Coder Social home page Coder Social logo

boost-math's Introduction

Boost Math Library Build Status

ANNOUNCEMENT: Support for C++03 is now deprecated in this library and will be supported in existing features only until March 2021. New features will require at least C++11, as will existing features from next year.

This library is divided into several interconnected parts:

Floating Point Utilities

Utility functions for dealing with floating point arithmetic, includes functions for floating point classification (fpclassify, isnan, isinf etc), sign manipulation, rounding, comparison, and computing the distance between floating point numbers.

Specific Width Floating Point Types

A set of typedefs similar to those provided by <cstdint> but for floating point types.

Mathematical Constants

A wide range of constants ranging from various multiples of ฯ€, fractions, Euler's constant, etc.

These are of course usable from template code, or as non-templates with a simplified interface if that is more appropriate.

Statistical Distributions

Provides a reasonably comprehensive set of statistical distributions, upon which higher level statistical tests can be built.

The initial focus is on the central univariate distributions. Both continuous (like normal & Fisher) and discrete (like binomial & Poisson) distributions are provided.

A comprehensive tutorial is provided, along with a series of worked examples illustrating how the library is used to conduct statistical tests.

Special Functions

Provides a small number of high quality special functions; initially these were concentrated on functions used in statistical applications along with those in the Technical Report on C++ Library Extensions.

The function families currently implemented are the gamma, beta & error functions along with the incomplete gamma and beta functions (four variants of each) and all the possible inverses of these, plus the digamma, various factorial functions, Bessel functions, elliptic integrals, hypergeometrics, sinus cardinals (along with their hyperbolic variants), inverse hyperbolic functions, Legrendre/Laguerre/Hermite/Chebyshev polynomials and various special power and logarithmic functions.

All the implementations are fully generic and support the use of arbitrary "real-number" types, including Boost.Multiprecision, although they are optimised for use with types with known significand (or mantissa) sizes: typically float, double or long double.

These functions also provide the basis of support for the TR1 special functions.

Root Finding and Function Minimisation

A comprehensive set of root-finding algorithms over the real line, both with derivatives and derivative free.

Also function minimisation via Brent's Method.

Polynomials and Rational Functions

Tools for manipulating polynomials and for efficient evaluation of rationals or polynomials.

Interpolation

Function interpolation via barycentric rational interpolation, compactly supported quadartic, cubic, and quintic B-splines, the Chebyshev transform, trigonometric polynomials, Makima, pchip, and cubic Hermite splines.

Numerical Integration and Differentiation

A reasonably comprehensive set of routines for integration (trapezoidal, Gauss-Legendre, Gauss-Kronrod, Gauss-Chebyshev, double-exponential, and Monte-Carlo) and differentiation (Chebyshev transform, finite difference, the complex step derivative, and forward-mode automatic differentiation).

The integration routines are usable for functions returning complex results - and hence can be used for computation of contour integrals.

Quaternions and Octonions

Quaternion and Octonians are class templates similar to std::complex.

The full documentation is available on boost.org.

Master Develop
Travis Build Status Build Status
Appveyor Build status Build status

Support, bugs and feature requests

Bugs and feature requests can be reported through the GitHub issue tracker (see open issues and closed issues).

You can submit your changes through a pull request.

There is no mailing-list specific to Boost Math, although you can use the general-purpose Boost mailing-list using the tag [math].

Development

Clone the whole boost project, which includes the individual Boost projects as submodules (see boost+git doc):

$ git clone https://github.com/boostorg/boost
$ cd boost
$ git submodule update --init

The Boost Math Library is located in libs/math/.

Running tests

First, make sure you are in libs/math/test. You can either run all the tests listed in Jamfile.v2 or run a single test:

test$ ../../../b2                        <- run all tests
test$ ../../../b2 static_assert_test     <- single test
test$ # A more advanced syntax, demoing various options for building the tests:
test$ ../../../b2 -a -j2 -q --reconfigure toolset=clang cxxflags="--std=c++14 -fsanitize=address -fsanitize=undefined" linkflags="-fsanitize=undefined -fsanitize=address"

Building documentation

Full instructions can be found here, but to reiterate slightly:

libs/math/doc$ brew install docbook-xsl # on mac
libs/math/doc$ touch ~/user-config.jam
libs/math/doc$ # now edit so that:
libs/math/doc$ cat ~/user-config.jam
using darwin ;

using xsltproc ;

using boostbook
    : /usr/local/opt/docbook-xsl/docbook-xsl
    ;

using doxygen ;
using quickbook ;
libs/math/doc$ ../../../b2

boost-math's People

Contributors

jzmaddock avatar pabristow avatar nathompson avatar jeremy-murphy avatar ckormanyos avatar ctmacuser avatar sguazt avatar antonbikineev avatar beman avatar kundor avatar danieljames avatar douggregor avatar jensmaurer avatar akumta avatar bwignall avatar pulver avatar vprus avatar evanmiller avatar sitmo avatar brunolalande avatar rogeeff avatar steveire avatar brycelelbach avatar nikhar avatar straszheim avatar suyash-patil avatar inkstink avatar grafikrobot avatar dabrahams avatar lakshayg avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.