import testWord2vec2 as tw

import tensorflow_util as tu

import numpy as np

model = tw.load_model()

namelist = tw.loadNameList()

import jieba

namelist1 = []

for name in namelist:

    seg_list = jieba.cut(name)

    temp_name = " ".join(seg_list)

    namelist1.append(temp_name)

from sklearn import feature_extraction

from sklearn.feature_extraction.text import TfidfTransformer

from sklearn.feature_extraction.text import CountVectorizer

vectorizer=CountVectorizer()

transformer=TfidfTransformer()

tfidf=transformer.fit_transform(vectorizer.fit_transform(namelist1))

word=vectorizer.get_feature_names()

weight=tfidf.toarray()

keyword = [];

for i in range(len(word)):

    wei = weight[i,:]

    re = np.where(wei == np.max(wei))

    print(word[re[0][0]],":",wei[re[0][0]])