def setUp(self):
self._model_filename = "hmm_m4n4.pkl"
self._train_filename = "m4n4.train.data"
self._num_hidden = 4
self._num_observ = 4
transition_matrix = np.random.rand(4, 4)
observation_matrix = np.random.rand(4, 4)
hmm = HMM(self._num_hidden, self._num_observ, transition_matrix=transition_matrix,
observation_matrix=observation_matrix)
sequences = hmm.generate_data(10000, 4, 51)
io.save_sequences(self._train_filename, sequences)
HMM.to_file(self._model_filename, hmm)
def setUp(self):
self._model_filename = "hmm_m4n4.pkl"
self._train_filename = "m4n4.train.data"
self._num_hidden = 4
self._num_observ = 4
transition_matrix = np.random.rand(4, 4)
observation_matrix = np.random.rand(4, 4)
hmm = HMM(self._num_hidden,
self._num_observ,
transition_matrix=transition_matrix,
observation_matrix=observation_matrix)
sequences = hmm.generate_data(10000, 4, 51)
io.save_sequences(self._train_filename, sequences)
HMM.to_file(self._model_filename, hmm)
# -*- 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)))
