Rebuild dev docs at master=85223b9

Author: sklearn-ci

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="r12" 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">[R12]</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="r13" 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">[R13]</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="r14" 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">[R14]</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="r15" 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">[R15]</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

@@ -132,7 +132,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></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><code class="descclassname">sklearn.cluster.</code><code class="descname">AgglomerativeClustering</code><span class="sig-paren">(</span><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 0x19adaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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><code class="descclassname">sklearn.cluster.</code><code class="descname">AgglomerativeClustering</code><span class="sig-paren">(</span><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 0x2650aa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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>
@@ -239,7 +239,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.AgglomerativeClustering.__init__">
-<code class="descname">__init__</code><span class="sig-paren">(</span><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 0x19adaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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>
+<code class="descname">__init__</code><span class="sig-paren">(</span><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 0x2650aa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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

@@ -132,7 +132,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></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><code class="descclassname">sklearn.cluster.</code><code class="descname">FeatureAgglomeration</code><span class="sig-paren">(</span><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 0x19adaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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><code class="descclassname">sklearn.cluster.</code><code class="descname">FeatureAgglomeration</code><span class="sig-paren">(</span><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 0x2650aa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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>
@@ -239,7 +239,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.FeatureAgglomeration.__init__">
-<code class="descname">__init__</code><span class="sig-paren">(</span><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 0x19adaa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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>
+<code class="descname">__init__</code><span class="sig-paren">(</span><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 0x2650aa0&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/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.datasets.make_biclusters.html

@@ -194,10 +194,10 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.datasets"
 <p class="last"><a class="reference internal" href="sklearn.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal"><span class="pre">make_checkerboard</span></code></a></p>
 </div>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r16" rules="none">
+<table class="docutils citation" frame="void" id="r5" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R16]</a></td><td>Dhillon, I. S. (2001, August). Co-clustering documents and
+<tr><td class="label"><a class="fn-backref" href="#id1">[R5]</a></td><td>Dhillon, I. S. (2001, August). Co-clustering documents and
 words using bipartite spectral graph partitioning. In Proceedings
 of the seventh ACM SIGKDD international conference on Knowledge
 discovery and data mining (pp. 269-274). ACM.</td></tr>

dev/modules/generated/sklearn.datasets.make_checkerboard.html

@@ -194,10 +194,10 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.datasets"
 <p class="last"><a class="reference internal" href="sklearn.datasets.make_biclusters.html#sklearn.datasets.make_biclusters" title="sklearn.datasets.make_biclusters"><code class="xref py py-obj docutils literal"><span class="pre">make_biclusters</span></code></a></p>
 </div>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r17" rules="none">
+<table class="docutils citation" frame="void" id="r6" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R17]</a></td><td>Kluger, Y., Basri, R., Chang, J. T., &amp; Gerstein, M. (2003).
+<tr><td class="label"><a class="fn-backref" href="#id1">[R6]</a></td><td>Kluger, Y., Basri, R., Chang, J. T., &amp; Gerstein, M. (2003).
 Spectral biclustering of microarray data: coclustering genes
 and conditions. Genome research, 13(4), 703-716.</td></tr>
 </tbody>

dev/modules/generated/sklearn.datasets.make_classification.html

@@ -255,10 +255,10 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.datasets"
 <p>The algorithm is adapted from Guyon [1] and was designed to generate
 the &#8220;Madelon&#8221; dataset.</p>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r18" rules="none">
+<table class="docutils citation" frame="void" id="r7" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R18]</a></td><td>I. Guyon, &#8220;Design of experiments for the NIPS 2003 variable
+<tr><td class="label"><a class="fn-backref" href="#id1">[R7]</a></td><td>I. Guyon, &#8220;Design of experiments for the NIPS 2003 variable
 selection benchmark&#8221;, 2003.</td></tr>
 </tbody>
 </table>

dev/modules/generated/sklearn.datasets.make_gaussian_quantiles.html

@@ -193,10 +193,10 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.datasets"
 <p class="rubric">Notes</p>
 <p>The dataset is from Zhu et al [1].</p>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r19" rules="none">
+<table class="docutils citation" frame="void" id="r8" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R19]</a></td><td><ol class="first last upperalpha simple" start="10">
+<tr><td class="label"><a class="fn-backref" href="#id1">[R8]</a></td><td><ol class="first last upperalpha simple" start="10">
 <li>Zhu, H. Zou, S. Rosset, T. Hastie, &#8220;Multi-class AdaBoost&#8221;, 2009.</li>
 </ol>
 </td></tr>

dev/modules/generated/sklearn.datasets.make_hastie_10_2.html

@@ -178,10 +178,10 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.datasets"
 </dl>
 </div>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r20" rules="none">
+<table class="docutils citation" frame="void" id="r9" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R20]</a></td><td>T. Hastie, R. Tibshirani and J. Friedman, &#8220;Elements of Statistical
+<tr><td class="label"><a class="fn-backref" href="#id1">[R9]</a></td><td>T. Hastie, R. Tibshirani and J. Friedman, &#8220;Elements of Statistical
 Learning Ed. 2&#8221;, Springer, 2009.</td></tr>
 </tbody>
 </table>

dev/modules/generated/sklearn.datasets.make_swiss_roll.html

@@ -172,10 +172,10 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.datasets"
 <p class="rubric">Notes</p>
 <p>The algorithm is from Marsland [1].</p>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r21" rules="none">
+<table class="docutils citation" frame="void" id="r10" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R21]</a></td><td>S. Marsland, &#8220;Machine Learning: An Algorithmic Perspective&#8221;,
+<tr><td class="label"><a class="fn-backref" href="#id1">[R10]</a></td><td>S. Marsland, &#8220;Machine Learning: An Algorithmic Perspective&#8221;,
 Chapter 10, 2009.
 <a class="reference external" href="http://www-ist.massey.ac.nz/smarsland/Code/10/lle.py">http://www-ist.massey.ac.nz/smarsland/Code/10/lle.py</a></td></tr>
 </tbody>

dev/modules/generated/sklearn.ensemble.AdaBoostClassifier.html

@@ -213,17 +213,17 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.ensemble"
 <p class="last"><a class="reference internal" href="sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-obj docutils literal"><span class="pre">AdaBoostRegressor</span></code></a>, <a class="reference internal" href="sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-obj docutils literal"><span class="pre">GradientBoostingClassifier</span></code></a>, <code class="xref py py-obj docutils literal"><span class="pre">DecisionTreeClassifier</span></code></p>
 </div>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r22" rules="none">
+<table class="docutils citation" frame="void" id="r11" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R22]</a></td><td>Y. Freund, R. Schapire, &#8220;A Decision-Theoretic Generalization of
+<tr><td class="label"><a class="fn-backref" href="#id1">[R11]</a></td><td>Y. Freund, R. Schapire, &#8220;A Decision-Theoretic Generalization of
 on-Line Learning and an Application to Boosting&#8221;, 1995.</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r23" rules="none">
+<table class="docutils citation" frame="void" id="r12" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id2">[R23]</a></td><td><ol class="first last upperalpha simple" start="10">
+<tr><td class="label"><a class="fn-backref" href="#id2">[R12]</a></td><td><ol class="first last upperalpha simple" start="10">
 <li>Zhu, H. Zou, S. Rosset, T. Hastie, &#8220;Multi-class AdaBoost&#8221;, 2009.</li>
 </ol>
 </td></tr>