def compute_pmi(self, words_dict):
#compute all kinds of combines for word
sub_part = gen_bigram(self.text)
if len(sub_part) > 0:
self.pmi = min(
map(
lambda (left, right): math.log(self.freq / words_dict[
left].freq / words_dict[right].freq), sub_part))
def compute_pmi(self, words_dict):
#compute all kinds of combines for word
sub_part = gen_bigram(self.text)
if len(sub_part) > 0:
self.pmi = min(
map(
lambda word: math.log(self.freq / words_dict[word[
0]].freq / words_dict[word[1]].freq), sub_part))
def compute_pmi(self, words_dict):
# 这里的words_dict是word_cad
# key:word,value:word_info
#compute all kinds of combines for word
sub_part = gen_bigram(self.text)
if len(sub_part) > 0:
# 使用一个具体的例子来概括就是:
# 计算min{p(电影院)/(p(电影)*p(院)),p(电影院)/(p(电)*p(影院))}
self.pmi = min(
map(
lambda word: math.log(self.freq / words_dict[word[
0]].freq / words_dict[word[1]].freq), sub_part))
def compute_pmi(self, words_dict):
# compute all kinds of combines for word
sub_part = gen_bigram(self.text)
# print(sub_part)
if len(sub_part) > 0:
# self.pmi = min(
# map(lambda x: math.log(self.freq / (words_dict[x[0]].freq *
# words_dict[x[1]].freq)),
# sub_part))
# print(self.freq, max(words_dict[sub_part[0][0]].freq - self.freq, 0.00001),
# max(words_dict[sub_part[0][1]].freq - self.freq, 0.00001))
# print(math.log(self.freq / (max(words_dict[sub_part[0][0]].freq - self.freq, 0.00001)) *
# (max(words_dict[sub_part[0][1]].freq - self.freq, 0.00001))))
self.pmi = min(
map(
lambda x: math.log(self.freq / (
(max(words_dict[x[0]].freq - self.freq, 0.00001)) *
(max(words_dict[x[1]].freq - self.freq, 0.00001)))),
sub_part))