HMM工具箱命令使用说明.docx

HOW TO USE THE HMM TOOLBOX (MATLAB)
一、离散输出的隐马尔科夫模型(DHMM,HMM with discrete outputs)
最大似然参数估计EM(Baum Welch算法)
The script gives an example of how to learn an HMM with discrete outputs. Let there be Q=2 states and O=3 output symbols. We create random stochastic matrices as follows.
O = 3;
Q = 2;
prior0 = normalise(rand(Q,1));
transmat0 = mk_stochastic(rand(Q,Q));
obsmat0 = mk_stochastic(rand(Q,O)); 
Now we sample nex=20 sequences of length T=10 each from this model, to use as training data.
T=10;   %序列长度
nex=20;  %样本序列数目
data = dhmm_sample(prior0, transmat0, obsmat0, nex, T);      
Here data is 20x10. Now we make a random guess as to what the parameters are,
prior1 = normalise(rand(Q,1)); %初始状态概率
transmat1 = mk_stochastic(rand(Q,Q)); %初始状态转移矩阵
obsmat1 = mk_stochastic(rand(Q,O)); %初始观测状态到隐藏状态间的概率转移矩阵
and improve our guess using 5 iterations of EM...
[LL, prior2, transmat2, obsmat2] = dhmm_em(data, prior1, transmat1, obsmat1, 'max_iter', 5);
%prior2, transmat2, obsmat2 为训练好后的初始概率,状态转移矩阵及混合状态概率转移矩阵
LL(t) is the log-likelihood after iteration t, so we can plot the learning curve.
序列分类
To evaluate the log-likelihood of a trained model given test data, proceed as follows:
loglik = dhmm_logprob(data, prior2, transmat2, obsmat2)  %HMM测试
Note: the discrete alphabet is assumed to be {1, 2, ..., O}, where O = size(obsmat, 2). Hence data cannot contain any 0s.
To classify a sequence into one of k classes, train up k HMMs, one per class, and pute the log-likelihood that each model gives to the test sequence; if the i'th model is the most likely, then declare the class of the sequence to be class i.
Computing the most probable sequence (Viterbi)
First you need to evaluate B(i,t) = P(y_t | Q_t=i) for all t,i:
B = multinomial_prob(data, obsmat);
Then you can use
[path] = viterbi_path(prior, transmat, B) 
 
二、具有高斯混合输出的隐马尔科夫模型(GHMM,HMM with mixture of Gaussians outputs)
Maximum likelihood parameter estimation using EM (Baum Welch)
Let us g