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<ul>
<li><a class="reference internal" href="#"><tt class="docutils literal"><span class="pre">sklearn.cluster</span></tt>.Ward</a></li>
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<div class="section" id="sklearn-cluster-ward">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></a>.Ward<a class="headerlink" href="#sklearn-cluster-ward" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.Ward">
<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">Ward</tt><big>(</big><em>n_clusters=2</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>pooling_func=<function mean at 0x2b8085912398></em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/hierarchical.py#L875"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Ward" title="Permalink to this definition">¶</a></dt>
<dd><p>Ward hierarchical clustering: constructs a tree and cuts it.</p>
<p>Recursively merges the pair of clusters that minimally increases
within-cluster variance.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>n_clusters</strong> : int or ndarray</p>
<blockquote>
<div><p>The number of clusters to find.</p>
</div></blockquote>
<p><strong>connectivity</strong> : sparse matrix (optional)</p>
<blockquote>
<div><p>Connectivity matrix. Defines for each sample the neighboring
samples following a given structure of the data.
Default is None, i.e, the hierarchical clustering algorithm is
unstructured.</p>
</div></blockquote>
<p><strong>memory</strong> : Instance of joblib.Memory or string (optional)</p>
<blockquote>
<div><p>Used to cache the output of the computation of the tree.
By default, no caching is done. If a string is given, it is the
path to the caching directory.</p>
</div></blockquote>
<p><strong>n_components</strong> : int (optional)</p>
<blockquote>
<div><p>The number of connected components in the graph defined by the
connectivity matrix. If not set, it is estimated.</p>
</div></blockquote>
<p><strong>compute_full_tree</strong> : bool or ‘auto’ (optional)</p>
<blockquote>
<div><p>Stop early the construction of the tree at n_clusters. This is
useful to decrease computation time if the number of clusters is
not small compared to the number of samples. This option is
useful only when specifying a connectivity matrix. Note also that
when varying the number of clusters and using caching, it may
be advantageous to compute the full tree.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><p class="first"><strong>labels_</strong> : array [n_features]</p>
<blockquote>
<div><p>cluster labels for each feature</p>
</div></blockquote>
<p><strong>n_leaves_</strong> : int</p>
<blockquote>
<div><p>Number of leaves in the hierarchical tree.</p>
</div></blockquote>
<p><strong>n_components_</strong> : int</p>
<blockquote>
<div><p>The estimated number of connected components in the graph.</p>
</div></blockquote>
<p><strong>children_</strong> : array-like, shape (n_nodes-1, 2)</p>
<blockquote class="last">
<div><p>The children of each non-leaf node. Values less than <cite>n_samples</cite>
refer to leaves of the tree. A greater value <cite>i</cite> indicates a node with
children <cite>children_[i - n_samples]</cite>.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering" title="sklearn.cluster.AgglomerativeClustering"><tt class="xref py py-obj docutils literal"><span class="pre">AgglomerativeClustering</span></tt></a></dt>
<dd>agglomerative hierarchical clustering</dd>
</dl>
</div>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.Ward.fit" title="sklearn.cluster.Ward.fit"><tt class="xref py py-obj docutils literal"><span class="pre">fit</span></tt></a>(X[, y])</td>
<td>Fit the hierarchical clustering on the data</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.Ward.fit_predict" title="sklearn.cluster.Ward.fit_predict"><tt class="xref py py-obj docutils literal"><span class="pre">fit_predict</span></tt></a>(X[, y])</td>
<td>Performs clustering on X and returns cluster labels.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.Ward.get_params" title="sklearn.cluster.Ward.get_params"><tt class="xref py py-obj docutils literal"><span class="pre">get_params</span></tt></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.Ward.set_params" title="sklearn.cluster.Ward.set_params"><tt class="xref py py-obj docutils literal"><span class="pre">set_params</span></tt></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.Ward.__init__">
<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>pooling_func=<function mean at 0x2b8085912398></em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/hierarchical.py#L932"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Ward.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cluster.Ward.fit">
<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/hierarchical.py#L699"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Ward.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the hierarchical clustering on the data</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>The samples a.k.a. observations.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><strong>self</strong> :</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.Ward.fit_predict">
<tt class="descname">fit_predict</tt><big>(</big><em>X</em>, <em>y=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L334"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Ward.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs clustering on X and returns cluster labels.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : ndarray, shape (n_samples, n_features)</p>
<blockquote>
<div><p>Input data.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>y</strong> : ndarray, shape (n_samples,)</p>
<blockquote class="last">
<div><p>cluster labels</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.Ward.get_params">
<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L189"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Ward.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>deep: boolean, optional</strong> :</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.Ward.set_params">
<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L226"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.Ward.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
<tt class="docutils literal"><span class="pre"><component>__<parameter></span></tt> so that it’s possible to update each
component of a nested object.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>self</strong> :</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
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