sakai

The {@link org.apache.maven.jrcs.diff diff} package implements the differencing engine that JRCS uses. The engine has the power of Unix diff, is simple to understand, and can be used independently of the archive handling functionality. The entry point to the differencing engine is class {@link org.apache.maven.jrcs.diff.Diff Diff}.

Text is represented as Object[] because the diff engine is capable of handling more than plain ascci. In fact, arrays of any type that implements {@link java.lang.Object#hashCode hashCode()} and {@link java.lang.Object#equals equals()} correctly can be subject to differencing using this library.

Two implementations of the differencing algorithm are provided.

• {@link SimpleDiff Simple Diff} is a verys imple algorithm that is fast and works well with very large input sequences, but that frequently produces result that are subotimal (at times four or more times larger than GNU diff).
• {@link org.apache.commons.jrcs.diff.myers.MyersDiff MyersDiff} is an implementation of Gene Myers differencing algorithm. Myer's algorithm produces optimum results (minimum diffs), but consumes considerably more memory than SimpleDiff, so its not suitable for very large files.

@author Juanco Anez
@version $Id: package.html 2891 2005-10-25 17:30:31Z ian@caret.cam.ac.uk $
@see Diff 
@see org.apache.maven.jrcs.rcs.Archive

The {@link org.apache.maven.jrcs.diff.myers diff.myers} package implements Gene Myers' differencing algorithm.

Myer's algorithm produces optimum results (minimum diffs), but consumes considerably more memory than SimpleDiff, so its not suitable for very large files.

JRCS is not intended to replace RCS nor CVS. JRCS was written to be able create archive analysis tools that can do things like identify hot spots in the source code, measure the contributions by each developer, or assess how bugs make it in.

The reasons why JRCS has the ability do do check-ins and save archives is API symmetry, and to simplify the writing of unit tests.

The {@link org.apache.maven.jrcs.rcs rcs} package implements th8e archive handling functionality. The entry point to the library is class {@link org.apache.maven.jrcs.rcs.Archive Archive}.

CAVEAT UTILITOR: Do not make modifications to your archives with JRCS. There needs to be an important amount of additional testing before it's safe to do that.

The {@link org.apache.maven.jrcs.diff diff} package implements the differencing engine that JRCS uses. The engine has the power of Unix diff, is simple to understand, and can be used independently of the archive handling functionality. The entry point to the differencing engine is class {@link org.apache.maven.jrcs.diff.Diff Diff}.

Package Dependencies

Jakarta ORO Regexp

The Jakarta ORO Regexp library is used for RCS keyword management. You can get a copy of Jakarta ORO from:

http://jakarta.apache.org/oro/

A copy of Jakarta Regexp is included in the full distribution of this library.

JavaCC

The RCS parser is constructed using the JavaCC compiler compiler. You can get a copy of JavaCC from:

http://www.metamata.com/javacc/

JavaCC is not distributable, so you must get a copy of it yourself if you intend to use it. Keep in mind that, as distributed, this library does not require JavaCC to be compiled because all the JavaCC generated .java files are included.

If you decide to use JavaCC, please adjust the "javacc.lib.dir" property as needed. You'll also need to download the set of optional tasks for Ant.

JUnit

The library tests are written for the JUnit Testing Framework. For distributions and documentation see:

http://www.junit.org/

@author Juanco Anez @version $Id: Archive.java,v 1.16 2002/02/27 20:58:58 sbailliez Exp $ @see Archive @see org.apache.maven.jrcs.diff.Diff

Example Usage

	IndexSearcher searcher = new IndexSearcher(ramDir);
	Query query = QueryParser.parse("Kenne*", FIELD_NAME, analyzer);
	query = query.rewrite(reader); //required to expand search terms
	Hits hits = searcher.search(query);

	Highlighter highlighter = new Highlighter(this, new QueryScorer(query));
	for (int i = 0; i < hits.length(); i++)
	{
		String text = hits.doc(i).get(FIELD_NAME);
		TokenStream tokenStream = analyzer.tokenStream(FIELD_NAME, new StringReader(text));
		// Get 3 best fragments and seperate with a "..."
		String result = highlighter.getBestFragments(tokenStream, text, 3, "...");
		System.out.println(result);
	}

New features 06/02/2005

New features 22/12/2004

1. Faster highlighting using Term vector support
2. New formatting options to use color intensity to show informational value
3. Options for better summarization by using term IDF scores to influence fragment selection

The highlighter takes a TokenStream as input. Until now these streams have typically been produced using an Analyzer but the new class TokenSources provides helper methods for obtaining TokenStreams from the new TermVector position support (see latest CVS version).

The new class GradientFormatter can use a scale of colors to highlight terms according to their score. A subtle use of color can help emphasise the reasons for matching (useful when doing "MoreLikeThis" queries and you want to see what the basis of the similarities are).

The QueryScorer class has a new constructor which can use an IndexReader to derive the IDF (inverse document frequency) for each term in order to influcence the score. This is useful for helping to extracting the most significant sections of a document and in supplying scores used by the new GradientFormatter to color significant words more strongly. The QueryScorer.getMaxWeight method is useful when passed to the GradientFormatter constructor to define the top score which is associated with the top color.

This package provides common exceptions for the Sakai Service APIs.

This package provides new UI components.