 24.0, 1.0, 9.0, 5.0))</td><td>0.3957470894175252</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 39, 52, 56, 58, 82, 935, 1497, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 64.0, 24.0, 1.0, 7.0, 3.0))</td><td>0.3880262849372963</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 147, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 24.0, 11.0, 3.0, 2.0))</td><td>0.2970037958772549</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 39.0, 9.0, 3.0, 2.0))</td><td>0.3086758239353416</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 102, 767, 1803, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 31.0, 8.0, 4.0, 9.0))</td><td>0.7443324665748963</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 95, 864, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 4.0, 20.0, 17.0, 8.0, 2.0))</td><td>0.4328996306135816</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 56, 58, 66, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 65.0, 37.0, 9.0, 5.0))</td><td>0.49327738015345335</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 335, 761, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 52.0, 31.0, 8.0))</td><td>0.42568291332430636</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 62, 824, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 92.0, 22.0, 2.0, 9.0, 1.0))</td><td>0.6728332622005713</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 95, 767, 1540, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 55.0, 18.0, 9.0))</td><td>0.4069588938274318</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 55, 58, 93, 754, 1525, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 56.0, 32.0, 6.0))</td><td>0.5150075716547744</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 52, 54, 58, 66, 1208, 1522, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 74.0, 23.0, 3.0, 9.0, 1.0))</td><td>0.05684286750600648</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 65, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 45.0, 18.0, 6.0, 4.0))</td><td>0.2870476999858153</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 287, 758, 1609, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 30.0, 15.0, 4.0, 9.0, 4.0))</td><td>0.6317638606590832</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 76, 754, 1488, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 38.0, 9.0, 9.0))</td><td>0.6020934374192533</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 77, 798, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 24.0, 16.0, 6.0))</td><td>0.4310870874502372</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 758, 1529, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 52.0, 13.0, 9.0))</td><td>0.5773723013899492</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 66, 769, 1546, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 51.0, 18.0, 9.0))</td><td>0.4561220390060513</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 24, 35, 52, 55, 58, 70, 771, 1894, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 52.0, 9.0, 7.0, 2.0))</td><td>0.5383403480617419</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 40, 52, 54, 58, 100, 759, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 45.0, 11.0, 1.0, 9.0))</td><td>0.475295863521345</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 186, 773, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 7.0, 8.0, 3.0))</td><td>0.4279630658073857</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 77, 753, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 40.0, 13.0, 5.0, 3.0))</td><td>0.3931532351491857</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 149, 754, 1497, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 58.0, 14.0, 9.0, 6.0))</td><td>0.44967040049806045</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 66, 825, 1485, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 82.0, 28.0, 9.0))</td><td>0.4994707439785534</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 67, 754, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 39.0, 12.0, 9.0, 1.0))</td><td>0.5014521170651254</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 196, 1148, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 54.0, 4.0, 7.0, 6.0))</td><td>0.39077288574791175</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 55, 58, 64, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 33.0, 7.0, 3.0, 2.0))</td><td>0.3055301093264299</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 52, 54, 58, 64, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 13.0, 6.0))</td><td>0.4358113050183041</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 70, 754, 1499, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 1.0, 45.0, 18.0, 9.0, 3.0))</td><td>0.5493849693302909</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 107, 781, 1619, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 22.0, 23.0, 8.0))</td><td>0.4453802681068516</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 104, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 47.0, 11.0, 4.0))</td><td>0.39100396760088746</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 11, 35, 52, 54, 58, 90, 754, 1528, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 66.0, 25.0, 7.0, 2.0))</td><td>0.5325028539881009</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 85, 819, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 50.0, 4.0, 9.0, 1.0))</td><td>0.5634012311382048</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 36, 53, 54, 58, 183, 758, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 29.0, 8.0, 6.0))</td><td>0.0038502207111076325</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 53.0, 15.0, 5.0, 1.0))</td><td>0.37593279379276345</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 58, 194, 878, 1522, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 6.0, 75.0, 28.0, 9.0, 3.0))</td><td>0.5419285829607133</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 791, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 42.0, 8.0, 5.0))</td><td>0.29528318188995617</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 73, 763, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 46.0, 21.0, 6.0, 8.0, 3.0))</td><td>0.7441716646699414</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 54, 58, 80, 764, 1554, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 42.0, 8.0, 2.0, 9.0, 3.0))</td><td>0.6291788025272267</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 98, 795, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 66.0, 10.0, 6.0, 1.0))</td><td>0.5151519280354544</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 64, 776, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 69.0, 6.0, 5.0, 1.0))</td><td>0.443660756352065</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 38, 52, 54, 58, 65, 754, 1643, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 62.0, 17.0, 1.0, 9.0, 1.0))</td><td>0.2955279114522038</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 36, 52, 55, 59, 142, 828, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2272, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 25.0, 16.0, 9.0))</td><td>0.645511980207143</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 62, 760, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 64.0, 11.0, 9.0))</td><td>0.5566166625470893</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 148, 1063, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 7.0, 2.0, 4.0))</td><td>0.141570421743417</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 53, 54, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 49.0, 10.0, 7.0, 3.0))</td><td>0.3617343525172901</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 64, 755, 1557, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 11.0, 4.0, 4.0))</td><td>0.3753752575962695</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 55, 58, 148, 754, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 37.0, 11.0, 4.0, 1.0))</td><td>0.38119212349051745</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 38, 53, 54, 58, 183, 758, 1796, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 10.0, 6.0, 1.0, 6.0))</td><td>-0.07085899234720677</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 79, 767, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 35.0, 9.0, 1.0, 2.0, 1.0))</td><td>0.5086274088290877</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 99, 753, 1628, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 25.0, 10.0, 8.0))</td><td>0.36105153676240864</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 41, 52, 54, 58, 111, 796, 1526, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 56.0, 35.0, 7.0, 1.0))</td><td>0.45729077266231466</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 414, 1530, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 19.0, 8.0, 8.0))</td><td>0.5946828710397873</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 304, 770, 1484, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 49.0, 11.0, 6.0, 3.0))</td><td>0.4632054651930009</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 96, 793, 1526, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 56.0, 17.0, 1.0, 9.0, 6.0))</td><td>0.5178581003214455</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 53, 54, 58, 62, 895, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 57.0, 21.0, 5.0))</td><td>0.5496912966651062</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 13, 35, 52, 54, 58, 105, 759, 1521, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 95.0, 26.0, 7.0))</td><td>0.47466911616385127</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 55, 58, 129, 827, 1991, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 22.0, 14.0, 4.0, 7.0))</td><td>0.1757985344762691</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 53, 54, 58, 75, 898, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 25.0, 17.0, 8.0, 2.0))</td><td>0.39135269364887604</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 754, 1488, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 66.0, 14.0, 9.0, 1.0))</td><td>0.5665452315540727</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 106, 755, 1585, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 17.0, 14.0, 1.0, 6.0, 2.0))</td><td>0.5182972664877341</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 139, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 62.0, 8.0, 7.0, 2.0))</td><td>0.5060924445162001</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 74, 758, 1641, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 45.0, 17.0, 9.0))</td><td>0.5209387935355109</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 113, 811, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2272, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 26.0, 16.0, 3.0, 8.0, 2.0))</td><td>0.6832149927675923</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 62, 760, 1651, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 45.0, 5.0, 5.0, 1.0))</td><td>0.5509121916391337</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 162, 766, 1753, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 58.0, 7.0, 1.0, 3.0, 5.0))</td><td>0.5398762014674534</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 93, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 12.0, 3.0))</td><td>0.2961833007075563</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 54, 58, 67, 772, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 53.0, 14.0, 9.0))</td><td>0.599025072141327</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 52, 54, 58, 128, 902, 1740, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 26.0, 21.0, 1.0, 8.0, 1.0))</td><td>0.09119721795510422</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(5, 7, 9, 43, 52, 54, 58, 263, 758, 1880, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 39.0, 27.0, 1.0, 9.0, 1.0))</td><td>0.7585314227982445</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 555, 834, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2266, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 27.0, 23.0, 8.0, 1.0))</td><td>0.4791682245047027</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 68, 818, 1743, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 75.0, 16.0, 5.0))</td><td>0.6939479448531908</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 54, 58, 69, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 19.0, 16.0, 3.0))</td><td>0.34726134892392196</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 326, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 1.0, 21.0, 2.0, 2.0))</td><td>0.3492546131495866</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 16, 36, 53, 54, 58, 138, 845, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 60.0, 18.0, 3.0, 1.0))</td><td>0.3319444377309123</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 149, 763, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 22.0, 12.0, 6.0, 3.0))</td><td>0.389083435798545</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 68, 779, 1626, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 63.0, 18.0, 4.0, 1.0, 4.0, 4.0))</td><td>0.6548041695855658</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 160, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2270, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 47.0, 10.0, 4.0, 3.0))</td><td>0.3215627619223204</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 200, 919, 1564, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 54.0, 17.0, 7.0, 2.0))</td><td>0.5067893800391566</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 101, 830, 1871, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 45.0, 18.0, 6.0, 2.0))</td><td>0.635402638507777</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 143, 778, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 57.0, 7.0, 4.0, 5.0))</td><td>0.2799282505014868</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 89, 799, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 70.0, 23.0, 5.0, 6.0))</td><td>0.4383577877668074</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 77, 753, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 55.0, 13.0, 1.0, 6.0))</td><td>0.4606678418194047</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 66, 759, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 42.0, 9.0, 9.0, 6.0))</td><td>0.41394080621332663</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 309, 989, 1806, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 36.0, 20.0, 9.0))</td><td>0.20189759554221606</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 38, 52, 54, 58, 104, 753, 1612, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 71.0, 16.0, 6.0, 4.0))</td><td>0.4126263032699512</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 55, 58, 63, 755, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 56.0, 32.0, 4.0, 1.0))</td><td>0.3420238993131625</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 53, 54, 58, 68, 756, 1548, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 61.0, 12.0, 9.0, 3.0))</td><td>0.5097418997786998</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 46.0, 10.0, 2.0, 5.0, 1.0))</td><td>0.46652575236901495</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 40, 52, 54, 58, 87, 753, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 8.0, 5.0, 1.0))</td><td>0.3954271123422852</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 46.0, 9.0, 4.0, 1.0))</td><td>0.3565297364149951</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 39, 52, 54, 58, 145, 794, 1503, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 9.0, 4.0, 5.0))</td><td>0.2905588541171152</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 81, 773, 1568, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 87.0, 35.0, 9.0, 1.0))</td><td>0.3409474221872153</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 78, 753, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 2.0, 102.0, 34.0, 7.0, 5.0))</td><td>0.47283336494407696</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 97, 754, 1646, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 46.0, 17.0, 9.0, 1.0))</td><td>0.5058842115994928</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 94, 753, 1532, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 57.0, 6.0, 1.0, 9.0, 2.0))</td><td>0.44369464506430945</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 54, 58, 62, 777, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 5.0, 69.0, 21.0, 2.0, 9.0))</td><td>0.7266002556266093</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 54, 58, 75, 758, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 36.0, 9.0, 5.0, 6.0))</td><td>0.13466962337323768</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 77, 885, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 27.0, 16.0, 1.0, 8.0))</td><td>0.6471660097959321</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 63, 777, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 30.0, 12.0, 8.0, 2.0))</td><td>0.4666274661526697</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 78, 754, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 91.0, 11.0, 1.0, 6.0, 4.0))</td><td>0.5065136503392239</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 55, 58, 65, 833, 1588, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 72.0, 11.0, 6.0, 3.0))</td><td>0.49110107644902995</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 53, 54, 58, 65, 761, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 48.0, 14.0, 3.0, 5.0))</td><td>0.24235606825012912</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 66, 754, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 66.0, 10.0, 9.0, 4.0))</td><td>0.503027790149992</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 54, 58, 73, 910, 1578, 2239, 2243, 2247, 2251, 2254, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 47.0, 17.0, 8.0, 5.0))</td><td>0.3876717471153184</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 24.0, 14.0, 4.0, 1.0))</td><td>0.27730970175803643</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 62, 768, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 28.0, 30.0, 7.0, 1.0))</td><td>0.6058060060369346</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 83, 868, 1690, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 45.0, 13.0, 8.0, 1.0))</td><td>0.5629367664865254</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 52, 54, 58, 85, 778, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 33.0, 7.0, 7.0, 1.0))</td><td>0.4168517062597744</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 74, 766, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 32.0, 7.0, 8.0))</td><td>0.4103758183483085</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 113, 775, 1496, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 51.0, 14.0, 9.0))</td><td>0.4606336202562616</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 1002, 1712, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2265, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 58.0, 17.0, 8.0, 4.0))</td><td>0.7891706513102545</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 756, 1494, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 46.0, 13.0, 8.0, 1.0))</td><td>0.4332330520397724</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 65, 932, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 45.0, 16.0, 3.0, 9.0, 2.0))</td><td>0.6622191927807854</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 42, 52, 54, 58, 174, 1041, 1719, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 2.0, 18.0, 4.0, 2.0))</td><td>0.31322564713820766</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 66, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 10.0, 6.0))</td><td>0.4605725697469614</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 63, 982, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 48.0, 40.0, 6.0, 1.0))</td><td>0.35292008901980043</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 70, 754, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 49.0, 25.0, 6.0))</td><td>0.5272149998026563</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 83, 773, 1562, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 18.0, 30.0, 5.0, 5.0))</td><td>0.5064506582404033</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 215, 867, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 34.0, 9.0, 6.0))</td><td>0.4842793773160605</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 765, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 53.0, 8.0, 1.0, 5.0))</td><td>0.5684399498923773</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 57, 58, 64, 1176, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 64.0, 14.0, 7.0))</td><td>0.42151011733548865</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 54, 58, 94, 754, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 1.0, 37.0, 20.0, 9.0, 1.0))</td><td>0.45468484513774016</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 68, 792, 1494, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 64.0, 14.0, 5.0, 1.0))</td><td>0.569888063608629</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 161, 780, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 3.0, 2.0))</td><td>0.3015862923395183</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 55, 58, 255, 1178, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 63.0, 9.0, 6.0, 1.0))</td><td>0.5812332501679669</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 88, 777, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 39.0, 10.0, 9.0, 5.0))</td><td>0.49218049020848653</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 64, 773, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 62.0, 21.0, 8.0))</td><td>0.49790169734601525</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 67, 759, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 76.0, 12.0, 9.0, 3.0))</td><td>0.503008142017501</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 62, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 51.0, 19.0, 9.0))</td><td>0.5275968826473193</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 56, 58, 80, 771, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 26.0, 10.0, 1.0, 1.0, 9.0))</td><td>0.6723782935753818</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 53, 54, 58, 68, 786, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 88.0, 12.0, 5.0, 2.0))</td><td>0.21492191556882984</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 67, 834, 1489, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 27.0, 19.0, 6.0))</td><td>0.48775649909948937</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 85, 755, 1590, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 44.0, 15.0, 8.0, 2.0))</td><td>0.4488221582905743</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 37, 52, 54, 58, 105, 761, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 27.0, 13.0, 8.0))</td><td>0.4804179858750661</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 96, 840, 1493, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 37.0, 2.0, 4.0, 1.0))</td><td>0.47548056425291024</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 62, 757, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 57.0, 12.0, 5.0, 1.0))</td><td>0.44781984794185603</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 92, 775, 1595, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 10.0, 5.0, 6.0, 1.0))</td><td>0.45940620724182857</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 62, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 32.0, 18.0, 9.0, 2.0))</td><td>0.4936141963526422</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 71.0, 12.0, 5.0, 2.0))</td><td>0.4488790158611313</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 97, 753, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 61.0, 7.0, 3.0, 5.0))</td><td>0.4111118146174487</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 75, 759, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 55.0, 18.0, 8.0, 3.0))</td><td>0.4739819325925695</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 85, 842, 1500, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 40.0, 8.0, 8.0, 2.0))</td><td>0.578791101578059</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 146, 790, 1706, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 5.0, 59.0, 35.0, 1.0, 1.0, 8.0, 7.0))</td><td>0.5730244213088527</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 267, 759, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 70.0, 13.0, 9.0, 1.0))</td><td>0.44384897955668356</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 16, 35, 52, 54, 58, 80, 754, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 74.0, 20.0, 1.0, 9.0, 1.0))</td><td>0.5889852982401871</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 161, 780, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 26.0, 9.0, 8.0, 3.0))</td><td>0.41526816231047764</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 137, 775, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 11.0, 6.0))</td><td>0.5085929032418037</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 66, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 38.0, 13.0, 7.0))</td><td>0.4860639562995077</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 19.0, 6.0))</td><td>0.3366660609338564</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 191, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 38.0, 14.0, 1.0, 9.0, 4.0))</td><td>0.4798200661226778</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 84, 811, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 67.0, 14.0, 3.0, 9.0, 4.0))</td><td>0.6223318749853471</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 66, 778, 1574, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 20.0, 22.0, 1.0, 8.0, 6.0))</td><td>0.4469233825232206</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 36, 52, 55, 58, 128, 897, 1542, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 40.0, 13.0, 1.0, 7.0, 1.0))</td><td>0.4441297928123564</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 62, 772, 1677, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 54.0, 9.0, 4.0, 1.0))</td><td>0.44079379310575934</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 38.0, 18.0, 2.0, 6.0))</td><td>0.44926541621002786</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 178, 1511, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 5.0, 58.0, 24.0, 9.0))</td><td>0.4169259766736698</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 68, 753, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2269, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 24.0, 26.0, 1.0, 8.0, 1.0))</td><td>0.5190266396360106</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 274, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 52.0, 16.0, 1.0, 2.0, 6.0, 1.0))</td><td>0.5900105173008461</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 93, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 42.0, 4.0, 6.0))</td><td>0.3884000934033597</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 118, 799, 1534, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 77.0, 33.0, 8.0, 2.0))</td><td>0.40036040144247825</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 73, 815, 1591, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 62.0, 26.0, 1.0, 9.0, 1.0, 9.0, 7.0))</td><td>0.8932523121341697</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 773, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 67.0, 16.0, 9.0, 2.0))</td><td>0.49652221586036716</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 41, 52, 54, 58, 92, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 49.0, 22.0, 7.0))</td><td>0.491437128578763</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 63, 762, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 11.0, 4.0, 1.0))</td><td>0.44266063795920996</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 66, 801, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 43.0, 13.0, 9.0))</td><td>0.4006174706384096</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 91, 764, 1520, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 17.0, 9.0, 4.0))</td><td>0.43742153769307035</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 170, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 43.0, 7.0, 8.0, 2.0))</td><td>0.3982701117425878</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 37, 53, 54, 58, 99, 754, 1532, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 20.0, 13.0, 8.0))</td><td>0.11460723300575287</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 36, 52, 54, 58, 133, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 47.0, 24.0, 7.0, 1.0))</td><td>0.5601738140332215</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 93, 758, 1549, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 65.0, 23.0, 8.0))</td><td>0.5201766749523712</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 756, 1485, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 48.0, 41.0, 9.0, 1.0))</td><td>0.4847876769599403</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 55, 58, 167, 857, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 43.0, 25.0, 4.0, 2.0))</td><td>0.26082714818259295</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 17, 36, 53, 54, 58, 63, 784, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 48.0, 22.0, 8.0, 2.0))</td><td>0.3532819761931362</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 74, 815, 1562, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 30.0, 7.0, 8.0, 2.0))</td><td>0.39593178934551415</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 81, 768, 1767, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 27.0, 16.0, 6.0))</td><td>0.45018861388714276</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 35, 52, 54, 58, 102, 754, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 75.0, 24.0, 1.0, 9.0, 3.0))</td><td>0.5697002774124691</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 91, 771, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 67.0, 18.0, 1.0, 9.0, 1.0))</td><td>0.596720796848351</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 368, 851, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 23.0, 9.0, 8.0))</td><td>0.5108205625750125</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 65, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 69.0, 19.0, 7.0, 2.0))</td><td>0.41813068042992485</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 90, 1073, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 47.0, 7.0, 5.0))</td><td>0.46416170043519955</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 55, 58, 89, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 53.0, 10.0, 1.0, 9.0))</td><td>0.49418993123648236</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 92, 765, 1579, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 51.0, 22.0, 6.0, 1.0))</td><td>0.4603462729577086</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 644, 999, 1611, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 46.0, 4.0, 6.0, 5.0))</td><td>0.07430249688387464</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 52, 54, 58, 65, 761, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 23.0, 13.0, 7.0))</td><td>0.3088569455500879</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 77, 773, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 47.0, 10.0, 1.0, 6.0, 6.0))</td><td>0.3811102820422535</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 36, 52, 54, 58, 69, 769, 1512, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 2.0, 20.0, 9.0, 1.0))</td><td>0.4231415227128722</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 53, 54, 58, 330, 931, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 34.0, 9.0, 9.0))</td><td>0.23714041346725243</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 9.0, 5.0))</td><td>0.27844896774495925</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 10, 36, 52, 54, 58, 211, 756, 1511, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 43.0, 20.0, 2.0, 9.0))</td><td>0.5502500745151035</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 239, 801, 1692, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 63.0, 10.0, 5.0, 2.0))</td><td>0.4316277264619956</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 62, 772, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 40.0, 12.0, 9.0))</td><td>0.4351796791693063</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 54, 58, 112, 753, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 48.0, 9.0, 9.0, 2.0))</td><td>0.4713157478809139</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 53, 54, 58, 99, 774, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 19.0, 7.0, 8.0, 3.0))</td><td>-0.005079381354845591</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 810, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 25.0, 21.0, 9.0, 1.0))</td><td>0.4874047685252522</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 137, 777, 1519, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 10.0, 1.0, 5.0, 5.0))</td><td>0.4509949908618473</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 63, 755, 1568, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 13.0, 7.0, 3.0, 2.0))</td><td>0.144642734610205</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 59, 96, 781, 1521, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 76.0, 25.0, 9.0, 1.0))</td><td>0.5006141203003888</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 119, 916, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 59.0, 14.0, 5.0))</td><td>0.3673291370229643</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 24, 36, 53, 54, 58, 70, 850, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 39.0, 12.0, 4.0, 4.0))</td><td>0.3390337326604843</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 36, 52, 54, 58, 63, 774, 1522, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 64.0, 22.0, 8.0, 1.0))</td><td>0.3411083726753153</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 36, 52, 54, 58, 69, 757, 1481, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 19.0, 30.0, 5.0, 2.0))</td><td>0.3979841047164755</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 142, 755, 1563, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 2.0, 15.0, 8.0, 2.0))</td><td>0.45991436240350075</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 58, 67, 756, 1685, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 49.0, 17.0, 7.0, 3.0))</td><td>0.49019388436680167</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 108, 774, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 62.0, 4.0, 9.0, 2.0))</td><td>0.3871313676216672</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 74, 827, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 46.0, 4.0, 6.0, 3.0))</td><td>0.47509498079832596</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 35, 52, 55, 58, 63, 754, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 62.0, 12.0, 6.0, 1.0))</td><td>0.3797618647275127</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 42, 52, 54, 58, 91, 797, 1489, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 35.0, 26.0, 9.0, 3.0))</td><td>0.6368869072620977</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 35, 53, 54, 58, 64, 761, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 38.0, 5.0, 7.0, 3.0))</td><td>0.3333706126625667</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 67, 754, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 16.0, 5.0, 1.0))</td><td>0.44052024944675866</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 36, 52, 55, 58, 116, 783, 1484, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 51.0, 25.0, 9.0))</td><td>0.4883266266131713</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 77, 941, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 69.0, 7.0, 9.0))</td><td>0.42866716385009207</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 62, 788, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 26.0, 12.0, 7.0, 1.0))</td><td>0.684669701061638</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 13, 38, 53, 54, 58, 79, 864, 1554, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 1.0, 47.0, 10.0, 1.0, 9.0, 2.0))</td><td>0.4234082655785796</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 67, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 33.0, 8.0, 4.0, 1.0))</td><td>0.3509915678111103</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 158, 759, 1506, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 64.0, 11.0, 7.0, 1.0))</td><td>0.5244632615075748</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 38, 52, 54, 59, 62, 756, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 31.0, 19.0, 3.0, 6.0))</td><td>0.552015878404113</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 53, 54, 58, 118, 779, 1579, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 58.0, 5.0, 6.0, 2.0))</td><td>0.4314049982720582</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 95, 774, 1607, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 32.0, 19.0, 8.0, 1.0))</td><td>0.2938255218240011</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 110, 790, 1614, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 63.0, 12.0, 8.0, 1.0))</td><td>0.3703235222774195</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 62, 772, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 59.0, 9.0, 3.0, 7.0))</td><td>0.7213703154146927</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 81, 768, 1511, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 24.0, 20.0, 8.0, 3.0))</td><td>0.4543533097791921</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 68, 754, 1700, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 47.0, 23.0, 9.0))</td><td>0.47132883033351863</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 55, 58, 75, 819, 1632, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 21.0, 20.0, 1.0, 8.0))</td><td>0.550446722471569</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 77, 798, 1740, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 47.0, 13.0, 1.0, 7.0))</td><td>0.5413261185148517</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 52, 54, 58, 165, 915, 1513, 2239, 2243, 2247, 2251, 2253, 2258, 2262, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 72.0, 27.0, 7.0, 3.0))</td><td>0.4151886099398936</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 35, 52, 54, 58, 63, 762, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 8.0, 4.0, 2.0))</td><td>0.47397688160219714</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 35, 53, 54, 58, 107, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 32.0, 8.0, 4.0, 4.0))</td><td>0.3620239078816743</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 226, 870, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 35.0, 18.0, 1.0, 9.0))</td><td>0.49153871747122135</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 80, 764, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 5.0, 45.0, 17.0, 3.0, 9.0, 2.0))</td><td>0.7317131966932886</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 99, 759, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 23.0, 20.0, 1.0, 8.0, 3.0))</td><td>0.4462808597045983</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 190, 820, 1684, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 22.0, 19.0, 6.0, 4.0))</td><td>0.5105910261865086</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 65, 763, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 36.0, 6.0, 4.0, 2.0))</td><td>0.3639578585841284</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 17, 36, 53, 54, 58, 106, 779, 1608, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 58.0, 3.0, 6.0, 4.0))</td><td>0.4067907980902132</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 53, 54, 58, 99, 1014, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 19.0, 11.0, 4.0, 3.0))</td><td>0.3665227318263936</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 64, 812, 1527, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 38.0, 6.0, 6.0, 2.0))</td><td>0.32825809266887646</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 35, 53, 54, 58, 187, 754, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 7.0, 5.0))</td><td>0.5746787209135704</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 59, 62, 760, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 44.0, 14.0, 1.0, 9.0))</td><td>0.6191405581068921</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 62, 781, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 11.0, 7.0, 2.0))</td><td>0.5016910077684852</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 7, 9, 36, 52, 54, 58, 63, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 63.0, 17.0, 4.0, 2.0))</td><td>0.29454636480385815</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 53, 54, 58, 93, 760, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 43.0, 8.0, 1.0, 7.0, 3.0))</td><td>0.5270511991678457</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 42, 52, 55, 58, 150, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 11.0, 13.0, 3.0, 3.0))</td><td>0.3552542523929211</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 150, 772, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 9.0, 9.0, 7.0))</td><td>0.4269682176502088</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 105, 763, 1499, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 47.0, 14.0, 9.0, 1.0))</td><td>0.4015133395079627</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 52, 54, 58, 62, 775, 1481, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 28.0, 11.0, 5.0, 3.0))</td><td>0.48549147009713534</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 64, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 39.0, 5.0, 2.0))</td><td>0.324124493479792</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 108, 773, 1487, 2239, 2243, 2247, 2251, 2256, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 26.0, 16.0, 1.0, 8.0, 3.0))</td><td>0.4678628555502993</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 55, 58, 92, 817, 1571, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 26.0, 21.0, 6.0, 1.0))</td><td>0.48196793022597306</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 76, 889, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 24.0, 9.0, 1.0, 6.0))</td><td>0.5875546054803815</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 56, 58, 433, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 51.0, 18.0, 4.0, 4.0))</td><td>0.22795369778560773</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 73, 786, 1763, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 26.0, 23.0, 1.0, 8.0, 1.0))</td><td>0.5327994243551198</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 53, 54, 58, 106, 763, 1542, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 45.0, 6.0, 4.0))</td><td>0.5213873642752693</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 17, 35, 52, 54, 58, 291, 753, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 70.0, 8.0, 3.0, 7.0))</td><td>0.4808811291171772</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 97, 757, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 53.0, 4.0, 2.0, 4.0))</td><td>0.33506780624611887</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 761, 1525, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 37.0, 13.0, 9.0))</td><td>0.43560616790667506</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 130, 847, 1544, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 13.0, 1.0, 8.0, 4.0))</td><td>0.5273876670901224</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 17, 36, 52, 55, 58, 95, 755, 1558, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 31.0, 11.0, 3.0, 1.0))</td><td>0.30561483794268307</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 52, 55, 58, 83, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 66.0, 6.0, 2.0, 7.0))</td><td>0.4237773491170048</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 758, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 45.0, 11.0, 8.0, 2.0))</td><td>0.559812526046173</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 110, 755, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 42.0, 27.0, 2.0, 3.0))</td><td>0.25318105613416975</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 73, 979, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 27.0, 16.0, 8.0))</td><td>0.4116290103123988</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 35, 52, 54, 58, 71, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 35.0, 13.0, 2.0, 9.0, 3.0))</td><td>0.6014641470875457</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 40, 53, 54, 58, 91, 797, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 5.0, 45.0, 28.0, 9.0, 2.0))</td><td>0.4754338384183618</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 36, 52, 54, 58, 100, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 36.0, 12.0, 8.0))</td><td>0.364275039214928</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 70, 771, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 50.0, 9.0, 4.0, 8.0))</td><td>0.3872770373788801</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 73, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 46.0, 18.0, 2.0, 6.0))</td><td>0.5632883450051287</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 474, 779, 1510, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 4.0, 4.0, 5.0))</td><td>0.26533471016902144</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 4.0, 46.0, 43.0, 3.0, 2.0))</td><td>0.384781273347365</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 136, 773, 1914, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 89.0, 27.0, 2.0, 7.0, 3.0))</td><td>0.7031063485286051</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 71, 777, 1581, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 25.0, 4.0, 1.0, 6.0, 1.0))</td><td>0.4499090971756395</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 287, 755, 1564, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 10.0, 6.0, 6.0, 1.0))</td><td>0.3760578435883072</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 222, 761, 1525, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 59.0, 15.0, 8.0))</td><td>0.5350103894767942</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 35, 53, 54, 58, 96, 754, 1601, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 63.0, 12.0, 9.0))</td><td>0.23104859697744992</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 46.0, 13.0, 8.0, 1.0))</td><td>0.4090797824611765</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 46.0, 20.0, 6.0, 2.0))</td><td>0.37465459691427205</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 231, 754, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 18.0, 14.0, 8.0))</td><td>0.4069436712168092</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 55, 58, 74, 786, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 16.0, 13.0, 1.0, 8.0))</td><td>0.4552700314940642</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 63, 755, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 23.0, 8.0, 3.0))</td><td>0.29028874332071886</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 75, 860, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 60.0, 10.0, 7.0, 1.0))</td><td>0.4342243636001503</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 38.0, 20.0, 9.0))</td><td>0.49688581409920735</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 11.0, 6.0))</td><td>0.3857454034225889</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 53, 54, 58, 73, 814, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 23.0, 25.0, 5.0, 2.0))</td><td>0.36016731341395514</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 777, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 56.0, 15.0, 9.0, 4.0))</td><td>0.4521481125706622</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 82, 869, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2282, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 16.0, 21.0, 8.0, 3.0))</td><td>0.25871116990348764</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 62, 762, 1513, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 62.0, 27.0, 9.0))</td><td>0.5580797430527182</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 42, 52, 55, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 61.0, 35.0, 5.0, 1.0))</td><td>0.42619282600490144</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 1.0, 6.0, 5.0, 1.0))</td><td>0.25438863705063613</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 221, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 36.0, 13.0, 5.0))</td><td>0.3058237561257835</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 54, 58, 69, 769, 1490, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 52.0, 15.0, 9.0))</td><td>0.45628083784601875</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 130, 765, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 59.0, 15.0, 1.0, 9.0))</td><td>0.54027264328348</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 151, 770, 1642, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 4.0, 1.0, 5.0, 5.0))</td><td>0.4532303008595634</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 73, 786, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 54.0, 7.0, 1.0, 9.0, 2.0))</td><td>0.507629113410816</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 35, 52, 55, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 48.0, 12.0, 2.0, 5.0))</td><td>0.5664654871530429</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 37, 52, 54, 58, 84, 763, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 24.0, 11.0, 1.0, 2.0, 7.0))</td><td>0.38300748561856135</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 92, 952, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 17.0, 21.0, 5.0, 1.0))</td><td>0.45806213505560334</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 288, 756, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 40.0, 25.0, 9.0))</td><td>0.29198116702186827</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 62, 756, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 58.0, 18.0, 1.0, 5.0))</td><td>0.5547832760065594</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 54, 58, 73, 1020, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 49.0, 21.0, 9.0, 2.0))</td><td>0.6367525721396594</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 65, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 4.0, 8.0, 4.0))</td><td>0.2638589913927377</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 35, 53, 54, 58, 66, 783, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 50.0, 13.0, 1.0, 9.0, 3.0))</td><td>0.21448364277033194</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 94, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 20.0, 8.0, 1.0, 4.0))</td><td>0.39386889406366843</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 55, 58, 69, 767, 1507, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 19.0, 30.0, 1.0, 7.0, 4.0))</td><td>0.47443791199440793</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 53, 54, 58, 70, 864, 1554, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 49.0, 20.0, 8.0, 4.0))</td><td>0.5362300337955492</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 53, 54, 58, 65, 761, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 24.0, 11.0, 1.0, 7.0, 1.0))</td><td>0.4561272278522751</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 75, 834, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 2.0, 53.0, 20.0, 1.0, 9.0, 1.0))</td><td>0.6416784200867793</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 24, 42, 52, 54, 58, 139, 898, 1696, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 4.0, 41.0, 11.0, 9.0, 3.0))</td><td>0.5190383972514416</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 52, 54, 58, 63, 754, 1486, 2239, 2243, 2249, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 63.0, 44.0, 9.0, 4.0))</td><td>0.45882603499856994</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 66, 758, 1877, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 43.0, 7.0, 6.0, 3.0))</td><td>0.415353328109322</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 57, 58, 683, 895, 1491, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 53.0, 22.0, 5.0, 1.0))</td><td>0.2712014366604007</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 53, 54, 58, 160, 813, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 48.0, 13.0, 8.0))</td><td>0.43580965470845345</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 175, 828, 1669, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 26.0, 12.0, 8.0))</td><td>0.40908436775367335</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 92, 864, 1535, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 25.0, 23.0, 8.0))</td><td>0.5937710023921263</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 53, 54, 58, 79, 754, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 45.0, 17.0, 2.0, 7.0))</td><td>0.4872026042526208</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 43, 52, 54, 58, 62, 772, 1667, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 45.0, 22.0, 9.0, 3.0))</td><td>0.6871701193736908</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 138, 932, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 26.0, 11.0, 8.0, 4.0))</td><td>0.5005004192898962</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 63, 767, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 47.0, 10.0, 6.0, 1.0))</td><td>0.37459191310848694</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 63, 762, 1482, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 44.0, 20.0, 7.0, 4.0))</td><td>0.2815627872751817</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 68, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 31.0, 9.0, 4.0, 1.0))</td><td>0.3960317426756295</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 76, 862, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 17.0, 9.0, 1.0, 7.0, 1.0))</td><td>0.36418902827054356</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 334, 802, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 55.0, 13.0, 9.0, 5.0))</td><td>0.38727400642428267</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 38, 52, 55, 58, 63, 763, 1540, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 88.0, 53.0, 9.0, 2.0))</td><td>0.2989267592608578</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 232, 760, 1550, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 13.0, 9.0, 2.0))</td><td>0.5402603731487501</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 55, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 38.0, 12.0, 6.0, 1.0))</td><td>0.29175869772594903</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 64, 770, 1557, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 38.0, 18.0, 1.0, 6.0, 1.0))</td><td>0.4038818066761407</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 116, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 10.0, 6.0))</td><td>0.38919198037344505</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 55, 58, 69, 1033, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 50.0, 27.0, 1.0, 7.0, 1.0))</td><td>0.49330293748221055</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 53, 54, 58, 274, 857, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 10.0, 6.0, 1.0))</td><td>0.3388074444453294</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 62, 784, 1505, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 67.0, 10.0, 9.0, 1.0))</td><td>0.39659711186215163</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 38, 52, 55, 58, 65, 754, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 6.0, 85.0, 47.0, 6.0, 1.0))</td><td>0.29984162009645654</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 54, 58, 167, 810, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 8.0, 8.0, 6.0))</td><td>0.12432186491105157</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 40, 52, 54, 58, 144, 825, 1553, 2239, 2244, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 5.0, 25.0, 27.0, 6.0, 1.0))</td><td>0.30906333191663093</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 16, 35, 53, 54, 58, 64, 761, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 3.0, 6.0, 5.0))</td><td>0.40902961715988395</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 38, 53, 54, 58, 65, 777, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 10.0, 9.0, 9.0, 1.0))</td><td>0.3227214426253103</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 56, 58, 63, 776, 1490, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 47.0, 8.0, 6.0, 3.0))</td><td>0.41302041105804327</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 257, 754, 1769, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 49.0, 33.0, 9.0, 1.0))</td><td>0.3364940188726825</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 13, 38, 53, 54, 58, 63, 754, 1530, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 45.0, 8.0, 9.0, 1.0))</td><td>0.36415135469624343</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 56, 58, 64, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 50.0, 9.0, 6.0, 1.0))</td><td>0.4127737658373679</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 40, 52, 54, 58, 163, 818, 1672, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 19.0, 16.0, 8.0))</td><td>0.322275543478831</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 74, 757, 1500, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 63.0, 9.0, 7.0))</td><td>0.5832277057723749</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 120, 758, 1585, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 48.0, 22.0, 1.0, 7.0, 3.0))</td><td>0.42942613555350495</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 63, 767, 1563, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 48.0, 11.0, 3.0, 3.0, 1.0))</td><td>0.5508004564375808</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 65, 761, 1493, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 22.0, 20.0, 1.0, 8.0))</td><td>0.38223866816809937</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 83, 755, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 46.0, 9.0, 1.0, 7.0, 2.0))</td><td>0.500447827143102</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 63, 762, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 36.0, 13.0, 6.0, 1.0))</td><td>0.5007817670597317</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 53, 54, 58, 74, 834, 1512, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 25.0, 11.0, 1.0, 1.0, 5.0))</td><td>0.4985563197014522</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 87, 963, 1623, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 40.0, 9.0, 8.0, 3.0))</td><td>0.3377048586681894</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 36, 52, 55, 58, 264, 761, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 21.0, 14.0, 1.0, 9.0))</td><td>0.6008151028063984</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 78, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 77.0, 18.0, 9.0, 4.0))</td><td>0.4656029444870715</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 95, 777, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 73.0, 19.0, 9.0, 2.0))</td><td>0.5059222479139088</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 57, 58, 69, 799, 1884, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 50.0, 16.0, 6.0))</td><td>0.28739084638154844</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 52, 55, 58, 80, 771, 1495, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2272, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 45.0, 18.0, 1.0, 9.0, 2.0))</td><td>0.6653707363893253</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 62, 761, 1491, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 21.0, 12.0, 1.0, 5.0, 1.0))</td><td>0.46044490029236496</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 36, 53, 54, 58, 73, 760, 1579, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 29.0, 23.0, 7.0))</td><td>0.4695997359820767</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 64, 808, 1485, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 64.0, 17.0, 9.0))</td><td>0.5218684126815426</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 38, 52, 54, 58, 100, 815, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 37.0, 17.0, 5.0, 2.0))</td><td>0.2846389956297924</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 36, 53, 54, 58, 144, 1012, 2013, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 55.0, 20.0, 7.0, 1.0))</td><td>0.031687958289136786</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 156, 779, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 26.0, 7.0, 1.0, 5.0, 2.0))</td><td>0.3573085589138351</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 57, 58, 84, 754, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 42.0, 11.0, 1.0, 8.0, 5.0))</td><td>0.5124833714480392</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 35, 52, 54, 58, 144, 770, 1836, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 37.0, 13.0, 5.0))</td><td>-0.037242752587392514</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 67, 755, 1497, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 35.0, 2.0, 5.0))</td><td>0.39245051842999074</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 22, 35, 53, 54, 58, 62, 759, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 42.0, 19.0, 2.0, 9.0))</td><td>0.32618642825580896</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 67, 756, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2313, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 21.0, 9.0, 1.0, 8.0))</td><td>0.16556384462843282</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 73, 910, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 47.0, 29.0, 9.0, 5.0))</td><td>0.4564969279215122</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 74, 770, 1693, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 74.0, 18.0, 9.0, 4.0))</td><td>0.34675279423211586</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 753, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 10.0, 2.0, 9.0))</td><td>0.5219129105870304</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 43, 52, 55, 58, 64, 812, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 4.0, 19.0, 18.0, 3.0, 1.0, 9.0, 2.0))</td><td>0.6265254091908732</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 36, 53, 54, 58, 174, 781, 1535, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 39.0, 7.0, 9.0, 4.0))</td><td>0.24199386721782623</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 59, 62, 762, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 49.0, 16.0, 3.0, 8.0))</td><td>0.6976022748438535</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 70, 767, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 25.0, 16.0, 1.0, 8.0, 1.0))</td><td>0.44625794281157943</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 90, 753, 1590, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 65.0, 5.0, 5.0, 5.0))</td><td>0.4312046480165353</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 36, 52, 54, 58, 114, 817, 1520, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 3.0, 61.0, 22.0, 5.0))</td><td>0.537218629626863</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 62, 772, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 48.0, 14.0, 1.0, 9.0))</td><td>0.5499916084135245</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 54, 58, 129, 2051, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 41.0, 10.0, 9.0, 5.0))</td><td>0.4252469337035876</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 52, 54, 58, 140, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 49.0, 15.0, 3.0))</td><td>0.4059630838217397</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 36, 52, 54, 58, 63, 777, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 54.0, 4.0, 6.0, 2.0))</td><td>0.47243365323721054</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 53, 54, 58, 91, 797, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 6.0, 61.0, 20.0, 7.0, 1.0))</td><td>0.4326046183288627</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 158, 777, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 6.0, 49.0, 22.0, 1.0, 9.0, 1.0))</td><td>0.563686314228413</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 54, 58, 62, 760, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 3.0, 23.0, 11.0, 9.0, 8.0))</td><td>0.9022512973675878</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 40, 52, 54, 58, 65, 864, 1563, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 39.0, 13.0, 9.0, 2.0))</td><td>0.4833048227042961</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 88, 1237, 1795, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 60.0, 9.0, 9.0, 4.0))</td><td>0.43208744006889854</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 22, 35, 53, 54, 58, 66, 864, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 51.0, 22.0, 9.0, 1.0))</td><td>0.3124888967766046</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 77, 760, 1506, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 50.0, 14.0, 9.0))</td><td>0.48591571626403873</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 81, 997, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 46.0, 14.0, 9.0, 1.0))</td><td>0.5087409637860709</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 63, 762, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 44.0, 47.0, 6.0, 2.0))</td><td>0.40505241606204073</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 342, 758, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 4.0, 21.0, 29.0, 1.0, 4.0, 1.0, 9.0))</td><td>0.7192263017853675</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 55, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 47.0, 41.0, 5.0, 2.0))</td><td>0.27770761472965205</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 62, 756, 1485, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 39.0, 7.0, 7.0, 1.0))</td><td>0.501866553649899</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 263, 855, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 26.0, 31.0, 1.0, 8.0))</td><td>0.5749994192547301</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 7, 9, 38, 52, 54, 58, 201, 755, 1545, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 53.0, 10.0, 1.0, 8.0, 1.0))</td><td>0.27675653833599945</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 55, 58, 71, 825, 1485, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 81.0, 15.0, 1.0, 9.0))</td><td>0.5460479891362512</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 37, 53, 54, 58, 62, 784, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 21.0, 14.0, 1.0, 8.0))</td><td>0.49190133093653</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 59, 66, 758, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 64.0, 9.0, 9.0))</td><td>0.48647939005473123</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 72, 917, 1520, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 38.0, 9.0, 7.0))</td><td>0.4024332326605647</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 96, 754, 1497, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 38.0, 12.0, 1.0, 8.0, 1.0))</td><td>0.47226151325351756</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 95, 755, 1491, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 11.0, 24.0, 4.0, 5.0))</td><td>0.25037186094636854</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 40, 53, 54, 58, 63, 762, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 53.0, 12.0, 7.0, 2.0))</td><td>0.3296633514295284</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 11, 40, 52, 55, 58, 304, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 67.0, 32.0, 5.0))</td><td>0.2955061359075314</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 64, 767, 1566, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 51.0, 20.0, 7.0, 2.0))</td><td>0.4762709733863837</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 53, 54, 58, 75, 759, 1612, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 79.0, 8.0, 9.0, 3.0))</td><td>0.14447430929265886</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 125, 801, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 51.0, 16.0, 7.0))</td><td>0.5077856413999889</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 88, 755, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 9.0, 4.0, 5.0))</td><td>0.3891704301140702</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 91, 764, 1520, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 36.0, 19.0, 7.0, 1.0))</td><td>0.5743322576870058</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 55, 58, 97, 753, 1485, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 11.0, 10.0, 8.0, 1.0))</td><td>0.48612478185893326</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 35, 52, 55, 58, 99, 754, 1485, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 56.0, 11.0, 8.0, 3.0))</td><td>0.13924914447007203</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 390, 757, 1565, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 43.0, 2.0, 4.0))</td><td>0.17628469235213223</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 99, 768, 1509, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 24.0, 16.0, 6.0))</td><td>0.45914823143726446</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 10, 40, 52, 54, 58, 126, 754, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 40.0, 35.0, 1.0, 9.0, 3.0))</td><td>0.4424033448814621</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 77, 764, 1504, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 67.0, 18.0, 1.0, 9.0))</td><td>0.5845693281460641</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 40, 52, 54, 58, 74, 932, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 55.0, 19.0, 1.0, 1.0, 9.0, 4.0))</td><td>0.5645012729889429</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 89, 872, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 11.0, 4.0))</td><td>0.4312989229349088</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 754, 1494, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 41.0, 12.0, 9.0, 4.0))</td><td>0.48757277855789893</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 216, 760, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 3.0, 61.0, 23.0, 3.0, 9.0, 1.0))</td><td>0.7067505976054085</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 55, 58, 137, 1052, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 7.0, 4.0))</td><td>0.39537639885360487</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 62, 758, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 24.0, 20.0, 9.0, 1.0))</td><td>0.6264739806499442</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 72, 767, 1523, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 74.0, 10.0, 7.0))</td><td>0.4235474801136093</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 97, 761, 1630, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 36.0, 4.0, 3.0, 6.0))</td><td>0.35821295547848875</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 81, 759, 1533, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 18.0, 14.0, 8.0, 3.0))</td><td>0.3896235530411808</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 176, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 47.0, 13.0, 9.0, 3.0))</td><td>0.4046368581897699</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 77, 769, 1551, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 65.0, 13.0, 8.0, 3.0))</td><td>0.4900054029248979</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 105, 758, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 34.0, 18.0, 8.0, 1.0))</td><td>0.4806973187485771</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 9.0, 1.0, 4.0, 2.0))</td><td>0.305127677821468</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 55, 58, 159, 757, 2003, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 16.0, 15.0, 4.0, 1.0))</td><td>0.08310159597072175</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 319, 756, 1567, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 25.0, 6.0, 5.0, 3.0))</td><td>0.41543633943394326</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(3, 6, 9, 37, 52, 54, 58, 163, 978, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 64.0, 23.0, 4.0, 1.0))</td><td>0.29212796030609434</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 141, 780, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 44.0, 15.0, 1.0, 9.0, 2.0))</td><td>0.5207624808567776</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 52, 54, 58, 82, 935, 1497, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 63.0, 19.0, 6.0, 3.0))</td><td>0.5380737578093815</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 94, 771, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 46.0, 13.0, 1.0, 9.0))</td><td>0.46143162663714576</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 99, 786, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 21.0, 13.0, 5.0, 6.0))</td><td>0.35672421495179957</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 98, 914, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 28.0, 26.0, 8.0, 1.0))</td><td>0.46346238755668095</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 87, 840, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 25.0, 13.0, 3.0))</td><td>0.3881625962407689</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 65, 774, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 6.0, 79.0, 42.0, 1.0, 9.0, 2.0))</td><td>0.4574688633566817</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 24, 35, 53, 54, 58, 76, 817, 1604, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 33.0, 7.0, 8.0, 2.0))</td><td>0.43516862678456986</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 55, 58, 106, 770, 1550, 2239, 2243, 2247, 2251, 2254, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 41.0, 12.0, 7.0, 4.0))</td><td>0.5118578332262529</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 76, 770, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 34.0, 7.0, 8.0, 3.0))</td><td>0.47333012535580843</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 39.0, 6.0, 4.0))</td><td>0.3738161389241558</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 64, 755, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 12.0, 1.0, 6.0, 1.0))</td><td>0.44812472062221503</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 69.0, 16.0, 7.0, 1.0))</td><td>0.5239176956421894</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 52, 54, 58, 186, 754, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 71.0, 15.0, 7.0))</td><td>0.5612664341989643</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 40, 52, 55, 58, 111, 822, 2028, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 56.0, 38.0, 9.0))</td><td>0.34851383082335763</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 55, 58, 187, 1013, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 1.0, 13.0, 4.0, 2.0))</td><td>0.49436222997073154</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 63, 762, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 35.0, 20.0, 7.0, 2.0))</td><td>0.47090097532375724</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 106, 770, 1483, 2239, 2243, 2247, 2251, 2255, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 67.0, 10.0, 3.0, 6.0, 4.0))</td><td>0.6482835950476369</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 144, 808, 1490, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 8.0, 1.0, 9.0, 1.0))</td><td>0.4053856336086401</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 18, 35, 53, 54, 58, 74, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 56.0, 14.0, 1.0, 9.0, 3.0))</td><td>0.5408511254371731</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 57, 58, 76, 764, 1504, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 6.0, 91.0, 46.0, 9.0))</td><td>0.5935657258084162</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 148, 948, 1547, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 3.0, 87.0, 15.0, 9.0))</td><td>0.4243854572715264</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 70, 769, 2001, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 19.0, 11.0, 8.0))</td><td>0.27938138717281896</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 55, 58, 113, 818, 1496, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 63.0, 7.0, 2.0, 8.0, 2.0))</td><td>0.6547451549427594</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 55, 58, 113, 756, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 26.0, 15.0, 5.0))</td><td>0.4785161761580555</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 58, 63, 775, 1517, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 6.0, 67.0, 52.0, 9.0, 2.0))</td><td>0.4202947178172482</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 67, 754, 1645, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 61.0, 17.0, 9.0, 3.0))</td><td>0.4562860032055088</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 64, 755, 1486, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 49.0, 10.0, 6.0, 2.0))</td><td>0.3724045218287271</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 123, 776, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 57.0, 22.0, 9.0, 3.0))</td><td>0.4639088609914652</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 75, 763, 1495, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 51.0, 10.0, 4.0, 9.0, 2.0))</td><td>0.6449383223805425</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 55, 58, 156, 848, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 12.0, 16.0, 8.0))</td><td>0.3343233517199663</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 36, 52, 54, 58, 405, 848, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 8.0, 3.0, 2.0, 3.0))</td><td>0.5301839551789832</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 64, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 6.0, 5.0, 4.0))</td><td>0.3219200376389505</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 62, 758, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 56.0, 24.0, 5.0, 8.0))</td><td>0.8465312082141419</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 6, 9, 35, 52, 55, 58, 66, 768, 1483, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 56.0, 23.0, 9.0))</td><td>0.4649137755846237</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 63, 776, 1712, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 6.0, 42.0, 25.0, 9.0, 4.0))</td><td>0.5040836592379156</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 86, 767, 1516, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 3.0, 58.0, 19.0, 9.0))</td><td>0.5151843991232065</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 62, 759, 1506, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 22.0, 15.0, 2.0, 7.0))</td><td>0.5390652449173093</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 132, 758, 1509, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 49.0, 16.0, 7.0, 1.0))</td><td>0.4392365279779221</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 198, 823, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 47.0, 29.0, 5.0, 2.0))</td><td>0.3459809222134437</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 62, 760, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 71.0, 13.0, 1.0, 9.0))</td><td>0.6732755830351487</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 94, 799, 1612, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 49.0, 19.0, 1.0, 9.0, 1.0))</td><td>0.5261437178016258</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 90, 753, 1562, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 80.0, 13.0, 4.0, 9.0))</td><td>0.31304824861735436</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 387, 772, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 50.0, 21.0, 1.0, 5.0, 7.0))</td><td>0.40875974780453767</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 663, 1556, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 45.0, 10.0, 5.0, 1.0))</td><td>0.6984549130649476</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 62, 760, 1526, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 51.0, 8.0, 5.0, 4.0))</td><td>0.47626212634743126</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 62, 780, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 39.0, 11.0, 2.0, 6.0, 1.0))</td><td>0.5543205279525943</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 107, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 1.0, 41.0, 14.0, 4.0))</td><td>0.37741901390378074</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 11, 38, 52, 54, 58, 62, 764, 1490, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 60.0, 18.0, 9.0, 1.0))</td><td>0.436575876477442</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 66, 755, 1512, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 76.0, 15.0, 5.0))</td><td>0.392209443689701</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 109, 770, 1843, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 53.0, 13.0, 9.0))</td><td>0.27658845715331143</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 38, 52, 54, 58, 75, 759, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 2.0, 12.0, 1.0, 6.0))</td><td>0.42250592143910926</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 36, 52, 54, 58, 67, 772, 1484, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 35.0, 15.0, 9.0, 1.0))</td><td>0.5193172405559268</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 755, 1557, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 74.0, 14.0, 4.0, 2.0))</td><td>0.4679050896610716</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 55, 58, 63, 767, 1765, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 88.0, 46.0, 7.0, 1.0))</td><td>0.44795953705714014</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 36, 52, 54, 58, 91, 915, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 57.0, 12.0, 5.0, 2.0))</td><td>0.49081379131591885</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 16, 35, 52, 55, 58, 62, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 60.0, 14.0, 9.0, 3.0))</td><td>0.4574585093934569</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 35, 53, 54, 58, 73, 764, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 35.0, 3.0, 7.0, 1.0))</td><td>0.5464124804950423</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 63, 762, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 34.0, 28.0, 9.0, 2.0))</td><td>0.3640007200660671</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 65, 754, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 56.0, 15.0, 9.0, 3.0))</td><td>0.4725337526997281</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 76, 760, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 60.0, 22.0, 1.0, 9.0, 2.0))</td><td>0.5092224265263113</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 73, 796, 1490, 2239, 2243, 2247, 2251, 2253, 2260, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 67.0, 14.0, 1.0, 9.0, 1.0))</td><td>0.504590367065239</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 45, 53, 54, 58, 88, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 13.0, 15.0, 4.0, 2.0))</td><td>0.32811934604927373</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 36, 53, 54, 58, 76, 800, 1492, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 36.0, 2.0, 1.0, 5.0))</td><td>0.32357811267553716</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 52, 55, 58, 126, 767, 1496, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 24.0, 15.0, 4.0, 5.0))</td><td>0.32072616229587914</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 17, 35, 53, 54, 58, 68, 753, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 46.0, 6.0, 8.0, 3.0))</td><td>0.5195936956386993</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 114, 764, 1483, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 1.0, 81.0, 18.0, 1.0, 9.0, 3.0))</td><td>0.6034716314859581</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 52, 55, 58, 230, 777, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 55.0, 17.0, 9.0))</td><td>0.5374201362168604</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 36, 53, 54, 58, 91, 785, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 34.0, 22.0, 3.0, 9.0, 1.0))</td><td>0.5998401448419213</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 40, 52, 54, 58, 298, 756, 1600, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 41.0, 13.0, 1.0, 7.0, 2.0))</td><td>0.3637079897003609</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 13, 37, 53, 54, 58, 63, 762, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 11.0, 8.0, 1.0))</td><td>0.40570651140881503</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 35, 53, 54, 58, 82, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 71.0, 14.0, 9.0))</td><td>0.3880790633505582</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 95, 763, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 8.0, 3.0, 3.0))</td><td>0.3016150997425576</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 65, 957, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 19.0, 10.0, 6.0, 3.0))</td><td>0.052079963452282996</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 95, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 49.0, 11.0, 3.0, 2.0))</td><td>0.26445119751582574</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 373, 777, 1507, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 45.0, 14.0, 4.0, 7.0))</td><td>0.4642955171811677</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 35, 53, 54, 58, 71, 774, 1627, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 4.0, 65.0, 26.0, 9.0, 1.0))</td><td>0.03747052817724689</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 74, 794, 1483, 2239, 2243, 2248, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 57.0, 15.0, 11.0, 1.0, 9.0, 1.0))</td><td>0.6475700709429233</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 55, 58, 63, 762, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 40.0, 12.0, 5.0, 2.0))</td><td>0.33680942817632764</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 81, 829, 1576, 2239, 2243, 2247, 2251, 2253, 2259, 2261, 2263, 2269, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 26.0, 40.0, 2.0, 8.0))</td><td>0.11381658953184509</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 16, 40, 52, 55, 58, 86, 804, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 23.0, 2.0, 1.0, 6.0, 2.0))</td><td>0.4102667242614921</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 67, 754, 1502, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 61.0, 19.0, 7.0))</td><td>0.45748498961471734</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 216, 771, 1488, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2269, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 69.0, 20.0, 3.0, 1.0))</td><td>0.49832550184052665</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 35, 52, 54, 58, 77, 797, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 13.0, 1.0, 75.0, 26.0, 2.0, 6.0))</td><td>0.6842430076127664</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 52, 54, 58, 77, 753, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 59.0, 16.0, 1.0, 9.0, 3.0))</td><td>0.5587658633925876</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 15, 37, 52, 54, 58, 81, 784, 1513, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 2.0, 26.0, 33.0, 2.0, 8.0))</td><td>0.23684920533388779</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 117, 801, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 71.0, 12.0, 9.0, 1.0))</td><td>0.4751680275945012</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 52, 54, 58, 63, 754, 1494, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 5.0, 40.0, 23.0, 8.0, 5.0))</td><td>0.34881275566174047</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 296, 842, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 65.0, 15.0, 8.0))</td><td>0.5353390290954155</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 55, 58, 71, 754, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 20.0, 11.0, 7.0))</td><td>0.42796324152945475</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 763, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 62.0, 19.0, 9.0))</td><td>0.5810019571202357</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 92, 797, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 3.0, 36.0, 21.0, 9.0, 1.0))</td><td>0.4180513235199662</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 16, 42, 52, 54, 58, 91, 797, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 38.0, 24.0, 4.0, 7.0, 2.0))</td><td>0.6743719449557839</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 180, 756, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 4.0, 68.0, 30.0, 7.0, 3.0))</td><td>0.510149552956042</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 90, 753, 1657, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 60.0, 15.0, 2.0, 9.0, 4.0))</td><td>0.539218491390401</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 16, 35, 52, 54, 58, 272, 773, 1515, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 53.0, 16.0, 4.0, 8.0, 5.0, 7.0, 5.0))</td><td>1.0271698323939857</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 767, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 44.0, 6.0, 6.0, 2.0))</td><td>0.5475817760635884</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 85, 769, 1481, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 57.0, 11.0, 2.0, 6.0, 1.0))</td><td>0.5368034319645332</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 54, 58, 107, 753, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 61.0, 7.0, 3.0, 4.0))</td><td>0.37242013697543985</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 265, 754, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 40.0, 20.0, 9.0, 3.0))</td><td>0.396775454044478</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 39, 53, 54, 58, 118, 756, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 47.0, 7.0, 4.0, 1.0))</td><td>0.25868245287897873</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 53, 54, 58, 110, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 40.0, 15.0, 3.0, 1.0))</td><td>0.3036297619419412</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 41, 53, 54, 58, 175, 809, 1892, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 23.0, 8.0, 2.0))</td><td>0.25036569732645886</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 52, 54, 58, 95, 758, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 64.0, 19.0, 8.0, 1.0))</td><td>0.3823363781345661</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 63, 754, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 41.0, 6.0, 1.0, 6.0))</td><td>0.4071784508634328</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 15, 36, 53, 54, 58, 168, 763, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 17.0, 9.0, 2.0))</td><td>0.12184957975416794</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 71, 756, 1524, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 28.0, 18.0, 6.0, 3.0))</td><td>0.4337638997470934</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 74, 896, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 23.0, 7.0, 2.0, 8.0, 1.0))</td><td>0.46154437788335234</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 67, 757, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 36.0, 7.0, 3.0, 5.0))</td><td>0.3289219602250157</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 77, 793, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 61.0, 12.0, 9.0, 3.0))</td><td>0.39717753193287153</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 46.0, 13.0, 4.0, 4.0))</td><td>0.2716219430948167</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 328, 760, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 5.0, 63.0, 18.0, 1.0, 9.0, 1.0))</td><td>0.5168172128422941</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 52, 54, 58, 63, 757, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 6.0, 45.0, 35.0, 3.0, 2.0))</td><td>0.3212924275095712</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 16, 39, 52, 54, 58, 113, 764, 1484, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 14.0, 11.0, 9.0, 1.0))</td><td>0.4714068379093845</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 53, 54, 58, 72, 887, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 31.0, 6.0, 1.0, 5.0))</td><td>0.38727030820558983</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 137, 900, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 34.0, 7.0, 4.0, 2.0))</td><td>0.3961342687926411</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 53, 54, 58, 105, 756, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 73.0, 13.0, 8.0, 2.0))</td><td>0.3942302898376439</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 63, 783, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 23.0, 9.0, 4.0, 2.0))</td><td>0.2234066169829491</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 84, 805, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 78.0, 17.0, 7.0, 1.0))</td><td>0.46556107236477295</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 62, 784, 1540, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 3.0, 62.0, 14.0, 1.0, 9.0, 1.0))</td><td>0.5042428270585666</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 107, 753, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 58.0, 13.0, 2.0, 4.0, 4.0))</td><td>0.52219977653365</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 62, 997, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 51.0, 17.0, 1.0, 2.0, 6.0, 2.0))</td><td>0.7903552108477647</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 40, 52, 54, 58, 63, 762, 1482, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 6.0, 61.0, 29.0, 7.0))</td><td>0.3702891579021531</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 54, 58, 65, 754, 1545, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 6.0, 76.0, 31.0, 9.0, 4.0))</td><td>0.41497552403750726</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 52, 55, 58, 116, 808, 1572, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 3.0, 43.0, 20.0, 9.0, 1.0))</td><td>0.4795911204368749</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 75, 829, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 62.0, 12.0, 1.0, 1.0, 6.0, 2.0))</td><td>0.48426519951964936</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 62, 756, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 53.0, 10.0, 7.0))</td><td>0.5289444612581095</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 86, 763, 1715, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 47.0, 12.0, 5.0, 5.0))</td><td>0.2916640900486493</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 106, 758, 1502, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 72.0, 11.0, 9.0, 2.0))</td><td>0.49241834369482285</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 53, 54, 58, 153, 889, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 44.0, 15.0, 3.0, 7.0))</td><td>0.5247602518812428</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 35, 53, 54, 58, 317, 753, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 61.0, 14.0, 9.0))</td><td>0.5243126490360481</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 35, 52, 54, 58, 105, 926, 1659, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 1.0, 49.0, 22.0, 7.0, 2.0, 1.0, 9.0))</td><td>0.6777365483189957</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 92, 761, 1567, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 26.0, 19.0, 1.0, 6.0, 2.0))</td><td>0.5133589458959473</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 52, 55, 58, 69, 1068, 1486, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 20.0, 24.0, 7.0))</td><td>0.4093535406518529</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 37, 52, 54, 58, 127, 782, 1516, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 25.0, 33.0, 2.0, 8.0))</td><td>0.5784690280884495</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 65, 753, 1496, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 47.0, 18.0, 4.0, 9.0))</td><td>0.5894326148184418</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 40, 52, 54, 58, 104, 755, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 23.0, 19.0, 2.0, 5.0, 4.0))</td><td>0.47039654501632805</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 518, 1474, 1481, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 37.0, 16.0, 4.0, 2.0))</td><td>0.41412375827826087</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 53, 54, 58, 99, 759, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 57.0, 19.0, 9.0, 3.0))</td><td>0.40519807306787203</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 15, 35, 52, 55, 58, 125, 882, 1617, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 2.0, 82.0, 20.0, 8.0, 2.0))</td><td>0.13722002334969502</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 38, 53, 54, 58, 79, 785, 1589, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 51.0, 9.0, 9.0, 1.0))</td><td>0.37602023323121536</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 10, 41, 52, 55, 58, 92, 817, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 2.0, 45.0, 16.0, 7.0, 2.0))</td><td>0.44387057285153003</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 11, 38, 52, 54, 58, 79, 864, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 25.0, 10.0, 5.0, 9.0, 2.0))</td><td>0.7438434586485152</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 67, 810, 1481, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 41.0, 8.0, 4.0))</td><td>0.3439339339842252</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 37, 52, 54, 58, 273, 753, 1546, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 27.0, 15.0, 8.0, 1.0))</td><td>0.48388415008911384</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 63, 762, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 6.0, 47.0, 14.0, 1.0, 8.0, 2.0))</td><td>0.46019064828720413</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 35, 52, 54, 58, 62, 983, 1643, 2239, 2243, 2249, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 83.0, 25.0, 9.0))</td><td>0.45633377853942864</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 65, 758, 1486, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 4.0, 57.0, 14.0, 6.0, 1.0))</td><td>0.44915072173224085</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 22, 36, 53, 54, 58, 91, 785, 1489, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 34.0, 20.0, 6.0, 4.0))</td><td>0.15122495825971694</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 18, 38, 52, 55, 58, 241, 1198, 1481, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 50.0, 21.0, 4.0, 2.0))</td><td>0.33029127328199775</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 52, 54, 58, 84, 943, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 73.0, 22.0, 7.0))</td><td>0.41218778246964294</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 36, 53, 54, 58, 92, 760, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 52.0, 20.0, 2.0, 9.0))</td><td>0.5830881246441572</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 17, 35, 52, 54, 58, 64, 756, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 3.0, 56.0, 19.0, 9.0))</td><td>0.5129118632445823</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 64, 795, 1593, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 67.0, 18.0, 9.0, 3.0))</td><td>0.4759861815453962</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 55, 58, 83, 794, 1645, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 42.0, 14.0, 1.0, 5.0, 2.0))</td><td>0.48082892562063995</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 94, 789, 1525, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 2.0, 3.0, 21.0, 1.0, 9.0))</td><td>0.39758350639609513</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 37, 52, 54, 58, 94, 771, 1603, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 2.0, 25.0, 14.0, 8.0, 5.0))</td><td>0.4812031793266297</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 108, 754, 1513, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 60.0, 19.0, 8.0, 1.0))</td><td>0.5009562961982895</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 118, 770, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 59.0, 5.0, 5.0, 2.0))</td><td>0.4863386249943671</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 24, 38, 52, 54, 58, 79, 782, 1535, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 52.0, 12.0, 1.0, 9.0, 4.0))</td><td>0.41016388479277605</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 155, 812, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 55.0, 11.0, 1.0, 9.0))</td><td>0.474459237708614</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 77, 767, 1541, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.0, 60.0, 15.0, 9.0, 2.0))</td><td>0.47552918279118006</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 72, 773, 1493, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 78.0, 11.0, 8.0))</td><td>0.48900539702585927</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 53, 54, 58, 65, 754, 1500, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 4.0, 57.0, 11.0, 7.0))</td><td>0.48427292690419943</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 53, 54, 58, 659, 1345, 1556, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 35.0, 8.0, 1.0, 8.0))</td><td>0.15561898523917767</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 40, 52, 55, 58, 69, 756, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 41.0, 33.0, 1.0, 9.0, 1.0))</td><td>0.4214864268638875</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 52, 57, 58, 63, 762, 1481, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 5.0, 38.0, 19.0, 3.0, 2.0))</td><td>0.35828753004436065</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 108, 757, 1527, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2313, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 5.0, 45.0, 34.0, 1.0, 1.0, 6.0, 2.0))</td><td>0.3698201782704784</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 149, 798, 1509, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 20.0, 10.0, 8.0))</td><td>0.41655478543574087</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 62, 882, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 11.0, 1.0, 92.0, 18.0, 9.0, 4.0))</td><td>0.5454401693578246</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 90, 753, 2063, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 5.0, 3.0, 8.0))</td><td>0.33742409302411747</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 53, 54, 58, 126, 806, 1501, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 27.0, 13.0, 2.0, 8.0, 3.0))</td><td>0.4539250539675345</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 37, 52, 54, 58, 63, 762, 1481, 2239, 2244, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2313, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 21.0, 9.0, 1.0, 7.0))</td><td>0.46209990706301185</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 327, 776, 1547, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 50.0, 14.0, 8.0, 6.0))</td><td>0.4609640216752623</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 37, 53, 54, 58, 84, 779, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 1.0, 20.0, 12.0, 7.0, 4.0))</td><td>0.3610032064580636</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 86, 755, 1482, 2239, 2243, 2249, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 48.0, 15.0, 6.0, 1.0))</td><td>0.29756729762978806</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 84, 791, 1481, 2239, 2243, 2247, 2251, 2256, 2257, 2261, 2266, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 42.0, 9.0, 7.0, 1.0))</td><td>0.48437257854059423</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 35, 52, 54, 58, 65, 764, 1565, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 4.0, 56.0, 24.0, 9.0))</td><td>0.4989444446205215</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 36, 53, 54, 58, 93, 767, 1486, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 16.0, 5.0))</td><td>0.40651226602978197</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 97, 780, 1885, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 11.0, 8.0, 2.0))</td><td>0.3697534346291355</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 36, 53, 54, 58, 92, 760, 1498, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 2.0, 49.0, 13.0, 2.0, 9.0, 1.0))</td><td>0.6117794480139507</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 19, 40, 52, 54, 58, 72, 820, 1528, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 44.0, 7.0, 9.0, 2.0))</td><td>0.37293115624232387</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 240, 770, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 35.0, 5.0, 9.0))</td><td>0.4637078738554014</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 55, 58, 106, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 41.0, 5.0, 4.0, 4.0))</td><td>0.3982274079181806</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 35, 53, 54, 58, 181, 1046, 1583, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 64.0, 15.0, 1.0, 9.0))</td><td>0.5208394995035719</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 18, 35, 52, 54, 58, 390, 753, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 68.0, 11.0, 9.0, 1.0))</td><td>0.3026216168331513</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 35, 53, 54, 58, 64, 754, 1487, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 36.0, 13.0, 9.0, 3.0))</td><td>0.48717381670283566</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 11, 36, 52, 57, 58, 69, 755, 1482, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2302, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0, 2.0, 23.0, 23.0, 4.0, 1.0))</td><td>0.33957321970130117</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 15, 35, 52, 54, 58, 65, 797, 1495, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 70.0, 19.0, 9.0, 6.0))</td><td>0.08201935922197534</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 17, 35, 52, 54, 58, 249, 1120, 1481, 2239, 2243, 2247, 2251, 2255, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 57.0, 13.0, 5.0, 5.0))</td><td>0.7704257630209403</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 10, 37, 52, 54, 58, 219, 828, 1766, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2268, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2311, 2312, 2314, 2315, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 16.0, 17.0, 1.0, 1.0, 8.0, 2.0))</td><td>0.49440763250434894</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 37, 52, 54, 58, 64, 759, 1834, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2305, 2309, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 22.0, 9.0, 1.0, 5.0, 2.0))</td><td>0.38788862196363877</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 772, 1570, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 40.0, 13.0, 1.0, 9.0, 1.0))</td><td>0.6874765843186574</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 6, 9, 35, 52, 54, 58, 62, 762, 1487, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 66.0, 13.0, 3.0, 8.0, 1.0))</td><td>0.6747053660195084</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 11, 35, 52, 54, 58, 108, 803, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 55.0, 5.0, 4.0, 1.0))</td><td>0.3607256581374703</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 52, 54, 58, 63, 763, 1491, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2286, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 7.0, 6.0, 86.0, 28.0, 4.0, 4.0))</td><td>0.35966979363607476</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 11, 38, 53, 54, 58, 65, 761, 1503, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 8.0, 6.0, 34.0, 12.0, 4.0, 2.0))</td><td>0.3072088565909671</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 35, 52, 54, 58, 78, 760, 1484, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 4.0, 79.0, 28.0, 2.0, 8.0, 4.0))</td><td>0.5717644803118319</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 11, 37, 52, 55, 58, 186, 839, 1631, 2239, 2243, 2247, 2251, 2254, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2305, 2309, 2310, 2311, 2312, 2313, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 5.0, 17.0, 23.0, 1.0, 8.0, 2.0))</td><td>0.571803342486731</td><td>0.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 9, 35, 52, 54, 58, 249, 773, 1657, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2285, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2314, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 14.0, 1.0, 61.0, 19.0, 1.0, 9.0, 4.0))</td><td>0.6003581500199596</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 16, 38, 53, 54, 58, 65, 754, 1485, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2302, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 3.0, 41.0, 11.0, 9.0))</td><td>0.35982217343259815</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 6, 9, 36, 53, 54, 58, 65, 775, 1646, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 6.0, 80.0, 39.0, 9.0, 2.0))</td><td>0.362765486586329</td><td>0.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 9, 36, 53, 54, 58, 659, 820, 1893, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2303, 2309, 2310, 2311, 2312, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 2.0, 43.0, 11.0, 2.0, 9.0))</td><td>0.46426137269946555</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(4, 7, 9, 38, 52, 55, 58, 63, 762, 1540, 2239, 2243, 2248, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2303, 2309, 2310, 2311, 2312, 2316, 2319), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 5.0, 37.0, 17.0, 9.0, 2.0))</td><td>0.2520510379480721</td><td>1.0</td><td>0</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(0, 7, 10, 35, 52, 54, 58, 62, 756, 1487, 2239, 2243, 2247, 2251, 2253, 2258, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2301, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 1.0, 50.0, 16.0, 9.0))</td><td>0.543242346735894</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(1, 7, 10, 35, 52, 55, 58, 62, 764, 1490, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2263, 2267, 2271, 2275, 2279, 2281, 2283, 2287, 2291, 2293, 2295, 2297, 2299, 2301, 2309, 2311, 2312, 2313, 2314, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 52.0, 15.0, 1.0, 2.0, 9.0))</td><td>0.6308150461947708</td><td>1.0</td><td>1</td></tr><tr><td>Map(vectorType -> sparse, length -> 2320, indices -> List(2, 6, 9, 37, 52, 54, 58, 64, 761, 1481, 2239, 2243, 2247, 2251, 2253, 2257, 2261, 2264, 2267, 2271, 2275, 2279, 2281, 2284, 2287, 2291, 2293, 2295, 2297, 2300, 2304, 2309, 2310, 2311, 2312, 2316), values -> List(1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 44.0, 24.0, 5.0))</td><td>0.373816686972977</td><td>1.0</td><td>0</td></tr></tbody></table>Showing the first 1000 rows.</div>"]}}],"execution_count":0},{"cell_type":"code","source":["testDF.columns"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"7be7792f-f881-4cf7-856d-664d89338e9a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[232]: [&#39;encounter_id&#39;,\n &#39;patient_nbr&#39;,\n &#39;race&#39;,\n &#39;gender&#39;,\n &#39;age&#39;,\n &#39;weight&#39;,\n &#39;admission_type_id&#39;,\n &#39;discharge_disposition_id&#39;,\n &#39;admission_source_id&#39;,\n &#39;time_in_hospital&#39;,\n &#39;payer_code&#39;,\n &#39;medical_specialty&#39;,\n &#39;num_lab_procedures&#39;,\n &#39;num_procedures&#39;,\n &#39;num_medications&#39;,\n &#39;number_outpatient&#39;,\n &#39;number_emergency&#39;,\n &#39;number_inpatient&#39;,\n &#39;diag_1&#39;,\n &#39;diag_2&#39;,\n &#39;diag_3&#39;,\n &#39;number_diagnoses&#39;,\n &#39;max_glu_serum&#39;,\n &#39;A1Cresult&#39;,\n &#39;metformin&#39;,\n &#39;repaglinide&#39;,\n &#39;nateglinide&#39;,\n &#39;chlorpropamide&#39;,\n &#39;glimepiride&#39;,\n &#39;acetohexamide&#39;,\n &#39;glipizide&#39;,\n &#39;glyburide&#39;,\n &#39;tolbutamide&#39;,\n &#39;pioglitazone&#39;,\n &#39;rosiglitazone&#39;,\n &#39;acarbose&#39;,\n &#39;miglitol&#39;,\n &#39;troglitazone&#39;,\n &#39;tolazamide&#39;,\n &#39;examide&#39;,\n &#39;citoglipton&#39;,\n &#39;insulin&#39;,\n &#39;glyburide-metformin&#39;,\n &#39;glipizide-metformin&#39;,\n &#39;glimepiride-pioglitazone&#39;,\n &#39;metformin-rosiglitazone&#39;,\n &#39;metformin-pioglitazone&#39;,\n &#39;change&#39;,\n &#39;diabetesMed&#39;,\n &#39;readmitted&#39;]</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[232]: [&#39;encounter_id&#39;,\n &#39;patient_nbr&#39;,\n &#39;race&#39;,\n &#39;gender&#39;,\n &#39;age&#39;,\n &#39;weight&#39;,\n &#39;admission_type_id&#39;,\n &#39;discharge_disposition_id&#39;,\n &#39;admission_source_id&#39;,\n &#39;time_in_hospital&#39;,\n &#39;payer_code&#39;,\n &#39;medical_specialty&#39;,\n &#39;num_lab_procedures&#39;,\n &#39;num_procedures&#39;,\n &#39;num_medications&#39;,\n &#39;number_outpatient&#39;,\n &#39;number_emergency&#39;,\n &#39;number_inpatient&#39;,\n &#39;diag_1&#39;,\n &#39;diag_2&#39;,\n &#39;diag_3&#39;,\n &#39;number_diagnoses&#39;,\n &#39;max_glu_serum&#39;,\n &#39;A1Cresult&#39;,\n &#39;metformin&#39;,\n &#39;repaglinide&#39;,\n &#39;nateglinide&#39;,\n &#39;chlorpropamide&#39;,\n &#39;glimepiride&#39;,\n &#39;acetohexamide&#39;,\n &#39;glipizide&#39;,\n &#39;glyburide&#39;,\n &#39;tolbutamide&#39;,\n &#39;pioglitazone&#39;,\n &#39;rosiglitazone&#39;,\n &#39;acarbose&#39;,\n &#39;miglitol&#39;,\n &#39;troglitazone&#39;,\n &#39;tolazamide&#39;,\n &#39;examide&#39;,\n &#39;citoglipton&#39;,\n &#39;insulin&#39;,\n &#39;glyburide-metformin&#39;,\n &#39;glipizide-metformin&#39;,\n &#39;glimepiride-pioglitazone&#39;,\n &#39;metformin-rosiglitazone&#39;,\n &#39;metformin-pioglitazone&#39;,\n &#39;change&#39;,\n &#39;diabetesMed&#39;,\n &#39;readmitted&#39;]</div>"]}}],"execution_count":0},{"cell_type":"code","source":["dbutils.fs.help()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"6257c056-522c-4ee0-a9be-994ec79dc9b6"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class = \"ansiout\"><b>dbutils.fs</b> provides utilities for working with FileSystems. Most methods in\nthis package can take either a DBFS path (e.g., \"/foo\" or \"dbfs:/foo\"), or\nanother FileSystem URI.\n\nFor more info about a method, use <b>dbutils.fs.help(\"methodName\")</b>.\n\nIn notebooks, you can also use the %fs shorthand to access DBFS. The %fs shorthand maps\nstraightforwardly onto dbutils calls. For example, \"%fs head --maxBytes=10000 /file/path\"\ntranslates into \"dbutils.fs.head(\"/file/path\", maxBytes = 10000)\".\n    <h3>fsutils</h3><b>cp(from: String, to: String, recurse: boolean = false): boolean</b> -> Copies a file or directory, possibly across FileSystems<br /><b>head(file: String, maxBytes: int = 65536): String</b> -> Returns up to the first 'maxBytes' bytes of the given file as a String encoded in UTF-8<br /><b>ls(dir: String): Seq</b> -> Lists the contents of a directory<br /><b>mkdirs(dir: String): boolean</b> -> Creates the given directory if it does not exist, also creating any necessary parent directories<br /><b>mv(from: String, to: String, recurse: boolean = false): boolean</b> -> Moves a file or directory, possibly across FileSystems<br /><b>put(file: String, contents: String, overwrite: boolean = false): boolean</b> -> Writes the given String out to a file, encoded in UTF-8<br /><b>rm(dir: String, recurse: boolean = false): boolean</b> -> Removes a file or directory<br /><br /><h3>mount</h3><b>mount(source: String, mountPoint: String, encryptionType: String = \"\", owner: String = null, extraConfigs: Map = Map.empty[String, String]): boolean</b> -> Mounts the given source directory into DBFS at the given mount point<br /><b>mounts: Seq</b> -> Displays information about what is mounted within DBFS<br /><b>refreshMounts: boolean</b> -> Forces all machines in this cluster to refresh their mount cache, ensuring they receive the most recent information<br /><b>unmount(mountPoint: String): boolean</b> -> Deletes a DBFS mount point<br /><br /></div>","textData":null,"removedWidgets":[],"addedWidgets":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","data":{"text/html":["<div class = \"ansiout\"><b>dbutils.fs</b> provides utilities for working with FileSystems. Most methods in\nthis package can take either a DBFS path (e.g., \"/foo\" or \"dbfs:/foo\"), or\nanother FileSystem URI.\n\nFor more info about a method, use <b>dbutils.fs.help(\"methodName\")</b>.\n\nIn notebooks, you can also use the %fs shorthand to access DBFS. The %fs shorthand maps\nstraightforwardly onto dbutils calls. For example, \"%fs head --maxBytes=10000 /file/path\"\ntranslates into \"dbutils.fs.head(\"/file/path\", maxBytes = 10000)\".\n    <h3>fsutils</h3><b>cp(from: String, to: String, recurse: boolean = false): boolean</b> -> Copies a file or directory, possibly across FileSystems<br /><b>head(file: String, maxBytes: int = 65536): String</b> -> Returns up to the first 'maxBytes' bytes of the given file as a String encoded in UTF-8<br /><b>ls(dir: String): Seq</b> -> Lists the contents of a directory<br /><b>mkdirs(dir: String): boolean</b> -> Creates the given directory if it does not exist, also creating any necessary parent directories<br /><b>mv(from: String, to: String, recurse: boolean = false): boolean</b> -> Moves a file or directory, possibly across FileSystems<br /><b>put(file: String, contents: String, overwrite: boolean = false): boolean</b> -> Writes the given String out to a file, encoded in UTF-8<br /><b>rm(dir: String, recurse: boolean = false): boolean</b> -> Removes a file or directory<br /><br /><h3>mount</h3><b>mount(source: String, mountPoint: String, encryptionType: String = \"\", owner: String = null, extraConfigs: Map = Map.empty[String, String]): boolean</b> -> Mounts the given source directory into DBFS at the given mount point<br /><b>mounts: Seq</b> -> Displays information about what is mounted within DBFS<br /><b>refreshMounts: boolean</b> -> Forces all machines in this cluster to refresh their mount cache, ensuring they receive the most recent information<br /><b>unmount(mountPoint: String): boolean</b> -> Deletes a DBFS mount point<br /><br /></div>"]}}],"execution_count":0},{"cell_type":"code","source":["import zipfile\n\ndbutils.fs.rm(\"dbfs:/lr_model.pkl\", True)\nlrModel.save('/lr_model.pkl')\ndbutils.fs.rm(\"file:/databricks/driver/lr_model.pkl\", True)\ndbutils.fs.cp(\"dbfs:/lr_model.pkl\", \"file:/databricks/driver/lr_model.pkl\", True)\n\nwith zipfile.ZipFile('pickle.zip', 'w') as myzip:\n    myzip.write('lr_model.pkl')\n\ndbutils.fs.cp(\"file:/databricks/driver/pickle.zip\", \"dbfs:/FileStore/\")\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e130e82b-bf33-4d04-9387-aed0bdf36ddd"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[348]: True</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[348]: True</div>"]}}],"execution_count":0},{"cell_type":"code","source":["display(dbutils.fs.ls(\"dbfs:/File\"))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a35e8dff-951c-4d4c-a2d9-6d3bb644e62e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[["dbfs:/lr_model.pkl/data/","data/",0],["dbfs:/lr_model.pkl/metadata/","metadata/",0]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"path","type":"\"string\"","metadata":"{}"},{"name":"name","type":"\"string\"","metadata":"{}"},{"name":"size","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/lr_model.pkl/data/</td><td>data/</td><td>0</td></tr><tr><td>dbfs:/lr_model.pkl/metadata/</td><td>metadata/</td><td>0</td></tr></tbody></table></div>"]}}],"execution_count":0},{"cell_type":"code","source":["import pickle\nfilename ='lr_model_pickle_dump.pkl'\nwith open('../'+filename,'wb')as file:\n    pickle.dump(lrModel, file)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ee6d3ecf-c3f0-4917-bc93-c32e8c9d0732"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">TypeError</span>                                 Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564236&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> filename <span class=\"ansi-blue-fg\">=</span><span class=\"ansi-blue-fg\">&#39;lr_model_pickle_dump.pkl&#39;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> <span class=\"ansi-green-fg\">with</span> open<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#39;../&#39;</span><span class=\"ansi-blue-fg\">+</span>filename<span class=\"ansi-blue-fg\">,</span><span class=\"ansi-blue-fg\">&#39;wb&#39;</span><span class=\"ansi-blue-fg\">)</span><span class=\"ansi-green-fg\">as</span> file<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">----&gt; 4</span><span class=\"ansi-red-fg\">     </span>pickle<span class=\"ansi-blue-fg\">.</span>dump<span class=\"ansi-blue-fg\">(</span>lrModel<span class=\"ansi-blue-fg\">,</span> file<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-red-fg\">TypeError</span>: can&#39;t pickle _thread.RLock objects</div>","errorSummary":"<span class=\"ansi-red-fg\">TypeError</span>: can&#39;t pickle _thread.RLock objects","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">TypeError</span>                                 Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564236&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> filename <span class=\"ansi-blue-fg\">=</span><span class=\"ansi-blue-fg\">&#39;lr_model_pickle_dump.pkl&#39;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> <span class=\"ansi-green-fg\">with</span> open<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#39;../&#39;</span><span class=\"ansi-blue-fg\">+</span>filename<span class=\"ansi-blue-fg\">,</span><span class=\"ansi-blue-fg\">&#39;wb&#39;</span><span class=\"ansi-blue-fg\">)</span><span class=\"ansi-green-fg\">as</span> file<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">----&gt; 4</span><span class=\"ansi-red-fg\">     </span>pickle<span class=\"ansi-blue-fg\">.</span>dump<span class=\"ansi-blue-fg\">(</span>lrModel<span class=\"ansi-blue-fg\">,</span> file<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-red-fg\">TypeError</span>: can&#39;t pickle _thread.RLock objects</div>"]}}],"execution_count":0},{"cell_type":"code","source":["dbutils.fs.put(\"/FileStore/my-stuff/my-file.txt\", \"Contents of my file\")\n# https://community.cloud.databricks.com/files/my-stuff/my-file.txt?o=4041069687372363\n# https://docs.microsoft.com/en-us/azure/databricks/data/filestore"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e51308d0-37cc-4aa6-8780-b4a223a3424c"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Wrote 19 bytes.\nOut[240]: True</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Wrote 19 bytes.\nOut[240]: True</div>"]}}],"execution_count":0},{"cell_type":"code","source":["# from pyspark.ml.regression import LinearRegressionModel\n# #lrModel.write().overwrite().save('/FileStore/lr_model5.txt')\n# print(x)\n#display(LinearRegressionModel.load('/FileStore/lr_model4.txt'))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4618ef07-de6e-4582-bc32-d5c294154f09"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">IllegalArgumentException</span>                  Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564238&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      1</span> <span class=\"ansi-green-fg\">from</span> pyspark<span class=\"ansi-blue-fg\">.</span>ml<span class=\"ansi-blue-fg\">.</span>regression <span class=\"ansi-green-fg\">import</span> LinearRegressionModel\n<span class=\"ansi-green-fg\">----&gt; 2</span><span class=\"ansi-red-fg\"> </span>lrModel <span class=\"ansi-blue-fg\">=</span> lr<span class=\"ansi-blue-fg\">.</span>fit<span class=\"ansi-blue-fg\">(</span>trainDF<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> <span class=\"ansi-red-fg\">#lrModel.write().overwrite().save(&#39;/FileStore/lr_model5.txt&#39;)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      4</span> print<span class=\"ansi-blue-fg\">(</span>x<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      5</span> <span class=\"ansi-red-fg\">#display(LinearRegressionModel.load(&#39;/FileStore/lr_model4.txt&#39;))</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/base.py</span> in <span class=\"ansi-cyan-fg\">fit</span><span class=\"ansi-blue-fg\">(self, dataset, params)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    127</span>                 <span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>copy<span class=\"ansi-blue-fg\">(</span>params<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">.</span>_fit<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    128</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 129</span><span class=\"ansi-red-fg\">                 </span><span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_fit<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    130</span>         <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    131</span>             raise ValueError(&#34;Params must be either a param map or a list/tuple of param maps, &#34;\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/wrapper.py</span> in <span class=\"ansi-cyan-fg\">_fit</span><span class=\"ansi-blue-fg\">(self, dataset)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    319</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    320</span>     <span class=\"ansi-green-fg\">def</span> _fit<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 321</span><span class=\"ansi-red-fg\">         </span>java_model <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>_fit_java<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    322</span>         model <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>_create_model<span class=\"ansi-blue-fg\">(</span>java_model<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    323</span>         <span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_copyValues<span class=\"ansi-blue-fg\">(</span>model<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/wrapper.py</span> in <span class=\"ansi-cyan-fg\">_fit_java</span><span class=\"ansi-blue-fg\">(self, dataset)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    316</span>         &#34;&#34;&#34;\n<span class=\"ansi-green-intense-fg ansi-bold\">    317</span>         self<span class=\"ansi-blue-fg\">.</span>_transfer_params_to_java<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">--&gt; 318</span><span class=\"ansi-red-fg\">         </span><span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_java_obj<span class=\"ansi-blue-fg\">.</span>fit<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">.</span>_jdf<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    319</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    320</span>     <span class=\"ansi-green-fg\">def</span> _fit<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py</span> in <span class=\"ansi-cyan-fg\">__call__</span><span class=\"ansi-blue-fg\">(self, *args)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1303</span>         answer <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>gateway_client<span class=\"ansi-blue-fg\">.</span>send_command<span class=\"ansi-blue-fg\">(</span>command<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1304</span>         return_value = get_return_value(\n<span class=\"ansi-green-fg\">-&gt; 1305</span><span class=\"ansi-red-fg\">             answer, self.gateway_client, self.target_id, self.name)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">   1306</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">   1307</span>         <span class=\"ansi-green-fg\">for</span> temp_arg <span class=\"ansi-green-fg\">in</span> temp_args<span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">deco</span><span class=\"ansi-blue-fg\">(*a, **kw)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    131</span>                 <span class=\"ansi-red-fg\"># Hide where the exception came from that shows a non-Pythonic</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    132</span>                 <span class=\"ansi-red-fg\"># JVM exception message.</span>\n<span class=\"ansi-green-fg\">--&gt; 133</span><span class=\"ansi-red-fg\">                 </span>raise_from<span class=\"ansi-blue-fg\">(</span>converted<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    134</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    135</span>                 <span class=\"ansi-green-fg\">raise</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">raise_from</span><span class=\"ansi-blue-fg\">(e)</span>\n\n<span class=\"ansi-red-fg\">IllegalArgumentException</span>: features does not exist. Available: encounter_id, patient_nbr, race, gender, age, weight, admission_type_id, discharge_disposition_id, admission_source_id, time_in_hospital, payer_code, medical_specialty, num_lab_procedures, num_procedures, num_medications, number_outpatient, number_emergency, number_inpatient, diag_1, diag_2, diag_3, number_diagnoses, max_glu_serum, A1Cresult, metformin, repaglinide, nateglinide, chlorpropamide, glimepiride, acetohexamide, glipizide, glyburide, tolbutamide, pioglitazone, rosiglitazone, acarbose, miglitol, troglitazone, tolazamide, examide, citoglipton, insulin, glyburide-metformin, glipizide-metformin, glimepiride-pioglitazone, metformin-rosiglitazone, metformin-pioglitazone, change, diabetesMed, readmitted</div>","errorSummary":"<span class=\"ansi-red-fg\">IllegalArgumentException</span>: features does not exist. Available: encounter_id, patient_nbr, race, gender, age, weight, admission_type_id, discharge_disposition_id, admission_source_id, time_in_hospital, payer_code, medical_specialty, num_lab_procedures, num_procedures, num_medications, number_outpatient, number_emergency, number_inpatient, diag_1, diag_2, diag_3, number_diagnoses, max_glu_serum, A1Cresult, metformin, repaglinide, nateglinide, chlorpropamide, glimepiride, acetohexamide, glipizide, glyburide, tolbutamide, pioglitazone, rosiglitazone, acarbose, miglitol, troglitazone, tolazamide, examide, citoglipton, insulin, glyburide-metformin, glipizide-metformin, glimepiride-pioglitazone, metformin-rosiglitazone, metformin-pioglitazone, change, diabetesMed, readmitted","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">IllegalArgumentException</span>                  Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564238&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      1</span> <span class=\"ansi-green-fg\">from</span> pyspark<span class=\"ansi-blue-fg\">.</span>ml<span class=\"ansi-blue-fg\">.</span>regression <span class=\"ansi-green-fg\">import</span> LinearRegressionModel\n<span class=\"ansi-green-fg\">----&gt; 2</span><span class=\"ansi-red-fg\"> </span>lrModel <span class=\"ansi-blue-fg\">=</span> lr<span class=\"ansi-blue-fg\">.</span>fit<span class=\"ansi-blue-fg\">(</span>trainDF<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> <span class=\"ansi-red-fg\">#lrModel.write().overwrite().save(&#39;/FileStore/lr_model5.txt&#39;)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      4</span> print<span class=\"ansi-blue-fg\">(</span>x<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      5</span> <span class=\"ansi-red-fg\">#display(LinearRegressionModel.load(&#39;/FileStore/lr_model4.txt&#39;))</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/base.py</span> in <span class=\"ansi-cyan-fg\">fit</span><span class=\"ansi-blue-fg\">(self, dataset, params)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    127</span>                 <span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>copy<span class=\"ansi-blue-fg\">(</span>params<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">.</span>_fit<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    128</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 129</span><span class=\"ansi-red-fg\">                 </span><span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_fit<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    130</span>         <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    131</span>             raise ValueError(&#34;Params must be either a param map or a list/tuple of param maps, &#34;\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/wrapper.py</span> in <span class=\"ansi-cyan-fg\">_fit</span><span class=\"ansi-blue-fg\">(self, dataset)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    319</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    320</span>     <span class=\"ansi-green-fg\">def</span> _fit<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 321</span><span class=\"ansi-red-fg\">         </span>java_model <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>_fit_java<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    322</span>         model <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>_create_model<span class=\"ansi-blue-fg\">(</span>java_model<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    323</span>         <span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_copyValues<span class=\"ansi-blue-fg\">(</span>model<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/wrapper.py</span> in <span class=\"ansi-cyan-fg\">_fit_java</span><span class=\"ansi-blue-fg\">(self, dataset)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    316</span>         &#34;&#34;&#34;\n<span class=\"ansi-green-intense-fg ansi-bold\">    317</span>         self<span class=\"ansi-blue-fg\">.</span>_transfer_params_to_java<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">--&gt; 318</span><span class=\"ansi-red-fg\">         </span><span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_java_obj<span class=\"ansi-blue-fg\">.</span>fit<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">.</span>_jdf<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    319</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    320</span>     <span class=\"ansi-green-fg\">def</span> _fit<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py</span> in <span class=\"ansi-cyan-fg\">__call__</span><span class=\"ansi-blue-fg\">(self, *args)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1303</span>         answer <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>gateway_client<span class=\"ansi-blue-fg\">.</span>send_command<span class=\"ansi-blue-fg\">(</span>command<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1304</span>         return_value = get_return_value(\n<span class=\"ansi-green-fg\">-&gt; 1305</span><span class=\"ansi-red-fg\">             answer, self.gateway_client, self.target_id, self.name)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">   1306</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">   1307</span>         <span class=\"ansi-green-fg\">for</span> temp_arg <span class=\"ansi-green-fg\">in</span> temp_args<span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">deco</span><span class=\"ansi-blue-fg\">(*a, **kw)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    131</span>                 <span class=\"ansi-red-fg\"># Hide where the exception came from that shows a non-Pythonic</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    132</span>                 <span class=\"ansi-red-fg\"># JVM exception message.</span>\n<span class=\"ansi-green-fg\">--&gt; 133</span><span class=\"ansi-red-fg\">                 </span>raise_from<span class=\"ansi-blue-fg\">(</span>converted<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    134</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    135</span>                 <span class=\"ansi-green-fg\">raise</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">raise_from</span><span class=\"ansi-blue-fg\">(e)</span>\n\n<span class=\"ansi-red-fg\">IllegalArgumentException</span>: features does not exist. Available: encounter_id, patient_nbr, race, gender, age, weight, admission_type_id, discharge_disposition_id, admission_source_id, time_in_hospital, payer_code, medical_specialty, num_lab_procedures, num_procedures, num_medications, number_outpatient, number_emergency, number_inpatient, diag_1, diag_2, diag_3, number_diagnoses, max_glu_serum, A1Cresult, metformin, repaglinide, nateglinide, chlorpropamide, glimepiride, acetohexamide, glipizide, glyburide, tolbutamide, pioglitazone, rosiglitazone, acarbose, miglitol, troglitazone, tolazamide, examide, citoglipton, insulin, glyburide-metformin, glipizide-metformin, glimepiride-pioglitazone, metformin-rosiglitazone, metformin-pioglitazone, change, diabetesMed, readmitted</div>"]}}],"execution_count":0},{"cell_type":"code","source":["dbutils.library.installPyPI(\"ModelExport\")\nfrom dbmlModelExport import ModelExport\n\nModelExport.exportModel(lrModel, \"/FileStore/lr_model5.txt\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e0162f1f-29c6-48ee-a443-5ed8c2025b28"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">Py4JJavaError</span>                             Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564241&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-fg\">----&gt; 1</span><span class=\"ansi-red-fg\"> </span>dbutils<span class=\"ansi-blue-fg\">.</span>library<span class=\"ansi-blue-fg\">.</span>installPyPI<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;ModelExport&#34;</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> <span class=\"ansi-green-fg\">from</span> dbmlModelExport <span class=\"ansi-green-fg\">import</span> ModelExport\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">      4</span> ModelExport<span class=\"ansi-blue-fg\">.</span>exportModel<span class=\"ansi-blue-fg\">(</span>lrModel<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">&#34;/FileStore/lr_model5.txt&#34;</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/local_disk0/tmp/1614956911283-0/dbutils.py</span> in <span class=\"ansi-cyan-fg\">installPyPI</span><span class=\"ansi-blue-fg\">(self, project, version, repo, extras)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    252</span>             self<span class=\"ansi-blue-fg\">.</span>print_dbuitilsLibrary_deprecation_message<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    253</span>             return self.print_and_return(self.entry_point.getSharedDriverContext() \\\n<span class=\"ansi-green-fg\">--&gt; 254</span><span class=\"ansi-red-fg\">                                      .addIsolatedPyPILibrary(project, version, repo, extras))\n</span><span class=\"ansi-green-intense-fg ansi-bold\">    255</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    256</span>         <span class=\"ansi-green-fg\">def</span> restartPython<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py</span> in <span class=\"ansi-cyan-fg\">__call__</span><span class=\"ansi-blue-fg\">(self, *args)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1303</span>         answer <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>gateway_client<span class=\"ansi-blue-fg\">.</span>send_command<span class=\"ansi-blue-fg\">(</span>command<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1304</span>         return_value = get_return_value(\n<span class=\"ansi-green-fg\">-&gt; 1305</span><span class=\"ansi-red-fg\">             answer, self.gateway_client, self.target_id, self.name)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">   1306</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">   1307</span>         <span class=\"ansi-green-fg\">for</span> temp_arg <span class=\"ansi-green-fg\">in</span> temp_args<span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">deco</span><span class=\"ansi-blue-fg\">(*a, **kw)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    125</span>     <span class=\"ansi-green-fg\">def</span> deco<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    126</span>         <span class=\"ansi-green-fg\">try</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 127</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">return</span> f<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    128</span>         <span class=\"ansi-green-fg\">except</span> py4j<span class=\"ansi-blue-fg\">.</span>protocol<span class=\"ansi-blue-fg\">.</span>Py4JJavaError <span class=\"ansi-green-fg\">as</span> e<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    129</span>             converted <span class=\"ansi-blue-fg\">=</span> convert_exception<span class=\"ansi-blue-fg\">(</span>e<span class=\"ansi-blue-fg\">.</span>java_exception<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py</span> in <span class=\"ansi-cyan-fg\">get_return_value</span><span class=\"ansi-blue-fg\">(answer, gateway_client, target_id, name)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    326</span>                 raise Py4JJavaError(\n<span class=\"ansi-green-intense-fg ansi-bold\">    327</span>                     <span class=\"ansi-blue-fg\">&#34;An error occurred while calling {0}{1}{2}.\\n&#34;</span><span class=\"ansi-blue-fg\">.</span>\n<span class=\"ansi-green-fg\">--&gt; 328</span><span class=\"ansi-red-fg\">                     format(target_id, &#34;.&#34;, name), value)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">    329</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    330</span>                 raise Py4JError(\n\n<span class=\"ansi-red-fg\">Py4JJavaError</span>: An error occurred while calling o17662.addIsolatedPyPILibrary.\n: org.apache.spark.SparkException: Process List(/local_disk0/pythonVirtualEnvDirs/virtualEnv-2045ae60-c3d1-4d78-b17b-f7599bd93b48/bin/python, /local_disk0/pythonVirtualEnvDirs/virtualEnv-2045ae60-c3d1-4d78-b17b-f7599bd93b48/bin/pip, install, ModelExport, --disable-pip-version-check) exited with code 1. ERROR: Could not find a version that satisfies the requirement ModelExport (from versions: none)\nERROR: No matching distribution found for ModelExport\n\n\tat org.apache.spark.util.Utils$.executeAndGetOutput(Utils.scala:1459)\n\tat org.apache.spark.util.Utils$.installLibrary(Utils.scala:877)\n\tat org.apache.spark.SparkContext.addFile(SparkContext.scala:1710)\n\tat org.apache.spark.SparkContext.addFile(SparkContext.scala:1636)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.$anonfun$addIsolatedPyPILibrary$1(SharedDriverContext.scala:746)\n\tat scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)\n\tat com.databricks.logging.UsageLogging.$anonfun$recordOperation$4(UsageLogging.scala:432)\n\tat com.databricks.logging.UsageLogging.$anonfun$withAttributionContext$1(UsageLogging.scala:240)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:62)\n\tat com.databricks.logging.UsageLogging.withAttributionContext(UsageLogging.scala:235)\n\tat com.databricks.logging.UsageLogging.withAttributionContext$(UsageLogging.scala:232)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.withAttributionContext(SharedDriverContext.scala:68)\n\tat com.databricks.logging.UsageLogging.withAttributionTags(UsageLogging.scala:277)\n\tat com.databricks.logging.UsageLogging.withAttributionTags$(UsageLogging.scala:270)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.withAttributionTags(SharedDriverContext.scala:68)\n\tat com.databricks.logging.UsageLogging.recordOperation(UsageLogging.scala:413)\n\tat com.databricks.logging.UsageLogging.recordOperation$(UsageLogging.scala:339)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.recordOperation(SharedDriverContext.scala:68)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.addIsolatedPyPILibrary(SharedDriverContext.scala:746)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\n\tat py4j.Gateway.invoke(Gateway.java:295)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:251)\n\tat java.lang.Thread.run(Thread.java:748)\n</div>","errorSummary":"org.apache.spark.SparkException: Process List(/local_disk0/pythonVirtualEnvDirs/virtualEnv-2045ae60-c3d1-4d78-b17b-f7599bd93b48/bin/python, /local_disk0/pythonVirtualEnvDirs/virtualEnv-2045ae60-c3d1-4d78-b17b-f7599bd93b48/bin/pip, install, ModelExport, --disable-pip-version-check) exited with code 1. ERROR: Could not find a version that satisfies the requirement ModelExport (from versions: none)","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">Py4JJavaError</span>                             Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564241&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-fg\">----&gt; 1</span><span class=\"ansi-red-fg\"> </span>dbutils<span class=\"ansi-blue-fg\">.</span>library<span class=\"ansi-blue-fg\">.</span>installPyPI<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;ModelExport&#34;</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> <span class=\"ansi-green-fg\">from</span> dbmlModelExport <span class=\"ansi-green-fg\">import</span> ModelExport\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">      4</span> ModelExport<span class=\"ansi-blue-fg\">.</span>exportModel<span class=\"ansi-blue-fg\">(</span>lrModel<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">&#34;/FileStore/lr_model5.txt&#34;</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/local_disk0/tmp/1614956911283-0/dbutils.py</span> in <span class=\"ansi-cyan-fg\">installPyPI</span><span class=\"ansi-blue-fg\">(self, project, version, repo, extras)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    252</span>             self<span class=\"ansi-blue-fg\">.</span>print_dbuitilsLibrary_deprecation_message<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    253</span>             return self.print_and_return(self.entry_point.getSharedDriverContext() \\\n<span class=\"ansi-green-fg\">--&gt; 254</span><span class=\"ansi-red-fg\">                                      .addIsolatedPyPILibrary(project, version, repo, extras))\n</span><span class=\"ansi-green-intense-fg ansi-bold\">    255</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    256</span>         <span class=\"ansi-green-fg\">def</span> restartPython<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py</span> in <span class=\"ansi-cyan-fg\">__call__</span><span class=\"ansi-blue-fg\">(self, *args)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1303</span>         answer <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>gateway_client<span class=\"ansi-blue-fg\">.</span>send_command<span class=\"ansi-blue-fg\">(</span>command<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1304</span>         return_value = get_return_value(\n<span class=\"ansi-green-fg\">-&gt; 1305</span><span class=\"ansi-red-fg\">             answer, self.gateway_client, self.target_id, self.name)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">   1306</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">   1307</span>         <span class=\"ansi-green-fg\">for</span> temp_arg <span class=\"ansi-green-fg\">in</span> temp_args<span class=\"ansi-blue-fg\">:</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">deco</span><span class=\"ansi-blue-fg\">(*a, **kw)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    125</span>     <span class=\"ansi-green-fg\">def</span> deco<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    126</span>         <span class=\"ansi-green-fg\">try</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 127</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">return</span> f<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    128</span>         <span class=\"ansi-green-fg\">except</span> py4j<span class=\"ansi-blue-fg\">.</span>protocol<span class=\"ansi-blue-fg\">.</span>Py4JJavaError <span class=\"ansi-green-fg\">as</span> e<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    129</span>             converted <span class=\"ansi-blue-fg\">=</span> convert_exception<span class=\"ansi-blue-fg\">(</span>e<span class=\"ansi-blue-fg\">.</span>java_exception<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py</span> in <span class=\"ansi-cyan-fg\">get_return_value</span><span class=\"ansi-blue-fg\">(answer, gateway_client, target_id, name)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    326</span>                 raise Py4JJavaError(\n<span class=\"ansi-green-intense-fg ansi-bold\">    327</span>                     <span class=\"ansi-blue-fg\">&#34;An error occurred while calling {0}{1}{2}.\\n&#34;</span><span class=\"ansi-blue-fg\">.</span>\n<span class=\"ansi-green-fg\">--&gt; 328</span><span class=\"ansi-red-fg\">                     format(target_id, &#34;.&#34;, name), value)\n</span><span class=\"ansi-green-intense-fg ansi-bold\">    329</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    330</span>                 raise Py4JError(\n\n<span class=\"ansi-red-fg\">Py4JJavaError</span>: An error occurred while calling o17662.addIsolatedPyPILibrary.\n: org.apache.spark.SparkException: Process List(/local_disk0/pythonVirtualEnvDirs/virtualEnv-2045ae60-c3d1-4d78-b17b-f7599bd93b48/bin/python, /local_disk0/pythonVirtualEnvDirs/virtualEnv-2045ae60-c3d1-4d78-b17b-f7599bd93b48/bin/pip, install, ModelExport, --disable-pip-version-check) exited with code 1. ERROR: Could not find a version that satisfies the requirement ModelExport (from versions: none)\nERROR: No matching distribution found for ModelExport\n\n\tat org.apache.spark.util.Utils$.executeAndGetOutput(Utils.scala:1459)\n\tat org.apache.spark.util.Utils$.installLibrary(Utils.scala:877)\n\tat org.apache.spark.SparkContext.addFile(SparkContext.scala:1710)\n\tat org.apache.spark.SparkContext.addFile(SparkContext.scala:1636)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.$anonfun$addIsolatedPyPILibrary$1(SharedDriverContext.scala:746)\n\tat scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)\n\tat com.databricks.logging.UsageLogging.$anonfun$recordOperation$4(UsageLogging.scala:432)\n\tat com.databricks.logging.UsageLogging.$anonfun$withAttributionContext$1(UsageLogging.scala:240)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:62)\n\tat com.databricks.logging.UsageLogging.withAttributionContext(UsageLogging.scala:235)\n\tat com.databricks.logging.UsageLogging.withAttributionContext$(UsageLogging.scala:232)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.withAttributionContext(SharedDriverContext.scala:68)\n\tat com.databricks.logging.UsageLogging.withAttributionTags(UsageLogging.scala:277)\n\tat com.databricks.logging.UsageLogging.withAttributionTags$(UsageLogging.scala:270)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.withAttributionTags(SharedDriverContext.scala:68)\n\tat com.databricks.logging.UsageLogging.recordOperation(UsageLogging.scala:413)\n\tat com.databricks.logging.UsageLogging.recordOperation$(UsageLogging.scala:339)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.recordOperation(SharedDriverContext.scala:68)\n\tat com.databricks.backend.daemon.driver.SharedDriverContext.addIsolatedPyPILibrary(SharedDriverContext.scala:746)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\n\tat py4j.Gateway.invoke(Gateway.java:295)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:251)\n\tat java.lang.Thread.run(Thread.java:748)\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["pipelineModel.save(\"/FileStore/pipelinemodel\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c2206700-b219-489a-a5b9-3ad7e1347506"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["%fs\nls /FileStore/pipelinemodel/stages/5_LinearRegression_754acd492fc5/data/part-00000-tid-1161563655149410688-9bfe5547-44e4-403a-9d9c-6ef69de365ec-3123-1-c000.snappy.parquet\n"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ce830c1d-a045-4c40-8835-b7f0d0e93bb2"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[["dbfs:/FileStore/pipelinemodel/stages/5_LinearRegression_754acd492fc5/data/part-00000-tid-1161563655149410688-9bfe5547-44e4-403a-9d9c-6ef69de365ec-3123-1-c000.snappy.parquet","part-00000-tid-1161563655149410688-9bfe5547-44e4-403a-9d9c-6ef69de365ec-3123-1-c000.snappy.parquet",20559]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"path","type":"\"string\"","metadata":"{}"},{"name":"name","type":"\"string\"","metadata":"{}"},{"name":"size","type":"\"long\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>name</th><th>size</th></tr></thead><tbody><tr><td>dbfs:/FileStore/pipelinemodel/stages/5_LinearRegression_754acd492fc5/data/part-00000-tid-1161563655149410688-9bfe5547-44e4-403a-9d9c-6ef69de365ec-3123-1-c000.snappy.parquet</td><td>part-00000-tid-1161563655149410688-9bfe5547-44e4-403a-9d9c-6ef69de365ec-3123-1-c000.snappy.parquet</td><td>20559</td></tr></tbody></table></div>"]}}],"execution_count":0},{"cell_type":"code","source":["import pickle\ndbutils.fs.put(\"/dbfs/mnt/sample.txt\", \"sample content\")\n\nwith open('/dbfs/mnt/sample.txt', 'a') as f:\n  f.write(\"??????\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d0f41f0d-d722-40d3-a181-5a8ad2e12436"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Wrote 14 bytes.\n</div>","removedWidgets":[],"addedWidgets":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Wrote 14 bytes.\n</div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">FileNotFoundError</span>                         Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564245&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> dbutils<span class=\"ansi-blue-fg\">.</span>fs<span class=\"ansi-blue-fg\">.</span>put<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;/dbfs/mnt/sample.txt&#34;</span><span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">&#34;sample content&#34;</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> \n<span class=\"ansi-green-fg\">----&gt; 4</span><span class=\"ansi-red-fg\"> </span><span class=\"ansi-green-fg\">with</span> open<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#39;/dbfs/mnt/sample.txt&#39;</span><span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">&#39;a&#39;</span><span class=\"ansi-blue-fg\">)</span> <span class=\"ansi-green-fg\">as</span> f<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      5</span>   f<span class=\"ansi-blue-fg\">.</span>write<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;??????&#34;</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-red-fg\">FileNotFoundError</span>: [Errno 2] No such file or directory: &#39;/dbfs/mnt/sample.txt&#39;</div>","errorSummary":"<span class=\"ansi-red-fg\">FileNotFoundError</span>: [Errno 2] No such file or directory: &#39;/dbfs/mnt/sample.txt&#39;","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">FileNotFoundError</span>                         Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3172634598564245&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> dbutils<span class=\"ansi-blue-fg\">.</span>fs<span class=\"ansi-blue-fg\">.</span>put<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;/dbfs/mnt/sample.txt&#34;</span><span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">&#34;sample content&#34;</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> \n<span class=\"ansi-green-fg\">----&gt; 4</span><span class=\"ansi-red-fg\"> </span><span class=\"ansi-green-fg\">with</span> open<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#39;/dbfs/mnt/sample.txt&#39;</span><span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">&#39;a&#39;</span><span class=\"ansi-blue-fg\">)</span> <span class=\"ansi-green-fg\">as</span> f<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      5</span>   f<span class=\"ansi-blue-fg\">.</span>write<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;??????&#34;</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-red-fg\">FileNotFoundError</span>: [Errno 2] No such file or directory: &#39;/dbfs/mnt/sample.txt&#39;</div>"]}}],"execution_count":0},{"cell_type":"code","source":[""],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9544bfb2-f21c-4835-bfcc-4540bbe9fbea"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"type":"ipynbError","data":"","errorSummary":"","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>"]}}],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"Test","dashboards":[],"language":"python","widgets":{},"notebookOrigID":3724877754035424}},"nbformat":4,"nbformat_minor":0}
diff --git a/server.py b/server.py
deleted file mode 100644
index dcd71ca..0000000
--- a/server.py
+++ /dev/null
@@ -1,95 +0,0 @@
-from flask import Flask, request
-from flask_cors import CORS, cross_origin
-from flask_restful import Resource, Api
-from json import dumps
-from flask_jsonpify import jsonify
-from lxml import html
-import requests
-import time
-
-from selenium import webdriver
-from selenium.webdriver.common.keys import Keys
-
-
-app = Flask(__name__)
-api = Api(app)
-
-CORS(app)
-
-
-@app.route("/")
-def hello():
-    return jsonify({'text':'Hello World!'})
-
-def getPrices(crop, crop_2):
-
-    price_page = requests.get('https://markets.businessinsider.com/commodities/' + crop + '-price')  # url
-    price_page_tree = html.fromstring(price_page.content)
-    raw_info = price_page_tree.xpath('//span//text()')
-
-    price = raw_info[29]
-    delta_usd = raw_info[34]
-    delta = raw_info[36][1:-2]
-
-    url = "https://www.cmegroup.com/trading/agricultural/grain-and-oilseed/" + crop_2 + "_quotes_globex.html";
-    driver = webdriver.Chrome("/Users/kitan/Documents/GitHub/GEWEEK/backend/chromedriver")
-
-    driver.get(url)
-    time.sleep(6)
-    forecast_page_content = bytes(driver.page_source, 'utf-8')
-
-    forecast_price_page_tree = html.fromstring(forecast_page_content)
-    raw_delta_fc = forecast_price_page_tree.xpath('//tr/td/text()')[6:]
-
-    delta_2m = float('.'.join(raw_delta_fc[0].split('\'')))
-    delta_6m = float('.'.join(raw_delta_fc[13].split('\'')))
-    delta_12m = float('.'.join(raw_delta_fc[26].split('\'')))
-
-
-    price_info = {
-        'name': crop,
-        'price': price,
-        'delta_usd': delta_usd,
-        'delta': delta,
-        'delta_2m': delta_2m,
-        'delta_6m': delta_6m,
-        'delta_12m': delta_12m
-    }
-
-    return price_info
-
-corn_info = getPrices("corn", "corn");
-rice_info = getPrices("rice","rough-rice");
-wheat_info = getPrices("wheat", "kc-wheat");
-
-price_info_db = {
-    'corn': corn_info,
-    'rice': rice_info,
-    'wheat': wheat_info
-}
-
-class Prices(Resource):
-
-    def get(self, crop):
-        return price_info_db[crop]
-
-
-
-class Employees(Resource):
-    def get(self):
-        return {'employees': [{'id':1, 'name':'Balram'},{'id':2, 'name':'Tom'}]}
-
-class Employees_Name(Resource):
-    def get(self, employee_id):
-        print('Employee id:' + employee_id)
-        result = {'data': {'id':1, 'name':'Balram'}}
-        return jsonify(result)
-
-
-api.add_resource(Employees, '/employees') # Route_1
-api.add_resource(Employees_Name, '/employees/<employee_id>') # Route_3
-api.add_resource(Prices, '/prices/<crop>') # Route_4
-
-
-if __name__ == '__main__':
-   app.run(port=5002
\ No newline at end of file