8.11.1. sklearn.hmm.GaussianHMM¶
- class sklearn.hmm.GaussianHMM(n_components=1, covariance_type='diag', startprob=None, transmat=None, startprob_prior=None, transmat_prior=None, algorithm='viterbi', means_prior=None, means_weight=0, covars_prior=0.01, covars_weight=1, random_state=None)¶
Hidden Markov Model with Gaussian emissions
Representation of a hidden Markov model probability distribution. This class allows for easy evaluation of, sampling from, and maximum-likelihood estimation of the parameters of a HMM.
Parameters : n_components : int
Number of states.
_covariance_type : string
String describing the type of covariance parameters to use. Must be one of ‘spherical’, ‘tied’, ‘diag’, ‘full’. Defaults to ‘diag’.
See also
- GMM
- Gaussian mixture model
Examples
>>> from sklearn.hmm import GaussianHMM >>> GaussianHMM(n_components=2) ... GaussianHMM(algorithm='viterbi', covariance_type='diag', covars_prior=0.01, covars_weight=1, means_prior=None, means_weight=0, n_components=2, random_state=None, startprob=None, startprob_prior=1.0, transmat=None, transmat_prior=1.0)
Attributes