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<ul>
<li><a class="reference internal" href="#"><tt class="docutils literal"><span class="pre">sklearn.cross_decomposition</span></tt>.PLSSVD</a></li>
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<div class="section" id="sklearn-cross-decomposition-plssvd">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cross_decomposition" title="sklearn.cross_decomposition"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_decomposition</span></tt></a>.PLSSVD<a class="headerlink" href="#sklearn-cross-decomposition-plssvd" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_decomposition.PLSSVD">
<em class="property">class </em><tt class="descclassname">sklearn.cross_decomposition.</tt><tt class="descname">PLSSVD</tt><big>(</big><em>n_components=2</em>, <em>scale=True</em>, <em>copy=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cross_decomposition/pls_.py#L682"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD" title="Permalink to this definition">¶</a></dt>
<dd><p>Partial Least Square SVD</p>
<p>Simply perform a svd on the crosscovariance matrix: X’Y
There are no iterative deflation here.</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_components</strong> : int, default 2</p>
<blockquote>
<div><p>Number of components to keep.</p>
</div></blockquote>
<p><strong>scale</strong> : boolean, default True</p>
<blockquote>
<div><p>Whether to scale X and Y.</p>
</div></blockquote>
<p><strong>copy</strong> : boolean, default True</p>
<blockquote>
<div><p>Whether to copy X and Y, or perform in-place computations.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><p class="first"><strong>x_weights_</strong> : array, [p, n_components]</p>
<blockquote>
<div><p>X block weights vectors.</p>
</div></blockquote>
<p><strong>y_weights_</strong> : array, [q, n_components]</p>
<blockquote>
<div><p>Y block weights vectors.</p>
</div></blockquote>
<p><strong>x_scores_</strong> : array, [n_samples, n_components]</p>
<blockquote>
<div><p>X scores.</p>
</div></blockquote>
<p><strong>y_scores_</strong> : array, [n_samples, n_components]</p>
<blockquote class="last">
<div><p>Y scores.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="sklearn.cross_decomposition.PLSCanonical.html#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition.PLSCanonical"><tt class="xref py py-obj docutils literal"><span class="pre">PLSCanonical</span></tt></a>, <a class="reference internal" href="sklearn.cross_decomposition.CCA.html#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition.CCA"><tt class="xref py py-obj docutils literal"><span class="pre">CCA</span></tt></a></p>
</div>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><tt class="xref py py-obj docutils literal"><span class="pre">fit</span></tt>(X, Y)</td>
<td></td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cross_decomposition.PLSSVD.fit_transform" title="sklearn.cross_decomposition.PLSSVD.fit_transform"><tt class="xref py py-obj docutils literal"><span class="pre">fit_transform</span></tt></a>(X[, y])</td>
<td>Learn and apply the dimension reduction on the train data.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cross_decomposition.PLSSVD.get_params" title="sklearn.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.set_params" title="sklearn.cross_decomposition.PLSSVD.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>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cross_decomposition.PLSSVD.transform" title="sklearn.cross_decomposition.PLSSVD.transform"><tt class="xref py py-obj docutils literal"><span class="pre">transform</span></tt></a>(X[, Y])</td>
<td>Apply the dimension reduction learned on the train data.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSSVD.__init__">
<tt class="descname">__init__</tt><big>(</big><em>n_components=2</em>, <em>scale=True</em>, <em>copy=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cross_decomposition/pls_.py#L719"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSSVD.fit_transform">
<tt class="descname">fit_transform</tt><big>(</big><em>X</em>, <em>y=None</em>, <em>**fit_params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cross_decomposition/pls_.py#L772"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Learn and apply the dimension reduction on the train data.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like of predictors, shape = [n_samples, p]</p>
<blockquote>
<div><p>Training vectors, where n_samples in the number of samples and
p is the number of predictors.</p>
</div></blockquote>
<p><strong>Y</strong> : array-like of response, shape = [n_samples, q], optional</p>
<blockquote>
<div><p>Training vectors, where n_samples in the number of samples and
q is the number of response variables.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><strong>x_scores if Y is not given, (x_scores, y_scores) otherwise.</strong> :</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSSVD.transform">
<tt class="descname">transform</tt><big>(</big><em>X</em>, <em>Y=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/a95203b/sklearn/cross_decomposition/pls_.py#L758"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the dimension reduction learned on the train data.</p>
</dd></dl>
</dd></dl>
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