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torchdr.quick_start.html
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<section id="quick-start-guide">
<span id="module-torchdr"></span><span id="quick-start"></span><h1>Quick Start Guide<a class="headerlink" href="#quick-start-guide" title="Link to this heading"></a></h1>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h2>
<p>To install TorchDR, run in the terminal:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>torchdr
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<h2>When to use TorchDR<a class="headerlink" href="#when-to-use-torchdr" title="Link to this heading"></a></h2>
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<li><p>For leveraging the power of <strong>GPU acceleration</strong> for faster computations. All the modules in TorchDR are designed to work seamlessly on the GPU by setting <code class="docutils literal notranslate"><span class="pre">device</span> <span class="pre">=</span> <span class="pre">'cuda'</span></code>.</p></li>
<li><p>For comparing different dimensionality reduction methods in a fair and reproducible way. TorchDR maximizes <strong>code sharing</strong> across various methods, ensuring a <strong>fair benchmarking</strong> that emphasizes core differences.</p></li>
<li><p>For developing new dimensionality reduction approaches. TorchDR provides a <strong>modular and extensible framework</strong> that allows you to focus on the core ideas of your method, while the rest of the pipeline is taken care of.</p></li>
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<section id="examples-using-affinitymatcher">
<h3>Examples using <code class="docutils literal notranslate"><span class="pre">AffinityMatcher</span></code>:<a class="headerlink" href="#examples-using-affinitymatcher" title="Link to this heading"></a></h3>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We show how to compute a PCA embedding using the closed form and using the AffinityMatcher class. Both approaches lead to the same solution."><img alt="" src="_images/sphx_glr_demo_pca_via_affinity_matcher_thumb.png" />
<p><a class="reference internal" href="auto_examples/basics/demo_pca_via_affinity_matcher.html#sphx-glr-auto-examples-basics-demo-pca-via-affinity-matcher-py"><span class="std std-ref">PCA via SVD and via AffinityMatcher</span></a></p>
<div class="sphx-glr-thumbnail-title">PCA via SVD and via AffinityMatcher</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We show how to compute a TSNE embedding with TorchDR on the swiss roll dataset."><img alt="" src="_images/sphx_glr_demo_tsne_swiss_roll_thumb.png" />
<p><a class="reference internal" href="auto_examples/basics/demo_tsne_swiss_roll.html#sphx-glr-auto-examples-basics-demo-tsne-swiss-roll-py"><span class="std std-ref">TSNE embedding of the swiss roll dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">TSNE embedding of the swiss roll dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We illustrate the basic usage of TorchDR with different neighbor embedding methods on the SNARE-seq gene expression dataset with given cell type labels."><img alt="" src="_images/sphx_glr_demo_ne_methods_affinity_matcher_thumb.png" />
<p><a class="reference internal" href="auto_examples/basics/demo_ne_methods_affinity_matcher.html#sphx-glr-auto-examples-basics-demo-ne-methods-affinity-matcher-py"><span class="std std-ref">Neighbor Embedding on genomics & equivalent affinity matcher formulation</span></a></p>
<div class="sphx-glr-thumbnail-title">Neighbor Embedding on genomics & equivalent affinity matcher formulation</div>
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