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        <h3>Contents</h3>
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<li><a class="reference internal" href="#">A demo of K-Means clustering on the handwritten digits data</a></li>
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  <div class="section" id="a-demo-of-k-means-clustering-on-the-handwritten-digits-data">
<span id="example-cluster-kmeans-digits-py"></span><h1>A demo of K-Means clustering on the handwritten digits data<a class="headerlink" href="#a-demo-of-k-means-clustering-on-the-handwritten-digits-data" title="Permalink to this headline">ΒΆ</a></h1>
<p>Comparing various initialization strategies in terms of runtime and quality of
the results.</p>
<p>TODO: explode the ouput of the cluster labeling and digits.target groundtruth
as categorical boolean arrays of shape (n_sample, n_unique_labels) and measure
the Pearson correlation as an additional measure of the clustering quality.</p>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/kmeans_digits.py"><tt class="xref download docutils literal"><span class="pre">kmeans_digits.py</span></tt></a></p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">print</span> <span class="n">__doc__</span>

<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">scikits.learn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">scikits.learn.datasets</span> <span class="kn">import</span> <span class="n">load_digits</span>
<span class="kn">from</span> <span class="nn">scikits.learn.pca</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">scikits.learn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span>

<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>

<span class="n">digits</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">()</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">scale</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>

<span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="n">n_digits</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">target</span><span class="p">))</span>

<span class="k">print</span> <span class="s">&quot;n_digits: </span><span class="si">%d</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">n_digits</span>
<span class="k">print</span> <span class="s">&quot;n_features: </span><span class="si">%d</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">n_features</span>
<span class="k">print</span> <span class="s">&quot;n_samples: </span><span class="si">%d</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">n_samples</span>
<span class="k">print</span>

<span class="k">print</span> <span class="s">&quot;Raw k-means with k-means++ init...&quot;</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">init</span><span class="o">=</span><span class="s">&#39;k-means++&#39;</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">n_digits</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;done in </span><span class="si">%0.3f</span><span class="s">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;inertia: </span><span class="si">%f</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">km</span><span class="o">.</span><span class="n">inertia_</span>
<span class="k">print</span>

<span class="k">print</span> <span class="s">&quot;Raw k-means with random centroid init...&quot;</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">init</span><span class="o">=</span><span class="s">&#39;random&#39;</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">n_digits</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;done in </span><span class="si">%0.3f</span><span class="s">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;inertia: </span><span class="si">%f</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">km</span><span class="o">.</span><span class="n">inertia_</span>
<span class="k">print</span>

<span class="k">print</span> <span class="s">&quot;Raw k-means with PCA-based centroid init...&quot;</span>
<span class="c"># in this case the seeding of the centers is deterministic, hence we run the</span>
<span class="c"># kmeans algorithm only once with n_init=1</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_digits</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">init</span><span class="o">=</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">n_digits</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;done in </span><span class="si">%0.3f</span><span class="s">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">)</span>
<span class="k">print</span> <span class="s">&quot;inertia: </span><span class="si">%f</span><span class="s">&quot;</span> <span class="o">%</span> <span class="n">km</span><span class="o">.</span><span class="n">inertia_</span>
<span class="k">print</span>
</pre></div>
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