def hmm_test():
st_time = time.time()
model_file = "hmm_model.json"
# load data
with open(model_file, 'r') as f:
data = json.load(f)
A = np.array(data['A'])
B = np.array(data['B'])
pi = np.array(data['pi'])
# observation symbols
obs_dict = data['observations']
# state symbols
states_symbols = dict()
for idx, item in enumerate(data['states']):
states_symbols[item] = idx
Osequence = np.array(data['Osequence'])
N = len(Osequence)
model = HMM(pi, A, B, obs_dict, states_symbols)
delta = model.forward(Osequence)
m_delta = np.array([[3.5000e-01, 1.3600e-01, 0.0000e+00, 0.0000e+00, 1.1136e-05, 1.1136e-05, 0.0000e+00],
[1.5000e-01, 3.2000e-02, 4.6400e-03, 2.7840e-04, 3.3408e-05, 1.1136e-05, 8.9088e-07]])
print("Your forward function output:", delta)
print("My forward function output:", m_delta)
gamma = model.backward(Osequence)
m_gamma = np.array([[1.6896e-06, 3.8400e-06, 6.4000e-05, 2.0000e-03, 1.4000e-02, 2.0000e-02, 1.0000e+00],
[1.9968e-06, 1.1520e-05, 1.9200e-04, 3.2000e-03, 2.2000e-02, 6.0000e-02, 1.0000e+00]])
print("Your backward function output:", gamma)
print("My backward function output:", m_gamma)
prob1 = model.sequence_prob(Osequence)
m_prob1 = 8.908800000000002e-07
print("Your sequence_prob function output:", prob1)
print("My sequence_prob function output:", m_prob1)
prob2 = model.posterior_prob(Osequence)
m_prob2 = np.array([[0.6637931, 0.5862069, 0., 0., 0.175, 0.25, 0.],
[0.3362069, 0.4137931, 1., 1., 0.825, 0.75, 1.]])
print("Your posterior_prob function output:", prob2)
print("My posterior_prob function output:", m_prob2)
viterbi_path = model.viterbi(Osequence)
m_viterbi_path = ['1', '1', '2', '2', '2', '2', '2']
print('Your viterbi function output: ', viterbi_path)
print('My viterbi function output: ', m_viterbi_path)
en_time = time.time()
print()
print("hmm total time: ", en_time - st_time)
def test02():
A = np.array([[0.5, 0.2, 0.3], [0.3, 0.5, 0.2], [0.2, 0.3, 0.5]])
B = np.array([[0.5, 0.5], [0.4, 0.6], [0.7, 0.3]])
pi = np.array([0.2, 0.4, 0.4])
hmm = HMM(A, B, pi)
pro = hmm.forward([0, 1, 0])
# 0.130218
print('前向算法：', pro)
pro = hmm.backward([0, 1, 0])
print('后向算法：', pro)
def hmm_test():
st_time = time.time()
model_file = "hmm_model.json"
# load data
with open(model_file, 'r') as f:
data = json.load(f)
A = np.array(data['A'])
B = np.array(data['B'])
pi = np.array(data['pi'])
# observation symbols
obs_dict = data['observations']
# state symbols
states_symbols = dict()
for idx, item in enumerate(data['states']):
states_symbols[item] = idx
Osequence = np.array(data['Osequence'])
N = len(Osequence)
model = HMM(pi, A, B, obs_dict, states_symbols)
delta = model.forward(Osequence)
print("Forward function output:", delta)
gamma = model.backward(Osequence)
print("Backward function output:", gamma)
prob1 = model.sequence_prob(Osequence)
print("Sequence_prob function output:", prob1)
prob2 = model.posterior_prob(Osequence)
print("Posterior_prob function output:", prob2)
prob3 = model.likelihood_prob(Osequence)
print("Likelihood_prob function output:", prob3)
viterbi_path = model.viterbi(Osequence)
print('Viterbi function output: ', viterbi_path)
en_time = time.time()
print()
print("hmm total time: ", en_time - st_time)
def hmm_test():
st_time = time.time()
model_file = "hmm_model.json"
# load data
with open(model_file, 'r') as f:
data = json.load(f)
A = np.array(data['A'])
B = np.array(data['B'])
pi = np.array(data['pi'])
# observation symbols
obs_dict = data['observations']
# state symbols
states_symbols = dict()
for idx, item in enumerate(data['states']):
states_symbols[item] = idx
Osequence = np.array(data['Osequence'])
N = len(Osequence)
# print("pi",pi,"\nobsdict",obs_dict,"\n","B", B,"\n states_symbols",states_symbols,"\n A",A,"\n osequence",Osequence)
model = HMM(pi, A, B, obs_dict, states_symbols)
alpha = model.forward(Osequence)
m_alpha = np.array([[
3.5000e-01, 1.3600e-01, 0.0000e+00, 0.0000e+00, 1.1136e-05, 1.1136e-05,
0.0000e+00
],
[
1.5000e-01, 3.2000e-02, 4.6400e-03, 2.7840e-04,
3.3408e-05, 1.1136e-05, 8.9088e-07
]])
print("Your forward function output:\n", alpha)
print("Correct forward function output:\n", m_alpha)
beta = model.backward(Osequence)
m_beta = np.array([[
1.6896e-06, 3.8400e-06, 6.4000e-05, 2.0000e-03, 1.4000e-02, 2.0000e-02,
1.0000e+00
],
[
1.9968e-06, 1.1520e-05, 1.9200e-04, 3.2000e-03,
2.2000e-02, 6.0000e-02, 1.0000e+00
]])
print("Your backward function output:\n", beta)
print("Correct backward function output:\n", m_beta)
prob1 = model.sequence_prob(Osequence)
m_prob1 = 8.908800000000002e-07
print("Your sequence_prob function output:\n", prob1)
print("Correct sequence_prob function output:\n", m_prob1)
prob2 = model.posterior_prob(Osequence)
m_prob2 = np.array([[0.6637931, 0.5862069, 0., 0., 0.175, 0.25, 0.],
[0.3362069, 0.4137931, 1., 1., 0.825, 0.75, 1.]])
print("Your posterior_prob function output:\n", prob2)
print("Correct posterior_prob function output:\n", m_prob2)
prob3 = model.likelihood_prob(Osequence)
m_prob3 = np.array([[[0.48275862, 0, 0, 0, 0.1, 0.],
[0.18103448, 0.5862069, 0, 0, 0.075, 0.25]],
[[0.10344828, 0., 0, 0.175, 0.15, 0.],
[0.23275862, 0.4137931, 1, 0.825, 0.675, 0.75]]])
print("Your likelihood_prob function output:\n", prob3)
print("Correct likelihood_prob function output:\n", m_prob3)
viterbi_path = model.viterbi(Osequence)
m_viterbi_path = ['1', '1', '2', '2', '2', '2', '2']
print('Your viterbi function output: \n', viterbi_path)
print('Correct viterbi function output: \n', m_viterbi_path)
en_time = time.time()
print()
print("HMM total time: ", en_time - st_time)
# -*- coding:utf-8 -*-
import numpy as np
from hmm import HMM
A = np.array([[0.7, 0.3], [0.4, 0.6]])
B = np.array([[0.5, 0.4, 0.1], [0.1, 0.3, 0.6]])
hmm = HMM(A, B, [0.3, 0.7])
# print(hmm.generate_data(5, seed=2018))
observations, states = hmm.generate_data(T=10, seed=2019)
print('observations: {}'.format(observations))
print('hidden states: {}'.format(states))
# 概率计算问题
print('backward prob: {}'.format(hmm.backward(observations)[1]))
print('forward prob: {}'.format(hmm.forward(observations)[1]))
# 学习问题
model = HMM(np.array([[0.5, 0.5], [0.5, 0.5]]),
np.array([[0.4, 0.4, 0.2], [0.2, 0.3, 0.5]]), np.array([0.5, 0.5]))
a, b, pi, count = model.baum_welch(observations, threshold=0.1)
print('EM iteration: {}'.format(count))
print('a: {}'.format(a))
print('b: {}'.format(b))
print('pi: {}'.format(pi))
# 预测问题
print("predict: {}".format(hmm.viterbi(observations)))
hmmfile = "test.hmm" seqfile = "test.seq" M, N, pi, A, B = read_hmm(hmmfile) T, obs = read_sequence(seqfile) hmm_object = HMM(pi, A, B) #test forward algorithm prob, alpha = hmm_object.forward(obs) print "forward probability is %f" % np.log(prob) prob, alpha, scale = hmm_object.forward_with_scale(obs) print "forward probability with scale is %f" % prob # test backward algorithm prob, beta = hmm_object.backward(obs) print "backward probability is %f" % prob beta = hmm_object.backward_with_scale(obs, scale) # test baum-welch algorithm logprobinit, logprobfinal = hmm_object.baum_welch(obs) print "------------------------------------------------" print "estimated parameters are: " print "pi is:" print hmm_object.pi print "A is:" print hmm_object.A print "B is:" print hmm_object.B print "------------------------------------------------" print "initial log probability is:"