Download JAR files tagged by apart with all dependencies

Tests for mirroring, split apart for dependencies.

A java library to take apart and assemble JPEG files

The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other breaking updates.

Krausening is a library that allows for the externalization of property and configuration files from deployment units, such as WARs, EARs, uber JARs, etc. By ensuring that property files are external to deployment packages, these packages can be easily reused as-is to deploy to heterogeneous environments, circumventing the need to hack apart or rebuild the package to support property file modification.

Krausening is a library that allows for the externalization of property and configuration files from deployment units, such as WARs, EARs, uber JARs, etc. By ensuring that property files are external to deployment packages, these packages can be easily reused as-is to deploy to heterogeneous environments, circumventing the need to hack apart or rebuild the package to support property file modification.

This maven plugin is responsible to create a Fat JAR bundle containing all the dependencies specified in a POM file. Sometimes users need to unpack specific dependency (eq. zip, tar etc) before packing the dependency as this dependency could contain other jar files. The primary motive is to collect all the dependencies including the plain jar dependencies and the other jar dependencies that reside in other dependency (eq. zip, tar etc). Apart from this, it could also update the versions of the specified dependencies before wrapping in a big fat JAR bundle. This internally uses bnd to wrap all the dependencies in a single fat JAR.

Dryuf Concurrent is enhancement over original Java Concurrent Future design. Apart from providing ListenableFuture functionality, it provides flexibility how to handle cancel() operations, can delay the notifications. It allows various types of notification listeners, distinguishing among successful end, failure and end due to cancel request. Last but not least, it has very low memory footprint and excellent performance, even outperforming JDK Future without listener support.

A NSGA-II implementation using Java. This implementation of NSGA-II algorithm is in pure reference to the original published paper. This is not an effort to convert the originally implemented C code in Java. The original C code by the authors has not be referred to while writing this implementation. This is a fully customizable implementation of the NSGA-II algorithm, made as generic as possible. This documentation assumes you have basic understanding of the NSGA-II algorithm. Apart from the core concepts of the algorithm, everything else in this package can be implemented as per the user's choice and plugged into the algorithm dynamically. Since NSGA-II is more like a set of protocols to follow as an algorithm rather than a concrete implementation of every aspect, this package has been re-written from scratch keeping complete customizability in mind. Apart from the core concepts of the algorithm, everything is considered to be a plugin external to the algorithm that can be implemented by the user and dynamically plugged into the algorithm during runtime as needed. This opens up the possibility of the package to be used simply as a PoC or be converted into something much more complex according to the users needs.

relay-jdbc allows a JDBC connection to be relayed through an intermediate relay server: it is both a JDBC driver (client) and a JDBC relay server. It is a fork of https://github.com/AugeoSoftware/VJDBC project, where Aleksei Yu. Semenov has implemented a number of brilliant improvements of the orignal VJDBC project hosted on http://vjdbc.sourceforge.net Original Developers were: Hunter Payne ([email protected]) and Michael Link ([email protected]). For all pre-existing code, credits go to the original respective author. This product brings the original VJDBC concept further and (apart from code quality improvements and bug fixes) implements complete separation of the underlying transport mechanism, introducing support for connecting directly through a SSH pipe, where the relay server interacts with the client through standard I/O pipes. The re-designed transport architecture supports easy development of new transport implementations, allowing this JDBC relay to be quickly adapted to a variety of exotic environments.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.