Download JAR files tagged by combinations with all dependencies

A Functional Workflow Language. Cuneiform is a workflow specification language which makes it easy to integrate heterogeneous tools and libraries and exploit data parallelism. Users do not have to create heavy-weight wrappers for establised tools or to reimplement them. Instead, they apply their existing software to partitioned data. Using the Hi-WAY application master Cuneiform can be executed on Hadoop YARN which makes it suitable for large scale data analysis. Cuneiform comes in the form of a functional programming language with a Foreign Function Interface (FFI) that lets users create functions in any suitable scripting language and apply these functions in a uniform way. Data paralelism is expressed by applying map, cross-product, dot-product, or combinations of the aforementioned algorithmic skeletons to collections of black-box data.

"Tests with Parameters" allows you to simply add parameters to your JUnit test methods. TwiP calls such methods with all possible combinations of their parameters... or at least some reasonable subset of commonly failing values in the case of Integers, etc. You can further reduce these values with an assume expression in an annotation, e.g. ">= 0". Alternatively you can specify a static method or field to provide the values for your test method(s), if you want to test with other than the default values. By using TwiP you change the semantics of your tests from existence to for-all quantifiers, i.e. you specify "all ravens are black" instead of "Abraxas is black", "Toni is black", etc. This moves your tests closer to an executable specification, so TwiP is a very nice addition to BDD.

This module contains a more general approach to construct AlignmentAlgorithms by relying on the theoretical concept of Algebraic Dynamic Programming (ADP) as developed by Giegerich et al. ADP defines four ingredients for an alignment algorithm: 1.) A signature that defines the permitted alignment operations. Operations are just function templates with an associated arity, meaning the number of arguments it takes from the left sequence and from the right sequence. In the TCSAlignmentToolbox we have a fixed signature with the following operations: REPLACEMENT(1, 1), DELETION(1, 0), INSERTION(0, 1), SKIPDELETION(1, 0) and SKIPINSERTION(0, 1) 2.) A regular tree grammar that produces alignments, that is: sequences of operations, in a restricted fashion. 3.) An algebra that can translate such trees to a cost. In the TCSAlignmentToolbox this is a Comparator. 4.) A choice function, in case of the TCSAlignmentToolbox: the strict minimum or the soft minimum. An alignment algorithm in the TCSAlignmentToolbox sense of the word then is the combination of choice function and grammar. While we provide hardcoded versions of these combinations in the main package, the adp package allows you to create your own grammars. You can combine them with a choice function by instantiating one of the Algorithm classes provided in this package with a grammar of your choice. For example: AlignmentAlgorithm algo = new SoftADPScoreAlgorithm(my_grammar, comparator); creates an alignment algorithm that implicitly produces all possible alignments your grammar can construct with the given input, translates them to a cost using the algebra/comparator you provided and applies the soft minimum to return the score. This all gets efficient by dynamic programming. Note that there is runtime overhead when using this method in comparison with the hardcoded algorithms. But for complicated grammars this is a much easier way to go. For more information on the theory, please refer to my master's thesis: "Adaptive Affine Sequence Alignment using Algebraic Dynamic Programming"