| 123456789101112131415161718192021 |
- function [theta] = trainLinearReg(X, y, lambda)
- %TRAINLINEARREG Trains linear regression given a dataset (X, y) and a
- %regularization parameter lambda
- % [theta] = TRAINLINEARREG (X, y, lambda) trains linear regression using
- % the dataset (X, y) and regularization parameter lambda. Returns the
- % trained parameters theta.
- %
- % Initialize Theta
- initial_theta = zeros(size(X, 2), 1);
- % Create "short hand" for the cost function to be minimized
- costFunction = @(t) linearRegCostFunction(X, y, t, lambda);
- % Now, costFunction is a function that takes in only one argument
- options = optimset('MaxIter', 200, 'GradObj', 'on');
- % Minimize using fmincg
- theta = fmincg(costFunction, initial_theta, options);
- end
|
