C# Principal Component Analysis
Principal Component Analysis is frequently referred to as PCA.
NMath from CenterSpace Software is a .NET class library that provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, and multivariate statistics.
Note that with the release of NMath 7, all statistical types were unified into the CenterSpace.NMath.Core namespace and the CenterSpace.NMath.Stats namespace was deprecated.
In NMath, class DoublePCA and FloatPCA perform principal component analyses. An instance is constructed from a matrix or a dataframe containing numeric data. Each column represents a variable, and each row represents an observation. The data may optionally be zero-centered and scaled to have unit variance. This functionality can be called from any .NET language including VB.NET and F#.
The NMath library provides building blocks for mathematical, financial, engineering, and scientific applications on the .NET platform. Features include matrix and vector classes, linear algebra, random number generators, numerical integration methods, interpolation, statistics, biostatistics, multiple linear regression, analysis of variance (ANOVA), optimization, and object-oriented interfaces to public domain computing packages such as the BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra PACKage). All NMath routines are callable from any .NET language, including C#, Visual Basic.NET, and F#.
Complete documentation for all NMath libraries is available online. For more information on principal component analysis, see:
- The section on principal component analysis in the NMath Stats User’s Guide
- API documentation for class DoublePCA and FloatPCA in the NMath Reference Guide.
- The CenterSpace blog has several articles on data clustering in general, and specifically on using NMath Stat’s PCA algorithm with code examples.
Addition background information on the PCA algorithm can be found at Wikipedia.
PCA Code Examples
All NMath libraries include extensive code examples in both C# and Visual Basic.NET. Studying these examples is one of the best ways to learn how to use NMath libraries. For more information on principal component analysis, see:
- PrincipalComponentExample [C#] [VB.NET]
Example showing how to perform a principal component analysis on a data set.
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