source: prextra/tree_train.m @ 99

%
%    w = tree_train(x,y,opt)
%
function w = tree_train(x,y,opt)

% how good are we in this node?
err = tree_gini(y,opt.K);
if (err==0)

        w = y(1); % just predict this label

else
        % we split further
        n = size(x,1);

        % optionally, choose only from a subset
        if (opt.featsubset>0)
                fss = randperm(size(x,2));
                fss = fss(1:opt.featsubset);
        else
                fss = 1:size(x,2);
        end

        % check each feature separately:
        besterr = inf; bestf = []; bestt = []; bestj = []; bestI = [];
        for i=fss
                % sort the data along feature i:
                [xi,I] = sort(x(:,i)); yi = y(I);
                % run over all possible splits:
                for j=1:n-1
                        % compute the gini
                        err = j*tree_gini(yi(1:j),opt.K) + (n-j)*tree_gini(yi(j+1:n),opt.K);
                        % and see if it is better than before.
                        if (err<besterr)
                                besterr = err;
                                bestf = i;
                                bestj = j;
                                bestt = mean(xi(j:j+1));
                                bestI = I;
                        end
                end
        end

        % store
        w.bestf = bestf;
        w.bestt = bestt;
        %  now find the children:
        w.l = tree_train(x(bestI(1:bestj),:),y(bestI(1:bestj)),opt);
        w.r = tree_train(x(bestI(bestj+1:end),:),y(bestI(bestj+1:end)),opt);
end