candidate_selection.m

greedy em 混和模型训练算法

%%%% Find a candidate model that maximizes log-likelihood   %%%%%
%%%% and compute the weight a'                              %%%%%
%%%% (exclude from the search motif neighborhoods)          %%%%%
%%%%%%                                                      %%%%%
%%%%%% Kostas Blekas, 19 Dec. 2001                          %%%%%
%%%%%% please contact at [e-mail] in case of problems %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


function [mpr_init,a,ind] = candidate_selection(fi,n,m,alpha,lambda,L,W,Ji,CP,C,extr)

a_value = zeros(1,m);
logl = zeros(1,m);

for i=1:m
    if (extr(i)==1)
        logl(i) = -1000000000000000.0;
    else
        delt = (fi - Ji(i,:)) ./ (fi + Ji(i,:));
        a_value(i) = 0.5 - 0.5 * sum(delt) / sum(delt.^2);
        if a_value(i) > 0 
           logl(i) = sum(log((fi + Ji(i,:))./2)) + 0.5*sum(delt)^2 / sum(delt.^2);
        else
            logl(i) = -1000000000000000.0;
        end
    end
end

[maxll ind] = max(logl);

mpr_init = ones(L,W) .* (1.0 - lambda)/(L-1);
for j=1:W 
    mpr_init(find(C(ind,j)==alpha),j) = lambda;
end

a = a_value(ind);