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)
plt.imshow(probas, cmap = cm.gray)
plt.show()
plt.show()
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)
plt.imshow(probas, cmap=cm.gray)
plt.show()
plt.show()
initial = [0.1, 0.1, 0.1]
A = [[0.3, 0.5, 0.3], [0.3, 0.3, 0.5], [0.5, 0.3, 0.3]]
alphabet = ['o', '*', 'p', 'h']
B = [[1.0, 0.5, 0.5, 0.5], [0.5, 1.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.5]]
my_markov_model = Markov_model(initial, A, B, alphabet)
data = my_markov_model.generate_data(20)
my_proba_computer = Proba_computer(initial, A, B, alphabet)
states = np.asarray([x['state'] for x in data])
observations = [x['obs'] for x in data]
gammas = my_proba_computer.compute_probas(observations)
epsilons = my_proba_computer.compute_epsilons(observations)
print epsilons
gammas_eps = np.zeros((epsilons.shape[0], epsilons.shape[2]))
for t in xrange(gammas_eps.shape[1]):
gammas_eps[:, t] = np.sum(epsilons[:, :, t], axis=1)
print gammas_eps
print gammas
plt.subplot(411)
initial = [0.1, 0.1, 0.1]
A = [[0.3, 0.5, 0.3], [0.3, 0.3, 0.5], [0.5, 0.3, 0.3]]
alphabet = ["o", "*", "p", "h"]
B = [[1.0, 0.5, 0.5, 0.5], [0.5, 1.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.5]]
my_markov_model = Markov_model(initial, A, B, alphabet)
data = my_markov_model.generate_data(20)
my_proba_computer = Proba_computer(initial, A, B, alphabet)
states = np.asarray([x["state"] for x in data])
observations = [x["obs"] for x in data]
gammas = my_proba_computer.compute_probas(observations)
epsilons = my_proba_computer.compute_epsilons(observations)
print epsilons
gammas_eps = np.zeros((epsilons.shape[0], epsilons.shape[2]))
for t in xrange(gammas_eps.shape[1]):
gammas_eps[:, t] = np.sum(epsilons[:, :, t], axis=1)
print gammas_eps
print gammas
