% moving to runDiffFeatures simulation
%numFeatures = 10;
numTrain = 10;
numTest = 100;
classPriors = [0.5 0.5];
okData = 0;
while okData == 0
    num1 = 0;
    num2 = 0;
    for i = 1:numTrain
        [f1 f2] = sim1overN(numFeatures);
        %   [f1 f2] = simMutuallyExclusive(numFeatures, 0.9);
        randNumber = rand;
        if( randNumber < classPriors(1) )
            trainMat(i,1:numFeatures) = f1;
            trainMat(i,numFeatures+1) = 1;
            num1 = num1 + 1;
        else
            trainMat(i,1:numFeatures) = f2;
            trainMat(i, numFeatures+1) = 2;
            num2 = num2 + 1;
        end
    end
    if(num1 == 0 || num2 == 0)
        okData = 0;
        disp('picked all training data from one class only');
    else
        okData = 1;
    end
end
for i =1:numTest
     [f1 f2] = sim1overN(numFeatures);
%    [f1 f2] = simMutuallyExclusive(numFeatures, 0.9);
    if(rand < classPriors(1))
        testMat(i,1:numFeatures) = f1;
        testMat(i,numFeatures+1) = 1;
    else
        testMat(i,1:numFeatures) = f2;
        testMat(i, numFeatures+1) = 2;
    end
end
% Call Classifiers
%centroidParamClassVect = calcCentroidalParametricClass(testMat(:,1:numFeatures), trainMat, classPriors);
centroidParamClassVect = calcCentroidalParametricClassX(testMat(:,1:numFeatures), trainMat, classPriors);
for i=1:numTest
    oneNNclassVect(i) = simplekNN(testMat(i,1:numFeatures), trainMat, 1);
    threeNNclassVect(i) = simplekNN(testMat(i,1:numFeatures), trainMat, 3);
    fiveNNclassVect(i) = simplekNN(testMat(i,1:numFeatures), trainMat, 5);    
    trainMat1 = trainMat(find(trainMat(:, numFeatures+1)==1),1:numFeatures);
    trainMat2 = trainMat(find(trainMat(:, numFeatures+1)==2),1:numFeatures);    
    centroidClassVect(i) = centroid(testMat(i,1:numFeatures), trainMat1, trainMat2);
    AvgcentroidClassVect(i) = Avgcentroid(testMat(i,1:numFeatures), trainMat1(:,1:numFeatures), trainMat2(:,1:numFeatures));    
end


testClass = testMat(:, numFeatures+1);
% Calculate error

oneNNError = sum(abs(testClass' - oneNNclassVect));
threeNNError = sum(abs(testClass' - threeNNclassVect));
fiveNNError = sum(abs(testClass' - fiveNNclassVect));
centroidClassError = sum(abs(testClass' - centroidClassVect));
AvgcentroidClassError = sum(abs(testClass' - AvgcentroidClassVect));
centroidParamClassError = sum(abs(testClass' - centroidParamClassVect));
% oneNNError
% threeNNError
% fiveNNError
% centroidClassError
% AvgcentroidClassError
% centroidParamClassError