Major changes

Switch out pomegranate HMM backend to hmmlearn. (#105)
Remove separate HMM and GMM-HMM implementations – only keep a single GMM-HMM implementation (in the GMMHMM class) and treat multivariate Gaussian emission HMM as a special case of GMM-HMM. (#105)
Support string and numeric labels by using label encodings (from sklearn.preprocessing.LabelEncoder). (#105)
Add support for Python v3.6, v3.7, v3.8, v3.9 and remove support for v3.5. (#105)
Switch from approximate DTW algorithm (fastdtw) to exact implementation (dtaidistance) for KNNClassifier. (#106)

Minor changes

Switch to use duck-typing for iterables instead of requiring lists. (#105)
Rename 'strict left-right' HMM topology to 'linear'. (#105)
Switch m2r to m2r2, as m2r is no longer maintained. (#105)
Change covariance to covariance_type, to match hmmlearn. (#105)
Use numpy.random.RandomState(seed=None) as default instead of numpy.random.RandomState(seed=0). (#105)
Switch KNNClassifier serialization from HDF5 to pickling. (#106)
Use intersphinx for external documentation links, e.g. to numpy. (#108)
Change MinMaxScale bounds to floats. (#112)
Add __repr__ function to GMMHMM, HMMClassifier and KNNClassifier. (#120)
Use feature-independent warping (DTWI). (#121)
Ensure minimum Sakoe-Chiba band width is 1. (#126)
Add .readthedocs.yml configuration file. (#127)