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<section id="module-torchdr">
<span id="id1"></span><span id="api-and-modules"></span><h1>API and Modules<a class="headerlink" href="#module-torchdr" title="Link to this heading">#</a></h1>
<p>This page provides a complete reference of all TorchDR classes and functions.
For conceptual background, see the <a class="reference internal" href="torchdr.user_guide.html#user-guide"><span class="std std-ref">User Guide</span></a>.</p>
<section id="dimensionality-reduction-methods">
<h2>Dimensionality Reduction Methods<a class="headerlink" href="#dimensionality-reduction-methods" title="Link to this heading">#</a></h2>
<p>TorchDR provides <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>-compatible estimators that work seamlessly with both NumPy arrays and PyTorch tensors. All methods support GPU acceleration via <code class="docutils literal notranslate"><span class="pre">device="cuda"</span></code> and can scale to large datasets using <code class="docutils literal notranslate"><span class="pre">backend="faiss"</span></code> or <code class="docutils literal notranslate"><span class="pre">backend="keops"</span></code>.</p>
<section id="neighbor-embedding">
<h3>Neighbor Embedding<a class="headerlink" href="#neighbor-embedding" title="Link to this heading">#</a></h3>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.UMAP.html#torchdr.UMAP" title="torchdr.UMAP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">UMAP</span></code></a>([n_neighbors, n_components, min_dist, ...])</p></td>
<td><p>UMAP introduced in <span id="id2">[<a class="reference internal" href="torchdr.bibliography.html#id9" title="Leland McInnes, John Healy, and James Melville. Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018.">McInnes <em>et al.</em>, 2018</a>]</span> and further studied in <span id="id3">[<a class="reference internal" href="torchdr.bibliography.html#id12" title="Sebastian Damrich and Fred A Hamprecht. On umap's true loss function. Advances in Neural Information Processing Systems, 34:5798–5809, 2021.">Damrich and Hamprecht, 2021</a>]</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.TSNE.html#torchdr.TSNE" title="torchdr.TSNE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TSNE</span></code></a>([perplexity, n_components, lr, ...])</p></td>
<td><p>t-Stochastic Neighbor Embedding (t-SNE) introduced in <span id="id4">[<a class="reference internal" href="torchdr.bibliography.html#id3" title="Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 2008.">Van der Maaten and Hinton, 2008</a>]</span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.InfoTSNE.html#torchdr.InfoTSNE" title="torchdr.InfoTSNE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InfoTSNE</span></code></a>([perplexity, n_components, lr, ...])</p></td>
<td><p>InfoTSNE algorithm introduced in <span id="id5">[<a class="reference internal" href="torchdr.bibliography.html#id14" title="Sebastian Damrich, Jan Niklas Böhm, Fred A Hamprecht, and Dmitry Kobak. From $ t $-sne to umap with contrastive learning. arXiv preprint arXiv:2206.01816, 2022.">Damrich <em>et al.</em>, 2022</a>]</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.LargeVis.html#torchdr.LargeVis" title="torchdr.LargeVis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LargeVis</span></code></a>([perplexity, n_components, lr, ...])</p></td>
<td><p>LargeVis algorithm introduced in <span id="id6">[<a class="reference internal" href="torchdr.bibliography.html#id13" title="Jian Tang, Jingzhou Liu, Ming Zhang, and Qiaozhu Mei. Visualizing large-scale and high-dimensional data. In Proceedings of the 25th international conference on world wide web, 287–297. 2016.">Tang <em>et al.</em>, 2016</a>]</span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.SNE.html#torchdr.SNE" title="torchdr.SNE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SNE</span></code></a>([perplexity, n_components, lr, ...])</p></td>
<td><p>Stochastic Neighbor Embedding (SNE) introduced in <span id="id7">[<a class="reference internal" href="torchdr.bibliography.html#id2" title="Geoffrey E Hinton and Sam Roweis. Stochastic neighbor embedding. Advances in neural information processing systems, 2002.">Hinton and Roweis, 2002</a>]</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.TSNEkhorn.html#torchdr.TSNEkhorn" title="torchdr.TSNEkhorn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TSNEkhorn</span></code></a>([perplexity, n_components, lr, ...])</p></td>
<td><p>TSNEkhorn algorithm introduced in <span id="id8">[<a class="reference internal" href="torchdr.bibliography.html#id4" title="Hugues Van Assel, Titouan Vayer, Rémi Flamary, and Nicolas Courty. Snekhorn: dimension reduction with symmetric entropic affinities. Advances in Neural Information Processing Systems, 2024.">Van Assel <em>et al.</em>, 2024</a>]</span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.COSNE.html#torchdr.COSNE" title="torchdr.COSNE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">COSNE</span></code></a>([perplexity, lambda1, gamma, ...])</p></td>
<td><p>Implementation of the CO-Stochastic Neighbor Embedding (CO-SNE) introduced in <span id="id9">[<a class="reference internal" href="torchdr.bibliography.html#id22" title="Yunhui Guo, Haoran Guo, and Stella X Yu. Co-sne: dimensionality reduction and visualization for hyperbolic data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 21–30. 2022.">Guo <em>et al.</em>, 2022</a>]</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.PACMAP.html#torchdr.PACMAP" title="torchdr.PACMAP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PACMAP</span></code></a>([n_neighbors, n_components, lr, ...])</p></td>
<td><p>PACMAP algorithm introduced in <span id="id10">[<a class="reference internal" href="torchdr.bibliography.html#id23" title="Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik. Understanding how dimension reduction tools work: an empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization. Journal of Machine Learning Research, 22(201):1–73, 2021.">Wang <em>et al.</em>, 2021</a>]</span>.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="spectral-methods">
<h3>Spectral Methods<a class="headerlink" href="#spectral-methods" title="Link to this heading">#</a></h3>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.PCA.html#torchdr.PCA" title="torchdr.PCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PCA</span></code></a>([n_components, device, distributed, ...])</p></td>
<td><p>Principal Component Analysis module.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.IncrementalPCA.html#torchdr.IncrementalPCA" title="torchdr.IncrementalPCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IncrementalPCA</span></code></a>([n_components, copy, ...])</p></td>
<td><p>Incremental Principal Components Analysis (IPCA) leveraging PyTorch for GPU acceleration.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.ExactIncrementalPCA.html#torchdr.ExactIncrementalPCA" title="torchdr.ExactIncrementalPCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExactIncrementalPCA</span></code></a>([n_components, device, ...])</p></td>
<td><p>Exact Incremental Principal Component Analysis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.KernelPCA.html#torchdr.KernelPCA" title="torchdr.KernelPCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KernelPCA</span></code></a>([affinity, n_components, device, ...])</p></td>
<td><p>Kernel Principal Component Analysis module.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.PHATE.html#torchdr.PHATE" title="torchdr.PHATE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PHATE</span></code></a>([k, n_components, t, alpha, ...])</p></td>
<td><p>Implementation of PHATE introduced in <span id="id11">[<a class="reference internal" href="torchdr.bibliography.html#id24" title="Kevin R Moon, David Van Dijk, Zheng Wang, Scott Gigante, Daniel B Burkhardt, William S Chen, Kristina Yim, Antonia van den Elzen, Matthew J Hirn, Ronald R Coifman, and others. Visualizing structure and transitions in high-dimensional biological data. Nature biotechnology, 37(12):1482–1492, 2019.">Moon <em>et al.</em>, 2019</a>]</span>.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>
<section id="affinities">
<h2>Affinities<a class="headerlink" href="#affinities" title="Link to this heading">#</a></h2>
<p>Affinities are the building blocks for constructing the input similarity matrix <span class="math notranslate nohighlight">\(\mathbf{P}\)</span>.
See <a class="reference internal" href="torchdr.user_guide.html#user-guide"><span class="std std-ref">User Guide</span></a> for details on how affinities are used in DR methods.</p>
<section id="adaptive-affinities">
<h3>Adaptive Affinities<a class="headerlink" href="#adaptive-affinities" title="Link to this heading">#</a></h3>
<p>Affinities that adapt bandwidth based on local neighborhood structure.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.EntropicAffinity.html#torchdr.EntropicAffinity" title="torchdr.EntropicAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EntropicAffinity</span></code></a>([perplexity, max_iter, ...])</p></td>
<td><p>Solve the directed entropic affinity problem introduced in <span id="id12">[<a class="reference internal" href="torchdr.bibliography.html#id2" title="Geoffrey E Hinton and Sam Roweis. Stochastic neighbor embedding. Advances in neural information processing systems, 2002.">Hinton and Roweis, 2002</a>]</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.SymmetricEntropicAffinity.html#torchdr.SymmetricEntropicAffinity" title="torchdr.SymmetricEntropicAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SymmetricEntropicAffinity</span></code></a>([perplexity, lr, ...])</p></td>
<td><p>Compute the symmetric entropic affinity (SEA) introduced in <span id="id13">[<a class="reference internal" href="torchdr.bibliography.html#id4" title="Hugues Van Assel, Titouan Vayer, Rémi Flamary, and Nicolas Courty. Snekhorn: dimension reduction with symmetric entropic affinities. Advances in Neural Information Processing Systems, 2024.">Van Assel <em>et al.</em>, 2024</a>]</span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.UMAPAffinity.html#torchdr.UMAPAffinity" title="torchdr.UMAPAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">UMAPAffinity</span></code></a>([n_neighbors, max_iter, ...])</p></td>
<td><p>Compute the input affinity used in UMAP <span id="id14">[<a class="reference internal" href="torchdr.bibliography.html#id9" title="Leland McInnes, John Healy, and James Melville. Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018.">McInnes <em>et al.</em>, 2018</a>]</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.PACMAPAffinity.html#torchdr.PACMAPAffinity" title="torchdr.PACMAPAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PACMAPAffinity</span></code></a>([n_neighbors, metric, ...])</p></td>
<td><p>Compute the input affinity used in PACMAP <span id="id15">[<a class="reference internal" href="torchdr.bibliography.html#id23" title="Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik. Understanding how dimension reduction tools work: an empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization. Journal of Machine Learning Research, 22(201):1–73, 2021.">Wang <em>et al.</em>, 2021</a>]</span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.SelfTuningAffinity.html#torchdr.SelfTuningAffinity" title="torchdr.SelfTuningAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SelfTuningAffinity</span></code></a>([K, normalization_dim, ...])</p></td>
<td><p>Self-tuning affinity introduced in <span id="id16">[<a class="reference internal" href="torchdr.bibliography.html#id19" title="Lihi Zelnik-Manor and Pietro Perona. Self-tuning spectral clustering. Advances in neural information processing systems, 2004.">Zelnik-Manor and Perona, 2004</a>]</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.MAGICAffinity.html#torchdr.MAGICAffinity" title="torchdr.MAGICAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MAGICAffinity</span></code></a>([K, metric, zero_diag, ...])</p></td>
<td><p>Compute the MAGIC affinity with alpha-decay kernel introduced in <span id="id17">[<a class="reference internal" href="torchdr.bibliography.html#id20" title="David Van Dijk, Roshan Sharma, Juozas Nainys, Kristina Yim, Pooja Kathail, Ambrose J Carr, Cassandra Burdziak, Kevin R Moon, Christine L Chaffer, Diwakar Pattabiraman, and others. Recovering gene interactions from single-cell data using data diffusion. Cell, 174(3):716–729, 2018.">Van Dijk <em>et al.</em>, 2018</a>]</span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.PHATEAffinity.html#torchdr.PHATEAffinity" title="torchdr.PHATEAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PHATEAffinity</span></code></a>([metric, device, backend, ...])</p></td>
<td><p>Compute the potential affinity used in PHATE <span id="id18">[<a class="reference internal" href="torchdr.bibliography.html#id24" title="Kevin R Moon, David Van Dijk, Zheng Wang, Scott Gigante, Daniel B Burkhardt, William S Chen, Kristina Yim, Antonia van den Elzen, Matthew J Hirn, Ronald R Coifman, and others. Visualizing structure and transitions in high-dimensional biological data. Nature biotechnology, 37(12):1482–1492, 2019.">Moon <em>et al.</em>, 2019</a>]</span>.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="other-normalized-affinities">
<h3>Other Normalized Affinities<a class="headerlink" href="#other-normalized-affinities" title="Link to this heading">#</a></h3>
<p>Other normalized affinity kernels.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.NormalizedGaussianAffinity.html#torchdr.NormalizedGaussianAffinity" title="torchdr.NormalizedGaussianAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NormalizedGaussianAffinity</span></code></a>([sigma, metric, ...])</p></td>
<td><p>Compute the Gaussian affinity matrix which can be normalized along a dimension.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.NormalizedStudentAffinity.html#torchdr.NormalizedStudentAffinity" title="torchdr.NormalizedStudentAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NormalizedStudentAffinity</span></code></a>([...])</p></td>
<td><p>Compute the Student affinity matrix which can be normalized along a dimension.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.SinkhornAffinity.html#torchdr.SinkhornAffinity" title="torchdr.SinkhornAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SinkhornAffinity</span></code></a>([eps, tol, max_iter, ...])</p></td>
<td><p>Compute the symmetric doubly stochastic affinity matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.DoublyStochasticQuadraticAffinity.html#torchdr.DoublyStochasticQuadraticAffinity" title="torchdr.DoublyStochasticQuadraticAffinity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DoublyStochasticQuadraticAffinity</span></code></a>([eps, ...])</p></td>
<td><p>Compute the symmetric doubly stochastic affinity.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>
<section id="base-classes">
<h2>Base Classes<a class="headerlink" href="#base-classes" title="Link to this heading">#</a></h2>
<p>These classes provide the foundation for implementing custom DR methods.</p>
<section id="core-base-classes">
<h3>Core Base Classes<a class="headerlink" href="#core-base-classes" title="Link to this heading">#</a></h3>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="stubs/torchdr.DRModule.html#torchdr.DRModule" title="torchdr.DRModule"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DRModule</span></code></a>([n_components, device, backend, ...])</p></td>
<td><p>Base class for DR methods.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="stubs/torchdr.AffinityMatcher.html#torchdr.AffinityMatcher" title="torchdr.AffinityMatcher"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AffinityMatcher</span></code></a>(affinity_in[, affinity_out, ...])</p></td>
<td><p>Perform dimensionality reduction by matching two affinity matrices.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="neighbor-embedding-base-classes">
<h3>Neighbor Embedding Base Classes<a class="headerlink" href="#neighbor-embedding-base-classes" title="Link to this heading">#</a></h3>
<p>Base classes for neighbor embedding methods. <a class="reference internal" href="gen_modules/torchdr.SparseNeighborEmbedding.html#torchdr.SparseNeighborEmbedding" title="torchdr.SparseNeighborEmbedding"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparseNeighborEmbedding</span></code></a> leverages input affinity sparsity for efficient attractive term computation. <a class="reference internal" href="gen_modules/torchdr.NegativeSamplingNeighborEmbedding.html#torchdr.NegativeSamplingNeighborEmbedding" title="torchdr.NegativeSamplingNeighborEmbedding"><code class="xref py py-class docutils literal notranslate"><span class="pre">NegativeSamplingNeighborEmbedding</span></code></a> adds approximate repulsive term computation via negative sampling.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.NeighborEmbedding.html#torchdr.NeighborEmbedding" title="torchdr.NeighborEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NeighborEmbedding</span></code></a>(affinity_in[, ...])</p></td>
<td><p>Solves the neighbor embedding problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.SparseNeighborEmbedding.html#torchdr.SparseNeighborEmbedding" title="torchdr.SparseNeighborEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SparseNeighborEmbedding</span></code></a>(affinity_in[, ...])</p></td>
<td><p>Solves the neighbor embedding problem with a sparse input affinity matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.NegativeSamplingNeighborEmbedding.html#torchdr.NegativeSamplingNeighborEmbedding" title="torchdr.NegativeSamplingNeighborEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NegativeSamplingNeighborEmbedding</span></code></a>(affinity_in)</p></td>
<td><p>Solves the neighbor embedding problem with both sparsity and sampling.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>
<section id="evaluation-metrics">
<h2>Evaluation Metrics<a class="headerlink" href="#evaluation-metrics" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.silhouette_score.html#torchdr.silhouette_score" title="torchdr.silhouette_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">silhouette_score</span></code></a>(X, labels[, weights, ...])</p></td>
<td><p>Compute the Silhouette score as the mean of silhouette coefficients.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.silhouette_samples.html#torchdr.silhouette_samples" title="torchdr.silhouette_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">silhouette_samples</span></code></a>(X, labels[, weights, ...])</p></td>
<td><p>Compute the silhouette coefficients for each data sample.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.knn_label_accuracy.html#torchdr.knn_label_accuracy" title="torchdr.knn_label_accuracy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">knn_label_accuracy</span></code></a>(X, labels[, k, metric, ...])</p></td>
<td><p>Compute k-NN label accuracy to evaluate class structure preservation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.neighborhood_preservation.html#torchdr.neighborhood_preservation" title="torchdr.neighborhood_preservation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighborhood_preservation</span></code></a>(X, Z, K[, metric, ...])</p></td>
<td><p>Compute K-ary neighborhood preservation between input data and embeddings.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.kmeans_ari.html#torchdr.kmeans_ari" title="torchdr.kmeans_ari"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kmeans_ari</span></code></a>(X, labels[, n_clusters, niter, ...])</p></td>
<td><p>Perform K-means clustering and compute Adjusted Rand Index.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="utils">
<h2>Utils<a class="headerlink" href="#utils" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.pairwise_distances.html#torchdr.pairwise_distances" title="torchdr.pairwise_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise_distances</span></code></a>(X[, Y, metric, backend, ...])</p></td>
<td><p>Compute pairwise distances between two tensors or from a DataLoader.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/torchdr.binary_search.html#torchdr.binary_search" title="torchdr.binary_search"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binary_search</span></code></a>(f, n[, begin, end, max_iter, ...])</p></td>
<td><p>Batched binary search root finding.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/torchdr.false_position.html#torchdr.false_position" title="torchdr.false_position"><code class="xref py py-obj docutils literal notranslate"><span class="pre">false_position</span></code></a>(f, n[, begin, end, max_iter, ...])</p></td>
<td><p>Batched false-position root finding.</p></td>
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</tbody>
</table>
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