Hmmlearn

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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.

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Released on September 12th, 2020.

.. warning::
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
``init_params_``.

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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.

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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).

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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.

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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 PR82 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.