def test_slfit(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
learner = SLHMM(self._num_hidden, self._num_observ)
learner.fit(sequences, verbose=True)
for sequence in sequences:
pprint("True probability: %f" % hmm.predict(sequence))
pprint("Infered probability: %f" % learner.predict(sequence))
def test_slfit(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
learner = SLHMM(self._num_hidden, self._num_observ)
learner.fit(sequences, verbose=True)
for sequence in sequences:
pprint("True probability: %f" % hmm.predict(sequence))
pprint("Infered probability: %f" % learner.predict(sequence))
def test_predict(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
for sequence in sequences:
self.assertEqual(hmm.predict(sequence), hmm.predict(sequence),
"HMM.prediction Error")
sequences = [[0, 1], [1, 2, 3, 0], [0, 0, 0, 1]]
for sequence in sequences:
self.assertEqual(hmm.predict(sequence), hmm.predict(sequence),
"HMM.prediction Error")
def test_predict(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
for sequence in sequences:
self.assertEqual(hmm.predict(sequence), hmm.predict(sequence),
"HMM.prediction Error")
sequences = [[0,1], [1,2,3,0], [0,0,0,1]]
for sequence in sequences:
self.assertEqual(hmm.predict(sequence), hmm.predict(sequence),
"HMM.prediction Error")
def test_decode(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
for sequence in sequences:
decoded_sequence = hmm.decode(sequence)
self.assertEqual(len(sequence), len(decoded_sequence), "HMM.decode Error")
for i in xrange(len(sequence)):
self.assertEqual(sequence[i], sequence[i], "HMM.decode Error")
sequences = [[0, 1], [1, 2], [0, 1, 2, 0]]
for sequence in sequences:
decoded_sequence = hmm.decode(sequence)
self.assertEqual(len(sequence), len(decoded_sequence), "HMM.decode Error")
for i in xrange(len(sequence)):
self.assertEqual(decoded_sequence[i], decoded_sequence[i],
"HMM.decode Error")
def test_decode(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
for sequence in sequences:
decoded_sequence = hmm.decode(sequence)
self.assertEqual(len(sequence), len(decoded_sequence),
"HMM.decode Error")
for i in xrange(len(sequence)):
self.assertEqual(sequence[i], sequence[i], "HMM.decode Error")
sequences = [[0, 1], [1, 2], [0, 1, 2, 0]]
for sequence in sequences:
decoded_sequence = hmm.decode(sequence)
self.assertEqual(len(sequence), len(decoded_sequence),
"HMM.decode Error")
for i in xrange(len(sequence)):
self.assertEqual(decoded_sequence[i], decoded_sequence[i],
"HMM.decode Error")
def test_emfit(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
learner = EMHMM(self._num_hidden, self._num_observ)
learner.fit(sequences, verbose=True, repeats=1)
for sequence in sequences:
pprint("True probability: %f" % hmm.predict(sequence))
pprint("Infered probability: %f" % learner.predict(sequence))
pprint("Learned parameter using EM algorithm:")
pprint("Transition matrix: ")
pprint(learner.transition_matrix)
pprint("Observation matrix: ")
pprint(learner.observation_matrix)
pprint("Initial distribution: ")
pprint(learner.initial_dist)
pprint("*" * 50)
pprint("True Transition matrix: ")
pprint(hmm.transition_matrix)
pprint("True Observation matrix: ")
pprint(hmm.observation_matrix)
pprint("True initial distribution: ")
pprint(hmm.initial_dist)
def test_emfit(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
learner = EMHMM(self._num_hidden, self._num_observ)
learner.fit(sequences, verbose=True, repeats=1)
for sequence in sequences:
pprint("True probability: %f" % hmm.predict(sequence))
pprint("Infered probability: %f" % learner.predict(sequence))
pprint("Learned parameter using EM algorithm:")
pprint("Transition matrix: ")
pprint(learner.transition_matrix)
pprint("Observation matrix: ")
pprint(learner.observation_matrix)
pprint("Initial distribution: ")
pprint(learner.initial_dist)
pprint("*" * 50)
pprint("True Transition matrix: ")
pprint(hmm.transition_matrix)
pprint("True Observation matrix: ")
pprint(hmm.observation_matrix)
pprint("True initial distribution: ")
pprint(hmm.initial_dist)
def test_loading(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
for sequence in sequences:
self.assertEqual(hmm.predict(sequence), hmm.predict(sequence),
"Inferred probability is wrong")
def test_loading(self):
sequences = io.load_sequences(self._train_filename)
hmm = HMM.from_file(self._model_filename)
for sequence in sequences:
self.assertEqual(hmm.predict(sequence), hmm.predict(sequence),
"Inferred probability is wrong")
