new_contents: from abc import abstractmethod
  import numpy as np
  
  class RandomPredictor(object):
      """"
      Abstract base class that implement the interface that we use for our
      predictors. Classic supervised learning.
  
      @author: Heiko
      """
  
      @abstractmethod
      def fit(self, X, y):
          """
          Method to fit the model. In this case, nothing to do.
  
          Parameters:
          X - 2d numpy array of training data
          y - 1d numpy array of training labels
          """
          pass
  
      @abstractmethod
      def predict(self, X):
          """
          Method to apply the model data
  
          Parameters:
          X - 2d numpy array of test data
          """
          return (hash(str(X)) % 100) / 100.0
  
  if __name__ == '__main__':
      X = np.random.rand(100, 100)
  
      predictor = RandomPredictor()
      y1 = predictor.predict(X)
      y2 = predictor.predict(X)
  
      assert(y1 == y2)
      assert(y1 > 0.0 and y2 < 1.0)
subject: Add simple random prediction.