From d51068ffd66e42f0d4f41d80847a0ef1ac1a6fd1 Mon Sep 17 00:00:00 2001 From: Robert Muir Date: Wed, 2 Mar 2011 14:59:02 +0000 Subject: [PATCH] LUCENE-2894: apply formatting to more code samples git-svn-id: https://svn.apache.org/repos/asf/lucene/dev/trunk@1076237 13f79535-47bb-0310-9956-ffa450edef68 --- .../lucene/search/highlight/package.html | 2 +- .../queryparser/src/java/overview.html | 2 +- .../org/apache/lucene/analysis/package.html | 26 ++++++------ .../org/apache/lucene/messages/package.html | 4 +- .../lucene/search/function/package.html | 10 ++--- .../apache/lucene/search/spans/package.html | 4 +- .../org/apache/lucene/collation/package.html | 12 +++--- modules/analysis/icu/src/java/overview.html | 40 +++++++++---------- 8 files changed, 50 insertions(+), 50 deletions(-) diff --git a/lucene/contrib/highlighter/src/java/org/apache/lucene/search/highlight/package.html b/lucene/contrib/highlighter/src/java/org/apache/lucene/search/highlight/package.html index a2e7d85d570..b890f250946 100755 --- a/lucene/contrib/highlighter/src/java/org/apache/lucene/search/highlight/package.html +++ b/lucene/contrib/highlighter/src/java/org/apache/lucene/search/highlight/package.html @@ -26,7 +26,7 @@ Fragmenter, fragment Scorer, and Formatter classes.

Example Usage

-
+
   //... Above, create documents with two fields, one with term vectors (tv) and one without (notv)
   IndexSearcher searcher = new IndexSearcher(directory);
   QueryParser parser = new QueryParser("notv", analyzer);
diff --git a/lucene/contrib/queryparser/src/java/overview.html b/lucene/contrib/queryparser/src/java/overview.html
index d3df6f0722c..57928004677 100644
--- a/lucene/contrib/queryparser/src/java/overview.html
+++ b/lucene/contrib/queryparser/src/java/overview.html
@@ -131,7 +131,7 @@ you don't need to worry about dealing with those.
 
 {@link org.apache.lucene.queryParser.standard.StandardQueryParser} usage:
 
-
+
       StandardQueryParser qpHelper = new StandardQueryParser();
       StandardQueryConfigHandler config =  qpHelper.getQueryConfigHandler();
       config.setAllowLeadingWildcard(true);
diff --git a/lucene/src/java/org/apache/lucene/analysis/package.html b/lucene/src/java/org/apache/lucene/analysis/package.html
index 28569e483ba..4ce7f49b590 100644
--- a/lucene/src/java/org/apache/lucene/analysis/package.html
+++ b/lucene/src/java/org/apache/lucene/analysis/package.html
@@ -130,7 +130,7 @@ There are many post tokenization steps that can be done, including (but not limi
     
   
   However an application might invoke Analysis of any text for testing or for any other purpose, something like:
-  
+  
       Analyzer analyzer = new StandardAnalyzer(); // or any other analyzer
       TokenStream ts = analyzer.tokenStream("myfield",new StringReader("some text goes here"));
       while (ts.incrementToken()) {
@@ -182,7 +182,7 @@ the source code of any one of the many samples located in this package.
   This allows phrase search and proximity search to seamlessly cross 
   boundaries between these "sections".
   In other words, if a certain field "f" is added like this:
-  
+  
       document.add(new Field("f","first ends",...);
       document.add(new Field("f","starts two",...);
       indexWriter.addDocument(document);
@@ -191,7 +191,7 @@ the source code of any one of the many samples located in this package.
   Where desired, this behavior can be modified by introducing a "position gap" between consecutive field "sections", 
   simply by overriding 
   {@link org.apache.lucene.analysis.Analyzer#getPositionIncrementGap(java.lang.String) Analyzer.getPositionIncrementGap(fieldName)}:
-  
+  
       Analyzer myAnalyzer = new StandardAnalyzer() {
          public int getPositionIncrementGap(String fieldName) {
            return 10;
@@ -220,7 +220,7 @@ the source code of any one of the many samples located in this package.
    tokens following a removed stop word, using
    {@link org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute#setPositionIncrement(int)}.
    This can be done with something like:
-   
+   
       public TokenStream tokenStream(final String fieldName, Reader reader) {
         final TokenStream ts = someAnalyzer.tokenStream(fieldName, reader);
         TokenStream res = new TokenStream() {
@@ -334,7 +334,7 @@ here to illustrate the usage of the new TokenStream API.

 Then we will develop a custom Attribute, a PartOfSpeechAttribute, and add another filter to the chain which
 utilizes the new custom attribute, and call it PartOfSpeechTaggingFilter.
 
Whitespace tokenization

-
+
 public class MyAnalyzer extends Analyzer {
 
   public TokenStream tokenStream(String fieldName, Reader reader) {
@@ -381,7 +381,7 @@ API
 
Adding a LengthFilter

 We want to suppress all tokens that have 2 or less characters. We can do that easily by adding a LengthFilter 
 to the chain. Only the tokenStream() method in our analyzer needs to be changed:
-
+
   public TokenStream tokenStream(String fieldName, Reader reader) {
     TokenStream stream = new WhitespaceTokenizer(reader);
     stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
@@ -398,7 +398,7 @@ TokenStream
 API
 

 Now let's take a look how the LengthFilter is implemented (it is part of Lucene's core):
-
+
 public final class LengthFilter extends TokenFilter {
 
   final int min;
@@ -448,7 +448,7 @@ is neccessary. The same is true for the consumer, which can simply use local ref
 
Adding a custom Attribute

 Now we're going to implement our own custom Attribute for part-of-speech tagging and call it consequently 
 PartOfSpeechAttribute. First we need to define the interface of the new Attribute:
-
+
   public interface PartOfSpeechAttribute extends Attribute {
     public static enum PartOfSpeech {
       Noun, Verb, Adjective, Adverb, Pronoun, Preposition, Conjunction, Article, Unknown
@@ -470,7 +470,7 @@ and returns an actual instance. You can implement your own factory if you need t
 Now here is the actual class that implements our new Attribute. Notice that the class has to extend
 {@link org.apache.lucene.util.AttributeImpl}:
 
-
+
 public final class PartOfSpeechAttributeImpl extends AttributeImpl 
                             implements PartOfSpeechAttribute{
   
@@ -513,7 +513,7 @@ This is a simple Attribute implementation has only a single variable that stores
 new AttributeImpl class and therefore implements its abstract methods clear(), copyTo(), equals(), hashCode().
 Now we need a TokenFilter that can set this new PartOfSpeechAttribute for each token. In this example we show a very naive filter
 that tags every word with a leading upper-case letter as a 'Noun' and all other words as 'Unknown'.
-
+
   public static class PartOfSpeechTaggingFilter extends TokenFilter {
     PartOfSpeechAttribute posAtt;
     CharTermAttribute termAtt;
@@ -544,7 +544,7 @@ Just like the LengthFilter, this new filter accesses the attributes it needs in
 stores references in instance variables. Notice how you only need to pass in the interface of the new
 Attribute and instantiating the correct class is automatically been taken care of.
 Now we need to add the filter to the chain:
-
+
   public TokenStream tokenStream(String fieldName, Reader reader) {
     TokenStream stream = new WhitespaceTokenizer(reader);
     stream = new LengthFilter(stream, 3, Integer.MAX_VALUE);
@@ -564,7 +564,7 @@ API
 Apparently it hasn't changed, which shows that adding a custom attribute to a TokenStream/Filter chain does not
 affect any existing consumers, simply because they don't know the new Attribute. Now let's change the consumer
 to make use of the new PartOfSpeechAttribute and print it out:
-
+
   public static void main(String[] args) throws IOException {
     // text to tokenize
     final String text = "This is a demo of the new TokenStream API";
@@ -606,7 +606,7 @@ API the reader could now write an Attribute and TokenFilter that can specify for
 of a sentence or not. Then the PartOfSpeechTaggingFilter can make use of this knowledge and only tag capitalized words
 as nouns if not the first word of a sentence (we know, this is still not a correct behavior, but hey, it's a good exercise). 
 As a small hint, this is how the new Attribute class could begin:
-
+
   public class FirstTokenOfSentenceAttributeImpl extends Attribute
                    implements FirstTokenOfSentenceAttribute {
     
diff --git a/lucene/src/java/org/apache/lucene/messages/package.html b/lucene/src/java/org/apache/lucene/messages/package.html
index 14219e895d1..24d41716497 100644
--- a/lucene/src/java/org/apache/lucene/messages/package.html
+++ b/lucene/src/java/org/apache/lucene/messages/package.html
@@ -45,7 +45,7 @@ Features:
 

 Lazy loading of Message Strings
 
-
+
 	public class MessagesTestBundle extends NLS {
 	
 	  private static final String BUNDLE_NAME = MessagesTestBundle.class.getName();
@@ -85,7 +85,7 @@ Lazy loading of Message Strings
 

 Normal loading of Message Strings
 
-
+
 	String message1 = NLS.getLocalizedMessage(MessagesTestBundle.Q0004E_INVALID_SYNTAX_ESCAPE_UNICODE_TRUNCATION);
 	String message2 = NLS.getLocalizedMessage(MessagesTestBundle.Q0004E_INVALID_SYNTAX_ESCAPE_UNICODE_TRUNCATION, Locale.JAPANESE);
 

diff --git a/lucene/src/java/org/apache/lucene/search/function/package.html b/lucene/src/java/org/apache/lucene/search/function/package.html
index 15dcbfa2cd6..5da5fe8dbc0 100755
--- a/lucene/src/java/org/apache/lucene/search/function/package.html
+++ b/lucene/src/java/org/apache/lucene/search/function/package.html
@@ -130,14 +130,14 @@
     Using field (byte) values to as scores:
     

     Indexing:
-    
+    
       f = new Field("score", "7", Field.Store.NO, Field.Index.UN_TOKENIZED);
       f.setOmitNorms(true);
       d1.add(f);
     

     

     Search:
-    
+    
       Query q = new FieldScoreQuery("score", FieldScoreQuery.Type.BYTE);
     

     Document d1 above would get a score of 7.
@@ -148,7 +148,7 @@
     

     Dividing the original score of each document by a square root of its docid
     (just to demonstrate what it takes to manipulate scores this way)
-    
+    
       Query q = queryParser.parse("my query text");
       CustomScoreQuery customQ = new CustomScoreQuery(q) {
         public float customScore(int doc, float subQueryScore, float valSrcScore) {
@@ -158,7 +158,7 @@
     

         

         For more informative debug info on the custom query, also override the name() method:
-        
+        
       CustomScoreQuery customQ = new CustomScoreQuery(q) {
         public float customScore(int doc, float subQueryScore, float valSrcScore) {
           return subQueryScore / Math.sqrt(docid);
@@ -171,7 +171,7 @@
         

         Taking the square root of the original score and multiplying it by a "short field driven score", ie, the
         short value that was indexed for the scored doc in a certain field:
-        
+        
       Query q = queryParser.parse("my query text");
       FieldScoreQuery qf = new FieldScoreQuery("shortScore", FieldScoreQuery.Type.SHORT);
       CustomScoreQuery customQ = new CustomScoreQuery(q,qf) {
diff --git a/lucene/src/java/org/apache/lucene/search/spans/package.html b/lucene/src/java/org/apache/lucene/search/spans/package.html
index 1b41b07a065..791af178db5 100644
--- a/lucene/src/java/org/apache/lucene/search/spans/package.html
+++ b/lucene/src/java/org/apache/lucene/search/spans/package.html
@@ -59,7 +59,7 @@ two starts and ends at the greater of the two ends.
 
For example, a span query which matches "John Kerry" within ten
 words of "George Bush" within the first 100 words of the document
 could be constructed with:
-
+
 SpanQuery john   = new SpanTermQuery(new Term("content", "john"));
 SpanQuery kerry  = new SpanTermQuery(new Term("content", "kerry"));
 SpanQuery george = new SpanTermQuery(new Term("content", "george"));
@@ -82,7 +82,7 @@ SpanQuery johnKerryNearGeorgeBushAtStart =
 So, for example, the above query can be restricted to documents which
 also use the word "iraq" with:
 
-
+
 Query query = new BooleanQuery();
 query.add(johnKerryNearGeorgeBushAtStart, true, false);
 query.add(new TermQuery("content", "iraq"), true, false);
diff --git a/modules/analysis/common/src/java/org/apache/lucene/collation/package.html b/modules/analysis/common/src/java/org/apache/lucene/collation/package.html
index 3be02313735..8698bda6856 100644
--- a/modules/analysis/common/src/java/org/apache/lucene/collation/package.html
+++ b/modules/analysis/common/src/java/org/apache/lucene/collation/package.html
@@ -52,7 +52,7 @@
 
Example Usages

 
 
Farsi Range Queries

-
+
   // "fa" Locale is not supported by Sun JDK 1.4 or 1.5
   Collator collator = Collator.getInstance(new Locale("ar"));
   CollationKeyAnalyzer analyzer = new CollationKeyAnalyzer(Version.LUCENE_40, collator);
@@ -76,10 +76,10 @@
   ScoreDoc[] result
     = is.search(aqp.parse("[ \u062F TO \u0698 ]"), null, 1000).scoreDocs;
   assertEquals("The index Term should not be included.", 0, result.length);
-

+

 
 
Danish Sorting

-
+
   Analyzer analyzer 
     = new CollationKeyAnalyzer(Version.LUCENE_40, Collator.getInstance(new Locale("da", "dk")));
   RAMDirectory indexStore = new RAMDirectory();
@@ -103,10 +103,10 @@
     Document doc = searcher.doc(result[i].doc);
     assertEquals(sortedTracerOrder[i], doc.getValues("tracer")[0]);
   }
-

+

 
 
Turkish Case Normalization

-
+
   Collator collator = Collator.getInstance(new Locale("tr", "TR"));
   collator.setStrength(Collator.PRIMARY);
   Analyzer analyzer = new CollationKeyAnalyzer(Version.LUCENE_40, collator);
@@ -121,7 +121,7 @@
   Query query = parser.parse("d\u0131gy");   // U+0131: dotless i
   ScoreDoc[] result = is.search(query, null, 1000).scoreDocs;
   assertEquals("The index Term should be included.", 1, result.length);
-

+

 
 
Caveats and Comparisons

 

diff --git a/modules/analysis/icu/src/java/overview.html b/modules/analysis/icu/src/java/overview.html
index 38dec11d741..729d4cb7571 100644
--- a/modules/analysis/icu/src/java/overview.html
+++ b/modules/analysis/icu/src/java/overview.html
@@ -66,12 +66,12 @@ algorithm.
 
 
Example Usages

 
Tokenizing multilanguage text

-
+
   /**
    * This tokenizer will work well in general for most languages.
    */
   Tokenizer tokenizer = new ICUTokenizer(reader);
-

+

 

 
Collation

 

@@ -111,7 +111,7 @@ algorithm.
 
Example Usages

 
 
Farsi Range Queries

-
+
   Collator collator = Collator.getInstance(new ULocale("ar"));
   ICUCollationKeyAnalyzer analyzer = new ICUCollationKeyAnalyzer(Version.LUCENE_40, collator);
   RAMDirectory ramDir = new RAMDirectory();
@@ -134,10 +134,10 @@ algorithm.
   ScoreDoc[] result
     = is.search(aqp.parse("[ \u062F TO \u0698 ]"), null, 1000).scoreDocs;
   assertEquals("The index Term should not be included.", 0, result.length);
-

+

 
 
Danish Sorting

-
+
   Analyzer analyzer 
     = new ICUCollationKeyAnalyzer(Version.LUCENE_40, Collator.getInstance(new ULocale("da", "dk")));
   RAMDirectory indexStore = new RAMDirectory();
@@ -161,10 +161,10 @@ algorithm.
     Document doc = searcher.doc(result[i].doc);
     assertEquals(sortedTracerOrder[i], doc.getValues("tracer")[0]);
   }
-

+

 
 
Turkish Case Normalization

-
+
   Collator collator = Collator.getInstance(new ULocale("tr", "TR"));
   collator.setStrength(Collator.PRIMARY);
   Analyzer analyzer = new ICUCollationKeyAnalyzer(Version.LUCENE_40, collator);
@@ -179,7 +179,7 @@ algorithm.
   Query query = parser.parse("d\u0131gy");   // U+0131: dotless i
   ScoreDoc[] result = is.search(query, null, 1000).scoreDocs;
   assertEquals("The index Term should be included.", 1, result.length);
-

+

 
 
Caveats and Comparisons

 

@@ -239,7 +239,7 @@ algorithm.
 
 
Example Usages

 
Normalizing text to NFC

-
+
   /**
    * Normalizer2 objects are unmodifiable and immutable.
    */
@@ -248,7 +248,7 @@ algorithm.
    * This filter will normalize to NFC.
    */
   TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, normalizer);
-

+

 

 
Case Folding

 

@@ -278,12 +278,12 @@ this integration. To perform case-folding, you use normalization with the form
 
 
Example Usages

 
Lowercasing text

-
+
   /**
    * This filter will case-fold and normalize to NFKC.
    */
   TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer);
-

+

 

 
Search Term Folding

 

@@ -305,13 +305,13 @@ many character foldings recursively.
 
 
Example Usages

 
Removing accents

-
+
   /**
    * This filter will case-fold, remove accents and other distinctions, and
    * normalize to NFKC.
    */
   TokenStream tokenstream = new ICUFoldingFilter(tokenizer);
-

+

 

 
Text Transformation

 

@@ -335,19 +335,19 @@ and
 
 
Example Usages

 
Convert Traditional to Simplified

-
+
   /**
    * This filter will map Traditional Chinese to Simplified Chinese
    */
   TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Traditional-Simplified"));
-

+

 
Transliterate Serbian Cyrillic to Serbian Latin

-  
+
   /**
    * This filter will map Serbian Cyrillic to Serbian Latin according to BGN rules
    */
   TokenStream tokenstream = new ICUTransformFilter(tokenizer, Transliterator.getInstance("Serbian-Latin/BGN"));
-

+

 

 
Backwards Compatibility

 

@@ -359,7 +359,7 @@ a specific Unicode Version by using a {@link com.ibm.icu.text.FilteredNormalizer
 

 
Example Usages

 
Restricting normalization to Unicode 5.0

-
+
   /**
    * This filter will do NFC normalization, but will ignore any characters that
    * did not exist as of Unicode 5.0. Because of the normalization stability policy
@@ -371,6 +371,6 @@ a specific Unicode Version by using a {@link com.ibm.icu.text.FilteredNormalizer
     set.freeze(); 
     FilteredNormalizer2 unicode50 = new FilteredNormalizer2(normalizer, set);
     TokenStream tokenstream = new ICUNormalizer2Filter(tokenizer, unicode50);
-

+