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# """"ranker """

# """

# 判断两个句子的相似度

# :param sentence_a,sentence_b 两个文本

# return disorder_degree : ？,similarly :输入文本的相似度.

# """

# def calcSentenceSimilarity(self,sentence_a,sentence_b):

# query_a_WordList = jieba.cut(sentence_a)

# query_b_WordList = jieba.cut(sentence_b)

# disorder_degree,similarly = self.calcSimilarityScoreByWordListByIdf(query_a_WordList,query_b_WordList)

# return disorder_degree,similarly

#

#

# """TopicResponse"""

#

# """

# 获取最相似的query 基于文本相似度（计算: Engine.Ranker.Ranker）

# 输入:sentence 内容 text ，topic 数据， 相似度的阈值（默认值:1.0;满足阈值内的可以返回，无满足返回空）

# """

# def getSimilarlyQueryByRanker(self, sentence_text, topic, threshold=0.7):

# simi_query = None

# min_val = threshold

#

# for query in topic["query"]:

# sen_text = query["text"]

# disorder_degree, simi_val = self.ranker.calcSentenceSimilarity(sen_text, sentence_text)

# if simi_query > min_val:

# simi_query = query

# min_val = simi_val

# return simi_query

#

#

# """

# 获取同意，或不同意回复时，的兜底回复。

# 基于文本相似度

# 输入：sentence 内容 text ,topic 数据，相似度阈值（默认值：0.5）

# """

# def getAgreeQueryByRanker(self, sentence_text, topic, threshold=0.5):

# response = None

# for sen in topic["agree_query"]:

# disorder_degree, simi_val = self.ranker.calcSentenceSimilarity(sen, sentence_text)

# if simi_val > threshold:

# response = random.choice(topic["agree_response"])

# if response:

# return response

#

# for sen in topic["disagree_query"]:

# if sen == sentence_text:

# response = random.choice(topic["disagree_response"])

# return response