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This project is about configuration of applications and about parameterization of Objects. This library offers annotations (and annotation processors), parsers, typing, observing changes, serialization, cloning and more when working with settings and/or object parameters. Settings and its features can be declared using an xml format. The library offers syntax support for settings and parameters in a loosely coupled manner. You are not restricted to built in syntax, you can provide your own. At runtime this library tracks keys for which a default is used because they are not found in settings. Also it tracks unused keys. You can stack features for settings such as caching, preparing keys and values, readonlyness, threadsafety, helpsupport, reading / parsing from input. You can easily develop your own features for settings.

This nifi-utils module should be a general purpose place to store widely and generally useful functions that any component might want to leverage. NO DEPENDENCIES should be added. This module is likely to be leveraged by every extension and should not bring along any other dependencies. The only dependency intended is the nifi-api and even this is expected to be already provided in any case where it would be used. The typical place this util would be found is within a nar and all nars already have nifi-api as a parent dependency. The nifi-api can be thought of as a NiFi Application Container level dependency.

Java implementation of the Ziggurat algorithm for generating Gaussian distributed pseudorandom numbers. The Ziggurat algorithm is significantly faster than the more commonly encountered Polar method, and has some other desirable statistical properties. The ZigguratGaussian class is a Java port of the GNU Scientific Library's C implementation (Voss, 2005) of the Ziggurat method. In porting to Java, we have made several optimizations, the details of which can be found in the source code comments, which highlights any differences between this Java implementation and the C implementation on which it is based. This package also includes an implementation of the Polar Method, included to enable comparing speed advantage of the Ziggurat algorithm.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "minimum description length" (MDL) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "evidence-driven state merging" (EDSM) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

Suji is a converter library from Japanese number notation to numerical value, and from numerical notation to Japanese Kansuji notation. Converter.values(src) to convert from Japanese number notation to numerical value: Japanese number notation can include Kansuji. The String "１つの価格が二兆30万五千十7円になります。" will be converted to two BigDecimal, 1 and 2000000005017. And also, 打率は三割二部五厘です。 will be a 0.325. The return value is a list of Suji Numeral objects. If the input string has no number notation, Suji returns a empty list. The Numeral object has three methods: value(), begin(), and end(): value(): a BigDecimal instance of a numerical value for the number notation. begin(): the begin index (int) of the found number notation at the input string. end(): the end index (int) of the found number notation. Converter.kansujis(src) to convert from numeric notation to Japanese Kansuji notation: The String 20兆30万五千十7円になります。 will be converted to the Kansuji string, 二十兆三十万五千十七. The boolean flag one is interpreted as whether to display the first character 一 or not. The output of Converter.kansujis('1000万', true) will be converted to 一千万, and the output of Converter.kansujis('1000万', false) will be converted to 千万. Note that kansujis does not support numerical notation after the decimal point. If the input string is 32.01, the output will 三十二, not 三十二割一厘. The return value is a list of Kansuji objects. If the input string has no number notation, Suji returns a empty list. The Kansuji object has three methods: value(), begin(), and end(): value(): a String instance of a Kansuji notation. begin(): the begin index (int) of the found number notation at the input string. end(): the end index (int) of the found number notation. Suji is a one-pass parser. That is, Suji parse a source text from the head to the end only once.

Bundle with a synchronous activator which will install OGEMA resources from XML (.ogx) or JSON (.ogj) files located in config/resources (default). This requires a running OGEMA framework when the bundle is started and enables the installation of basic resources before other applications are activated. Also adds a service for felix fileinstall that will install such resource files. Files installed through fileinstall will be reinstalled when they are updated or when a newer version of an installed file is found after a restart (using the file's last modification timestamp). Files installed from the configuration directory will only be reinstalled on a restart, if they have been changed.

Provides a time series forecasting environment for Weka. Includes a wrapper for Weka regression schemes that automates the process of creating lagged variables and date-derived periodic variables and provides the ability to do closed-loop forecasting. New evaluation routines are provided by a special evaluation module and graphing of predictions/forecasts are provided via the JFreeChart library. Includes both command-line and GUI user interfaces. Sample time series data can be found in ${WEKA_HOME}/packages/timeseriesForecasting/sample-data.

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.