Package de.jungblut.classification

Class AbstractClassifier

java.lang.Object

de.jungblut.classification.AbstractPredictor

de.jungblut.classification.AbstractClassifier

All Implemented Interfaces:

Classifier, Predictor

Direct Known Subclasses:

AbstractKNearestNeighbours, DecisionTree, HMM, LogisticRegression, MaxEntMarkovModel, MultilayerPerceptron, MultinomialNaiveBayes, RandomForest, Voter

public abstract class AbstractClassifier
extends AbstractPredictor
implements Classifier

Abstract base class for classifiers.

Author:

thomas.jungblut

Constructor Summary

Constructors

Constructor	Description
AbstractClassifier()

Method Summary

Modifier and Type	Method	Description
void	train(de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome)	Trains this classifier with the given features and the outcome.
void	train(java.lang.Iterable<de.jungblut.math.DoubleVector> features, java.lang.Iterable<de.jungblut.math.DoubleVector> outcome)	Trains this classifier with the given features and the outcome.

Methods inherited from class de.jungblut.classification.AbstractPredictor

extractPredictedClass, extractPredictedClass, predictedClass, predictedClass, predictProbability

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.jungblut.classification.Predictor

asClassifier, extractPredictedClass, extractPredictedClass, predict, predictedClass, predictedClass, predictProbability

Constructor Detail

AbstractClassifier

public AbstractClassifier()

Method Detail

train

public void train(de.jungblut.math.DoubleVector[] features,
                  de.jungblut.math.DoubleVector[] outcome)

Description copied from interface: Classifier

Trains this classifier with the given features and the outcome.

Specified by:

train in interface Classifier

outcome - the outcome must have classes labeled as doubles. E.G. in the binary case you have a single element and decide between 0d and 1d. In higher dimensional cases you have each of these single elements mapped to a dimension.

train

public void train(java.lang.Iterable<de.jungblut.math.DoubleVector> features,
                  java.lang.Iterable<de.jungblut.math.DoubleVector> outcome)

Description copied from interface: Classifier

Trains this classifier with the given features and the outcome. This is the streaming method for training, it takes parallel iterables.

Specified by:

train in interface Classifier

outcome - the outcome must have classes labeled as doubles. E.G. in the binary case you have a single element and decide between 0d and 1d. In higher dimensional cases you have each of these single elements mapped to a dimension.