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Change Log

Changes to minihmm are documented here.

Version numbers follow following the conventions described in PEP440 and Semantic versioning. Because we're still below v1, minor updates might break backward compatibility.

v0.3.3 = [2024-03-28]

v0.3.2 = [2022-05-10]

  • Python 2.7 support dropped
  • Test environments changed to Python 3.6 and 3.9
  • Dockerized

v0.3.0 = [2018-08-28]

  • BREAKING: footprint of serialized objects now smaller
  • Improved screen output in DefaultLoggerFactory()
  • Improved code styling

v0.2.1 = [2017-11-26]

  • Restored Python 3 compatibility

v0.2.0 = [2017-11-25]

  • Migrated serialization setup to jsonpickle, a practice much saner than what I had been previously doing. This breaks backward compatibility with v0.1.4 JSON blobs, but enables far more flexibility in what can be serialized
  • replace older (de)serialize() methods with much saner get_header() and get_row() for export of models as DataFrame rows (e.g. to watch parameter trajectories during training
  • train_baum_welch() can now weight individual observations
  • changed logging in train_baum_welch(). This led to some backward-incompatible changes, but is much nicer. It also includes a DefaultLoggerFactory() so people don't have to comb through insane logs anymore
  • Unit tests and multiple improvments for train_baum_welch()

v0.1.4 = [2017-06-08]

  • Some class properties are now manged, saving me from myself
  • to_dict() and from_dict() methods specified for serializing models as JSON blobs
  • Convenience methods for building HMM tables from known observations, optionally with weights
  • Speed improvements under the hood
  • Suppression of non-useful warnings, and creation of useful ones
  • Unit tests for key features

v0.1.3 = [2017-05-09]

  • Model reduction tested and working, even though unit tests not yet fleshed out
  • Valid pseudocount arrays now generated for state priors in high order space (before was only for transition tables)
  • Added warnings in places where unexpected side effects could be caused by valid calculations (e.g. uncaught nan)
  • Serialization sketched out for FirstOrderHMMs

v0.1.2 = [2017-05-08]

Added

  • Methods for serializing and deserializing large matrices to JSON
  • Methods for reducing high-order models to first-order models, and for converting state sequences between orders
  • Yet more unit tests

v0.1.1

Added

  • Can now sample state paths given an observation sequence, from the
    conditional distribution P(Path | observation sequence)
  • Unit tests

Changed

  • miniHMM factored out of unpublished scientific project
  • Migration from SVN to GIT repo
  • get_free_parameters() and from_parameters() replaced by serialize() and deserialize() methods in all factors