smile.classification

Interface OnlineClassifier<T>

Type Parameters:

T - the type of input object

All Superinterfaces:

Classifier<T>, java.io.Serializable, java.util.function.ToDoubleFunction<T>, java.util.function.ToIntFunction<T>

All Known Implementing Classes:

DiscreteNaiveBayes, LogisticRegression, LogisticRegression.Binomial, LogisticRegression.Multinomial, Maxent, Maxent.Binomial, Maxent.Multinomial, MLP, SparseLogisticRegression, SparseLogisticRegression.Binomial, SparseLogisticRegression.Multinomial

public interface OnlineClassifier<T>
extends Classifier<T>

Classifier with online learning capability. Online learning is a model of induction that learns one instance at a time. More formally, an online algorithm proceeds in a sequence of trials.

Method Summary

Modifier and Type	Method and Description
default void	update(T[] x, int[] y) Updates the model with a (micro-)batch of new samples.
void	update(T x, int y) Online update the classifier with a new training instance.

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, f, predict, predict

Method Detail

update

default void update(T[] x,
                    int[] y)

Updates the model with a (micro-)batch of new samples.

Parameters:

x - the training instances.

y - the target values.

update

void update(T x,
            int y)

Online update the classifier with a new training instance. In general, this method may be NOT multi-thread safe.

Parameters:

x - training instance.

y - training label.