本文整理汇总了Python中hmm.HMM.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Python HMM.evaluate方法的具体用法？Python HMM.evaluate怎么用？Python HMM.evaluate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类hmm.HMM的用法示例。
在下文中一共展示了HMM.evaluate方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: HMMClassifier
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import evaluate [as 别名]
class HMMClassifier(object):
def __init__(self, **kwarg): # lname, url, other prior knowledge
super(HMMClassifier, self).__init__()
self.HMMauthor = HMM('author', 2)
self.HMMvenue = HMM('venue', 2) # Not important
self.HMMentire = HMM('entire', 6) # Set empirically
self.observations_raw = []
self.observation_sequences = []
self.labels = []
def predict(self, segment):
author_likelihood = self.HMMauthor.evaluate(segment)
venue_likelihood = self.HMMvenue.evaluate(segment)
print segment
print 'author likelihood:\t' , author_likelihood
print 'venue likelihood:\t' , venue_likelihood
def decode(self, segment):
# print segment
observation_sequence, decoded_sequence = self.HMMentire.decode(segment)
self.observations_raw.append(segment)
self.observation_sequences.append(observation_sequence)
self.labels.append(decoded_sequence)
# segment the labeling into parts
author_field = []
title_field = []
venue_field = []
year_field = []
raw_tokens = Tokens(segment).tokens
for i in range(len(decoded_sequence)):
token_i = raw_tokens[i]
label_i = decoded_sequence[i]
if label_i in [0,1]:
author_field.append(token_i)
if label_i == 2:
continue
if label_i == 3:
title_field.append(token_i)
if label_i == 4:
venue_field.append(token_i)
if label_i == 5:
year_field.append(token_i)
return ' '.join(author_field), ' '.join(title_field), ' '.join(venue_field), list(set(year_field))
# Additional step: to calculate the overall sum of P(X1|FN,LN,DL...) + P(X2|TI,TI,TI...) + P(X3|VN,VN,VN...) + P(X4|DT)
# 1. Find boundaries:
# boundaries = [[], [], []]
# label_ranges = [[0,1,2], [3], [2,4,5]]
# for i in range(len(label_ranges)):
# label_range = label_ranges[i]
# for j in range(len(decoded_sequence)):
# if decoded_sequence[j] in label_range:
# boundaries[i].append()
def decode_without_constraints(self, segment):
print segment
observation_sequence, decoded_sequence = self.HMMentire.decode_without_constraints(segment)
self.observations_raw.append(segment)
self.observation_sequences.append(observation_sequence)
self.labels.append(decoded_sequence)
for vector, decoding, token in zip(observation_sequence, decoded_sequence, Tokens(segment).tokens):
if decoding == 0:
label = 'FN'
elif decoding == 1:
label = 'LN'
elif decoding == 2:
label = 'DL'
elif decoding == 3:
label = 'TI'
elif decoding == 4:
label = 'VN'
elif decoding == 5:
label = 'YR'
else:
label = str(decoding) + ', PROBLEM'
print vector, '\t', label, '\t', token
print '\n\n'
def cross_correct(self):
absolute_correct = []
absolute_wrong = []
# 1. Confirm what's the big structure of the publication inside this specific domain
counter = {}
for l in self.labels:
first_label = str(l[0])
if counter.has_key(first_label):
counter[first_label] += 1
else:
counter[first_label] = 1
sorted_counter = sorted(counter.iteritems(), key=operator.itemgetter(1), reverse=True)
print 'First labels distribution: ', sorted_counter
#.........这里部分代码省略.........
示例2: HMM
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import evaluate [as 别名]
model = HMM()
model.init_random(3,4)
print model.trans
for line in model.trans:
print sum(line)
print model.emit
for line in model.emit:
print sum(line)
def dataFormatter(filename):
with open(filename,'r') as f:
tmp = [ list(s) for s in f.read().split()]
sample = []
for seq in tmp:
seq = map(lambda x:ord(x)-ord('A'), seq)
sample.append(seq)
return sample
#sample = sample[0]
sample = dataFormatter('hmm_test1.in')
print sample
model.train(sample)
forEvaluation = dataFormatter('hmm_evaluation.in')
for seq in forEvaluation:
print model.evaluate(seq)