Merge branch 'master' of https://github.com/everpeace/ml-class-assignments

Author: everpeace

ex5/mlclass-ex5/learningCurve.m

@@ -53,16 +53,13 @@
 %       end
 %
 
-% ---------------------- Sample Solution ----------------------
-
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-% -------------------------------------------------------------
+for i= 1:m
+    theta          = trainLinearReg(X(1:i,:), y(1:i), lambda);
+    error_train(i) = linearRegCostFunction(X(1:i,:)   , y(1:i)   , theta, 0);
+    error_val(i)   = linearRegCostFunction(Xval, yval, theta, 0);
+end
 
+%
 % =========================================================================
 
 end

ex5/mlclass-ex5/linearRegCostFunction.m

@@ -19,16 +19,16 @@
 %               You should set J to the cost and grad to the gradient.
 %
 
-
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+% calculate cost function
+diff = X*theta - y;
+% calculate penalty
+% excluded the first theta value
+theta1 = [0 ; theta(2:end, :)];
+p = lambda*(theta1'*theta1);
+J = (diff'*diff)/(2*m) + p/(2*m);
+
+% calculate grads
+grad = (X'*diff+lambda*theta1)/m;
 
 % =========================================================================
 

ex5/mlclass-ex5/polyFeatures.m

@@ -15,10 +15,10 @@
 %
 % 
 
-
-
-
-
+X_poly(:,1) = X;
+for i=2:p
+    X_poly(:,i) = X.*X_poly(:,i-1);
+end
 
 % =========================================================================
 

ex5/mlclass-ex5/validationCurve.m

@@ -39,14 +39,12 @@
 %
 %
 
-
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+for i = 1:length(lambda_vec)
+    lambda = lambda_vec(i);
+    theta = trainLinearReg(X,y,lambda);
+    error_train(i) = linearRegCostFunction(X   , y   , theta, 0);
+    error_val(i)   = linearRegCostFunction(Xval, yval, theta, 0);
+end
 
 % =========================================================================