def phoneme_rec_accuracy_hmm(acModelDir, trPhData, testPhData, debug=True):
syspath = 'recsystem'
train = []
trTarget = []
for ph in trPhData:
train += trPhData[ph]
trTarget += [ph] * len(trPhData[ph])
print len(train), len(trTarget)
hmmcl = HMMClassifier(nStates=3, nMix=1)
#hmmcl.train(train, trTarget)
hmmcl.load(trPhData.keys(), pathToHmm=acModelDir)
# hmmcl.refine_cov_matrix()
print len(hmmcl.modelsDict)
test = []
testTarget = []
for ph in testPhData:
test += testPhData[ph]
testTarget += [ph] * len(testPhData[ph])
print len(test), len(testTarget)
predRes = hmmcl.predict(test)
if debug:
f = open('results', 'w')
yaml.dump(zip(testTarget, predRes), f, default_flow_style=False)
f.close()
return sum([1 if t == p else 0 for t, p in zip(testTarget, predRes)]) / float(len(testTarget))
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()
