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<title>ADAPT &mdash; adapt 0.1.0 documentation</title>
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<p class="caption"><span class="caption-text">API Documentation</span></p>
<p class="caption" role="heading"><span class="caption-text">API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="##adapt-feature-based">Feature-based</a><ul>
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<li class="toctree-l1"><a class="reference internal" href="##adapt-instance-based">Instance-based</a><ul>
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<p class="caption"><span class="caption-text">Examples Gallery</span></p>
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<p class="caption" role="heading"><span class="caption-text">Examples Gallery</span></p>
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<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.feature_based.MDD.html#adapt.feature_based.MDD" title="adapt.feature_based.MDD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_based.MDD</span></code></a>([encoder, task, ...])</p></td>
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<td><p>WDGRL (Wasserstein Distance Guided Representation Learning) is an unsupervised domain adaptation method on the model of the <span class="xref std std-ref">DANN</span>.</p></td>
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<title>Classification &mdash; adapt 0.1.0 documentation</title>
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<li class="toctree-l1"><a class="reference internal" href="https://github.com/adapt-python/adapt">Github</a></li>
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<p class="caption"><span class="caption-text">API Documentation</span></p>
<p class="caption" role="heading"><span class="caption-text">API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="#">Classification</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="##Src-Only">Src Only</a></li>
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<li class="toctree-l2"><a class="reference internal" href="##See-also">See also</a></li>
<li class="toctree-l2"><a class="reference internal" href="#Experimental-Setup">Experimental Setup</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#mSDA">mSDA</a></li>
<li class="toctree-l2"><a class="reference internal" href="#DANN">DANN</a></li>
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</ul>
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<section id="Classification">
<h1>Classification<a class="headerlink" href="#Classification" title="Permalink to this headline"></a></h1>
<div class="btn btn-notebook" role="button"><p><img alt="09bc92d00b19480cb23f3b0fe9c986ff" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
</div><div class="btn btn-notebook" role="button"><p><img alt="d524f1683c254916b86993a49a4e365e" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Classification.ipynb">View on GitHub</a></p>
<div class="btn btn-notebook" role="button"><p><img alt="aaf720d69a4845cfb3f988129bcfeff6" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
</div><div class="btn btn-notebook" role="button"><p><img alt="e63355d9179b498cbdca4110c5e996cd" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Classification.ipynb">View on GitHub</a></p>
</div><p>You will find here the application of DA methods from the ADAPT package on a simple two dimensional DA classification problem.</p>
<p>First we import packages needed in the following. We will use <code class="docutils literal notranslate"><span class="pre">matplotlib</span> <span class="pre">Animation</span></code> tools in order to get a visual understanding of the mselected methods:</p>
<div class="nbinput nblast docutils container">
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