java.lang.Object
- org.apache.lucene.queries.mlt.MoreLikeThis

public final class MoreLikeThis
extends java.lang.Object

Generate "more like this" similarity queries. Based on this mail:


 Lucene does let you access the document frequency of terms, with IndexReader.docFreq().
 Term frequencies can be computed by re-tokenizing the text, which, for a single document,
 is usually fast enough.  But looking up the docFreq() of every term in the document is
 probably too slow.
 
 You can use some heuristics to prune the set of terms, to avoid calling docFreq() too much,
 or at all.  Since you're trying to maximize a tf*idf score, you're probably most interested
 in terms with a high tf. Choosing a tf threshold even as low as two or three will radically
 reduce the number of terms under consideration.  Another heuristic is that terms with a
 high idf (i.e., a low df) tend to be longer.  So you could threshold the terms by the
 number of characters, not selecting anything less than, e.g., six or seven characters.
 With these sorts of heuristics you can usually find small set of, e.g., ten or fewer terms
 that do a pretty good job of characterizing a document.
 
 It all depends on what you're trying to do.  If you're trying to eek out that last percent
 of precision and recall regardless of computational difficulty so that you can win a TREC
 competition, then the techniques I mention above are useless.  But if you're trying to
 provide a "more like this" button on a search results page that does a decent job and has
 good performance, such techniques might be useful.
 
 An efficient, effective "more-like-this" query generator would be a great contribution, if
 anyone's interested.  I'd imagine that it would take a Reader or a String (the document's
 text), analyzer Analyzer, and return a set of representative terms using heuristics like those
 above.  The frequency and length thresholds could be parameters, etc.
 
 Doug

Initial Usage

This class has lots of options to try to make it efficient and flexible. The simplest possible usage is as follows. The bold fragment is specific to this class.

 IndexReader ir = ...
 IndexSearcher is = ...

 MoreLikeThis mlt = new MoreLikeThis(ir);
 Reader target = ... // orig source of doc you want to find similarities to
 Query query = mlt.like( target);
 
 Hits hits = is.search(query);
 // now the usual iteration thru 'hits' - the only thing to watch for is to make sure
 //you ignore the doc if it matches your 'target' document, as it should be similar to itself

Thus you:

do your normal, Lucene setup for searching,
create a MoreLikeThis,
get the text of the doc you want to find similarities to
then call one of the like() calls to generate a similarity query
call the searcher to find the similar docs

More Advanced Usage

You may want to use setFieldNames(...) so you can examine multiple fields (e.g. body and title) for similarity.

Depending on the size of your index and the size and makeup of your documents you may want to call the other set methods to control how the similarity queries are generated:

setMinTermFreq(...)
setMinDocFreq(...)
setMaxDocFreq(...)
setMaxDocFreqPct(...)
setMinWordLen(...)
setMaxWordLen(...)
setMaxQueryTerms(...)
setMaxNumTokensParsed(...)
setStopWord(...)

 Changes: Mark Harwood 29/02/04
 Some bugfixing, some refactoring, some optimisation.
 - bugfix: retrieveTerms(int docNum) was not working for indexes without a termvector -added missing code
 - bugfix: No significant terms being created for fields with a termvector - because
 was only counting one occurrence per term/field pair in calculations(ie not including frequency info from TermVector)
 - refactor: moved common code into isNoiseWord()
 - optimise: when no termvector support available - used maxNumTermsParsed to limit amount of tokenization

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`private static class`	`MoreLikeThis.FreqQ`	PriorityQueue that orders words by score.
`private static class`	`MoreLikeThis.Int`	Use for frequencies and to avoid renewing Integers.
`private static class`	`MoreLikeThis.ScoreTerm`

Field Summary

Fields
Modifier and Type	Field	Description
`private Analyzer`	`analyzer`	Analyzer that will be used to parse the doc.
`private boolean`	`boost`	Should we apply a boost to the Query based on the scores?
`private float`	`boostFactor`	Boost factor to use when boosting the terms
`static boolean`	`DEFAULT_BOOST`	Boost terms in query based on score.
`static java.lang.String[]`	`DEFAULT_FIELD_NAMES`	Default field names.
`static int`	`DEFAULT_MAX_DOC_FREQ`	Ignore words which occur in more than this many docs.
`static int`	`DEFAULT_MAX_NUM_TOKENS_PARSED`	Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
`static int`	`DEFAULT_MAX_QUERY_TERMS`	Return a Query with no more than this many terms.
`static int`	`DEFAULT_MAX_WORD_LENGTH`	Ignore words greater than this length or if 0 then this has no effect.
`static int`	`DEFAULT_MIN_DOC_FREQ`	Ignore words which do not occur in at least this many docs.
`static int`	`DEFAULT_MIN_TERM_FREQ`	Ignore terms with less than this frequency in the source doc.
`static int`	`DEFAULT_MIN_WORD_LENGTH`	Ignore words less than this length or if 0 then this has no effect.
`static java.util.Set<?>`	`DEFAULT_STOP_WORDS`	Default set of stopwords.
`private java.lang.String[]`	`fieldNames`	Field name we'll analyze.
`private IndexReader`	`ir`	IndexReader to use
`private int`	`maxDocFreq`	Ignore words which occur in more than this many docs.
`private int`	`maxNumTokensParsed`	The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
`private int`	`maxQueryTerms`	Don't return a query longer than this.
`private int`	`maxWordLen`	Ignore words if greater than this len.
`private int`	`minDocFreq`	Ignore words which do not occur in at least this many docs.
`private int`	`minTermFreq`	Ignore words less frequent that this.
`private int`	`minWordLen`	Ignore words if less than this len.
`private TFIDFSimilarity`	`similarity`	For idf() calculations.
`private java.util.Set<?>`	`stopWords`	Current set of stop words.

Constructor Summary

Constructors
Constructor Description

MoreLikeThis(IndexReader ir)
Constructor requiring an IndexReader.

MoreLikeThis(IndexReader ir, TFIDFSimilarity sim)

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private void`	`addTermFrequencies(java.io.Reader r, java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> perFieldTermFrequencies, java.lang.String fieldName)`	Adds term frequencies found by tokenizing text from reader into the Map words
`private void`	`addTermFrequencies(java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> field2termFreqMap, Terms vector, java.lang.String fieldName)`	Adds terms and frequencies found in vector into the Map termFreqMap
`private Query`	`createQuery(PriorityQueue<MoreLikeThis.ScoreTerm> q)`	Create the More like query from a PriorityQueue
`private PriorityQueue<MoreLikeThis.ScoreTerm>`	`createQueue(java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> perFieldTermFrequencies)`	Create a PriorityQueue from a word->tf map.
`java.lang.String`	`describeParams()`	Describe the parameters that control how the "more like this" query is formed.
`Analyzer`	`getAnalyzer()`	Returns an analyzer that will be used to parse source doc with.
`float`	`getBoostFactor()`	Returns the boost factor used when boosting terms
`java.lang.String[]`	`getFieldNames()`	Returns the field names that will be used when generating the 'More Like This' query.
`int`	`getMaxDocFreq()`	Returns the maximum frequency in which words may still appear.
`int`	`getMaxNumTokensParsed()`
`int`	`getMaxQueryTerms()`	Returns the maximum number of query terms that will be included in any generated query.
`int`	`getMaxWordLen()`	Returns the maximum word length above which words will be ignored.
`int`	`getMinDocFreq()`	Returns the frequency at which words will be ignored which do not occur in at least this many docs.
`int`	`getMinTermFreq()`	Returns the frequency below which terms will be ignored in the source doc.
`int`	`getMinWordLen()`	Returns the minimum word length below which words will be ignored.
`TFIDFSimilarity`	`getSimilarity()`
`java.util.Set<?>`	`getStopWords()`	Get the current stop words being used.
`private int`	`getTermsCount(java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> perFieldTermFrequencies)`
`boolean`	`isBoost()`	Returns whether to boost terms in query based on "score" or not.
`private boolean`	`isNoiseWord(java.lang.String term)`	determines if the passed term is likely to be of interest in "more like" comparisons
`Query`	`like(int docNum)`	Return a query that will return docs like the passed lucene document ID.
`Query`	`like(java.lang.String fieldName, java.io.Reader... readers)`	Return a query that will return docs like the passed Readers.
`Query`	`like(java.util.Map<java.lang.String,java.util.Collection<java.lang.Object>> filteredDocument)`
`java.lang.String[]`	`retrieveInterestingTerms(int docNum)`
`java.lang.String[]`	`retrieveInterestingTerms(java.io.Reader r, java.lang.String fieldName)`	Convenience routine to make it easy to return the most interesting words in a document.
`private PriorityQueue<MoreLikeThis.ScoreTerm>`	`retrieveTerms(int docNum)`	Find words for a more-like-this query former.
`private PriorityQueue<MoreLikeThis.ScoreTerm>`	`retrieveTerms(java.io.Reader r, java.lang.String fieldName)`	Find words for a more-like-this query former.
`private PriorityQueue<MoreLikeThis.ScoreTerm>`	`retrieveTerms(java.util.Map<java.lang.String,java.util.Collection<java.lang.Object>> field2fieldValues)`
`void`	`setAnalyzer(Analyzer analyzer)`	Sets the analyzer to use.
`void`	`setBoost(boolean boost)`	Sets whether to boost terms in query based on "score" or not.
`void`	`setBoostFactor(float boostFactor)`	Sets the boost factor to use when boosting terms
`void`	`setFieldNames(java.lang.String[] fieldNames)`	Sets the field names that will be used when generating the 'More Like This' query.
`void`	`setMaxDocFreq(int maxFreq)`	Set the maximum frequency in which words may still appear.
`void`	`setMaxDocFreqPct(int maxPercentage)`	Set the maximum percentage in which words may still appear.
`void`	`setMaxNumTokensParsed(int i)`
`void`	`setMaxQueryTerms(int maxQueryTerms)`	Sets the maximum number of query terms that will be included in any generated query.
`void`	`setMaxWordLen(int maxWordLen)`	Sets the maximum word length above which words will be ignored.
`void`	`setMinDocFreq(int minDocFreq)`	Sets the frequency at which words will be ignored which do not occur in at least this many docs.
`void`	`setMinTermFreq(int minTermFreq)`	Sets the frequency below which terms will be ignored in the source doc.
`void`	`setMinWordLen(int minWordLen)`	Sets the minimum word length below which words will be ignored.
`void`	`setSimilarity(TFIDFSimilarity similarity)`
`void`	`setStopWords(java.util.Set<?> stopWords)`	Set the set of stopwords.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - DEFAULT_MAX_NUM_TOKENS_PARSED
```
public static final int DEFAULT_MAX_NUM_TOKENS_PARSED
```
    Default maximum number of tokens to parse in each example doc field that is not stored with TermVector support.
    
    See Also:
    
    getMaxNumTokensParsed(), Constant Field Values
  - DEFAULT_MIN_TERM_FREQ
```
public static final int DEFAULT_MIN_TERM_FREQ
```
    Ignore terms with less than this frequency in the source doc.
    
    See Also:
    
    getMinTermFreq(), setMinTermFreq(int), Constant Field Values
  - DEFAULT_MIN_DOC_FREQ
```
public static final int DEFAULT_MIN_DOC_FREQ
```
    Ignore words which do not occur in at least this many docs.
    
    See Also:
    
    getMinDocFreq(), setMinDocFreq(int), Constant Field Values
  - DEFAULT_MAX_DOC_FREQ
```
public static final int DEFAULT_MAX_DOC_FREQ
```
    Ignore words which occur in more than this many docs.
    
    See Also:
    
    getMaxDocFreq(), setMaxDocFreq(int), setMaxDocFreqPct(int), Constant Field Values
  - DEFAULT_BOOST
```
public static final boolean DEFAULT_BOOST
```
    Boost terms in query based on score.
    
    See Also:
    
    isBoost(), setBoost(boolean), Constant Field Values
  - DEFAULT_FIELD_NAMES
```
public static final java.lang.String[] DEFAULT_FIELD_NAMES
```
    Default field names. Null is used to specify that the field names should be looked up at runtime from the provided reader.
  - DEFAULT_MIN_WORD_LENGTH
```
public static final int DEFAULT_MIN_WORD_LENGTH
```
    Ignore words less than this length or if 0 then this has no effect.
    
    See Also:
    
    getMinWordLen(), setMinWordLen(int), Constant Field Values
  - DEFAULT_MAX_WORD_LENGTH
```
public static final int DEFAULT_MAX_WORD_LENGTH
```
    Ignore words greater than this length or if 0 then this has no effect.
    
    See Also:
    
    getMaxWordLen(), setMaxWordLen(int), Constant Field Values
  - DEFAULT_STOP_WORDS
```
public static final java.util.Set<?> DEFAULT_STOP_WORDS
```
    Default set of stopwords. If null means to allow stop words.
    
    See Also:
    
    setStopWords(java.util.Set<?>), getStopWords()
  - stopWords
```
private java.util.Set<?> stopWords
```
    Current set of stop words.
  - DEFAULT_MAX_QUERY_TERMS
```
public static final int DEFAULT_MAX_QUERY_TERMS
```
    Return a Query with no more than this many terms.
    
    See Also:
    
    BooleanQuery.getMaxClauseCount(), getMaxQueryTerms(), setMaxQueryTerms(int), Constant Field Values
  - analyzer
```
private Analyzer analyzer
```
    Analyzer that will be used to parse the doc.
  - minTermFreq
```
private int minTermFreq
```
    Ignore words less frequent that this.
  - minDocFreq
```
private int minDocFreq
```
    Ignore words which do not occur in at least this many docs.
  - maxDocFreq
```
private int maxDocFreq
```
    Ignore words which occur in more than this many docs.
  - boost
```
private boolean boost
```
    Should we apply a boost to the Query based on the scores?
  - fieldNames
```
private java.lang.String[] fieldNames
```
    Field name we'll analyze.
  - maxNumTokensParsed
```
private int maxNumTokensParsed
```
    The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
  - minWordLen
```
private int minWordLen
```
    Ignore words if less than this len.
  - maxWordLen
```
private int maxWordLen
```
    Ignore words if greater than this len.
  - maxQueryTerms
```
private int maxQueryTerms
```
    Don't return a query longer than this.
  - similarity
```
private TFIDFSimilarity similarity
```
    For idf() calculations.
  - ir
```
private final IndexReader ir
```
    IndexReader to use
  - boostFactor
```
private float boostFactor
```
    Boost factor to use when boosting the terms
- Constructor Detail
  - MoreLikeThis
```
public MoreLikeThis(IndexReader ir)
```
    Constructor requiring an IndexReader.
  - MoreLikeThis
```
public MoreLikeThis(IndexReader ir,
                    TFIDFSimilarity sim)
```
- Method Detail
  - getBoostFactor
```
public float getBoostFactor()
```
    Returns the boost factor used when boosting terms
    
    Returns:
    
    the boost factor used when boosting terms
    
    See Also:
    
    setBoostFactor(float)
  - setBoostFactor
```
public void setBoostFactor(float boostFactor)
```
    Sets the boost factor to use when boosting terms
    
    See Also:
    
    getBoostFactor()
  - getSimilarity
```
public TFIDFSimilarity getSimilarity()
```
  - setSimilarity
```
public void setSimilarity(TFIDFSimilarity similarity)
```
  - getAnalyzer
```
public Analyzer getAnalyzer()
```
    Returns an analyzer that will be used to parse source doc with. The default analyzer is not set.
    
    Returns:
    
    the analyzer that will be used to parse source doc with.
  - setAnalyzer
```
public void setAnalyzer(Analyzer analyzer)
```
    Sets the analyzer to use. An analyzer is not required for generating a query with the like(int) method, all other 'like' methods require an analyzer.
    
    Parameters:
    
    analyzer - the analyzer to use to tokenize text.
  - getMinTermFreq
```
public int getMinTermFreq()
```
    Returns the frequency below which terms will be ignored in the source doc. The default frequency is the DEFAULT_MIN_TERM_FREQ.
    
    Returns:
    
    the frequency below which terms will be ignored in the source doc.
  - setMinTermFreq
```
public void setMinTermFreq(int minTermFreq)
```
    Sets the frequency below which terms will be ignored in the source doc.
    
    Parameters:
    
    minTermFreq - the frequency below which terms will be ignored in the source doc.
  - getMinDocFreq
```
public int getMinDocFreq()
```
    Returns the frequency at which words will be ignored which do not occur in at least this many docs. The default frequency is DEFAULT_MIN_DOC_FREQ.
    
    Returns:
    
    the frequency at which words will be ignored which do not occur in at least this many docs.
  - setMinDocFreq
```
public void setMinDocFreq(int minDocFreq)
```
    Sets the frequency at which words will be ignored which do not occur in at least this many docs.
    
    Parameters:
    
    minDocFreq - the frequency at which words will be ignored which do not occur in at least this many docs.
  - getMaxDocFreq
```
public int getMaxDocFreq()
```
    Returns the maximum frequency in which words may still appear. Words that appear in more than this many docs will be ignored. The default frequency is DEFAULT_MAX_DOC_FREQ.
    
    Returns:
    
    get the maximum frequency at which words are still allowed, words which occur in more docs than this are ignored.
  - setMaxDocFreq
```
public void setMaxDocFreq(int maxFreq)
```
    Set the maximum frequency in which words may still appear. Words that appear in more than this many docs will be ignored.
    
    Parameters:
    
    maxFreq - the maximum count of documents that a term may appear in to be still considered relevant
  - setMaxDocFreqPct
```
public void setMaxDocFreqPct(int maxPercentage)
```
    Set the maximum percentage in which words may still appear. Words that appear in more than this many percent of all docs will be ignored. This method calls setMaxDocFreq(int) internally (both conditions cannot be used at the same time).
    
    Parameters:
    
    maxPercentage - the maximum percentage of documents (0-100) that a term may appear in to be still considered relevant.
  - isBoost
```
public boolean isBoost()
```
    Returns whether to boost terms in query based on "score" or not. The default is DEFAULT_BOOST.
    
    Returns:
    
    whether to boost terms in query based on "score" or not.
    
    See Also:
    
    setBoost(boolean)
  - setBoost
```
public void setBoost(boolean boost)
```
    Sets whether to boost terms in query based on "score" or not.
    
    Parameters:
    
    boost - true to boost terms in query based on "score", false otherwise.
    
    See Also:
    
    isBoost()
  - getFieldNames
```
public java.lang.String[] getFieldNames()
```
    Returns the field names that will be used when generating the 'More Like This' query. The default field names that will be used is DEFAULT_FIELD_NAMES.
    
    Returns:
    
    the field names that will be used when generating the 'More Like This' query.
  - setFieldNames
```
public void setFieldNames(java.lang.String[] fieldNames)
```
    Sets the field names that will be used when generating the 'More Like This' query. Set this to null for the field names to be determined at runtime from the IndexReader provided in the constructor.
    
    Parameters:
    
    fieldNames - the field names that will be used when generating the 'More Like This' query.
  - getMinWordLen
```
public int getMinWordLen()
```
    Returns the minimum word length below which words will be ignored. Set this to 0 for no minimum word length. The default is DEFAULT_MIN_WORD_LENGTH.
    
    Returns:
    
    the minimum word length below which words will be ignored.
  - setMinWordLen
```
public void setMinWordLen(int minWordLen)
```
    Sets the minimum word length below which words will be ignored.
    
    Parameters:
    
    minWordLen - the minimum word length below which words will be ignored.
  - getMaxWordLen
```
public int getMaxWordLen()
```
    Returns the maximum word length above which words will be ignored. Set this to 0 for no maximum word length. The default is DEFAULT_MAX_WORD_LENGTH.
    
    Returns:
    
    the maximum word length above which words will be ignored.
  - setMaxWordLen
```
public void setMaxWordLen(int maxWordLen)
```
    Sets the maximum word length above which words will be ignored.
    
    Parameters:
    
    maxWordLen - the maximum word length above which words will be ignored.
  - setStopWords
```
public void setStopWords(java.util.Set<?> stopWords)
```
    Set the set of stopwords. Any word in this set is considered "uninteresting" and ignored. Even if your Analyzer allows stopwords, you might want to tell the MoreLikeThis code to ignore them, as for the purposes of document similarity it seems reasonable to assume that "a stop word is never interesting".
    
    Parameters:
    
    stopWords - set of stopwords, if null it means to allow stop words
    
    See Also:
    
    getStopWords()
  - getStopWords
```
public java.util.Set<?> getStopWords()
```
    Get the current stop words being used.
    
    See Also:
    
    setStopWords(java.util.Set<?>)
  - getMaxQueryTerms
```
public int getMaxQueryTerms()
```
    Returns the maximum number of query terms that will be included in any generated query. The default is DEFAULT_MAX_QUERY_TERMS.
    
    Returns:
    
    the maximum number of query terms that will be included in any generated query.
  - setMaxQueryTerms
```
public void setMaxQueryTerms(int maxQueryTerms)
```
    Sets the maximum number of query terms that will be included in any generated query.
    
    Parameters:
    
    maxQueryTerms - the maximum number of query terms that will be included in any generated query.
  - getMaxNumTokensParsed
```
public int getMaxNumTokensParsed()
```
    Returns:
    
    The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
    
    See Also:
    
    DEFAULT_MAX_NUM_TOKENS_PARSED
  - setMaxNumTokensParsed
```
public void setMaxNumTokensParsed(int i)
```
    Parameters:
    
    i - The maximum number of tokens to parse in each example doc field that is not stored with TermVector support
  - like
```
public Query like(int docNum)
           throws java.io.IOException
```
    Return a query that will return docs like the passed lucene document ID.
    
    Parameters:
    
    docNum - the documentID of the lucene doc to generate the 'More Like This" query for.
    
    Returns:
    
    a query that will return docs like the passed lucene document ID.
    
    Throws:
    
    java.io.IOException
  - like
```
public Query like(java.util.Map<java.lang.String,java.util.Collection<java.lang.Object>> filteredDocument)
           throws java.io.IOException
```
    Parameters:
    
    filteredDocument - Document with field values extracted for selected fields.
    
    Returns:
    
    More Like This query for the passed document.
    
    Throws:
    
    java.io.IOException
  - like
```
public Query like(java.lang.String fieldName,
                  java.io.Reader... readers)
           throws java.io.IOException
```
    Return a query that will return docs like the passed Readers. This was added in order to treat multi-value fields.
    
    Returns:
    
    a query that will return docs like the passed Readers.
    
    Throws:
    
    java.io.IOException
  - createQuery
```
private Query createQuery(PriorityQueue<MoreLikeThis.ScoreTerm> q)
```
    Create the More like query from a PriorityQueue
  - createQueue
```
private PriorityQueue<MoreLikeThis.ScoreTerm> createQueue(java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> perFieldTermFrequencies)
                                                   throws java.io.IOException
```
    Create a PriorityQueue from a word->tf map.
    
    Parameters:
    
    perFieldTermFrequencies - a per field map of words keyed on the word(String) with Int objects as the values.
    
    Throws:
    
    java.io.IOException
  - getTermsCount
```
private int getTermsCount(java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> perFieldTermFrequencies)
```
  - describeParams
```
public java.lang.String describeParams()
```
    Describe the parameters that control how the "more like this" query is formed.
  - retrieveTerms
```
private PriorityQueue<MoreLikeThis.ScoreTerm> retrieveTerms(int docNum)
                                                     throws java.io.IOException
```
    Find words for a more-like-this query former.
    
    Parameters:
    
    docNum - the id of the lucene document from which to find terms
    
    Throws:
    
    java.io.IOException
  - retrieveTerms
```
private PriorityQueue<MoreLikeThis.ScoreTerm> retrieveTerms(java.util.Map<java.lang.String,java.util.Collection<java.lang.Object>> field2fieldValues)
                                                     throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - addTermFrequencies
```
private void addTermFrequencies(java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> field2termFreqMap,
                                Terms vector,
                                java.lang.String fieldName)
                         throws java.io.IOException
```
    Adds terms and frequencies found in vector into the Map termFreqMap
    
    Parameters:
    
    field2termFreqMap - a Map of terms and their frequencies per field
    
    vector - List of terms and their frequencies for a doc/field
    
    Throws:
    
    java.io.IOException
  - addTermFrequencies
```
private void addTermFrequencies(java.io.Reader r,
                                java.util.Map<java.lang.String,java.util.Map<java.lang.String,MoreLikeThis.Int>> perFieldTermFrequencies,
                                java.lang.String fieldName)
                         throws java.io.IOException
```
    Adds term frequencies found by tokenizing text from reader into the Map words
    
    Parameters:
    
    r - a source of text to be tokenized
    
    perFieldTermFrequencies - a Map of terms and their frequencies per field
    
    fieldName - Used by analyzer for any special per-field analysis
    
    Throws:
    
    java.io.IOException
  - isNoiseWord
```
private boolean isNoiseWord(java.lang.String term)
```
    determines if the passed term is likely to be of interest in "more like" comparisons
    
    Parameters:
    
    term - The word being considered
    
    Returns:
    
    true if should be ignored, false if should be used in further analysis
  - retrieveTerms
```
private PriorityQueue<MoreLikeThis.ScoreTerm> retrieveTerms(java.io.Reader r,
                                                            java.lang.String fieldName)
                                                     throws java.io.IOException
```
    Find words for a more-like-this query former. The result is a priority queue of arrays with one entry for every word in the document. Each array has 6 elements. The elements are:
    
    The word (String)
    The top field that this word comes from (String)
    The score for this word (Float)
    The IDF value (Float)
    The frequency of this word in the index (Integer)
    The frequency of this word in the source document (Integer)
    This is a somewhat "advanced" routine, and in general only the 1st entry in the array is of interest. This method is exposed so that you can identify the "interesting words" in a document. For an easier method to call see retrieveInterestingTerms().
    Parameters:
    
    r - the reader that has the content of the document
    
    fieldName - field passed to the analyzer to use when analyzing the content
    
    Returns:
    
    the most interesting words in the document ordered by score, with the highest scoring, or best entry, first
    
    Throws:
    
    java.io.IOException
    
    See Also:
    
    retrieveInterestingTerms(int)
  - retrieveInterestingTerms
```
public java.lang.String[] retrieveInterestingTerms(int docNum)
                                            throws java.io.IOException
```
    Throws:
    
    java.io.IOException
    
    See Also:
    
    retrieveInterestingTerms(java.io.Reader, String)
  - retrieveInterestingTerms
```
public java.lang.String[] retrieveInterestingTerms(java.io.Reader r,
                                                   java.lang.String fieldName)
                                            throws java.io.IOException
```
    Convenience routine to make it easy to return the most interesting words in a document. More advanced users will call retrieveTerms() directly.
    
    Parameters:
    
    r - the source document
    
    fieldName - field passed to analyzer to use when analyzing the content
    
    Returns:
    
    the most interesting words in the document
    
    Throws:
    
    java.io.IOException
    
    See Also:
    
    retrieveTerms(java.io.Reader, String), setMaxQueryTerms(int)

Constructor	Description
`MoreLikeThis(IndexReader ir)`	Constructor requiring an IndexReader.
`MoreLikeThis(IndexReader ir, TFIDFSimilarity sim)`

Class MoreLikeThis

Initial Usage

More Advanced Usage

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_MAX_NUM_TOKENS_PARSED

DEFAULT_MIN_TERM_FREQ

DEFAULT_MIN_DOC_FREQ

DEFAULT_MAX_DOC_FREQ

DEFAULT_BOOST

DEFAULT_FIELD_NAMES

DEFAULT_MIN_WORD_LENGTH

DEFAULT_MAX_WORD_LENGTH

DEFAULT_STOP_WORDS

stopWords

DEFAULT_MAX_QUERY_TERMS

analyzer

minTermFreq

minDocFreq

maxDocFreq

boost

fieldNames

maxNumTokensParsed

minWordLen

maxWordLen

maxQueryTerms

similarity

ir

boostFactor

Constructor Detail

MoreLikeThis

MoreLikeThis

Method Detail

getBoostFactor

setBoostFactor

getSimilarity

setSimilarity

getAnalyzer

setAnalyzer

getMinTermFreq

setMinTermFreq

getMinDocFreq

setMinDocFreq

getMaxDocFreq

setMaxDocFreq

setMaxDocFreqPct

isBoost

setBoost

getFieldNames

setFieldNames

getMinWordLen

setMinWordLen

getMaxWordLen

setMaxWordLen

setStopWords

getStopWords

getMaxQueryTerms

setMaxQueryTerms

getMaxNumTokensParsed

setMaxNumTokensParsed

like

like

like

createQuery

createQueue

getTermsCount

describeParams

retrieveTerms

retrieveTerms

addTermFrequencies

addTermFrequencies

isNoiseWord

retrieveTerms

retrieveInterestingTerms

retrieveInterestingTerms