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- function [J, grad] = linearRegCostFunction(X, y, theta, lambda)
- %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
- % 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
- J = 0;
- grad = zeros(size(theta));
- % ====================== YOUR CODE HERE ======================
- % 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);
- reg = lambda / m * theta .* [0; ones(rows(theta)-1, 1)];
- grad = 1 / m * X' * (X * theta - y) + reg;
- %size(grad)
- % =========================================================================
- grad = grad(:);
- end
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