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‎docs/contents.html

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<li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
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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" role="heading"><span class="caption-text">Tutorials</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="map.html">Choosing the right algorithm</a></li>
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<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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<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.feature_based.FE.html#adapt.feature_based.FE" title="adapt.feature_based.FE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_based.FE</span></code></a>([estimator, Xt, yt, copy, ...])</p></td>
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<td><p>FE: Frustratingly Easy Domain Adaptation.</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.feature_based.FA.html#adapt.feature_based.FA" title="adapt.feature_based.FA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_based.FA</span></code></a>([estimator, Xt, yt, copy, ...])</p></td>
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<td><p>FA: Feature Augmentation.</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.feature_based.CORAL.html#adapt.feature_based.CORAL" title="adapt.feature_based.CORAL"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_based.CORAL</span></code></a>([estimator, Xt, ...])</p></td>
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<td><p>CORAL: CORrelation ALignment</p></td>

‎docs/examples/Classification.html

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<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../map.html">Choosing the right algorithm</a></li>
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<section id="Classification">
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<h1>Classification<a class="headerlink" href="#Classification" title="Permalink to this headline"></a></h1>
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<div class="btn btn-notebook" role="button"><p><img alt="e7039483b2dc4ea18ee11f560be137a4" 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>
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</div><div class="btn btn-notebook" role="button"><p><img alt="b78330d4ef564a759835480a763370d1" 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>
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<div class="btn btn-notebook" role="button"><p><img alt="7e5febbae9be4cb49a22890f1a8ef47d" 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>
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</div><div class="btn btn-notebook" role="button"><p><img alt="6600b9179aab4842916326eb2be34ace" 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>
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</div><p>You will find here the application of DA methods from the ADAPT package on a simple two dimensional DA classification problem.</p>
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<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>
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<section id="Multi-Fidelity">
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<h1>Multi-Fidelity<a class="headerlink" href="#Multi-Fidelity" title="Permalink to this headline"></a></h1>
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<div class="btn btn-notebook" role="button"><p><img alt="f630b8d69d7647e986fa8aeb2411c923" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Cc9TVY_Tl_boVzZDNisQnqe6Qx78svqe?usp=sharing">Run in Google Colab</a></p>
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</div><div class="btn btn-notebook" role="button"><p><img alt="c68caa818b9847faa67ab0b96d821b88" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Multi_fidelity.ipynb">View on GitHub</a></p>
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<div class="btn btn-notebook" role="button"><p><img alt="39fb27f4651d4fbf9ef92e084815a7a7" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Cc9TVY_Tl_boVzZDNisQnqe6Qx78svqe?usp=sharing">Run in Google Colab</a></p>
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</div><div class="btn btn-notebook" role="button"><p><img alt="16a9427b4d9a458a893dff0faa024dd9" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Multi_fidelity.ipynb">View on GitHub</a></p>
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</div><p>The following example is a 1D regression multi-fidelity issue. Blue points are low fidelity observations and orange points are high fidelity observations. The goal is to use both datasets to learn the task on the [0, 1] interval.</p>
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<p>To tackle this challenge, we use here the parameter-based method: <a class="reference external" href="#RegularTransferNN">RegularTransferNN</a></p>
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<section id="Toy-Regression">
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<h1>Toy Regression<a class="headerlink" href="#Toy-Regression" title="Permalink to this headline"></a></h1>
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<div class="btn btn-notebook" role="button"><p><img alt="4401e0ae9ac7495f8fefef6c5fcfa2b7" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1adhqoV6b0uEavLDmMfkiwtRjam0DrXux?usp=sharing">Run in Google Colab</a></p>
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</div><div class="btn btn-notebook" role="button"><p><img alt="ee6cd43caa46488cb94b7936201e55e2" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Regression.ipynb">View on GitHub</a></p>
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<div class="btn btn-notebook" role="button"><p><img alt="e424096f38164c73a44b40c56b827341" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1adhqoV6b0uEavLDmMfkiwtRjam0DrXux?usp=sharing">Run in Google Colab</a></p>
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</div><div class="btn btn-notebook" role="button"><p><img alt="ce200f7c0da44b108ad1b64974134025" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Regression.ipynb">View on GitHub</a></p>
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</div><p>You will find here the application of DA methods from the ADAPT package on a simple one dimensional DA regression problem.</p>
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<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 selected methods:</p>
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<div class="btn btn-notebook" role="button"><p><img alt="f370479d0d9548a6b065dee562e1a44a" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Tz-TIkHI8ashHP90Im6D3tMjZ3lkR7s6?usp=sharing">Run in Google Colab</a></p>
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</div><div class="btn btn-notebook" role="button"><p><img alt="af0be599c0dd4c5b8d5a45ec1b5f0a7e" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Two_moons.ipynb">View on GitHub</a></p>
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<div class="btn btn-notebook" role="button"><p><img alt="92321d0ef1d54e1fb424a825284362a2" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Tz-TIkHI8ashHP90Im6D3tMjZ3lkR7s6?usp=sharing">Run in Google Colab</a></p>
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</div><div class="btn btn-notebook" role="button"><p><img alt="698097286ed34c5a8ee3b33e9d257483" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Two_moons.ipynb">View on GitHub</a></p>
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</div><p>The following example is a binary classification domain adaptation issue. The goal is to learn the classification task on the target data (black points) knowing only the labels on the source data (red and blue points).</p>
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<p>The following methods are being tested:</p>
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