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Descripción del Proyecto

dnAnalytics is a numerical library for the .NET Framework and Mono. The library is written in C# and is available as a fully managed library or with a wrapper around the Intel® Math Kernel Library (MKL). The MKL wrapped version provides significantly better performance when working with large data sets. dnAnalytics is compatible with .NET 2.0 or later and Mono. The managed version will run on Windows XP or newer and on any platform that supports Mono. The MKL wrapped version supports 32-bit and 64-bit versions of Windows XP or newer and 32-bit and 64-bit versions of Linux.

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Information regarding Project Releases and Project Resources. Note that the information here is a quote from Freecode.com page, and the downloads themselves may not be hosted on OSDN.

2009-04-29 23:25
2009.4

Esta versión incluye un F # interface inicial, capaces de resolver escasa, los lectores de la matriz de Matlab / escritores, depuradores visuales para las matrices y los vectores, las distribuciones de probabilidad, las clases de generación de números aleatorios (incluidos los de Mersenne Twister MT19937), y una clase de estadística descriptiva, histograma, y el Coeficiente de Correlación de Pearson .
Tags: Major, Stable
This release adds an initial F# interface, sparse solvers, Matlab matrix readers/writers, visual debuggers for matrices and vectors, probability distributions, random number generation classes (including Mersenne Twister MT19937), and a descriptive statistics class, histogram, and Pearson Correlation Coefficient.

2008-12-07 20:09
0.3.1 Beta

Esta versión añade una interfaz de C #, capaces de resolver escasa, los lectores de la matriz de Matlab / escritores, depuradores visuales para las matrices y los vectores, las distribuciones de probabilidad, generadores de números aleatorios, y una clase de estadística descriptiva.
Tags: Major feature enhancements
This versions adds a F# interface, sparse solvers,
Matlab matrix readers/writers, visual debuggers
for matrices and vectors, probability
distributions, random number generators, and a
descriptive statistics class.

Project Resources

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