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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="67b41b0b23164b5db56ec7c9a8d8c44a" 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="5d4b69f72ec04403b56798c75de6b25b" 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="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><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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<p>&#169; Copyright 2020, Antoine de Mathelin, Michelin and Centre Borelli, ENS Paris-Saclay.</p>
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<p>&#169; Copyright 2020, ADAPT team, Michelin and Centre Borelli, ENS Paris-Saclay.</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="b7c8cb9a159a435e835cc6e73c75f4a2" 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="2126694077ef4c9d8beed060550c5e93" 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="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><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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<p>&#169; Copyright 2020, Antoine de Mathelin, Michelin and Centre Borelli, ENS Paris-Saclay.</p>
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<p>&#169; Copyright 2020, ADAPT team, Michelin and Centre Borelli, ENS Paris-Saclay.</p>
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