clear; close all; clc

%% Load Data

data = load('data.txt');

X = data(:, 2:101); y = data(:, 1);

data_test = load('datatest.txt');

X_test = data_test(:, 2:101);  y_test = data_test(:, 1);

%% ============ Compute Cost and Gradient ============

[m, n] = size(X);

[m1, n1] = size(X_test);

X = [ones(m, 1) X];

X_test = [ones(m1, 1) X_test];

initial_theta = zeros(n + 1, 1);

% Compute and display initial cost and gradient

[cost, grad] = costFunction(initial_theta, X, y);

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

% fprintf('Gradient at initial theta (zeros): \n');

% fprintf(' %f \n', grad);

%% ============= Optimizing using fminunc  =============

options = optimset('GradObj', 'on', 'MaxIter', 400,'TolFun', 01e-10);

[theta, cost] = ...

    fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);

fprintf('Cost at theta found by fminunc: %f\n', cost);

% fprintf('theta: \n');

% fprintf(' %f \n', theta);

%% ============== Predict and Accuracies ==============

p = predict(theta, X);

fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);

ptest = predict(theta, X_test);

fprintf('Testing Accuracy: %f\n', mean(double(ptest == y_test)) * 100);