Download de.cit-ec.tcs.alignment JAR files with all dependencies

This module permits exporting and importing of Sequence objects to CSV files. The underlying NodeSpecification is stored via a JSON file. The module aims at human- readable and compatible storage. For storage efficiency, further compression is advised.

This module provides a very basic support for the parallel computing of tasks (Engine class) and entries of a matrix (MatrixEngine). This is basically just a wrapper around the java standard functionality for parallel computing (mainly the Standard Thread Pool and the Future interface). Additional functionality is provided by the ProgressReporter interface, which can be used as a hook to provide information on the current state of the parallel computing task to other modules or the user.

This module permits exporting and importing of Sequence objects to CSV files. The underlying NodeSpecification is stored via a JSON file. The module aims at human- readable and compatible storage. For storage efficiency, further compression is advised.

This module contains means to visualize Alignments. The most important interface is the Visualizer class. A trivial implementation (essentially just using toString()) is the StringVisualizer. A more sophisticated example that is recommended for outside use is the HTMLVisualizer. All other classes are helper classes for said HTMLVisualizer.

This module contains some wrappers to make usage of the TCSAlignmentToolbox easier. Most important for beginners is the StringEditDistance, which provides convenience functions for simple string comparisons. For sequences of vectors we provide the VectorialSequences wrapper. The RandomSequenceGenerator enables you to generate random multi-modal sequences for testing purposes.

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

This module permits exporting and importing of Sequence objects to CSV files. The underlying NodeSpecification is stored via a JSON file. The module aims at human- readable and compatible storage. For storage efficiency, further compression is advised.

This module contains some wrappers to make usage of the TCSAlignmentToolbox easier. Most important for beginners is the StringEditDistance, which provides convenience functions for simple string comparisons. For sequences of vectors we provide the VectorialSequences wrapper. The RandomSequenceGenerator enables you to generate random multi-modal sequences for testing purposes.

This module contains convenience functions to interface with primitive datatypes more comfortably.

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.