ds: r7 octave

ds/25-1/r/7/mlclass-ex2/costFunctionReg.m

@@ -0,0 +1,27 @@
+function [J, grad] = costFunctionReg(theta, X, y, lambda)
+%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
+%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
+%   theta as the parameter for regularized logistic regression and the
+%   gradient of the cost w.r.t. to the parameters.
+
+% Initialize some useful values
+m = length(y); % number of training examples
+
+% You need to return the following variables correctly
+J = 0;
+grad = zeros(size(theta));
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the cost of a particular choice of theta.
+%               You should set J to the cost.
+%               Compute the partial derivatives and set grad to the partial
+%               derivatives of the cost w.r.t. each parameter in theta
+
+[J_, grad_] = costFunction(theta, X, y);
+J = J_ + lambda / (2 * m) * (theta' * theta);
+grad = grad_ + theta * lambda / m;
+grad(1) = grad_(1);
+
+% =============================================================
+
+end