Crate rand_distr
source · [−]Expand description
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
Distributiontrait andDistIterhelper type - The
Standard,Alphanumeric,Uniform,OpenClosed01,Open01,Bernoulli, andWeightedIndexdistributions
Distributions
This crate provides the following probability distributions:
- Related to real-valued quantities that grow linearly
(e.g. errors, offsets):
Normaldistribution, andStandardNormalas a primitiveSkewNormaldistributionCauchydistribution
- Related to Bernoulli trials (yes/no events, with a given probability):
BinomialdistributionGeometricdistributionHypergeometricdistribution
- Related to positive real-valued quantities that grow exponentially
(e.g. prices, incomes, populations):
LogNormaldistribution
- Related to the occurrence of independent events at a given rate:
- Gamma and derived distributions:
GammadistributionChiSquareddistributionStudentTdistributionFisherFdistribution
- Triangular distribution:
BetadistributionTriangulardistribution
- Multivariate probability distributions
DirichletdistributionUnitSpheredistributionUnitBalldistributionUnitCircledistributionUnitDiscdistribution
- Alternative implementation for weighted index sampling
WeightedAliasIndexdistribution
- Misc. distributions
InverseGaussiandistributionNormalInverseGaussiandistribution
Re-exports
pub use weighted_alias::WeightedAliasIndex;pub use num_traits;Modules
A distribution uniformly sampling numbers within a given range.
This module contains an implementation of alias method for sampling random indices with probabilities proportional to a collection of weights.
Structs
Sample a u8, uniformly distributed over ASCII letters and numbers:
a-z, A-Z and 0-9.
The Bernoulli distribution.
The Beta distribution with shape parameters alpha and beta.
The binomial distribution Binomial(n, p).
The Cauchy distribution Cauchy(median, scale).
The chi-squared distribution χ²(k), where k is the degrees of
freedom.
The Dirichlet distribution Dirichlet(alpha).
An iterator that generates random values of T with distribution D,
using R as the source of randomness.
The exponential distribution Exp(lambda).
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.
The Fisher F distribution F(m, n).
Samples floating-point numbers according to the Fréchet distribution
The Gamma distribution Gamma(shape, scale) distribution.
The geometric distribution Geometric(p) bounded to [0, u64::MAX].
Samples floating-point numbers according to the Gumbel distribution
The hypergeometric distribution Hypergeometric(N, K, n).
The log-normal distribution ln N(mean, std_dev**2).
The normal distribution N(mean, std_dev**2).
A distribution to sample floating point numbers uniformly in the open
interval (0, 1), i.e. not including either endpoint.
A distribution to sample floating point numbers uniformly in the half-open
interval (0, 1], i.e. including 1 but not 0.
Samples floating-point numbers according to the Pareto distribution
The PERT distribution.
The Poisson distribution Poisson(lambda).
The skew normal distribution SN(location, scale, shape).
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.
Samples integers according to the geometric distribution with success
probability p = 0.5. This is equivalent to Geometeric::new(0.5),
but faster.
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.
The Student t distribution, t(nu), where nu is the degrees of
freedom.
The triangular distribution.
Sample values uniformly between two bounds.
Samples uniformly from the unit ball (surface and interior) in three dimensions.
Samples uniformly from the edge of the unit circle in two dimensions.
Samples uniformly from the unit disc in two dimensions.
Samples uniformly from the surface of the unit sphere in three dimensions.
Samples floating-point numbers according to the Weibull distribution
A distribution using weighted sampling of discrete items
Samples integers according to the zeta distribution.
Samples integers according to the Zipf distribution.
Enums
Error type returned from Bernoulli::new.
Error type returned from Beta::new.
Error type returned from Binomial::new.
Error type returned from Cauchy::new.
Error type returned from ChiSquared::new and StudentT::new.
Error type returned from Dirchlet::new.
Error type returned from Exp::new.
Error type returned from FisherF::new.
Error type returned from Frechet::new.
Error type returned from Gamma::new.
Error type returned from Geometric::new.
Error type returned from Gumbel::new.
Error type returned from Hypergeometric::new.
Error type returned from InverseGaussian::new
Error type returned from Normal::new and LogNormal::new.
Error type returned from NormalInverseGaussian::new
Error type returned from Pareto::new.
Error type returned from Poisson::new.
Error type returned from SkewNormal::new.
Error type returned from Triangular::new.
Error type returned from Weibull::new.
Error type returned from WeightedIndex::new.
Error type returned from Zeta::new.
Error type returned from Zipf::new.
Traits
Types (distributions) that can be used to create a random instance of T.