Download JAR files tagged by number with all dependencies

The MAJ API was written by Richard Cartwright, with the help and support of Guillaume Belrose and Fang Ren. To contact the author, please raise an issue. The software is provided AS IS with no warranty whatsoever. The MAJ API (pronounced madge) is a pure Java API for creating, reading, manipulating and writing MXF (SMPTE ST 377), AAF structured storage and Reg-XML (SMPTE ST 2001) files. MXF files are commonly used as a container for professional media file formats and AAF is supported by a number of professional video editing packages. MXF and Reg-XML are used as part of the Interoperable Mastering Format suite of specifications.

This tool allows setting the version number of a specified dependency in a given Maven pom.xml file. All original comments and formatting will be kept. Usage example: mvn com.soerensen.maven.plugins:version-edit-plugin:1.0.1:setDependencyVersion -DpomFile="pom.xml" -Dgavtc="commons-io:commons-io:4.8.0" mvn com.soerensen.maven.plugins:version-edit-plugin:0.0.1:setPropertyDependencyVersion -DpropertyFile="test.property" -Dkey="junit_junit" -Dvalue="24.0.0" gavtc -> groupId:artifactId:version:type*:classifier* * optional Extra property: - noBackupFile (default:false), used to ignore the creation of a backup file.

JSON is a light-weight, language independent, data interchange format. See http://www.JSON.org/ The files in this package implement JSON encoders/decoders in Java. It also includes the capability to convert between JSON and XML, HTTP headers, Cookies, and CDL. This is a reference implementation. There is a large number of JSON packages in Java. Perhaps someday the Java community will standardize on one. Until then, choose carefully. The license includes this restriction: "The software shall be used for good, not evil." If your conscience cannot live with that, then choose a different package.

JSON is a light-weight, language independent, data interchange format. See http://www.JSON.org/ The files in this package implement JSON encoders/decoders in Java. It also includes the capability to convert between JSON and XML, HTTP headers, Cookies, and CDL. This is a reference implementation. There is a large number of JSON packages in Java. Perhaps someday the Java community will standardize on one. Until then, choose carefully. The license includes this restriction: "The software shall be used for good, not evil." If your conscience cannot live with that, then choose a different package.

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 simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model. Note: at prediction time, a missing value is returned if no cluster is found for the instance. The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.

ISO 13616-1:2007 specifies the elements of an international bank account number (IBAN) used to facilitate the processing of data internationally in data interchange, in financial environments as well as within and between other industries. The IBAN is designed for automated processing, but can also be used conveniently in other media interchange when appropriate (e.g. paper document exchange, etc.). ISO 13616-1:2007 does not specify internal procedures, file organization techniques, storage media, languages, etc. to be used in its implementation, nor is it designed to facilitate the routing of messages within a network. It is applicable to the textual data which might be conveyed through a system (network). This project provides a general purpose ISO 13616-1:2007 compatible IBAN Java class.

The UnboundID LDAP SDK for Java is a fast, comprehensive, and easy-to-use Java API for communicating with LDAP directory servers and performing related tasks like reading and writing LDIF, encoding and decoding data using base64 and ASN.1 BER, and performing secure communication. This package contains the Minimal Edition of the LDAP SDK, which is a stripped-down version of the Standard Edition that includes full support for the LDAP protocol and LDIF parsing, but omits a number of optional elements, like many of the controls and extended operations, the LDAP persistence framework, and LDAP listener framework. The Minimal Edition is primarily intended for use in space-constrained and/or memory-constrained environments, like in Android apps or embedded systems.

This is an open source library that allows generating performance indicators easily for java based application. This library is configurable through an xml file (similar to log4j) in order to define the performance object types, dimensions, counters, the output format and statistics generation periodicity. Those statistics can be saved as a file (xml/json) or injected directly in a database. Then they can be correlated and analyzed through any Business Intelligence solution in order to check the application performance and quality: number of transactions, ratio of successful and failure ...

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.