HMM 隐马尔科夫 Python 代码

 import numpy as np
 # -*- codeing:utf-8 -*-
 __author__ = 'youfei'

 #   隐状态
 hidden_state = ['sunny', 'rainy']

 #   观测序列
 obsevition = ['walk', 'shop', 'clean']

 #   根据观测序列、发射概率、状态转移矩阵、发射概率
 #   返回最佳路径
 def compute(obs, states, start_p, trans_p, emit_p):
     #   max_p（3*2）每一列存储第一列不同隐状态的最大概率
     max_p = np.zeros((len(obs), len(states)))

     #   path（2*3）每一行存储上max_p对应列的路径
     path = np.zeros((len(states), len(obs)))

     #   初始化
     for i in range(len(states)):
         max_p[0][i] = start_p[i] * emit_p[i][obs[0]]
         path[i][0] = i

     for t in range(1, len(obs)):
         newpath = np.zeros((len(states), len(obs)))
         for y in range(len(states)):
             prob = -1
             for y0 in range(len(states)):
                 nprob = max_p[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]]
                 if nprob > prob:
                     prob = nprob
                     state = y0
                     #   记录路径
                     max_p[t][y] = prob
                     for m in range(t):
                         newpath[y][m] = path[state][m]
                     newpath[y][t] = y

         path = newpath

     max_prob = -1
     path_state = 0
     #   返回最大概率的路径
     for y in range(len(states)):
         if max_p[len(obs)-1][y] > max_prob:
             max_prob = max_p[len(obs)-1][y]
             path_state = y

     return path[path_state]

 state_s = [0, 1]
 obser = [0, 1, 2]

 #   初始状态，测试集中，0.6概率观测序列以sunny开始
 start_probability = [0.6, 0.4]

 #   转移概率，0.7：sunny下一天sunny的概率
 transititon_probability = np.array([[0.7, 0.3], [0.4, 0.6]])

 #   发射概率，0.4：sunny在0.4概率下为shop
 emission_probability = np.array([[0.1, 0.4, 0.5], [0.6, 0.3, 0.1]])

 result = compute(obser, state_s, start_probability, transititon_probability, emission_probability)

 for k in range(len(result)):
     print(hidden_state[int(result[k])])