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svg2vector from group de.vandermeer (version 2.0.0)

SVG to vector converter. This tool converts an SVG graphic to a vector format. Currently supported targets are EMF, PDF and SVG. The tool does support SVG and SVGZ input formats. It also can deal with SVG layers. Input can be a file or a URL. Output can be a single file or a set of files, i.e. handling SVG layers individually. This means that the tool can be part of a tool chain for creating animations. Simply create an SVG image with several layers, one per step of an animation. Then use this tool to create an image per layer and use them in the target publication platform.

Group: de.vandermeer Artifact: svg2vector
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10 downloads
Artifact svg2vector
Group de.vandermeer
Version 2.0.0
Last update 13. April 2017
Organization not specified
URL http://www.vandermeer.de/projects/skb/java/svg2vector
License Apache 2
Dependencies amount 8
Dependencies execs, ST4, batik-bridge, freehep-graphicsio, freehep-graphics2d, freehep-graphicsio-emf, freehep-graphicsio-pdf, freehep-graphicsio-svg,
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SVMAttributeEval from group nz.ac.waikato.cms.weka (version 1.0.2)

Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking. For more information see: I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.

Group: nz.ac.waikato.cms.weka Artifact: SVMAttributeEval
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2 downloads
Artifact SVMAttributeEval
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SVMAttributeEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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jcobyla from group de.xypron.jcobyla (version 1.4)

COBYLA2 is an implementation of Powell's nonlinear derivative free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular shaped simplex over iterations. It solves nonsmooth NLP with a moderate number of variables (about 100). Inequality constraints only. The initial point X is taken as one vertex of the initial simplex with zero being another, so, X should not be entered as the zero vector.

Group: de.xypron.jcobyla Artifact: jcobyla
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3 downloads
Artifact jcobyla
Group de.xypron.jcobyla
Version 1.4
Last update 31. May 2022
Organization not specified
URL https://github.com/xypron/jcobyla
License The MIT License
Dependencies amount 0
Dependencies No dependencies
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sequences from group de.cit-ec.tcs.alignment (version 3.1.1)

This module contains the sequence datastructure of the TCS Alignment Toolbox. It defines the possible value sets in the ValueType enum as well as the different KeywordSpecification classes, namely: 1.) StringKeywordSpecification for string type values. 2.) SymbolicKeywordSpecification for values from a discrete alphabet (also refer to the Alphabet class) 3.) VectorialKeywordSpecification for vectors of some length (or for scalars) A NodeSpecification is a vector of such KeywordSpecifications and defines the order of value sets. A node, then, is defined as a vector of values from these value sets (also refer to the Value interface as well as the StringValue, SymbolicValue and VectorialValue classes). Finally a sequence is defined as a list of such nodes.

Group: de.cit-ec.tcs.alignment Artifact: sequences
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0 downloads
Artifact sequences
Group de.cit-ec.tcs.alignment
Version 3.1.1
Last update 26. October 2018
Organization not specified
URL http://openresearch.cit-ec.de/projects/tcs
License The GNU Affero General Public License, Version 3
Dependencies amount 1
Dependencies lombok,
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mrglvq from group de.cit-ec.ml (version 0.1.0)

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.

Group: de.cit-ec.ml Artifact: mrglvq
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0 downloads
Artifact mrglvq
Group de.cit-ec.ml
Version 0.1.0
Last update 27. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/median_relational_glvq
License The GNU General Public License, Version 3
Dependencies amount 1
Dependencies rng,
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jsci from group net.sf.jsci (version 1.2)

JSci is a set of open source Java packages. The aim is to encapsulate scientific methods/principles in the most natural way possible. As such they should greatly aid the development of scientific based software. It offers: abstract math interfaces, linear algebra (support for various matrix and vector types), statistics (including probability distributions), wavelets, newtonian mechanics, chart/graph components (AWT and Swing), MathML DOM implementation, ... Note: some packages, like javax.comm, for the astro and instruments package aren't listed as dependencies (not available).

Group: net.sf.jsci Artifact: jsci
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12 downloads
Artifact jsci
Group net.sf.jsci
Version 1.2
Last update 28. April 2012
Organization physics.org
URL http://jsci.sourceforge.net/
License GNU Lesser General Public License 2.1
Dependencies amount 3
Dependencies lpsolve, mathml-dom-java, xercesImpl,
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rng from group de.cit-ec.ml (version 1.0.0)

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.

Group: de.cit-ec.ml Artifact: rng
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0 downloads
Artifact rng
Group de.cit-ec.ml
Version 1.0.0
Last update 26. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/relational_neural_gas
License The GNU General Public License, Version 3
Dependencies amount 0
Dependencies No dependencies
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chips-n-salsa from group org.cicirello (version 5.0.0)

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

Group: org.cicirello Artifact: chips-n-salsa
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0 downloads
Artifact chips-n-salsa
Group org.cicirello
Version 5.0.0
Last update 03. June 2022
Organization Cicirello.Org
URL https://chips-n-salsa.cicirello.org/
License GPL-3.0-or-later
Dependencies amount 0
Dependencies No dependencies
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