Download JAR files tagged by time with all dependencies

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.

The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment. The current version can be used for classification using numeric predictors. For more information see: I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis: Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support. In: 1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003), Gdansk, Poland, 2003; V. G. Kaburlasos, I. N. Athanasiadis, P. A. Mitkas, V. Petridis (2003). Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration.

The XRadar is an open extensible code report tool currently supporting all Java based systems. The batch-processing framework produces HTML/SVG reports of the systems current state and the development over time - all presented in sexy tables and graphs. It gets results from several brilliant open source projects and a couple of in house grown projects and presents the results as massive unified html/svg reports. The architecture is based on java, xml and xsl. Presently it only supports Java, but there are plans to produce plug ins for other leading languages.

The XRadar is an open extensible code report tool currently supporting all Java based systems. The batch-processing framework produces HTML/SVG reports of the systems current state and the development over time - all presented in sexy tables and graphs. It gets results from several brilliant open source projects and a couple of in house grown projects and presents the results as massive unified html/svg reports. The architecture is based on java, xml and xsl. Presently it only supports Java, but there are to produce plug ins for other leading languages.

Weighing less than 50KB, Rupy is probably the smallest Java NIO application server in the world. Rupy is inherently non-blocking asynchronous, which makes it the ideal candidate for high concurrency real-time applications pushing dynamic data. Tested with acme, rupy performs on average ~1500 requests per second. To put that figure in perspective; acme doesn't use keep-alive, so that means 1500 unique TCP connections serving dynamic content per second! Thanks to NIO and an event queue to avoid selector trashing, this figure degrades gracefully under high concurrency.

The XRadar is an open extensible code report tool currently supporting all Java based systems. The batch-processing framework produces HTML/SVG reports of the systems current state and the development over time - all presented in sexy tables and graphs. It gets results from several brilliant open source projects and a couple of in house grown projects and presents the results as massive unified html/svg reports. The architecture is based on java, xml and xsl. Presently it only supports Java, but there are plans to produce plug ins for other leading languages.

A tool to help visualise API differences between two different versions of a project. Jardiff takes two jar files and outputs all the public API changes as xml, html or plain text. Currently this is command line driven, though we are working on an ant plugin. It requires java 1.4 to run. <h2>Background</h2> At OSjava, we were using a shell script to work out API differences between our release versions. This unfortunately doesn't scale very well to larger projects, the main reason for jardiff was to do the same in a much shorter time (From memory it was taking minutes to get the API diff from two fairly small jars). In it's current state, jardiff can generate the api differences between rt.jar for java 1.4 and java 1.5 in under 20 seconds, so it should scale to even the largest of projects.