BLD: Prepare a new release

This will be v.0.3.6

ChangeLog

@@ -1,4 +1,5 @@
-Version 0.3.5+
+Version 0.3.6 2010-12-17 luispedro
+	* Unsupervised (1-class) kernel density modeling
 	* Fix for when SDA returns empty
 	* weights option to some learners
 	* stump learner

README.rst

@@ -22,6 +22,19 @@ There is a strong emphasis on speed and low memory usage. Therefore, most of
 the performance sensitive code is in C++. This is behind Python-based
 interfaces for convenience.
 
+New in 0.3.5
+------------
+- Unsupervised (1-class) kernel density modeling
+- Fix for when SDA returns empty
+- weights option to some learners
+- stump learner
+- Adaboost (result of above changes)
+
+New in 0.3.5
+------------
+- Fixes for 64 bit machines
+- Functions in measures.py all have same interface now.
+
 New in 0.3.4
 ------------
 - Random forest learners
@@ -40,5 +53,6 @@ Features
 
 License: MIT
 Author: Luis Pedro Coelho (with code from LibSVM and scikits.learn)
-Website: http://luispedro.org/software/milk
+Website: `http://luispedro.org/software/milk
+<http://luispedro.org/software/milk>`__
 API Documentation: `http://packages.python.org/milk/ <http://packages.python.org/milk/>`_

docs/source/readme.rst

@@ -3,6 +3,7 @@ MILK: MACHINE LEARNING TOOLKIT
 ==============================
 Machine Learning in Python
 --------------------------
+
 Milk is a machine learning toolkit in Python.
 
 Its focus is on supervised classification with several classifiers available:
@@ -21,14 +22,37 @@ There is a strong emphasis on speed and low memory usage. Therefore, most of
 the performance sensitive code is in C++. This is behind Python-based
 interfaces for convenience.
 
+New in 0.3.5
+------------
+- Unsupervised (1-class) kernel density modeling
+- Fix for when SDA returns empty
+- weights option to some learners
+- stump learner
+- Adaboost (result of above changes)
+
+New in 0.3.5
+------------
+- Fixes for 64 bit machines
+- Functions in measures.py all have same interface now.
+
+New in 0.3.4
+------------
+- Random forest learners
+- Decision trees sped up 20x
+- Much faster gridsearch  (finds optimum without computing all folds)
+
 Features
 --------
+- Random forests
+- Self organising maps
 - SVMs. Using the libsvm solver with a pythonesque wrapper around it.
 - Stepwise Discriminant Analysis for feature selection.
+- Non-negative matrix factorisation
 - K-means using as little memory as possible.
+- Affinity propagation
 
 License: MIT
-Author: Luis Pedro Coelho (with code from LibSVM)
+Author: Luis Pedro Coelho (with code from LibSVM and scikits.learn)
 Website: `http://luispedro.org/software/milk
 <http://luispedro.org/software/milk>`__
 API Documentation: `http://packages.python.org/milk/ <http://packages.python.org/milk/>`_

milk/milk_version.py

@@ -1 +1 @@
-__version__ = '0.3.5'
+__version__ = '0.3.6'