function [U, S] = pca(X)
%PCA Run principal component analysis on the dataset X
%   [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X
%   Returns the eigenvectors U, the eigenvalues (on diagonal) in S
%

% Useful values
[m, n] = size(X);

% You need to return the following variables correctly.
U = zeros(n);
S = zeros(n);

% ====================== YOUR CODE HERE ======================
% Instructions: You should first compute the covariance matrix. Then, you
%               should use the "svd" function to compute the eigenvectors
%               and eigenvalues of the covariance matrix. 
%
% Note: When computing the covariance matrix, remember to divide by m (the
%       number of examples).
%

Sigma = (1 / m) * X' * X;
[U, S] = svd(Sigma);

% =========================================================================

end

JY-112553
github-repos/MATLAB/JY-112553-machine-learning/machine-learning-db9c6e5a5175739821acd97787453472b8f46cac/machine-learning-ex7/ex7/pca.m
machine-learning
github-repos/MATLAB/JY-112553-machine-learning
db9c6e5a5175739821acd97787453472b8f46cac