old_contents: # coding: utf-8
  
  import matplotlib.pyplot as plt
  from IPython import display
  
  import numpy as np
  import pandas as pd
  from scipy.sparse.linalg import svds
  
  import itertools
  import pickle
  import math
  import re
  import sys
  
  from bs4 import BeautifulSoup
  from nltk.corpus import stopwords
  from gensim.models import word2vec
  
  from word2vec_as_MF import Word2vecMF
  from functions import *
  
  
  model_enwik = Word2vecMF()
  model_enwik.load_matrices(from_file='enwik-200/matrices250aa.npz')
  
  MAX_ITER = 15
  words = ["him", "five", "main"]
  # for i in range(MAX_ITER):
  #     model_enwik.load_CW('enwik-200/PS40iter_fromSVD_dim200_step5e-05_factors', i)
  #     MF = model_enwik.MF(model_enwik.C, model_enwik.W)
  #     print MF
  model_enwik.load_CW('enwik-200/250aa', 7)
  x = datasets_corr(model_enwik, "datasets", "enwik-200/250aa",MAX_ITER,plot_corrs=False)
  for i in x:
      print i
      print x[i]
  
new_contents: # coding: utf-8
  
  import matplotlib.pyplot as plt
  from IPython import display
  
  import numpy as np
  import pandas as pd
  from scipy.sparse.linalg import svds
  
  import itertools
  import pickle
  import math
  import re
  import sys
  
  from bs4 import BeautifulSoup
  from nltk.corpus import stopwords
  from gensim.models import word2vec
  
  from word2vec_as_MF import Word2vecMF
  from functions import *
  
  
  model_enwik = Word2vecMF()
  model_enwik.load_matrices(from_file='enwik-200/matrices250.npz')
  
  MAX_ITER = 8
  words = ["him", "five", "main"]
  for i in range(MAX_ITER):
      model_enwik.load_CW('enwik-200/250ac', i)
      MF = model_enwik.MF(model_enwik.C, model_enwik.W)
      print MF
  model_enwik.load_CW('enwik-200/250ac', 7)
  x = datasets_corr(model_enwik, "datasets", "enwik-200/250ac",MAX_ITER,plot_corrs=False)
  for i in x:
      print i
      print x[i]
  
subject: test 250ac