code: def make_labels(X):
    
  
    return numpy.hstack([k*numpy.ones(len(X[k]), dtype=int) for k in range(len(X))])
docs: Helper function that generates a single 1D numpy.ndarray with labels which
    are good targets for stock logistic regression.
  
  
    Parameters:
  
      X (numpy.ndarray): The input data matrix. This must be a numpy.ndarray
        with 3 dimensions or an iterable containing 2 numpy.ndarrays with 2
        dimensions each. Each correspond to the data for one of the two classes,
        every row corresponds to one example of the data set, every column, one
        different feature.
  
  
    Returns:
  
      numpy.ndarray: With a single dimension, containing suitable labels for all
        rows and for all classes defined in X (depth).