Download JAR files tagged by basis with all dependencies

Wicket Web Beans (WWB) is an Apache Wicket component toolkit for displaying and editing POJOs that conform to the JavaBeans specification. Web pages are automatically generated based on bean properties and certain conventions. If necessary, the layout, editability, and actions of these pages can be customized on an exception basis. In other words, the toolkit normally does what you'd expect, but when it doesn't, you can override its behavior.

Evita is a specialized database with easy-to-use API for e-commerce systems. Purpose of this research is creating fast and scalable engine that handles all complex tasks that e-commerce systems has to deal with on daily basis. Evita should operate as a fast secondary lookup / search index used by application frontends. We aim for order of magnitude better latency (10x faster or better) for common e-commerce tasks than other solutions based on SQL or NoSQL databases on the same hardware specification. Evita should not be used for storing and handling primary data, and we don't aim for ACID properties nor data corruption guarantees. Evita "index" must be treated as something that could be dropped any time and built up from scratch easily again.

This Tool provides the means to monitor the performance of Spring Batch applications without the need to manipulate the respective code basis. Through Listeners and AOP it evaluates the throughput of a monitored Job and writes the result to a simble database-schema (a file-based H2 by default). It measures the performance of Job, Step, Chunk, Reader/Processor/Writer/Tasklet down to indivdual Items. Installation/Usage: 1. Add the springBatchPerformanceMonitoring-dependency to your POM 2. Add the de.viadee.spring.batch.infrastructure.Configurator.class to your Spring-Application-Context (via @Import on your Configuration) 3. Run the Job 4. By default the monitoring result is written to project-folder/target/database/monitoringDB.mv.db

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.