Package ai.sklearn4j.naive_bayes

Class BaseNaiveBayes

java.lang.Object

ai.sklearn4j.base.ClassifierMixin

ai.sklearn4j.naive_bayes.BaseNaiveBayes

Direct Known Subclasses:

BernoulliNaiveBayes, CategoricalNaiveBayes, ComplementNaiveBayes, GaussianNaiveBayes, MultinomialNaiveBayes

public abstract class BaseNaiveBayes
extends ClassifierMixin

Abstract base class for naive Bayes estimators

Field Summary

Fields inherited from class ai.sklearn4j.base.ClassifierMixin

classCounts, classes

Constructor Summary

Constructors

Constructor	Description
BaseNaiveBayes()

Method Summary

Modifier and Type	Method	Description
protected abstract NumpyArray<Double>	jointLogLikelihood(NumpyArray<Double> x)	Compute the unnormalized posterior log probability of X.
NumpyArray<Long>	predict(NumpyArray<Double> x)	Perform classification on an array of test vectors X.
NumpyArray<Double>	predictLogProbabilities(NumpyArray<Double> x)	Return log-probability estimates for the test vector X.
NumpyArray<Double>	predictProbabilities(NumpyArray<Double> x)	Return probability estimates for the test vector X.

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

BaseNaiveBayes

public BaseNaiveBayes()

Method Detail

jointLogLikelihood

protected abstract 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.

Parameters:

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

predict

public NumpyArray<Long> predict(NumpyArray<Double> x)

Perform classification on an array of test vectors X.

Specified by:

predict in class ClassifierMixin

Parameters:

x - Array-like of shape (n_samples, n_features) The input samples.

Returns:

NumpyArray of shape (n_samples,) Predicted target values for X.

predictLogProbabilities

public NumpyArray<Double> predictLogProbabilities(NumpyArray<Double> x)

Return log-probability estimates for the test vector X.

Specified by:

predictLogProbabilities in class ClassifierMixin

Parameters:

x - array-like of shape (n_samples, n_features) The input samples.

Returns:

array-like of shape (n_samples, n_classes) Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`.

predictProbabilities

public NumpyArray<Double> predictProbabilities(NumpyArray<Double> x)

Return probability estimates for the test vector X.

Specified by:

predictProbabilities in class ClassifierMixin

Parameters:

x - array-like of shape (n_samples, n_features) The input samples.

Returns:

array-like of shape (n_samples, n_classes) Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`.