Author: Ken Baldwin

Setting the NMath License Key

Posted on by Ken Baldwin
NMath license information is stored in a license key which must be found at runtime. When you purchase one or more developer seats of NMath, you will be issued a license key describing the terms of your license. If no license key is found at runtime, a default evaluation license key is used which provides a free 30-day evaluation period for NMath on the current machine. Three mechanisms are supp...
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New Versions of NMath Libraries Released

Posted on by Ken Baldwin
CenterSpace is proud to announce the immediate availability of new versions of our .NET math libraries, NMath 5.2 and NMath Stats 3.5. This release adds many new features and performance enhancements. Changes for version 5.2 of NMath include: Upgraded to Intel MKL 10.3 Update 11 with resulting performance increases. Added class NMathConfiguration for controlling the loading of the NMath lice...
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NMath Configuration

Posted on by Ken Baldwin
Beginning with the release of NMath 5.2 and NMath Stats 3.5, NMath includes a new configuration system for controlling the loading of the NMath license key, kernel assembly, and native library. Based on customer feedback, we've designed this system to provide greater flexibility and security at deployment, and greater convenience in group development environments. We've also added optional logging...
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Distribution Fitting Demo

Posted on by Ken Baldwin
CDF() of fitted distribution
A customer recently asked how to fit a normal (Gaussian) distribution to a vector of experimental data. Here's a demonstration of how to do it. Let's start by creating a data set: 100 values drawn from a normal distribution with known parameters (mean = 0.5, variance = 2.0). int n = 100; double mean = .5; double variance = 2.0; var data = new DoubleVector( n, new RandGenNormal( mean, variance ) )...
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The Importance of Graphing Your Data

Posted on by Ken Baldwin
In his classic book The Visual Display of Quantitative Information, Edward R. Tufte argued that "graphics can be more precise and revealing than conventional statistical computations". As an example, he described Anscombe's Quartet--four datasets that have identical simple statistical properties, yet appear very different when graphed. These data sets--each consisting of 11 x,y points--were con...
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