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<h3>Contents</h3>
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<li><a class="reference internal" href="#">PCA 2d projection of of Iris dataset</a></li>
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<div class="section" id="pca-2d-projection-of-of-iris-dataset">
<span id="example-plot-pca-py"></span><h1>PCA 2d projection of of Iris dataset<a class="headerlink" href="#pca-2d-projection-of-of-iris-dataset" title="Permalink to this headline">ΒΆ</a></h1>
<p>The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
and petal width.</p>
<p>Principal Component Analysis (PCA) applied to this data identifies the
combination of attributes (principal components, or directions in the
feature space) that account for the most variance in the data. Here we
plot the different samples on the 2 first principal components.</p>
<img alt="auto_examples/images/plot_pca.png" class="align-center" src="auto_examples/images/plot_pca.png" />
<p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/plot_pca.py"><tt class="xref download docutils literal"><span class="pre">plot_pca.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">import</span> <span class="nn">pylab</span> <span class="kn">as</span> <span class="nn">pl</span>
<span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">datasets</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="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
<span class="n">target_names</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target_names</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="mi">2</span><span class="p">)</span>
<span class="n">X_r</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c"># Percentage of variance explained for each components</span>
<span class="k">print</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_</span>
<span class="n">pl</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">target_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="s">"rgb"</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">target_names</span><span class="p">):</span>
<span class="n">pl</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_r</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">i</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_r</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">i</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">target_name</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pl</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">'PCA of IRIS dataset'</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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