hmmlearn Changelog

Here you can see the full list of changes between each hmmlearn release.

Version 0.2.0

Relased on March 1st, 2016

The release contains a known bug: fitting GMMHMM with covariance types other than "diag" does not work. This is going to be fixed in the following version. See issue #78 on GitHub for details.

  • Removed deprecated re-exports from hmmlean.hmm.
  • Speed up forward-backward algorithms and Viterbi decoding by using Cython typed memoryviews. Thanks to @cfarrow. See PR#82 on GitHub.
  • Changed the API to accept multiple sequences via a single feature matrix X and an array of sequence lengths. This allowed to use the HMMs as part of scikit-learn Pipeline. The idea was shamelessly plugged from seqlearn package by @larsmans. See issue #29 on GitHub.
  • Removed params and init_params from internal methods. Accepting these as arguments was redundant and confusing, because both available as instance attributes.
  • Implemented ConvergenceMonitor, a class for convergence diagnostics. The idea is due to @mvictor212.
  • Added support for non-fully connected architectures, e.g. left-right HMMs. Thanks to @matthiasplappert. See issue #33 and PR #38 on GitHub.
  • Fixed normalization of emission probabilities in MultinomialHMM, see issue #19 on GitHub.
  • GaussianHMM is now initialized from all observations, see issue #1 on GitHub.
  • Changed the models to do input validation lazily as suggested by the scikit-learn guidelines.
  • Added min_covar parameter for controlling overfitting of GaussianHMM, see issue #2 on GitHub.
  • Accelerated M-step fro GaussianHMM with full and tied covariances. See PR #97 on GitHub. Thanks to @anntzer.
  • Fixed M-step for GMMHMM, which incorrectly expeced GMM.score_samples to return log-probabilities. See PR #4 on GitHub for discussion. Thanks to @mvictor212 and @michcio1234.

Version 0.1.1

Initial release, released on February 9th 2015