Download JAR files tagged by compute with all dependencies

This package contains Microsoft Azure Compute Management SDK. This package has been deprecated. A replacement package com.azure.resourcemanager:azure-resourcemanager-compute is available as of 31-March-2022. We strongly encourage you to upgrade to continue receiving updates. See Migration Guide https://aka.ms/java-track2-migration-guide for guidance on upgrading. Refer to our deprecation policy https://azure.github.io/azure-sdk/policies_support.html for more details.

This module provides two packages, 'trees' and 'forests', which provide algorithms to compute edit distances on trees and forests (that is, unordered or ordered lists of trees) respectively. The edit distance is computed according to the tree edit distance algorithm of Zhang and Shasha (1989). The basic tree data structure is defined by the Tree interface in the trees module. Please refer to the javadoc for more detailed information.

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.

A filter that applies the LOF (Local Outlier Factor) algorithm to compute an outlier score for each instance in the data. Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable. For more information, see: Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.

YET ANOTHER IMPLEMENTATION OF LEVENSHTEIN DISTANCE GenericLevenshtein is an implementation of Minimum Edit Distance, also called Levenshtein Distance, written by Ramon Ziai and Niels Ott. This algorithm is very popular and it is often used to compute the similarity of strings. The difference in the presented implementation is that it can operate on sequences of any Java object implementing equals(Object). So no matter if you want to compare genome sequences or sequences of numbers, or just strings, here you go! Furthermore, the costs of the replace, insert, and delete operations can be customized by implementing the simple WeightCalculator<T> interface. In that case it is not a requirement to rely on equals(Object) as your implementation can do whatever you like it to do in oder to compare objects.

The Java Algebra System (JAS) is an object oriented, type safe and multi-threaded approach to computer algebra. JAS provides a well designed software library using generic types for algebraic computations implemented in the Java programming language using the JVM runtime infrastructure. The library can be used as any other Java software package or it can be used interactively or interpreted through a jython (Java Python) or jruby (Java Ruby) front end, there is also an Android App based on Ruboto (jruby for Android). The focus of JAS is at the moment on commutative, solvable and non-commutative polynomials, power series, Groebner bases, factorization, real and complex roots and applications. By the use of Java as implementation language JAS is 64-bit and multi-core CPU ready and can make use of mutiple CPUs where available. JAS can run on a wide variety of devices ranging from Android to compute clusters (using MPJ a Java Message Passing Interface (MPI) or OpenMPI).

Tool used to generate the preferred class information for downloadable JAR files in the form of a META-INF/PREFERRED.LIST required for use by the {@link net.jini.loader.pref.PreferredClassLoader}. The list is generated by examining the dependencies of classes contained within a target JAR file and zero or more additional supporting JAR files. Through various command-line options, a set of "root" classes are identified as belonging to a public API. These root classes provide the starting point for recursively computing a dependency graph, finding all of the classes referenced in the public API of the root classes, finding all of the classes referenced in turn by the public API of those classes, and so on, until no new classes are found. The results of the dependency analysis are combined with the preferred list information in the additional supporting JAR files to compute a preferred list having the smallest number of entries that describes the preferred state of the classes and resources contained in all of the JAR files. The output of the tool is a new version of the target JAR file containing the generated preferred list, and/or a copy of the list printed to System.out.