classify_knn.m

function [ accuraccy, TP, FP, TN, FN ] = classify_knn( PATH_TRAIN, PATH_TEST, stWin, stStep, mtWin, mtStep, Statistics, K )

    MODEL_NAME = ['knn_' datestr(now,'ddmmyyyy_HHMMSSFFF') '.mat'];
    MODEL_PATH = ['models\' MODEL_NAME];
    
    kNN_model_add_class(MODEL_PATH, 'good', [PATH_TRAIN 'good'], Statistics, stWin, stStep, mtWin, mtStep);
    kNN_model_add_class(MODEL_PATH, 'bad', [PATH_TRAIN 'bad'], Statistics, stWin, stStep, mtWin, mtStep);           
    
    correct_predictions = 0;
    wrong_predictions = 0;
    
    TP = 0;
    FP = 0;
    TN = 0;
    FN = 0;

    files_to_test = dir(PATH_TEST);
    files_to_test(1) = []; files_to_test(1) = [];

    [Features, ClassNames, MEAN, STD, Statistics, ...
        stWin, stStep, mtWin, mtStep] = kNN_model_load(MODEL_PATH);

    for l = 1:length(files_to_test)
        [y, Fs] = wavread([PATH_TEST files_to_test(l).name]);
        stF = stFeatureExtraction(y, Fs, stWin, stStep);
        mtWinRatio = mtWin/stWin;
        mtStepRatio = mtStep/stStep;
        [mtFeatures] = mtFeatureExtraction(stF, mtWinRatio, mtStepRatio, Statistics);
        mtFeatures = mean(mtFeatures, 2);
        [P, label] = classifyKNN_D_Multi(Features, (mtFeatures - MEAN') ./ STD', K, 1);

        if(strfind(files_to_test(l).name, 'b'))
            if(label == 1)
                correct_predictions = correct_predictions + 1;
                TP = TP + 1;
            else
                wrong_predictions = wrong_predictions + 1;
                FP = FP + 1;
            end;
        elseif(strfind(files_to_test(l).name, 'r'))
            if(label == 2)
                correct_predictions = correct_predictions + 1;
                TN = TN + 1;
            else
                wrong_predictions = wrong_predictions + 1;
                FN = FN + 1;
            end;
        end;

    end;

    
    accuraccy = (TP+TN)/(TP+TN+FP+FN);
end