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<div class="sphx-glr-example-title section" id="nearest-neighbors-classification">
<span id="sphx-glr-auto-examples-neighbors-plot-classification-py"></span><h1>Nearest Neighbors Classification<a class="headerlink" href="#nearest-neighbors-classification" title="Permalink to this headline">¶</a></h1>
<p>Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.</p>
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<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none"><div class="highlight"><pre><span></span>
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<div class="highlight-default"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="k">import</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.colors.ListedColormap.html#matplotlib.colors.ListedColormap" title="View documentation for matplotlib.colors.ListedColormap"><span class="n">ListedColormap</span></a>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">datasets</span>
<span class="n">n_neighbors</span> <span class="o">=</span> <span class="mi">15</span>
<span class="c1"># import some data to play with</span>
<span class="n">iris</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="View documentation for sklearn.datasets.load_iris"><span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span></a><span class="p">()</span>
<span class="c1"># we only take the first two features. We could avoid this ugly</span>
<span class="c1"># slicing by using a two-dim dataset</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="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</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">h</span> <span class="o">=</span> <span class="o">.</span><span class="mi">02</span> <span class="c1"># step size in the mesh</span>
<span class="c1"># Create color maps</span>
<span class="n">cmap_light</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.colors.ListedColormap.html#matplotlib.colors.ListedColormap" title="View documentation for matplotlib.colors.ListedColormap"><span class="n">ListedColormap</span></a><span class="p">([</span><span class="s1">'#FFAAAA'</span><span class="p">,</span> <span class="s1">'#AAFFAA'</span><span class="p">,</span> <span class="s1">'#AAAAFF'</span><span class="p">])</span>
<span class="n">cmap_bold</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.colors.ListedColormap.html#matplotlib.colors.ListedColormap" title="View documentation for matplotlib.colors.ListedColormap"><span class="n">ListedColormap</span></a><span class="p">([</span><span class="s1">'#FF0000'</span><span class="p">,</span> <span class="s1">'#00FF00'</span><span class="p">,</span> <span class="s1">'#0000FF'</span><span class="p">])</span>
<span class="k">for</span> <span class="n">weights</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'uniform'</span><span class="p">,</span> <span class="s1">'distance'</span><span class="p">]:</span>
<span class="c1"># we create an instance of Neighbours Classifier and fit the data.</span>
<span class="n">clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="View documentation for sklearn.neighbors.KNeighborsClassifier"><span class="n">neighbors</span><span class="o">.</span><span class="n">KNeighborsClassifier</span></a><span class="p">(</span><span class="n">n_neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span>
<span class="n">clf</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="n">y</span><span class="p">)</span>
<span class="c1"># Plot the decision boundary. For that, we will assign a color to each</span>
<span class="c1"># point in the mesh [x_min, x_max]x[y_min, y_max].</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.meshgrid.html#numpy.meshgrid" title="View documentation for numpy.meshgrid"><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span></a><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange" title="View documentation for numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange" title="View documentation for numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.c_.html#numpy.c_" title="View documentation for numpy.c_"><span class="n">np</span><span class="o">.</span><span class="n">c_</span></a><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="c1"># Put the result into a color plot</span>
<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="View documentation for matplotlib.pyplot.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.pcolormesh.html#matplotlib.pyplot.pcolormesh" title="View documentation for matplotlib.pyplot.pcolormesh"><span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span></a><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_light</span><span class="p">)</span>
<span class="c1"># Plot also the training points</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="View documentation for matplotlib.pyplot.scatter"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</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">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_bold</span><span class="p">,</span>
<span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="View documentation for matplotlib.pyplot.xlim"><span class="n">plt</span><span class="o">.</span><span class="n">xlim</span></a><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="View documentation for matplotlib.pyplot.ylim"><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span></a><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">"3-Class classification (k = </span><span class="si">%i</span><span class="s2">, weights = '</span><span class="si">%s</span><span class="s2">')"</span>
<span class="o">%</span> <span class="p">(</span><span class="n">n_neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">))</span>
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