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// Copyright 2019 Developers of the Rand project.
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
//> or the MIT license
// <LICENSE-MIT or>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.
#![doc(html_logo_url = "",
html_favicon_url = "",
html_root_url = "")]
#![allow(clippy::excessive_precision, clippy::float_cmp, clippy::unreadable_literal)]
#![allow(clippy::neg_cmp_op_on_partial_ord)] // suggested fix too verbose
//! 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`] and [`Bernoulli`] distributions
//! - The [`weighted`] sub-module
//! ## 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
//! - 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
//! - [`Exp`]onential 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
pub use rand::distributions::{Distribution, DistIter, Standard,
Alphanumeric, Uniform, OpenClosed01, Open01, Bernoulli, uniform, weighted};
pub use self::unit_sphere::UnitSphere;
pub use self::unit_ball::UnitBall;
pub use self::unit_circle::UnitCircle;
pub use self::unit_disc::UnitDisc;
pub use self::gamma::{Gamma, Error as GammaError, ChiSquared, ChiSquaredError,
FisherF, FisherFError, StudentT, Beta, BetaError};
pub use self::normal::{Normal, Error as NormalError, LogNormal, StandardNormal};
pub use self::exponential::{Exp, Error as ExpError, Exp1};
pub use self::pareto::{Pareto, Error as ParetoError};
pub use self::pert::{Pert, PertError};
pub use self::poisson::{Poisson, Error as PoissonError};
pub use self::binomial::{Binomial, Error as BinomialError};
pub use self::cauchy::{Cauchy, Error as CauchyError};
pub use self::dirichlet::{Dirichlet, Error as DirichletError};
pub use self::triangular::{Triangular, TriangularError};
pub use self::weibull::{Weibull, Error as WeibullError};
pub use self::utils::Float;
mod unit_sphere;
mod unit_ball;
mod unit_circle;
mod unit_disc;
mod gamma;
mod normal;
mod exponential;
mod pareto;
mod pert;
mod poisson;
mod binomial;
mod cauchy;
mod dirichlet;
mod triangular;
mod weibull;
mod utils;
mod ziggurat_tables;
mod test {
// Notes on testing
// Testing random number distributions correctly is hard. The following
// testing is desired:
// - Construction: test initialisation with a few valid parameter sets.
// - Erroneous usage: test that incorrect usage generates an error.
// - Vector: test that usage with fixed inputs (including RNG) generates a
// fixed output sequence on all platforms.
// - Correctness at fixed points (optional): using a specific mock RNG,
// check that specific values are sampled (e.g. end-points and median of
// distribution).
// - Correctness of PDF (extra): generate a histogram of samples within a
// certain range, and check this approximates the PDF. These tests are
// expected to be expensive, and should be behind a feature-gate.
// TODO: Vector and correctness tests are largely absent so far.
// NOTE: Some distributions have tests checking only that samples can be
// generated. This is redundant with vector and correctness tests.
/// Construct a deterministic RNG with the given seed
pub fn rng(seed: u64) -> impl rand::RngCore {
// For tests, we want a statistically good, fast, reproducible RNG.
// PCG32 will do fine, and will be easy to embed if we ever need to.
const INC: u64 = 11634580027462260723;
rand_pcg::Pcg32::new(seed, INC)