net.yacy.cora.bayes

Class BayesClassifier<T,K>

java.lang.Object

net.yacy.cora.bayes.Classifier<T,K>

net.yacy.cora.bayes.BayesClassifier<T,K>

Type Parameters:

T - The feature class.

K - The category class.

public class BayesClassifier<T,K>
extends Classifier<T,K>

A concrete implementation of the abstract Classifier class. The Bayes classifier implements a naive Bayes approach to classifying a given set of features: classify(feat1,...,featN) = argmax(P(cat)*PROD(P(featI|cat)

Author:

Philipp Nolte

See Also:

http://en.wikipedia.org/wiki/Naive_Bayes_classifier

Constructor Summary

Constructors

Constructor and Description
BayesClassifier()

Method Summary

Methods

Modifier and Type	Method and Description
private java.util.SortedSet<Classification<T,K>>	categoryProbabilities(java.util.Collection<T> features) Retrieves a sorted Set of probabilities that the given set of features is classified as the available categories.
private float	categoryProbability(java.util.Collection<T> features, K category) Calculates the probability that the features can be classified as the category given.
Classification<T,K>	classify(java.util.Collection<T> features) Classifies the given set of features.
java.util.Collection<Classification<T,K>>	classifyDetailed(java.util.Collection<T> features) Classifies the given set of features.
private float	featuresProbabilityProduct(java.util.Collection<T> features, K category) Calculates the product of all feature probabilities: PROD(P(featI|cat)

Methods inherited from class net.yacy.cora.bayes.Classifier

categoryCount, decrementCategory, decrementFeature, featureCount, featureProbability, featureWeighedAverage, featureWeighedAverage, featureWeighedAverage, featureWeighedAverage, getCategories, getCategoriesTotal, getFeatures, getMemoryCapacity, incrementCategory, incrementFeature, learn, learn, reset, setMemoryCapacity

Methods inherited from class java.lang.Object

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

Constructor Detail

BayesClassifier

public BayesClassifier()

Method Detail

featuresProbabilityProduct

private float featuresProbabilityProduct(java.util.Collection<T> features,
                               K category)

Calculates the product of all feature probabilities: PROD(P(featI|cat)

Parameters:

features - The set of features to use.

category - The category to test for.

Returns:

The product of all feature probabilities.

categoryProbability

private float categoryProbability(java.util.Collection<T> features,
                        K category)

Calculates the probability that the features can be classified as the category given.

Parameters:

features - The set of features to use.

category - The category to test for.

Returns:

The probability that the features can be classified as the category.

categoryProbabilities

private java.util.SortedSet<Classification<T,K>> categoryProbabilities(java.util.Collection<T> features)

Retrieves a sorted Set of probabilities that the given set of features is classified as the available categories.

Parameters:

features - The set of features to use.

Returns:

A sorted Set of category-probability-entries.

classify

public Classification<T,K> classify(java.util.Collection<T> features)

Classifies the given set of features.

Specified by:

classify in class Classifier<T,K>

Parameters:

features - The features to classify.

Returns:

The category the set of features is classified as.

classifyDetailed

public java.util.Collection<Classification<T,K>> classifyDetailed(java.util.Collection<T> features)

Classifies the given set of features. and return the full details of the classification.

Returns:

The set of categories the set of features is classified as.