완료

ex3/lrCostFunction.m

@@ -36,10 +36,11 @@ grad = zeros(size(theta));
 %           grad = grad + YOUR_CODE_HERE (using the temp variable)
 %
 
+hx = sigmoid(X*theta);
+%J = sum(-y.*log(hx)-(1.-y).*log(1.-hx))/m + lambda*sum([0; theta(2:end)].^2)/(2*m);
+J = (-y'*log(hx)-(1.-y)'*log(1.-hx))/m + lambda*sum([0; theta(2:end)].^2)/(2*m);
 
-
+grad = X'*(hx-y)/m + lambda*[0; theta(2:end)]/m;
-
-
 
 
 

ex3/oneVsAll.m

@@ -49,13 +49,13 @@ X = [ones(m, 1) X];
 %                 initial_theta, options);
 %
 
+for c=1:num_labels
 
+  initial_theta = zeros(n + 1, 1);
+  options = optimset('GradObj', 'on', 'MaxIter', 50);
+  [theta] = fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), initial_theta, options);
 
-
+  all_theta(c,:) = theta';
-
-
-
-
 
 
 

ex3/predict.m

@@ -21,11 +21,13 @@ p = zeros(size(X, 1), 1);
 %       can use max(A, [], 2) to obtain the max for each row.
 %
 
+X = [ones(m, 1), X];
+a2 = sigmoid(X*Theta1');
+a2 = [ones(m, 1), a2];
+a3 = sigmoid(a2*Theta2');
 
-
+[val, idx] = max(a3, [], 2);
-
+p = idx;
-
-
 
 
 

ex3/predictOneVsAll.m

@@ -30,9 +30,8 @@ X = [ones(m, 1) X];
 %       for each row.
 %       
 
-
+[val, idx] = max(X*all_theta', [], 2);
-
+p = idx;
-
 
 
 

ex3/token.mat

@@ -0,0 +1,15 @@
+# Created by Octave 4.2.1, Thu May 11 16:43:37 2017 GMT <unknown@unknown>
+# name: email
+# type: sq_string
+# elements: 1
+# length: 16
+mjjo53@gmail.com
+
+
+# name: token
+# type: sq_string
+# elements: 1
+# length: 16
+77Tay6oLh6AIePHt
+
+