Changelog

Fabian Pedregosa · Fabian Pedregosa · commit d671948c1c1d · 2011-05-10T14:17:15.000+02:00
diff --git a/AUTHORS.rst b/AUTHORS.rst
@@ -50,14 +50,17 @@ People
 
   * `Gael Varoquaux <http://gael-varoquaux.info/blog/>`_
 
-  * `Jake VanderPlas <http://www.astro.washington.edu/users/vanderplas/>`_ 
+  * `Jake VanderPlas <http://www.astro.washington.edu/users/vanderplas/>`_
     contributed the BallTree module in February 2010.
 
   * `Alexandre Gramfort
     <http://www-sop.inria.fr/members/Alexandre.Gramfort/index.fr.html>`_
 
   * `Olivier Grisel <http://twitter.com/ogrisel>`_
 
+  * Bertrand Thirion, contributed the hierarchical clustering module together
+    with Gael Varoquaux, Alexandre Gramfort and Vincent Michel.
+
   * Vincent Michel.
 
   * Chris Filo Gorgolewski
@@ -91,6 +94,13 @@ People
   * `Alexandre Passos <http://atpassos.posterous.com>`_ joined the
     project in November 2010 contributed the fast SVD variant.
 
+  * `Vlad Niculae <http://vene.ro>`_ joined the project in March 2011 and
+    contributed the non-negative matrix factorization module.
+
+
+  * Thouis (Ray) Jones joined the project in contributed the Cython bindings
+  for the BallTree class.
+
 
 If I forgot anyone, do not hesitate to send me an email to
 fabian.pedregosa@inria.fr and I'll include you in the list.
diff --git a/doc/install.rst b/doc/install.rst
@@ -30,7 +30,7 @@ Installing an official release
 Installing from source
 ----------------------
 
-Installing from source requires you to have installed numpy, 
+Installing from source requires you to have installed numpy,
 scipy, setuptools, python development headers and a working C++
 compiler. Under debian-like systems you can get all this by executing
 with root privileges::
@@ -117,7 +117,7 @@ executing the command::
     python setup.py install
 
 
-To build a precompiled package like the ones distributed at 
+To build a precompiled package like the ones distributed at
 `the downloads section <https://sourceforge.net/projects/scikit-learn/files/>`_,
 the command to execute is::
 
@@ -132,7 +132,7 @@ Third party distributions of scikits.learn
 ==========================================
 
 Some third-party distributions are now providing versions of
-scikits.learn integrated with their package-management systems. 
+scikits.learn integrated with their package-management systems.
 
 These can make installation and upgrading much easier for users since
 the integration includes the ability to automatically install
@@ -151,6 +151,14 @@ using the following commands with root privileges::
       apt-get install python-scikits-learn
 
 
+Python(x, y)
+------------
+
+The `Python(x, y) <http://pythonxy.com>`_ distributes scikit-learn as an additional plugin, which can
+be found in the `Additional plugins <http://code.google.com/p/pythonxy/wiki/AdditionalPlugins>`_
+page.
+
+
 Enthought python distribution
 -----------------------------
 
@@ -189,7 +197,7 @@ Testing
 
 Testing requires having the `nose
 <http://somethingaboutorange.com/mrl/projects/nose/>`_ library. After
-installation, the package can be tested by executing from outside the
+installation, the package can be tested by executing *from outside* the
 source directory::
 
     python -c "import scikits.learn as skl; skl.test()"
@@ -200,8 +208,9 @@ eventually should finish with the a text similar to::
            Ran 601 tests in 27.920s
            OK (SKIP=2)
 
-otherwise please consider submitting a bug in the :ref:`bug_tracker`
-or to the :ref:`mailing_lists`.
+otherwise please consider posting an issue into the `bug tracker
+<https://github.com/scikit-learn/scikit-learn/issues>`_ or to the
+:ref:`mailing_lists`.
 
 scikits.learn can also be tested without having the package
 installed. For this you must compile the sources inplace from the
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
@@ -339,6 +339,18 @@ Signal Decomposition
 
    decomposition.fastica
 
+
+Linear Discriminant Analysis
+============================
+
+.. autosummary::
+
+   :toctree: generated
+   :template: class.rst
+
+   lda.LDA
+
+
 Cross Validation
 ================
 
diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
@@ -31,6 +31,8 @@ data can be found in the `labels_` attribute.
     long as a similarity measure exists for such objects.
 
 
+.. _k_means:
+
 K-means
 =======
 
diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst
@@ -4,6 +4,9 @@
 Covariance estimation
 ===================================================
 
+.. currentmodule:: scikits.learn.covariance
+
+
 Many statistical problems require at some point the estimation of a
 population's covariance matrix, which can be seen as an estimation of
 data set scatter plot shape. Most of the time, such an estimation has
@@ -20,7 +23,6 @@ observations are independant and identically distributed.
 Empirical covariance
 ====================
 
-.. currentmodule:: scikits.learn.covariance
 
 The covariance matrix of a data set is known to be well approximated
 with the classical `Maximum Likelihood Estimator` (or `empirical
@@ -38,15 +40,14 @@ whether the data are centered or not, the result will be different, so
 one may want to use the `assume_centered` parameter accurately.
 
 .. topic:: Examples:
-   
+
    * See :ref:`example_covariance_plot_covariance_estimation.py` for
      an example on how to fit an :class:`EmpiricalCovariance` object
      to data.
 
 Shrunk Covariance
 =================
 
-.. curentmodule:: scikits.learn.covariance
 
 Basic shrinkage
 ---------------
@@ -77,7 +78,7 @@ whether the data are centered or not, the result will be different, so
 one may want to use the `assume_centered` parameter accurately.
 
 .. topic:: Examples:
-   
+
    * See :ref:`example_covariance_plot_covariance_estimation.py` for
      an example on how to fit a :class:`ShrunkCovariance` object
      to data.
@@ -101,7 +102,7 @@ fitting a :class:`LedoitWolf` object to the same sample.
     Volume 88, Issue 2, February 2004, pages 365-411.
 
 .. topic:: Examples:
-   
+
    * See :ref:`example_covariance_plot_covariance_estimation.py` for
      an example on how to fit a :class:`LedoitWolf` object to data and
      for visualizing the performances of the Ledoit-Wolf estimator in
@@ -112,6 +113,9 @@ fitting a :class:`LedoitWolf` object to the same sample.
    :align: center
    :scale: 75%
 
+
+.. _oracle_apprroximating_shrinkage:
+
 Oracle Approximating Shrinkage
 ------------------------------
 
@@ -132,13 +136,13 @@ from the matlab programm available from the authors webpage
 
 [2] "Shrinkage Algorithms for MMSE Covariance Estimation" Chen et al.,
     IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010.
-    
+
 .. topic:: Examples:
-   
+
    * See :ref:`example_covariance_plot_covariance_estimation.py` for
      an example on how to fit an :class:`OAS` object
      to data.
-     
+
    * See :ref:`example_covariance_plot_lw_vs_oas.py` to visualize the
      Mean Squared Error difference between a :class:`LedoitWolf` and
      an :class:`OAS` estimator of the covariance.
diff --git a/doc/modules/datasets.rst b/doc/modules/datasets.rst
@@ -39,7 +39,7 @@ Dataset generators
 
 TODO
 
-
+.. _labeled_faces_in_the_wild:
 
 The Labeled Faces in the Wild face recognition dataset
 ======================================================
diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst
@@ -102,6 +102,8 @@ is not the exact inverse transform of `transform` even when
       <http://arxiv.org/abs/0909.4061>`_
       Halko, et al., 2009
 
+.. _kernel_PCA:
+
 Kernel PCA
 ----------
 
@@ -173,14 +175,14 @@ sparse components found by :class:`NMF` on the digits dataset.
 
 
 The :attr:`init` attribute determines the initialization method applied, which
-has a great impact on the performance of the method. :class:`NMF` implements 
+has a great impact on the performance of the method. :class:`NMF` implements
 the method Nonnegative Double Singular Value Decomposition. NNDSVD is based on
-two SVD processes, one approximating the data matrix, the other approximating 
-positive sections of the resulting partial SVD factors utilizing an algebraic 
+two SVD processes, one approximating the data matrix, the other approximating
+positive sections of the resulting partial SVD factors utilizing an algebraic
 property of unit rank matrices. The basic NNDSVD algorithm is better fit for
-sparse factorization. Its variants NNDSVDa (in which all zeros are set equal to 
-the mean of all elements of the data), and NNDSVDar (in which the zeros are set 
-to random perturbations less than the mean of the data divided by 100) are 
+sparse factorization. Its variants NNDSVDa (in which all zeros are set equal to
+the mean of all elements of the data), and NNDSVDar (in which the zeros are set
+to random perturbations less than the mean of the data divided by 100) are
 recommended in the dense case.
 
 :class:`NMF` can also be initialized with random non-negative matrices, by
@@ -189,7 +191,7 @@ passing an integer seed or a `RandomState` to :attr:`init`.
 In :class:`NMF`, sparseness can be enforced by setting the attribute
 :attr:`sparseness` to `data` or `components`. Sparse components lead to
 localized features, and sparse data leads to a more efficient representation
-of the data. 
+of the data.
 
 .. topic:: Examples:
 
@@ -212,4 +214,4 @@ of the data.
     * `"SVD based initialization: A head start for nonnegative
       matrix factorization"
       <http://www.cs.rpi.edu/~boutsc/files/nndsvd.pdf>`_
-      C. Boutsidis, E. Gallopoulos, 2008 
+      C. Boutsidis, E. Gallopoulos, 2008
diff --git a/doc/support.rst b/doc/support.rst
@@ -24,7 +24,7 @@ IRC
 Some devs like to hang out on channel #learn on irc.freenode.net
 
 If you do not have an irc client or are behind a firewall this web
-client works fine: http://webchat.freenode.net 
+client works fine: http://webchat.freenode.net
 
 
 .. _documentation_resources:
@@ -36,6 +36,7 @@ This documentation is relative to |release|. Documentation for other
 versions can be found here:
 
     * `Development version <http://scikit-learn.sf.net/dev/>`_
+    * `0.8 <http://scikit-learn.sf.net/0.8/>`_
     * `0.7 <http://scikit-learn.sf.net/0.7/>`_
     * `0.6 <http://scikit-learn.sf.net/0.6/>`_
     * `0.5 <http://scikit-learn.sf.net/0.5/>`_
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
