jsat.classifiers

Interface WarmClassifier

All Superinterfaces:

Classifier, Cloneable, Serializable

All Known Implementing Classes:

DCDs, LinearBatch, LSSVM, NewGLMNET, PlattSMO

public interface WarmClassifier
extends Classifier

This interface is meant for models that support efficient warm starting from the solution of a previous model. Training with a warm start means that instead of solving the problem from scratch, the code can use a previous solution to start closer towards its goal.

Some algorithm may be able to warm start from solutions of the same form, even if they were trained by a different algorithm. Other algorithms may only be able to warm start from the same algorithm. There may also be restrictions that the warm start can only be from a solution trained on the exact same data set. The latter case is indicated by the warmFromSameDataOnly() method.

Just because a classifier fits the type that the warm start interface states doesn't mean that it is a valid classifier to warm start from. Classifiers of the same class trained on the same data must always be valid to warm start from.

Note: The use of this class is still under development, and may change in the future.

Author:

Edward Raff

Method Summary

Modifier and Type	Method and Description
void	trainC(ClassificationDataSet dataSet, Classifier warmSolution) Trains the classifier and constructs a model for classification using the given data set.
void	trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool) Trains the classifier and constructs a model for classification using the given data set.
boolean	warmFromSameDataOnly() Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from.

Methods inherited from interface jsat.classifiers.Classifier

classify, clone, supportsWeightedData, trainC, trainC

Method Detail

warmFromSameDataOnly

boolean warmFromSameDataOnly()

Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from. If this is the case true will be returned. The behavior for training on a different data set when this is defined is undefined. It may cause an error, or it may cause the algorithm to take longer or reach a worse solution.
When true, it is important that the data set be unaltered - this includes mutating the values stored or re-arranging the data points within the data set.

Returns:

true if the algorithm can only be warm started from the model trained on the exact same data set.

trainC

void trainC(ClassificationDataSet dataSet,
            Classifier warmSolution,
            ExecutorService threadPool)

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Parameters:

dataSet - the data set to train on

warmSolution - the solution to use to warm start this model

threadPool - the source of threads to use.

trainC

void trainC(ClassificationDataSet dataSet,
            Classifier warmSolution)

Trains the classifier and constructs a model for classification using the given data set.

Parameters:

dataSet - the data set to train on

warmSolution - the solution to use to warm start this model