function [err,r,inx] = andrerr( model, distrib )% ANDRERR Classification error of the Generalized Anderson's task.%% Synopsis:%  [err,r,inx] = andrerr( model, distrib )%% Description:  %  This function computes the classification error of%  the given linear classifier and underlying set of Gaussian %  distributions as defined in the Generalized Anderson's %  task [SH10].%% Input:%  model [struct] Linear classifier:%   .W [dim x 1] Normal vector the separating hyperplane.%   .b [real] Bias the hyperplane.%  %  distrib [struct] Set of Gaussians with assigned binary labels:%   .Mean [dim x ncomp] Mean vectors.%   .Cov [dim x dim x ncomp] Covariance matrices.%   .y [1 x ncomp] Lables of Gaussians (1 or 2).%  % Output:%  err [real] Probability of misclassification.%  r [real] Mahalanobis distance of the cloasest Gaussian.%  inx [int] Index of the cloasest Gaussian.%% Example:%  distrib = load('mars');%  model = eanders(distrib,{'err',0.06'});%  figure; pandr( model, distrib );%  error = andrerr( model, distrib )%% See also %  ANDRORIG, GANDERS, EANDERS, GGRADANDR.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 4-may-2004, VF% 17-sep-2003, VFif ~isfield(distrib,'y'), distrib.y = [1,2]; end[dim,ncomp] = size(distrib.Mean);Radius = zeros(ncomp,1);for i=1:ncomp,    if distrib.y(i) == 1,    Radius(i) = (model.W'*distrib.Mean(:,i)+model.b)/...        sqrt(model.W'*distrib.Cov(:,:,i)*model.W);  else    Radius(i) = -(model.W'*distrib.Mean(:,i)+model.b)/...        sqrt(model.W'*distrib.Cov(:,:,i)*model.W);  end    end[r,inx] = min( Radius );err=1-cdf('norm',r,0,1);return;