mln.m

四种SVM工具箱的分类与回归算法别人的

function [logL] = mln(X,MI,SIGMA,Pk)
% MLN logarithm of value of the likelihood function.
% [logL] = mln(X,MI,SIGMA,Pk)
%
% MLN computes logarithm of value of the likelihood
%  function which is defined as product of probabilities p(x) of
%  all vectors from the point set X. It is considered that
%  conditional p.d. functions p(x|k) are normaly distributed 
%  and their parameters are given. Futher, it is considered that 
%  apriori probabilities p(k) are known, then the logarithm holds 
%                N       K
%        logL = sum log sum p(x_i|k) p(k)
%               i=1     k=1
%
% 
% Input:
%  X [DxN] contains N vectors which are D-dimensional.
%  MI [DxK] contains K vectors of mean values, MI=[mi_1,mi_2,...mi_K].
%  SIGMA [(DxD)xK] contains K covariance matrices which are D-by-D
%     dimensional, SIGMA=[sigma_1,sigma_2,...,sigma_K].
%     The pair mi_1,sigma_1 describes the first normaly distributed 
%     p.d. function p(x|k=1) and so one for k=1,2,...K. 
%  Pk [1xK] contains K values of apriori probabilities.
%  
% Output:
%  logL [1x1] logarithm of value of the Maximal-Likelihood function.
%  
% See also UNSUNI, UNSUND, UNSUPER.
%

% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
% Written Vojtech Franc (diploma thesis) 4.8.2000
% Modifications
 

D=size(MI,1);   % dimension
K=size(MI,2);   % % of classes
N=size(X,2);    % # of points

A=zeros(N,K);

for k=1:K,
    pxk=normald(X,MI(:,k),SIGMA(:,1+(k-1)*D:k*D));

    A(:,k)=pxk(:)*Pk(k);
end

logL=sum(log(sum(A,2)));