Download JAR files tagged by class with all dependencies

It helps to quickly make settings for class and its fields of with properties XML. Instead of create a lot of files to describe every class and its fields it use describing by type, by name. E.g. setting "java.lang.Integer"-"INTEGER NOT NULL" will be assigned for every field of this type. Of course it can be overridden by settings for field name and so on. It is used by beige-web to describe how to render an entity and convert from HTML value. It is used by beige-orm to describe how to map an entity to a DBMS. It make simple settings - Map<String, String>, e.g. "title"-"Beige ORM 1.1", class settings Map<String, Map<String, String>>, e.g. "org.model.Customer"-["defaultOrder"-"ITSNAME", "wdgFilterOrder"-"filterOrderStd"], fields settings Map<String, Map<String, Map<String, String>>>, e.g. "org.model.Customer"-["itsId"["wdgNew"-"null", "wdgFilter"-"filterNumber"]]

Tool used to analyze a set of classes and determine on what other classes they directly or indirectly depend. Typically this tool is used to compute the necessary and sufficient set of classes to include in a JAR file, for use in the class path of a client or service, or for use in the codebase of a client or service. The tool starts with a set of "root" classes and recursively computes a dependency graph, finding all of the classes referenced directly by the root classes, finding all of the classes referenced in turn by those classes, and so on, until no new classes are found or until classes that are not of interest are found. The normal output of the tool is a list of all of the classes in the dependency graph. The output from this command can be used as input to the jar tool, to create a JAR file containing precisely those classes.

Package containing a class that rescales the attributes in a classification problem based on their discriminative power. This is useful as a pre-processing step for learning algorithms such as the k-nearest-neighbour method, to replace simple normalization. Each attribute is rescaled by multiplying it with a learned weight. All attributes excluding the class are assumed to be numeric and missing values are not permitted. To achieve the rescaling, this package also contains an implementation of non-negative logistic regression, which produces a logistic regression model with non-negative weights .

This module contains the sequence datastructure of the TCS Alignment Toolbox. It defines the possible value sets in the ValueType enum as well as the different KeywordSpecification classes, namely: 1.) StringKeywordSpecification for string type values. 2.) SymbolicKeywordSpecification for values from a discrete alphabet (also refer to the Alphabet class) 3.) VectorialKeywordSpecification for vectors of some length (or for scalars) A NodeSpecification is a vector of such KeywordSpecifications and defines the order of value sets. A node, then, is defined as a vector of values from these value sets (also refer to the Value interface as well as the StringValue, SymbolicValue and VectorialValue classes). Finally a sequence is defined as a list of such nodes.

This bundle wraps Pi4j (http://pi4j.com) that wraps the native code Wiring Pi (http://wiringpi.com). It wraps these libraries to make them OSGi friendly and allow them to work together with the OSGi enRoute IoT circuit library (osgi.enroute.iot.circuit). The bundle will first use Pi4J to detect on what hardware it runs. If it runs on an appropriate type, it will register a component that can be configured with Metatype. The Metatype defines a full blown configuration template for all the Pi's functions. The GPIO's are registered as components for the circuit. Regardless of the success of the configuration, this bundle will also register a GpioController service, which is the main Pi4J class.

An empty artifact that Guava depends on to signal that it is providing ListenableFuture -- but is also available in a second "version" that contains com.google.common.util.concurrent.ListenableFuture class, without any other Guava classes. The idea is: - If users want only ListenableFuture, they depend on listenablefuture-1.0. - If users want all of Guava, they depend on guava, which, as of Guava 27.0, depends on listenablefuture-9999.0-empty-to-avoid-conflict-with-guava. The 9999.0-... version number is enough for some build systems (notably, Gradle) to select that empty artifact over the "real" listenablefuture-1.0 -- avoiding a conflict with the copy of ListenableFuture in guava itself. If users are using an older version of Guava or a build system other than Gradle, they may see class conflicts. If so, they can solve them by manually excluding the listenablefuture artifact or manually forcing their build systems to use 9999.0-....

A plugin helping with installing ImageJ 1.x plugins and dependencies. In ImageJ 1.x, .jar files containing an underscore are automatically scanned for classes whose names contain underscores. Every such class is considered to be a plugin. That is not true, however, for jai_codec.jar's classes. Therefore, Fiji introduced the split between plugins/ and jars/, where third-party libraries are expected to live in jars/. ImageJ2 uses the same structure since it is backwards compatible.