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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
- J = 0;
- grad = zeros(size(theta));
-
- % ====================== 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
-
- hx = sigmoid(X * theta); %hypothesis, m * 1
-
- J = 1 / m * sum(-y' * log(hx) - (1 - y)' * log(1 - hx)) + lambda / (2 * m) * theta(2:end)' * theta(2:end);
-
- gradf = (1 / m) * (X(:, 1)' * (hx - y));
- gradb = (1 / m) * (X(:, 2:end)' * (hx - y)) + lambda * theta(2:end) / m;
-
- grad = [gradf;gradb];
-
-
- % =============================================================
-
- end
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