#coding=utf-8
'''
Created on 2017-12-4

本例为天气和行为的关系
'''
import numpy as np
import matplotlib.pyplot as plt

hmmlearn可以在安装numpy以后，再使用pip install hmmlearn安装

from hmmlearn import hmm

states = ["Rainy", "Sunny"]##隐藏状态
n_states = len(states)##隐藏状态长度

observations = ["walk", "shop", "clean"]##可观察的状态
n_observations = len(observations)##可观察序列的长度

start_probability = np.array([0.6, 0.4])##开始转移概率，即开始是Rainy和Sunny的概率
##隐藏间天气转移混淆矩阵，即Rainy和Sunny之间的转换关系，例如[0,0]表示今天Rainy，明天Rainy的概率
transition_probability = np.array([
[0.7, 0.3],
[0.4, 0.6]
])
##隐藏状态天气和可视行为混淆矩阵，例如[0,0]表示今天Rainy，walk行为的概率为0.1
emission_probability = np.array([
[0.1, 0.4, 0.5],
[0.6, 0.3, 0.1]
])

#构建了一个MultinomialHMM模型，这模型包括开始的转移概率，隐藏间天气转换混淆矩阵（transmat），隐藏状态天气和可视行为混淆矩阵emissionprob，对模型参数初始化
model = hmm.MultinomialHMM(n_components=n_states)
model.startprob_= start_probability
model.transmat_ = transition_probability
model.emissionprob_ = emission_probability

#给出一个可见序列
bob_Actions = np.array([[2, 0, 1, 1, 2, 0]]).T

解决问题1,解码问题,已知模型参数和X，估计最可能的Z； 维特比算法

logprob, weathers = model.decode(bob_Actions, algorithm="viterbi")
print "Bob Actions:", ", ".join(map(lambda x: observations[x], bob_Actions))
print "weathers:", ", ".join(map(lambda x: states[x], weathers))
print logprob#该参数反映模型拟合的好坏,数值越大越好

解决问题2,概率问题，已知模型参数和X，估计X出现的概率, 向前-向后算法

score = model.score(bob_Actions, lengths=None)
#最后输出结果
print score

TypeError Traceback (most recent call last)
in ()
39 # 解决问题1,解码问题,已知模型参数和X，估计最可能的Z； 维特比算法
40 logprob, weathers = model.decode(bob_Actions, algorithm="viterbi")
---> 41 print("Bob Actions:", ", ".join(map(lambda x: observations[x], bob_Actions)))
42 print( "weathers:", ", ".join(map(lambda x: states[x], weathers)))
43 print (logprob)#该参数反映模型拟合的好坏,数值越大越好

in (x)
39 # 解决问题1,解码问题,已知模型参数和X，估计最可能的Z； 维特比算法
40 logprob, weathers = model.decode(bob_Actions, algorithm="viterbi")
---> 41 print("Bob Actions:", ", ".join(map(lambda x: observations[x], bob_Actions)))
42 print( "weathers:", ", ".join(map(lambda x: states[x], weathers)))
43 print (logprob)#该参数反映模型拟合的好坏,数值越大越好

TypeError: only integer scalar arrays can be converted to a scalar index

TypeError: only integer scalar arrays can be converted to a scalar index

类型错误：只有整数可以转换成索引

打乱数据报错:only integer scalar arrays can be converted to a scalar index - liangxinxin - 博客园 batch_x=[1,2,3,4,5,6,7,8,9] indices = np.random.permutation(len(batch_x)) batch_x = batch_x[indices] https://www.cnblogs.com/liangxinxinbo/p/13066951.html