Crate rand_distr

Generating random samples from probability distributions.

Re-exports

This crate is a super-set of the rand::distributions module. See the rand::distributions module documentation for an overview of the core Distribution trait and implementations.

The following are re-exported:

• The Distribution trait and DistIter helper type
• The Standard, Alphanumeric, Uniform, OpenClosed01, Open01, Bernoulli, and WeightedIndex distributions

Distributions

This crate provides the following probability distributions:

• Related to real-valued quantities that grow linearly (e.g. errors, offsets):
  • Normal distribution, and StandardNormal as a primitive
  • Cauchy distribution
• Related to Bernoulli trials (yes/no events, with a given probability):
  • Binomial distribution
  • Geometric distribution
  • Hypergeometric distribution
• Related to positive real-valued quantities that grow exponentially (e.g. prices, incomes, populations):
  • LogNormal distribution
• Related to the occurrence of independent events at a given rate:
  • Pareto distribution
  • Poisson distribution
  • Exponential distribution, and Exp1 as a primitive
  • Weibull distribution
• Gamma and derived distributions:
  • Gamma distribution
  • ChiSquared distribution
  • StudentT distribution
  • FisherF distribution
• Triangular distribution:
  • Beta distribution
  • Triangular distribution
• Multivariate probability distributions
  • Dirichlet distribution
  • UnitSphere distribution
  • UnitBall distribution
  • UnitCircle distribution
  • UnitDisc distribution
• Alternative implementation for weighted index sampling
  • WeightedAliasIndex distribution
• Misc. distributions
  • InverseGaussian distribution
  • NormalInverseGaussian distribution

Re-exports

pub use weighted_alias::WeightedAliasIndex;

pub use num_traits;

Modules

uniform	A distribution uniformly sampling numbers within a given range.
weighted_alias	This module contains an implementation of alias method for sampling random indices with probabilities proportional to a collection of weights.

Structs

Alphanumeric	Sample a u8, uniformly distributed over ASCII letters and numbers: a-z, A-Z and 0-9.
Bernoulli	The Bernoulli distribution.
Beta	The Beta distribution with shape parameters alpha and beta.
Binomial	The binomial distribution Binomial(n, p).
Cauchy	The Cauchy distribution Cauchy(median, scale).
ChiSquared	The chi-squared distribution χ²(k), where k is the degrees of freedom.
Dirichlet	The Dirichlet distribution Dirichlet(alpha).
DistIter	An iterator that generates random values of T with distribution D, using R as the source of randomness.
Exp	The exponential distribution Exp(lambda).
Exp1	Samples floating-point numbers according to the exponential distribution, with rate parameter λ = 1. This is equivalent to Exp::new(1.0) or sampling with -rng.gen::<f64>().ln(), but faster.
FisherF	The Fisher F distribution F(m, n).
Gamma	The Gamma distribution Gamma(shape, scale) distribution.
Geometric	The geometric distribution Geometric(p) bounded to [0, u64::MAX].
Hypergeometric	The hypergeometric distribution Hypergeometric(N, K, n).
InverseGaussian	The inverse Gaussian distribution
LogNormal	The log-normal distribution ln N(mean, std_dev**2).
Normal	The normal distribution N(mean, std_dev**2).
NormalInverseGaussian	The normal-inverse Gaussian distribution
Open01	A distribution to sample floating point numbers uniformly in the open interval (0, 1), i.e. not including either endpoint.
OpenClosed01	A distribution to sample floating point numbers uniformly in the half-open interval (0, 1], i.e. including 1 but not 0.
Pareto	Samples floating-point numbers according to the Pareto distribution
Pert	The PERT distribution.
Poisson	The Poisson distribution Poisson(lambda).
Standard	A generic random value distribution, implemented for many primitive types. Usually generates values with a numerically uniform distribution, and with a range appropriate to the type.
StandardGeometric	Samples integers according to the geometric distribution with success probability p = 0.5. This is equivalent to Geometeric::new(0.5), but faster.
StandardNormal	Samples floating-point numbers according to the normal distribution N(0, 1) (a.k.a. a standard normal, or Gaussian). This is equivalent to Normal::new(0.0, 1.0) but faster.
StudentT	The Student t distribution, t(nu), where nu is the degrees of freedom.
Triangular	The triangular distribution.
Uniform	Sample values uniformly between two bounds.
UnitBall	Samples uniformly from the unit ball (surface and interior) in three dimensions.
UnitCircle	Samples uniformly from the edge of the unit circle in two dimensions.
UnitDisc	Samples uniformly from the unit disc in two dimensions.
UnitSphere	Samples uniformly from the surface of the unit sphere in three dimensions.
Weibull	Samples floating-point numbers according to the Weibull distribution
WeightedIndex	A distribution using weighted sampling of discrete items

Enums

BernoulliError	Error type returned from Bernoulli::new.
BetaError	Error type returned from Beta::new.
BinomialError	Error type returned from Binomial::new.
CauchyError	Error type returned from Cauchy::new.
ChiSquaredError	Error type returned from ChiSquared::new and StudentT::new.
DirichletError	Error type returned from Dirchlet::new.
ExpError	Error type returned from Exp::new.
FisherFError	Error type returned from FisherF::new.
GammaError	Error type returned from Gamma::new.
GeoError	Error type returned from Geometric::new.
HyperGeoError	Error type returned from Hypergeometric::new.
InverseGaussianError	Error type returned from InverseGaussian::new
NormalError	Error type returned from Normal::new and LogNormal::new.
NormalInverseGaussianError	Error type returned from NormalInverseGaussian::new
ParetoError	Error type returned from Pareto::new.
PertError	Error type returned from Pert constructors.
PoissonError	Error type returned from Poisson::new.
TriangularError	Error type returned from Triangular::new.
WeibullError	Error type returned from Weibull::new.
WeightedError	Error type returned from WeightedIndex::new.

Traits

Distribution

Types (distributions) that can be used to create a random instance of T.