Coursera Machine Learning Week 3 - Programming Exercise 2: Logistic Regression

sigmoid.m

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';

-eof-