jsat.regression

Interface UpdateableRegressor

All Superinterfaces:

Cloneable, Regressor, Serializable

All Known Implementing Classes:

BaseUpdateableRegressor, KernelRLS, KernelSGD, LinearSGD, PassiveAggressive, STGD, UpdatableStacking

public interface UpdateableRegressor
extends Regressor

UpdateableRegressor is an interface for one type of Online learner. The main characteristic of an online learner is that new example points can be added incrementally after the classifier was initially trained, or as part of its initial training.
Some Online learners behave differently in when they are updated. The UpdateableRegressor is an online learner that specifically only performs additional learning when a new example is provided via the update(jsat.classifiers.DataPoint, double) method.
The standard behavior for an UpdateableRegressor is that the user first calls Regressor.train(jsat.regression.RegressionDataSet) to first train the classifier, or setUp(jsat.classifiers.CategoricalData[], int) to prepare for online updates. Once one of these is called, it should then be safe to call update(jsat.classifiers.DataPoint, double) without getting a FailedToFitException. Some online learners may require one of the train methods to be called first.

Author:

Edward Raff

Method Summary

Modifier and Type	Method and Description
UpdateableRegressor	clone()
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes) Prepares the classifier to begin learning from its update(jsat.classifiers.DataPoint, double) method.
void	update(DataPoint dataPoint, double targetValue) Updates the classifier by giving it a new data point to learn from.

Methods inherited from interface jsat.regression.Regressor

regress, supportsWeightedData, train, train

Method Detail

setUp

void setUp(CategoricalData[] categoricalAttributes,
           int numericAttributes)

Prepares the classifier to begin learning from its update(jsat.classifiers.DataPoint, double) method.

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

update

void update(DataPoint dataPoint,
            double targetValue)

Updates the classifier by giving it a new data point to learn from.

Parameters:

dataPoint - the data point to learn

targetValue - the target value of the data point

clone

UpdateableRegressor clone()

Specified by:

clone in interface Regressor