smile.classification

Interface SoftClassifier<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:

AdaBoost, DecisionTree, DiscreteNaiveBayes, GradientTreeBoost, KNN, LDA, LogisticRegression, LogisticRegression.Binomial, LogisticRegression.Multinomial, Maxent, Maxent.Binomial, Maxent.Multinomial, MLP, NaiveBayes, OneVersusOne, OneVersusRest, QDA, RandomForest, RDA, SparseLogisticRegression, SparseLogisticRegression.Binomial, SparseLogisticRegression.Multinomial

public interface SoftClassifier<T>
extends Classifier<T>

Soft classifiers calculate a posteriori probabilities besides the class label of an instance.

Method Summary

Modifier and Type	Method and Description
default int[]	predict(T[] x, double[][] posteriori) Predicts the class labels of an array of instances.
int	predict(T x, double[] posteriori) Predicts the class label of an instance and also calculate a posteriori probabilities.

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, predict, predict, score

Method Detail

predict

int predict(T x,
            double[] posteriori)

Predicts the class label of an instance and also calculate a posteriori probabilities. Classifiers may NOT support this method since not all classification algorithms are able to calculate such a posteriori probabilities.

Parameters:

x - an instance to be classified.

posteriori - a posteriori probabilities on output.

Returns:

the predicted class label

predict

default int[] predict(T[] x,
                      double[][] posteriori)

Predicts the class labels of an array of instances.

Parameters:

x - the instances to be classified.

posteriori - a posteriori probabilities on output.

Returns:

the predicted class labels.