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JAXB 2.1.x XJC Boolean-Getter Plugin This plugin causes getter methods for Boolean Objects to be called "getXX" instead of "isXX". Downloaded from http://fisheye5.atlassian.com/browse/~raw,r=1.1/jaxb2-commons/www/boolean-getter/index.html on 18/03/10 Yiannis Paschalidis Sanity4J was created to simplify running multiple static code analysis tools on the Java projects. It provides a single entry point to run all the selected tools and produce a consolidated report, which presents all findings in an easily accessible manner.

The RestFixture is a FitNesse (http://fitnesse.org) fixture that allows developers and/or product owners to write test fixtures for REST services with simplicity in mind. The idea is to write tests that are self documenting and easy to write and read, without the need to write Java code. The fixture allows test writers to express tests as actions (any of the allowed HTTP methods) to operate on resource URIs and express expectations on the content of the return code, headers and body. All without writing one single line of Java code. And it also works as a living/executable documentation of the API.

ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers many data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions in particular of new methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.

The Universal Java Matrix Package (UJMP) is an open source library for dense and sparse matrix computations and linear algebra in Java. In addition to the basic operations like matrix multiplication, matrix inverse or decomposition methods, it also supports visualization, JDBC import/export and many other useful functions such as mean, correlation, standard deviation, mutual information, or the replacement of missing values. It's a swiss army knife for data processing in Java, tailored to machine learning applications.

Skeletal implementation of interface javax.lang.model.util.Types, plus concrete realization backed by core Java Reflection API, akin to JEP 119. This module implements those methods in interface javax.lang.model.util.Types that pertain to the Java type system, plus it provides a method for resolving formal type parameters to actual type arguments. These methods have no equivalent in the Java Reflection API. The abstract skeletal implementation in this module is meant to be specialized for a particular javax.lang.model implementation. The abstraction makes this module well-suited to be used in projects that contain javax.lang.model as part of their domain model (for instance, implementations of alternative languages on top of the JVM).

Asynchronous publish-subscribe communication library. Features: 1. Listeners are stored using WeakReferences to prevent memory leaks when they are not unsubscribed. 2. Uses fork-join framework for concurrent event delivery. 3. Publish methods returns collection of Futures that represent event notifications. 4. Supports ordered publishing: guaranteed to notify of the events in order they were published. 5. Provides EventSource and EventService interfaces for better integration with IOC containers and alternative implementations.

A filter that applies the LOF (Local Outlier Factor) algorithm to compute an outlier score for each instance in the data. Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable. For more information, see: Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.

This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.