week 8 done

Author: Mark Linderman

machine-learning-ex7/ex7/ex7_pca.m

@@ -144,6 +144,8 @@
 %  If you are applying a machine learning algorithm 
 fprintf('\nDimension reduction for face dataset.\n\n');
 
+fprintf('\nSize of X_norm before projection onto Z: ')
+fprintf('%d ', size(X_norm));
 K = 100;
 Z = projectData(X_norm, U, K);
 

machine-learning-ex7/ex7/projectData.m

@@ -18,8 +18,10 @@
 %                    projection_k = x' * U(:, k);
 %
 
-Z = X * U(:, K);
+% result should be 5000 x 100 for the faces dataset because we're projecting down to 100 features from 1024
+% because X is 5000 x 1024 and U is 1024 x 1024 (remember, U was calculated by multiplying X by transpose(X)
 
+Z = X * U(:, 1:K);
 
 % =============================================================
 

machine-learning-ex7/ex7/recoverData.m

@@ -21,7 +21,8 @@
 %               Notice that U(j, 1:K) is a row vector.
 %               
 
-X_rec = Z * transpose(U(:, K));
+
+X_rec = Z * transpose(U(:,1:K));
 
 % =============================================================
 

machine-learning-ex7/ex7/token.mat

@@ -1,4 +1,4 @@
-# Created by Octave 4.4.1, Sun Feb 17 21:26:38 2019 EST <[email protected]>
+# Created by Octave 4.4.1, Sat Feb 23 17:12:59 2019 EST <[email protected]>
 # name: email
 # type: sq_string
 # elements: 1