def train(sentences):
print("Training the model...")
obs = defaultdict(lambda: defaultdict(lambda: 1))
prevObs = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: 1)))
tr = defaultdict(lambda: defaultdict(lambda: 0))
uniObs = defaultdict(lambda: defaultdict(lambda: 0))
prevBigram = "start"
for raw,indices in sentences:
j = 0
i = 0
cstate = 'B'
while i < len(raw) - 1:
bigram = raw[i:i+2]
if indices[j] == i+1:
obs['B'][bigram] += 1
j += 1
tr[cstate]['B'] += 1
cstate = 'B'
prevObs['B'][bigram][prevBigram] += 1
else:
obs['C'][bigram] += 1
tr[cstate]['C'] += 1
cstate = 'C'
prevObs['C'][bigram][prevBigram] += 1
i += 1
prevBigram = bigram
uniObs[cstate][uniBlock.block(bigram[0]) +'::'+ uniBlock.block(bigram[1])] += 1
return [obs,tr,prevObs,uniObs]
def nextProbas(model,cstate,bigram,prevBigram):
observations = model[0]
transitions = model[1]
prevObservations = model[2]
uniObs = model[3]
d = False
if not bigram in observations['B'] or not bigram in observations['C']:
bCoeff = 1
for bg in observations['B']:
if bigram[1] == bg[1]:
bCoeff += 1
cCoeff = 1
for bg in observations['C']:
if bigram[1] == bg[1]:
cCoeff += 1
bPb = transitions[cstate]['B'] * bCoeff
cPb = transitions[cstate]['C'] * cCoeff
if abs((float(min(bPb,cPb)) / max(bPb,cPb))) < 0.1:
d = True
if not d:
bPb = transitions[cstate]['B'] * observations['B'][bigram] * uniObs['B'][uniBlock.block(bigram[0]) +'::'+ uniBlock.block(bigram[1])]
cPb = transitions[cstate]['C'] * observations['C'][bigram] * uniObs['C'][uniBlock.block(bigram[0]) +'::'+ uniBlock.block(bigram[1])]
return dict({'B':bPb,'C':cPb})
def nextProbas(model,cstate,bigram,prevBigram):
observations = model[0]
transitions = model[1]
prevObservations = model[2]
uniObs = model[3]
bPb = transitions[cstate]['B'] * observations['B'][bigram] * uniObs['B'][uniBlock.block(bigram[0]) +'::'+ uniBlock.block(bigram[1])]
cPb = transitions[cstate]['C'] * observations['C'][bigram] * uniObs['C'][uniBlock.block(bigram[0]) +'::'+ uniBlock.block(bigram[1])]
return dict({'B':bPb,'C':cPb})
