smile.classification

Class NaiveBayes

java.lang.Object

smile.classification.NaiveBayes

All Implemented Interfaces:

java.io.Serializable, java.util.function.ToDoubleFunction<double[]>, java.util.function.ToIntFunction<double[]>, Classifier<double[]>, SoftClassifier<double[]>

public class NaiveBayes
extends java.lang.Object
implements SoftClassifier<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

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and 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.

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, f, predict

Constructor Detail

NaiveBayes

public NaiveBayes(double[] priori,
                  smile.stat.distribution.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,
                  smile.stat.distribution.Distribution[][] condprob,
                  smile.util.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 - class labels

Method Detail

priori

public double[] priori()

Returns a priori probabilities.

predict

public int predict(double[] x)

Predict the class of an instance.

Specified by:

predict in interface Classifier<double[]>

Parameters:

x - the instance to be classified.

Returns:

the predicted class label.

predict

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

Predict the class of an instance.

Specified by:

predict in interface SoftClassifier<double[]>

Parameters:

x - the instance to be classified.

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

Returns:

the predicted class label.