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- 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
- z = hypothesis(theta, X);
- t = lambda*(sum(theta .^ 2)-theta(1)^2)/2/m;
- J = mean(- y .* log(z) + (y - 1) .* log(1 - z)) + t;
- grad = mean((z - y) .* X)' + lambda /m * theta;
- grad(1) = grad(1) - lambda /m * theta(1);
- % ====================== 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
- % =============================================================
- end
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