old_contents: import numpy as np
  from sklearn.naive_bayes import GaussianNB
  
  training_arr = np.loadtxt('task7629/train.txt', str, '#', ' ')
  testing_arr = np.loadtxt('task7629/check.txt', str, '#', ' ')
  
  arr_y = map(lambda ele: int(ele[11]), training_arr)
  arr_x = map(lambda ele: map(lambda x: float(x), ele[1:10]), training_arr)
  
  testing_arr = map(lambda ele: map(lambda x: float(x), ele[1:10]), testing_arr)
  
  print arr_x[0]
  print testing_arr[0]
  
  clf = GaussianNB()
  clf.fit(arr_x, arr_y)
  res_arr = clf.predict(testing_arr)
  
  print res_arr
  
  print reduce(lambda x, y: str(x) + str(y), res_arr)
  
new_contents: import numpy as np
  from sklearn.naive_bayes import GaussianNB
  
  training_arr = np.loadtxt('task7629/train.txt', str, '#', ' ')
  testing_arr = np.loadtxt('task7629/check.txt', str, '#', ' ')
  
  arr_y = map(lambda ele: int(ele[11]), training_arr)
  arr_x = map(lambda ele: map(lambda x: float(x), ele[1:10]), training_arr)
  
  testing_arr = map(lambda ele: map(lambda x: float(x), ele[1:10]), testing_arr)
  
  print arr_x[0]
  print testing_arr[0]
  
  clf = GaussianNB()
  clf.fit(arr_x, arr_y)
  res_arr = clf.predict(testing_arr)
  
  print res_arr
  
  print ''.join(res_arr)
  
subject: refactor a little bit