machine-learning-hw7: pca.m comparison

Implement PCA

author	Jordi Gutiérrez Hermoso <jordigh@octave.org>
date	Tue, 06 Dec 2011 03:22:07 -0500
parents	ded78d0b4987
children

comparison

equal deleted inserted replaced

-:be1f915bd52a
+:069653867b3b
 function [U, S] = pca(X)
-%PCA Run principal component analysis on the dataset X
+##PCA Run principal component analysis on the dataset X
-%   [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X
+##   [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X
-%   Returns the eigenvectors U, the eigenvalues (on diagonal) in S
+##   Returns the eigenvectors U, the eigenvalues (on diagonal) in S
-%
+##
-% Useful values
+[U, S, ~] = svd (X'*X/rows (X));
-[m, n] = size(X);
-% You need to return the following variables correctly.
+endfunction
-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).
-%
-% =========================================================================
-end

Mercurial > hg > machine-learning-hw7