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