词向量之加载word2vec和glove_cpu加载txt2vec

1 Google用word2vec预训练了300维的新闻语料的词向量googlenews-vecctors-negative300.bin，解压后3.39个G。

可以用gensim加载进来，但是需要内存足够大。

#加载Google训练的词向量
import gensim
model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',binary=True)
print(model['love'])

Glove300维的词向量有5.25个G。

# 用gensim打开glove词向量需要在向量的开头增加一行：所有的单词数 词向量的维度
import gensim
import os
import shutil
import hashlib
from sys import platform
#计算行数，就是单词数
def getFileLineNums(filename):
	f = open(filename, 'r')
	count = 0
	for line in f:
		count += 1
	return count
 
#Linux或者Windows下打开词向量文件，在开始增加一行
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 load(filename):
	num_lines = getFileLineNums(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)
	
	model = gensim.models.KeyedVectors.load_word2vec_format(gensim_file)
 
load('glove.840B.300d.txt')