def _train(self, num_train_inst):
'''
Train a Hidden Markov Model with differnt learning algorithms
'''
num_train_inst = min(num_train_inst, self._training_data[0].shape[0])
training_data = self._training_data[0][:num_train_inst]
pprint("=" * 50)
pprint("Training set length: %d" % num_train_inst)
# Spectral learning algorithm
start_time = time.time()
self._sl_learner = SLHMM(self._num_hidden, self._num_observ)
self._sl_learner.fit([training_data])
end_time = time.time()
pprint("Time used for Spectral Learner: %f" % (end_time - start_time))
sl_time = end_time - start_time
# Expectation Maximization algorithm
#self._em_learners = []
em_times = np.zeros(self._num_em_restarts, dtype=np.float)
#for i in xrange(self._num_em_restarts):
#self._em_learners.append(EMHMM(self._num_hidden, self._num_observ))
#start_time = time.time()
#self._em_learners[i].fit([training_data], max_iters=20, verbose=True)
#end_time = time.time()
#pprint("Time used for Expectation Maximization: %f" % (end_time - start_time))
#em_times[i] = end_time - start_time
return (sl_time, np.mean(em_times))
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))
