import matplotlib.pyplot as plt
import matplotlib.cm as cm
from surrogate.Markov_model import Markov_model
from HMM_algos import Proba_computer
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)
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()
