[release] 0.10.0 🎉 #127
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* Finish bulk of pomegranate -> hmmlearn migration * Remove pomegranate dependency + add hmmlearn * Finish GMMHMM tests * Finish HMMClassifier tests * Finish _Validator tests * Redo GMMHMM documentation * Redo GMMHMM documentation * Update README * Update README * Change strict left-right topology to linear * Update notebook * Improve GMMHMM initial/transitions documentation * Improve documentation diagrams * Update HMM classifier diagram size * Fix README issues * Fix README superscript * Fix FastDTW reference * Specify sphinx version and switch m2r to m2r2 * Center sequentia title in docs * Fix tests for Python >=3.6 * Remove Python 3.5, add 3.8+3.9 * Change assertion tolerances + Python versions * Remove Python 3.9 support (temp)
* Add notebooks/requirements.txt * Re-implement DTW KNN without FastDTW * Remove list_of_strings validator * Rename _parallel_predict to _chunk_predict * Redo KNNClassifier documentation * Fix KNNClassifier validations * Fix KNNClassifier validations * Clean up KNNClassifier multiprocessing * Add note about KNNClassifier verbose+njobs * Finish KNNClassifier documentation * Remove test notebook * Finish KNNClassifier tests * Finish KNNClassifier tests * Specify n_jobs range * Add properties * Specify k range * Update README * Fix README.md * Fix README.md * Fix README.md * Update README * Finish README.md
* Remove h5py dependency and add Python v3.9 * Upgrade Python version requirement
* Use intersphinx for external documentation links * Iterable -> array-like * Fix GMMHMM iterable type * Add space between parameters and types * Fix classes type
* Change List[X] to list of X
* Fix minor typos in README.md
* Add warning to TrimZeros documentation
* Change min-max scale bounds to floats
* Allow array-like of transforms instead of list
* Always have a newline after class docstring
* Add docstrings to test cases
* Add HMMClassifier serialization/deserialization
* Change input format notebook
* Use relative (fractional) Sakoe-Chiba window width
* Add __repr__ function to all classifiers
* Use feature-independent warping (DTWI)
* Update copyright and move references
* Ensure minimum Sakoe-Chiba band width is 1 * Add Sakoe–Chiba to README.md
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Major changes
pomegranateHMM backend tohmmlearn. (#105)GMMHMMclass) and treat multivariate Gaussian emission HMM as a special case of GMM-HMM. (#105)sklearn.preprocessing.LabelEncoder). (#105)fastdtw) to exact implementation (dtaidistance) forKNNClassifier. (#106)Minor changes
m2rtom2r2, asm2ris no longer maintained. (#105)covariancetocovariance_type, to matchhmmlearn. (#105)numpy.random.RandomState(seed=None)as default instead ofnumpy.random.RandomState(seed=0). (#105)KNNClassifierserialization from HDF5 to pickling. (#106)intersphinxfor external documentation links, e.g. tonumpy. (#108)MinMaxScalebounds to floats. (#112)__repr__function toGMMHMM,HMMClassifierandKNNClassifier. (#120).readthedocs.ymlconfiguration file. (#127)