old_contents: # -*- coding: utf-8 -*-
  
  # The MIT License (MIT)
  
  # Copyright (c) 2016 MIT Probabilistic Computing Project
  
  # Permission is hereby granted, free of charge, to any person obtaining a
  # copy of this software and associated documentation files (the
  # "Software"), to deal in the Software without restriction, including
  # without limitation the rights to use, copy, modify, merge, publish,
  # distribute, sublicense, and/or sell copies of the Software, and to permit
  # persons to whom the Software is furnished to do so, subject to the
  # following conditions:
  
  # The above copyright notice and this permission notice shall be included
  # in all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
  # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
  # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
  # NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
  # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
  # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
  # USE OR OTHER DEALINGS IN THE SOFTWARE.
  
  import unittest
  
  import numpy as np
  
  import gpmcc.engine
  
  DATA_NUM_0 = 100
  DATA_NUM_1 = 200
  NUM_SIM = 10000
  NUM_ITER = 5
  
  class TestBinomial(unittest.TestCase):
  
      def test_binomial(self):
          # Create categorical data of DATA_NUM_0 zeros and DATA_NUM_1 ones.
          data = np.transpose(np.array([[0] * DATA_NUM_0 + [1] * DATA_NUM_1]))
          # Run a single chain for a few iterations.
          engine = gpmcc.engine.Engine(
              data, ['categorical'], distargs=[{'k': 2}], initialize=True)
          engine.transition(NUM_ITER)
          # Simulate from hypothetical row and compute the proportion of ones.
          xx = engine.simulate(-1, [0], N=NUM_SIM)[0]
          sum_b = np.sum(xx[:,0])
          observed_prob_of_1 = (float(sum_b) / float(NUM_SIM))
          true_prob_of_1 = float(DATA_NUM_1) / float(DATA_NUM_0 + DATA_NUM_1)
          # Check that the observed proportion of ones is about 2/3, out to two
          # places. If this were a plain binomial model, we'd expect the sample
          # proportion to have standard deviation sqrt(2/3*1/3/10000) = 0.005, so
          # check out to two places.
          self.assertAlmostEqual(true_prob_of_1, observed_prob_of_1, places=1)
  
  if __name__ == '__main__':
      unittest.main()
  
new_contents: # -*- coding: utf-8 -*-
  
  # The MIT License (MIT)
  
  # Copyright (c) 2016 MIT Probabilistic Computing Project
  
  # Permission is hereby granted, free of charge, to any person obtaining a
  # copy of this software and associated documentation files (the
  # "Software"), to deal in the Software without restriction, including
  # without limitation the rights to use, copy, modify, merge, publish,
  # distribute, sublicense, and/or sell copies of the Software, and to permit
  # persons to whom the Software is furnished to do so, subject to the
  # following conditions:
  
  # The above copyright notice and this permission notice shall be included
  # in all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
  # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
  # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
  # NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
  # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
  # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
  # USE OR OTHER DEALINGS IN THE SOFTWARE.
  
  import unittest
  
  import numpy as np
  
  import gpmcc.engine
  
  DATA_NUM_0 = 100
  DATA_NUM_1 = 200
  NUM_SIM = 10000
  NUM_ITER = 5
  
  class TestBinomial(unittest.TestCase):
  
      def test_binomial(self):
          # Create categorical data of DATA_NUM_0 zeros and DATA_NUM_1 ones.
          data = np.transpose(np.array([[0] * DATA_NUM_0 + [1] * DATA_NUM_1]))
          # Run a single chain for a few iterations.
          engine = gpmcc.engine.Engine(
              data, ['categorical'], distargs=[{'k': 2}], seeds=[12],
              initialize=True)
          engine.transition(NUM_ITER)
          # Simulate from hypothetical row and compute the proportion of ones.
          xx = engine.simulate(-1, [0], N=NUM_SIM)[0]
          sum_b = np.sum(xx[:,0])
          observed_prob_of_1 = (float(sum_b) / float(NUM_SIM))
          true_prob_of_1 = float(DATA_NUM_1) / float(DATA_NUM_0 + DATA_NUM_1)
          # Check that the observed proportion of ones is about 2/3, out to two
          # places. If this were a plain binomial model, we'd expect the sample
          # proportion to have standard deviation sqrt(2/3*1/3/10000) = 0.005, so
          # check out to two places.
          self.assertAlmostEqual(true_prob_of_1, observed_prob_of_1, places=1)
  
  if __name__ == '__main__':
      unittest.main()
  
subject: Choose a seed with good initialization.