def __init__(self): self.pi = [] self.transitions = dd(list) self.emissions = [] self.states = [] self.nstates = 0 self.guesser = Guesser(10) self.vocab = set()
class Learner:
def __init__(self):
self.pi = []
self.transitions = dd(list)
self.emissions = []
self.states = []
self.nstates = 0
self.guesser = Guesser(10)
self.vocab = set()
def train(self, sentences):
for sentence in sentences:
self.guesser.loadWords(sentence)
self.vocab.add(sentence[0][0])
prevTag = sentence[0][1]
word = sentence[0][0]
firstState = -1
try:
firstState = self.states.index(prevTag)
except:
firstState = len(self.states)
self.pi.append(1)
for state in range(len(self.states)):
self.transitions[state].append(0)
self.states.append(prevTag)
self.nstates += 1
self.transitions[firstState] = [0] * len(self.states)
self.emissions.append({})
self.pi[firstState] += 1
self.emissions[firstState][word] = self.emissions[firstState].get(word, 0) + 1
for word, tag in sentence[1:]:
self.vocab.add(word)
state = -1
try:
state = self.states.index(tag)
except:
self.pi.append(0)
state = len(self.states)
for s in range(len(self.states)):
self.transitions[s].append(0)
self.states.append(tag)
self.nstates += 1
self.transitions[state] = [0] * len(self.states)
self.emissions.append({})
self.emissions[state][word] = self.emissions[state].get(word, 0) + 1
self.transitions[self.states.index(prevTag)][state] += 1
prevTag = tag
self.guesser.setTags(self.states)
self.guesser.computeTheta()
self.normalize()
def normalize(self):
totalStart = sum(self.pi)
assert totalStart != 0
self.pi = map(lambda x:x * 1.0 / totalStart, self.pi)
for state in range(len(self.states)):
totalEmisOut = sum(self.emissions[state].itervalues())
assert totalEmisOut != 0
self.emissions[state] = dict(map(lambda x:(x[0], x[1] * 1.0 / totalEmisOut), self.emissions[state].items()))
totalTransOut = sum(self.transitions[state])
assert totalTransOut != 0
self.transitions[state] = map(lambda x:x * 1.0 / totalTransOut, self.transitions[state])
def laplaceSmoothTransitions(self):
alpha = 1.0/len(self.states)
for state in range(len(self.states)):
for trState in range(len(self.states)):
if self.transitions[state][trState] == 0:
self.transitions[state][trState] = alpha
totalTransOut = sum(self.transitions[state])
self.transitions[state] = map(lambda x:x * 1.0 / totalTransOut, self.transitions[state])
