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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="#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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<div class="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 headline">¶</a></h1>
<dl class="py function">
<dt id="sklearn.cluster.kmeans_plusplus">
<code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">kmeans_plusplus</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">n_clusters</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">x_squared_norms</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">n_local_trials</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1929"><span class="viewcode-link">[source]</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 simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<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>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)).</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</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 inital 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, 4],</span>
<span class="go"> [ 1, 0]])</span>
<span class="gp">>>> </span><span class="n">indices</span>
<span class="go">array([4, 2])</span>
</pre></div>
</div>
</dd></dl>
<div class="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 headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="An example to show the output of the sklearn.cluster.kmeans_plusplus function for generating in..."><div class="figure align-default" id="id1">
<img alt="An example of K-Means++ initialization" src="../../_images/sphx_glr_plot_kmeans_plusplus_thumb.png" />
<p class="caption"><span class="caption-text"><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></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
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