Google词向量word2vec的使用

 """
     1.在自然语言处理中常常使用预训练的word2vec，这个预训练的词向量可以使用google的GoogleNews-vectors-negative300.bin
     2.GoogleNews-vectors-negative300.bin是训练好的300维的新闻语料词向量
     3.本函数的作用就是把一个词转换成词向量，以供我们后期使用。没有在该word2vec中的词采用其他的方式构建，如采用均匀分布或者高斯分布等随机初始化
 """
 import numpy as np # loads 300x1 word vectors from file.
 def load_bin_vec(fname, vocab):
     word_vecs = {}
     with open(fname, "rb") as f:
         header = f.readline()
         vocab_size, layer1_size = map(int, header.split()) # 3000000 300
         binary_len = np.dtype('float32').itemsize * layer1_size #
         for line in range(vocab_size):
             word = []
             while True:
                 ch = f.read(1)
                 if ch == ' ':
                     word = ''.join(word)
                     break
                 if ch != '\n':
                     word.append(ch)
             if word in vocab:
                 word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')
             else:
                 f.read(binary_len)
     return word_vecs # add random vectors of unknown words which are not in pre-trained vector file.
 # if pre-trained vectors are not used, then initialize all words in vocab with random value.
 def add_unknown_words(word_vecs, vocab, min_df=1, k=300):
     for word in vocab:
         if word not in word_vecs and vocab[word] >= min_df:
             word_vecs[word] = np.random.uniform(-0.25, 0.25, k) vectors_file = './GoogleNews-vectors-negative300.bin'
 vocab = ['I', 'can', 'do'] vectors = load_bin_vec(vectors_file, vocab)  # pre-trained vectors
 add_unknown_words(vectors, vocab)
 print(vectors['I'])
 print('*'*40)
 print(vectors['can'])
 print('*'*40)
 print(vectors['do'])