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
%


predictions =  sigmoid(X*theta);

leftPart = -y' * log(predictions);

rightPart = (1 - y') * log(1 - predictions);

J = (1 / m) * (leftPart - rightPart);

grad = (1 / m) * ((predictions - y)' * X);



% =============================================================

end

vugsus
github-repos/MATLAB/vugsus-coursera-machine-learning/coursera-machine-learning-4c2d45cb729355593509abcd41779d19de5a1970/mlclass-ex2/costFunction.m
coursera-machine-learning
github-repos/MATLAB/vugsus-coursera-machine-learning
4c2d45cb729355593509abcd41779d19de5a1970