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- function [J, grad] = costFunction(theta, X, y)
- %COSTFUNCTION Compute cost and gradient for logistic regression
- % J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
- % parameter for 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);
- J = mean(- y .* log(z) + (y - 1) .* log(1 - z));
- grad = mean((z - y) .* X);
- % ====================== 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
- %
- % Note: grad should have the same dimensions as theta
- %
- % =============================================================
- end
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