for datasetCounter = 1:3
    % define datasets of experiment
    allMyDatasets = {'desharnais','coc81_1_1','COCOMO_NASA93'};
    
    eval(['load ',char(allMyDatasets(datasetCounter)),'.csv;']);
    eval(['dataset = ',char(allMyDatasets(datasetCounter)),';']);
    eval(['clear ',char(allMyDatasets(datasetCounter)),';']);
    
    for kernelCounter = 1:1
        
        % define datasets of experiment
        %                 allMyKernels = {'epanechnikov','gaussian','uniform','triangular'};
        allMyKernels = {'mendes'};
        %define current kernel
        currentKernel = allMyKernels(kernelCounter);
        
        for bandwidthCounter = 1:1
            % define bandwidths of experiment
            allMyBandwidhts = [1;2;3;4;5]; %1,2,3,4,5 are codes not actuals
            
            % define current bandwidth
            currentBandwidht = allMyBandwidhts(bandwidthCounter);
            
            actuals = -1 * ones(20,size(dataset,1));
            actualsWithFeatures = [];
            
            pred1 = -1 * ones(20,size(dataset,1));
            mre1 = -1 * ones(20,size(dataset,1));
            pred1 = -1 * ones(20,size(dataset,1));
            mre1Kernel = -1 * ones(20,size(dataset,1));
            
            pred3 = -1 * ones(20,size(dataset,1));
            mre3 = -1 * ones(20,size(dataset,1));
            pred3Kernel = -1 * ones(20,size(dataset,1));
            mre3Kernel = -1 * ones(20,size(dataset,1));
            
            pred5 = -1 * ones(20,size(dataset,1));
            mre5 = -1 * ones(20,size(dataset,1));
            pred5Kernel = -1 * ones(20,size(dataset,1));
            mre5Kernel = -1 * ones(20,size(dataset,1));
            
            pred7 = -1 * ones(20,size(dataset,1));
            mre7 = -1 * ones(20,size(dataset,1));
            pred7Kernel = -1 * ones(20,size(dataset,1));
            mre7Kernel = -1 * ones(20,size(dataset,1));
            
            pred9 = -1 * ones(20,size(dataset,1));
            mre9 = -1 * ones(20,size(dataset,1));
            pred9Kernel = -1 * ones(20,size(dataset,1));
            mre9Kernel = -1 * ones(20,size(dataset,1));
            
            predBestK = -1 * ones(20,size(dataset,1));
            mreBestK = -1 * ones(20,size(dataset,1));
            predBestKKernel = -1 * ones(20,size(dataset,1));
            mreBestKKernel = -1 * ones(20,size(dataset,1));
            
            % a dummy to keep track of whether the best k value was found
            bestKFound = 0;
            % find the best k value for the train data
            if bestKFound == 0
                myBestK = bestk(dataset);
            end
            
            % repeat 20 times
            for counter = 1:20
                
                % randomize dataset
                dataset = randomizeDataset(dataset);
                
                % for each row in the randomized dataset do the following
                i = 0;
                while i < size(dataset,1)
                    % increment i by 1
                    i = i + 1;
                    
                    % pick up the row
                    myRow = dataset(i,:);
                    
                    % record it into actuals
                    actuals(counter,i) = myRow(1,size(myRow,2));
                    % record also the features of that instance (since a lot of instances
                    % have the same effort value, I needed to record the features too)
                    actualsWithFeatures = [actualsWithFeatures;myRow(1,:)];
                    
                    
                    % train is the dataset minus the selected row
                    train = dataset;
                    train(i,:) = [];
                    
                    % now start predictions
                    % below are the ones for various k values
                    [pred3(counter,i), mre3(counter,i), sortedEffortValues] = nnkForKernel(myRow,train,3);
                    [pred3Kernel(counter,i), mre3Kernel(counter,i)] = myKernelEstimator(myRow, train, currentKernel, sortedEffortValues, currentBandwidht);
                    
                    [pred5(counter,i), mre5(counter,i), sortedEffortValues] = nnkForKernel(myRow,train,5);
                    [pred5Kernel(counter,i), mre5Kernel(counter,i)] = myKernelEstimator(myRow, train, currentKernel, sortedEffortValues, currentBandwidht);
                    
                    [pred7(counter,i), mre7(counter,i), sortedEffortValues] = nnkForKernel(myRow,train,7);
                    [pred7Kernel(counter,i), mre7Kernel(counter,i)] = myKernelEstimator(myRow, train, currentKernel, sortedEffortValues, currentBandwidht);
                    
                    [pred9(counter,i), mre9(counter,i), sortedEffortValues] = nnkForKernel(myRow,train,9);
                    [pred9Kernel(counter,i), mre9Kernel(counter,i)] = myKernelEstimator(myRow, train, currentKernel, sortedEffortValues, currentBandwidht);
                    
                    [predBestK(counter,i), mreBestK(counter,i), sortedEffortValues] = nnkForKernel(myRow,train,myBestK);
                    [predBestKKernel(counter,i), mreBestKKernel(counter,i)] = myKernelEstimator(myRow, train, currentKernel, sortedEffortValues, currentBandwidht);
                    
                end
                %                                 counter
            end
            currentKernel
            currentBandwidht
            %             kernelMdMREMMRECalculator(mre3,mre3Kernel,mre5,mre5Kernel,mre7,mre7Kernel,mre9,mre9Kernel,mreBestK,mreBestKKernel);
            kernelPred25Calculator(mre3,mre3Kernel,mre5,mre5Kernel,mre7,mre7Kernel,mre9,mre9Kernel,mreBestK,mreBestKKernel);
            
        end
    end
end