Package ai.sklearn4j.naive_bayes

Class CategoricalNaiveBayes

java.lang.Object

ai.sklearn4j.base.ClassifierMixin

ai.sklearn4j.naive_bayes.BaseNaiveBayes

ai.sklearn4j.naive_bayes.CategoricalNaiveBayes

public class CategoricalNaiveBayes
extends BaseNaiveBayes

Naive Bayes classifier for categorical features.

The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.

Field Summary

Fields inherited from class ai.sklearn4j.base.ClassifierMixin

classCounts, classes

Constructor Summary

Constructors

Constructor	Description
CategoricalNaiveBayes()

Method Summary

Modifier and Type	Method	Description
NumpyArray<Double>	getClassLogPrior()	Gets the log probability of each class (smoothed).
List<NumpyArray<Double>>	getFeatureLogProbabilities()	Gets the empirical log probability of features given a class, P(x_i|y).
protected NumpyArray<Double>	jointLogLikelihood(NumpyArray<Double> x)	Compute the unnormalized posterior log probability of X.
void	setClassLogPrior(NumpyArray<Double> classLogPrior)	Sets the log probability of each class (smoothed).
void	setFeatureLogProbabilities(List<NumpyArray<Double>> featureLogProbabilities)	Sets the empirical log probability of features given a class, P(x_i|y).

Methods inherited from class ai.sklearn4j.naive_bayes.BaseNaiveBayes

predict, predictLogProbabilities, predictProbabilities

Methods inherited from class ai.sklearn4j.base.ClassifierMixin

getClassCounts, getClasses, getFeatureNamesIn, getNumberOfFeatures, setClassCounts, setClasses, setFeatureNamesIn, setNumberOfFeatures

Methods inherited from class java.lang.Object

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

Constructor Detail

CategoricalNaiveBayes

public CategoricalNaiveBayes()

Method Detail

jointLogLikelihood

protected NumpyArray<Double> jointLogLikelihood(NumpyArray<Double> x)

Compute the unnormalized posterior log probability of X.

I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of shape (n_samples, n_classes).

predict, predictProbabilities, and predictLogProbabilities pass the input over to jointLogLikelihood.

Specified by:

jointLogLikelihood in class BaseNaiveBayes

Parameters:

x - An array-like of shape (n_samples, n_classes).

getClassLogPrior

public NumpyArray<Double> getClassLogPrior()

Gets the log probability of each class (smoothed).

Returns:

Log probability of each class (smoothed).

setClassLogPrior

public void setClassLogPrior(NumpyArray<Double> classLogPrior)

Sets the log probability of each class (smoothed).

Parameters:

classLogPrior - The log probability of each class (smoothed).

getFeatureLogProbabilities

public List<NumpyArray<Double>> getFeatureLogProbabilities()

Gets the empirical log probability of features given a class, P(x_i|y).

Returns:

Empirical log probability of features given a class, P(x_i|y).

setFeatureLogProbabilities

public void setFeatureLogProbabilities(List<NumpyArray<Double>> featureLogProbabilities)

Sets the empirical log probability of features given a class, P(x_i|y).

Parameters:

featureLogProbabilities - The empirical log probability of features given a class, P(x_i|y).