numerics.mathdotnet.com
Constants
http://numerics.mathdotnet.com/Constants.html
MathNET Numerics contains a set of often used mathematical and scientific constants. Mathematical and defined scientific constants are as accurate as double precision allows, while measured constants are implemented according to 2007 CODATA. All constants are defined as static constant fields of the. Log e{ pi} ). Frac{1}{2} log e{2 pi} ). Sqrt{ frac{1}{2} = frac{1}{ sqrt{2} = frac{ sqrt{2} {2} ). Sqrt{2 pi e} ). Log e{ sqrt{2 pi} = frac{1}{2} log e{2 pi} ). Log e{ sqrt{2 pi e} ). Frac{1}{ sqrt{ pi} ).
numerics.mathdotnet.com
Contribute to Math.NET Numerics
http://numerics.mathdotnet.com/Contributing.html
Contribute to Math.NET Numerics. MathNET Numerics is driven by the community and contributors like you. I'm excited that you're interested to help us move forward and improve Numerics. We usually accept contributions and try to attribute them properly, provided they keep the library consistent, focused and mathematically accurate. Have a look at the following tips to get started quickly. I'm looking forward to your pull requests! Make sure you have a GitHub account. Fork the mainline repository. Solution...
numerics.mathdotnet.com
Descriptive Statistics
http://numerics.mathdotnet.com/DescriptiveStatistics.html
We need to reference Math.NET Numerics and open the statistics namespace:. MathNet.Numerics.Statistics;. The primary class for statistical analysis is. Which provides common descriptive statics as static extension methods to. Sequences. However, various statistics can be computed much more efficiently if the data source has known properties or structure, that's why the following classes provide specialized static implementations:. Statistics.Maximum; var. Statistics.Minimum; var. Statistics.Mean; var.
numerics.mathdotnet.com
Special Functions
http://numerics.mathdotnet.com/Functions.html
All the following special functions are available in the static. Prod {k=1} {x} k = Gamma(x 1) ]. X mapsto ln x! Ln Gamma(x 1) ]. Binom{n}{k} = mathrm{C} n k = frac{n! Ln binom{n}{k} = ln n! Binom{n}{k 1,k 2, dots,k r} = frac{n! Prod {i=1} {r}k i! E n(x) = int 1 infty t {-n} e {-xt} , mathrm{d}t ]. Gamma(a) = int 0 infty t {a-1} e {-t} , mathrm{d}t ]. Lower incomplete Gamma function, unregularized. Gamma(a,x) = int 0 x t {a-1} e {-t} , mathrm{d}t ]. Upper incomplete Gamma function, unregularized. Z mapst...
numerics.mathdotnet.com
Euclid & Number Theory
http://numerics.mathdotnet.com/Euclid.html
Euclid and Number Theory. Namespace provides routines related to the domain of integers. Remainder vs. Canonical Modulus. Remainder and modulus are closely related operations with a long tradition of confusing on with the other. The % operator in most computer languages implements one of the two, but some even leave which one as an implementation detail (e.g. C-1990). Warning: In C#, like most languages, % is the remainder operator, not the modulus! In C# and F#, the remainder is available as. 1, congrue...
numerics.mathdotnet.com
Curve Fitting: Linear Regression
http://numerics.mathdotnet.com/Regression.html
Curve Fitting: Linear Regression. Regression is all about fitting a low order parametric model or curve to data, so we can reason about it or make predictions on points not covered by the data. Both data and model are known, but we'd like to find the model parameters that make the model fit best or good enough to the data according to some metric. We may also be interested in how well the model supports the data or whether we better look for another more appropriate model. Simple Regression: Fit to a Line.
numerics.mathdotnet.com
Probability Distributions
http://numerics.mathdotnet.com/Probability.html
MathNET Numerics provides a wide range of probability distributions. Given the distribution parameters they can be used to investigate their statistical properties or to sample non-uniform random numbers. MathNet.Numerics.Distributions; using. MathNet.Numerics.Random; / create a parametrized distribution instance. GammaEntropy; / distribution functions. Both probability functions and sampling are also available as static functions for simpler usage scenarios:. Normal.WithMeanPrecision( 0.0. Sampling a Pr...
numerics.mathdotnet.com
Matrices and Vectors
http://numerics.mathdotnet.com/Matrix.html
MathNET Numerics includes rich types for matrices and vectors. They support both single and double precision, real and complex floating point numbers. Mathbf{A}= begin{bmatrix} a {0,0} and a {0,1} and cdots and a {0,(n-1)} a {1,0} and a {1,1} and cdots and a {1,(n-1)} vdots and vdots and ddots and vdots a {(m-1),0} and a {(m-1),1} and cdots and a {(m-1),(n-1)} end{bmatrix}, quad mathbf{v}= begin{bmatrix}v 0 v 1 vdots v {n-1} end{bmatrix} ]. Both dense and sparse vectors are supported:. Stores non-zero va...
numerics.mathdotnet.com
Pseudo-Random Numbers
http://numerics.mathdotnet.com/Random.html
The Net Framework base class library (BCL) includes a pseudo-random number generator for non-cryptography use in the form of the. Class Math.NET Numerics provides a few alternatives with different characteristics in randomness, bias, sequence length, performance and thread-safety. All these classes inherit from. So you can use them as a drop-in replacement even in third-party code. We need to reference Math.NET Numerics and open the namespaces for random numbers and probability distributions:. In F# we c...
