function [train_data, d, test_data, d_test] = readTrainingData(distribution)

total_files = 10; % Összes fájl száma
total_data_per_file = 1000; % Egy fájlban lévő összes adat
if(distribution>=1)
    distribution = 0.9;
    error("Distribution can't be bigger or equal than 1.")
end

train_data_per_file = round(total_data_per_file*distribution);

test_data_per_file = round(total_data_per_file*(1-distribution));
train_data = zeros(train_data_per_file*total_files, 785); % Tanító halmaz inicializálása
d = zeros(train_data_per_file * total_files, 10);
d(:) = -1;

test_data = zeros(total_files * test_data_per_file, 785); % Teszt halmaz inicializálása
d_test = zeros(total_files * test_data_per_file, 10);
d_test(:) = -1;

d_counter = 1;
d_test_counter = 1;

for file_index = 0:(total_files - 1)
    file_name = sprintf('data%d', file_index); % Aktuális fájl neve
    fid = fopen(file_name, 'r');

    for i = 1:total_data_per_file
        t = fread(fid, [28 28], 'uchar');% Beolvasás

        % 2D mátrix átalakítása 1D vektorrá, ahol 0 van a mátrixban, ott is 0 marad, egyébként 1
        t_vector = double(t(:)'./255);
        t_vector_with_ones = [1, t_vector]; % Minden sor elejére beszúrunk egy 1-est

        if i <= train_data_per_file
            % Tanító halmazba mentés
            train_data(d_counter, :) = t_vector_with_ones;
            d(d_counter, file_index + 1) = 1;
            d_counter = d_counter + 1;
        else
            % Teszt halmazba mentés
            test_data(d_test_counter, :) = t_vector_with_ones;
            d_test(d_test_counter, file_index + 1) = 1;
            d_test_counter = d_test_counter + 1;
        end
    end

    fclose(fid);
end
end