Hmmlearn

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Released on July 18th, 2021.

- Fixed support for multi-sequence GMM-HMM fit.
- Deprecated ``utils.iter_from_X_lengths``.
- 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.

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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.