高斯混合模型的matlab源代码

function ll = gmcv(data, ks, nfold, nrep, sig) % ll = gmcv(data, ks, nfold, nrep, [sig]) % % performs n-fold cross validation on data using Gaussian Mixtures % of complexities supplied in ks. % % INPUT: % % data(n,d) : data matrix (one data point per ROW) % ks(nk) : vector specifying models (i.e. ks = [1:10]) % nfold : specifies how to split data. If it is a single % number, it specifies how many folds to do (i.e. 10 fold cv). % if it is a vector of length 2: % nfold(1) : number of restarts of algorithm (different than nrep) % nfold(2) : size of *training* set (i.e. 0.7) % nrep : number of random restarts per model per fold % sig : optional: 0/1 matrix of indicators of what elements to include % in the sigma matrices. % % OUTPUT: % % ll(nfold,nk) : set of log-likelihoods. Each column represents a single model. % % by Igor Cadez, 10/27/98, revised 01/18/99. %initialize variables [n d] = size(data); nk = length(ks); if(~exist('sig')) sig = ones(d); end %figure out whether to do folds or random draws... nr = nfold(1); if(length(nfold)>1) foldflag = 0; omega = nfold(2); spl = round(n*omega); else foldflag = 1; par = round((0:nr)*n/nr); ind = randperm(n); end ll = zeros(nr, nk); %the main loop for(j=1:nr) ik = 1; for(i = ks) if(foldflag) xtest = data( ind(par(j)+1:par(j+1)) , : ); xtrain = [data( ind(1:par(j)), : ); data( ind(par(j+1)+1:end), : )]; else ind = randperm(n); xtrain = data(ind(1:spl),:); xtest = data(ind((spl+1):end),:); end %do some random restarts to get the best insample fit [alphabest mubest sigmabest lkhbest] = gm(xtrain, i, nrep, sig); %get the out of sample ll, pack the results ll(j, ik) = gmscore(xtest,alphabest,mubest,sigmabest); ik = ik+1; %note: ks might skip some models, that's why ik is needed end end