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Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.

DICTIONARY-BASED COMPOUND SPLITTER FOR GERMAN BananaSplit is a compound splitter for German that uses a dictionary resource. The dictionary can be either a simple word list, or a word list equipped with POS values, or an XML based dictionary. The original version was able to use GermaNet as a dictionary. This is useful in applications that rely on GermaNet anyway: no additional lexicon needs to be generated and held in memory. This was also the original purpose of BananaSplit. It served as a compound splitter for a tool called BananaRelation. BananaRelation cannot be published here as it makes heavy use of unpublished code by EML Research, Heidelberg. BananaSplit can either be used as a standalone application or it can be integrated into other Java programs (as a library). This program emerged from the seminar Lexical Semantic Processing in NLP (winter term 2005/2006) taught by Iryna Gurevych at the Seminar für Sprachwissenschaft, Tübingen. Both BananaSplit and BananaRelation were introduced to the seminar participants on 17th of December, 2005. The key algorithm for compound splitting is based on Langer (1998). The program came to use in Müller and Gurevych (2006). Please note that the program splits compounds into two parts only. Details are given in the documents linked below.

Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes: forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set. Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions. Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set. Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc. It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes. Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

This project aims to offer an easy-to-extend Java DAS server framework. It offers several advantages: * Implementing data sources is very easy but also flexible and powerful. * Data caching is built into the system, with access to the caching mechanism made available to the data sources. * All aspects of the server are highly configurable, including selecting options where the DAS 1.53 specification offers choices to the implementor. * The latest Java technologies have been used throughout the system to optimise performance and simplify data source development. * Wherever possible the same terminology is used in the API as in the DAS specification and XML - again, making data source development more easy. * The server allows XSLT transforms of the DAS XML to be configured to provide a simple DAS client view (limited to the single DAS source). More details of the DAS protocol, DAS servers and DAS clients can be found at http://www.biodas.org/wiki/Main_Page. The first version of this server is a complete implementation of Distributed Sequence Annotation System (DAS) Version 1.53. If you are interested in learning more about DAS 1.53, the specification is highly recommended as a concise and complete description of the DAS protocol that can be obtained from: http://biodas.org/documents/spec.html

IronPDF Java library offers an extensive compatibility range, making it a go-to solution for a wide array of developers. It fully supports JVM languages like Java, Scala, and Kotlin, making it incredibly versatile. This Java PDF library is also compatible with Java 8 and above, providing optimum performance across multiple platforms. It's been designed with a wide range of users in mind Here's a look at what it supports: JVM Languages: Java, Scala, Kotlin.Platforms: Java 8 and above.Operating Systems: Microsoft Windows, Linux, Docker, Azure, AWS.IDEs: Jetbrains IntelliJ IDEA, Eclipse. You can deploy IronPDF Java across various platforms, including Microsoft Windows, Linux, Docker, Azure, and AWS. It is also fully compatible with popular IDEs like Jetbrains IntelliJ IDEA and Eclipse, facilitating smooth project development and management. Your pom.xml file is essentially the backbone of your project when you're using Maven. It's here where you introduce new dependencies that you wish to include. To make IronPDF Java package a part of your Maven project, you simply need to add the following snippets to your pom.xml: Remember to replace '20xx.xx.xxxx' with the latest version of IronPDF. IronPDF Java simplifies the process of creating PDF files. Convert HTML files, HTML strings, or URLs directly to new PDF documents in a few lines of code. The variety of file formats it handles is vast, as it can even transform images into PDF documents and vice versa. Need to use base 64 encoding, base URLs, or custom file paths? No problem! IronPDF Java has got you coveredFor more detail about installing and using IronPDF Java. When you run your project for the first time post-integration, IronPDF's engine binaries will automatically be downloaded. The engine starts its journey when you call any IronPDF function for the first time and takes a breather when your application is either closed or enters an idle state. It is not an open source java PDF library but here's the best part - IronPDF Java is offering a 30-day free trial. So, why wait? Give it a go and boost your PDF operations today.

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

pact-jvm-consumer-specs2 ======================== ## Specs2 Bindings for the pact-jvm library ## Dependency In the root folder of your project in build.sbt add the line: ```scala libraryDependencies += "au.com.dius.pact.consumer" %% "specs2" % "4.0.1" ``` or if you are using Gradle: ```groovy dependencies { testCompile "au.com.dius.pact.consumer:specs2_2.13:4.0.1" } ``` __*Note:*__ `PactSpec` requires spec2 3.x. Also, for spray users there's an incompatibility between specs2 v3.x and spray. Follow these instructions to resolve that problem: https://groups.google.com/forum/#!msg/spray-user/2T6SBp4OJeI/AJlnJuAKPRsJ ## Usage To author a test, mix `PactSpec` into your spec First we define a service client called `ConsumerService`. In our example this is a simple wrapper for `dispatch`, an HTTP client. The source code can be found in the test folder alongside the `ExamplePactSpec`. Here is a simple example: ``` import au.com.dius.pact.consumer.PactSpec class ExamplePactSpec extends Specification with PactSpec { val consumer = "My Consumer" val provider = "My Provider" override def is = uponReceiving("a request for foo") .matching(path = "/foo") .willRespondWith(body = "{}") .withConsumerTest { providerConfig => Await.result(ConsumerService(providerConfig.url).simpleGet("/foo"), Duration(1000, MILLISECONDS)) must beEqualTo(200, Some("{}")) } } ``` This spec will be run along with the rest of your specs2 unit tests and will output your pact json to ``` /target/pacts/<Consumer>_<Provider>.json ``` # Forcing pact files to be overwritten (3.6.5+) By default, when the pact file is written, it will be merged with any existing pact file. To force the file to be overwritten, set the Java system property `pact.writer.overwrite` to `true`. # Test Analytics We are tracking anonymous analytics to gather important usage statistics like JVM version and operating system. To disable tracking, set the 'pact_do_not_track' system property or environment variable to 'true'.