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              <ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.SpectralBiclustering</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering</span></code></a><ul>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.biclusters_"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.biclusters_</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.fit"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.fit</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_indices"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.get_indices</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.get_metadata_routing</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_params"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.get_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_shape"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.get_shape</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_submatrix"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.get_submatrix</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.set_params"><code class="docutils literal notranslate"><span class="pre">SpectralBiclustering.set_params</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-spectralbiclustering">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.SpectralBiclustering</span></code></a></li>
</ul>
</li>
</ul>

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  <section id="sklearn-cluster-spectralbiclustering">
<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>.SpectralBiclustering<a class="headerlink" href="#sklearn-cluster-spectralbiclustering" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">SpectralBiclustering</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</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">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bistochastic'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">svd_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'randomized'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_svd_vecs</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">mini_batch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k-means++'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/cluster/_bicluster.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.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>
<p>Read more in the <a class="reference internal" href="../biclustering.html#spectral-biclustering"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>n_clusters</strong><span class="classifier">int or tuple (n_row_clusters, n_column_clusters), default=3</span></dt><dd><p>The number of row and column clusters in the checkerboard
structure.</p>
</dd>
<dt><strong>method</strong><span class="classifier">{‘bistochastic’, ‘scale’, ‘log’}, default=’bistochastic’</span></dt><dd><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’.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>if <code class="docutils literal notranslate"><span class="pre">method='log'</span></code>, the data must not be sparse.</p>
</div>
</dd>
<dt><strong>n_components</strong><span class="classifier">int, default=6</span></dt><dd><p>Number of singular vectors to check.</p>
</dd>
<dt><strong>n_best</strong><span class="classifier">int, default=3</span></dt><dd><p>Number of best singular vectors to which to project the data
for clustering.</p>
</dd>
<dt><strong>svd_method</strong><span class="classifier">{‘randomized’, ‘arpack’}, default=’randomized’</span></dt><dd><p>Selects the algorithm for finding singular vectors. May be
‘randomized’ or ‘arpack’. If ‘randomized’, uses
<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 notranslate"><span class="pre">randomized_svd</span></code></a>, which may be faster
for large matrices. If ‘arpack’, uses
<code class="docutils literal notranslate"><span class="pre">scipy.sparse.linalg.svds</span></code>, which is more accurate, but
possibly slower in some cases.</p>
</dd>
<dt><strong>n_svd_vecs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of vectors to use in calculating the SVD. Corresponds
to <code class="docutils literal notranslate"><span class="pre">ncv</span></code> when <code class="docutils literal notranslate"><span class="pre">svd_method=arpack</span></code> and <code class="docutils literal notranslate"><span class="pre">n_oversamples</span></code> when
<code class="docutils literal notranslate"><span class="pre">svd_method</span></code> is ‘randomized`.</p>
</dd>
<dt><strong>mini_batch</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to use mini-batch k-means, which is faster but may get
different results.</p>
</dd>
<dt><strong>init</strong><span class="classifier">{‘k-means++’, ‘random’} or ndarray of shape (n_clusters, n_features),             default=’k-means++’</span></dt><dd><p>Method for initialization of k-means algorithm; defaults to
‘k-means++’.</p>
</dd>
<dt><strong>n_init</strong><span class="classifier">int, default=10</span></dt><dd><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>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance, default=None</span></dt><dd><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>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>rows_</strong><span class="classifier">array-like of shape (n_row_clusters, n_rows)</span></dt><dd><p>Results of the clustering. <code class="docutils literal notranslate"><span class="pre">rows[i,</span> <span class="pre">r]</span></code> is True if
cluster <code class="docutils literal notranslate"><span class="pre">i</span></code> contains row <code class="docutils literal notranslate"><span class="pre">r</span></code>. Available only after calling <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
<dt><strong>columns_</strong><span class="classifier">array-like of shape (n_column_clusters, n_columns)</span></dt><dd><p>Results of the clustering, like <code class="docutils literal notranslate"><span class="pre">rows</span></code>.</p>
</dd>
<dt><strong>row_labels_</strong><span class="classifier">array-like of shape (n_rows,)</span></dt><dd><p>Row partition labels.</p>
</dd>
<dt><strong>column_labels_</strong><span class="classifier">array-like of shape (n_cols,)</span></dt><dd><p>Column partition labels.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.biclusters_" title="sklearn.cluster.SpectralBiclustering.biclusters_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">biclusters_</span></code></a><span class="classifier">tuple of two ndarrays</span></dt><dd><p>Convenient way to get row and column indicators together.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.cluster.SpectralCoclustering.html#sklearn.cluster.SpectralCoclustering" title="sklearn.cluster.SpectralCoclustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SpectralCoclustering</span></code></a></dt><dd><p>Spectral Co-Clustering algorithm (Dhillon, 2001).</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<ul class="simple">
<li><p><a class="reference external" href="https://doi.org/10.1101/gr.648603">Kluger, Yuval, et. al., 2003. Spectral biclustering of microarray
data: coclustering genes and conditions.</a></p></li>
</ul>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">SpectralBiclustering</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </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">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</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">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span> <span class="o">=</span> <span class="n">SpectralBiclustering</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="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span><span class="o">.</span><span class="n">row_labels_</span>
<span class="go">array([1, 1, 1, 0, 0, 0], dtype=int32)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span><span class="o">.</span><span class="n">column_labels_</span>
<span class="go">array([1, 0], dtype=int32)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span>
<span class="go">SpectralBiclustering(n_clusters=2, random_state=0)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.fit" title="sklearn.cluster.SpectralBiclustering.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y])</p></td>
<td><p>Create a biclustering for X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_indices" title="sklearn.cluster.SpectralBiclustering.get_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_indices</span></code></a>(i)</p></td>
<td><p>Row and column indices of the <code class="docutils literal notranslate"><span class="pre">i</span></code>'th bicluster.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_metadata_routing" title="sklearn.cluster.SpectralBiclustering.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_params" title="sklearn.cluster.SpectralBiclustering.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_shape" title="sklearn.cluster.SpectralBiclustering.get_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_shape</span></code></a>(i)</p></td>
<td><p>Shape of the <code class="docutils literal notranslate"><span class="pre">i</span></code>'th bicluster.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_submatrix" title="sklearn.cluster.SpectralBiclustering.get_submatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submatrix</span></code></a>(i, data)</p></td>
<td><p>Return the submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.set_params" title="sklearn.cluster.SpectralBiclustering.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.biclusters_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">biclusters_</span></span><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.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 notranslate"><span class="pre">rows_</span></code> and <code class="docutils literal notranslate"><span class="pre">columns_</span></code> members.</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.fit">
<span class="sig-name descname"><span class="pre">fit</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">y</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/_bicluster.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a biclustering for X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Training data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>SpectralBiclustering instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_indices">
<span class="sig-name descname"><span class="pre">get_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/base.py#L814"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_indices" title="Permalink to this definition">¶</a></dt>
<dd><p>Row and column indices of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
<p>Only works if <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and <code class="docutils literal notranslate"><span class="pre">columns_</span></code> attributes exist.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>i</strong><span class="classifier">int</span></dt><dd><p>The index of the cluster.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>row_ind</strong><span class="classifier">ndarray, dtype=np.intp</span></dt><dd><p>Indices of rows in the dataset that belong to the bicluster.</p>
</dd>
<dt><strong>col_ind</strong><span class="classifier">ndarray, dtype=np.intp</span></dt><dd><p>Indices of columns in the dataset that belong to the bicluster.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_metadata_routing" title="Permalink to this definition">¶</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_shape">
<span class="sig-name descname"><span class="pre">get_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/base.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Shape of the <code class="docutils literal notranslate"><span class="pre">i</span></code>’th bicluster.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>i</strong><span class="classifier">int</span></dt><dd><p>The index of the cluster.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>n_rows</strong><span class="classifier">int</span></dt><dd><p>Number of rows in the bicluster.</p>
</dd>
<dt><strong>n_cols</strong><span class="classifier">int</span></dt><dd><p>Number of columns in the bicluster.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.get_submatrix">
<span class="sig-name descname"><span class="pre">get_submatrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/base.py#L854"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_submatrix" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>i</strong><span class="classifier">int</span></dt><dd><p>The index of the cluster.</p>
</dd>
<dt><strong>data</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>submatrix</strong><span class="classifier">ndarray of shape (n_rows, n_cols)</span></dt><dd><p>The submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>Works with sparse matrices. Only works if <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and
<code class="docutils literal notranslate"><span class="pre">columns_</span></code> attributes exist.</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralBiclustering.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.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 <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<section id="examples-using-sklearn-cluster-spectralbiclustering">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.SpectralBiclustering</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-spectralbiclustering" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spe..."><img alt="" src="../../_images/sphx_glr_plot_spectral_biclustering_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py"><span class="std std-ref">A demo of the Spectral Biclustering algorithm</span></a></p>
  <div class="sphx-glr-thumbnail-title">A demo of the Spectral Biclustering algorithm</div>
</div></div><div class="clearer"></div></section>
</section>


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