Package org.apache.lucene.classification

# Class SimpleNaiveBayesClassifier

java.lang.Object
org.apache.lucene.classification.SimpleNaiveBayesClassifier

All Implemented Interfaces:
  `Classifier<BytesRef>`

Direct Known Subclasses:
  `CachingNaiveBayesClassifier`, `SimpleNaiveBayesDocumentClassifier`

public class SimpleNaiveBayesClassifier extends Object implements Classifier<BytesRef>
A simplistic Lucene based NaiveBayes classifier, see `http://en.wikipedia.org/wiki/Naive_Bayes_classifier`

WARNING: This API is experimental and might change in incompatible ways in the next release.

## Field Summary

Fields
Modifier and Type
Field
Description
`protected final Analyzer`
`analyzer`
`Analyzer` to be used for tokenizing unseen input text
`protected final String`
`classFieldName`
name of the field to be used as a class / category output
`protected final IndexReader`
`indexReader`
`IndexReader` used to access the `Classifier`'s index
`protected final IndexSearcher`
`indexSearcher`
`IndexSearcher` to run searches on the index for retrieving frequencies
`protected final Query`
`query`
`Query` used to eventually filter the document set to be used to classify
`protected final String[]`
`textFieldNames`
names of the fields to be used as input text

## Constructor Summary

Constructors
Constructor
Description
`SimpleNaiveBayesClassifier(IndexReader indexReader, Analyzer analyzer, Query query, String classFieldName, String... textFieldNames)`
Creates a new NaiveBayes classifier.

## Method Summary

Modifier and Type
Method
Description
`ClassificationResult<BytesRef>`
`assignClass(String inputDocument)`
Assign a class (with score) to the given text String
`protected List<ClassificationResult<BytesRef>>`
`assignClassNormalizedList(String inputDocument)`
Calculate probabilities for all classes for a given input text
`protected int`
`countDocsWithClass()`
count the number of documents in the index having at least a value for the 'class' field
`List<ClassificationResult<BytesRef>>`
`getClasses(String text)`
Get all the classes (sorted by score, descending) assigned to the given text String.
`List<ClassificationResult<BytesRef>>`
`getClasses(String text, int max)`
Get the first `max` classes (sorted by score, descending) assigned to the given text String.
`protected ArrayList<ClassificationResult<BytesRef>>`
`normClassificationResults(List<ClassificationResult<BytesRef>> assignedClasses)`
Normalize the classification results based on the max score available
`protected String[]`
`tokenize(String text)`
tokenize a `String` on this classifier's text fields and analyzer

### Methods inherited from class java.lang.Object

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

## Field Details

### indexReader

protected final IndexReader indexReader
`IndexReader` used to access the `Classifier`'s index

### textFieldNames

protected final String[] textFieldNames
names of the fields to be used as input text

### classFieldName

protected final String classFieldName
name of the field to be used as a class / category output

### analyzer

protected final Analyzer analyzer
`Analyzer` to be used for tokenizing unseen input text

### indexSearcher

protected final IndexSearcher indexSearcher
`IndexSearcher` to run searches on the index for retrieving frequencies

### query

protected final Query query
`Query` used to eventually filter the document set to be used to classify

## Constructor Details

### SimpleNaiveBayesClassifier

public SimpleNaiveBayesClassifier(IndexReader indexReader, Analyzer analyzer, Query query, String classFieldName, String... textFieldNames)
Creates a new NaiveBayes classifier.

Parameters:
  `indexReader` - the reader on the index to be used for classification
  `analyzer` - an `Analyzer` used to analyze unseen text
  `query` - a `Query` to eventually filter the docs used for training the classifier, or `null` if all the indexed docs should be used
  `classFieldName` - the name of the field used as the output for the classifier NOTE: must not be havely analyzed as the returned class will be a token indexed for this field
  `textFieldNames` - the name of the fields used as the inputs for the classifier, NO boosting supported per field

## Method Details

### assignClass

public ClassificationResult<BytesRef> assignClass(String inputDocument) throws IOException
Description copied from interface: `Classifier`
Assign a class (with score) to the given text String

Specified by:
  `assignClass` in interface `Classifier<BytesRef>`
Parameters:
  `inputDocument` - a String containing text to be classified
Returns:
  a `ClassificationResult` holding assigned class of type `T` and score
Throws:
  `IOException` - If there is a low-level I/O error.

### getClasses

public List<ClassificationResult<BytesRef>> getClasses(String text) throws IOException
Description copied from interface: `Classifier`
Get all the classes (sorted by score, descending) assigned to the given text String.

Specified by:
  `getClasses` in interface `Classifier<BytesRef>`
Parameters:
  `text` - a String containing text to be classified
Returns:
  the whole list of `ClassificationResult`, the classes and scores. Returns `null` if the classifier can't make lists.
Throws:
  `IOException` - If there is a low-level I/O error.

### getClasses

public List<ClassificationResult<BytesRef>> getClasses(String text, int max) throws IOException
Description copied from interface: `Classifier`
Get the first `max` classes (sorted by score, descending) assigned to the given text String.

Specified by:
  `getClasses` in interface `Classifier<BytesRef>`
Parameters:
  `text` - a String containing text to be classified
  `max` - the number of return list elements
Returns:
  the whole list of `ClassificationResult`, the classes and scores. Cut for "max" number of elements. Returns `null` if the classifier can't make lists.
Throws:
  `IOException` - If there is a low-level I/O error.

### assignClassNormalizedList

protected List<ClassificationResult<BytesRef>> assignClassNormalizedList(String inputDocument) throws IOException
Calculate probabilities for all classes for a given input text

Parameters:
  `inputDocument` - the input text as a `String`
Returns:
  a `List` of `ClassificationResult`, one for each existing class
Throws:
  `IOException` - if assigning probabilities fails

### countDocsWithClass

protected int countDocsWithClass() throws IOException
count the number of documents in the index having at least a value for the 'class' field

Returns:
  the no. of documents having a value for the 'class' field
Throws:
  `IOException` - if accessing to term vectors or search fails

### tokenize

protected String[] tokenize(String text) throws IOException
tokenize a `String` on this classifier's text fields and analyzer

Parameters:
  `text` - the `String` representing an input text (to be classified)
Returns:
  a `String` array of the resulting tokens
Throws:
  `IOException` - if tokenization fails

### normClassificationResults

protected ArrayList<ClassificationResult<BytesRef>> normClassificationResults(List<ClassificationResult<BytesRef>> assignedClasses)
Normalize the classification results based on the max score available

Parameters:
  `assignedClasses` - the list of assigned classes
Returns:
  the normalized results