org.apache.ignite.ml.naivebayes.discrete

Class DiscreteNaiveBayesModel

java.lang.Object

org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

All Implemented Interfaces:

Serializable, AutoCloseable, Exportable<DiscreteNaiveBayesModel>, IgniteModel<Vector,Double>, Model<Vector,Double>

public final class DiscreteNaiveBayesModel
extends Object
implements IgniteModel<Vector,Double>, Exportable<DiscreteNaiveBayesModel>, Serializable

Discrete naive Bayes model which predicts result value y belongs to a class C_k, k in [0..K] as p(C_k,y) =x_1*p_k1^x *...*x_i*p_ki^x_i. Where x_i is a discrete feature, p_ki is a prior probability probability of class p(x|C_k). Returns the number of the most possible class.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DiscreteNaiveBayesModel(double[][][] probabilities, double[] clsProbabilities, double[] labels, double[][] bucketThresholds, DiscreteNaiveBayesSumsHolder sumsHolder)

Method Summary

Modifier and Type	Method and Description
double[][]	getBucketThresholds()
double[]	getClsProbabilities()
double[][][]	getProbabilities()
DiscreteNaiveBayesSumsHolder	getSumsHolder()
Double	predict(Vector vector) Make a prediction for the specified input arguments.
<P> void	saveModel(Exporter<DiscreteNaiveBayesModel,P> exporter, P path) Save model by the given path.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.ignite.ml.IgniteModel

andBefore, andThen, andThen, close, combine, toString

Constructor Detail

DiscreteNaiveBayesModel

public DiscreteNaiveBayesModel(double[][][] probabilities,
                               double[] clsProbabilities,
                               double[] labels,
                               double[][] bucketThresholds,
                               DiscreteNaiveBayesSumsHolder sumsHolder)

Parameters:

probabilities - Probabilities of features for classes.

clsProbabilities - Prior probabilities for classes.

bucketThresholds - The threshold to convert a feature to a binary value.

sumsHolder - Amount values which are abouve the threshold per label.

labels - Labels.

Method Detail

saveModel

public <P> void saveModel(Exporter<DiscreteNaiveBayesModel,P> exporter,
                          P path)

Save model by the given path.

Specified by:

saveModel in interface Exportable<DiscreteNaiveBayesModel>

Parameters:

exporter - Exporter.

path - Path to saved model.

predict

public Double predict(Vector vector)

Description copied from interface: Model

Make a prediction for the specified input arguments.

Specified by:

predict in interface Model<Vector,Double>

Parameters:

vector - features vector.

Returns:

A label with max probability.

getProbabilities

public double[][][] getProbabilities()

getClsProbabilities

public double[] getClsProbabilities()

getBucketThresholds

public double[][] getBucketThresholds()

getSumsHolder

public DiscreteNaiveBayesSumsHolder getSumsHolder()