ml_hw_5_pca.md

mean = [5;5;10];
sigma = [6 -3 0; -3 6 0; 0 0 10];
no_of_samples = 100000;
samples = mvnrnd(mean, sigma, no_of_samples);
covariances = cov(samples);
[v, D, W] = eig(covariances);
v(:,1)
D(1,1)

v(:,2)
D(2,2)

v(:,3)
D(3,3)


% alternatively
[coeff, scores, latent ] = pca(samples)
coeff % columns are pca basis
latent % eigenvals