function [Cen,cinv,ppri]=mltrain_uni(Pr,Tr)
% Usage: [Cen,cinv,ppri]=mltrain_uni(Pr,Tr)
% ML- Maximum Likelihood Classifier training program
% Modified from ML.m
% Pr: training set feature vector  K by N
% Tr: training set target vector K by S  
%    [1 0 ... 0] is class 1, [0 ... 0 1] is class S
% S: number of classes
% N: feature vector dimension
% K: # of feature vectors in training set
% Cen: an array of Mean vector of each class 
% cinv: an array of inverse of covariance matrix of each class
% ppri: a priori probability of each class

% Pr is K x N,  Tr is K x S
[K,N]=size(Pr); [tmp,S]=size(Tr);
for ns=1:S, % for each class
    idx=find(Tr(:,ns)==1);  % indices of class ns
    Nc(ns)=length(idx);  % # of training samples in class ns
    F=Pr(idx,:);   % feature vectors Nc(ns) x N
    if Nc(ns)> 1,
        Cen{ns}=mean(F)';  % N x 1 mean vector of class ns
        t1=F'-Cen{ns}*ones(1,Nc(ns));  % subtract the mean vector N x Nc(ns)
        C=(1/Nc(ns))*t1*t1';  % covariance matrix of class ns
        cinv{ns}=pinv(C);  % inverse of covariance matrix, use pseudo inverse 
                           % incase C is singular
    elseif Nc(ns)==1;  % singular case
        Cen{ns}=F'; 
        cinv{ns}=eye(N);  
    end
end
ppri=Nc/K;  % prior probability vector