Download JAR files tagged by computes with all dependencies

A Java implementation of the Nilsimsa locality sensitive hash. The Nilsimsa algorithm computes a 256 bit hash value that indicates how different two strings are. The more similar the strings the smaller will be the bitwise difference between their respective Nilsimsa hashes. Therefore, Nilsimsa is well suited to detect texts of the same origin, such as slightly modified spam messages, updated newspaper articles, etc.

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.

This module defines the interface for AlignmentAlgorithms as well as some helper classes. An AlignmentAlgorithm computes an Alignment of two given input sequences, given a Comparator that works in these sequences. More details on the AlignmentAlgorithm can be found in the respective interface. More information on Comparators can be found in the comparators module. The resulting 'Alignment' may be just a real-valued dissimilarity between the input sequence or may incorporate additional information, such as a full Alignment, a PathList, a PathMap or a CooptimalModel. If those results support the calculation of a Gradient, they implement the DerivableAlignmentDistance interface. In more detail, the Alignment class represents the result of a backtracing scheme, listing all Operations that have been applied in one co-optimal Alignment. A classic AlignmentAlgorithm does not result in a differentiable dissimilarity, because the minimum function is not differentiable. Therefore, this package also contains utility functions for a soft approximation of the minimum function, namely Softmin. For faster (parallel) computation of many different alignments or gradients we also provide the ParallelProcessingEngine, the SquareParallelProcessingEngine and the ParallelGradientEngine.