Copyright (C) 2013, 2014
Benjamin Paaßen, Charlie Krüger, Georg Zentgraf
AG Theoretical Computer Science
Centre of Excellence Cognitive Interaction Technology (CITEC)
University of Bielefeld
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
This Java Toolbox provides several algorithms to align two input sequences, where your sequential data is allowed to be multimodal and multidimensional. We also provide additional tools to inspect the alignment results in more detail or even calculate derivatives of the alignment w.r.t. metric parameters such that you can optimize the alignment parameters according to some cost function. It is written in Java 1.7. and is also compatible with Matlab (version 2013b or higher).
To use this software is recommended if
This toolbox is not the right tool for you if you want to do standard computation like DTW on one-dimensional double sequences or simple string edit distance. In that case there are far better and faster tools out there.
The TCSAlignmentToolbox can be directly used in your Matlab/Java-program as a .jar file or via maven. As the project is not yet part of the central maven repository you need to install the toolbox manually via the command
mvn install:install-file -Dfile=TCSAlignmentToolbox-1.4.jar -DgroupId=de.unibi.citec.tcs -DartifactId=TCSAlignmentToolbox -Dversion=1.4 -Dpackaging=jar
Of course you can also compile and install from the source code.
mvn install
If you do not want to use maven you can use the binary .jar file.
If you want to use the Toolbox as fast as possible it is recommended to use one of the code examples in the "examples" directory and develop your application code from there on.
The idea behind the toolbox is to enable you to not only align DNA/symbol sequences or sequences of vectorial data, but sequences of nodes that each contain multiple values of type 'symbol', 'vector' or 'string'. This allows for much more complex input data. Furthermore we not only provide the alignment score for your specified scoring scheme but also the alignment paths, soft approximations of the generic alignment scheme and means to calculate the derivative of an alignment result.
To understand the toolboxes architecture it is helpful to keep the following concepts in mind:
The architecture is shown in detail in UML diagrams in the "uml" directory.
In order to use the Toolbox, you'll need to execute (some of) the following steps:
Examples are also provided in the "examples" directory.
In our algorithms we currently differentiate between the following types:
Note that different algorithms have different runtime. If you intend to calculate all pairwise alignments of N sequences with maximum length L and K keywords the worst case runtime is O(N^2 * L^2 * K). The constant factors, however, can be improved by the choice of algorithm or parallel processing.
The following figures show the runtime for one alignment in seconds versus the sequence length for all available algorithms. The number of keywords was set to 3 (one symbolic keyword, one vectorial keyword and one string keyword). The sequences where generated randomly.
The following figure shows (for the case of the global alignment strict score algorithm) how much runtime can be saved by using parallel processing instead of iterative processing. All pairwise alignments where calculated for an ascending number of sequences. The sequence length was fixed to 50 and - as before - 3 keywords were used. The sequences where generated randomly.
The following figures show the runtime for one alignment in seconds versus the number of keywords for all available algorithms. The sequence length was set to 50. One third of the keywords where always symbolic, one third vectorial and one third strings. The sequences where generated randomly.
The following figures show the runtime for one alignment/derivative in seconds versus the alphabet size in a string edit distance setting with only one symbolic keyword. The sequence length was set to 100. The sequences where generated randomly. As can be seen the runtime is constant with regard to the alphabet size for alignment calculations as well as strict derivatives. This is due to the fact that in strict derivative calculations only those parameters will be considered that are actually used within the alignment. In soft derivative calculations, however, all possible alignments are taken into account which makes it much harder to exploit this "parameter sparsity". So for a given alphabet A you should be prepared for an additional factor of A^2 in runtime which leads to a total worst case runtime of O(N^2 * L^2 * K * A^2).
You can find further documentation in the "javadoc" and "uml" directory. Examples are also provided in the "examples" directory.
This Toolbox was developed in 2014 in the Theoretical Computer Science for Cognitive Systems Workgroup at the Centre of Excellence 'Cognitive Interaction Technology' (CITEC), Bielefeld University. Development was part of the research project 'Learning Feedback for Intelligent Tutoring Systems' (FIT), which is part of the DFG priority program 'Autonomous Learning'.
The main purpose of this toolbox is metric learning, which is described in more detail in our papers:
For visualization we use the Apache Common Lang library. For the normalized compression distance we use the fast LZ4 implementation by jpountz. Both are used in accordance with the Apache License, Version 2.
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