def __init__(self, filename=None, text=None, **bigram_parameters):
if filename:
with open(filename) as fp:
self.text = fp.read()
elif text:
self.text = text
else:
print "Error: need to pass in either filename or text"
pairs = zip(*map(lambda x: x.strip().split(' '), \
filter(bool, self.text.strip().split('\n'))))
self.tags = pairs[0]
self.observations = pairs[1]
self.observation_set = set(self.observations)
self.tag_buckets = {}
self.tag_counts = {}
for index, tag in enumerate(self.tags):
if tag in self.tag_buckets:
self.tag_counts[tag] += 1
if self.observations[index] in self.tag_buckets[tag]:
self.tag_buckets[tag][self.observations[index]] += 1
else:
self.tag_buckets[tag][self.observations[index]] = 1
else:
self.tag_counts[tag] = 1
self.tag_buckets[tag]= {self.observations[index]: 1}
self.bigram_model = Bigram(prepared_tokens=self.tags, **bigram_parameters)
self.likelihood_cache, self.prior_cache, self.in_vocab_cache = {}, {}, {}
class HiddenMarkovModel():
def __init__(self, filename=None, text=None, **bigram_parameters):
if filename:
with open(filename) as fp:
self.text = fp.read()
elif text:
self.text = text
else:
print "Error: need to pass in either filename or text"
pairs = zip(*map(lambda x: x.strip().split(' '), \
filter(bool, self.text.strip().split('\n'))))
self.tags = pairs[0]
self.observations = pairs[1]
self.observation_set = set(self.observations)
self.tag_buckets = {}
self.tag_counts = {}
for index, tag in enumerate(self.tags):
if tag in self.tag_buckets:
self.tag_counts[tag] += 1
if self.observations[index] in self.tag_buckets[tag]:
self.tag_buckets[tag][self.observations[index]] += 1
else:
self.tag_buckets[tag][self.observations[index]] = 1
else:
self.tag_counts[tag] = 1
self.tag_buckets[tag]= {self.observations[index]: 1}
self.bigram_model = Bigram(prepared_tokens=self.tags, **bigram_parameters)
self.likelihood_cache, self.prior_cache, self.in_vocab_cache = {}, {}, {}
def getPriorProbability(self, bigram):
first, second = bigram
if first not in self.prior_cache:
self.prior_cache[first] = { second: self.bigram_model.get_probability(bigram)}
elif second not in self.prior_cache[first]:
self.prior_cache[first][second] = self.bigram_model.get_probability(bigram)
return self.prior_cache[first][second]
def getLikelihoodProbability(self, word, tag):
try:
value = self.likelihood_cache[word][tag]
except KeyError:
if tag not in self.tag_buckets or word not in self.tag_buckets[tag]:
value = 0.
else:
value = float(self.tag_buckets[tag][word])/float(self.tag_counts[tag])
if word not in self.likelihood_cache:
self.likelihood_cache[word] = { tag: value}
elif tag not in self.likelihood_cache[word]:
self.likelihood_cache[word][tag] = value
return value
def isInVocabulary(self, word):
if word not in self.in_vocab_cache:
self.in_vocab_cache[word] = word in self.observation_set
return self.in_vocab_cache[word]
