my_markov_model = Markov_model(initial, A, B, alphabet) data = my_markov_model.generate_data(10) my_proba_computer = Proba_computer(initial, A, B, alphabet) print [x['state'] for x in data] print [x['obs'] for x in data] forwards = my_proba_computer.compute_forward_probas([x['obs'] for x in data]) plt.subplot(311) plt.imshow(forwards, cmap = cm.gray) backwards = my_proba_computer.compute_backward_probas([x['obs'] for x in data]) plt.subplot(312) plt.imshow(backwards, cmap = cm.gray) probas = my_proba_computer.compute_probas([x['obs'] for x in data]) for i in xrange(probas.shape[1]): print probas[:,i] plt.subplot(313)
initial = [0.1, 0.1, 0.1] A = [[0.1, 0.5, 0.1], [0.1, 0.1, 0.5], [0.5, 0.1, 0.1]] alphabet = ['a', 'b', 'c'] B = [[0.8, 0.1, 0.1], [0.1, 0.8, 0.1], [0.1, 0.1, 0.8]] my_markov_model = Markov_model(initial, A, B, alphabet) data = my_markov_model.generate_data(10) my_proba_computer = Proba_computer(initial, A, B, alphabet) print[x['state'] for x in data] print[x['obs'] for x in data] forwards = my_proba_computer.compute_forward_probas([x['obs'] for x in data]) plt.subplot(311) plt.imshow(forwards, cmap=cm.gray) backwards = my_proba_computer.compute_backward_probas([x['obs'] for x in data]) plt.subplot(312) plt.imshow(backwards, cmap=cm.gray) probas = my_proba_computer.compute_probas([x['obs'] for x in data]) for i in xrange(probas.shape[1]): print probas[:, i] plt.subplot(313)
from surrogate.Markov_model import Markov_model from HMM_algos import Proba_computer initial = [0.1, 0.1, 0.1] A = [[0.1, 0.5, 0.1], [0.1, 0.1, 0.5], [0.5, 0.1, 0.1]] alphabet = ['a', 'b', 'c'] B = [[1.0, 0.5, 0.5], [0.5, 1.0, 0.5], [0.5, 0.5, 1.0]] my_markov_model = Markov_model(initial, A, B, alphabet) data = my_markov_model.generate_data(10) my_forward_proba = Proba_computer(initial, A, B, alphabet) print [x['state'] for x in data] print [x['obs'] for x in data] forward_probas = my_forward_proba.compute_forward_probas([x['obs'] for x in data]) for i in xrange(forward_probas.shape[1]): print forward_probas[:,i] plt.imshow(forward_probas, cmap = cm.gray) plt.clim() plt.show()
from surrogate.Markov_model import Markov_model from HMM_algos import Proba_computer initial = [0.1, 0.1, 0.1] A = [[0.1, 0.5, 0.1], [0.1, 0.1, 0.5], [0.5, 0.1, 0.1]] alphabet = ['a', 'b', 'c'] B = [[1.0, 0.5, 0.5], [0.5, 1.0, 0.5], [0.5, 0.5, 1.0]] my_markov_model = Markov_model(initial, A, B, alphabet) data = my_markov_model.generate_data(10) my_forward_proba = Proba_computer(initial, A, B, alphabet) print [x['state'] for x in data] print [x['obs'] for x in data] forward_probas = my_forward_proba.compute_forward_probas([x['obs'] for x in data]) for i in xrange(forward_probas.shape[1]): print forward_probas[:,i] plt.imshow(forward_probas, cmap = cm.gray) plt.clim() plt.show()