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This Lutra submodule provides a common framework that other submodules in Lutra use and extend. It includes an implementation of OTTR's data model with classes for templates, instances, types, etc.; a system for handling results and error messages; core interfaces and abstract classes for streamed pipelines for parsing and writing templates and instances; as well as implementation of the TemplateStore, a container for templates, with algorithms for checking correctness of templates and expanding and checking correctness of instances. Lutra is the reference implementation of the OTTR framework. For more information about OTTR, see http://ottr.xyz.

Maltcms, short for "Modular Application Toolkit for Chromatography Mass-Spectrometry" is an application framework mainly suited for developers working in the domain of bioinformatics for metabolomics and proteomics. Its aim is to provide reusable, efficient datastructures, abstracting from the various low-level data-formats like netcdf (ANDIMS), mzXML, mzData and mzML and providing consistent access to data features like mass spectra, chromatograms and metadata. Furthermore, Maltcms provides a platform for the implementation and execution of processing pipelines. New and existing algorithms can be easily implemented or included and executed together with existing processing elements.

This package contains several example programs of the usage of the Chips-n-Salsa library. Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. Chips-n-Salsa's source code is maintained on GitHub, and the prebuilt jars of the library can be imported from Maven Central using maven or other build tools. The purpose of the package chips-n-salsa-examples is to demonstrate usage of the major functionality of the Chips-n-Salsa library. You can find out more about the Chips-n-Salsa library itself from its website: https://chips-n-salsa.cicirello.org/.

fastutil extends the Java Collections Framework by providing type-specific maps, sets, lists, and queues with a small memory footprint and fast operations; it provides also big (64-bit) arrays, sets, and lists, sorting algorithms, fast, practical I/O classes for binary and text files, and facilities for memory mapping large files. This jar (fastutil-core.jar) contains data structures based on integers, longs, doubles, and objects, only; fastutil.jar contains all classes. If you have both jars in your dependencies, this jar should be excluded.

Rule Learner is an open source Java library for learning business rules from historical data. It supports supervised Machine Learning by incorporating well-known algorithms such as RIPPER and C4.5 provided by open source tools such as Weka. Rule Learner is implemented as a Machine Learning component of OpenRules Decision Manager. It allows business analysts to place their historical data in simple Excel tables and to automatically discover business rules that find certain pattern in this data. The generated rules are human readable and presented in Excel format executable by OpenRules Decision Manager.

Package containing a class that rescales the attributes in a classification problem based on their discriminative power. This is useful as a pre-processing step for learning algorithms such as the k-nearest-neighbour method, to replace simple normalization. Each attribute is rescaled by multiplying it with a learned weight. All attributes excluding the class are assumed to be numeric and missing values are not permitted. To achieve the rescaling, this package also contains an implementation of non-negative logistic regression, which produces a logistic regression model with non-negative weights .

This module provides algorithms to compare sets, that is, order-invariant lists. These algorithms are implementations of the AlignmentAlgorithm interface defined in the algorithms module. In particular, this module contains the StrictSetAlignmentScoreAlgorithm for computing the cost of the optimal unordered alignment of two sets, the StrictSetAlignmentFullAlgorithm which provides the Alignment itself as well, and the GreedySetAlignmentScoreAlgorithm as well as the GreedySetAlignmentFullAlgorithm for computing a potentially sub-optimal but faster alignment of two sets. The optimal alignments rely on the HungarianAlgorithm for solving the assignment problem in bipartite graphs. Here, we rely on the implementation provided by Kevin L. Stern which is provided within this distribution.