Rebuild dev docs at master=5f3cbb5

Author: sklearn-ci

dev/_sources/auto_examples/ensemble/plot_voting_decision_regions.txt

@@ -35,6 +35,6 @@ when the averaged probability is calculated.
 .. literalinclude:: plot_voting_decision_regions.py
     :lines: 21-
 
-**Total running time of the example:**  0.46 seconds
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+**Total running time of the example:**  0.47 seconds
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dev/auto_examples/ensemble/plot_partial_dependence.html

@@ -176,7 +176,7 @@
 (see <a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor.feature_importances_" title="sklearn.ensemble.GradientBoostingRegressor.feature_importances_"><tt class="xref py py-attr docutils literal"><span class="pre">feature_importances_</span></tt></a>).</p>
 <p>This example shows how to obtain partial dependence plots from a
 <a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><tt class="xref py py-class docutils literal"><span class="pre">GradientBoostingRegressor</span></tt></a> trained on the California
-housing dataset. The example is taken from <a class="reference internal" href="../../modules/ensemble.html#htf2009" id="id2">[HTF2009]</a>.</p>
+housing dataset. The example is taken from <a class="reference internal" href="#htf2009" id="id2">[HTF2009]</a>.</p>
 <p>The plot shows four one-way and one two-way partial dependence plots.
 The target variables for the one-way PDP are:
 median income (<cite>MedInc</cite>), avg. occupants per household (<cite>AvgOccup</cite>),

dev/auto_examples/ensemble/plot_voting_decision_regions.html

@@ -234,8 +234,8 @@
 <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p><strong>Total running time of the example:</strong>  0.46 seconds
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+<p><strong>Total running time of the example:</strong>  0.47 seconds
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 </div>
 
 

dev/modules/generated/sklearn.calibration.CalibratedClassifierCV.html

@@ -237,31 +237,31 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 </tbody>
 </table>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r104" rules="none">
+<table class="docutils citation" frame="void" id="r1" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R104]</a></td><td>Obtaining calibrated probability estimates from decision trees
+<tr><td class="label"><a class="fn-backref" href="#id1">[R1]</a></td><td>Obtaining calibrated probability estimates from decision trees
 and naive Bayesian classifiers, B. Zadrozny &amp; C. Elkan, ICML 2001</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r105" rules="none">
+<table class="docutils citation" frame="void" id="r2" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id2">[R105]</a></td><td>Transforming Classifier Scores into Accurate Multiclass
+<tr><td class="label"><a class="fn-backref" href="#id2">[R2]</a></td><td>Transforming Classifier Scores into Accurate Multiclass
 Probability Estimates, B. Zadrozny &amp; C. Elkan, (KDD 2002)</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r106" rules="none">
+<table class="docutils citation" frame="void" id="r3" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id3">[R106]</a></td><td>Probabilistic Outputs for Support Vector Machines and Comparisons to
+<tr><td class="label"><a class="fn-backref" href="#id3">[R3]</a></td><td>Probabilistic Outputs for Support Vector Machines and Comparisons to
 Regularized Likelihood Methods, J. Platt, (1999)</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r107" rules="none">
+<table class="docutils citation" frame="void" id="r4" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id4">[R107]</a></td><td>Predicting Good Probabilities with Supervised Learning,
+<tr><td class="label"><a class="fn-backref" href="#id4">[R4]</a></td><td>Predicting Good Probabilities with Supervised Learning,
 A. Niculescu-Mizil &amp; R. Caruana, ICML 2005</td></tr>
 </tbody>
 </table>

dev/modules/generated/sklearn.cluster.AgglomerativeClustering.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></a>.AgglomerativeClustering<a class="headerlink" href="#sklearn-cluster-agglomerativeclustering" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.cluster.AgglomerativeClustering">
-<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">AgglomerativeClustering</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f611b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L607"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">AgglomerativeClustering</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2c531b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L607"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
 <dd><p>Agglomerative Clustering</p>
 <p>Recursively merges the pair of clusters that minimally increases
 a given linkage distance.</p>
@@ -291,7 +291,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.AgglomerativeClustering.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f611b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L690"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2c531b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L690"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">

dev/modules/generated/sklearn.cluster.FeatureAgglomeration.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></a>.FeatureAgglomeration<a class="headerlink" href="#sklearn-cluster-featureagglomeration" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.cluster.FeatureAgglomeration">
-<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">FeatureAgglomeration</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f611b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L779"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">FeatureAgglomeration</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2c531b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L779"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Permalink to this definition">¶</a></dt>
 <dd><p>Agglomerate features.</p>
 <p>Similar to AgglomerativeClustering, but recursively merges features
 instead of samples.</p>
@@ -300,7 +300,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.FeatureAgglomeration.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2f611b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L690"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>n_components=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x2c531b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3cbb5/sklearn/cluster/hierarchical.py#L690"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">

dev/modules/generated/sklearn.cluster.MiniBatchKMeans.html

@@ -525,10 +525,6 @@ <h2>Examples using <tt class="docutils literal"><span class="pre">sklearn.cluste
 <a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_faces_decomposition.html"><img alt="../../_images/plot_faces_decomposition1.png" src="../../_images/plot_faces_decomposition1.png" /></a>
 <p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_faces_decomposition.html#example-decomposition-plot-faces-decomposition-py"><em>Faces dataset decompositions</em></a></p>
 </div>
-</div><div class="thumbnailContainer" tooltip="This example applies to :ref:`olivetti_faces` different unsupervised matrix decomposition (dime..."><div class="figure">
-<a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_faces_decomposition.html"><img alt="../../_images/plot_faces_decomposition1.png" src="../../_images/plot_faces_decomposition1.png" /></a>
-<p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_faces_decomposition.html#example-decomposition-plot-faces-decomposition-py"><em>Faces dataset decompositions</em></a></p>
-</div>
 </div><div class="thumbnailContainer" tooltip="This is an example showing how the scikit-learn can be used to cluster documents by topics usin..."><div class="figure">
 <a class="reference external image-reference" href="./../../auto_examples/text/document_clustering.html"><img alt="../../_images/document_clustering1.png" src="../../_images/document_clustering1.png" /></a>
 <p class="caption"><a class="reference internal" href="../../auto_examples/text/document_clustering.html#example-text-document-clustering-py"><em>Clustering text documents using k-means</em></a></p>

dev/modules/generated/sklearn.covariance.LedoitWolf.html

@@ -469,10 +469,6 @@ <h2>Examples using <tt class="docutils literal"><span class="pre">sklearn.covari
 <a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html"><img alt="../../_images/plot_pca_vs_fa_model_selection1.png" src="../../_images/plot_pca_vs_fa_model_selection1.png" /></a>
 <p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#example-decomposition-plot-pca-vs-fa-model-selection-py"><em>Model selection with Probabilistic PCA and Factor Analysis (FA)</em></a></p>
 </div>
-</div><div class="thumbnailContainer" tooltip="Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik..."><div class="figure">
-<a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html"><img alt="../../_images/plot_pca_vs_fa_model_selection1.png" src="../../_images/plot_pca_vs_fa_model_selection1.png" /></a>
-<p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#example-decomposition-plot-pca-vs-fa-model-selection-py"><em>Model selection with Probabilistic PCA and Factor Analysis (FA)</em></a></p>
-</div>
 </div><div class="clearer"></div></div>
 </div>
 

dev/modules/generated/sklearn.covariance.ShrunkCovariance.html

@@ -457,10 +457,6 @@ <h2>Examples using <tt class="docutils literal"><span class="pre">sklearn.covari
 <a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html"><img alt="../../_images/plot_pca_vs_fa_model_selection1.png" src="../../_images/plot_pca_vs_fa_model_selection1.png" /></a>
 <p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#example-decomposition-plot-pca-vs-fa-model-selection-py"><em>Model selection with Probabilistic PCA and Factor Analysis (FA)</em></a></p>
 </div>
-</div><div class="thumbnailContainer" tooltip="Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik..."><div class="figure">
-<a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html"><img alt="../../_images/plot_pca_vs_fa_model_selection1.png" src="../../_images/plot_pca_vs_fa_model_selection1.png" /></a>
-<p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#example-decomposition-plot-pca-vs-fa-model-selection-py"><em>Model selection with Probabilistic PCA and Factor Analysis (FA)</em></a></p>
-</div>
 </div><div class="clearer"></div></div>
 </div>
 

dev/modules/generated/sklearn.cross_validation.cross_val_score.html

@@ -270,10 +270,6 @@ <h2>Examples using <tt class="docutils literal"><span class="pre">sklearn.cross_
 <a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html"><img alt="../../_images/plot_pca_vs_fa_model_selection1.png" src="../../_images/plot_pca_vs_fa_model_selection1.png" /></a>
 <p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#example-decomposition-plot-pca-vs-fa-model-selection-py"><em>Model selection with Probabilistic PCA and Factor Analysis (FA)</em></a></p>
 </div>
-</div><div class="thumbnailContainer" tooltip="Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik..."><div class="figure">
-<a class="reference external image-reference" href="./../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html"><img alt="../../_images/plot_pca_vs_fa_model_selection1.png" src="../../_images/plot_pca_vs_fa_model_selection1.png" /></a>
-<p class="caption"><a class="reference internal" href="../../auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#example-decomposition-plot-pca-vs-fa-model-selection-py"><em>Model selection with Probabilistic PCA and Factor Analysis (FA)</em></a></p>
-</div>
 </div><div class="thumbnailContainer" tooltip="A tutorial exercise using Cross-validation with an SVM on the Digits dataset."><div class="figure">
 <a class="reference external image-reference" href="./../../auto_examples/exercises/plot_cv_digits.html"><img alt="../../_images/plot_cv_digits1.png" src="../../_images/plot_cv_digits1.png" /></a>
 <p class="caption"><a class="reference internal" href="../../auto_examples/exercises/plot_cv_digits.html#example-exercises-plot-cv-digits-py"><em>Cross-validation on Digits Dataset Exercise</em></a></p>