def get_embedding(source):
# weight = alpah/(alpah + p)
# alpha is a parameter, 1e-3 ~ 1e-5
alpha = 1e-4
if os.path.exists(words_frequence_path):
with open(words_frequence_path, 'rb') as f:
frequence = pickle.load(f)
else:
from word2vec.save_words_frequence import save_words_frequence
save_words_frequence(words_frequence_path)
with open(words_frequence_path, 'rb') as f:
frequence = pickle.load(f)
max_fre = max(frequence.values())
words = cut(''.join(token(source)))
word2vec = get_word2vec(word2vec_path)
embedding = np.zeros_like(word2vec.wv['测试'])
words = [w for w in words if w in word2vec]
for w in words:
weight = alpha / (alpha + frequence.get(w, max_fre))
embedding += weight * word2vec[w]
embedding /= len(words)
return embedding
def name(self):
if self.type == 0:
if not self._args:
raise Exception('`table` lack of args')
return self._args[0]
if self._name:
return self._name
self._name = token()
return self._name
def set_tokens(self, line):
text = ''
counter = 0
while counter <= len(line) - 1:
w = line[counter]
if w in [' ', '\t']:
text = ''
counter += 1
continue
text += w
# print(w, ' ', text)
if text in grammer.keyword:
tok = util.token(text, 'keyword')
self.tokens.append(tok)
text = ''
if text == ' ':
text = ''
if text in grammer.assignment:
tok = util.token(text, 'assign')
self.tokens.append(tok)
text = ''
if text in ["\'", '\"']:
callback = self.string_action(line, counter)
counter = callback['counter']
text = callback['string']
tok = util.Token(text, 'string')
self.tokens.append(tok)
text = ''
continue
counter += 1
def __init__(self):
super().__init__("postgres")
self.config["ADMIN_PANEL_PASSWORD"] = token()
self.config["ADMIN_DB_PASSWORD"] = token()
self.config["GITEA_DB_PASSWORD"] = token()
self.config["COURSE_DB_PASSWORD"] = token()
self.config["AUTH_DB_PASSWORD"] = token()
self.config["CHAT_DB_PASSWORD"] = token()
def get_sentence_cos(original_text, title):
stop_words, dictionary, lda = get_model()
sentences = split_sentence(original_text)
if sentences == []:
raise NameError
sentences_cos = {}
if title:
original_text += title
content_ndarray = get_ndarray(cut(''.join(token(original_text))),
stop_words, dictionary, lda)
for i, sentence in enumerate(sentences):
sentences_cos[i] = get_cos_with_content(sentence, content_ndarray,
stop_words, dictionary, lda)
return sentences, sentences_cos
def save_words_frequence(corpus_path):
news_content = pd.read_csv(news_path, encoding='gb18030')
news_content.dropna(subset=['content'], inplace=True)
news_content.drop_duplicates(subset=['content'], inplace=True)
news_content_cut = [token(n) for n in news_content['content']]
news_content_cut = [''.join(n) for n in news_content_cut]
news_content_cut = [cut(n) for n in news_content_cut]
words = []
for document in news_content_cut:
words += [w for w in document.split()]
if os.path.exists(corpus_path):
print('文件已存在, 请勿重复写入')
else:
with open(corpus_path, 'w', encoding='utf-8') as f:
for sent_cut in news_content_cut:
f.write(sent_cut)
f.write('\n')
def save_words_frequence(words_frequence_path):
news_content = pd.read_csv(news_path, encoding='gb18030')
news_content.dropna(subset=['content'], inplace=True)
news_content.drop_duplicates(subset=['content'], inplace=True)
news_content_cut = [token(n) for n in news_content['content']]
news_content_cut = [''.join(n) for n in news_content_cut]
news_content_cut = [cut(n) for n in news_content_cut]
words = []
for document in news_content_cut:
words += [w for w in document.split()]
words_counter = Counter(words)
frequence = {w:count/len(words) for w, count in words_counter.items()}
if os.path.exists(words_frequence_path):
print('文件已存在, 请勿重复写入')
else:
with open(words_frequence_path, 'wb') as f:
pickle.dump(frequence, f)
currword = ""
ch = getchar()
if not ch:
break
currword += ch
if ch in [str(x) for x in range(10)]:
while True:
ch = getchar()
if not ch:
break
if ch not in [str(x) for x in range(10)]:
pushchar(ch)
break
currword += ch
tokens.append(util.token("NUMBER", currword, linenum))
elif currword in OPERATORS:
while True:
ch = getchar()
if not ch:
break
if ch not in "".join(OPERATORS):
pushchar(ch)
break
currword += ch
tokens.append(util.token("OPERATOR", currword, linenum))
elif currword == '"':
ending = currword
while True:
def table(self):
if isinstance(self.query['table'], basestring):
return Table(self.query['table'])
if not self.query['table'] and isinstance(self._bindobj, Query):
return Table(self._bindobj._alias or token())
return clause(self.query['table'])
def run(self): # pylint: disable=arguments-differ
print(f"Starting up. Using YoutubeDL [{ydl_version}]")
super().run(token())
def get_cos_with_content(sentence, content_ndarray, stop_words, dictionary,
lda):
sentence_ndarray = get_ndarray(cut(''.join(token(sentence))), stop_words,
dictionary, lda)
return cosine(sentence_ndarray, content_ndarray)
def __init__(self):
super().__init__("auth")
self.config["AUTH_FLASK_SECRET_KEY"] = token()
def get_sentence_cos(original_text, title):
word2vec = get_word2vec(word2vec_path)
stop_words = get_stop_words(stopwords_path)
sentences = split_sentence(original_text)
if sentences == []:
raise NameError
sentences_cut = [cut(''.join(token(n))) for n in sentences]
sentences_cut_del_stopwords = []
is_title = False
# 处理标题
if title:
title_cut = [cut(''.join(token(title)))]
words = title_cut[0].split()
title_cut_del_stopwords = list(set(words) - set(stop_words))
if title_cut_del_stopwords != []:
is_title = True
for s in sentences_cut:
words = s.split()
sentence_cut_del_stopwords = list(set(words) - set(stop_words))
if sentence_cut_del_stopwords != []:
sentences_cut_del_stopwords.append(sentence_cut_del_stopwords)
if sentence_cut_del_stopwords == []:
raise NameError
# 得到文章向量，句子向量
sentences_vec = []
additional_wordvec = {}
text_vec = np.zeros_like(word2vec.wv['测试'])
for i, sentence in enumerate(sentences_cut_del_stopwords):
sentence_vec = np.zeros_like(word2vec.wv['测试'])
for word in sentence:
if word in word2vec.wv.vocab:
sentence_vec += word2vec.wv[word]
elif word in additional_wordvec:
sentence_vec += additional_wordvec[word]
else:
additional_wordvec[word] = np.random.random(
word2vec.wv['测试'].shape)
sentence_vec = sentence_vec / len(sentence)
# 第一句话比较重要，说三遍
if i == 0:
text_vec += sentence_vec * 3
else:
text_vec += sentence_vec
sentences_vec.append(sentence_vec)
if is_title:
title_vec = np.zeros_like(word2vec.wv['测试'])
for word in title_cut_del_stopwords:
if word in word2vec.wv.vocab:
title_vec += word2vec.wv[word]
elif word in additional_wordvec:
title_vec += additional_wordvec[word]
else:
additional_wordvec[word] = np.random.random(
word2vec.wv['测试'].shape)
text_vec += text_vec * 3
# 多加了两遍第一句话,三遍title
text_vec /= len(sentences) + 5
# 求句子向量与文章向量的cosine
sentences_cos = {}
for i, sentence_vec in enumerate(sentences_vec):
sentences_cos[i] = cosine(sentence_vec, text_vec)
return sentences, sentences_cos
