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algorithms from group com.googlecode.princeton-java-algorithms (version 4.0.1)
Code examples from Princeton's "Algorithms" textbook, 4th Edition.
21 downloads
algorithms from group com.graphaware.neo4j (version 3.0.1.38.5)
GraphAware Framework Module exposing custom graph algorithms as Java and REST APIs
algorithms from group com.graphaware.neo4j (version 3.0.2.39.5)
GraphAware Framework Module exposing custom graph algorithms as Java and REST APIs
algorithms from group com.graphaware.neo4j (version 3.0.4.43.5)
GraphAware Framework Module exposing custom graph algorithms as Java and REST APIs
algorithms from group com.imsweb (version 1.4.3)
Java implementation of cancer-related algorithms (NHIA, NAPIIA, Survival Time, etc...)
algorithms from group fr.nargit.rating (version 1.0)
The Rating Algorithm library implement a rating system (Based on ELO) in order to handle multiple types
of games (1v1, 2v2, 1v1v1...)
algorithms from group de.cit-ec.tcs.alignment (version 2.1.2)
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.
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