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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.

This is a fork of jfreechart. The purpose is to the build it with the Java module sytem and Java 17. This to be able to use it in building java applications with reduced runtimes. Three servlet classes have been removed as they depended on the automatic module servlet.api. No functionality has beed added. All credit to the original project. Introduzing the module system has broken the unit tests. Everything seems to work as expected though. If you stumble upon this, then it might be useful if you need to use jfreechart in a Java module application. This project will not be maintained though. It looks like jfreechart is adopting the module system and that it might be released soon. jfreechart with Java 11 and modules for java fx applications was recently released. JFreeChart is a class library, written in Java, for generating charts. Utilising the Java2D API, it supports a wide range of chart types including bar charts, pie charts, line charts, XY-plots, time series plots, Sankey charts and more.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Stepping is a framework designed to ease the implementation of data processing solutions. In use cases where we need to implement data or data-streaming algorithms or any other processing on data, we need to first handle many different infrastructure issues. For example, we need to decide how to split the data processing logic into different steps, think about our threading policy, how to handle communication between the different steps, error handling etc. One of the most important subjects is the Threading Policy of our solution. For example, we need to think how many threads to open, have the option to distribute the processing of data to multiple 'executors' in parallel, have a thread-safe communication layer between the threads etc. On top of that we also care a lot about the performance of our solution, we want to make sure that the latency added by these infrastructures is minimal as possible. Stepping aims to handle many of these aspects so developers can spend their time on the business logic instead of solving these infrastructure and data flow issues issues over and over again.