fprintf('Loading DS...\n');
load ds.mat; DS=ds;

recogRate1=ldaPerfViaKnncLoo(DS);
DS2=DS; DS2.input=inputNormalize(DS2.input);	% input normalization
recogRate2=ldaPerfViaKnncLoo(DS2);
[featureNum, dataNum] = size(DS.input);
plot(1:featureNum, 100*recogRate1, 'o-', 1:featureNum, 100*recogRate2, '^-'); grid on
legend('Raw data', 'Normalized data');
xlabel('No. of projected features based on LDA');
ylabel('LOO recognition rates using KNNC (%)');

DS.input=inputNormalize(DS.input);	% input normalization
opt=ldaPerfViaKnncLoo('defaultOpt');
opt.mode='approximate';
tic; recogRate1=ldaPerfViaKnncLoo(DS, opt); time1=toc;
opt.mode='exact';
tic; recogRate2=ldaPerfViaKnncLoo(DS, opt); time2=toc;
[featureNum, dataNum] = size(DS.input);
figure;
plot(1:featureNum, 100*recogRate1, 'o-', 1:featureNum, 100*recogRate2, '^-'); grid on
legend('mode=''approximate''', 'mode=''exact''');
xlabel('No. of projected features based on LDA');
ylabel('LOO recognition rates using KNNC (%)');
fprintf('Time for ''approximate'' mode = %g sec, time for ''exact'' mode = %g sec\n', time1, time2);