| AArch64 | |
| ppc64le | |
| s390x | |
| x86-64 |
- Disable py2 build due to missing deps
- Update to Version 0.12.0 + Highlights * MarkovNetwork models have been added in and include both inference and structure learning. * Support for Python 2 has been depricated. * Markov network, data generator, and callback tutorials have been added in * A robust `from_json` method has been added in to __init__.py that can deserialize JSONs from any pomegranate model. + MarkovNetwork * MarkovNetwork models have been added in as a new probabilistic model. * Loopy belief propagation inference has been added in using the FactorGraph backend * Structure learning has been added in using Chow-Liu trees + BayesianNetwork * Chow-Liu tree building has been sped up slightly, courtesy of @alexhenrie * Chow-Liu tree building was further sped up by almost an order of magnitude * Constraint Graphs no longer fail when passing in graphs with self loops, courtesy of @alexhenrie + BayesClassifier * Updated the `from_samples` method to accept BayesianNetwork as an emission. This will build one Bayesian network for each class and use them as the emissions. + Distributions * Added a warning to DiscreteDistribution when the user passes in an empty dictionary. * Fixed the sampling procedure for JointProbabilityTables. * GammaDistributions should have their shape issue resolved * The documentation for BetaDistributions has been updated to specify that it is a Beta-Bernoulli distribution. + io * New file added, io.py, that contains data generators that can be operated on * Added DataGenerator, DataFrameGenerator, and a BaseGenerator class to inherit from + HiddenMarkovModel * Added RandomState parameter to `from_samples` to account for randomness when building discrete models. + Misc * Unneccessary calls to memset have been removed, courtesy of @alexhenrie * Checking for missing values has been slightly refactored to be cleaner, courtesy of @mareksmid-lucid * Include the LICENSE file in MANIFEST.in and simplify a bit, courtesy of @toddrme2178 * Added in a robust from_json method that can be used to deseralize a JSON for any pomegranate model. + docs * Added io.rst to briefly describe data generators * Added MarkovNetwork.rst to describe Markov networks * Added links to tutorials that did not have tutorials linked to them. + Tutorials * Added in a tutorial notebook for Markov networks * Added in a tutorial notebook for data generators * Added in a tutorial notebook for callbacks + CI * Removed unit tests for Py2.7 from AppVeyor and Travis * Added unit tests for Py3.8 to AppVeyor and Travis - Dropped python2 support
- Initial version