function [X, t] = file_creation_2(A, B, g, t0, tf, N, Nh, N_samples, control_index, add_noise, path, mode)
% Creates train/test dataset
% Inputs :
%           A : Matrix of the ODE system
%           B : Matrix of linear combination
%           g : Linear part in the ODE system
%           choices : choices for functions
%           t0 : Initial time            
%           tf : Final time
%           N : Number of input/output parameters
%           Nh : Resolution of the grid
%           N_samples : Number of samples desired
%           add_noise : Boolean, for adding noise or not
%           path : path to the folder where we create the dataset
%           mode : 'test' or 'train'
%
% Outputs :
%           X : Dataset
%           y : Labels
%           t : time interval


if add_noise
    noise = random('Normal', 0, 1e-0, [N,Nh+1]);
else
    noise = 0;
end

% Iterate over the samples
for i=0:N_samples-1
    % Generate random initial condition
    y0 = randi(20,1)*rand(N,1);
        
    % Solve the system of ODEs using Backward Euler
    [t, X] = backward_euler(A, g, y0, t0, tf, Nh);
    
    % Add noise
    X = X + noise;
    
    % Apply the linear combination of the inputs
    C = X' * B;
    C = C';
    
    % Open the target and input files to write the solutions
    fileID2 = fopen(strcat(path, 'files_', num2str(N), '/', mode, '/target/file_', num2str(i), '.txt'),'w');
    fileID3 = fopen(strcat(path, 'files_', num2str(N), '/', mode, '/input/file_', num2str(i), '.txt'),'w');

    for j = 1:N
        
        if j == control_index
            % Write in the files...
            for t_=1:Nh+1
                % ... the input.
                fprintf(fileID3, '%f1 ', X(j,t_));
            end
        else
            % Write in the files...
            for t_=1:Nh+1
                % ... the target.
                fprintf(fileID2, '%f2 ', X(j,t_));
            end
        end
        
        fprintf(fileID2,'\n');
        fprintf(fileID3,'\n');
    end
    fclose(fileID2);
    fclose(fileID3);
    
end

not_control_indexes = [];
for j = 1:N
    if j ~= control_index
        not_control_indexes = [not_control_indexes, j];
    end
end
% Plot a sample of the inputs and the targets
figure
plot(t, X(not_control_indexes(1), :), '-r', t, X(not_control_indexes(2), :), 'b-', t, X(control_index, :), 'c--');
title(mode)
legend('sample\_output1', 'sample\_ouput2', 'sample\_label1')
grid; 


return