K-means with HMM

Shazeb86

Hello, I am new to this discussion group and I hope it will very helpful for me and for the other user to learn about many things. I want to use K-means and Hidden Markov Model (HMM) for my gesture recognition problem. I found a combined code, but I couldn't understand the code very well. So I need to help to understand that and need your suggestions.

D = 3; % the number of dimensions to use: X, Y, Z  
M = 12; % output symbols  
N = 8; % states  
LR = 2; % degree of play in the left-to-right HMM transition matrix   

train_gesture = 'circle';  
test_gesture = 'circle';  

training = get_xyz_data('data/train',train_gesture);  
testing = get_xyz_data('data/test',test_gesture);   


%************************************************************  
%  
%  Initialize  
%   
%************************************************************  

gestureRecThreshold = 0; % set below  

[centroids N] = get_point_centroids(training,N,D);  
ATrainBinned = get_point_clusters(training,centroids,D);  
ATestBinned = get_point_clusters(testing,centroids,D);  

%************************************************************  
%  
%  Training  
%  
%************************************************************  
% Set priors:  
pP = prior_transition_matrix(M,LR);  

% Train the model:  
cyc = 50;  
[E,P,Pi,LL] = dhmm_numeric(ATrainBinned,pP,[1:N]',M,cyc,.00001);  



%************************************************************  
%  
%  Testing  
%  
%************************************************************  

sumLik = 0;  
minLik = Inf;  
for j=1:length(ATrainBinned)  
    lik = pr_hmm(ATrainBinned{j},P,E',Pi);  
    if (lik < minLik)  
        minLik = lik;  
    end  
    sumLik = sumLik + lik;  
end  
gestureRecThreshold = 2.0*sumLik/length(ATrainBinned);  

    fprintf('\n\n********************************************************************\n');  
fprintf('Testing %i sequences for a log likelihood greater than %f\n',length(ATestBinned),gestureRecThreshold);  
fprintf('********************************************************************\n\n');  

recs = 0;  
tLL = zeros(length(ATestBinned),1);  
for j=1:length(ATestBinned)  
    tLL(j,1) = pr_hmm(ATestBinned{j},P,E',Pi);  
    if (tLL(j,1) > gestureRecThreshold)  
        recs = recs + 1;  
        fprintf('Log likelihood: %f > %f (threshold) -- FOUND %s GESTURE!\n',tLL(j,1),gestureRecThreshold,test_gesture);  
    else  
        fprintf('Log likelihood: %f < %f (threshold) -- NO %s GESTURE.\n',tLL(j,1),gestureRecThreshold,test_gesture);  
    end  
end  
fprintf('Recognition success rate: %f percent\n',100*recs/length(ATestBinned));  
`