i'm using Kevin Murphy's HMM Toolbox. But i have a problem in understanding the results.
My problem is to classify some sequences with Hidden Markov Model. I'm using a (test) dataset build with random sequences of symbol '1' '3' '6'. this one is my training dataset: data = [ 1 1 1 3 6; 1 1 3 3 6; 1 3 3 6 6] - 3 observations of 5 symbols.
i use EM baum welch to train and log_prob to evaluate this new observation: data1 = [1 3 6]
i'm expecting logprob near 0 like -0.1 instead i have thoose result:
loglik = -110.04
loglik = -110.63
loglik = -91.679
why have i those weird result??
this is my MATLAB code:
O = 10;
Q = 12;
% training data
T = 5;
nex = 3;
data = [ 1 1 1 3 6;
1 1 3 3 6;
1 3 3 6 6]
% initial guess of parameters
prior1 = normalise(rand(Q,1));
transmat1 = mk_stochastic(rand(Q,Q));
obsmat1 = mk_stochastic(rand(Q,O));
% improve guess of parameters using EM
[LL, prior2, transmat2, obsmat2] = dhmm_em(data, prior1, transmat1, obsmat1, 'max_iter', 50);
LL
% use model to compute log likelihood
data1 = [1 3 6]
loglik = dhmm_logprob(data1, prior2, transmat2, obsmat2)
EDIT: also giving the same data of testing i recive weird result! why that?