완료

ex2/costFunction.m

@@ -20,8 +20,10 @@ grad = zeros(size(theta));
 % Note: grad should have the same dimensions as theta
 %
 
+hx = sigmoid(X*theta);
+J = sum(-y.*log(hx) - (1.-y).*log(1.-hx))/m;
 
-
+grad = sum((hx-y).*X)/m;
 
 
 

ex2/costFunctionReg.m

@@ -18,9 +18,9 @@ grad = zeros(size(theta));
 %               derivatives of the cost w.r.t. each parameter in theta
 
 
-
-
-
+hx = sigmoid(X*theta);
+J = sum(-y.*log(hx)-(1.-y).*log(1.-hx))/m + lambda*sum([0; theta(2:end)].^2)/(2*m);
+grad = (sum((hx-y).*X)./m)' + lambda*[0; theta(2:end)]/m;
 
 % =============================================================
 

ex2/ex2_reg.m

@@ -75,7 +75,7 @@ fprintf('Expected gradients (approx) - first five values only:\n');
 fprintf(' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n');
 
 fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
+%pause;
 
 % Compute and display cost and gradient
 % with all-ones theta and lambda = 10
@@ -90,7 +90,7 @@ fprintf('Expected gradients (approx) - first five values only:\n');
 fprintf(' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n');
 
 fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
+%pause;
 
 %% ============= Part 2: Regularization and Accuracies =============
 %  Optional Exercise:
@@ -107,7 +107,7 @@ pause;
 initial_theta = zeros(size(X, 2), 1);
 
 % Set regularization parameter lambda to 1 (you should vary this)
-lambda = 1;
+lambda = 100;
 
 % Set Options
 options = optimset('GradObj', 'on', 'MaxIter', 400);

ex2/plotData.m

@@ -12,10 +12,15 @@ figure; hold on;
 %               examples and 'ko' for the negative examples.
 %
 
+pos = find(y==1);
+neg = find(y == 0);
 
+plot(X(pos, 1), X(pos, 2), 'k+','LineWidth', 2, 'MarkerSize', 7);
+plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);
 
-
-
+ylabel('Exam 2 score');
+xlabel('Exam 1 score');
+legend('Admitted', 'Not admitted');
 
 
 

ex2/predict.m

@@ -17,7 +17,8 @@ p = zeros(m, 1);
 
 
 
-
+hx = X*theta;
+p = hx >= 0;
 
 
 

ex2/sigmoid.m

@@ -10,7 +10,7 @@ g = zeros(size(z));
 %               vector or scalar).
 
 
-
+g = 1./(1+exp(-z));
 
 
 % =============================================================

ex2/token.mat

@@ -0,0 +1,15 @@
+# Created by Octave 4.2.1, Wed May 10 12:15:18 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
+fZMMhxjQUsSZ1osy
+
+