def test_baum_welch_training_without(self):
"""Baum-Welch training without known state sequences."""
training_seq = Trainer.TrainingSequence(self.rolls, ())
def stop_training(log_likelihood_change, num_iterations):
"""Tell the training model when to stop."""
if VERBOSE:
print(f"ll change: {log_likelihood_change:f}")
if log_likelihood_change < 0.01:
return 1
elif num_iterations >= 10:
return 1
else:
return 0
baum_welch_mm = self.mm_builder.get_markov_model()
trainer = Trainer.BaumWelchTrainer(baum_welch_mm)
trained_mm = trainer.train([training_seq], stop_training)
if VERBOSE:
print(trained_mm.transition_prob)
print(trained_mm.emission_prob)
test_rolls, test_states = generate_rolls(300)
predicted_states, prob = trained_mm.viterbi(test_rolls,
dice_type_alphabet)
if VERBOSE:
print(f"Prediction probability: {prob:f}")
Utilities.pretty_print_prediction(self.test_rolls, test_states,
predicted_states)
states.append(random.choice('123'))
sequence = MutableSeq('',DNA())
for i in range(num):
sequence.append(random.choice('ACTG'))
return states.toseq(),sequence.toseq()
n_seq = 30
states = []
sequence = []
seq = []
for i in range(n_seq):
num = 10
state2, sequence2 = random_generator(num)
states.append(state2)
sequence.append(sequence2)
seq.append(Trainer.TrainingSequence(sequence[i],states[i]))
trainer = Trainer.BaumWelchTrainer(baum_welch)
trained = trainer.train(seq, stop_training)
print('\n\nProbabilitas Transisi: ',trained.transition_prob)
print('\nProbabilitas Emisi: ',trained.emission_prob)
prediction, prob = trained.viterbi(sequence[0], state())
print('\nProbabilitas Prediksi: ', prob)
Utilities.pretty_print_prediction(sequence[0], states[0], prediction)
