Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex7-008/mlclass-ex7/pca.m

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+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).
+%
+M = X'*X / m;
+
+[U S x ] = svd(M);
+
+
+
+
+
+
+% =========================================================================
+
+end