API Design

If you wish to create your own models and optimizers from our library, we already have set a standard for our API design.

Models

• All our models have train() and predict() functions. They will be called when using some parts in “Others” section of library.

• train() function takes in featureMatrix / tableOfTokenSequenceArray (mandatory for all models) and labelVector / tableOfTokenSequenceArray (not available for some models) in order.

• predict() function takes in featureMatrix or featureVector or tableOfTokenSequenceArray.

• The code for the models are object-oriented.

Optimizers

• All our optimizers have calculate() and reset() functions. They will be called inside called inside our models.

• calculate() function takes in learningRate (number) and costFunctionDerivatives (matrix) in order. It returns the adjusted costFunctionDerivatives.

• reset() does not take in any parameters.

• The code for the optimizers are object-oriented.

• You can get more optimizer formulas here.

Regularization

• All our regularization objects have calculateRegularization() and calculateRegularizationDerivatives() functions. They will be called inside called inside our models.

• Both takes in modelParameters (matrix) and numberOfData (integer) in order.

• calculateRegularization() returns regularization values for ModelParameters (matrix).

• calculateRegularizationDerivatives() returns regularization values for costFunctionDerivatives (matrix).

• The code for the regularization objects are object-oriented.