Classifier (thomasjungblut-common 2.0.0 API)

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

All Methods Instance Methods Abstract Methods
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 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(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. 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.