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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 = 0.5 * ( sum((hypothesis(theta, X) - y) .^ 2) + lambda * sum(theta(2:end) .^ 2)) / m;
- grad = (X' * (hypothesis(theta, X) - y) + theta * lambda) / m;
- grad(1) = grad(1) - lambda * theta(1) / m;
- % ====================== 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.
- %
- % =========================================================================
- grad = grad(:);
- end
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