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Copy file name to clipboardExpand all lines: docs/examples/Classification.html
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<sectionid="Classification">
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<h1>Classification<aclass="headerlink" href="#Classification" title="Permalink to this headline"></a></h1>
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<divclass="btn btn-notebook" role="button"><p><imgalt="d369b276b4744b0cab3f36e8b5537d40" src="../_images/colab_logo_32px.png" /><aclass="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
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</div><divclass="btn btn-notebook" role="button"><p><imgalt="894a8bd476554520a14e5f91493961df" src="../_images/github_logo_32px.png" /><aclass="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Classification.ipynb">View on GitHub</a></p>
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<divclass="btn btn-notebook" role="button"><p><imgalt="1c1527ebd2d8455f981eac128806a646" src="../_images/colab_logo_32px.png" /><aclass="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
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</div><divclass="btn btn-notebook" role="button"><p><imgalt="6bd6895ad3024031bf63c3e2f1c4207a" src="../_images/github_logo_32px.png" /><aclass="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 <codeclass="docutils literal notranslate"><spanclass="pre">matplotlib</span><spanclass="pre">Animation</span></code> tools in order to get a visual understanding of the mselected methods:</p>
Copy file name to clipboardExpand all lines: docs/examples/Multi_fidelity.html
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<sectionid="Multi-Fidelity">
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<h1>Multi-Fidelity<aclass="headerlink" href="#Multi-Fidelity" title="Permalink to this headline"></a></h1>
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<divclass="btn btn-notebook" role="button"><p><imgalt="52a07bcadbe84b1ba9f4c59e21069b62" src="../_images/colab_logo_32px.png" /><aclass="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><divclass="btn btn-notebook" role="button"><p><imgalt="517a6ae3dc404179ae304967a0784b5e" src="../_images/github_logo_32px.png" /><aclass="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Multi_fidelity.ipynb">View on GitHub</a></p>
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<divclass="btn btn-notebook" role="button"><p><imgalt="fe758e855f4c4b1dbec0d2a24bfbd1d8" src="../_images/colab_logo_32px.png" /><aclass="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><divclass="btn btn-notebook" role="button"><p><imgalt="fa66fbcf91614ae1a57ca11f12d0b398" src="../_images/github_logo_32px.png" /><aclass="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: <aclass="reference external" href="#RegularTransferNN">RegularTransferNN</a></p>
<spanclass="n">plt</span><spanclass="o">.</span><spanclass="n">title</span><spanclass="p">(</span><spanclass="s2">"Low Fidelity Only -- MAE = </span><spanclass="si">%.3f</span><spanclass="s2">"</span><spanclass="o">%</span><spanclass="k">sc</span>ore,<spanclass="nv">fontsize</span><spanclass="o">=</span><spanclass="m">18</span><spanclass="o">)</span>
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<spanclass="n">plt</span><spanclass="o">.</span><spanclass="n">title</span><spanclass="p">(</span><spanclass="s2">"Low Fidelity Only -- MAE = </span><spanclass="si">%.3f</span><spanclass="s2">"</span><spanclass="o">%</span><spanclass="k">sc</span>ore,<spanclass="w"></span><spanclass="nv">fontsize</span><spanclass="o">=</span><spanclass="m">18</span><spanclass="o">)</span>
<spanclass="n">plt</span><spanclass="o">.</span><spanclass="n">title</span><spanclass="p">(</span><spanclass="s2">"Low Fidelity Only -- MAE = </span><spanclass="si">%.3f</span><spanclass="s2">"</span><spanclass="o">%</span><spanclass="k">sc</span>ore,<spanclass="nv">fontsize</span><spanclass="o">=</span><spanclass="m">18</span><spanclass="o">)</span>
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<spanclass="n">plt</span><spanclass="o">.</span><spanclass="n">title</span><spanclass="p">(</span><spanclass="s2">"Low Fidelity Only -- MAE = </span><spanclass="si">%.3f</span><spanclass="s2">"</span><spanclass="o">%</span><spanclass="k">sc</span>ore,<spanclass="w"></span><spanclass="nv">fontsize</span><spanclass="o">=</span><spanclass="m">18</span><spanclass="o">)</span>
<spanclass="n">plt</span><spanclass="o">.</span><spanclass="n">title</span><spanclass="p">(</span><spanclass="s2">"Low Fidelity Only -- MAE = </span><spanclass="si">%.3f</span><spanclass="s2">"</span><spanclass="o">%</span><spanclass="k">sc</span>ore,<spanclass="nv">fontsize</span><spanclass="o">=</span><spanclass="m">18</span><spanclass="o">)</span>
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<spanclass="n">plt</span><spanclass="o">.</span><spanclass="n">title</span><spanclass="p">(</span><spanclass="s2">"Low Fidelity Only -- MAE = </span><spanclass="si">%.3f</span><spanclass="s2">"</span><spanclass="o">%</span><spanclass="k">sc</span>ore,<spanclass="w"></span><spanclass="nv">fontsize</span><spanclass="o">=</span><spanclass="m">18</span><spanclass="o">)</span>
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