package owl

L1 regularisation and its subgradient

L2 regularisation and its grandient

Elastic net regularisation and its gradient "a" is the weight on l1 regularisation term.

No regularisation and its gradient

least square loss function

hinge loss function

squared hinge loss function

softmax loss function

Stochastic Gradient Descent (SGD) algorithm b : batch size s : step size t : stop criteria l : loss function g : gradient function of the loss function r : regularisation function o : gradient fucntion of the regularisation function a : weight on the regularisation term, common setting is 0.0001 i : whether to include intercept or not, default value is false p : model parameters (k * m), each column is a classifier. So we have m classifier of k features. x : data matrix (n x k), each row is a data point. So we have n datea points of k features each. y : labelled data (n x m), n data points and each is labeled with m classifiers

wrapper of the _sgd_basic fucntion

Support Vector Machine regression i : whether to include intercept bias in parameters note that the values in y are either +1 or -1.

Ordinary Least Square regression i : whether to include intercept bias in parameters

Ridge regression i : whether to include intercept bias in parameters a : weight on the regularisation term TODO: how to choose a automatically

Lasso regression i : whether to include intercept bias in parameters a : weight on the regularisation term TODO: how to choose a automatically

Logistic regression i : whether to include intercept bias in parameters a : weight on the regularisation term note that the values in y are either +1 or 0.