function [] = network_main_v22()
%UNTITLED Summary of this function goes here
%   Detailed explanation goes here
ads = audioDatastore('onlineDataset_v5-3','IncludeSubfolders',true,'LabelSource','foldernames');
labelTable = countEachLabel(ads);
numLabels = size(labelTable,1);
numAudios = length(ads.Labels);

overlapPercentage=75;
num_folds=5;
for fold_idx=1:num_folds
    fprintf('processing %d among %d folds \n',fold_idx,num_folds);
    test_idx=fold_idx:num_folds:numAudios;
    adsTest=subset(ads,test_idx);
    testLabels = adsTest.Labels;
    testAudios = adsTest.Files;
%     testFeatures = yammet_features(testAudios);
    [testFeatures,testLabels,segmentsPerFile] = vggishPreprocess(adsTest,overlapPercentage);
    train_idx=setdiff(1:length(ads.Files),test_idx);
    adsTrainVlidation=subset(ads,train_idx);
    [adsTrain, adsValidation] = splitEachLabel(adsTrainVlidation,0.9,'randomized');
    
    trainLabels = adsTrain.Labels;
    uniqueLabels = unique(trainLabels);
    trainAudios =adsTrain.Files;
%     trainFeatures = yammet_features(trainAudios);
     [trainFeatures,trainLabels] = vggishPreprocess(adsTrain,overlapPercentage);
     
    validationLabels = adsValidation.Labels;
    validationAudios = adsValidation.Files;
%     validationFeatures = yammet_features(validationAudios);
    [validationFeatures,validationLabels] = vggishPreprocess(adsValidation,overlapPercentage);
    clear net;
    net = load('yamnet1.mat');
    net =net.trainedNet;
    lgraph = layerGraph(net.Layers);
    lgraph = replaceLayer(lgraph,"dense",fullyConnectedLayer(numLabels,"Name","dense"));
    lgraph = replaceLayer(lgraph,"Sounds",classificationLayer("Name","Sounds","Classes",uniqueLabels));
    layers = lgraph.Layers;
    for i = 1:21
        if isprop(layers(i),'WeightLearnRateFactor')
            layers(i).WeightLearnRateFactor = 0;
        end
        if isprop(layers(i),'WeightL2Factor')
            layers(i).WeightL2Factor = 0;
        end
        if isprop(layers(i),'BiasLearnRateFactor')
            layers(i).BiasLearnRateFactor = 0;
        end
        if isprop(layers(i),'BiasL2Factor')
            layers(i).BiasL2Factor = 0;
        end
   end
    lgraph = layerGraph(layers);
     miniBatchSize = 32;
     options = trainingOptions('adam',...
        'MaxEpochs',20,'MiniBatchSize',miniBatchSize,...
        'Shuffle','every-epoch', ...
         'ValidationData',{single(validationFeatures),validationLabels}, ...
        'Verbose',false, ...
        'Plots','training-progress',...
        'ValidationPatience',7,'ExecutionEnvironment','gpu','InitialLearnRate',0.0001);
  
    [trainedNet, netInfo] =trainNetwork(single(trainFeatures),trainLabels,lgraph,options);
    [testPredictions scores] = classify(trainedNet,single(testFeatures));
    save(['BW-yamnet.1',num2str(fold_idx),'.mat'],'trainedNet');
    save(['info',num2str(fold_idx),'.mat'],'netInfo');
    idx = 1;
    for ii = 1:numel(adsTest.Files)
        testPredictionsPerFile(1,test_idx(ii)) = mode(testPredictions(idx:idx+segmentsPerFile(ii)-1));
%         scoresPerFile(1,test_idx(ii)) = mode(scores(idx:idx+segmentsPerFile(ii)-1));
         scoresPerFile(test_idx(ii),:) = mode(scores(idx:idx+segmentsPerFile(ii)-1,:));
        idx = idx + segmentsPerFile(ii);
    end
end
actual_labels=ads.Labels;

% Confusion Matrix
figure;
plotconfusion(actual_labels,testPredictionsPerFile')
title('');

% figure;
% [X,Y,T,AUC]=perfcurve(actual_labels,scoresPerFile,'brainwave entrainment');
% AUC
% plot(X,Y);
% xlabel('False positive rate') 
% ylabel('True positive rate')
% title('ROC Curve')
% figure;
% [X1,Y1,T,AUC1]=perfcurve(actual_labels,scoresPerFile(:,1),'alpha');
% plot(X1,Y1);
% hold on
% 
% [X2,Y2,T,AUC2]=perfcurve(actual_labels,scoresPerFile(:,2),'beta');
% plot(X2,Y2);
% hold on
% 
% [X3,Y3,T,AUC3]=perfcurve(actual_labels,scoresPerFile(:,3),'delta');
% plot(X3,Y3);
% hold on
% 
% [X4,Y4,T,AUC4]=perfcurve(actual_labels,scoresPerFile(:,6),'theta');
% plot(X4,Y4);
% hold on
% [X5,Y5,T,AUC5]=perfcurve(actual_labels,scoresPerFile(:,4),'gamma');
% plot(X5,Y5);
% hold off
% AUC = (AUC1+AUC2+AUC3+AUC4+AUC5)/5
% legend('alpha','beta','delta','theta','gamma','Location','SE');
% xlabel('False positive rate') 
% ylabel('True positive rate')
% title('ROC Curve')
end