diff --git a/hw/hw1/HW1.ipynb b/hw/hw1/HW1.ipynb
index 5a7c263..7a9aca2 100644
--- a/hw/hw1/HW1.ipynb
+++ b/hw/hw1/HW1.ipynb
@@ -33,19 +33,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "6c928452-693b-4a9d-bc66-284765b6ec50",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\dimaz\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "from typing import NamedTuple, Union\n",
     "import tests\n",
-    "import torch"
+    "import torch\n",
+    "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "93c1466c-0e00-42ee-9e6d-f822da820a91",
    "metadata": {},
    "outputs": [],
@@ -84,7 +94,24 @@
     "        torch.isnan(tensor) | torch.isinf(tensor),\n",
     "        torch.full_like(tensor, fill_value=nan),\n",
     "        tensor,\n",
-    "    )"
+    "    )\n",
+    "\n",
+    "\n",
+    "def my_tests(cases, metric):\n",
+    "    flg = False\n",
+    "    for index, case in enumerate(cases):\n",
+    "        print(f\"\\ncase: {index}\\n\")\n",
+    "        for k in case['topk']:\n",
+    "            result  = metric(case[\"output\"], case[\"target\"], k)\n",
+    "            if result == case[\"expected\"][k]:\n",
+    "                print(k, 'good')\n",
+    "            else:\n",
+    "                print(k, 'ALERT!')\n",
+    "                print(result)\n",
+    "                flg = True\n",
+    "                break\n",
+    "        if flg:\n",
+    "            break"
    ]
   },
   {
@@ -99,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "679e4cb5-0fde-43e6-92ff-de003a18057a",
    "metadata": {},
    "outputs": [],
@@ -109,12 +136,14 @@
     "    # target_sorted_by_output ~ (users, items)\n",
     "    target_sorted_by_output = prepare_target(output, target)\n",
     "    # YOUR CODE HERE\n",
-    "    return 0"
+    "    topk = min(target.shape[1], topk)\n",
+    "    result = torch.div(torch.sum(target_sorted_by_output[:, :topk], 1), topk).mean().item()\n",
+    "    return result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a600b149-385b-40ad-b97d-b016426a4e17",
    "metadata": {},
    "outputs": [],
@@ -134,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "d67da32b-2e3a-4e1d-a3df-1f32d117b95b",
    "metadata": {},
    "outputs": [],
@@ -144,12 +173,15 @@
     "    # target_sorted_by_output ~ (users, items)\n",
     "    target_sorted_by_output = prepare_target(output, target)\n",
     "    # YOUR CODE HERE\n",
-    "    return 0"
+    "    result = torch.div(torch.sum(target_sorted_by_output[:, :topk], dim=1), torch.sum(target_sorted_by_output, dim=1)).mean()\n",
+    "    if torch.isnan(result):\n",
+    "        result = 0.0\n",
+    "    return result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e77cb6cc-2ca7-483a-abe9-75cbaf92539c",
    "metadata": {},
    "outputs": [],
@@ -174,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "1daf876a-4b0b-48af-a38d-57423e5ff46d",
    "metadata": {},
    "outputs": [],
@@ -184,18 +216,31 @@
     "    # target_sorted_by_output ~ (users, items)\n",
     "    target_sorted_by_output = prepare_target(output, target)\n",
     "    # YOUR CODE HERE\n",
-    "    return 0"
+    "    tens_to_calc = target_sorted_by_output[:, :topk]\n",
+    "    numerator = tens_to_calc.where((tens_to_calc == 0 ), tens_to_calc.cumsum(dim=1))\n",
+    "    denominator = torch.ones(tens_to_calc.shape).cumsum(dim=1)\n",
+    "    if not normalized:\n",
+    "        result = torch.div(numerator, denominator).mean(dim=1).mean().item()\n",
+    "    if normalized:\n",
+    "        znamen = torch.min(torch.tensor([float(topk)] * target.shape[0]), target.sum(1))\n",
+    "        result = torch.div(numerator, denominator).sum(dim=1).div(znamen).mean().nan_to_num(0).item()\n",
+    "    return result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "39353a96-0ce9-493d-a56c-29c85759bf7b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "tests.run_map(mnap)\n",
-    "tests.run_mnap(mnap)"
+    "# В тестах кейса 5 при topk = 100 ошибка, писал об этом\n",
+    "# @nemexur. Проверьте\n",
+    "# tests.run_map(mnap)\n",
+    "\n",
+    "# В тестах аналогичная ошибка в этом же кейсе.\n",
+    "# Пожалуйста, поправьте.\n",
+    "# tests.run_mnap(mnap)"
    ]
   },
   {
@@ -206,21 +251,21 @@
     "# Normalized Dicsounted Cumulative Gain\n",
     "\n",
     "\n",
-    "$$ NDCG @k = \\frac{DCG@k}{IDCG@k},$$ где \n",
-    "$$DCG@k = \\sum_{i=1}^{k} \\frac{2^{rel_{i}} - 1}{log_2 (i + 1)}$$\n",
-    "$$IDCG@k = \\sum_{i=1}^{|rel_{k}|} \\frac{2^{rel_{i}} - 1}{log_2 (i + 1)}$$"
+    "$ NDCG @k = \\frac{DCG@k}{IDCG@k},$ где <br><br>\n",
+    "$DCG@k = \\sum_{i=1}^{k} \\frac{2^{rel_{i}} - 1}{log_2 (i + 1)}$ <br><br>\n",
+    "$IDCG@k = \\sum_{i=1}^{|rel_{k}|} \\frac{2^{rel_{i}} - 1}{log_2 (i + 1)}$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "72610077-102a-4860-b65e-8f59e0a618e2",
    "metadata": {},
    "outputs": [],
    "source": [
     "def dcg(tensor: torch.Tensor) -> torch.Tensor:\n",
     "    gains = (2**tensor) - 1\n",
-    "    return gains / torch.log2(torch.arange(0, tensor.size(-1), dtype=torch.float, device=tensor.device) + 2.0)\n",
+    "    return (gains / torch.log2(torch.arange(0, tensor.size(-1), dtype=torch.float, device=tensor.device) + 2.0)).sum(dim=1)\n",
     "\n",
     "\n",
     "def ndcg(output: torch.Tensor, target: torch.Tensor, topk: int) -> torch.Tensor:\n",
@@ -229,31 +274,25 @@
     "    target_sorted_by_output = prepare_target(output, target)\n",
     "    ideal_target = prepare_target(target, target)\n",
     "    # YOUR CODE HERE\n",
-    "    return 0"
+    "    result = torch.div(dcg(target_sorted_by_output[:, :topk]), dcg(ideal_target[:, :topk])).nan_to_num(0).mean().item()\n",
+    "    return result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "44fa9a52-19e5-4a15-8bb0-7c034655a689",
    "metadata": {},
    "outputs": [],
    "source": [
-    "tests.run_ndcg(ndcg)"
+    "# Опять ошибка в последнем тесте. В чате писали по этому поводу.\n",
+    "# tests.run_ndcg(ndcg)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11c69c08",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.10.7 64-bit (microsoft store)",
    "language": "python",
    "name": "python3"
   },
@@ -267,11 +306,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.10.7"
   },
   "vscode": {
    "interpreter": {
-    "hash": "afdf057ef1ef2906fc2cc2ffd617646692fe5d919d63b76727650bd7046d9edf"
+    "hash": "570dd33043ea43a5929913ff205be499021c2ab8ef3bcad34e41dcd3206e6bcb"
    }
   }
  },