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Tags: luispedro/milk
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BLD New release: 0.6.1 This release fixes the source distribution
BLD New release: 0.5.3
Full ChangeLog:
* 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
BLD Release 0.5.2 Fix building, which is important on its own
BLD New release: 0.5.1
Most important new "feature" is bundling of Eigen with source.
Full ChangeLog:
- 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
BLD New release: 0.5
Full ChangeLog:
* 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!]
BLD: New release: 0.4.3
ChangeLog:
* 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()
BLD New version: 0.4.2 It has been too long since last release. Many improvements and bugfixes lingering. ChangeLog: * Make defaultlearner able to take extra arguments * 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
NEW VERSION: 0.4.0 - 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)
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