def apply_hmm(data, clabs, kMVA):
kMVA.estim_kbasis(data, clabs)
data = kMVA.transform(data, k=2)
model = init_hmm(3, 3, 2)
data = [list(x) for x in data]
dt, T = 1, 2
seq_set = ghmm.SequenceSet(ghmm.Float(), [sum(list(data[i:i+T]), []) for i in range(0, len(data) - T, dt)])
model.baumWelch(seq_set, 10, 0.01)
plotGHMMEmiss(model, stInd=0, dimInd=1)
def check_particular_phoneme(phData):
syspath = 'recsystem'
somePh = phData.keys()[0]
print somePh, len(phData[somePh][0]), len(phData[somePh][0][0])
for sample in phData[somePh][0]:
p = plt.plot(sample)
plt.grid(True)
plt.show()
hmm = init_hmm(nStates=3, nMix=1, dim=len(phData[somePh][0][0]))
seq_set = ghmm.SequenceSet(ghmm.Float(), [sum(phSample, []) for phSample in phData[somePh]])
print 'Let us train it!'
hmm.baumWelch(seq_set)
# print os.path.join(syspath, 'hmm', somePh)
# hmm = hmm_built_from(HmmFromGHMMBuilder, os.path.join(syspath, 'hmm', somePh))
hmmReloaded = HMMClassifier.reassigned_ghmm_object(hmm, 0.5)
loglikel = [hmmReloaded.loglikelihood(seq) for seq in seq_set]
pl = plt.plot(loglikel)
plt.show()
