function theta = gradient_descent(X, y, theta, alpha, iterations, do_plot)
    %GRADIENT_DESCENT do Gradient Descent for a given X, y, theta, alpha
    %for a specified number of iterations

    %if less than 6 arguments was given, then set do_plot to be false
    if nargin < 6
        do_plot = false;
    end
    if(do_plot)
        plot_hypothesis(X, y, theta);
        drawnow; pause(0.1); 
    end

    m = size(X, 1); %number of training examples
    cost_vector = []; %will store the results of our cost function

    for it = 1:iterations
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        % gradient descent
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

        % fill temporary array with values of theta
        theta_temp = theta;
        for ind = 1:length(theta_temp)
            %update theta(1) and store in temporary variable theta_0
            sigma = 0.0;

            for i = 1:m
                hypothesis = calculate_hypothesis(X, theta, i);
                output = y(i);
                sigma = sigma + (hypothesis - output) * X(i, ind);
            end

            theta_temp(ind) = theta_temp(ind) - ((alpha * 1.0) / m) * sigma;
        end

        %update theta
        theta = theta_temp;

        %update cost_vector
        cost_vector = [cost_vector; compute_cost(X, y, theta)];

        if do_plot
            plot_hypothesis(X, y, theta);
            drawnow; pause(0.1); 
        end
    end

    disp 'Gradient descent is finished.'
        
    if do_plot
        disp 'Press enter!'
        pause;
    end

    plot_cost(cost_vector);
        
    disp 'Press enter!';
    pause;
end