def test_external_file_model_compatibility(self):
"""
Test StringIO streams for dynamic programming.
"""
# define the dishonest casino model
fair_state = HMM.HiddenDieState(1 / 6.0)
loaded_state = HMM.HiddenDieState(0.5)
M = np.array([[0.95, 0.05], [0.1, 0.9]])
T = TransitionMatrix.MatrixTransitionObject(M)
hidden_states = [fair_state, loaded_state]
# define a sequence of observations
observations = [1, 2, 6, 6, 1, 2, 3, 4, 5, 6]
# define the observation stream
o_converter = lineario.IntConverter()
o_stream = lineario.SequentialStringIO(o_converter)
o_stream.open_write()
for x in observations:
o_stream.write(x)
o_stream.close()
# create the reference hidden markov model object
hmm_old = HMM.TrainedModel(M, hidden_states)
# create the testing hidden markov model object
names = ('tmp_f.tmp', 'tmp_s.tmp', 'tmp_b.tmp')
hmm_new = ExternalModel(T, hidden_states, names)
# get posterior distributions
distributions_old = hmm_old.scaled_posterior_durbin(observations)
hmm_new.init_dp(o_stream)
distributions_new = list(hmm_new.posterior())
# assert that the distributions are the same
self.assertTrue(np.allclose(distributions_old, distributions_new))
def test_scaled_ntransitions_expected_compatibility(self):
fair_state = HMM.HiddenDieState(1 / 6.0)
loaded_state = HMM.HiddenDieState(0.5)
states = [fair_state, loaded_state]
prandom = 0.1
# define the old hmm
transition_matrix = TransitionMatrix.get_uniform_transition_matrix(
prandom, len(states))
old_hmm = HMM.TrainedModel(transition_matrix, states)
# define the new hmm
cache_size = 100
transition_object = TransitionMatrix.UniformTransitionObject(
prandom, len(states))
new_hmm = Model(transition_object, [fair_state, loaded_state],
cache_size)
# define a sequence of observations
observations = [1, 2, 6, 6, 1, 2, 3, 4, 5, 6]
# define the (degenerate) distances between observations
distances = [1] * (len(observations) - 1)
# use the old algorithm to get the expected number of transitions
e_initial, A = old_hmm.scaled_transition_expectations_durbin(
observations)
ntransitions_expected_old = np.sum(A) - np.sum(np.diag(A))
# use the new algorithm to get the expected number of transitions
dp_info = new_hmm.get_dp_info(observations, distances)
ntransitions_expected_new = new_hmm.scaled_ntransitions_expected(
dp_info)
# assert that the expected number of transitions are almost the same
self.assertAlmostEqual(ntransitions_expected_old,
ntransitions_expected_new)
def test_model_compatibility(self):
# define the dishonest casino model
fair_state = HMM.HiddenDieState(1 / 6.0)
loaded_state = HMM.HiddenDieState(0.5)
M = np.array([[0.95, 0.05], [0.1, 0.9]])
T = TransitionMatrix.MatrixTransitionObject(M)
hidden_states = [fair_state, loaded_state]
# define a sequence of observations
observations = [1, 2, 6, 6, 1, 2, 3, 4, 5, 6]
# create the reference hidden markov model object
hmm_old = HMM.TrainedModel(M, hidden_states)
# create the testing hidden markov model object
hmm_new = InternalModel(T, hidden_states)
# get posterior distributions
distributions_old = hmm_old.scaled_posterior_durbin(observations)
distributions_new = hmm_new.posterior(
hmm_new.get_dp_info(observations))
# assert that the distributions are the same
self.assertTrue(np.allclose(distributions_old, distributions_new))
