text: from numpy import array
  from numpy import argmax
  from numpy.random import laplace
  import numpy as np
  from sklearn.preprocessing import LabelEncoder
  from sklearn.preprocessing import OneHotEncoder
  
  class Prototype:
    def __init__(self, epsilon=1):
      self.epsilon = epsilon
  
    def one_hot(self, data):
      """ Encode data with one-hot representation. """
      label_encoder = LabelEncoder()
      onehot_encoder = OneHotEncoder(sparse=False)
      integer_encoded = label_encoder.fit_transform(data).reshape(len(data), 1)
      onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
      return onehot_encoded
    
    def randomize(self, oh_data, private=True):
      """ Add Laplace noise. """
      for row in oh_data:
        for i in range(len(row)):
          b = laplace(scale=1.0/self.epsilon) if private else 0
          row[i] += b
      return oh_data
  
    def get_histogram(self, data, private=True):
      """ Return count of each unique element in data. """
      oh = self.one_hot(data)
      return np.sum(self.randomize(oh, private=private), axis=0)
  
  