Here you can see the full list of changes between each hmmlearn release.
Released on July 18th, 2021.
Fixed support for multi-sequence GMM-HMM fit.
Previously, APIs taking a lengths parameter would silently drop the last samples if the total length was less than the number of samples. This behavior is deprecated and will raise an exception in the future.
Released on February 3rd, 2021.
Fixed typo in implementation of covariance maximization for GMMHMM.
Changed history of ConvergenceMonitor to include the whole history for evaluation purposes. It can no longer be assumed that it has a maximum length of two.
Released on September 12th, 2020.
GMMHMM covariance maximization was incorrect in this release. This bug was fixed in the following release.
Bumped previously incorrect dependency bound on scipy to 0.19.
Bug fix for ‘params’ argument usage in GMMHMM.
Warn when an explicitly set attribute would be overridden by
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.
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).
get_stationary_distributionreturns 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).
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.
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
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
Xand an array of sequence
lengths. This allowed to use the HMMs as part of scikit-learn
Pipeline. The idea was shamelessly plugged from
seqlearnpackage by @larsmans. See issue #29 on GitHub.
init_paramsfrom internal methods. Accepting these as arguments was redundant and confusing, because both available as instance attributes.
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.
GaussianHMMis 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.
min_covarparameter for controlling overfitting of
GaussianHMM, see issue #2 on GitHub.
Accelerated M-step fro
GaussianHMMwith full and tied covariances. See PR #97 on GitHub. Thanks to @anntzer.
Fixed M-step for
GMMHMM, which incorrectly expected
GMM.score_samplesto return log-probabilities. See PR #4 on GitHub for discussion. Thanks to @mvictor212 and @michcio1234.
Initial release, released on February 9th 2015.