hmmlearn Changelog

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

Version 0.2.3

Released on December 17th, 2019.

Fitting of degenerate GMMHMMs appears to fail in certain cases on macOS; help with troubleshooting would be welcome.

  • Dropped support for Py2.7, Py3.4.
  • Log warning if not enough data is passed to fit() for a meaningful fit.
  • Better handle degenerate fits.
  • Allow missing observations in input multinomial data.
  • Avoid repeatedly rechecking validity of Gaussian covariance matrices.

Version 0.2.2

Released on May 5th, 2019.

This version was cut in particular in order to clear up the confusion between the “real” v0.2.1 and the pseudo-0.2.1 that were previously released by various third-party packagers.

  • Custom ConvergenceMonitors subclasses can be used (#218).
  • MultinomialHMM now accepts unsigned symbols (#258).
  • The get_stationary_distribution returns the stationary distribution of the transition matrix (i.e., the rescaled left-eigenvector of the transition matrix that is associated with the eigenvalue 1) (#141).

Version 0.2.1

Released on October 17th, 2018.

  • GMMHMM was fully rewritten (#107).
  • Fixed underflow when dealing with logs. Thanks to @aubreyli. See PR #105 on GitHub.
  • Reduced worst-case memory consumption of the M-step from O(S^2 T) to O(S T). See issue #313 on GitHub.
  • Dropped support for Python 2.6. It is no longer supported by scikit-learn.

Version 0.2.0

Released 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 expected 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.