function [cost,retained] = trimmedmse(R,beta,V);% Calculate trimmed mean of the squared value of the residuals.%%  cost = trimmedmse(R);%% The factor where one trimms off the normed residuals is% optimized. However, one can pass a default value when one to% exclude this optimization, e.g.:%%  cost = trimmedmse(R,0.15);%% One can overrule the default norm (norm='abs') by passing the norm function.% %  cost = trimmedmse(R,[],norm);%% As an additional output, the index of the retained points ca be% received:%%  [cost,retained] = trimmedmse(R);%% see also:%   mse, misclass% Copyright (c) 2002,  KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab% default trimming?eval('beta;','beta=[];');eval('R = feval(V,R);','R = R.^2;');[Rs,si] = sort(R);N = max(size(Rs));%figure; hist(Rs,50); pauseif ~isempty(beta),  nb = N - floor(N*beta);  mu = mean(Rs(1:nb));    cost = mu;else% optimize trimming factor    best_variance = inf;  t = 1;  %betas = 0:.01:.45;  %betas = [0 0.05 0.10 0.175 0.30 0.45];  betas = 0.05;  for beta = betas,        %beta = beta*2;    nb = N - floor(N*beta);    mu = mean(Rs(1:nb));      %variance = 1/((1-beta)^2) * (sum((Rs(1:nb)-mu).^2)/N+ (beta*(Rs(nb)-mu)^2));    variance = sum((Rs(1:nb)-mu).^2) + ...        (floor(N*beta)+1)*(Rs(nb)-mu)^2 - ...        1/N*(floor(N*beta)*(Rs(nb)-mu)^2);    variance = variance/(nb*nb-1);    %v(t,1) = variance; t=t+1;    if variance <= best_variance,      best_variance = variance;      cost = mu;      best_beta = beta;    end  endend%figure; plot(betas',[v sum(v,2)]);%figure; hist(Rs,50); % which are the retained data pointsretained = si(1:nb);