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51 lines
1.9 KiB
Matlab
51 lines
1.9 KiB
Matlab
function [J, grad] = lrCostFunction(theta, X, y, lambda)
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%LRCOSTFUNCTION Compute cost and gradient for logistic regression with
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%regularization
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% J = LRCOSTFUNCTION(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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%
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% Hint: The computation of the cost function and gradients can be
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% efficiently vectorized. For example, consider the computation
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%
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% sigmoid(X * theta)
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%
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% Each row of the resulting matrix will contain the value of the
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% prediction for that example. You can make use of this to vectorize
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% the cost function and gradient computations.
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%
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J = 1/m * (-y'*log(sigmoid(X*theta)) - (1-y)'*log(1-sigmoid(X*theta))) ...
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+ lambda/(2*m) * theta(2:end)' * theta(2:end);
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% Hint: When computing the gradient of the regularized cost function,
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% there're many possible vectorized solutions, but one solution
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% looks like:
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% grad = (unregularized gradient for logistic regression)
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% temp = theta;
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% temp(1) = 0; % because we don't add anything for j = 0
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% grad = grad + YOUR_CODE_HERE (using the temp variable)
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%
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regularization_term = lambda/m * vertcat([0], theta(2:end));
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grad = 1/m * X' * (sigmoid(X*theta) - y) + regularization_term;
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% =============================================================
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grad = grad(:);
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end
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