lg_reg2.asv

%% Initialization
clear ; close all; clc

%% Load Data
%  The first two columns contains the X values and the third column
%  contains the label (y).

trainingdata = csvread('finaldata.csv');
X = trainingdata(:, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); y = trainingdata(:, 11);



%% Regularized Logistic Regression
% Doing Feature Normalization
[X, mu, sigma] = featureNormalize(X);

% Initialize fitting parameters
initial_theta = ones(size(X, 2), 1);
display(initial_theta);

% Set regularization parameter lambda to 1
lambda = 0.02;


% Compute and display initial cost and gradient for regularized logistic
% regression
[cost, grad] = costFunctionReg(initial_theta, X, y, lambda);

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

fprintf('\nProgram paused. Press enter to continue.\n');
pause;

% Set Options
options = optimset('GradObj', 'on', 'MaxIter', 400);

% Optimizing to get theta
[theta, J, exit_flag] = ...
	fmincg(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);

% Compute accuracy on our training set
p = predict(theta, X);

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

testdata = csvread('testdata.csv');
X2 = testdata(:, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); y = testdata(:, 11);

p2 = predict(theta, X);