function [ error, falseNeg, falsePos ] = UseBoostedFeatures

Faces = ReadBmps('G:\Download\project2\Face16');
faceSize = size(Faces, 1);

Nonfaces = ReadBmps('G:\Download\project2\Nonface16');
nonFaceSize = size(Nonfaces, 1);

Results = zeros(200, 3);

%test start here

for w=2:200

    resultFromAdaboost = zeros(8, w); %change this for how many boosted youre testing
    resultFromAdaboost = dlmread(strcat('selectedFeaturesBoosted', num2str(w), '.txt'));

    halfSumTotalWeight = 0;

    for y=1:size(resultFromAdaboost, 2);
        beta = resultFromAdaboost(8, y) / (1 - resultFromAdaboost(8, y));
        alpha = log10(1/beta);
        halfSumTotalWeight = halfSumTotalWeight + alpha;
    end

    halfSumTotalWeight = .5 * halfSumTotalWeight;

    falseNeg = 0;
    falsePos = 0;
    totalError = 0;

    for faceImage=1:faceSize
        accumulation = 0;
        for l=1:size(resultFromAdaboost, 2);
            classification = 0;
             i = resultFromAdaboost(1, l);
             j = resultFromAdaboost(2, l);
             rows = resultFromAdaboost(3, l);
             columns = resultFromAdaboost(4, l);
             threshold = resultFromAdaboost(5, l);
             type = resultFromAdaboost(7, l);

             if(type == 1)
                upperLeft = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j, i+(rows/2)-1, j+(columns/2)-1);
                upperRight = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j+(columns/2), i+(rows/2)-1, j+(columns)-1);
                lowerRight = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i+(rows/2), j+(columns/2), i+(rows)-1, j+(columns)-1);
                lowerLeft = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i+(rows/2), j, i+(rows)-1, j+(columns/2)-1);
                Result = upperRight + lowerLeft - upperLeft - lowerRight;

                if Result >= threshold %correct
                    classification = 1;
                else
                    classification = 0;
                end
             elseif(type == 2)
                top = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j, i+(rows/2)-1, j+(columns)-1);
                bottom = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i+(rows/2), j, i+(rows)-1, j+(columns)-1);
                Result = top - bottom;

                if Result >= threshold %correct
                    classification = 1;
                else
                    classification = 0;
                end
             elseif(type == 3)
                left = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j, i+(rows)-1, j+(columns/2)-1);
                right = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j+(columns/2), i+(rows)-1, j+(columns)-1);
                Result = right - left;

                if Result >= threshold %correct
                    classification = 1;
                else
                    classification = 0;
                end
             elseif(type == 4)
                leftThird = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j, i+(rows)-1, j+(columns/3)-1);
                middleThird = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j+(columns/3), i+(rows)-1, j+(2*columns/3)-1);
                rightThird = IntegralSum(reshape(Faces(faceImage,:,:), 16, 16), i, j+(2*columns/3), i+(rows)-1, j+(columns)-1);
                Result = middleThird - leftThird - rightThird;

                if Result >= threshold %correct
                    classification = 1;
                else
                    classification = 0;
                end
             end

            if(classification == 1)
                beta = resultFromAdaboost(8, l) / (1 - resultFromAdaboost(8, l));
                alpha = log10(1/beta);
                accumulation = accumulation + alpha;
            end

        end

        if accumulation >= halfSumTotalWeight %correct classification
        else
            falseNeg = falseNeg + 1;
            totalError = totalError + 1;
        end

    end

    for nonFaceImage=1:nonFaceSize
        accumulation = 0;
         for l=1:size(resultFromAdaboost, 2);
             classification = 0;
             i = resultFromAdaboost(1, l);
             j = resultFromAdaboost(2, l);
             rows = resultFromAdaboost(3, l);
             columns = resultFromAdaboost(4, l);
             threshold = resultFromAdaboost(5, l);
             type = resultFromAdaboost(7, l);

             if(type == 1)
                upperLeft = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j, i+(rows/2)-1, j+(columns/2)-1);
                upperRight = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j+(columns/2), i+(rows/2)-1, j+(columns)-1);
                lowerRight = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i+(rows/2), j+(columns/2), i+(rows)-1, j+(columns)-1);
                lowerLeft = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i+(rows/2), j, i+(rows)-1, j+(columns/2)-1);
                Result = upperRight + lowerLeft - upperLeft - lowerRight;

                if Result >= threshold %incorrect
                    classification = 1;
                else
                    classification = 0;
                end
             elseif(type == 2)
                top = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j, i+(rows/2)-1, j+(columns)-1);
                bottom = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i+(rows/2), j, i+(rows)-1, j+(columns)-1);
                Result = top - bottom;

                if Result >= threshold %incorrect
                    classification = 1;
                else
                    classification = 0;
                end
             elseif(type == 3)
                left = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j, i+(rows)-1, j+(columns/2)-1);
                right = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j+(columns/2), i+(rows)-1, j+(columns)-1);
                Result = right - left;

                if Result >= threshold %incorrect for non face
                    classification = 1;
                else
                    classification = 0;
                end
             elseif(type == 4)
                leftThird = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j, i+(rows)-1, j+(columns/3)-1);
                middleThird = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j+(columns/3), i+(rows)-1, j+(2*columns/3)-1);
                rightThird = IntegralSum(reshape(Nonfaces(nonFaceImage,:,:), 16, 16), i, j+(2*columns/3), i+(rows)-1, j+(columns)-1);
                Result = middleThird - leftThird - rightThird;

                if Result >= threshold %incorrect
                    classification = 1;
                else
                    classification = 0;
                end
             end

            if(classification == 1)
                beta = resultFromAdaboost(8, l) / (1 - resultFromAdaboost(8, l));
                alpha = log10(1/beta);
                accumulation = accumulation + alpha;
            end

        end

        if accumulation >= halfSumTotalWeight
            falsePos = falsePos + 1;
            totalError = totalError + 1;
        else

        end

    end

        error = totalError / 15175;

        sprintf('%f %f %f', error, falseNeg, falsePos)
        Results(w, 1) = error;
        Results(w, 2) = falseNeg;
        Results(w, 3) = falsePos;

end

dlmwrite('trainingErrors.txt', Results);

end