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docs/examples/Classification.html

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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="7e3bb452f80347b487182be357720633" 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="6a4ad1d956994777a65593e9754f1901" 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="90261b65e96a49bda32ab21b102f74b7" 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="6cdd8bd2cae5471faa798a6e17abda6c" 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">

docs/examples/Multi_fidelity.html

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 </style>
 <section id="Multi-Fidelity">
 <h1>Multi-Fidelity<a class="headerlink" href="#Multi-Fidelity" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="e9948ad0381144438747a97902f2e299" 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>
-</div><div class="btn btn-notebook" role="button"><p><img alt="9593f1d04e8d487fae6614d483a068c2" 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>
+<div class="btn btn-notebook" role="button"><p><img alt="b7f17e160c584f35ab77f72abe1630cd" 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>
+</div><div class="btn btn-notebook" role="button"><p><img alt="9fbcccf624ad441da6b5a8b95fdb6ecf" 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>
 </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>
 <p>To tackle this challenge, we use here the parameter-based method: <a class="reference external" href="#RegularTransferNN">RegularTransferNN</a></p>
 <div class="nbinput nblast docutils container">

docs/examples/Regression.html

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 <section id="Toy-Regression">
 <h1>Toy Regression<a class="headerlink" href="#Toy-Regression" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="62b1ca1306cd4c57a20eb56924e43e17" 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>
-</div><div class="btn btn-notebook" role="button"><p><img alt="3fe4ac38ac9140e1b46e11b9cce97d7d" 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>
+<div class="btn btn-notebook" role="button"><p><img alt="42efb8e5fdb54707a96bda2dbf050521" 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>
+</div><div class="btn btn-notebook" role="button"><p><img alt="b5b70ecbde3a4a18a877bc6be554a0fd" 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>
 </div><p>You will find here the application of DA methods from the ADAPT package on a simple one dimensional DA regression 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 selected methods:</p>
 <div class="nbinput nblast docutils container">

docs/examples/Rotation.html

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 </style>
 <section id="Rotation">
 <h1>Rotation<a class="headerlink" href="#Rotation" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="e16e134fec644d039528551ebe346449" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1XePW12UF80PKzvLu9cyRJKWQoZIxk_J2?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="2d1057f8218f4128b5904b6c3e0dff1a" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Rotation.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="37770c72d1ec444c8cb82b1eb3eaeecd" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1XePW12UF80PKzvLu9cyRJKWQoZIxk_J2?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="dccc5835c5f3415b97eb84f9b4aa5c5f" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Rotation.ipynb">View on GitHub</a></p>
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 </pre></div>

docs/examples/Two_moons.html

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 <section id="Two-Moons">
 <h1>Two Moons<a class="headerlink" href="#Two-Moons" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="71b610b43b4d4146af82022e3a324853" 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>
-</div><div class="btn btn-notebook" role="button"><p><img alt="cbb67d5e18cf4dd4844ac33cb8aa1293" 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>
+<div class="btn btn-notebook" role="button"><p><img alt="0fe2bb6b54a34673aec0f391f69be190" 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>
+</div><div class="btn btn-notebook" role="button"><p><img alt="5d59ec720b2a47e1b6704452654ce44d" 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>
 </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>
 <p>The following methods are being tested:</p>
 <ul class="simple">

docs/examples/sample_bias.html

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 <section id="Sample-Bias-1D">
 <h1>Sample Bias 1D<a class="headerlink" href="#Sample-Bias-1D" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="ea0bed12768342f1b69e50a4be56886f" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Hbg2kDXKjKzeQKJSwxzaV7pwbmORhyA3?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="a1021324aa814b83b92ec30bd04a12ec" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/sample_bias.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="75f6048a976d497eaf205a4391ed2284" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Hbg2kDXKjKzeQKJSwxzaV7pwbmORhyA3?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="83a950246e294f09ac98bc4ae90232fd" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/sample_bias.ipynb">View on GitHub</a></p>
 </div><p>The following example is a 1D regression domain adaptation issue. The goal is to learn the regression task on the target data (orange points) knowing only the labels on the source data (blue points).</p>
 <p>In this example, there is a sample bias between the source and target datasets. The sources are drawn according to a gaussian distribution whereas the targets are uniformly distributed.</p>
 <p>The following methods are being tested:</p>

docs/examples/sample_bias_2d.html

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 </style>
 <section id="Sample-Bias-2D">
 <h1>Sample Bias 2D<a class="headerlink" href="#Sample-Bias-2D" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="04896b8e48ed48c7ad2ae1e0b7734dae" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/12_9rgPXobyeaKYlXh_fJPNfrbODdvuHY?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="df8dc67b1f6f4ee29bc27dd2314f55da" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/sample_bias_2d.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="b6a0900ad16f4150b4e3ff9a65bffe9b" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/12_9rgPXobyeaKYlXh_fJPNfrbODdvuHY?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="306310bc195e4c90ba243244657d05f0" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/sample_bias_2d.ipynb">View on GitHub</a></p>
 </div><p>The following example is a 2D regression domain adaptation issue. The goal is to learn the regression task on the target data (orange points) knowing only the labels on the source data (blue points).</p>
 <p>In this example, there is a sample bias between the source and target datasets. The sources are mostly located in X1=0 whereas the targets are uniformly distributed.</p>
 <p>The following methods are being tested:</p>

docs/examples/tradaboost_experiments.html

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 <section id="Reproduction-of-the-TrAdaBoost-experiments">
 <h1>Reproduction of the TrAdaBoost experiments<a class="headerlink" href="#Reproduction-of-the-TrAdaBoost-experiments" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="6dc109b89c8a4931ab40c072d45fc36c" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/adapt/blob/master/src_docs/examples/tradaboost_experiments.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="374b4b51781943cb88314a114ae60ee3" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/adapt/blob/master/src_docs/examples/tradaboost_experiments.ipynb">View on GitHub</a></p>
 </div><p>The purpose of this example is to reproduce the results obtained in the paper <a class="reference external" href="https://cse.hkust.edu.hk/~qyang/Docs/2007/tradaboost.pdf">Boosting for Transfer Learning (2007)</a>. In this work, the authors developed a transfer algorithm called TrAdaBoost dedicated for <a class="reference external" href="https://adapt-python.github.io/adapt/map.html">supervised domain adaptation</a>. You can find more details about this algorithm <a class="reference external" href="https://adapt-python.github.io/adapt/generated/adapt.instance_based.TrAdaBoost.html">here</a>. The
 goal of this algorithm is to combine a source dataset with many labeled instances to a target dataset with few labels in order to learn a good model on the target domain.</p>
 <p>We try to reproduce the two following exepriments:</p>