def cycle(self, max_iters, min_change):
self.trans_list = [(self.A[0][1], self.A[1][0])]
for i in range(max_iters):
sum_of_probs = 0.0
for word in self.words:
alpha, unused_beta = self.forward_backward(word)
sum_of_probs += sum(alpha[len(alpha) - 1])
if self.VERBOSE_FLAG:
output.sum_of_probs(sum_of_probs, i, self.out)
diff = sum_of_probs - self.sum_of_probs
if diff < min_change:
break
self.sum_of_probs = sum_of_probs
self.sum_of_probs_list.append(self.sum_of_probs)
for word in self.words:
self.counts[word] = self.soft_count(word)
new_B_values, normalizers = self.maximize_emission()
new_A_values = self.maximize_transition(normalizers)
new_Pi_values = self.maximize_Pi()
self.B = new_B_values
self.A = new_A_values
self.Pi = new_Pi_values
self.trans_list.append((self.A[0][1], self.A[1][0]))
if self.VERBOSE_FLAG:
output.letter_prob(self.B, self.states, self.out)
output.log_letter_prob(self.B, self.states, self.out)
output.A_output(self.A, self.states, self.out)
output.show_Pi(self.Pi, self.out)
output.sum_of_probs(sum_of_probs, i, self.out)
def maximize_emission(self):
new_B = [{} for i in self.states]
# new_B[state][letter] gives TOTAL soft count
# for given letter FROM given state
for word in self.words:
count = self.counts[word]
for t in range(len(word)):
for from_state in self.states:
if word[t] in new_B[from_state]:
to_sum = [count[t][from_state][to_state] for to_state in self.states]
new_B[from_state][word[t]] += sum(to_sum)
else:
to_sum = [count[t][from_state][to_state] for to_state in self.states]
new_B[from_state][word[t]] = sum(to_sum)
if self.VERBOSE_FLAG:
output.init_emissions(self.out)
normalizers = []
for from_state in self.states:
normalizer = sum(new_B[from_state].values())
normalizers.append(normalizer)
for letter in new_B[from_state].keys():
new_B[from_state][letter] = new_B[from_state][letter] / normalizer
if self.VERBOSE_FLAG:
output.letter_prob(new_B, self.states, self.out)
return new_B, normalizers
