def _constructHmmNetwork(self, numStates, ALPHA, withModels):
'''
top level-function: costruct self.hmmNEtwork that confirms to guyz's code
'''
# sequencePhonemes = sequencePhonemes[0:4]
######## construct transition matrix
#######
if not WITH_DURATIONS:
transMAtrix = self._constructTransMatrixHMMNetwork(
self.lyricsWithModels.phonemesNetwork)
# DEBUG
# writeListOfListToTextFile(transMAtrix, None , '/Users/joro/Documents/Phd/UPF/voxforge/myScripts/AlignmentStep/transMatrix')
# construct means, covars, and all the rest params
#########
if numStates == None:
numStates = len(self.lyricsWithModels.statesNetwork)
means, covars, weights, pi = self._constructHMMNetworkParameters(
numStates, withModels)
if WITH_DURATIONS:
self.hmmNetwork = GMHMM(numStates,
numMixtures,
numDimensions,
None,
means,
covars,
weights,
pi,
init_type='user',
verbose=True)
self.hmmNetwork.setALPHA(ALPHA)
else:
self.hmmNetwork = GMHMM(numStates,
numMixtures,
numDimensions,
transMAtrix,
means,
covars,
weights,
pi,
init_type='user',
verbose=True)
def test_rand():
n = 5
m = 4
d = 2
atmp = numpy.random.random_sample((n, n))
row_sums = atmp.sum(axis=1)
a = numpy.array(atmp / row_sums[:, numpy.newaxis], dtype=numpy.double)
wtmp = numpy.random.random_sample((n, m))
row_sums = wtmp.sum(axis=1)
w = numpy.array(wtmp / row_sums[:, numpy.newaxis], dtype=numpy.double)
means = numpy.array((0.6 * numpy.random.random_sample((n, m, d)) - 0.3), dtype=numpy.double)
covars = numpy.zeros( (n,m,d,d) )
for i in range(n):
for j in range(m):
for k in range(d):
covars[i][j][k][k] = 1
pitmp = numpy.random.random_sample((n))
pi = numpy.array(pitmp / sum(pitmp), dtype=numpy.double)
gmmhmm = GMHMM(n,m,d,a,means,covars,w,pi,init_type='user',verbose=True)
obs = numpy.array((0.6 * numpy.random.random_sample((40,d)) - 0.3), dtype=numpy.double)
print("Doing Baum-welch")
gmmhmm.train(obs,1000)
print()
print("Pi",gmmhmm.pi)
print("A",gmmhmm.A)
print("weights", gmmhmm.w)
print("means", gmmhmm.means)
print("covars", gmmhmm.covars)
def test_simple():
n = 2
m = 2
d = 2
pi = numpy.array([0.5, 0.5])
A = numpy.ones((n, n), dtype=numpy.double) / float(n)
w = numpy.ones((n, m), dtype=numpy.double)
means = numpy.ones((n, m, d), dtype=numpy.double)
covars = [[numpy.matrix(numpy.eye(d, d)) for j in xrange(m)]
for i in xrange(n)]
w[0][0] = 0.5
w[0][1] = 0.5
w[1][0] = 0.5
w[1][1] = 0.5
means[0][0][0] = 0.5
means[0][0][1] = 0.5
means[0][1][0] = 0.5
means[0][1][1] = 0.5
means[1][0][0] = 0.5
means[1][0][1] = 0.5
means[1][1][0] = 0.5
means[1][1][1] = 0.5
gmmhmm = GMHMM(n,
m,
d,
A,
means,
covars,
w,
pi,
init_type='user',
verbose=True)
obs = numpy.array([[0.3, 0.3], [0.1, 0.1], [0.2, 0.2]])
print "Doing Baum-welch"
gmmhmm.train(obs, 10)
print
print "Pi", gmmhmm.pi
print "A", gmmhmm.A
print "weights", gmmhmm.w
print "means", gmmhmm.means
print "covars", gmmhmm.covars
def trainModel(self, obs):
pi = numpy.array([0.2, 0.2, 0.2, 0.2, 0.2])
A = numpy.ones((self.n, self.n), dtype=numpy.double) / float(self.n)
w = numpy.ones((self.n, self.m), dtype=numpy.double)
means = numpy.ones((self.n, self.m, self.d), dtype=numpy.double)
covars = [[
numpy.matrix(numpy.eye(self.d, self.d)) for j in xrange(self.m)
] for i in xrange(self.n)]
n_iter = 20
'''w[0][0] = 0.5
w[0][1] = 0.5
w[1][0] = 0.5
w[1][1] = 0.5
means[0][0][0] = 0.5
means[0][0][1] = 0.5
means[0][1][0] = 0.5
means[0][1][1] = 0.5
means[1][0][0] = 0.5
means[1][0][1] = 0.5
means[1][1][0] = 0.5
means[1][1][1] = 0.5 '''
gmmhmm = GMHMM(self.n,
self.m,
self.d,
A,
means,
covars,
w,
pi,
init_type='user',
verbose=True)
print "Doing Baum-welch"
#gmmhmm.train(obs,10)
if len(obs.shape) == 2:
gmmhmm.train(obs)
return self
elif len(obs.shape) == 3:
count = obs.shape[0]
for n in range(count):
gmmhmm.train(obs[n, :, :])
return self
# w[1][0] = 0.5
# w[1][1] = 0.5
# btmp = np.random.random_sample((N, M))
# row_sums = btmp.sum(axis=1)
# B = btmp / row_sums[:, np.newaxis]
# print("Initial B: ", B)
print("HMM node started... Call a service")
#print("--- Check rows sum up to 1 ---")
HMM_model = GMHMM(N,
M,
D,
A,
means,
covars,
w,
pi,
init_type='user',
verbose=True)
def get_training_set_srv(req):
print("Start training requested")
print("Run a bag...")
global train_hmm
train_hmm = True
return
def start_training_srv(req):
w = numpy.ones((n, m), dtype=numpy.double)
hmm_means = numpy.ones((n, m, d), dtype=numpy.double)
hmm_means[0][0] = model.means_[0]
hmm_means[1][0] = model.means_[1]
hmm_means[2][0] = model.means_[2]
hmm_covars = numpy.array(
[[numpy.matrix(numpy.eye(d, d)) for j in xrange(m)]
for i in xrange(n)])
hmm_covars[0][0] = model.covars_[0]
hmm_covars[1][0] = model.covars_[1]
hmm_covars[2][0] = model.covars_[2]
gmmhmm = GMHMM(n,
m,
d,
A,
hmm_means,
hmm_covars,
w,
pi,
init_type='user',
verbose=False)
# hidden_state = model.predict(obs)
hidden_state = gmmhmm.decode(obs)
mean_sequence = [None] * len(obs)
var_sequence = [None] * len(obs)
for i in range(len(obs)):
mean_sequence[i] = model.means_[hidden_state[i]]
var_sequence[i] = numpy.diag(model.covars_[hidden_state[i]])
means.append(mean_sequence)
