Download JAR files tagged by uses with all dependencies

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

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.

An low latency, lock free, primitive bounded blocking queue backed by an int[]. This class mimics the interface of {@linkplain java.util.concurrent.BlockingQueue BlockingQueue}, however works with primitive ints rather than objects, so is unable to actually implement the BlockingQueue. This class takes advantage of the Unsafe.putOrderedObject, which allows us to create non-blocking code with guaranteed writes. These writes will not be re-orderd by instruction reordering. Under the covers it uses the faster store-store barrier, rather than the the slower store-load barrier, which is used when doing a volatile write. One of the trade off with this improved performance is we are limited to a single producer, single consumer.

Siena is a persistence abstraction layer made in Java with various implementations: * siena-jdbc: persists objects into a relational database using JDBC (status: deeply tested) * siena-gae: persists objects into the Google App Engine's datastore using its low-level API (status: beta) * siena-sdb: persists objects using Amazon's SimpleDB (very experimental) * siena-remote: persists objects remotely using a remote web application that uses siena. (planning) Siena is also: * Intrusive. You make a class persistent by extending a certain class. Relationships are defined also in an intrusive way. * Simple. Simplicity is the most important thing. * Limited. Due to the simplicity there are several unsupported things such as JOINs. * Extensible. There are two interfaces you can implement on your own way.

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).

BRAP is a Java and Android remoting protocol that uses native Java object serialization, encapsulated in HTTP. It aims to be an alternative to Spring HttpInvoker and Spring Security especially when you don't need or want the dependencies of Spring in your client, for example when building a rich client app or an Android app where size is important. The authentication mechanism lets you use your own domain objects as credentials. Server configuration can be expressed solely in web.xml or by subclassing the ProxyServlet but there is also an optional SpringProxyServlet available for seamless Spring Integration. BRAP focuses on being easy to use, small in size (9 kb), yet powerful and extensible. The client uses a pluggable TransportProvider with default implementations for HttpURLConnection and Apache HttpClient.

OpenCL4Java is a thin Java wrapper around OpenCL's C API. It uses JNA as its interop layer library, which means it works on all of the (many) JNA-supported platforms (see http://jna.dev.java.net/). It is autogenerated by JNAerator (http://jnaerator.googlecode.com/), so updates to newer OpenCL specs are a matter of seconds. Note that OpenCL4Java is used by JavaCL, an Object-Oriented API that presents OpenCL in a much more practical, powerful and idiomatic way to Java. For more info, please visit http://code.google.com/p/nativelibs4java/wiki/OpenCL.

This plugin will support various tasks within the Weblogic 8.1, 9.x, 10.x and 12.x environment. This version starts to support weblogic 10 and 11. The mojo supports such tasks as deploy, undeploy,clientgen,servicegen, and appc are supported as well as many others. The plugin uses exposed API's that are subject to change but have been tested in 8.1 SP 4-6 and 9.0 - 9.2 MP3, 10.x. There are two versions of the plugin to support the two environments based on differences in the JDK. The 9.x version is currently being refactored to support the standard JSR supported deployment interface. The code used in the plugin has been contributed to the Cargo project however to date has not be integrated into the codebase. Please feel free to suggest improvements or help support this plugin effort.

Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.