From 6530916642a82a0a1d2d14c2ded24922d3d8d313 Mon Sep 17 00:00:00 2001 From: neingeist Date: Thu, 6 Nov 2014 00:53:49 +0100 Subject: [PATCH] Regularized linear regression cost function --- ex5/linearRegCostFunction.m | 22 +++++++--------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/ex5/linearRegCostFunction.m b/ex5/linearRegCostFunction.m index 6addf6b..1d70418 100644 --- a/ex5/linearRegCostFunction.m +++ b/ex5/linearRegCostFunction.m @@ -1,34 +1,26 @@ function [J, grad] = linearRegCostFunction(X, y, theta, lambda) -%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear +%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear %regression with multiple variables -% [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the -% cost of using theta as the parameter for linear regression to fit the +% [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the +% cost of using theta as the parameter for linear regression to fit the % data points in X and y. Returns the cost in J and the gradient in grad % Initialize some useful values m = length(y); % number of training examples -% You need to return the following variables correctly +% You need to return the following variables correctly J = 0; grad = zeros(size(theta)); % ====================== YOUR CODE HERE ====================== -% Instructions: Compute the cost and gradient of regularized linear +% Instructions: Compute the cost and gradient of regularized linear % regression for a particular choice of theta. % % You should set J to the cost and grad to the gradient. % - - - - - - - - - - +J = 1/(2*m) * sum(((X*theta)-y).^2) ... + + lambda/(2*m) * sum(theta(2:end).^2); % =========================================================================