lose all;
clear all;

mnistImgs   = loadMNISTImages('../../../Database/MNIST/train-images.idx3-ubyte');
mnistLabels = loadMNISTLabels('../../../Database/MNIST/train-labels.idx1-ubyte');

nDigits     = 10;
labelsLen   = length(mnistLabels);

% Transform the labels to correct target values.
targetValues = zeros(nDigits, labelsLen);
for n = 1 : labelsLen
    % +1 to avoid 0 index
    targetValues(mnistLabels(n) + 1, n) = 1;
end;

nNeurons       = 20;
batchSize      = 100;
epochs         = 500;
learningRate   = 0.1;
activFunction  = @logisticSigmoid;
dActivFunction = @dLogisticSigmoid;

fprintf('Number of neurons: %d\n', nNeurons);
fprintf('Number of Epochs:  %d\n', epochs);
fprintf('Batch number:      %d\n', batchSize);
fprintf('Learning rate:     %d\n', learningRate);

[hiddenWeights, ...
 outputWeights, ...
 error] = trainStochasticSquaredErrorTwoLayerPerceptron(activFunction,
                                                        dActivFunction,
                                                        nNeurons,
                                                        mnistImgs,
                                                        targetValues,
                                                        epochs,
                                                        batchSize,
                                                        learningRate);

toFile(hiddenWeights, "hiddenweights");

toFile(outputWeights, "outputweights");

# NOWE

labels = [0,1,2,3,4,5,6,7,8,9];

predicted_labels = zeros(1, length(digit_data_base));

for idx_img in range(length(digit_data_base)):
            inputVector = digit_data_base(idx_img)
            hiddenActualInput  = hiddenWeights * inputVector;
            hiddenOutputVector = activFunction(hiddenActualInput);

            outputActualInput  = outputWeights * hiddenOutputVector;
            outputVector       = activFunction(outputActualInput);

            predicted_labels(idx_img) = labels(idx_from_max(outputVector))


cnf_matrix = confusion_matrix(predicted_labels , mnistLabels )