bwang482

from __future__ import division
import gensim
import itertools
import numpy as np
from collections import Counter
from sklearn.decomposition import PCA


def gensim_load_vec(path):
    w2v_model = gensim.models.Word2Vec.load_word2vec_format(path, binary=False)

bwang482

#!/usr/bin/python

import re, math, collections
from collections import Counter
 
def tokenize(_str):
	stopwords = ['and', 'for', 'if', 'the', 'then', 'be', 'is', 'are', 'will', 'in', 'it', 'to', 'that']
	tokens = collections.defaultdict(lambda: 0.)
	for m in re.finditer(r"(\w+)", _str, re.UNICODE):
		m = m.group(1).lower()