text: """
  page 132
  """
  from sklearn.naive_bayes import GaussianNB
  
  data_table = [
      [1, 1, 1, 0],
      [0, 0, 0, 1],
      [0, 1, 0, 0],
      [1, 0, 0, 0],
      [1, 1, 0, 1],
      [1, 0, 0, 1],
      [0, 1, 1, 1],
      [0, 0, 0, 0],
      [1, 0, 1, 0],
      [0, 1, 0, 1]
  ]
  
  X = [x[0:2] for x in data_table]
  print(X)
  y1 = [x[2] for x in data_table]
  print(y1)
  clf1 = GaussianNB().fit(X, y1)
  p1 = [[1, 0]]
  print(clf1.predict(p1))
  
  y2 = [x[3] for x in data_table]
  print(y2)
  clf2 = GaussianNB().fit(X, y2)
  p2 = [[1, 0]]
  print(clf1.predict(p2))
  