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  <div class="section" id="sklearn-cluster-bicluster-spectralcoclustering">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster.bicluster" title="sklearn.cluster.bicluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster.bicluster</span></code></a>.SpectralCoclustering<a class="headerlink" href="#sklearn-cluster-bicluster-spectralcoclustering" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering">
<em class="property">class </em><code class="descclassname">sklearn.cluster.bicluster.</code><code class="descname">SpectralCoclustering</code><span class="sig-paren">(</span><em>n_clusters=3</em>, <em>svd_method='randomized'</em>, <em>n_svd_vecs=None</em>, <em>mini_batch=False</em>, <em>init='k-means++'</em>, <em>n_init=10</em>, <em>n_jobs=1</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/cluster/bicluster.py#L182"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering" title="Permalink to this definition">¶</a></dt>
<dd><p>Spectral Co-Clustering algorithm (Dhillon, 2001).</p>
<p>Clusters rows and columns of an array <cite>X</cite> to solve the relaxed
normalized cut of the bipartite graph created from <cite>X</cite> as follows:
the edge between row vertex <cite>i</cite> and column vertex <cite>j</cite> has weight
<cite>X[i, j]</cite>.</p>
<p>The resulting bicluster structure is block-diagonal, since each
row and each column belongs to exactly one bicluster.</p>
<p>Supports sparse matrices, as long as they are nonnegative.</p>
<p>Read more in the <a class="reference internal" href="../biclustering.html#spectral-coclustering"><span class="std std-ref">User Guide</span></a>.</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> : integer, optional, default: 3</p>
<blockquote>
<div><p>The number of biclusters to find.</p>
</div></blockquote>
<p><strong>svd_method</strong> : string, optional, default: ‘randomized’</p>
<blockquote>
<div><p>Selects the algorithm for finding singular vectors. May be
‘randomized’ or ‘arpack’. If ‘randomized’, use
<a class="reference internal" href="sklearn.utils.extmath.randomized_svd.html#sklearn.utils.extmath.randomized_svd" title="sklearn.utils.extmath.randomized_svd"><code class="xref py py-func docutils literal"><span class="pre">sklearn.utils.extmath.randomized_svd</span></code></a>, which may be faster
for large matrices. If ‘arpack’, use
<a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.svds.html#scipy.sparse.linalg.svds" title="(in SciPy v1.1.0)"><code class="xref py py-func docutils literal"><span class="pre">scipy.sparse.linalg.svds</span></code></a>, which is more accurate, but
possibly slower in some cases.</p>
</div></blockquote>
<p><strong>n_svd_vecs</strong> : int, optional, default: None</p>
<blockquote>
<div><p>Number of vectors to use in calculating the SVD. Corresponds
to <cite>ncv</cite> when <cite>svd_method=arpack</cite> and <cite>n_oversamples</cite> when
<cite>svd_method</cite> is ‘randomized`.</p>
</div></blockquote>
<p><strong>mini_batch</strong> : bool, optional, default: False</p>
<blockquote>
<div><p>Whether to use mini-batch k-means, which is faster but may get
different results.</p>
</div></blockquote>
<p><strong>init</strong> : {‘k-means++’, ‘random’ or an ndarray}</p>
<blockquote>
<div><p>Method for initialization of k-means algorithm; defaults to
‘k-means++’.</p>
</div></blockquote>
<p><strong>n_init</strong> : int, optional, default: 10</p>
<blockquote>
<div><p>Number of random initializations that are tried with the
k-means algorithm.</p>
<p>If mini-batch k-means is used, the best initialization is
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.</p>
</div></blockquote>
<p><strong>n_jobs</strong> : int, optional, default: 1</p>
<blockquote>
<div><p>The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.</p>
<p>If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.</p>
</div></blockquote>
<p><strong>random_state</strong> : int, RandomState instance or None (default)</p>
<blockquote class="last">
<div><p>Used for randomizing the singular value decomposition and the k-means
initialization. Use an int to make the randomness deterministic.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>rows_</strong></td>
<td>(array-like, shape (n_row_clusters, n_rows)) Results of the clustering. <cite>rows[i, r]</cite> is True if cluster <cite>i</cite> contains row <cite>r</cite>. Available only after calling <code class="docutils literal"><span class="pre">fit</span></code>.</td>
</tr>
<tr class="row-even"><td><strong>columns_</strong></td>
<td>(array-like, shape (n_column_clusters, n_columns)) Results of the clustering, like <cite>rows</cite>.</td>
</tr>
<tr class="row-odd"><td><strong>row_labels_</strong></td>
<td>(array-like, shape (n_rows,)) The bicluster label of each row.</td>
</tr>
<tr class="row-even"><td><strong>column_labels_</strong></td>
<td>(array-like, shape (n_cols,)) The bicluster label of each column.</td>
</tr>
</tbody>
</table>
<p class="rubric">References</p>
<ul class="simple">
<li>Dhillon, Inderjit S, 2001. <a class="reference external" href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3011">Co-clustering documents and words using
bipartite spectral graph partitioning</a>.</li>
</ul>
<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.bicluster.SpectralCoclustering.fit" title="sklearn.cluster.bicluster.SpectralCoclustering.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[,&nbsp;y])</td>
<td>Creates a biclustering for X.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.bicluster.SpectralCoclustering.get_indices" title="sklearn.cluster.bicluster.SpectralCoclustering.get_indices"><code class="xref py py-obj docutils literal"><span class="pre">get_indices</span></code></a>(i)</td>
<td>Row and column indices of the i’th bicluster.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.bicluster.SpectralCoclustering.get_params" title="sklearn.cluster.bicluster.SpectralCoclustering.get_params"><code class="xref py py-obj docutils literal"><span class="pre">get_params</span></code></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.bicluster.SpectralCoclustering.get_shape" title="sklearn.cluster.bicluster.SpectralCoclustering.get_shape"><code class="xref py py-obj docutils literal"><span class="pre">get_shape</span></code></a>(i)</td>
<td>Shape of the i’th bicluster.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.bicluster.SpectralCoclustering.get_submatrix" title="sklearn.cluster.bicluster.SpectralCoclustering.get_submatrix"><code class="xref py py-obj docutils literal"><span class="pre">get_submatrix</span></code></a>(i,&nbsp;data)</td>
<td>Returns the submatrix corresponding to bicluster <cite>i</cite>.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.bicluster.SpectralCoclustering.set_params" title="sklearn.cluster.bicluster.SpectralCoclustering.set_params"><code class="xref py py-obj docutils literal"><span class="pre">set_params</span></code></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_clusters=3</em>, <em>svd_method='randomized'</em>, <em>n_svd_vecs=None</em>, <em>mini_batch=False</em>, <em>init='k-means++'</em>, <em>n_init=10</em>, <em>n_jobs=1</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/cluster/bicluster.py#L269"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="attribute">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering.biclusters_">
<code class="descname">biclusters_</code><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.biclusters_" title="Permalink to this definition">¶</a></dt>
<dd><p>Convenient way to get row and column indicators together.</p>
<p>Returns the <code class="docutils literal"><span class="pre">rows_</span></code> and <code class="docutils literal"><span class="pre">columns_</span></code> members.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/cluster/bicluster.py#L113"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a biclustering for X.</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>
<p class="last"><strong>y</strong> : Ignored</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering.get_indices">
<code class="descname">get_indices</code><span class="sig-paren">(</span><em>i</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/base.py#L414"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.get_indices" title="Permalink to this definition">¶</a></dt>
<dd><p>Row and column indices of the i’th bicluster.</p>
<p>Only works if <code class="docutils literal"><span class="pre">rows_</span></code> and <code class="docutils literal"><span class="pre">columns_</span></code> attributes exist.</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>i</strong> : int</p>
<blockquote>
<div><p>The index of the cluster.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>row_ind</strong> : np.array, dtype=np.intp</p>
<blockquote>
<div><p>Indices of rows in the dataset that belong to the bicluster.</p>
</div></blockquote>
<p><strong>col_ind</strong> : np.array, dtype=np.intp</p>
<blockquote class="last">
<div><p>Indices of columns in the dataset that belong to the bicluster.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/base.py#L213"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.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</strong> : boolean, optional</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.bicluster.SpectralCoclustering.get_shape">
<code class="descname">get_shape</code><span class="sig-paren">(</span><em>i</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/base.py#L436"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.get_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Shape of the i’th bicluster.</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>i</strong> : int</p>
<blockquote>
<div><p>The index of the cluster.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>shape</strong> : (int, int)</p>
<blockquote class="last">
<div><p>Number of rows and columns (resp.) in the bicluster.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering.get_submatrix">
<code class="descname">get_submatrix</code><span class="sig-paren">(</span><em>i</em>, <em>data</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/base.py#L452"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.get_submatrix" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the submatrix corresponding to bicluster <cite>i</cite>.</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>i</strong> : int</p>
<blockquote>
<div><p>The index of the cluster.</p>
</div></blockquote>
<p><strong>data</strong> : array</p>
<blockquote>
<div><p>The 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>submatrix</strong> : array</p>
<blockquote class="last">
<div><p>The submatrix corresponding to bicluster i.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>Works with sparse matrices. Only works if <code class="docutils literal"><span class="pre">rows_</span></code> and
<code class="docutils literal"><span class="pre">columns_</span></code> attributes exist.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralCoclustering.set_params">
<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralCoclustering.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 latter have parameters of the form
<code class="docutils literal"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> 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">self</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

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