Exercise3 - Week4 Finished

Author: ProgrammerYuan

machine-learning-ex3/ex3/lrCostFunction.m

@@ -35,7 +35,14 @@
 %           temp(1) = 0;   % because we don't add anything for j = 0  
 %           grad = grad + YOUR_CODE_HERE (using the temp variable)
 %
-
+h = sigmoid(X * theta);
+temp = theta;
+temp(1) = 0;
+positive_J = -y .* log(h);
+negative_J = (1 - y) .* log(1 - h);
+J = sum((positive_J - negative_J) / m) + lambda * sum(temp.^2) / (2 * m);
+
+grad = (X' * (h - y) + lambda * temp) / m;
 
 
 

machine-learning-ex3/ex3/oneVsAll.m

@@ -48,7 +48,14 @@
 %         fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
 %                 initial_theta, options);
 %
+initial_theta = zeros(n + 1, 1);
 
+for i = 1:num_labels
+    options = optimset('GradObj', 'on', 'MaxIter', 50);
+    [theta] = fmincg(@(t)(lrCostFunction(t, X, (y == i), lambda)), ...
+        initial_theta, options);
+    all_theta(i, :) = theta';
+end
 
 
 

machine-learning-ex3/ex3/predict.m

@@ -20,9 +20,15 @@
 %       information see 'help max'. If your examples are in rows, then, you
 %       can use max(A, [], 2) to obtain the max for each row.
 %
-
-
-
+X = [ones(m,1), X];
+
+z1 = Theta1 * X';
+a2 = sigmoid(z1);
+a2 = [ones(1, m);a2];
+z2 = Theta2 * a2;
+a3 = sigmoid(z2);
+[M, I] = max(a3);
+p = I';
 
 
 

machine-learning-ex3/ex3/predictOneVsAll.m

@@ -30,7 +30,9 @@
 %       for each row.
 %       
 
-
+result = all_theta * X';
+[M, I] = max(result);
+p = I';