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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.kmeans_plusplus</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.kmeans_plusplus"><code class="docutils literal notranslate"><span class="pre">kmeans_plusplus</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-kmeans-plusplus">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.kmeans_plusplus</span></code></a></li>
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<section id="sklearn-cluster-kmeans-plusplus">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.kmeans_plusplus<a class="headerlink" href="#sklearn-cluster-kmeans-plusplus" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.cluster.kmeans_plusplus">
<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">kmeans_plusplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_squared_norms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_local_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/cluster/_kmeans.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.kmeans_plusplus" title="Permalink to this definition">¶</a></dt>
<dd><p>Init n_clusters seeds according to k-means++.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The data to pick seeds from.</p>
</dd>
<dt><strong>n_clusters</strong><span class="classifier">int</span></dt><dd><p>The number of centroids to initialize.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>The weights for each observation in <code class="docutils literal notranslate"><span class="pre">X</span></code>. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, all observations
are assigned equal weight. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is ignored if <code class="docutils literal notranslate"><span class="pre">init</span></code>
is a callable or a user provided array.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.3.</span></p>
</div>
</dd>
<dt><strong>x_squared_norms</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Squared Euclidean norm of each data point.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int or RandomState instance, default=None</span></dt><dd><p>Determines random number generation for centroid initialization. Pass
an int for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>n_local_trials</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of seeding trials for each center (except the first),
of which the one reducing inertia the most is greedily chosen.
Set to None to make the number of trials depend logarithmically
on the number of seeds (2+log(k)) which is the recommended setting.
Setting to 1 disables the greedy cluster selection and recovers the
vanilla k-means++ algorithm which was empirically shown to work less
well than its greedy variant.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>centers</strong><span class="classifier">ndarray of shape (n_clusters, n_features)</span></dt><dd><p>The initial centers for k-means.</p>
</dd>
<dt><strong>indices</strong><span class="classifier">ndarray of shape (n_clusters,)</span></dt><dd><p>The index location of the chosen centers in the data array X. For a
given index and center, X[index] = center.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>Selects initial cluster centers for k-mean clustering in a smart way
to speed up convergence. see: Arthur, D. and Vassilvitskii, S.
“k-means++: the advantages of careful seeding”. ACM-SIAM symposium
on Discrete algorithms. 2007</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">kmeans_plusplus</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</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="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">centers</span><span class="p">,</span> <span class="n">indices</span> <span class="o">=</span> <span class="n">kmeans_plusplus</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">centers</span>
<span class="go">array([[10, 2],</span>
<span class="go"> [ 1, 0]])</span>
<span class="gp">>>> </span><span class="n">indices</span>
<span class="go">array([3, 2])</span>
</pre></div>
</div>
</dd></dl>
<section id="examples-using-sklearn-cluster-kmeans-plusplus">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.kmeans_plusplus</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-kmeans-plusplus" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="An example to show the output of the sklearn.cluster.kmeans_plusplus function for generating in..."><img alt="" src="../../_images/sphx_glr_plot_kmeans_plusplus_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_plusplus.html#sphx-glr-auto-examples-cluster-plot-kmeans-plusplus-py"><span class="std std-ref">An example of K-Means++ initialization</span></a></p>
<div class="sphx-glr-thumbnail-title">An example of K-Means++ initialization</div>
</div></div><div class="clearer"></div></section>
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