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<ul>
<li><a class="reference internal" href="#"><tt class="docutils literal"><span class="pre">sklearn.cluster.bicluster</span></tt>.SpectralBiclustering</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-bicluster-spectralbiclustering">Examples using <tt class="docutils literal"><span class="pre">sklearn.cluster.bicluster.SpectralBiclustering</span></tt></a></li>
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<div class="section" id="sklearn-cluster-bicluster-spectralbiclustering">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster.bicluster" title="sklearn.cluster.bicluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster.bicluster</span></tt></a>.SpectralBiclustering<a class="headerlink" href="#sklearn-cluster-bicluster-spectralbiclustering" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.bicluster.SpectralBiclustering">
<em class="property">class </em><tt class="descclassname">sklearn.cluster.bicluster.</tt><tt class="descname">SpectralBiclustering</tt><big>(</big><em>n_clusters=3</em>, <em>method='bistochastic'</em>, <em>n_components=6</em>, <em>n_best=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><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/bicluster.py#L287"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralBiclustering" title="Permalink to this definition">¶</a></dt>
<dd><p>Spectral biclustering (Kluger, 2003).</p>
<p>Partitions rows and columns under the assumption that the data has
an underlying checkerboard structure. For instance, if there are
two row partitions and three column partitions, each row will
belong to three biclusters, and each column will belong to two
biclusters. The outer product of the corresponding row and column
label vectors gives this checkerboard structure.</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 or tuple (n_row_clusters, n_column_clusters)</p>
<blockquote>
<div><p>The number of row and column clusters in the checkerboard
structure.</p>
</div></blockquote>
<p><strong>method</strong> : string, optional, default: ‘bistochastic’</p>
<blockquote>
<div><p>Method of normalizing and converting singular vectors into
biclusters. May be one of ‘scale’, ‘bistochastic’, or ‘log’.
The authors recommend using ‘log’. If the data is sparse,
however, log normalization will not work, which is why the
default is ‘bistochastic’. CAUTION: if <cite>method=’log’</cite>, the
data must not be sparse.</p>
</div></blockquote>
<p><strong>n_components</strong> : integer, optional, default: 6</p>
<blockquote>
<div><p>Number of singular vectors to check.</p>
</div></blockquote>
<p><strong>n_best</strong> : integer, optional, default: 3</p>
<blockquote>
<div><p>Number of best singular vectors to which to project the data
for clustering.</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’, uses
<cite>sklearn.utils.extmath.randomized_svd</cite>, which may be faster
for large matrices. If ‘arpack’, uses
<cite>sklearn.utils.arpack.svds</cite>, 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 seed, RandomState instance, or None (default)</p>
<blockquote>
<div><p>A pseudo random number generator used by the K-Means
initialization.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><p class="first"><strong>rows_</strong> : array-like, shape (n_row_clusters, n_rows)</p>
<blockquote>
<div><p>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 <tt class="docutils literal"><span class="pre">fit</span></tt>.</p>
</div></blockquote>
<p><strong>columns_</strong> : array-like, shape (n_column_clusters, n_columns)</p>
<blockquote>
<div><p>Results of the clustering, like <cite>rows</cite>.</p>
</div></blockquote>
<p><strong>row_labels_</strong> : array-like, shape (n_rows,)</p>
<blockquote>
<div><p>Row partition labels.</p>
</div></blockquote>
<p><strong>column_labels_</strong> : array-like, shape (n_cols,)</p>
<blockquote class="last">
<div><p>Column partition labels.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">References</p>
<ul class="simple">
<li>Kluger, Yuval, et. al., 2003. <a class="reference external" href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.1608">Spectral biclustering of microarray
data: coclustering genes and conditions</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.SpectralBiclustering.fit" title="sklearn.cluster.bicluster.SpectralBiclustering.fit"><tt class="xref py py-obj docutils literal"><span class="pre">fit</span></tt></a>(X)</td>
<td>Creates a biclustering for X.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.bicluster.SpectralBiclustering.get_indices" title="sklearn.cluster.bicluster.SpectralBiclustering.get_indices"><tt class="xref py py-obj docutils literal"><span class="pre">get_indices</span></tt></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.SpectralBiclustering.get_params" title="sklearn.cluster.bicluster.SpectralBiclustering.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.bicluster.SpectralBiclustering.get_shape" title="sklearn.cluster.bicluster.SpectralBiclustering.get_shape"><tt class="xref py py-obj docutils literal"><span class="pre">get_shape</span></tt></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.SpectralBiclustering.get_submatrix" title="sklearn.cluster.bicluster.SpectralBiclustering.get_submatrix"><tt class="xref py py-obj docutils literal"><span class="pre">get_submatrix</span></tt></a>(i, 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.SpectralBiclustering.set_params" title="sklearn.cluster.bicluster.SpectralBiclustering.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.bicluster.SpectralBiclustering.__init__">
<tt class="descname">__init__</tt><big>(</big><em>n_clusters=3</em>, <em>method='bistochastic'</em>, <em>n_components=6</em>, <em>n_best=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><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/bicluster.py#L384"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralBiclustering.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="attribute">
<dt id="sklearn.cluster.bicluster.SpectralBiclustering.biclusters_">
<tt class="descname">biclusters_</tt><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralBiclustering.biclusters_" title="Permalink to this definition">¶</a></dt>
<dd><p>Convenient way to get row and column indicators together.</p>
<p>Returns the <tt class="docutils literal"><span class="pre">rows_</span></tt> and <tt class="docutils literal"><span class="pre">columns_</span></tt> members.</p>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralBiclustering.fit">
<tt class="descname">fit</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cluster/bicluster.py#L112"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralBiclustering.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"><strong>X</strong> : array-like, shape (n_samples, n_features)</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralBiclustering.get_indices">
<tt class="descname">get_indices</tt><big>(</big><em>i</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L364"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralBiclustering.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 <tt class="docutils literal"><span class="pre">rows_</span></tt> and <tt class="docutils literal"><span class="pre">columns_</span></tt> 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">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.SpectralBiclustering.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.bicluster.SpectralBiclustering.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.bicluster.SpectralBiclustering.get_shape">
<tt class="descname">get_shape</tt><big>(</big><em>i</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L381"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralBiclustering.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">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.SpectralBiclustering.get_submatrix">
<tt class="descname">get_submatrix</tt><big>(</big><em>i</em>, <em>data</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/base.py#L392"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.bicluster.SpectralBiclustering.get_submatrix" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the submatrix corresponding to bicluster <cite>i</cite>.</p>
<p>Works with sparse matrices. Only works if <tt class="docutils literal"><span class="pre">rows_</span></tt> and
<tt class="docutils literal"><span class="pre">columns_</span></tt> attributes exist.</p>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.bicluster.SpectralBiclustering.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.bicluster.SpectralBiclustering.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>
<div class="section" id="examples-using-sklearn-cluster-bicluster-spectralbiclustering">
<h2>Examples using <tt class="docutils literal"><span class="pre">sklearn.cluster.bicluster.SpectralBiclustering</span></tt><a class="headerlink" href="#examples-using-sklearn-cluster-bicluster-spectralbiclustering" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spe..."><div class="figure">
<a class="reference external image-reference" href="./../../auto_examples/bicluster/plot_spectral_biclustering.html"><img alt="../../_images/plot_spectral_biclustering1.png" src="../../_images/plot_spectral_biclustering1.png" /></a>
<p class="caption"><a class="reference internal" href="../../auto_examples/bicluster/plot_spectral_biclustering.html#example-bicluster-plot-spectral-biclustering-py"><em>A demo of the Spectral Biclustering algorithm</em></a></p>
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