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<li><a class="reference internal" href="#">Recognizing hand-written digits</a></li>
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  <div class="section" id="recognizing-hand-written-digits">
<span id="example-plot-digits-classification-py"></span><h1>Recognizing hand-written digits<a class="headerlink" href="#recognizing-hand-written-digits" title="Permalink to this headline">ΒΆ</a></h1>
<p>An example showing how the scikit-learn can be used to recognize images of
hand-written digits.</p>
<img alt="auto_examples/images/plot_digits_classification.png" class="align-center" src="auto_examples/images/plot_digits_classification.png" />
<p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/plot_digits_classification.py"><tt class="xref download docutils literal"><span class="pre">plot_digits_classification.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="c"># Author: Gael Varoquaux &lt;gael dot varoquaux at normalesup dot org&gt;</span>
<span class="c"># License: Simplified BSD</span>

<span class="c"># Standard scientific Python imports</span>
<span class="kn">import</span> <span class="nn">pylab</span> <span class="kn">as</span> <span class="nn">pl</span>

<span class="c"># The digits dataset</span>
<span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>

<span class="c"># The data that we are interesting in is made of 8x8 images of digits,</span>
<span class="c"># let&#39;s have a look at the first 3 images. We know which digit they</span>
<span class="c"># represent: it is given in the &#39;target&#39; of the dataset.</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</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="mi">4</span><span class="p">]):</span>
    <span class="n">pl</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">index</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">pl</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray_r</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">&#39;Training: </span><span class="si">%i</span><span class="s">&#39;</span> <span class="o">%</span> <span class="n">label</span><span class="p">)</span>

<span class="c"># To apply an classifier on this data, we need to flatten the image, to</span>
<span class="c"># turn the data in a (samples, feature) matrix:</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">n_samples</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>

<span class="c"># Import a classifier:</span>
<span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">svm</span>
<span class="kn">from</span> <span class="nn">scikits.learn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">scikits.learn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">()</span>

<span class="c"># We learn the digits on the first half of the digits</span>
<span class="n">classifier</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">n_samples</span><span class="o">/</span><span class="mi">2</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="n">n_samples</span><span class="o">/</span><span class="mi">2</span><span class="p">])</span>

<span class="c"># Now predict the value of the digit on the second half:</span>
<span class="n">expected</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span><span class="p">[</span><span class="n">n_samples</span><span class="o">/</span><span class="mi">2</span><span class="p">:]</span>
<span class="n">predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">n_samples</span><span class="o">/</span><span class="mi">2</span><span class="p">:])</span>

<span class="k">print</span> <span class="s">&quot;Classification report for classifier:&quot;</span>
<span class="k">print</span> <span class="n">classifier</span>
<span class="k">print</span>
<span class="k">print</span> <span class="n">classification_report</span><span class="p">(</span><span class="n">expected</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
<span class="k">print</span>
<span class="k">print</span> <span class="s">&quot;Confusion matrix:&quot;</span>
<span class="k">print</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">expected</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>

<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">prediction</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
    <span class="nb">zip</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="n">n_samples</span><span class="o">/</span><span class="mi">2</span><span class="p">:],</span> <span class="n">predicted</span><span class="p">)[:</span><span class="mi">4</span><span class="p">]):</span>
    <span class="n">pl</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">index</span><span class="o">+</span><span class="mi">5</span><span class="p">)</span>
    <span class="n">pl</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray_r</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">&#39;Prediction: </span><span class="si">%i</span><span class="s">&#39;</span> <span class="o">%</span> <span class="n">prediction</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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