Download JAR files tagged by apart with all 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 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.

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.