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anly_words_mapping.py
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anly_words_mapping.py
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# anly_words_mapping.py
# -*- coding: utf8 -*-
import sys
import jieba
import json
# import jieba.posseg as pseg
import jieba.analyse
from openpyxl import load_workbook
from openpyxl import Workbook
reload(sys)
sys.setdefaultencoding('utf8')
reload(sys)
# 创建词向量
def createVocabLst(dataSet):
vocabSet = set([])
for document in dataSet:
vocabSet = vocabSet | set(document)
return list(vocabSet)
# 计算夹角余余弦
def cosVector(x,y):
if(len(x)!=len(y)):
print('error input,x and y is not in the same space')
return;
result1=0.0;
result2=0.0;
result3=0.0;
for i in range(len(x)):
result1+=x[i]*y[i] #sum(X*Y)
result2+=x[i]**2 #sum(X*X)
result3+=y[i]**2 #sum(Y*Y)
# print("result is "+str(result1/((result2*result3)**0.5))) #结果显示
cosVector = result1/((result2*result3)**0.5)
return cosVector
# 词向量转化为数值向量
def setOfWords2Vec(vocabList,inputSet):
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)] = 1
else :print "the word: %s is not in my Vocabulary!" % word
return returnVec
# 中文打印
def printChinese(List):
str = json.dumps(List,ensure_ascii=False)
return str
def test_tfidf():
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
if __name__ == "__main__":
corpus=["我 来到 北京 清华大学",#第一类文本切词后的结果,词之间以空格隔开
"他 来到 了 网易 杭研 大厦",#第二类文本的切词结果
"小明 硕士 毕业 与 中国 科学院",#第三类文本的切词结果
"我 爱 北京 天安门"]#第四类文本的切词结果
vectorizer=CountVectorizer()#该类会将文本中的词语转换为词频矩阵,矩阵元素a[i][j] 表示j词在i类文本下的词频
transformer=TfidfTransformer()#该类会统计每个词语的tf-idf权值
tfidf=transformer.fit_transform(vectorizer.fit_transform(corpus))#第一个fit_transform是计算tf-idf,第二个fit_transform是将文本转为词频矩阵
word=vectorizer.get_feature_names()#获取词袋模型中的所有词语
weight=tfidf.toarray()#将tf-idf矩阵抽取出来,元素a[i][j]表示j词在i类文本中的tf-idf权重
for i in range(len(weight)):#打印每类文本的tf-idf词语权重,第一个for遍历所有文本,第二个for便利某一类文本下的词语权重
print u"-------这里输出第",i,u"类文本的词语tf-idf权重------"
for j in range(len(word)):
print word[j],weight[i][j]
def main():
lib = load_workbook('D:\project\project-carNew\csv\dealer_name_mapping.xlsx')
# 创建新的excel文件
out_file = 'D:\project\project-carNew\csv\dealer_name_mapping_result.xlsx'
wt_wb = Workbook(write_only=True)
wt_ws = wt_wb.create_sheet()
wordsList = []
std_wordsList = []
all_wordsList = []
for row in lib['Sheet1'].iter_rows():
fenci_list = list(jieba.cut(row[0].value, cut_all=False))
wordsList.append(fenci_list)
all_wordsList.append(fenci_list)
for row in lib['Sheet2'].iter_rows():
fenci_list = list(jieba.cut(row[0].value, cut_all=False))
std_wordsList.append(fenci_list)
all_wordsList.append(fenci_list)
print printChinese(all_wordsList)
myVocabLst = createVocabLst(all_wordsList)
row_nm=0
for words in wordsList:
words_vec = setOfWords2Vec(myVocabLst, words)
# print words_vec
n=0
right_word= []
for std_words in std_wordsList:
std_word_vec = setOfWords2Vec(myVocabLst, std_words)
cos_nm = cosVector(words_vec,std_word_vec)
# print cos_nm
if cos_nm>n:
print cos_nm
n = cos_nm
right_word = std_words
left_word = ''.join(words)
ok_word = ''.join(right_word)
writeRowList = [left_word,ok_word,n]
print printChinese(writeRowList)
if row_nm == 0:
wt_ws.append(['left_word','ok_word','cos_nm'])
else:
wt_ws.append(writeRowList)
row_nm+=1
# if row_nm == 10:break
wt_wb.save(out_file)
if __name__=='__main__':
main()
# test_tfidf()