Download JAR files tagged by properties 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.

This plugin provides integration between Spring and Maven. Plugin goals support loading a Spring context XML file as part of the Maven build lifecycle. The XML file can be on the local file system or be accessible via any URL Spring's resource loading mechanism can understand. Spring's "classpath:context.xml" style notation is supported. Annotated Java classes can also be used to load a Spring context. Maven properties are injected into the Spring context (both XML and annotation style) as a java.util.Properties bean named "mavenProperties". Maven properties are also registered as a top level PropertySource so that Spring's placeholder resolution framework automatically considers them. See Project Reports -> Plugin Documentation for details on plugin goals. By default, the profile "maven" is set as an active Spring profile along with any other active Maven profiles.

Reco4j is an open source project aims at developing a recommendation framework based on graph data sources. We choose graph databases for several reasons. They are NoSQL databases, so "schemaless". This means that it is possible to extend the basic data structure with intermediate information, i.e. similarity value between item and so on. Moreover, since every information is expressed with properties, nodes and relations, the recommendation process can be customized to work on every graph. Reco4j can be used on every graph where "user" and "item" is represented by node and the preferences are modelled as relationship between them. Current implementation leverage on Neo4j as first graph database integrated in our framework.

The goal of quality-test is to provide a small Java library for basic code quality checks within unit tests. It is discouraged to use quality-test in production code. The checks provided in this library are designed to be used in unit-tests. The checks and utilities provided in this package check static properties of classes, for example whether classes are marked final or constructors are private. Additionally, there are utils to give additional code coverage for private constructors. Quality-Test often can be used together with Google Reflections (http://code.google.com/p/reflections/) to perform checks such as, assure that every class in package *.dto.* is final or make sure that no class in the package *.controller.* contains any non-final static variables.

Jetty context XML configuration plugin generator helper. This plugin helps you to generate your Context XML file with a dynamic content such as: * classpath entries * web app resources * maven properties Plugin needs a template Jetty context XML file where the dynamic content is injected. Injection of dynamic content is supported as: * altering the DOM with new values * with enabled filtering dynamic values can be replaced as properties (see bellow) Filtering of the template file is supported. Basically it means you can include content from your maven properties in your context XML file. If desired plugin sets two maven properties with the dynamic content: * jetty.conf-plugin.classpath * jetty.conf-plugin.webapp These properties might be used for filtering as well. Plugin resolves project dependency artifacts. These artifacts are transformed into webapp resources and classpath entries. Works with multi-module projects. Works in Eclipse with enabled m2e.

How many classes does it take to make a cache? 60? 80? You might think so if you look at the other caches on the market. Ashkay is a streamlined, simple cache that in many ways is more powerful than the other mainstream caches. Ashkay is a strategy based caching tool. In most cases, you might be using a configuration file to tell the cache what rules it should obey. Or, you might just be setting properties on the cache. I have never liked this model as it limits me, the user of the cache to strategies the developer decided were important. Instead, Ashkay lets you choose the strategy(ies) to use when caching certain objects. Of course, a few of the most handy strategies are pre-packaged, but implementing a new one is a simple as can be. Ashkay is a fork of <a href="http://xot.sf.net">xot</a> caching code. Made more sense on its own.

[] (https://drone.io/bitbucket.org/tidalwave/thesefoolishthings-superpom-src/latest) A feature-rich SuperPOM for building Java projects. It features: * explicit version configuration for a number of plugins; * easy configurability by means of pre-defined properties to avoid cut & copy of plugin sections. A number of profiles, that can be easily activated, are available for: * Spring-AOP configuration; * different kinds of Continuous Integration tasks, including a full run of QA tools such as JaCoCo, FindBugs, PMD, etc... * deploying WARs and locally running them with Tomcat or Jetty; * creating a Mac OS X bundle for JavaFX applications; * creating .deb packages for both application and services; * a customized release cycle, including all requirements for the Maven Central such as signing, with a 'transactional' behaviour (all artifacts, both the DSCM and the Maven artifacts are prepared on the local disk, so they can be uploaded in a second moment); Remember to customise it ------------------------ If you use it, please remember to change the ```description```,```url```, ```organization```, ```developers```, ```license```, etc... to override those related to the development of this POM.

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.