Search Results for "python ieee lom"
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Showing 5 open source projects for "python ieee lom"
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Double Conversion
Efficient binary-decimal & decimal-binary conversion routines for IEEE
Double Conversion is a high-performance C++ library that provides precise and efficient binary-decimal and decimal-binary conversion routines for IEEE 754 double-precision floating-point numbers. Originally extracted from the V8 JavaScript engine, it was refactored into a standalone library to make its robust number conversion algorithms easily reusable in other projects. The library ensures consistent and accurate results for converting between double values and their string... -
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pyfilesysobjects
Search and match of file type resource paths and addresses
The 'filesysobjects' package provides cross-platform-utilities for path addresses of file like resources. This includes the search and navigation features on file system structures with the application of regular expressions for pathnames intermixed with globs. - filesysobjects - constants - filesysobjects.apppaths - application resource path processing - filesysobjects.paths - file systems path processing - filesysobjects.pathtools - search, enumeration, and iteration... -
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PETRILab
Simulador de Redes de Petri Interpretadas para Controle
...Para utilizar o programa, baixe a versão mais recente em https://sourceforge.net/projects/petrilab/files/, extraia no local de preferência e execute o arquivo 'petrilab.pyw'. [1] M. V. Moreira e J. C. Basílio, “Bridging the Gap Between Design and Implementation of Discrete-Event Controllers”.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERIN -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer... -
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This is a Java API binding of the IEEE LTSC LOM (Learning Object Metadata) specification. It defines an implementation neutral Java interface for the LOM, as well as providing a default implementation of that interface, and XML marshalling functionality
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