%%Variable Declarations
count = 0;
numShips = 20;
numFeatures = 7;
numPlanets = 4;
fMatrix = zeros(numFeatures,numShips);
AMatrix = zeros(numPlanets,numFeatures);
gMatrix = zeros(numPlanets,numShips);
numPermutations = 10;
kConstant = 0.25;
errorFormula = 0;
errorMatrix = zeros(numFeatures,numShips);
errorVector = zeros(numPermutations * numShips, 1);

%%Read-in training data
fid = fopen('trainingdata.txt');
trainingMatrix = textscan(fid, '%s%s%f32%f32%s%f32%s');
fclose(fid);

trainingNameVector = trainingMatrix{1};
trainingPlanetVector = trainingMatrix{2};
trainingWarpVector = trainingMatrix{3};
trainingFreqVector = trainingMatrix{4};
trainingColorVector = trainingMatrix{5};
trainingRatioVector = trainingMatrix{6};
trainingActionVector = trainingMatrix{7};

%%Read-in noisy data
fid = fopen('noisydata.txt');
noisyMatrix = textscan(fid, '%s%s%s%s%s%s%s');
fclose(fid);

%%Put data into vectors based on features
noisyNameVector = noisyMatrix{1};
noisyPlanetVector = noisyMatrix{2};
noisyWarpVector = noisyMatrix{3};
noisyFreqVector = noisyMatrix{4};
noisyColorVector = noisyMatrix{5};
noisyRatioVector = noisyMatrix{6};
noisyActionVector = noisyMatrix{7};

%%Feature training
for i = 1:numShips
    %%Klingon Features
    if strcmp(trainingColorVector{i},'Black')
        fMatrix(5,i) = 1;
    elseif strcmp(trainingColorVector{i},'DarkGray')
        fMatrix(5,i) = 1;
    elseif trainingFreqVector(i) > 1000 && trainingFreqVector(i) < 1010
        fMatrix(4,i) = 1;
    end

    %%Romulan Features
    if strcmp(trainingColorVector{i},'Green')
        fMatrix(5,i) = 1;
    elseif strcmp(trainingColorVector{i},'DarkGreen')
        fMatrix(5,i) = 1;
    elseif ~strcmp(trainingColorVector{i},'DarkGray') && trainingWarpVector(i) > 7.1
        fMatrix(3,i) = 0.5;
    elseif ~strcmp(trainingColorVector{i},'Black') && trainingFreqVector(i) < 1000
        fMatrix(4,i) = 0.5;
    elseif ~strcmp(trainingColorVector{i},'DarkGray') && trainingFreqVector(i) < 1000
        fMatrix(4,i) = 0.5;
    end

    %%Antarean Features
    if strcmp(trainingColorVector{i},'Pink')
        fMatrix(5,i) = 1;
    elseif strcmp(trainingColorVector{i},'Yellow')
        fMatrix(5,i) = 1;
    elseif strcmp(trainingColorVector{i},'Orange')
        fMatrix(5,i) = 1;
    elseif findstr('A',trainingNameVector{i})
        fMatrix(1,i) = 1;
    elseif findstr('E',trainingNameVector{i})
        fMatrix(1,i) = 1;
    elseif findstr('0' || '1' || '2' || '3' || '4' || '5' || '6' || '7' || '8' || '9',trainingNameVector{i})
        fMatrix(1,i) = 1;
    end

    %%Federation Features
    if strcmp(trainingColorVector{i},'White')
        fMatrix(5,i) = 1;
    elseif trainingWarpVector(i) < 6.8 && trainingFreqVector(i) > 1040
        fMatrix(3,i) = 1;
    elseif strcmp('LightGray',trainingColorVector{i}) && trainingFreqVector(i) > 1040
        fMatrix(4,i) = 1;
    elseif strcmp('LightBlue',trainingColorVector{i}) && trainingFreqVector(i) > 1040
        fMatrix(4,i) = 1;
    elseif strcmp('LightColor',trainingColorVector{i}) && trainingFreqVector(i) > 1040
        fMatrix(4,i) = 1;
    end
end

%%Create g Matrix
for i = 1:numShips
    if i < 6
        gMatrix(1,i) = 1;
    elseif i < 11 && i > 5
        gMatrix(2,i) = 1;
    elseif i < 16 && i > 10
        gMatrix(3,i) = 1;
    elseif i < 21 && i > 15
        gMatrix(4,i) = 1;
    end
end

for i = 1:numPermutations
    %%Determine the order in which variables will be extracted
    order = randperm(numShips);

    for j = 1:numShips
        %%Keep track of the position in the total error vector
        count = count + 1;

        %%Extract corresponding vectors f and g based on order
        f = fMatrix(:,order(j));
        g = gMatrix(:,order(j));

        %%Find g' based on the current A
        gPrime = AMatrix * f;

        %%Calculate the error based on the given formula
        errorFormula = kConstant * (g - gPrime) * f';

        %%Make modifications to A based on the results from the formula
        AMatrix = AMatrix + errorFormula;

        %%Create a matrix of g'
        gPrimeMatrix = AMatrix * fMatrix;

        %%Calculate a matrix of errors based on what g is supposed to be
        %%compared to what we have our g'
        errorMatrix = gMatrix - gPrimeMatrix;

        %%Calculate total error and get the average
        totalError = 0;
        for k = 1:numShips
            totalError = totalError + norm(errorMatrix(:,k));
        end
        errorVector(count) = totalError / numShips;
    end
end