Coursera Machine Learning Week 3 - Programming Exercise 2: Logistic Regression
2016-08-26 16:55
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sigmoid.m
costFunction.m
predict.m
costFunctionReg.m
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g = 1 ./ (1 + exp(-z));
costFunction.m
S = sigmoid(X * theta); J = ( (-y' * log(S)) - ((1 - y') * log(1-S)) ) / m; grad = (S - y)' * X / m;
predict.m
p = round(sigmoid(X * theta));
costFunctionReg.m
T = theta; T(1) = 0; S = sigmoid(X * theta); J = ( (-y' * log(S)) - ((1 - y') * log(1-S)) ) / m + lambda / (2 * m) * sum(T .^ 2); grad = (S - y)' * X / m + lambda / m * T';
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