def run_hmm(labels, observations, axis_divs, angle_divs, smooth_iterations,
smooth_radius, validation_runs):
train_runs = 6000 - validation_runs
pd = PointDiscretizer(axis_divs)
ad = AngleDiscretizer(angle_divs)
print("Loading HMM Data...", end='')
start_time = time.time()
start_probabilities, transition_probabilities, emission_probabilities = load_hmm_data(
pd, ad, labels, observations, smooth_iterations, smooth_radius,
train_runs)
print("Done ({:.3f} secs)".format(time.time() - start_time))
model = Hmm(start_probabilities, transition_probabilities,
emission_probabilities)
# grade the model on the validation runs
# grading scheme: mean squared error (lower is better)
print("Grading Model...", end='')
start_time = time.time()
hidden_states = pd.states()
errors = []
for validation_run in range(train_runs, 6000):
run_labels = filter(lambda l: l[0] == validation_run, labels)
observation_list = [
ad.discretize(angle) for angle in observations[validation_run]
]
hidden_state_list = model.viterbi(observation_list, hidden_states)
for label in run_labels:
label_timestep = label[1]
label_state = (
label[2],
label[3],
)
guessed_state = pd.un_discretize(
hidden_state_list[label_timestep][0],
hidden_state_list[label_timestep][1])
errors.append(euclidean(guessed_state + label_state))
# get Mean Squared Error
MSE = statistics.mean(map(lambda x: x * x, errors))
print("Done! ({}s)".format(time.time() - start_time))
print(
"MSE of {:.3f} from AxDivs: {} AngDivs: {} SmIts: {} SmR: {} ValSet: {}"
.format(MSE, axis_divs, angle_divs, smooth_iterations, smooth_radius,
validation_runs))
return MSE
from hmm import Hmm
import time
label_map_file = '../dataset/phones/48_39.map'
chr_map_file = '../dataset/48_idx_chr.map_b'
label_file = '../dataset/label/train.lab'
post_file = '../dataset/posteriorgram/test6.post'#'../dataset/posteriorgram/train2.post'
Hmm.loadgetprob(label_map_file, chr_map_file, label_file, post_file)#'test_y_.txt'
setting = 0.57
for i in xrange(3):
n, p, path = Hmm.viterbi(setting)
#count,counttotal = Hmm.check(path)
#print (str(count) +'/'+ str(counttotal))
Hmm.save_result(path, setting)
setting += 0.05
model_file1 = "model1.json"
hmm1 = Hmm(os.path.join(model_file1))
model_file2 = "model2.json"
hmm2 = Hmm(os.path.join(model_file2))
print("Machine 1 generated samples:")
hmm1.generator()
print("\nFORWARD ALGORITHM:")
for obs in observations:
p1 = hmm1.forward(obs)
p2 = hmm2.forward(obs)
#
print("Observations = ", obs, " Fwd Prob (Machine 1) = ", p1,
", Fwd Prob (Machine 2) = ", p2, ", Fwd Prob log (Machine 1) = ",
(math.log(p1) if p1 != 0 else "NA"),
", Fwd Prob log (Machine 2) = ",
(math.log(p2) if p2 != 0 else "NA"),
", Best sequence coming from - ",
"Machine 1" if p1 > p2 else "Machine 2")
# print("Viterbi (Machine 1): ""Observations = ", obs, "Prob = ", prob,"Log Probability = ", (math.log(p1) if prob!=0 else "NA"), " Hidden State Sequence = ", hidden_states)
print("\nViterbi (Machine 2): ")
for obs in observations:
prob, hidden_states = hmm2.viterbi(obs)
print("Observations = ", obs, "Prob = ", prob, ", Log Probability = ",
(math.log(prob) if prob != 0 else "NA"),
", Hidden State Sequence = ",
hidden_states if prob != 0 else "NA")