def train(trainingData):
# train one GMM for each state
mixes = list()
for state in xrange(1,6):
# select data with current state label
d = trainingData[trainingData.rating==state]
# prepare data shape
d = np.array(zip(*[d[f].values for f in pcas]))
# init GMM
gmm = GMM(num_mixc,cov_type)
# train
gmm.fit(d)
mixes.append(gmm)
# train HMM with init, trans, GMMs=mixes
init,trans = hmm.hmmMlParams(trainingData,[1,2,3,4,5])
model = GMMHMM(n_components=5,init_params='',gmms=mixes)
model.transmat_ = trans
model.startprob_ = init
return model
