from hmmlearn import hmm
import pickle
def GMM_HMM(O, lengths, n_states, verbose=False):
# the first step initial a GMM_HMM model
# input:
# O, array, (n_samples, n_features), observation
# lengths, list, lengths of sequence
# n_states, number of states
# output:
# S, the best state sequence
# A, the transition probability matrix of the HMM model
# model = hmm.GMMHMM(n_components=n_states, n_mix=4, covariance_type="diag", n_iter=1000, verbose=verbose).fit(O, lengths)
model = hmm.GaussianHMM(n_components=n_states, covariance_type='diag', n_iter=1000, verbose=verbose).fit(O, lengths)
pi = model.startprob_
A = model.transmat_
_, S = model.decode(O, algorithm='viterbi')
gamma = model.predict_proba(O)
pickle.dump(model, open('C:/Users/Administrator/Desktop/HMM_program/save/GMM_HMM_model.pkl', 'wb'))
return S, A, gamma
