gensim是一個(gè)方便的nlp工具，特別是用來導(dǎo)入詞向量，這里簡單記錄一下gensim導(dǎo)入詞向量的方法

import gensim

w2v = gensim.models.KeyedVectors.load_word2vec_format("small_embedding.txt")
print(w2v['a'])

gensim導(dǎo)入詞向量需要詞向量文件的首行是：所有的單詞數(shù) 詞向量的維度
如果不是這個(gè)格式，需要做額外的操作

利用下面的代碼，可以跨平臺的自動添加第一行，生成符合要求的詞向量文件，記錄下來方便使用

def get_line_nums(filename):
    f = open(filename, 'r')
    count = 0
    for line in f:
        count += 1
    return count


# Linux或者Windows下打開詞向量文件，在開始增加一行
def prepend_line(infile, outfile, line):
    with open(infile, 'r') as old:
        with open(outfile, 'w') as new:
            new.write(str(line) + "\n")
            shutil.copyfileobj(old, new)


def prepend_slow(infile, outfile, line):
    with open(infile, 'r') as fin:
        with open(outfile, 'w') as fout:
            fout.write(line + "\n")
            for line in fin:
                fout.write(line)
    

def add_first_line(filename):
    num_lines = get_line_nums(filename)
    gensim_file = 'glove_model.txt'
    gensim_first_line = "{} {}".format(num_lines, 300)
    # Prepends the line.
    if platform == "linux" or platform == "linux2":
        prepend_line(filename, gensim_file, gensim_first_line)
    else:
        prepend_slow(filename, gensim_file, gensim_first_line)