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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>.SpectralCoclustering</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-spectralcoclustering">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.SpectralCoclustering</span></code></a></li>
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<section id="sklearn-cluster-spectralcoclustering">
<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>.SpectralCoclustering<a class="headerlink" href="#sklearn-cluster-spectralcoclustering" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.cluster.SpectralCoclustering">
<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">SpectralCoclustering</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">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/6cb2c5237/sklearn/cluster/_bicluster.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.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 <code class="docutils literal notranslate"><span class="pre">X</span></code> to solve the relaxed
normalized cut of the bipartite graph created from <code class="docutils literal notranslate"><span class="pre">X</span></code> as follows:
the edge between row vertex <code class="docutils literal notranslate"><span class="pre">i</span></code> and column vertex <code class="docutils literal notranslate"><span class="pre">j</span></code> has weight
<code class="docutils literal notranslate"><span class="pre">X[i,</span> <span class="pre">j]</span></code>.</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>
<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, default=3</span></dt><dd><p>The number of biclusters to find.</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’, 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 notranslate"><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 v1.9.0)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.sparse.linalg.svds</span></code></a>, 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>The bicluster label of each row.</p>
</dd>
<dt><strong>column_labels_</strong><span class="classifier">array-like of shape (n_cols,)</span></dt><dd><p>The bicluster label of each column.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.cluster.SpectralCoclustering.biclusters_" title="sklearn.cluster.SpectralCoclustering.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.SpectralBiclustering.html#sklearn.cluster.SpectralBiclustering" title="sklearn.cluster.SpectralBiclustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SpectralBiclustering</span></code></a></dt><dd><p>Partitions rows and columns under the assumption that the data has an underlying checkerboard structure.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<ul class="simple">
<li><p><a class="reference external" href="https://doi.org/10.1145/502512.502550">Dhillon, Inderjit S, 2001. Co-clustering documents and words using
bipartite spectral graph partitioning.</a></p></li>
</ul>
<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">SpectralCoclustering</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">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">>>> </span><span class="n">clustering</span> <span class="o">=</span> <span class="n">SpectralCoclustering</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">>>> </span><span class="n">clustering</span><span class="o">.</span><span class="n">row_labels_</span>
<span class="go">array([0, 1, 1, 0, 0, 0], dtype=int32)</span>
<span class="gp">>>> </span><span class="n">clustering</span><span class="o">.</span><span class="n">column_labels_</span>
<span class="go">array([0, 0], dtype=int32)</span>
<span class="gp">>>> </span><span class="n">clustering</span>
<span class="go">SpectralCoclustering(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.SpectralCoclustering.fit" title="sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.get_indices" title="sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.get_params" title="sklearn.cluster.SpectralCoclustering.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-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralCoclustering.get_shape" title="sklearn.cluster.SpectralCoclustering.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-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralCoclustering.get_submatrix" title="sklearn.cluster.SpectralCoclustering.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-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralCoclustering.set_params" title="sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.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.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 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.SpectralCoclustering.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/6cb2c5237/sklearn/cluster/_bicluster.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.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/6cb2c5237/sklearn/base.py#L769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.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/6cb2c5237/sklearn/base.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.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/6cb2c5237/sklearn/base.py#L790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.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/6cb2c5237/sklearn/base.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.SpectralCoclustering.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.SpectralCoclustering.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/6cb2c5237/sklearn/base.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.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 <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"><component>__<parameter></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-spectralcoclustering">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.SpectralCoclustering</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-spectralcoclustering" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clus..."><figure class="align-default" id="id1">
<img alt="A demo of the Spectral Co-Clustering algorithm" src="../../_images/sphx_glr_plot_spectral_coclustering_thumb.png" />
<figcaption>
<p><span class="caption-text"><a class="reference internal" href="../../auto_examples/bicluster/plot_spectral_coclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-coclustering-py"><span class="std std-ref">A demo of the Spectral Co-Clustering algorithm</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset..."><figure class="align-default" id="id2">
<img alt="Biclustering documents with the Spectral Co-clustering algorithm" src="../../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" />
<figcaption>
<p><span class="caption-text"><a class="reference internal" href="../../auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">Biclustering documents with the Spectral Co-clustering algorithm</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
</div><div class="clearer"></div></section>
</section>
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