% below are the load commands for different databases
% depending on which database you would like to be processed
% just uncomment the related lines

load 'MSR Data and Results\coc81_1_1.csv;
dataset = coc81_1_1;
clear coc81_1_1;



% define the variables to keep tract of.. well pretty much everything
selectedBestLevels = -1 * ones(20,size(dataset,1));
varianceOfSelectedBestPlaces = -1 * ones(20,size(dataset,1));
varianceOfSelectedBestPlacesGAC2 = -1 * ones(20,size(dataset,1));
%selectedBestKValues = -1 * ones(20,size(dataset,1));

actuals = -1 * ones(20,size(dataset,1));
actualsWithFeatures = [];

pred0 = -1 * ones(20,size(dataset,1));
mre0 = -1 * ones(20,size(dataset,1));

predGac2 = -1 * ones(20,size(dataset,1));
mreGac2 = -1 * ones(20,size(dataset,1));
instanceSizeGac2 = -1 * ones(20,size(dataset,1));

pred1 = -1 * ones(20,size(dataset,1));
mre1 = -1 * ones(20,size(dataset,1));
pred2 = -1 * ones(20,size(dataset,1));
mre2 = -1 * ones(20,size(dataset,1));
pred4 = -1 * ones(20,size(dataset,1));
mre4 = -1 * ones(20,size(dataset,1));
pred8  = -1 * ones(20,size(dataset,1));
mre8 = -1 * ones(20,size(dataset,1));
pred16 = -1 * ones(20,size(dataset,1));
mre16 = -1 * ones(20,size(dataset,1));
predx = -1 * ones(20,size(dataset,1));
mrex = -1 * ones(20,size(dataset,1));
predFloatingK = -1 * ones(20,size(dataset,1));
mreFloatingK = -1 * ones(20,size(dataset,1));
floatingKValues = -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
        % the one below -treeK- is our guy to defend
        [pred0(counter,i), mre0(counter,i),selectedBestLevels(counter,i),varianceOfSelectedBestPlaces(counter,i), gac2Tree, gac2Root] = treeK(myRow,train);
        
        % at this point we have build our GAC tree -above executable line- and we can check the
        % instances with the second tree
        
        [predGac2(counter,i), mreGac2(counter,i),instanceSizeGac2(counter,i),varianceOfSelectedBestPlacesGAC2(counter,i)] = secondGac(gac2Tree, gac2Root, myRow);
        if predGac2(counter,i) == -1    % meaning there were not enough instances for gac2 tree
            i = i-1;
            continue;
        end
        
        % below are the ones for various k values
        [pred1(counter,i), mre1(counter,i)] = nnk(myRow,train,1);
        [pred2(counter,i), mre2(counter,i)] = nnk(myRow,train,2);
        [pred4(counter,i), mre4(counter,i)] = nnk(myRow,train,4);
        [pred8(counter,i), mre8(counter,i)] = nnk(myRow,train,8);
        [pred16(counter,i), mre16(counter,i)] = nnk(myRow,train,16);
        floatingKValues(counter,i) = bestFloatingK(myRow,train);
        %[predFloatingK(counter,i), mreFloatingK(counter,i)] = nnk(myRow,train,floatingKValues(counter,i));
        [predx(counter,i), mrex(counter,i)] = nnk(myRow,train,myBestK);
    end
    counter
end