Rebuild dev docs at master=0cb93b0

Author: sklearn-ci

dev/_sources/whats_new.txt

@@ -15,26 +15,40 @@ Changelog
 New features
 ............
 
-   - The Gaussian Process module has been reimplemented and now offers classification 
-     and regression estimators through :class:`gaussian_process.GaussianProcessClassifier` 
-     and  :class:`gaussian_process.GaussianProcessRegressor`. Among other things, the new 
-     implementation supports kernel engineering, gradient-based hyperparameter optimization or 
-     sampling of functions from GP prior and GP posterior. Extensive documentation and 
+   - The Gaussian Process module has been reimplemented and now offers classification
+     and regression estimators through :class:`gaussian_process.GaussianProcessClassifier`
+     and  :class:`gaussian_process.GaussianProcessRegressor`. Among other things, the new
+     implementation supports kernel engineering, gradient-based hyperparameter optimization or
+     sampling of functions from GP prior and GP posterior. Extensive documentation and
      examples are provided. By `Jan Hendrik Metzen`_.
- 
+
 Enhancements
 ............
- 
+
 Bug fixes
 .........
-    - Fixed bug in :func:`manifold.spectral_embedding` where diagonal of unnormalized 
+
+    - :class:`RandomizedPCA` default number of `iterated_power` is 2 instead of 3.
+      This is a speed up with a minor precision decrease. By `Giorgio Patrini`_.
+
+    - :func:`randomized_svd` performs 2 power iterations by default, instead or 0.
+      In practice this is often enough for obtaining a good approximation of the
+      true eigenvalues/vectors in the presence of noise. By `Giorgio Patrini`_.
+
+    - :func:`randomized_range_finder` is more numerically stable when many
+      power iterations are requested, since it applies LU normalization by default.
+      If `n_iter<2` numerical issues are unlikely, thus no normalization is applied.
+      Other normalization options are available: 'none', 'LU' and 'QR'. By
+      `Giorgio Patrini`_.
+
+    - Fixed bug in :func:`manifold.spectral_embedding` where diagonal of unnormalized
       Laplacian matrix was incorrectly set to 1. By `Peter Fischer`_.
 
 
 API changes summary
 -------------------
- 
- 
+
+
 .. _changes_0_17:
 
 Version 0.17
@@ -271,7 +285,7 @@ Bug fixes
       in the final fit. By `Manoj Kumar`_.
 
     - Fixed bug in :class:`ensemble.forest.ForestClassifier` while computing
-      oob_score and X is a sparse.csc_matrix. By `Ankur Ankan`_.     
+      oob_score and X is a sparse.csc_matrix. By `Ankur Ankan`_.
 
     - All regressors now consistently handle and warn when given ``y`` that is of
       shape ``(n_samples, 1)``. By `Andreas Müller`_.
@@ -3799,3 +3813,4 @@ David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.
 .. _Jean Kossaifi: https://github.com/JeanKossaifi
 .. _Andrew Lamb: https://github.com/andylamb
 .. _Graham Clenaghan: https://github.com/gclenaghan
+.. _Giorgio Patrini: https://github.com/giorgiop

dev/modules/generated/sklearn.base.BaseEstimator.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></tt></a>.BaseEstimator<a class="headerlink" href="#sklearn-base-baseestimator" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.base.BaseEstimator">
-<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">BaseEstimator</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/718a7df/sklearn/base.py#L169"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">BaseEstimator</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/0cb93b0/sklearn/base.py#L169"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all estimators in scikit-learn</p>
 <p class="rubric">Notes</p>
 <p>All estimators should specify all the parameters that can be set
@@ -201,7 +201,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.BaseEstimator.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/718a7df/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_params" title="Permalink to this definition">¶</a></dt>
+<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/0cb93b0/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.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" />
@@ -226,7 +226,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.BaseEstimator.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/718a7df/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.set_params" title="Permalink to this definition">¶</a></dt>
+<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/0cb93b0/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.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

dev/modules/generated/sklearn.base.TransformerMixin.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></tt></a>.TransformerMixin<a class="headerlink" href="#sklearn-base-transformermixin" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.base.TransformerMixin">
-<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">TransformerMixin</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/718a7df/sklearn/base.py#L435"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.base.</tt><tt class="descname">TransformerMixin</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/0cb93b0/sklearn/base.py#L435"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mixin class for all transformers in scikit-learn.</p>
 <p class="rubric">Methods</p>
 <table border="1" class="longtable docutils">
@@ -194,7 +194,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.TransformerMixin.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/718a7df/sklearn/base.py#L438"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
+<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/0cb93b0/sklearn/base.py#L438"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to X and y with optional parameters fit_params
 and returns a transformed version of X.</p>