V, prev = h.viterbi(observations_index)
label = ""
for i in observations_index:
label += "".join("%10s" % observations_index_label[i])
print(" " * 7 + label)
for s in range(0, 2):
print("%7s: " % states_index_label[s] + " ".join("%10s" % ("%f" % v)
for v in V[s]))
print("\nThe most possible states and probability are:")
p, ss = h.state_path(observations_index)
for s in ss:
print(states_index_label[s])
print("%5f" % p)
# 下面测试 Baum-Welch 算法
print("---------Baum-Welch---------")
observations_data = np.array([1, 0, 0, 1, 0, 1, 0, 2, 1, 2])
states_data = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0])
guess = HMM(np.array([[0.5, 0.5], [0.5, 0.5]]),
np.array([[0.3, 0.3, 0.3], [0.3, 0.3, 0.3]]),
np.array([0.5, 0.5]))
newA, newB, newpi = guess.baum_welch(observations_data)
states_out = guess.state_path(observations_data)[1]
p = 0.0
for s in states_data:
if next(states_out) == s:
p += 1
print(p / len(states_data))
print("new A:\n", newA)
print("new B:\n", newB)
print("new pi:\n", newpi)
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:" print logprobinit print "final log probability is:" print logprobfinal
# -*- 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)))
# for i in range(len(obs)): # print str(obs[i])+' '+path[i] # Learning Test hmmLearn = HMM(a2, b2, pi2) #Defines the influence that new observations have on previous probabilities hmmLearn.influence = (3, 14) # hmmLearn.generateMatrix(states, alphabet) ##Supervised Learning # for x in range(50): # hmmLearn.supertrain(hmm.generate(2000)) # print "-----" # print hmmLearn.pi # print hmmLearn.a # print hmmLearn.b ##Unsupervised Learning (EM/Baum-Welch) for x in range(100): hmmLearn.baum_welch(hmm.generate(100, True)) print pi print a print b print "---" print hmmLearn.pi print hmmLearn.a print hmmLearn.b
