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<li><a class="reference internal" href="#">K-means Clustering</a></li>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-cluster-plot-cluster-iris-py"><span class="std std-ref">here</span></a>
to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="k-means-clustering">
<span id="sphx-glr-auto-examples-cluster-plot-cluster-iris-py"></span><h1>K-means Clustering<a class="headerlink" href="#k-means-clustering" title="Permalink to this heading">¶</a></h1>
<p>The plot shows:</p>
<ul class="simple">
<li><p>top left: What a K-means algorithm would yield using 8 clusters.</p></li>
<li><p>top right: What the effect of a bad initialization is
on the classification process: By setting n_init to only 1
(default is 10), the amount of times that the algorithm will
be run with different centroid seeds is reduced.</p></li>
<li><p>bottom left: What using eight clusters would deliver.</p></li>
<li><p>bottom right: The ground truth.</p></li>
</ul>
<img src="../../_images/sphx_glr_plot_cluster_iris_001.png" srcset="../../_images/sphx_glr_plot_cluster_iris_001.png" alt="8 clusters, 3 clusters, 3 clusters, bad initialization, Ground Truth" class = "sphx-glr-single-img"/><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Code source: Gaël Varoquaux</span>
<span class="c1"># Modified for documentation by Jaques Grobler</span>
<span class="c1"># License: BSD 3 clause</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="c1"># Though the following import is not directly being used, it is required</span>
<span class="c1"># for 3D projection to work with matplotlib < 3.2</span>
<span class="kn">import</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="c1"># noqa: F401</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html#numpy.random.seed" title="numpy.random.seed" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span></a><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="n">iris</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span></a><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">estimators</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="s2">"k_means_iris_8"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"k_means_iris_3"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)),</span>
<span class="p">(</span><span class="s2">"k_means_iris_bad_init"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KMeans</span></a><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</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="n">init</span><span class="o">=</span><span class="s2">"random"</span><span class="p">)),</span>
<span class="p">]</span>
<span class="n">fig</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">titles</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"8 clusters"</span><span class="p">,</span> <span class="s2">"3 clusters"</span><span class="p">,</span> <span class="s2">"3 clusters, bad initialization"</span><span class="p">]</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">((</span><span class="n">name</span><span class="p">,</span> <span class="n">est</span><span class="p">),</span> <span class="n">title</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">estimators</span><span class="p">,</span> <span class="n">titles</span><span class="p">)):</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s2">"3d"</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="mi">48</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">134</span><span class="p">)</span>
<span class="n">est</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">labels</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">labels_</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">3</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="n">X</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">labels</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">zaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Petal width"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Sepal length"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s2">"Petal length"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="c1"># Plot the ground truth</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">2</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">projection</span><span class="o">=</span><span class="s2">"3d"</span><span class="p">,</span> <span class="n">elev</span><span class="o">=</span><span class="mi">48</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">134</span><span class="p">)</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="p">[(</span><span class="s2">"Setosa"</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="s2">"Versicolour"</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="s2">"Virginica"</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text3D</span><span class="p">(</span>
<span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
<span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
<span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span>
<span class="n">name</span><span class="p">,</span>
<span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">"center"</span><span class="p">,</span>
<span class="n">bbox</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"w"</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">"w"</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># Reorder the labels to have colors matching the cluster results</span>
<span class="n">y</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.choose.html#numpy.choose" title="numpy.choose" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">choose</span></a><span class="p">(</span><span class="n">y</span><span class="p">,</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="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">3</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="n">X</span><span class="p">[:,</span> <span class="mi">2</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">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">zaxis</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">([])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Petal width"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Sepal length"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s2">"Petal length"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Ground Truth"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="matplotlib.pyplot.subplots_adjust" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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