Package smile.classification

Class NaiveBayes

java.lang.Object

smile.classification.AbstractClassifier<double[]>

smile.classification.NaiveBayes

All Implemented Interfaces:

Serializable, ToDoubleFunction<double[]>, ToIntFunction<double[]>, Classifier<double[]>

public class NaiveBayes
extends AbstractClassifier<double[]>

Naive Bayes classifier. A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions. Depending on the precise nature of the probability model, naive Bayes classifiers can be trained very efficiently in a supervised learning setting.

For a general purpose naive Bayes classifier without any assumptions about the underlying distribution of each variable, we don't provide a learning method to infer the variable distributions from the training data. Instead, the users can fit any appropriate distributions on the data by themselves with various Distribution classes. Although the predict(double[]) method takes an array of double values as a general form of independent variables, the users are free to use any discrete distributions to model categorical or ordinal random variables.

References

1. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze. Introduction to Information Retrieval, Chapter 13, 2009.
2. Kevin P. Murphy. Machina Learning A Probability Perspective, Chapter 3, 2012.

See Also:

• Distribution
• LDA
• QDA
• RDA
• Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface smile.classification.Classifier

Classifier.Trainer<T,M extends Classifier<T>>

Field Summary

Fields inherited from class smile.classification.AbstractClassifier

classes

Constructor Summary

Constructors

Constructor	Description
NaiveBayes(double[] priori, Distribution[][] condprob)	Constructor of general naive Bayes classifier.
NaiveBayes(double[] priori, Distribution[][] condprob, IntSet labels)	Constructor of general naive Bayes classifier.

Method Summary

Modifier and Type	Method	Description
int	predict(double[] x)	Predict the class of an instance.
int	predict(double[] x, double[] posteriori)	Predict the class of an instance.
double[]	priori()	Returns a priori probabilities.
boolean	soft()	Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.

Methods inherited from class smile.classification.AbstractClassifier

classes, numClasses

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, online, predict, predict, predict, predict, predict, predict, score, update, update, update

Constructor Details

NaiveBayes

public NaiveBayes(double[] priori,
 Distribution[][] condprob)

Constructor of general naive Bayes classifier.

Parameters:

priori - the priori probability of each class.

condprob - the conditional distribution of each variable in each class. In particular, condprob[i][j] is the conditional distribution P(x_j | class i).

NaiveBayes

public NaiveBayes(double[] priori,
 Distribution[][] condprob,
 IntSet labels)

Constructor of general naive Bayes classifier.

Parameters:

priori - the priori probability of each class.

condprob - the conditional distribution of each variable in each class. In particular, condprob[i][j] is the conditional distribution P(x_j | class i).

labels - the class label encoder.

Method Details

priori

public double[] priori()

Returns a priori probabilities.

Returns:

a priori probabilities.

predict

public int predict(double[] x)

Predict the class of an instance.

Parameters:

x - the instance to be classified.

Returns:

the predicted class label.

soft

public boolean soft()

Description copied from interface: Classifier

Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.

Returns:

true if soft classifier.

predict

public int predict(double[] x,
 double[] posteriori)

Predict the class of an instance.

Parameters:

x - the instance to be classified.

posteriori - the array to store a posteriori probabilities on output.

Returns:

the predicted class label.