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‎0.17/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 07adb35262cb49a1888499e43bdecceb
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config: b302606df6a16951f69a742171a74860
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎0.17/_build/html/stable/_static/ML_MAPS_README.html

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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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<head>
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<title>Machine Learning Cheat Sheet (for scikit-learn) &mdash; scikit-learn 0.17.1 documentation</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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var DOCUMENTATION_OPTIONS = {
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VERSION: '0.17.1',
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FILE_SUFFIX: '.html',
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HAS_SOURCE: true
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<link rel="author" title="About these documents" href="../../../../about.html" />
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<script src="../../../../_static/js/bootstrap.min.js" type="text/javascript"></script>
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<link rel="canonical" href="http://scikit-learn.org/stable/_build/html/stable/_static/ML_MAPS_README.html" />
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<p class="logo"><a href="../../../../index.html">
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<img src="../../../../_static/scikit-learn-logo-small.png" alt="Logo"/>
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</a>
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</p><div class="navbar">
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<ul>
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<li><a href="../../../../index.html">Home</a></li>
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<li class="link-title">Scikit-learn 0.17 (stable)</li>
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<li><a href="../../../../_downloads/scikit-learn-docs.pdf">PDF documentation</a></li>
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<li><a href="../../../../auto_examples/index.html">Examples</a></li>
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</ul>
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<div id="cse" style="width: 100%;"></div>
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</div> <!-- end navbar --></div>
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<!-- Github "fork me" ribbon -->
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style="position: absolute; top: 0; right: 0; border: 0;"
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src="../../../../_static/img/forkme.png"
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alt="Fork me on GitHub" />
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</a>
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<div class="content-wrapper">
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<div class="sphinxsidebar">
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<div class="sphinxsidebarwrapper">
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<p class="doc-version">This documentation is for scikit-learn <strong>version 0.17.1</strong> &mdash; <a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
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<p class="citing">If you use the software, please consider <a href="../../../../about.html#citing-scikit-learn">citing scikit-learn</a>.</p>
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<ul>
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<li><a class="reference internal" href="#">Machine Learning Cheat Sheet (for scikit-learn)</a><ul>
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<li><a class="reference internal" href="#editing-the-layout-of-the-map-and-it-s-paths">1. Editing the layout of the map and it&#8217;s paths.</a></li>
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<li><a class="reference internal" href="#accessing-the-paths-of-the-svg-file-and-exporting-them">2. Accessing the paths of the SVG file and exporting them.</a></li>
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<li><a class="reference internal" href="#export-paths-as-svg-files">3. Export paths as SVG files</a></li>
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<li><a class="reference internal" href="#edit-the-svg-file">4. Edit the SVG file</a></li>
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<li><a class="reference internal" href="#from-svg-to-html-map">5. From SVG to HTML map</a></li>
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<li><a class="reference internal" href="#add-the-new-map-to-the-main-html-file">6. Add the new map to the main html file</a></li>
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</ul>
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</li>
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</ul>
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</div>
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</div>
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<input type="checkbox" id="nav-trigger" class="nav-trigger" checked />
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<label for="nav-trigger"></label>
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<div class="content">
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<div class="documentwrapper">
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<div class="bodywrapper">
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<div class="body" role="main">
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<div class="section" id="machine-learning-cheat-sheet-for-scikit-learn">
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<h1>Machine Learning Cheat Sheet (for scikit-learn)<a class="headerlink" href="#machine-learning-cheat-sheet-for-scikit-learn" title="Permalink to this headline"></a></h1>
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<p>This document is intended to explain how to edit
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the machine learning cheat sheet, originally created
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by Andreas Mueller:</p>
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<p>(<a class="reference external" href="http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html">http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html</a>)</p>
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<p>The image is made interactive using an imagemap, and uses the jQuery Map Hilight plugin module
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by David Lynch (<a class="reference external" href="http://davidlynch.org/projects/maphilight/docs/">http://davidlynch.org/projects/maphilight/docs/</a>) to highlight
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the different items on the image upon mouseover.</p>
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<p>Modifying the map on the docs is currently a little bit tedious,
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so I&#8217;ll try to make it as simple as possible.</p>
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<div class="section" id="editing-the-layout-of-the-map-and-it-s-paths">
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<h2>1. Editing the layout of the map and it&#8217;s paths.<a class="headerlink" href="#editing-the-layout-of-the-map-and-it-s-paths" title="Permalink to this headline"></a></h2>
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<p>Use a Graphics editor like Inkscape Vector Graphics Editor
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to open the ml_map.svg file, in this folder. From there
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you can move objects around, ect. as you need.</p>
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<p>Save when done, and make sure to export a .PNG file
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to replace the old-outdated ml_map.png, as that file
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is used as a background image.</p>
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</div>
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<div class="section" id="accessing-the-paths-of-the-svg-file-and-exporting-them">
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<h2>2. Accessing the paths of the SVG file and exporting them.<a class="headerlink" href="#accessing-the-paths-of-the-svg-file-and-exporting-them" title="Permalink to this headline"></a></h2>
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<p>Use an image manipulation package like GIMP Image Editor to open
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the ml_map.svg file, in this folder. With GIMP, make sure
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to select &#8216;Import paths&#8217;.</p>
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<p>Once the image has been opened, you can see all imported paths on the paths tab.
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You can edit these or create new paths. In GIMP, right-clicking one of the
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paths and choosing: Path Tool will allow you to see the paths on
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the image. The paths will be exported later and will be used to
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make the click able regions on our image map.</p>
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</div>
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<div class="section" id="export-paths-as-svg-files">
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<h2>3. Export paths as SVG files<a class="headerlink" href="#export-paths-as-svg-files" title="Permalink to this headline"></a></h2>
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<p>After you&#8217;ve edited a path or created a new one, right click it on
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the paths menu and choose &#8216;Export Path..&#8217;. This way we extract just
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that path on its own as &#8216;new_area.svg&#8217; for example.</p>
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</div>
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<div class="section" id="edit-the-svg-file">
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<h2>4. Edit the SVG file<a class="headerlink" href="#edit-the-svg-file" title="Permalink to this headline"></a></h2>
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<p>Using a script made by David Lynch, we will convert the svg files into
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html maps. To do this, open the svg file in question in any text editor.
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Make sure that the &#8216;width&#8217; and &#8216;height&#8217; are not in &#8216;in&#8217; or &#8216;px&#8217;, i.e
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&#8220;100&#8221; is OK, but &#8220;100px&#8221; or &#8220;1.25in&#8221; are not.</p>
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<p>Then wrap the &lt;path&gt; tags in &lt;g&gt; and &lt;/g&gt; tags.
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Then the file is ready for the script.</p>
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</div>
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<div class="section" id="from-svg-to-html-map">
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<h2>5. From SVG to HTML map<a class="headerlink" href="#from-svg-to-html-map" title="Permalink to this headline"></a></h2>
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<p>Use the provided svg2imagemap.py script on your edited svg file:</p>
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<p>$ python svg2imagemap.py new_area.svg</p>
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<p>where new_area.svg is our file.</p>
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</div>
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<div class="section" id="add-the-new-map-to-the-main-html-file">
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<h2>6. Add the new map to the main html file<a class="headerlink" href="#add-the-new-map-to-the-main-html-file" title="Permalink to this headline"></a></h2>
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<p>Copy the code from the newly created &#8216;new_area.html&#8217;
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file. Open the ml_map.html file.</p>
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<p>Add the &lt;area href=....... &gt;&lt;/area&gt; that you copied
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after the last &lt;/area&gt; tag in the ml_map.html file.</p>
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<p>Add the link address to &#8216;href&#8217; and a tooltip to
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&#8216;title&#8217; within your &lt;area ...&gt; tag.</p>
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<p>If you wish to add the green and blue hover effect
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to the area, add
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data-maphilight=&#8217;{&#8220;strokeColor&#8221;:&#8221;0000ff&#8221;,&#8221;strokeWidth&#8221;:5,&#8221;fillColor&#8221;:&#8221;66FF66&#8221;,&#8221;fillOpacity&#8221;:0.4}&#8217;</p>
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<p>to your area tag, as done in the other &lt;area..&gt; tags above.</p>
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<p>Save the file, and you&#8217;re done.</p>
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<hr class="docutils" />
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<p>I&#8217;ll take some time to make some scripts to automate this process
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a bit more at some point, as it is not difficult to do,
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but tedious.</p>
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<p>-Jaques Grobler</p>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="clearer"></div>
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</div>
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</div>
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<div class="footer">
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&copy; 2010 - 2014, scikit-learn developers (BSD License).
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<a href="../../../../_sources/_build/html/stable/_static/ML_MAPS_README.txt" rel="nofollow">Show this page source</a>
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‎0.17/_downloads/plot_lena_compress.py renamed to ‎0.17/_downloads/plot_face_compress.py

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Vector Quantization Example
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=========================================================
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The classic image processing example, Lena, an 8-bit grayscale
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bit-depth, 512 x 512 sized image, is used here to illustrate
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how `k`-means is used for vector quantization.
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Face, a 1024 x 768 size image of a raccoon face,
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is used here to illustrate how `k`-means is
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used for vector quantization.
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"""
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print(__doc__)
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import matplotlib.pyplot as plt
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from sklearn import cluster
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from sklearn.utils.testing import SkipTest
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from sklearn.utils.fixes import sp_version
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n_clusters = 5
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np.random.seed(0)
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if sp_version < (0, 12):
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raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
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"thus does not include the scipy.misc.face() image.")
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try:
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lena = sp.lena()
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face = sp.face(gray=True)
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except AttributeError:
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# Newer versions of scipy have lena in misc
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# Newer versions of scipy have face in misc
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from scipy import misc
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lena = misc.lena()
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X = lena.reshape((-1, 1)) # We need an (n_sample, n_feature) array
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face = misc.face(gray=True)
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n_clusters = 5
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np.random.seed(0)
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X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array
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k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
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k_means.fit(X)
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values = k_means.cluster_centers_.squeeze()
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labels = k_means.labels_
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# create an array from labels and values
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lena_compressed = np.choose(labels, values)
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lena_compressed.shape = lena.shape
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face_compressed = np.choose(labels, values)
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face_compressed.shape = face.shape
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vmin = lena.min()
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vmax = lena.max()
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vmin = face.min()
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vmax = face.max()
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# original lena
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# original face
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plt.figure(1, figsize=(3, 2.2))
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plt.imshow(lena, cmap=plt.cm.gray, vmin=vmin, vmax=256)
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plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)
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# compressed lena
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# compressed face
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plt.figure(2, figsize=(3, 2.2))
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plt.imshow(lena_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
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plt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
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# equal bins lena
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# equal bins face
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regular_values = np.linspace(0, 256, n_clusters + 1)
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regular_labels = np.searchsorted(regular_values, lena) - 1
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regular_labels = np.searchsorted(regular_values, face) - 1
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regular_values = .5 * (regular_values[1:] + regular_values[:-1]) # mean
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regular_lena = np.choose(regular_labels.ravel(), regular_values)
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regular_lena.shape = lena.shape
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regular_face = np.choose(regular_labels.ravel(), regular_values, mode="clip")
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regular_face.shape = face.shape
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plt.figure(3, figsize=(3, 2.2))
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plt.imshow(regular_lena, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
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plt.imshow(regular_face, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
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# histogram
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plt.figure(4, figsize=(3, 2.2))

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