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Math.NET Numerics Features Overview

You can suggest or vote for new features or ideas here.

Core¶

Namespace: MathNet.Numerics

• Complex Numbers (including trigonometric functions)
• Mathematical & Scientific Constants and Prefixes (2007 CODATA)
• Precision class, for safe floating point handling
• Various helper and data structure classes
  

Trigonometry Functions¶

Namespace: MathNet.Numerics
Class: MathNet.Numerics.Trig

Real and complex trigonometry functions

• Conversion between degree, radian and grad.
• Sine, Cosine, Tangent, Cotangent, Secant, Cosecant
• Inverse: Sine, Cosine, Tangent, Cotangent, Secant, Cosecant
• Hyperbolic: Sine, Cosine, Tangent, Cotangent, Secant, Cosecant
• Inverse Hyperbolic: Sine, Cosine, Tangent, Cotangent, Secant, Cosecant
  

Number Theory¶

Namespace: MathNet.Numerics.NumberTheory
Class: MathNet.Numerics.NumberTheory.IntegerTheory

• IsEven, IsOdd
• IsPowerOfTwo
• PowerOfTwo, CeilingToPowerOfTwo
• IsPerfectSquare
• GreatestCommonDivisor (gcd) of two or more integers
• ExtendedGreatestCommonDivisor (egcd) of two integers
• LeastCommonMultiple (lcm) of two or more integers
  

Numerically Stable Functions¶

Namespace: MathNet.Numerics
Class: MathNet.Numerics.SpecialFunctions

• Hypotenuse: (a,b) -> sqrt(a^2+b^2)
• ExponentialMinusOne: x -> exp(x)-1
  

Special Functions¶

Namespace: MathNet.Numerics
Class: MathNet.Numerics.Fn

• Logarithmic Factorial
• Factorial
• Logarithmic Binomial Coefficient
• Binomial Coefficient
  

• Logarithmic Gamma
• Gamma (supports negative numbers as well)
• Regularized Gamma
  

• Digamma (Psi)
• Inverse Digamma
  

• Logarithmic Beta
• Regularized Beta
  

• Error Function (erf) and complement (erfc)
• Inverse Error Function and complement
  

Combinatorics¶

Namespace: MathNet.Numerics
Class: MathNet.Numerics.Combinatorics

• Counting: Variations, Variations with repetition, Combinations, Combinations with repetition, Permutations
  

Probability Distributions¶

Namespace: MathNet.Numerics.Distributions

Continuous Probability Distributions¶

Continuous probability distributions support both the probability density function (pdf) and the cumulative distribution function (cdf), as well as the usual probability parameters. Additionally, random numbers can be generated based on the configured probability model parameters and some random number source.

• Uniform
• Normal (Gaussian with mean and variance)
• Gamma
• Beta
• Weibull
  

Discrete Probability Distributions¶

Discrete probability distributions support both the probability mass function (pmf) and the cumulative distribution function (cdf), as well as the usual probability parameters. Additionally, random numbers can be generated based on the configured probability model parameters and some random number source.

• Bernoulli
  

Multivariate Probability Distributions¶

• Multinomial
• Dirichlet
  

Random Sources¶

Namespace: MathNet.Numerics.Random

All implementations inherit the .Net framework provided System.Random class for interoperability.

• Mersenne Twister
  

Note that random sources should be reused, so be careful to create only one instance (per thread) and share it internally.

Interpolation¶

Namespace: MathNet.Numerics.Interpolation

Most interpolation algorithms also support numeric differentiation and integration. A facade class Interpolate is provided for easy access, but if needed the algorithms can also be used directly in the Algorithms sub-namespace. All implementations implement the interface IInterpolation.

• Rational pole-free, on arbitrary points (Barycentric Floater-Hormann Algorithm)
• Rational with poles, on arbitrary points (Bulirsch & Stoer Algorithm)
• Neville Polynomial, on arbitrary points (Neville Algorithm)
• Polynomial, on equidistant points (Barycentric Algorithm)
• Linear Spline, on arbitrary points
• Cubic Spline, with boundary conditions on arbitrary points
• Natural Cubic Spline, on arbitrary points
• Akima Cubic Spline, on arbitrary points
• Custom Barycentric Interpolation, based on provided barycentric weights
• Custom Spline Interpolation, based on provided spline coefficients
• Custom Cubic Hermite Spline Interpolation, based on provided derivatives
  

If unsure what to choose, we recommend to simply use Interpolate.Common(x,y) which internally uses the barycentric rational pole-free interpolation.

Code Sample¶

double[] t = new double[] { -2.0, -1.0, 0.0, 1.0, 2.0 };
double[] x = new double[] { 1.0, 2.0, -1.0, 0.0, 1.0 };
IInterpolation interp = Interpolate.RationalWithoutPoles(t, x);
double a = interp.Interpolate(-0.5);

Linear Algebra¶

Namespace: MathNet.Numerics.LinearAlgebra

• Vector: Dense Real Double
  

Integral Transforms¶

Namespace: MathNet.Numerics.IntegralTransforms

• Complex Fast Fourier Transformation
• Real Hartley Transform
  

The transformation behavior can be configured (scaling, exponent sign, etc). See here for more details and code samples around Fourier transforms.