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Version 0.5.3 1013-06-19 by luispedro
* Fix MDS for non-array inputs
* Fix MDS bug
* Add return_* arguments to kmeans
* Extend zscore() to work on non-ndarrays
* Add frac_precluster_learner
* Work with older C++ compilers

Version 0.5.2 2013-03-07 by luispedro
* Fix distribution of Eigen with source

Version 0.5.1 2013-01-11 by luispedro
* Add subspace projection kNN
* Export ``pdist`` in milk namespace
* Add Eigen to source distribution
* Add measures.curves.roc
* Add ``mds_dists`` function
* Add ``verbose`` argument to milk.tests.run

Version 0.5 2012-11-05 by luispedro
* Add coordinate-descent based LASSO
* Add unsupervised.center function
* Make zscore work with NaNs (by ignoring them)
* Propagate apply_many calls through transformers
* Much faster SVM classification with means a much faster defaultlearner()
[measured 2.5x speedup on yeast dataset!]

Version 0.4.3 2012-09-19 by luispedro
* Add select_n_best & rank_corr to featureselection
* Add Euclidean MDS
* Add tree multi-class strategy
* Fix adaboost with boolean weak learners (issue #6, reported by audy
(Austin Richardson))
* Add ``axis`` arguments to zscore()

Version 0.4.2 2012-01-16 by luispedro
* Make defaultlearner able to take extra argumentsaudy (Austin Richardson)
* Make ctransforms_model a supervised_model (adds apply_many)
* Add expanded argument to defaultlearner
* Fix corner case in SDA
* Fix repeated_kmeans
* Fix parallel gridminimise on Windows
* Add multi_label argument to normaliselabels
* Add multi_label argument to nfoldcrossvalidation.foldgenerator
* Do not fork a process in gridminimise if nprocs == 1 (makes for easier
debugging, at the cost of slightly more complex code).
* Add milk.supervised.multi_label
* Fix ext.jugparallel when features is a Task
* Add milk.measures.bayesian_significance

Version 0.4.1 2011-08-25 by luispedro
* Fix important bug in multi-process gridsearch

Version 0.4.0 2011-08-24 by luispedro
* Use multiprocessing to take advantage of multi core machines (off by
default).
* Add perceptron learner
* Set random seed in random forest learner
* Add warning to milk/__init__.py if import fails
* Add return value to ``gridminimise``
* Set random seed in ``precluster_learner``
* Implemented Error-Correcting Output Codes for reduction of multi-class
to binary (including probability estimation)
* Add ``multi_strategy`` argument to ``defaultlearner()``
* Make the dot kernel in svm much, much, faster
* Make sigmoidal fitting for SVM probability estimates faster
* Fix bug in randomforest (patch by Wei on milk-users mailing list)

Version 0.3.10 2011-05-10 by luispedro
* Add ext.jugparallel
* parallel nfold crossvalidation using jug
* parallel multiple kmeans runs using jug
* cluster_agreement for non-ndarrays
* Add histogram & normali(z|s)e options to ``milk.kmeans.assign_centroid``
* Fix bug in sda when features were constant for a class
* Add select_best_kmeans
* Added defaultlearner as a better name than defaultclassifier
* Add `measures.curves.precision_recall`
* Add `unsupervised.parzen.parzen`

Version 0.3.9 2011-03-15 by luispedro
* Improve speed of k-nearest neighbour (10x on scikits-learn benchmark)
* Fix gridminize for low count labels
* Improve kmeans on newer numpy (works for larger datasets)
* Add ``folds`` argument to ``nfoldcrossvalidation``
* Add ``assign_centroid`` function in milk.unsupervised.nfoldcrossvalidation
* Faster kmeans by coding centroid recalculation in C++
* Fix bug with non-integer labels for tree learning

Version 0.3.8 2011-02-12 luispedro
* Fix compilation on Windows

Version 0.3.7 2011-02-10 luispedro
* Logistic regression
* Source demos included (in source and documentation)
* Add cluster agreement metrics
* Fix nfoldcrossvalidation bug when using origins

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
* Adaboost (result of above changes)

Version 0.3.5 2010-11-3
* Fixes for 64 bit machines.
* Functions in measures.py all have same interface now.

Version 0.3.4 2010-10-31
* Random forest learners
* Decision trees sped up 20x
* Much faster gridsearch (finds optimum without computing all folds)

Version 0.3.3 2010-10-22
* Missing file added to distribution

Version 0.3.2
* kmeans() for distance=mahalanobis
* minimise dependency on scipy
* self-organising maps
* important bug fix in repeated_kmeans
* faster feature selection

Version 0.3.1 2010-09-25
* fix sparse non-negative matrix factorisation
* mean grouped classifier
* update multi classifier to newer interface

Version 0.3 2010-09-23
* no scipy.weave dependency
* flatter namespace
* faster kmeans
* affinity propagation (borrowed from scikits-learn & slightly improved)
* pdist()

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