ComponentSource News | CenterSpace Software

NMath adds data visualization capabilities

Wed, 05 Oct 2011 14:03:51 GMT

Random number generation in NMath.

Version 5.1 provides static methods for plotting NMath types using the Microsoft Chart Controls for .NET.

NMath provides numerical components for math, financial, engineering, and scientific applications on the .NET platform. NMath includes: NMath Core (The foundational library includes general vector and matrix classes, complex number classes, random number generators, and numerical integration and differentiation mathematical methods), NMath Matrix (an advanced matrix manipulation library that extends the general matrix classes of NMath Core to include structured sparse matrix classes and factorizations, general matrix decompositions, and advanced least squares solutions), NMath Analysis (NMath Analysis is an advanced function manipulation library that extends the general analysis classes of NMath Core to include function minimization, root-finding, and linear programming). The following editions are available:

Updates in V5.1

Added assembly NMathChartMicrosoft.dll containing class NMathChart, which provides static methods for plotting NMath types using the Microsoft Chart Controls for .NET.
Upgraded to Intel MKL 10.3 Update 6 with resulting performance increases.
Added classes for solving first order initial value differential equations by the Runge-Kutta method. Class FirstOrderInitialValueProblem encapsulates a first order initial value differential equation, and class RungeKuttaSolver solves them.
Added static method NMathFunctions.NaNSort() for sorting vectors containing NaN's. NMathFunctions.Sort() no longer supports vectors containing NaN's.
NMathFunctions.Sort() now does a much faster in-place sort for compact vectors and an out-of-place sort for strided vectors. Previously all sorting was done out-of-place.
Added method NMathFunctions.Sign() that applies the signum function over real vectors.
Redesigned method NMathFunctions.Median(), which is now approximately 25X faster.
Added new Fill() method to vectorized random number generators for filling a DoubleVector.
Added NMathFunctions.CumulativeProduct() methods.
Improved performance of NMathFunctions.OuterProduct().
Added exponential moving average filter weights to class MovingWindowFilter.
Added MatrixFunctions.Solve() methods for factoring and solving general sparse matrices.
Added class LevenbergMarquardtMinimizer and deprecated LevenburgMarquardtMinimizer.
Added static method NMathKernel.Init() for explicitly loading the kernel assembly (for example, on application start-up).

About CenterSpace Software

CenterSpace Software was founded in December, 2002. Its mission is to develop enterprise class numerical component libraries for the .NET platform. The team has extensive experience developing high-performance, analytic software tools. The NMath Suite from CenterSpace Software provides building blocks for mathematical, financial, engineering, and scientific applications on the .NET platform. CenterSpace has satisfied customers in 48 countries around the world, primarily in financial, engineering, scientific, and academic markets.

NMath adds new random number generators

Thu, 17 Feb 2011 17:15:00 GMT

Random number generation in NMath.

Version 5.0 also features classes for solving quadratic programming (QP) and nonlinear programming (NLP) problems.

NMath Stats contains probability distributions, combinatorial functions, descriptive statistics, multi-type data table, multiple linear regression, hypothesis tests, analysis of variance (ANOVA), partial least squares (PLS), non-negative matrix factorization (NNMF) and multivariate statistics for the .NET platform.

Updates in NMath V5.0

A new set of random number generator classes based on MKL vectorized random number generators
Classes for creating independent streams of random numbers using the leapfrop and skip-ahead methods
Classes for generating quasirandom sequences using Niederreiter and Sobal methods
Classes for solving quadratic programming (QP) and nonlinear programming (NLP) problems

Updates in NMath Stats V3.3

Added a class for performing a Pearson's chi-square hypothesis test.

For more information visit:
http://www.centerspace.net/content/centerspace-software-releases-new-versions-nmath-net-numerical-libraries-0

About CenterSpace Software

NMath improves performance

Mon, 02 Aug 2010 17:12:00 GMT

Random number generation in NMath.

NMath V4.1 and NMath Stats V3.2 increase performance with upgrade to Intel Math Kernel Library 10.2 Update 5.

Updates in NMath V4.1

Upgraded to Intel MKL 10.2 Update 5 with resulting performance increases
Replaced CLI C++ kernel with a pure C# kernel that invokes MKL using PInvoke() to improve .NET 4.0 experience
Added class FirstOrderInitialValueProblem to encapsulate a first order initial value differential equation
Added class RungeKuttaSolver for solving first order initial value differential equations by the Runge-Kutta method
Added SavitzkyGolay class to generate filtering coefficients for data smoothing or for computing smooth Nth-derivatives in noisy data
Added a SavitzkyGolayFilter class to both smooth data or compute the smoothed n-derivative of the the data. New boundary handling options for smooth transitions to the data boundaries
Added INonlinearLeastSqMinimizer and IBoundedNonlinearLeastSqMinimizer interfaces. Class TrustRegionMinimizer now implements IBoundedNonlinearLeastSqMinimizer. Class LevenburgMarquardtMinimizer now implements INonlinearLeastSqMinimizer
Added classes BoundedOneVariableFunctionFitter and BoundedMultiVariableFunctionFitter which derive from OneVariableFunctionFitter and MultiVariableFunctionFitter, respectively, and accept linear bounds on the solution
Class OneVariableFunctionFitter is now templatized on INonlinearLeastSqMinimizer, and BoundedOneVariableFunctionFitter is templatized on IBoundedNonlinearLeastSqMinimizer
Class MultiVariableFunctionFitter is now templatized on INonlinearLeastSqMinimizer, and class BoundedMultiVariableFunction is templatized on IBoundedNonlinearLeastSqMinimizer
Added <Type>GSVDecomp and <Type>GSVDecompServer classes to compute the generalized singular value decomposition (GSVD) of a pair of general rectangular matrices
Added class BoxCoxTransformation which performs a Box-Cox power transformation to make non-normal data resemble normally-distributed data
Added accessor for the Hessian in class VariableMetricMinimizer
Added "force through origin" option to class PolynomialLeastSquares
Fixed several minor issues with CLS compliance

Updates in NMath Stats V3.2

Upgraded to Intel MKL 10.2 Update 5 with resulting performance increases
Added classes InputVariableCorrelator and ReducedVarianceInputCorrelator to induce a desired rank correlation among a set of random input variables
Added class KruskalWallisTest for performing a Kruskal-Wallis rank sum test
Added class JohnsonDistribution to encapsulate the Johnson system of distributions
Added classes GoodnessOfFit and GoodnessOfFitParameter for testing the goodness of fit of least squares model-fitting classes, such as LinearRegression, PolynomialLeastSquares, and OneVariableFunctionFitter
Added class BoxCox for computing the Box-Cox power transformations
Added StatsFunction.Median() for float types
Fixed issue where StatsFunctions.SumOfSquares() could occasionally return a negative value
Improved support for missing values in DataFrame.Load()

For more information visit:
http://www.centerspace.net/content/centerspace-software-releases-new-versions-nmath-net-numerical-libraries

About CenterSpace Software

CenterSpace Software updates NMath Suite

Mon, 16 Nov 2009 17:25:00 GMT

C# samples showing estimation and random number generation.

New release adds C# kernel, multi-core aware FFT classes and K-Means Clustering.

NMath lets you add math, financial, engineering and scientific functionality to your .NET applications. It contains the following components:

NMath Core
The foundational library includes general vector and matrix classes, complex number classes, random number generators, and numerical integration and differentiation mathematical methods.
NMath Matrix
An advanced matrix manipulation library that extends the general matrix classes of NMath Core to include structured sparse matrix classes and factorizations, general matrix decompositions, and advanced least squares solutions.
NMath Analysis
An advanced function manipulation library that extends the general analysis classes of NMath Core to include function minimization, root-finding, and linear programming.

NMath Suite is also available, and contains the three components listed above, plus:

NMath Stats
Provides math functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, and multivariate statistics.

Updates in NMath V4.0

C# Kernel
In addition to the CLI C++ kernel, the latest NMath 4.0 version includes a C# kernel. NMath is written primarily in C#, but includes a managed C++ (CLI C++) kernel, which provides an immutable and very fast interface to MKL. CLI C++ methods "pin" pointers to memory allocated on the managed heap before calling MKL. The managed memory referenced by a pinned pointer will not be touched by the .NET garbage collector as long as it is pinned. (Note that memory is passed by reference to native code, without any copying.) While the CLI C++ kernel is ideal for most cases, there are some applications that currently don't support it. For those cases, a C# kernel has been included.
Mono
The open source Mono Project provides a UNIX version of the .NET development platform, including a C# compiler and a Common Language Runtime. Its objective is to enable UNIX developers to build and deploy cross-platform .NET applications. However, because the Mono Project does not currently include a CLI C++ compiler, a C# kernel is required.
SQL Server CLR
It prohibits loading an assembly that contains both managed and unmanaged code. However, through the new C# kernel, you can open a managed assembly that makes Platform Invoke (P/Invoke) calls to native unmanaged methods. For most computations, NMath uses the Intel Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of the C and FORTRAN public domain computing packages known as the BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage). This gives the NMath class libraries performance levels comparable to C, and often results in performance an order of magnitude faster than non-platform-optimized implementations.
Fast Fourier Transforms (FFTs)
NMath 4.0 contains high performance, multi-core aware FFT classes. This set of classes will support all common 1D and 2D FFT computations in a robust easy-to-use object-oriented interface. All classes will efficiently support FFT's of arbitrary length, with a simple interface for both in-place and out-of-place computations. Additionally, there is a parallel set of classes for single precision computation.
Levenberg-Marquardt Nonlinear Least Squares
NMath 4.0 includes class LevenbergMarquardtMinimizer for minimizing the L2 norm of a function using the Levenberg-Marquardt algorithm (LMA). LMA is a standard in non-linear optimization, and is widely used for solving generic curve-fitting problems. LMA significantly outperforms gradient descent and conjugate gradient methods for medium sized problems, and complements the Trust Region minimizer already in NMath.
Linear Spline Interpolation
NMath 4.0 includes a class LinearSpline for representing a function whose values are determined by linear interpolation between tabulated values.
MKL Version 10.2
NMath 4.0 now leverages Intel's MKL Version 10.2. This provides many performance improvements for faster execution and taking advantage of more cores. It now also can take advantage of threading on more cores and more CPUs. These speed improvements generally range from 10% to 25%, and if you have a 24 core machine, the improvement can be as high as 130 times as great.

Updates in NMath Stats V3.1

K-Means Clustering
Includes a class for k-means clustering, to complement the hierarchical cluster analysis already in the library. The k-means clustering method assigns data points into k groups such that the sum of squares from points to the computed cluster centers is minimized. In general, k-means clustering uses far less memory that constructing a full hierarchical cluster tree.
Cronbach's Alpha
Includes a method for computing Cronbach's alpha, a measure of reliability (or consistency) in the framework of the domain sampling model. Cronbach's alpha measures how well a set of items (or variables) measures a single unidimensional latent construct.
Smoothing Filters
Includes classes for smoothing data by replacing each data point with a linear combination of the data points immediately to the left and right of the data point. You can provide the coefficients to use in the linear combination, or use provided methods to generate coefficients that implement a moving average filter and a Savitzky-Golay filter. A Savitzky-Golay filter performs a local polynomial regression on the data to determine the smoothed value for each point. This tends to preserve features of the data which are flattened by moving averages, for example, peak finding.

For more information visit:
http://www.centerspace.net/company/press-releases/centerspace-software-releases-new-versions-nmath-numerical-libraries