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An implementation of multi-valued logic

Java Processor that creates a class with static fields valued with Maven properties

Supervises the readings of sensors with real-valued readings and triggers an on/off switch according to the alarm state.

Provides differential evolution algorithm implementation for optimizing real-valued functions with real-value parameters

Description=Provides a set of filters for time series. Currently contains PAA and SAX transformation filters and a filter that converts symbolic time series to string attribute values. The time series need to be given as values of a relation-valued attribute in the ARFF file. For example data in ARFF format, check the data directory of this package.

A Multi-valued, generic Metadata container class. The class uses an internal Map of string keys pointing to vectors of strings. The data structure looks like the following: [std:string key]⇒std:vector of std:strings The multi-valued nature of the class is handled transparently by this Metadata container. Since all values are stored internally as string vectors, the difference between a scalar value and a non-scalar is handled by determining whether the list of values for a particular key is greater than 1.

JMapper is a framework that permits to write dynamic mapping, bringing several advantages as: create and enrich target objects, apply a specific logic to the mapping (only valued fields, only null fields, etc..), automated management of the XML file, possibility of implementing the 1 to N and N to 1 relationships, define explicit conversions, define inherited configurations,

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.