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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
- 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
- %
- % Note: grad should have the same dimensions as theta
- %
-
- hx = sigmoid(X * theta); %hypothesis, m * 1
- J = 1 / m * sum(-y' * log(hx) - (1 .- y)' * log(1 -hx));
- grad = 1 / m * X' *(hx - y);
-
-
-
-
- % =============================================================
-
- end
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