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Showing 8 open source projects for "development"
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CoRoPa stands for Computational Rough Paths. The aim of CoRoPa is to provide a software framework for various ideas related to Rough Path Theory, including rough differential equations and the digital description of serial data streams.
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math lib for .NET. n-dim arrays, complex numbers, linear algebra, FFT, sorting, cells- and logical arrays as well as 3D plotting classes help developing algorithms on every platform supporting .NET. Sources from SVN, binaries: http://ilnumerics.net
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C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.
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matImage
An image processing and analysis library for Matlab
MatImage is a Matlab library for image processing and analysis in 2D and 3D. It contains more than 250 fonctions for image enhancement, filtering, analysis, or visualisation, as well as for creating basic test shapes. It is built as a complement of the Mathworks image processing toolbox (IPT). Project is being transfered to GitHub: http://github.com/dlegland/matImage -
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BRAHMS is a Modular Execution Framework for dynamical systems. It knits together independently-authored software modules implementing dynamical processes into an integrated system, and supervises the deployment and execution of that system.
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Design and develop Recommendation and Adaptive Prediction Engines to address eCommerce opportunities. Build a portfolio of engines by creating and porting algorithms from multiple disciplines to a usable form. Try to solve NetFlix and other challenges.
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The class libraries here provide infrastructure for creating simulations of low energy nuclear physics experiments, as well as some useful working programs that do simple simulations and analysis of experiments performed with magnetic spectrographs.
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nBoost is a suite of boosting algorithms designed to solve binary classification problems on data that is not linearly separable by a convex combination of base hypotheses, i.e. noisy data. WARNING: Active development. Underlying algorithm is unstable.
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