de.jungblut.classification

Interface Classifier

All Superinterfaces:

Predictor

All Known Implementing Classes:

AbstractClassifier, AbstractKNearestNeighbours, DecisionTree, HMM, KNearestNeighbours, LogisticRegression, MaxEntMarkovModel, MultilayerPerceptron, MultinomialNaiveBayes, RandomForest, SparseKNearestNeighbours, UntrainableClassifier, Voter

public interface Classifier
extends Predictor

Classifier interface for predicting categorial variables.

Author:

thomas.jungblut

Method Summary

Modifier and Type	Method and 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(Iterable<de.jungblut.math.DoubleVector> features, Iterable<de.jungblut.math.DoubleVector> outcome) Trains this classifier with the given features and the outcome.

Methods inherited from interface de.jungblut.classification.Predictor

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

Method Detail

train

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

Trains this classifier with the given features and the outcome.

Parameters:

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

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

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

Parameters:

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.