% Classifier design. Here we use the quadratic classifier as an example.

toolboxAdd;
load DS.mat
suvOpt=mySuvOptSet;
% ====== Input selection
if suvOpt.useInputSelect
	DS.input=DS.input(suvOpt.input.bestIndex, :);		% Use the selected features
	DS.inputName=DS.inputName(suvOpt.input.bestIndex);	% Update the input names based on the selected features
end
% ====== Input normalization
if suvOpt.useInputNormalize
	[DS.input, mu, sigma]=inputNormalize(DS.input);		% Input normalization based on the given mu and sigma
end
% ====== Use QC as an example
[cPrm, logProb, recogRate, hitIndex]=qcTrain(DS);
fprintf('Recognition rate via QC = %f%%\n', recogRate*100);
DS.hitIndex=hitIndex;		% Attach hitIndex to DS for plotting
figure; qcPlot(DS, cPrm, '2dPdf');
figure; qcPlot(DS, cPrm, 'decBoundary');
fprintf('Saving classifier parameters "cPrm" to "cPrm.mat"...\n');
save cPrm.mat cPrm