smile.classification

Class NaiveBayes.Trainer

java.lang.Object

smile.classification.ClassifierTrainer<double[]>

smile.classification.NaiveBayes.Trainer

Enclosing class:

NaiveBayes

public static class NaiveBayes.Trainer
extends ClassifierTrainer<double[]>

Trainer for naive Bayes classifier for document classification.

Constructor Summary

Constructors

Constructor and Description
NaiveBayes.Trainer(NaiveBayes.Model model, double[] priori, int p) Constructor.
NaiveBayes.Trainer(NaiveBayes.Model model, int k, int p) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	setPriori(double[] priori) Sets a priori probabilities of each class.
void	setSmooth(double sigma) Sets add-k prior count of terms for smoothing.
NaiveBayes	train(double[][] x, int[] y) Learns a classifier with given training data.

Methods inherited from class smile.classification.ClassifierTrainer

setAttributes

Methods inherited from class java.lang.Object

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

Constructor Detail

NaiveBayes.Trainer

public NaiveBayes.Trainer(NaiveBayes.Model model,
                  int k,
                  int p)

Constructor.

Parameters:

model - the generation model of naive Bayes classifier.

k - the number of classes.

p - the dimensionality of input space.

NaiveBayes.Trainer

public NaiveBayes.Trainer(NaiveBayes.Model model,
                  double[] priori,
                  int p)

Constructor.

Parameters:

model - the generation model of naive Bayes classifier.

priori - a priori probabilities of each class.

p - the dimensionality of input space.

Method Detail

setPriori

public void setPriori(double[] priori)

Sets a priori probabilities of each class.

Parameters:

priori - a priori probabilities of each class.

setSmooth

public void setSmooth(double sigma)

Sets add-k prior count of terms for smoothing.

Parameters:

sigma - the prior count of add-k smoothing of evidence.

train

public NaiveBayes train(double[][] x,
               int[] y)

Description copied from class: ClassifierTrainer

Learns a classifier with given training data.

Specified by:

train in class ClassifierTrainer<double[]>

Parameters:

x - the training instances.

y - the training labels.

Returns:

a trained classifier.