csci5521/gauss_class/gauss_class_2D.m

% CSCI 5521 Introduction to Machine Learning
% Rui Kuang
% Demonstration of Classification by 2-D Gaussians 

clf;
prior1 = 0.3;
prior2 = 0.7;

% mvndis = @(X, mu, Sigma, prior) ( ...
%     -1/2 * log(2*pi) ...
%     - log(Sigma) ...
%     - power(X - mu, 2) / (2 * power(Sigma, 2)) ...
%     + log(prior) ...
% );

mu1 = [-1 -1];
mu2 = [1 1];

% Equal diagnoal covariance matrix
% Sigma1 = [1 0; 0 1];
% Sigma2 = [1 0; 0 1];

% Diagnoal covariance matrix
% Sigma1 = [1 0; 0 0.5];
% Sigma2 = [1 0; 0 0.5];

% Shared covariance matrix
% Sigma1 = [1 0.3; 0.3 0.5];
% Sigma2 = [1 0.3; 0.3 0.5];

 x1 = -10:.1:10; x2 = -10:.1:10;

% covariance matrix (increase the range for visualization)
Sigma1 = [1 0.1; 0.1 0.5];
Sigma2 = [0.5 0.3; 0.3 1];
x1 = -40:.1:40; x2 = -40:.1:40;

[X1,X2] = meshgrid(x1,x2);
%pdf1
% F1 = mvnpdf([X1(:) X2(:)],mu1,Sigma1);
F1 = mvndis([X1(:) X2(:)], mu1, Sigma1, prior1);
F1 = reshape(F1,length(x2),length(x1));
subplot(1,2,1);
surf(x1,x2,F1); hold on;

%pdf2
% F2 = mvnpdf([X1(:) X2(:)],mu2,Sigma2);
F2 = mvndis([X1(:) X2(:)], mu2, Sigma2, prior2);
F2 = reshape(F2,length(x2),length(x1));
surf(x1,x2,F2);
caxis([min(F2(:))-.5*range(F2(:)),max(F2(:))]);
axis([-4 4 -4 4 0 .4])
xlabel('x1'); ylabel('x2'); zlabel('Probability Density');

%decosopm boundary
%F1 = mvnpdf([X1(:) X2(:)],mu1,Sigma1);
%F1 = reshape(F1,length(x2),length(x1));
%F2 = mvnpdf([X1(:) X2(:)],mu2,Sigma2);
%F2 = reshape(F2,length(x2),length(x1));

cmp = F1 > F2;
subplot(1,2,2);
imagesc(X1(:),X2(:),cmp);

xlabel('x1'); ylabel('x2');


function res = mvndis(X, mu, Sigma, prior)
    [len, d] = size(X);
    res = zeros(len, 1);

    for i = 1:len
        x = X(i,:);
        mdist = (x - mu) * inv(Sigma) * (x - mu).';
        res(i) = -d/2*log(2*pi) - 1/2*log(det(Sigma)) - 1/2*mdist + log(prior);
    end
    % 1 x 2
    % (1 x 2) x ((2 x 2) x (2 x 1))
    % X - mu = 40401 x 2
    % (40401 x 2) x (2 x 2) x (2 x 40401)
    % mdist = (X - mu) * inv(Sigma) * (X - mu).';
    % res = -log(2*pi) - 1/2*log(det(Sigma)) - 1/2*mdist + log(prior)
    % res = zeros(size(X));
    % [l1, l2] = size(X);
    % 
    % for i1 = 1:l1
    %     for i2 = 1:l2
    %         cell = -log(2*pi) - 1/2*log(det(Sigma))
    %         res(i1, i2) = cell
    %     end
    % end
end