org.apache.lucene.classification

## Class CachingNaiveBayesClassifier

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

- All Implemented Interfaces:
    Classifier<BytesRef>

      public class CachingNaiveBayesClassifier
      extends SimpleNaiveBayesClassifier

  A simplistic Lucene based NaiveBayes classifier, with caching feature, see `http://en.wikipedia.org/wiki/Naive_Bayes_classifier`

  This is NOT an online classifier.

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

- - ### Field Summary

    - ### Fields inherited from class org.apache.lucene.classification.SimpleNaiveBayesClassifier

      `analyzer, classFieldName, indexReader, indexSearcher, query, textFieldNames`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `CachingNaiveBayesClassifier(IndexReader indexReader, Analyzer analyzer, Query query, String classFieldName, String... textFieldNames)` Creates a new NaiveBayes classifier with inside caching.

  - ### Method Summary

    All Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `protected List<ClassificationResult<BytesRef>>` | `assignClassNormalizedList(String inputDocument)` Calculate probabilities for all classes for a given input text
    `void` | `reInitCache(int minTermOccurrenceInCache, boolean justCachedTerms)` This function is building the frame of the cache.

    - ### Methods inherited from class org.apache.lucene.classification.SimpleNaiveBayesClassifier

      `assignClass, countDocsWithClass, getClasses, getClasses, normClassificationResults, tokenize`

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

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

- - ### Constructor Detail

    - #### CachingNaiveBayesClassifier

          public CachingNaiveBayesClassifier(IndexReader indexReader,
                                             Analyzer analyzer,
                                             Query query,
                                             String classFieldName,
                                             String... textFieldNames)

      Creates a new NaiveBayes classifier with inside caching. If you want less memory usage you could call `reInitCache()`.
      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
        `textFieldNames` - the name of the fields used as the inputs for the classifier

  - ### Method Detail

    - #### assignClassNormalizedList

          protected List<ClassificationResult<BytesRef>> assignClassNormalizedList(String inputDocument)
                                                                            throws IOException

      Description copied from class: `SimpleNaiveBayesClassifier`
      Calculate probabilities for all classes for a given input text
      Overrides:
        `assignClassNormalizedList` in class `SimpleNaiveBayesClassifier`
      Parameters:
        `inputDocument` - the input text as a `String`
      Returns:
        a `List` of `ClassificationResult`, one for each existing class
      Throws:
        `IOException` - if assigning probabilities fails

    - #### reInitCache

          public void reInitCache(int minTermOccurrenceInCache,
                                  boolean justCachedTerms)
                           throws IOException

      This function is building the frame of the cache. The cache is storing the word occurrences to the memory after those searched once. This cache can made 2-100x speedup in proper use, but can eat lot of memory. There is an option to lower the memory consume, if a word have really low occurrence in the index you could filter it out. The other parameter is switching between the term searching, if it true, just the terms in the skeleton will be searched, but if it false the terms whoes not in the cache will be searched out too (but not cached).
      Parameters:
        `minTermOccurrenceInCache` - Lower cache size with higher value.
        `justCachedTerms` - The switch for fully exclude low occurrence docs.
      Throws:
        `IOException` - If there is a low-level I/O error.

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