function [fracSV, normW, nbSV, nbBoundSV, posSV, negSV, posBound, negBound] = svmstat(net, doDisplay)% SVMSTAT - Support Vector Machine statistics% %   [FRACSV,NORMW,NBSV,NBBOUNDSV,NBPOSSV,NBNEGSV,NBPOSBOUND,NBNEGBOUND]%    = SVMSTAT(NET) %   For a trained SVM in structure NET the most important figures are%   computed, that are%   FRACSV (the fraction of Support Vectors in the training set)%   NORMW (the norm of the separating hyperplane)%   NBSV (the total number of Support Vectors)%   NBBOUNDSV (number of SVs where the coefficient is at the upper bound,%     these are the misclassified training examples)%   POSSV (indices of Support Vectors from positive examples)%   NEGSV (indices of Support Vectors from negative examples)%   POSBOUND (indices of SVs from positive examples that are at the bound)%   NEGBOUND (indices of SVs from negative examples that are at the bound)%   If the SVM has been trained with the 2norm of the errors (slack%   variables), then NBBOUNDSV, POSBOUND and NEGBOUND will be 0 resp. [].%   All the indices are for use in NET.sv respectively NET.svind%   SVMSTAT(NET,1) prints out all that values.%%   See also%   SVM, SVMTRAIN, SVMFWD%% % Copyright (c) Anton Schwaighofer (2001)% $Revision: 1.3 $ $Date: 2001/02/14 09:05:20 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% if nargin<2,  doDisplay = 0;endnbSV = 0;fracSV = 0;nbPosSV = 0;nbNegSV = 0;normW = 0;if (net.nbexamples<=0) | (size(net.svcoeff,1)==0),  warning('The given SVM has not been trained yet');  return;endif isfield(net, 'use2norm'),  use2norm = net.use2norm;else  use2norm = 0;endposSV = find(net.svcoeff>0);negSV = find(net.svcoeff<0);% Indices of the positive and negative examples that have become Support% VectorsnbPosSV = length(posSV);nbNegSV = length(negSV);nbSV = nbPosSV+nbNegSV;fracSV = nbSV/net.nbexamples;if (nargout<2) & ~doDisplay,  % shortcut to avoid the expensive computation of the norm  return;end% Extract the upper bound for the examples that are Support Vectorsif length(net.c(:))==1,  C = repmat(net.c, [length(net.svcoeff) 1]);  % The same upper bound for all exampleselseif length(net.c(:))==2,  C = zeros([length(net.svcoeff) 1]);  C(posSV) = net.c(1);  C(negSV) = net.c(2);  % Different upper bounds C for the positive and negative exampleselse  C = net.c(net.svind);endif isfield(net, 'alphatol'),  tol = net.alphatol;else  tol = net.accur;  % old versions of SVM used only one field NET.accurendif use2norm,  posBound = [];  negBound = [];else    posBound = find(abs(net.svcoeff(posSV))>(C(posSV)-tol));  negBound = find(abs(net.svcoeff(negSV))>(C(negSV)-tol));endnbPosBound = length(posBound);nbNegBound = length(negBound);nbBoundSV = nbPosBound+nbNegBound;if strcmp(net.kernel, 'linear') & isfield(net, 'normalw'),  normW = norm(net.normalw);  % linear kernel: norm of the separating hyperplane can be computed  % directlyelse  if use2norm,    alpha = abs(net.svcoeff);    % For the 2norm SVM, the norm of the hyperplance is easy to compute    normW = sqrt(sum(alpha)+sum((alpha.^2)./C));  else    % normal 1norm SVM:    [dummy, svOutput] = svmfwd(net, net.sv);    svOutput = svOutput-net.bias;    % norm of the hyperplane is computed using the output    % of the SVM for all Support Vectors without the bias term    normW = sqrt(net.svcoeff'*svOutput);    % norm is basically the quadratic term of the SVM objective function  endendif doDisplay,  fprintf('Number of Support Vectors (SV): %i\n', nbSV);  fprintf('  (that is a fraction of %2.3f%% of the training examples)\n', ...	  100*fracSV);  if ~use2norm,    fprintf('  %i of the SV have a coefficient ALPHA at the upper bound\n', ...            nbBoundSV);  end  fprintf('  %i Support Vectors from positive examples\n', nbPosSV);  if ~use2norm,    fprintf('     %i of them have a coefficient ALPHA at the upper bound\n', ...            nbPosBound);  end  fprintf('  %i Support Vectors from negative examples\n', nbNegSV);  if ~use2norm,    fprintf('     %i of them have a coefficient ALPHA at the upper bound\n', ...            nbNegBound);  end  fprintf('Norm of the separating hyperplane: %g\n', normW);end