clear all; close all; clc;

alpha = 0.01;
num_iters = 1000;

%% Plotting data
x1 = linspace(0,3,50);
mqtrue = 5;
cqtrue = 30;
dat1 = mqtrue*x1+5*randn(1,50);

x2 = linspace(7,10,50);
dat2 = mqtrue*x2 + (cqtrue + 5*randn(1,50));

x = [x1 x2]'; % X

subplot(2,2,1);
dat = [dat1 dat2]'; % Y

scatter(x1, dat1); hold on;
scatter(x2, dat2, '*'); hold on;
classdata = (dat>40);

%  Setup the data matrix appropriately, and add ones for the intercept term
[m, n] = size(x);

% Add intercept term to x and X_test
x = [ones(m, 1) x];

% Initialize fitting parameters
theta = zeros(n + 1, 1);
%initial_theta = [0.2; 0.2];

J_history = zeros(num_iters, 1);

plot_x = [min(x(:,2))-2,  max(x(:,2))+2]

for iter = 1:num_iters
% Compute and display initial cost and gradient
    [cost, grad] = logistic_costFunction(theta, x, classdata);
    theta = theta - alpha * grad;
    J_history(iter) = cost;

    fprintf('Iteration #%d - Cost = %d... rn',iter, cost);


    subplot(2,2,2);
    hold on; grid on;
    plot(iter, J_history(iter), '.r');  title(sprintf('Plot of cost against number of iterations. Cost is %g',J_history(iter)));
    xlabel('Iterations')
    ylabel('MSE')
    drawnow

    subplot(2,2,3);
    grid on;
    plot3(theta(1), theta(2), J_history(iter),'o')
    title(sprintf('Tita0 = %g, Tita1=%g', theta(1), theta(2)))
    xlabel('Tita0')
    ylabel('Tita1')
    zlabel('Cost')
    hold on;
    drawnow

    subplot(2,2,1);
    grid on;
    % Calculate the decision boundary line
    plot_y = theta(2).*plot_x + theta(1);  % <--- Boundary line
    % Plot, and adjust axes for better viewing
    plot(plot_x, plot_y)
    hold on;
    drawnow

end

fprintf('Cost at initial theta (zeros): %fn', cost);
fprintf('Gradient at initial theta (zeros): n');
fprintf(' %f n', grad);