Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex8-008/mlclass-ex8/checkCostFunction.m

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+function checkCostFunction(lambda)
+%CHECKCOSTFUNCTION Creates a collaborative filering problem 
+%to check your cost function and gradients
+%   CHECKCOSTFUNCTION(lambda) Creates a collaborative filering problem 
+%   to check your cost function and gradients, it will output the 
+%   analytical gradients produced by your code and the numerical gradients 
+%   (computed using computeNumericalGradient). These two gradient 
+%   computations should result in very similar values.
+
+% Set lambda
+if ~exist('lambda', 'var') || isempty(lambda)
+    lambda = 0;
+end
+
+%% Create small problem
+X_t = rand(4, 3);
+Theta_t = rand(5, 3);
+
+% Zap out most entries
+Y = X_t * Theta_t';
+Y(rand(size(Y)) > 0.5) = 0;
+R = zeros(size(Y));
+R(Y ~= 0) = 1;
+
+%% Run Gradient Checking
+X = randn(size(X_t));
+Theta = randn(size(Theta_t));
+num_users = size(Y, 2);
+num_movies = size(Y, 1);
+num_features = size(Theta_t, 2);
+
+numgrad = computeNumericalGradient( ...
+                @(t) cofiCostFunc(t, Y, R, num_users, num_movies, ...
+                                num_features, lambda), [X(:); Theta(:)]);
+
+[cost, grad] = cofiCostFunc([X(:); Theta(:)],  Y, R, num_users, ...
+                          num_movies, num_features, lambda);
+
+disp([numgrad grad]);
+fprintf(['The above two columns you get should be very similar.\n' ...
+         '(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']);
+
+diff = norm(numgrad-grad)/norm(numgrad+grad);
+fprintf(['If your backpropagation implementation is correct, then \n' ...
+         'the relative difference will be small (less than 1e-9). \n' ...
+         '\nRelative Difference: %g\n'], diff);
+
+end