def csd_selfterm(self):
if not self.csd_st_:
self.csd_st_ = 0
for w1, g1 in zip(self.weight, self.gaussian):
for w2, g2, in zip(self.weight, self.gaussian):
g = Gaussian(g2.mean, g2.variance + g1.variance)
self.csd_st_ += w1 * w2 * g.likelihood(g1.mean)
return self.csd_st_
def load_gmm_hmm(ctxfile, modelfile):
model = HMMSet()
with open(ctxfile, 'r') as fin:
for line in fin:
# Prepare as if new HMM.
data = [int(x) for x in line.strip().split()]
hmm = HMM()
hmm.context = data[2:]
hmm.center = int(math.floor(len(hmm.context) / 2.0))
# Check if this HMM already exists
if hmm.context_id() in model.cd_index:
idx = model.cd_index[hmm.context_id() ]
else:
model.hmm.append(copy.deepcopy(hmm))
idx = len(model.hmm) - 1
model.cd_index[hmm.context_id()] = idx
hmm = model.hmm[idx]
while len(hmm.modelid) <= data[1]: # Make sure we put the models in
hmm.modelid.append(None) # the right place if presented
hmm.modelid[data[1]] = data[0] # out of order.
for h in model.hmm:
h.fill_transitions()
model.build_ci_index()
model.build_cd_index()
with open(modelfile, 'r') as fin:
triple = []
trans = []
trans_count = {}
line = fin.readline() # <TransitionModel>
while '</Topology>' not in line:
while '<ForPhones' not in line:
line = fin.readline()
phones = [int(x) for x in fin.readline().strip().split()]
fin.readline() # </ForPhones>
line = fin.readline() # First state
state = 0
while '<State>' in line:
count = line.count('<Transition>')
for p in phones:
trans_count[ str(p) + ' ' + str(state) ] = count
state = state + 1
line = fin.readline()
line = fin.readline() # <Topology> or </TopologyEntry>
while '<Triples>' not in line:
line = fin.readline().strip()
total = int(line.split()[1])
for i in xrange(total):
triple.append([int(x) for x in fin.readline().strip().split() ])
fin.readline() # </Triples>
fin.readline() # <LogProbs>
line = fin.readline().strip()
line = line.replace('[', '').replace(']', '')
trans = [math.exp(float(x)) for x in line.strip().split()]
idx = 1 # The transitions start counting at 1.
for t in triple:
model.transition.append( trans[idx] )
# Add pointer to associated hmms (use reverse index)
for hidx in model.ci_index.get(t[0], []):
if t[1] < len(model.hmm[hidx].modelid) and model.hmm[hidx].modelid[t[1]] == t[2]:
model.hmm[hidx].transitionid[t[1]] = len(model.transition) - 1
idx += trans_count[ str(t[0]) + ' ' + str(t[1]) ]
# Now we read in the actual models (MOG).
fin.readline()
fin.readline() # </TransitionModel>
line = fin.readline() # <DIMENSION> XX <NUMPDFS> XXXX <DiagGMM>
data = line.split()
dimension = int(data[1])
numpdfs = int(data[3])
mog = MOG()
in_mean = False
in_var = False
mean = []
var = []
for line in fin:
if "</DiagGMM>" in line:
for g,m,v in zip(mog.gaussian, mean, var):
for i,x in enumerate(m):
g.variance[i] = 1 / v[i]
g.mean[i] = x * g.variance[i]
model.model.append(copy.deepcopy(mog)) # Change mean/var ?
mog.clear()
elif "<WEIGHTS>" in line:
weights = [float(x) for x in line.strip().split()[2:-1]]
for w in weights:
mog.add_gaussian(Gaussian.with_dimension(dimension), w)
elif "<MEANS_INVVARS>" in line:
in_mean = True
mean = []
elif "<INV_VARS>" in line:
in_var = True
var = []
elif in_mean:
if ']' in line:
in_mean = False
mean.append([float(x) for x in line.strip().split()[:-1]])
else:
mean.append([float(x) for x in line.strip().split()])
elif in_var:
if ']' in line:
in_var = False
var.append([float(x) for x in line.strip().split()[:-1]])
else:
var.append([float(x) for x in line.strip().split()])
model.verify()
return model
