% Input selection based on KNNR and LOO

toolboxAdd;
load DS.mat				% Load the dataset
DS.input=inputNormalize(DS.input);	% Input (feature) normalization
inputNum=2;				% Select 2 features at most
plotOpt=1;				% Plot the result of input selection
classifier='knncLoo';	% Use knncLoo.m for input selection
cPrm.k=1;				% Parameters for the classifier
fprintf('inputName=%s\n', cell2str(DS.inputName));	% Print the input names
selectionMethod='exhaustive';		% Method for input selection
switch selectionMethod
	case 'sequential'
		% ====== Sequential forward selection
		[bestInputIndex, bestRecogRate, allSelectedInput, allRecogRate, elapsedTime] = inputSelectSequential(DS, inputNum, classifier, cPrm, plotOpt);
	case 'exhaustive'
		% ====== Exhaustive search
		[bestInputIndex, bestRecogRate, allSelectedInput, allRecogRate, elapsedTime] = inputSelectExhaustive(DS, inputNum, classifier, cPrm, plotOpt);
	otherwise
		disp('Unknown method!');
end

fprintf('Recognition rate = %f\n', bestRecogRate*100);
fprintf('bestInputIndex = %s\n', mat2str(bestInputIndex));
fprintf('bestInputIndex = %s\n', cell2str(DS.inputName(bestInputIndex)));

[allRecogRate, index]=sort(allRecogRate);
allSelectedInput=allSelectedInput(index);
figure; inputSelectPlot(allRecogRate*100, allSelectedInput, DS.inputName, mfilename);
fprintf('Save the indices of the selected features to bestInputIndex.mat...\n');
save bestInputIndex bestInputIndex