Created
November 1, 2011 03:14
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logistic-regression-cost.m
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| function [J, grad] = costFunction(theta, X, y) | |
| % Logistic Regression cost function and partial derivative (gradient) | |
| m = length(y); % number of training examples | |
| cost = (y' * log(h)) + ((1-y')*log(1-h)); | |
| J = -1/m * cost; | |
| grad = 1/m * (X' * (h - y)); | |
| end |
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