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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def calculateEntropy(activeColumns, type='binary'):
 """ calculate the mean entropy given activation history @param activeColumns (array) 2D numpy array of activation history @return entropy (float) mean entropy """

  activationProb   = np.mean(activeColumns, 0)
  if type == 'binary':
    totalEntropy     = np.sum(binaryEntropyVectorized(activationProb))
  elif type == 'renyi':
    totalEntropy = np.sum(renyiEntropyVectorized(activationProb))
  else:
    raise ValueError('unknown entropy type')

  numberOfColumns  = activeColumns.shape[1]
  # return mean entropy
  return totalEntropy/numberOfColumns