From 8d225e4e14af687c435f46cfda323367aeac452c Mon Sep 17 00:00:00 2001 From: Diggory Hardy Date: Mon, 9 Mar 2020 14:26:01 +0000 Subject: [PATCH 1/6] rand: remove wasm_bindgen test This test is now redundant with testing within the getrandom repository. --- Cargo.toml | 1 - tests/wasm_bindgen/Cargo.toml | 16 ----------- tests/wasm_bindgen/js/index.js | 7 ----- tests/wasm_bindgen/src/lib.rs | 49 ---------------------------------- 4 files changed, 73 deletions(-) delete mode 100644 tests/wasm_bindgen/Cargo.toml delete mode 100644 tests/wasm_bindgen/js/index.js delete mode 100644 tests/wasm_bindgen/src/lib.rs diff --git a/Cargo.toml b/Cargo.toml index ac64187cf69..f19791defa1 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -47,7 +47,6 @@ members = [ "rand_chacha", "rand_hc", "rand_pcg", - "tests/wasm_bindgen", ] [dependencies] diff --git a/tests/wasm_bindgen/Cargo.toml b/tests/wasm_bindgen/Cargo.toml deleted file mode 100644 index e83c1746420..00000000000 --- a/tests/wasm_bindgen/Cargo.toml +++ /dev/null @@ -1,16 +0,0 @@ -[package] -name = "rand_wasm_bindgen_test" -description = "Minimal crate to test that rand can be build for web assembly target" -version = "0.1.0" -authors = ["The Rand Project Developers"] -publish = false -license = "MIT OR Apache-2.0" -edition = "2018" - -[lib] -crate-type = ["cdylib"] - -[dependencies] -rand = { path = "../..", features = ["wasm-bindgen"] } -wasm-bindgen = "0.2" -wasm-bindgen-test = "0.2" diff --git a/tests/wasm_bindgen/js/index.js b/tests/wasm_bindgen/js/index.js deleted file mode 100644 index a02fb59b165..00000000000 --- a/tests/wasm_bindgen/js/index.js +++ /dev/null @@ -1,7 +0,0 @@ -'use strict'; - -const rand_wasm_bindgen_test = require('./rand_wasm_bindgen_test'); - -console.log(rand_wasm_bindgen_test.generate_from_entropy()); -console.log(rand_wasm_bindgen_test.generate_from_os_rand()); -console.log(rand_wasm_bindgen_test.generate_from_seed()); diff --git a/tests/wasm_bindgen/src/lib.rs b/tests/wasm_bindgen/src/lib.rs deleted file mode 100644 index 9af0b9e535c..00000000000 --- a/tests/wasm_bindgen/src/lib.rs +++ /dev/null @@ -1,49 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -// Crate to test WASM with the `wasm-bindgen` lib. - -#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png")] - -use rand::rngs::{OsRng, StdRng}; -use rand::{Rng, SeedableRng}; -use wasm_bindgen::prelude::*; - -#[wasm_bindgen] -pub fn generate_from_seed(seed: u32) -> i32 { - StdRng::seed_from_u64(seed as u64).gen() -} - -#[wasm_bindgen] -pub fn generate_from_os_rand() -> i32 { - OsRng.gen() -} - -#[wasm_bindgen] -pub fn generate_from_entropy() -> i32 { - StdRng::from_entropy().gen() -} - -pub mod tests { - use wasm_bindgen_test::*; - - #[wasm_bindgen_test] - fn generate_from_seed() { - let _ = super::generate_from_seed(42); - } - - #[wasm_bindgen_test] - fn generate_from_os_rand() { - let _ = super::generate_from_os_rand(); - } - - #[wasm_bindgen_test] - fn generate_from_entropy() { - let _ = super::generate_from_entropy(); - } -} From 0aa461715b2a677cb6c62248e889c06d2534b441 Mon Sep 17 00:00:00 2001 From: Diggory Hardy Date: Mon, 9 Mar 2020 14:35:52 +0000 Subject: [PATCH 2/6] rand: remove wasm-bindgen and stdweb feature flags These feature flags are re-exports from getrandom and were already scheduled for removal in 0.8. --- Cargo.toml | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/Cargo.toml b/Cargo.toml index f19791defa1..545d08e272c 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -29,12 +29,7 @@ serde1 = [] # does nothing, deprecated # Optional dependencies: std = ["rand_core/std", "rand_chacha/std", "alloc", "getrandom", "libc"] alloc = ["rand_core/alloc"] # enables Vec and Box support (without std) -# re-export optional WASM dependencies to avoid breakage: -# Warning: wasm-bindgen and stdweb features will be removed in rand 0.8; -# recommended to activate via the getrandom crate instead. -wasm-bindgen = ["getrandom_package/wasm-bindgen"] -stdweb = ["getrandom_package/stdweb"] -getrandom = ["getrandom_package", "rand_core/getrandom"] +getrandom = ["rand_core/getrandom"] # Configuration: simd_support = ["packed_simd"] # enables SIMD support @@ -52,10 +47,6 @@ members = [ [dependencies] rand_core = { path = "rand_core", version = "0.5.1" } rand_pcg = { path = "rand_pcg", version = "0.2", optional = true } -# Do not depend on 'getrandom_package' directly; use the 'getrandom' feature! -# This is a dependency because: we forward wasm feature flags -# This is renamed because: we need getrandom to depend on rand_core/getrandom -getrandom_package = { version = "0.1.1", package = "getrandom", optional = true } log = { version = "0.4.4", optional = true } [dependencies.packed_simd] From abcd587bd8cfd590a1013abcc4f11a4c5682b2be Mon Sep 17 00:00:00 2001 From: Diggory Hardy Date: Mon, 9 Mar 2020 15:22:13 +0000 Subject: [PATCH 3/6] Rand: add std_rng feature flag --- Cargo.toml | 26 ++++++++++++++++++-------- README.md | 14 ++++++-------- src/lib.rs | 10 ++++++---- src/prelude.rs | 5 +++-- src/rngs/adapter/reseeding.rs | 1 + src/rngs/mod.rs | 8 ++++---- src/rngs/thread.rs | 4 ++-- 7 files changed, 40 insertions(+), 28 deletions(-) diff --git a/Cargo.toml b/Cargo.toml index 545d08e272c..4a5e4456829 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -22,18 +22,28 @@ appveyor = { repository = "rust-random/rand" } [features] # Meta-features: -default = ["std"] # without "std" rand uses libcore +default = ["std", "std_rng"] nightly = ["simd_support"] # enables all features requiring nightly rust serde1 = [] # does nothing, deprecated -# Optional dependencies: +# Option: without "std" rand uses libcore; this option enables functionality +# expected to be available on a standard platform. std = ["rand_core/std", "rand_chacha/std", "alloc", "getrandom", "libc"] -alloc = ["rand_core/alloc"] # enables Vec and Box support (without std) + +# Option: "alloc" enables support for Vec and Box when not using "std" +alloc = ["rand_core/alloc"] + +# Option: use getrandom package for seeding getrandom = ["rand_core/getrandom"] -# Configuration: -simd_support = ["packed_simd"] # enables SIMD support -small_rng = ["rand_pcg"] # enables SmallRng +# Option: experimental SIMD support +simd_support = ["packed_simd"] + +# Option: enable StdRng (enabled by default) +std_rng = ["rand_chacha", "rand_hc"] + +# Option: enable SmallRng +small_rng = ["rand_pcg"] [workspace] members = [ @@ -63,9 +73,9 @@ libc = { version = "0.2.22", optional = true, default-features = false } # Emscripten does not support 128-bit integers, which are used by ChaCha code. # We work around this by using a different RNG. [target.'cfg(not(target_os = "emscripten"))'.dependencies] -rand_chacha = { path = "rand_chacha", version = "0.2.1", default-features = false } +rand_chacha = { path = "rand_chacha", version = "0.2.1", default-features = false, optional = true } [target.'cfg(target_os = "emscripten")'.dependencies] -rand_hc = { path = "rand_hc", version = "0.2" } +rand_hc = { path = "rand_hc", version = "0.2", optional = true } [dev-dependencies] rand_pcg = { path = "rand_pcg", version = "0.2" } diff --git a/README.md b/README.md index c4bd676a98c..bd7ec768a5a 100644 --- a/README.md +++ b/README.md @@ -90,19 +90,17 @@ Rand release if required, but the change must be noted in the changelog. Rand is built with these features enabled by default: - `std` enables functionality dependent on the `std` lib -- `alloc` (implied by `std`) enables functionality requiring an allocator (when using this feature in `no_std`, Rand requires Rustc version 1.36 or greater) +- `alloc` (implied by `std`) enables functionality requiring an allocator + (when using this feature in `no_std`, Rand requires Rustc version 1.36 or + greater) - `getrandom` (implied by `std`) is an optional dependency providing the code behind `rngs::OsRng` +- `std_rng` enables inclusion of `StdRng`, `thread_rng` and `random` + (the latter two *also* require that `std` be enabled) Optionally, the following dependencies can be enabled: -- `log` enables logging via the `log` crate -- `stdweb` implies `getrandom/stdweb` to enable - `getrandom` support on `wasm32-unknown-unknown` - (will be removed in rand 0.8; activate via `getrandom` crate instead) -- `wasm-bindgen` implies `getrandom/wasm-bindgen` to enable - `getrandom` support on `wasm32-unknown-unknown` - (will be removed in rand 0.8; activate via `getrandom` crate instead) +- `log` enables logging via the `log` crate` crate Additionally, these features configure Rand: diff --git a/src/lib.rs b/src/lib.rs index f0f9f148aa8..a8483945ecb 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -100,10 +100,12 @@ pub mod rngs; pub mod seq; // Public exports -#[cfg(feature = "std")] pub use crate::rngs::thread::thread_rng; +#[cfg(all(feature = "std", feature = "std_rng"))] +pub use crate::rngs::thread::thread_rng; pub use rng::{Fill, Rng}; -#[cfg(feature = "std")] use crate::distributions::{Distribution, Standard}; +#[cfg(all(feature = "std", feature = "std_rng"))] +use crate::distributions::{Distribution, Standard}; /// Generates a random value using the thread-local random number generator. /// @@ -147,7 +149,7 @@ pub use rng::{Fill, Rng}; /// ``` /// /// [`Standard`]: distributions::Standard -#[cfg(feature = "std")] +#[cfg(all(feature = "std", feature = "std_rng"))] #[inline] pub fn random() -> T where Standard: Distribution { @@ -167,7 +169,7 @@ mod test { } #[test] - #[cfg(feature = "std")] + #[cfg(all(feature = "std", feature = "std_rng"))] fn test_random() { // not sure how to test this aside from just getting some values let _n: usize = random(); diff --git a/src/prelude.rs b/src/prelude.rs index 98ae3bb4315..51c457e3f9e 100644 --- a/src/prelude.rs +++ b/src/prelude.rs @@ -22,12 +22,13 @@ #[cfg(feature = "small_rng")] #[doc(no_inline)] pub use crate::rngs::SmallRng; +#[cfg(feature = "std_rng")] #[doc(no_inline)] pub use crate::rngs::StdRng; #[doc(no_inline)] -#[cfg(feature = "std")] +#[cfg(all(feature = "std", feature = "std_rng"))] pub use crate::rngs::ThreadRng; #[doc(no_inline)] pub use crate::seq::{IteratorRandom, SliceRandom}; #[doc(no_inline)] -#[cfg(feature = "std")] +#[cfg(all(feature = "std", feature = "std_rng"))] pub use crate::{random, thread_rng}; #[doc(no_inline)] pub use crate::{CryptoRng, Rng, RngCore, SeedableRng}; diff --git a/src/rngs/adapter/reseeding.rs b/src/rngs/adapter/reseeding.rs index 5460e3431f9..c67196123f6 100644 --- a/src/rngs/adapter/reseeding.rs +++ b/src/rngs/adapter/reseeding.rs @@ -325,6 +325,7 @@ mod fork { } +#[cfg(feature = "std_rng")] #[cfg(test)] mod test { use super::ReseedingRng; diff --git a/src/rngs/mod.rs b/src/rngs/mod.rs index 11121960255..fc0f8cbc2ca 100644 --- a/src/rngs/mod.rs +++ b/src/rngs/mod.rs @@ -102,15 +102,15 @@ pub mod adapter; pub mod mock; // Public so we don't export `StepRng` directly, making it a bit // more clear it is intended for testing. #[cfg(feature = "small_rng")] mod small; -mod std; -#[cfg(feature = "std")] pub(crate) mod thread; +#[cfg(feature = "std_rng")] mod std; +#[cfg(all(feature = "std", feature = "std_rng"))] pub(crate) mod thread; #[allow(deprecated)] #[cfg(feature = "std")] pub use self::entropy::EntropyRng; #[cfg(feature = "small_rng")] pub use self::small::SmallRng; -pub use self::std::StdRng; -#[cfg(feature = "std")] pub use self::thread::ThreadRng; +#[cfg(feature = "std_rng")] pub use self::std::StdRng; +#[cfg(all(feature = "std", feature = "std_rng"))] pub use self::thread::ThreadRng; #[cfg(feature = "getrandom")] pub use rand_core::OsRng; diff --git a/src/rngs/thread.rs b/src/rngs/thread.rs index 91ed4c30a8e..6ec62c763dd 100644 --- a/src/rngs/thread.rs +++ b/src/rngs/thread.rs @@ -8,8 +8,8 @@ //! Thread-local random number generator -use std::cell::UnsafeCell; -use std::ptr::NonNull; +use core::cell::UnsafeCell; +use core::ptr::NonNull; use super::std::Core; use crate::rngs::adapter::ReseedingRng; From 3abd83a4f7e390e6e24555ff53e56c19b2885187 Mon Sep 17 00:00:00 2001 From: Diggory Hardy Date: Mon, 9 Mar 2020 15:27:01 +0000 Subject: [PATCH 4/6] Remove deprecated distributions --- src/distributions/binomial.rs | 321 ----------------------- src/distributions/cauchy.rs | 99 ------- src/distributions/dirichlet.rs | 126 --------- src/distributions/exponential.rs | 114 -------- src/distributions/gamma.rs | 373 --------------------------- src/distributions/mod.rs | 51 ---- src/distributions/normal.rs | 177 ------------- src/distributions/pareto.rs | 70 ----- src/distributions/poisson.rs | 151 ----------- src/distributions/triangular.rs | 83 ------ src/distributions/unit_circle.rs | 102 -------- src/distributions/unit_sphere.rs | 97 ------- src/distributions/utils.rs | 112 -------- src/distributions/weibull.rs | 67 ----- src/distributions/ziggurat_tables.rs | 283 -------------------- 15 files changed, 2226 deletions(-) delete mode 100644 src/distributions/binomial.rs delete mode 100644 src/distributions/cauchy.rs delete mode 100644 src/distributions/dirichlet.rs delete mode 100644 src/distributions/exponential.rs delete mode 100644 src/distributions/gamma.rs delete mode 100644 src/distributions/normal.rs delete mode 100644 src/distributions/pareto.rs delete mode 100644 src/distributions/poisson.rs delete mode 100644 src/distributions/triangular.rs delete mode 100644 src/distributions/unit_circle.rs delete mode 100644 src/distributions/unit_sphere.rs delete mode 100644 src/distributions/weibull.rs delete mode 100644 src/distributions/ziggurat_tables.rs diff --git a/src/distributions/binomial.rs b/src/distributions/binomial.rs deleted file mode 100644 index c096e4a8629..00000000000 --- a/src/distributions/binomial.rs +++ /dev/null @@ -1,321 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2016-2017 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The binomial distribution. -#![allow(deprecated)] -#![allow(clippy::all)] - -use crate::distributions::{Distribution, Uniform}; -use crate::Rng; - -/// The binomial distribution `Binomial(n, p)`. -/// -/// This distribution has density function: -/// `f(k) = n!/(k! (n-k)!) p^k (1-p)^(n-k)` for `k >= 0`. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Binomial { - /// Number of trials. - n: u64, - /// Probability of success. - p: f64, -} - -impl Binomial { - /// Construct a new `Binomial` with the given shape parameters `n` (number - /// of trials) and `p` (probability of success). - /// - /// Panics if `p < 0` or `p > 1`. - pub fn new(n: u64, p: f64) -> Binomial { - assert!(p >= 0.0, "Binomial::new called with p < 0"); - assert!(p <= 1.0, "Binomial::new called with p > 1"); - Binomial { n, p } - } -} - -/// Convert a `f64` to an `i64`, panicing on overflow. -// In the future (Rust 1.34), this might be replaced with `TryFrom`. -fn f64_to_i64(x: f64) -> i64 { - assert!(x < (::std::i64::MAX as f64)); - x as i64 -} - -impl Distribution for Binomial { - fn sample(&self, rng: &mut R) -> u64 { - // Handle these values directly. - if self.p == 0.0 { - return 0; - } else if self.p == 1.0 { - return self.n; - } - - // The binomial distribution is symmetrical with respect to p -> 1-p, - // k -> n-k switch p so that it is less than 0.5 - this allows for lower - // expected values we will just invert the result at the end - let p = if self.p <= 0.5 { self.p } else { 1.0 - self.p }; - - let result; - let q = 1. - p; - - // For small n * min(p, 1 - p), the BINV algorithm based on the inverse - // transformation of the binomial distribution is efficient. Otherwise, - // the BTPE algorithm is used. - // - // Voratas Kachitvichyanukul and Bruce W. Schmeiser. 1988. Binomial - // random variate generation. Commun. ACM 31, 2 (February 1988), - // 216-222. http://dx.doi.org/10.1145/42372.42381 - - // Threshold for prefering the BINV algorithm. The paper suggests 10, - // Ranlib uses 30, and GSL uses 14. - const BINV_THRESHOLD: f64 = 10.; - - if (self.n as f64) * p < BINV_THRESHOLD && self.n <= (::std::i32::MAX as u64) { - // Use the BINV algorithm. - let s = p / q; - let a = ((self.n + 1) as f64) * s; - let mut r = q.powi(self.n as i32); - let mut u: f64 = rng.gen(); - let mut x = 0; - while u > r as f64 { - u -= r; - x += 1; - r *= a / (x as f64) - s; - } - result = x; - } else { - // Use the BTPE algorithm. - - // Threshold for using the squeeze algorithm. This can be freely - // chosen based on performance. Ranlib and GSL use 20. - const SQUEEZE_THRESHOLD: i64 = 20; - - // Step 0: Calculate constants as functions of `n` and `p`. - let n = self.n as f64; - let np = n * p; - let npq = np * q; - let f_m = np + p; - let m = f64_to_i64(f_m); - // radius of triangle region, since height=1 also area of region - let p1 = (2.195 * npq.sqrt() - 4.6 * q).floor() + 0.5; - // tip of triangle - let x_m = (m as f64) + 0.5; - // left edge of triangle - let x_l = x_m - p1; - // right edge of triangle - let x_r = x_m + p1; - let c = 0.134 + 20.5 / (15.3 + (m as f64)); - // p1 + area of parallelogram region - let p2 = p1 * (1. + 2. * c); - - fn lambda(a: f64) -> f64 { - a * (1. + 0.5 * a) - } - - let lambda_l = lambda((f_m - x_l) / (f_m - x_l * p)); - let lambda_r = lambda((x_r - f_m) / (x_r * q)); - // p1 + area of left tail - let p3 = p2 + c / lambda_l; - // p1 + area of right tail - let p4 = p3 + c / lambda_r; - - // return value - let mut y: i64; - - let gen_u = Uniform::new(0., p4); - let gen_v = Uniform::new(0., 1.); - - loop { - // Step 1: Generate `u` for selecting the region. If region 1 is - // selected, generate a triangularly distributed variate. - let u = gen_u.sample(rng); - let mut v = gen_v.sample(rng); - if !(u > p1) { - y = f64_to_i64(x_m - p1 * v + u); - break; - } - - if !(u > p2) { - // Step 2: Region 2, parallelograms. Check if region 2 is - // used. If so, generate `y`. - let x = x_l + (u - p1) / c; - v = v * c + 1.0 - (x - x_m).abs() / p1; - if v > 1. { - continue; - } else { - y = f64_to_i64(x); - } - } else if !(u > p3) { - // Step 3: Region 3, left exponential tail. - y = f64_to_i64(x_l + v.ln() / lambda_l); - if y < 0 { - continue; - } else { - v *= (u - p2) * lambda_l; - } - } else { - // Step 4: Region 4, right exponential tail. - y = f64_to_i64(x_r - v.ln() / lambda_r); - if y > 0 && (y as u64) > self.n { - continue; - } else { - v *= (u - p3) * lambda_r; - } - } - - // Step 5: Acceptance/rejection comparison. - - // Step 5.0: Test for appropriate method of evaluating f(y). - let k = (y - m).abs(); - if !(k > SQUEEZE_THRESHOLD && (k as f64) < 0.5 * npq - 1.) { - // Step 5.1: Evaluate f(y) via the recursive relationship. Start the - // search from the mode. - let s = p / q; - let a = s * (n + 1.); - let mut f = 1.0; - if m < y { - let mut i = m; - loop { - i += 1; - f *= a / (i as f64) - s; - if i == y { - break; - } - } - } else if m > y { - let mut i = y; - loop { - i += 1; - f /= a / (i as f64) - s; - if i == m { - break; - } - } - } - if v > f { - continue; - } else { - break; - } - } - - // Step 5.2: Squeezing. Check the value of ln(v) againts upper and - // lower bound of ln(f(y)). - let k = k as f64; - let rho = (k / npq) * ((k * (k / 3. + 0.625) + 1. / 6.) / npq + 0.5); - let t = -0.5 * k * k / npq; - let alpha = v.ln(); - if alpha < t - rho { - break; - } - if alpha > t + rho { - continue; - } - - // Step 5.3: Final acceptance/rejection test. - let x1 = (y + 1) as f64; - let f1 = (m + 1) as f64; - let z = (f64_to_i64(n) + 1 - m) as f64; - let w = (f64_to_i64(n) - y + 1) as f64; - - fn stirling(a: f64) -> f64 { - let a2 = a * a; - (13860. - (462. - (132. - (99. - 140. / a2) / a2) / a2) / a2) / a / 166320. - } - - if alpha - > x_m * (f1 / x1).ln() - + (n - (m as f64) + 0.5) * (z / w).ln() - + ((y - m) as f64) * (w * p / (x1 * q)).ln() - // We use the signs from the GSL implementation, which are - // different than the ones in the reference. According to - // the GSL authors, the new signs were verified to be - // correct by one of the original designers of the - // algorithm. - + stirling(f1) - + stirling(z) - - stirling(x1) - - stirling(w) - { - continue; - } - - break; - } - assert!(y >= 0); - result = y as u64; - } - - // Invert the result for p < 0.5. - if p != self.p { - self.n - result - } else { - result - } - } -} - -#[cfg(test)] -mod test { - use super::Binomial; - use crate::distributions::Distribution; - use crate::Rng; - - fn test_binomial_mean_and_variance(n: u64, p: f64, rng: &mut R) { - let binomial = Binomial::new(n, p); - - let expected_mean = n as f64 * p; - let expected_variance = n as f64 * p * (1.0 - p); - - let mut results = [0.0; 1000]; - for i in results.iter_mut() { - *i = binomial.sample(rng) as f64; - } - - let mean = results.iter().sum::() / results.len() as f64; - assert!( - (mean as f64 - expected_mean).abs() < expected_mean / 50.0, - "mean: {}, expected_mean: {}", - mean, - expected_mean - ); - - let variance = - results.iter().map(|x| (x - mean) * (x - mean)).sum::() / results.len() as f64; - assert!( - (variance - expected_variance).abs() < expected_variance / 10.0, - "variance: {}, expected_variance: {}", - variance, - expected_variance - ); - } - - #[test] - #[cfg_attr(miri, ignore)] // Miri is too slow - fn test_binomial() { - let mut rng = crate::test::rng(351); - test_binomial_mean_and_variance(150, 0.1, &mut rng); - test_binomial_mean_and_variance(70, 0.6, &mut rng); - test_binomial_mean_and_variance(40, 0.5, &mut rng); - test_binomial_mean_and_variance(20, 0.7, &mut rng); - test_binomial_mean_and_variance(20, 0.5, &mut rng); - } - - #[test] - fn test_binomial_end_points() { - let mut rng = crate::test::rng(352); - assert_eq!(rng.sample(Binomial::new(20, 0.0)), 0); - assert_eq!(rng.sample(Binomial::new(20, 1.0)), 20); - } - - #[test] - #[should_panic] - fn test_binomial_invalid_lambda_neg() { - Binomial::new(20, -10.0); - } -} diff --git a/src/distributions/cauchy.rs b/src/distributions/cauchy.rs deleted file mode 100644 index dc54c98a35b..00000000000 --- a/src/distributions/cauchy.rs +++ /dev/null @@ -1,99 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2016-2017 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The Cauchy distribution. -#![allow(deprecated)] -#![allow(clippy::all)] - -use crate::distributions::Distribution; -use crate::Rng; -use std::f64::consts::PI; - -/// The Cauchy distribution `Cauchy(median, scale)`. -/// -/// This distribution has a density function: -/// `f(x) = 1 / (pi * scale * (1 + ((x - median) / scale)^2))` -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Cauchy { - median: f64, - scale: f64, -} - -impl Cauchy { - /// Construct a new `Cauchy` with the given shape parameters - /// `median` the peak location and `scale` the scale factor. - /// Panics if `scale <= 0`. - pub fn new(median: f64, scale: f64) -> Cauchy { - assert!(scale > 0.0, "Cauchy::new called with scale factor <= 0"); - Cauchy { median, scale } - } -} - -impl Distribution for Cauchy { - fn sample(&self, rng: &mut R) -> f64 { - // sample from [0, 1) - let x = rng.gen::(); - // get standard cauchy random number - // note that π/2 is not exactly representable, even if x=0.5 the result is finite - let comp_dev = (PI * x).tan(); - // shift and scale according to parameters - let result = self.median + self.scale * comp_dev; - result - } -} - -#[cfg(test)] -mod test { - use super::Cauchy; - use crate::distributions::Distribution; - - fn median(mut numbers: &mut [f64]) -> f64 { - sort(&mut numbers); - let mid = numbers.len() / 2; - numbers[mid] - } - - fn sort(numbers: &mut [f64]) { - numbers.sort_by(|a, b| a.partial_cmp(b).unwrap()); - } - - #[test] - fn test_cauchy_averages() { - // NOTE: given that the variance and mean are undefined, - // this test does not have any rigorous statistical meaning. - let cauchy = Cauchy::new(10.0, 5.0); - let mut rng = crate::test::rng(123); - let mut numbers: [f64; 1000] = [0.0; 1000]; - let mut sum = 0.0; - for i in 0..1000 { - numbers[i] = cauchy.sample(&mut rng); - sum += numbers[i]; - } - let median = median(&mut numbers); - println!("Cauchy median: {}", median); - assert!((median - 10.0).abs() < 0.4); // not 100% certain, but probable enough - let mean = sum / 1000.0; - println!("Cauchy mean: {}", mean); - // for a Cauchy distribution the mean should not converge - assert!((mean - 10.0).abs() > 0.4); // not 100% certain, but probable enough - } - - #[test] - #[should_panic] - fn test_cauchy_invalid_scale_zero() { - Cauchy::new(0.0, 0.0); - } - - #[test] - #[should_panic] - fn test_cauchy_invalid_scale_neg() { - Cauchy::new(0.0, -10.0); - } -} diff --git a/src/distributions/dirichlet.rs b/src/distributions/dirichlet.rs deleted file mode 100644 index a75678a8504..00000000000 --- a/src/distributions/dirichlet.rs +++ /dev/null @@ -1,126 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2013 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The dirichlet distribution. -#![allow(deprecated)] -#![allow(clippy::all)] - -use crate::distributions::gamma::Gamma; -use crate::distributions::Distribution; -use crate::Rng; - -/// The dirichelet distribution `Dirichlet(alpha)`. -/// -/// The Dirichlet distribution is a family of continuous multivariate -/// probability distributions parameterized by a vector alpha of positive reals. -/// It is a multivariate generalization of the beta distribution. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Debug)] -pub struct Dirichlet { - /// Concentration parameters (alpha) - alpha: Vec, -} - -impl Dirichlet { - /// Construct a new `Dirichlet` with the given alpha parameter `alpha`. - /// - /// # Panics - /// - if `alpha.len() < 2` - #[inline] - pub fn new>>(alpha: V) -> Dirichlet { - let a = alpha.into(); - assert!(a.len() > 1); - for i in 0..a.len() { - assert!(a[i] > 0.0); - } - - Dirichlet { alpha: a } - } - - /// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`. - /// - /// # Panics - /// - if `alpha <= 0.0` - /// - if `size < 2` - #[inline] - pub fn new_with_param(alpha: f64, size: usize) -> Dirichlet { - assert!(alpha > 0.0); - assert!(size > 1); - Dirichlet { - alpha: vec![alpha; size], - } - } -} - -impl Distribution> for Dirichlet { - fn sample(&self, rng: &mut R) -> Vec { - let n = self.alpha.len(); - let mut samples = vec![0.0f64; n]; - let mut sum = 0.0f64; - - for i in 0..n { - let g = Gamma::new(self.alpha[i], 1.0); - samples[i] = g.sample(rng); - sum += samples[i]; - } - let invacc = 1.0 / sum; - for i in 0..n { - samples[i] *= invacc; - } - samples - } -} - -#[cfg(test)] -mod test { - use super::Dirichlet; - use crate::distributions::Distribution; - - #[test] - fn test_dirichlet() { - let d = Dirichlet::new(vec![1.0, 2.0, 3.0]); - let mut rng = crate::test::rng(221); - let samples = d.sample(&mut rng); - let _: Vec = samples - .into_iter() - .map(|x| { - assert!(x > 0.0); - x - }) - .collect(); - } - - #[test] - fn test_dirichlet_with_param() { - let alpha = 0.5f64; - let size = 2; - let d = Dirichlet::new_with_param(alpha, size); - let mut rng = crate::test::rng(221); - let samples = d.sample(&mut rng); - let _: Vec = samples - .into_iter() - .map(|x| { - assert!(x > 0.0); - x - }) - .collect(); - } - - #[test] - #[should_panic] - fn test_dirichlet_invalid_length() { - Dirichlet::new_with_param(0.5f64, 1); - } - - #[test] - #[should_panic] - fn test_dirichlet_invalid_alpha() { - Dirichlet::new_with_param(0.0f64, 2); - } -} diff --git a/src/distributions/exponential.rs b/src/distributions/exponential.rs deleted file mode 100644 index 5fdf7aa74fb..00000000000 --- a/src/distributions/exponential.rs +++ /dev/null @@ -1,114 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2013 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The exponential distribution. -#![allow(deprecated)] - -use crate::distributions::utils::ziggurat; -use crate::distributions::{ziggurat_tables, Distribution}; -use crate::Rng; - -/// 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::().ln()`, but faster. -/// -/// See `Exp` for the general exponential distribution. -/// -/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method. The exact -/// description in the paper was adjusted to use tables for the exponential -/// distribution rather than normal. -/// -/// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to -/// Generate Normal Random Samples*]( -/// https://www.doornik.com/research/ziggurat.pdf). -/// Nuffield College, Oxford -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Exp1; - -// This could be done via `-rng.gen::().ln()` but that is slower. -impl Distribution for Exp1 { - #[inline] - fn sample(&self, rng: &mut R) -> f64 { - #[inline] - fn pdf(x: f64) -> f64 { - (-x).exp() - } - #[inline] - fn zero_case(rng: &mut R, _u: f64) -> f64 { - ziggurat_tables::ZIG_EXP_R - rng.gen::().ln() - } - - ziggurat( - rng, - false, - &ziggurat_tables::ZIG_EXP_X, - &ziggurat_tables::ZIG_EXP_F, - pdf, - zero_case, - ) - } -} - -/// The exponential distribution `Exp(lambda)`. -/// -/// This distribution has density function: `f(x) = lambda * exp(-lambda * x)` -/// for `x > 0`. -/// -/// Note that [`Exp1`](crate::distributions::Exp1) is an optimised implementation for `lambda = 1`. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Exp { - /// `lambda` stored as `1/lambda`, since this is what we scale by. - lambda_inverse: f64, -} - -impl Exp { - /// Construct a new `Exp` with the given shape parameter - /// `lambda`. Panics if `lambda <= 0`. - #[inline] - pub fn new(lambda: f64) -> Exp { - assert!(lambda > 0.0, "Exp::new called with `lambda` <= 0"); - Exp { - lambda_inverse: 1.0 / lambda, - } - } -} - -impl Distribution for Exp { - fn sample(&self, rng: &mut R) -> f64 { - let n: f64 = rng.sample(Exp1); - n * self.lambda_inverse - } -} - -#[cfg(test)] -mod test { - use super::Exp; - use crate::distributions::Distribution; - - #[test] - fn test_exp() { - let exp = Exp::new(10.0); - let mut rng = crate::test::rng(221); - for _ in 0..1000 { - assert!(exp.sample(&mut rng) >= 0.0); - } - } - #[test] - #[should_panic] - fn test_exp_invalid_lambda_zero() { - Exp::new(0.0); - } - #[test] - #[should_panic] - fn test_exp_invalid_lambda_neg() { - Exp::new(-10.0); - } -} diff --git a/src/distributions/gamma.rs b/src/distributions/gamma.rs deleted file mode 100644 index f19738dbe8e..00000000000 --- a/src/distributions/gamma.rs +++ /dev/null @@ -1,373 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2013 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The Gamma and derived distributions. -#![allow(deprecated)] - -use self::ChiSquaredRepr::*; -use self::GammaRepr::*; - -use crate::distributions::normal::StandardNormal; -use crate::distributions::{Distribution, Exp, Open01}; -use crate::Rng; - -/// The Gamma distribution `Gamma(shape, scale)` distribution. -/// -/// The density function of this distribution is -/// -/// ```text -/// f(x) = x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k) -/// ``` -/// -/// where `Γ` is the Gamma function, `k` is the shape and `θ` is the -/// scale and both `k` and `θ` are strictly positive. -/// -/// The algorithm used is that described by Marsaglia & Tsang 2000[^1], -/// falling back to directly sampling from an Exponential for `shape -/// == 1`, and using the boosting technique described in that paper for -/// `shape < 1`. -/// -/// [^1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method for -/// Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3 -/// (September 2000), 363-372. -/// DOI:[10.1145/358407.358414](https://doi.acm.org/10.1145/358407.358414) -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Gamma { - repr: GammaRepr, -} - -#[derive(Clone, Copy, Debug)] -enum GammaRepr { - Large(GammaLargeShape), - One(Exp), - Small(GammaSmallShape), -} - -// These two helpers could be made public, but saving the -// match-on-Gamma-enum branch from using them directly (e.g. if one -// knows that the shape is always > 1) doesn't appear to be much -// faster. - -/// Gamma distribution where the shape parameter is less than 1. -/// -/// Note, samples from this require a compulsory floating-point `pow` -/// call, which makes it significantly slower than sampling from a -/// gamma distribution where the shape parameter is greater than or -/// equal to 1. -/// -/// See `Gamma` for sampling from a Gamma distribution with general -/// shape parameters. -#[derive(Clone, Copy, Debug)] -struct GammaSmallShape { - inv_shape: f64, - large_shape: GammaLargeShape, -} - -/// Gamma distribution where the shape parameter is larger than 1. -/// -/// See `Gamma` for sampling from a Gamma distribution with general -/// shape parameters. -#[derive(Clone, Copy, Debug)] -struct GammaLargeShape { - scale: f64, - c: f64, - d: f64, -} - -impl Gamma { - /// Construct an object representing the `Gamma(shape, scale)` - /// distribution. - /// - /// Panics if `shape <= 0` or `scale <= 0`. - #[inline] - pub fn new(shape: f64, scale: f64) -> Gamma { - assert!(shape > 0.0, "Gamma::new called with shape <= 0"); - assert!(scale > 0.0, "Gamma::new called with scale <= 0"); - - let repr = if shape == 1.0 { - One(Exp::new(1.0 / scale)) - } else if shape < 1.0 { - Small(GammaSmallShape::new_raw(shape, scale)) - } else { - Large(GammaLargeShape::new_raw(shape, scale)) - }; - Gamma { repr } - } -} - -impl GammaSmallShape { - fn new_raw(shape: f64, scale: f64) -> GammaSmallShape { - GammaSmallShape { - inv_shape: 1. / shape, - large_shape: GammaLargeShape::new_raw(shape + 1.0, scale), - } - } -} - -impl GammaLargeShape { - fn new_raw(shape: f64, scale: f64) -> GammaLargeShape { - let d = shape - 1. / 3.; - GammaLargeShape { - scale, - c: 1. / (9. * d).sqrt(), - d, - } - } -} - -impl Distribution for Gamma { - fn sample(&self, rng: &mut R) -> f64 { - match self.repr { - Small(ref g) => g.sample(rng), - One(ref g) => g.sample(rng), - Large(ref g) => g.sample(rng), - } - } -} -impl Distribution for GammaSmallShape { - fn sample(&self, rng: &mut R) -> f64 { - let u: f64 = rng.sample(Open01); - - self.large_shape.sample(rng) * u.powf(self.inv_shape) - } -} -impl Distribution for GammaLargeShape { - fn sample(&self, rng: &mut R) -> f64 { - loop { - let x = rng.sample(StandardNormal); - let v_cbrt = 1.0 + self.c * x; - if v_cbrt <= 0.0 { - // a^3 <= 0 iff a <= 0 - continue; - } - - let v = v_cbrt * v_cbrt * v_cbrt; - let u: f64 = rng.sample(Open01); - - let x_sqr = x * x; - if u < 1.0 - 0.0331 * x_sqr * x_sqr - || u.ln() < 0.5 * x_sqr + self.d * (1.0 - v + v.ln()) - { - return self.d * v * self.scale; - } - } - } -} - -/// The chi-squared distribution `χ²(k)`, where `k` is the degrees of -/// freedom. -/// -/// For `k > 0` integral, this distribution is the sum of the squares -/// of `k` independent standard normal random variables. For other -/// `k`, this uses the equivalent characterisation -/// `χ²(k) = Gamma(k/2, 2)`. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct ChiSquared { - repr: ChiSquaredRepr, -} - -#[derive(Clone, Copy, Debug)] -enum ChiSquaredRepr { - // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1, - // e.g. when alpha = 1/2 as it would be for this case, so special- - // casing and using the definition of N(0,1)^2 is faster. - DoFExactlyOne, - DoFAnythingElse(Gamma), -} - -impl ChiSquared { - /// Create a new chi-squared distribution with degrees-of-freedom - /// `k`. Panics if `k < 0`. - pub fn new(k: f64) -> ChiSquared { - let repr = if k == 1.0 { - DoFExactlyOne - } else { - assert!(k > 0.0, "ChiSquared::new called with `k` < 0"); - DoFAnythingElse(Gamma::new(0.5 * k, 2.0)) - }; - ChiSquared { repr } - } -} -impl Distribution for ChiSquared { - fn sample(&self, rng: &mut R) -> f64 { - match self.repr { - DoFExactlyOne => { - // k == 1 => N(0,1)^2 - let norm = rng.sample(StandardNormal); - norm * norm - } - DoFAnythingElse(ref g) => g.sample(rng), - } - } -} - -/// The Fisher F distribution `F(m, n)`. -/// -/// This distribution is equivalent to the ratio of two normalised -/// chi-squared distributions, that is, `F(m,n) = (χ²(m)/m) / -/// (χ²(n)/n)`. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct FisherF { - numer: ChiSquared, - denom: ChiSquared, - // denom_dof / numer_dof so that this can just be a straight - // multiplication, rather than a division. - dof_ratio: f64, -} - -impl FisherF { - /// Create a new `FisherF` distribution, with the given - /// parameter. Panics if either `m` or `n` are not positive. - pub fn new(m: f64, n: f64) -> FisherF { - assert!(m > 0.0, "FisherF::new called with `m < 0`"); - assert!(n > 0.0, "FisherF::new called with `n < 0`"); - - FisherF { - numer: ChiSquared::new(m), - denom: ChiSquared::new(n), - dof_ratio: n / m, - } - } -} -impl Distribution for FisherF { - fn sample(&self, rng: &mut R) -> f64 { - self.numer.sample(rng) / self.denom.sample(rng) * self.dof_ratio - } -} - -/// The Student t distribution, `t(nu)`, where `nu` is the degrees of -/// freedom. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct StudentT { - chi: ChiSquared, - dof: f64, -} - -impl StudentT { - /// Create a new Student t distribution with `n` degrees of - /// freedom. Panics if `n <= 0`. - pub fn new(n: f64) -> StudentT { - assert!(n > 0.0, "StudentT::new called with `n <= 0`"); - StudentT { - chi: ChiSquared::new(n), - dof: n, - } - } -} -impl Distribution for StudentT { - fn sample(&self, rng: &mut R) -> f64 { - let norm = rng.sample(StandardNormal); - norm * (self.dof / self.chi.sample(rng)).sqrt() - } -} - -/// The Beta distribution with shape parameters `alpha` and `beta`. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Beta { - gamma_a: Gamma, - gamma_b: Gamma, -} - -impl Beta { - /// Construct an object representing the `Beta(alpha, beta)` - /// distribution. - /// - /// Panics if `shape <= 0` or `scale <= 0`. - pub fn new(alpha: f64, beta: f64) -> Beta { - assert!((alpha > 0.) & (beta > 0.)); - Beta { - gamma_a: Gamma::new(alpha, 1.), - gamma_b: Gamma::new(beta, 1.), - } - } -} - -impl Distribution for Beta { - fn sample(&self, rng: &mut R) -> f64 { - let x = self.gamma_a.sample(rng); - let y = self.gamma_b.sample(rng); - x / (x + y) - } -} - -#[cfg(test)] -mod test { - use super::{Beta, ChiSquared, FisherF, StudentT}; - use crate::distributions::Distribution; - - const N: u32 = 100; - - #[test] - fn test_chi_squared_one() { - let chi = ChiSquared::new(1.0); - let mut rng = crate::test::rng(201); - for _ in 0..N { - chi.sample(&mut rng); - } - } - #[test] - fn test_chi_squared_small() { - let chi = ChiSquared::new(0.5); - let mut rng = crate::test::rng(202); - for _ in 0..N { - chi.sample(&mut rng); - } - } - #[test] - fn test_chi_squared_large() { - let chi = ChiSquared::new(30.0); - let mut rng = crate::test::rng(203); - for _ in 0..N { - chi.sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_chi_squared_invalid_dof() { - ChiSquared::new(-1.0); - } - - #[test] - fn test_f() { - let f = FisherF::new(2.0, 32.0); - let mut rng = crate::test::rng(204); - for _ in 0..N { - f.sample(&mut rng); - } - } - - #[test] - fn test_t() { - let t = StudentT::new(11.0); - let mut rng = crate::test::rng(205); - for _ in 0..N { - t.sample(&mut rng); - } - } - - #[test] - fn test_beta() { - let beta = Beta::new(1.0, 2.0); - let mut rng = crate::test::rng(201); - for _ in 0..N { - beta.sample(&mut rng); - } - } - - #[test] - #[should_panic] - fn test_beta_invalid_dof() { - Beta::new(0., 0.); - } -} diff --git a/src/distributions/mod.rs b/src/distributions/mod.rs index e7865c27b90..7b3ea82c69a 100644 --- a/src/distributions/mod.rs +++ b/src/distributions/mod.rs @@ -103,58 +103,8 @@ pub use self::other::Alphanumeric; #[cfg(feature = "alloc")] pub use self::weighted::{WeightedError, WeightedIndex}; -// The following are all deprecated after being moved to rand_distr -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::binomial::Binomial; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::cauchy::Cauchy; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::dirichlet::Dirichlet; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::exponential::{Exp, Exp1}; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::gamma::{Beta, ChiSquared, FisherF, Gamma, StudentT}; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::normal::{LogNormal, Normal, StandardNormal}; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::pareto::Pareto; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::poisson::Poisson; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::triangular::Triangular; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::unit_circle::UnitCircle; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::unit_sphere::UnitSphereSurface; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::weibull::Weibull; - mod bernoulli; -#[cfg(feature = "std")] mod binomial; -#[cfg(feature = "std")] mod cauchy; -#[cfg(feature = "std")] mod dirichlet; -#[cfg(feature = "std")] mod exponential; -#[cfg(feature = "std")] mod gamma; -#[cfg(feature = "std")] mod normal; -#[cfg(feature = "std")] mod pareto; -#[cfg(feature = "std")] mod poisson; -#[cfg(feature = "std")] mod triangular; pub mod uniform; -#[cfg(feature = "std")] mod unit_circle; -#[cfg(feature = "std")] mod unit_sphere; -#[cfg(feature = "std")] mod weibull; #[cfg(feature = "alloc")] pub mod weighted; mod float; @@ -165,7 +115,6 @@ pub mod hidden_export { mod integer; mod other; mod utils; -#[cfg(feature = "std")] mod ziggurat_tables; /// Types (distributions) that can be used to create a random instance of `T`. /// diff --git a/src/distributions/normal.rs b/src/distributions/normal.rs deleted file mode 100644 index ec62fa9abe9..00000000000 --- a/src/distributions/normal.rs +++ /dev/null @@ -1,177 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2013 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The normal and derived distributions. -#![allow(deprecated)] - -use crate::distributions::utils::ziggurat; -use crate::distributions::{ziggurat_tables, Distribution, Open01}; -use crate::Rng; - -/// 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. -/// -/// See `Normal` for the general normal distribution. -/// -/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method. -/// -/// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to -/// Generate Normal Random Samples*]( -/// https://www.doornik.com/research/ziggurat.pdf). -/// Nuffield College, Oxford -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct StandardNormal; - -impl Distribution for StandardNormal { - fn sample(&self, rng: &mut R) -> f64 { - #[inline] - fn pdf(x: f64) -> f64 { - (-x * x / 2.0).exp() - } - #[inline] - fn zero_case(rng: &mut R, u: f64) -> f64 { - // compute a random number in the tail by hand - - // strange initial conditions, because the loop is not - // do-while, so the condition should be true on the first - // run, they get overwritten anyway (0 < 1, so these are - // good). - let mut x = 1.0f64; - let mut y = 0.0f64; - - while -2.0 * y < x * x { - let x_: f64 = rng.sample(Open01); - let y_: f64 = rng.sample(Open01); - - x = x_.ln() / ziggurat_tables::ZIG_NORM_R; - y = y_.ln(); - } - - if u < 0.0 { - x - ziggurat_tables::ZIG_NORM_R - } else { - ziggurat_tables::ZIG_NORM_R - x - } - } - - ziggurat( - rng, - true, // this is symmetric - &ziggurat_tables::ZIG_NORM_X, - &ziggurat_tables::ZIG_NORM_F, - pdf, - zero_case, - ) - } -} - -/// The normal distribution `N(mean, std_dev**2)`. -/// -/// This uses the ZIGNOR variant of the Ziggurat method, see [`StandardNormal`] -/// for more details. -/// -/// Note that [`StandardNormal`] is an optimised implementation for mean 0, and -/// standard deviation 1. -/// -/// [`StandardNormal`]: crate::distributions::StandardNormal -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Normal { - mean: f64, - std_dev: f64, -} - -impl Normal { - /// Construct a new `Normal` distribution with the given mean and - /// standard deviation. - /// - /// # Panics - /// - /// Panics if `std_dev < 0`. - #[inline] - pub fn new(mean: f64, std_dev: f64) -> Normal { - assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0"); - Normal { mean, std_dev } - } -} -impl Distribution for Normal { - fn sample(&self, rng: &mut R) -> f64 { - let n = rng.sample(StandardNormal); - self.mean + self.std_dev * n - } -} - - -/// The log-normal distribution `ln N(mean, std_dev**2)`. -/// -/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, std_dev**2)` -/// distributed. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct LogNormal { - norm: Normal, -} - -impl LogNormal { - /// Construct a new `LogNormal` distribution with the given mean - /// and standard deviation. - /// - /// # Panics - /// - /// Panics if `std_dev < 0`. - #[inline] - pub fn new(mean: f64, std_dev: f64) -> LogNormal { - assert!(std_dev >= 0.0, "LogNormal::new called with `std_dev` < 0"); - LogNormal { - norm: Normal::new(mean, std_dev), - } - } -} -impl Distribution for LogNormal { - fn sample(&self, rng: &mut R) -> f64 { - self.norm.sample(rng).exp() - } -} - -#[cfg(test)] -mod tests { - use super::{LogNormal, Normal}; - use crate::distributions::Distribution; - - #[test] - fn test_normal() { - let norm = Normal::new(10.0, 10.0); - let mut rng = crate::test::rng(210); - for _ in 0..1000 { - norm.sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_normal_invalid_sd() { - Normal::new(10.0, -1.0); - } - - - #[test] - fn test_log_normal() { - let lnorm = LogNormal::new(10.0, 10.0); - let mut rng = crate::test::rng(211); - for _ in 0..1000 { - lnorm.sample(&mut rng); - } - } - #[test] - #[should_panic] - fn test_log_normal_invalid_sd() { - LogNormal::new(10.0, -1.0); - } -} diff --git a/src/distributions/pareto.rs b/src/distributions/pareto.rs deleted file mode 100644 index ac5473b8c84..00000000000 --- a/src/distributions/pareto.rs +++ /dev/null @@ -1,70 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The Pareto distribution. -#![allow(deprecated)] - -use crate::distributions::{Distribution, OpenClosed01}; -use crate::Rng; - -/// Samples floating-point numbers according to the Pareto distribution -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Pareto { - scale: f64, - inv_neg_shape: f64, -} - -impl Pareto { - /// Construct a new Pareto distribution with given `scale` and `shape`. - /// - /// In the literature, `scale` is commonly written as xm or k and - /// `shape` is often written as α. - /// - /// # Panics - /// - /// `scale` and `shape` have to be non-zero and positive. - pub fn new(scale: f64, shape: f64) -> Pareto { - assert!((scale > 0.) & (shape > 0.)); - Pareto { - scale, - inv_neg_shape: -1.0 / shape, - } - } -} - -impl Distribution for Pareto { - fn sample(&self, rng: &mut R) -> f64 { - let u: f64 = rng.sample(OpenClosed01); - self.scale * u.powf(self.inv_neg_shape) - } -} - -#[cfg(test)] -mod tests { - use super::Pareto; - use crate::distributions::Distribution; - - #[test] - #[should_panic] - fn invalid() { - Pareto::new(0., 0.); - } - - #[test] - fn sample() { - let scale = 1.0; - let shape = 2.0; - let d = Pareto::new(scale, shape); - let mut rng = crate::test::rng(1); - for _ in 0..1000 { - let r = d.sample(&mut rng); - assert!(r >= scale); - } - } -} diff --git a/src/distributions/poisson.rs b/src/distributions/poisson.rs deleted file mode 100644 index ce94d7542b3..00000000000 --- a/src/distributions/poisson.rs +++ /dev/null @@ -1,151 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2016-2017 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The Poisson distribution. -#![allow(deprecated)] - -use crate::distributions::utils::log_gamma; -use crate::distributions::{Cauchy, Distribution}; -use crate::Rng; - -/// The Poisson distribution `Poisson(lambda)`. -/// -/// This distribution has a density function: -/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Poisson { - lambda: f64, - // precalculated values - exp_lambda: f64, - log_lambda: f64, - sqrt_2lambda: f64, - magic_val: f64, -} - -impl Poisson { - /// Construct a new `Poisson` with the given shape parameter - /// `lambda`. Panics if `lambda <= 0`. - pub fn new(lambda: f64) -> Poisson { - assert!(lambda > 0.0, "Poisson::new called with lambda <= 0"); - let log_lambda = lambda.ln(); - Poisson { - lambda, - exp_lambda: (-lambda).exp(), - log_lambda, - sqrt_2lambda: (2.0 * lambda).sqrt(), - magic_val: lambda * log_lambda - log_gamma(1.0 + lambda), - } - } -} - -impl Distribution for Poisson { - fn sample(&self, rng: &mut R) -> u64 { - // using the algorithm from Numerical Recipes in C - - // for low expected values use the Knuth method - if self.lambda < 12.0 { - let mut result = 0; - let mut p = 1.0; - while p > self.exp_lambda { - p *= rng.gen::(); - result += 1; - } - result - 1 - } - // high expected values - rejection method - else { - let mut int_result: u64; - - // we use the Cauchy distribution as the comparison distribution - // f(x) ~ 1/(1+x^2) - let cauchy = Cauchy::new(0.0, 1.0); - - loop { - let mut result; - let mut comp_dev; - - loop { - // draw from the Cauchy distribution - comp_dev = rng.sample(cauchy); - // shift the peak of the comparison ditribution - result = self.sqrt_2lambda * comp_dev + self.lambda; - // repeat the drawing until we are in the range of possible values - if result >= 0.0 { - break; - } - } - // now the result is a random variable greater than 0 with Cauchy distribution - // the result should be an integer value - result = result.floor(); - int_result = result as u64; - - // this is the ratio of the Poisson distribution to the comparison distribution - // the magic value scales the distribution function to a range of approximately 0-1 - // since it is not exact, we multiply the ratio by 0.9 to avoid ratios greater than 1 - // this doesn't change the resulting distribution, only increases the rate of failed drawings - let check = 0.9 - * (1.0 + comp_dev * comp_dev) - * (result * self.log_lambda - log_gamma(1.0 + result) - self.magic_val).exp(); - - // check with uniform random value - if below the threshold, we are within the target distribution - if rng.gen::() <= check { - break; - } - } - int_result - } - } -} - -#[cfg(test)] -mod test { - use super::Poisson; - use crate::distributions::Distribution; - - #[test] - #[cfg_attr(miri, ignore)] // Miri is too slow - fn test_poisson_10() { - let poisson = Poisson::new(10.0); - let mut rng = crate::test::rng(123); - let mut sum = 0; - for _ in 0..1000 { - sum += poisson.sample(&mut rng); - } - let avg = (sum as f64) / 1000.0; - println!("Poisson average: {}", avg); - assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough - } - - #[test] - fn test_poisson_15() { - // Take the 'high expected values' path - let poisson = Poisson::new(15.0); - let mut rng = crate::test::rng(123); - let mut sum = 0; - for _ in 0..1000 { - sum += poisson.sample(&mut rng); - } - let avg = (sum as f64) / 1000.0; - println!("Poisson average: {}", avg); - assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough - } - - #[test] - #[should_panic] - fn test_poisson_invalid_lambda_zero() { - Poisson::new(0.0); - } - - #[test] - #[should_panic] - fn test_poisson_invalid_lambda_neg() { - Poisson::new(-10.0); - } -} diff --git a/src/distributions/triangular.rs b/src/distributions/triangular.rs deleted file mode 100644 index 37be19867e8..00000000000 --- a/src/distributions/triangular.rs +++ /dev/null @@ -1,83 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The triangular distribution. -#![allow(deprecated)] - -use crate::distributions::{Distribution, Standard}; -use crate::Rng; - -/// The triangular distribution. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Triangular { - min: f64, - max: f64, - mode: f64, -} - -impl Triangular { - /// Construct a new `Triangular` with minimum `min`, maximum `max` and mode - /// `mode`. - /// - /// # Panics - /// - /// If `max < mode`, `mode < max` or `max == min`. - #[inline] - pub fn new(min: f64, max: f64, mode: f64) -> Triangular { - assert!(max >= mode); - assert!(mode >= min); - assert!(max != min); - Triangular { min, max, mode } - } -} - -impl Distribution for Triangular { - #[inline] - fn sample(&self, rng: &mut R) -> f64 { - let f: f64 = rng.sample(Standard); - let diff_mode_min = self.mode - self.min; - let diff_max_min = self.max - self.min; - if f * diff_max_min < diff_mode_min { - self.min + (f * diff_max_min * diff_mode_min).sqrt() - } else { - self.max - ((1. - f) * diff_max_min * (self.max - self.mode)).sqrt() - } - } -} - -#[cfg(test)] -mod test { - use super::Triangular; - use crate::distributions::Distribution; - - #[test] - fn test_new() { - for &(min, max, mode) in &[ - (-1., 1., 0.), - (1., 2., 1.), - (5., 25., 25.), - (1e-5, 1e5, 1e-3), - (0., 1., 0.9), - (-4., -0.5, -2.), - (-13.039, 8.41, 1.17), - ] { - println!("{} {} {}", min, max, mode); - let _ = Triangular::new(min, max, mode); - } - } - - #[test] - fn test_sample() { - let norm = Triangular::new(0., 1., 0.5); - let mut rng = crate::test::rng(1); - for _ in 0..1000 { - norm.sample(&mut rng); - } - } -} diff --git a/src/distributions/unit_circle.rs b/src/distributions/unit_circle.rs deleted file mode 100644 index 37885d8eb8e..00000000000 --- a/src/distributions/unit_circle.rs +++ /dev/null @@ -1,102 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -#![allow(deprecated)] -#![allow(clippy::all)] - -use crate::distributions::{Distribution, Uniform}; -use crate::Rng; - -/// Samples uniformly from the edge of the unit circle in two dimensions. -/// -/// Implemented via a method by von Neumann[^1]. -/// -/// [^1]: von Neumann, J. (1951) [*Various Techniques Used in Connection with -/// Random Digits.*](https://mcnp.lanl.gov/pdf_files/nbs_vonneumann.pdf) -/// NBS Appl. Math. Ser., No. 12. Washington, DC: U.S. Government Printing -/// Office, pp. 36-38. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct UnitCircle; - -impl UnitCircle { - /// Construct a new `UnitCircle` distribution. - #[inline] - pub fn new() -> UnitCircle { - UnitCircle - } -} - -impl Distribution<[f64; 2]> for UnitCircle { - #[inline] - fn sample(&self, rng: &mut R) -> [f64; 2] { - let uniform = Uniform::new(-1., 1.); - let mut x1; - let mut x2; - let mut sum; - loop { - x1 = uniform.sample(rng); - x2 = uniform.sample(rng); - sum = x1 * x1 + x2 * x2; - if sum < 1. { - break; - } - } - let diff = x1 * x1 - x2 * x2; - [diff / sum, 2. * x1 * x2 / sum] - } -} - -#[cfg(test)] -mod tests { - use super::UnitCircle; - use crate::distributions::Distribution; - - /// Assert that two numbers are almost equal to each other. - /// - /// On panic, this macro will print the values of the expressions with their - /// debug representations. - macro_rules! assert_almost_eq { - ($a:expr, $b:expr, $prec:expr) => { - let diff = ($a - $b).abs(); - if diff > $prec { - panic!(format!( - "assertion failed: `abs(left - right) = {:.1e} < {:e}`, \ - (left: `{}`, right: `{}`)", - diff, $prec, $a, $b - )); - } - }; - } - - #[test] - fn norm() { - let mut rng = crate::test::rng(1); - let dist = UnitCircle::new(); - for _ in 0..1000 { - let x = dist.sample(&mut rng); - assert_almost_eq!(x[0] * x[0] + x[1] * x[1], 1., 1e-15); - } - } - - #[test] - fn value_stability() { - let mut rng = crate::test::rng(2); - let expected = [ - [-0.9965658683520504, -0.08280380447614634], - [-0.9790853270389644, -0.20345004884984505], - [-0.8449189758898707, 0.5348943112253227], - ]; - let samples = [ - UnitCircle.sample(&mut rng), - UnitCircle.sample(&mut rng), - UnitCircle.sample(&mut rng), - ]; - assert_eq!(samples, expected); - } -} diff --git a/src/distributions/unit_sphere.rs b/src/distributions/unit_sphere.rs deleted file mode 100644 index 5b8c8ad55f8..00000000000 --- a/src/distributions/unit_sphere.rs +++ /dev/null @@ -1,97 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -#![allow(deprecated)] -#![allow(clippy::all)] - -use crate::distributions::{Distribution, Uniform}; -use crate::Rng; - -/// Samples uniformly from the surface of the unit sphere in three dimensions. -/// -/// Implemented via a method by Marsaglia[^1]. -/// -/// [^1]: Marsaglia, George (1972). [*Choosing a Point from the Surface of a -/// Sphere.*](https://doi.org/10.1214/aoms/1177692644) -/// Ann. Math. Statist. 43, no. 2, 645--646. -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct UnitSphereSurface; - -impl UnitSphereSurface { - /// Construct a new `UnitSphereSurface` distribution. - #[inline] - pub fn new() -> UnitSphereSurface { - UnitSphereSurface - } -} - -impl Distribution<[f64; 3]> for UnitSphereSurface { - #[inline] - fn sample(&self, rng: &mut R) -> [f64; 3] { - let uniform = Uniform::new(-1., 1.); - loop { - let (x1, x2) = (uniform.sample(rng), uniform.sample(rng)); - let sum = x1 * x1 + x2 * x2; - if sum >= 1. { - continue; - } - let factor = 2. * (1.0_f64 - sum).sqrt(); - return [x1 * factor, x2 * factor, 1. - 2. * sum]; - } - } -} - -#[cfg(test)] -mod tests { - use super::UnitSphereSurface; - use crate::distributions::Distribution; - - /// Assert that two numbers are almost equal to each other. - /// - /// On panic, this macro will print the values of the expressions with their - /// debug representations. - macro_rules! assert_almost_eq { - ($a:expr, $b:expr, $prec:expr) => { - let diff = ($a - $b).abs(); - if diff > $prec { - panic!(format!( - "assertion failed: `abs(left - right) = {:.1e} < {:e}`, \ - (left: `{}`, right: `{}`)", - diff, $prec, $a, $b - )); - } - }; - } - - #[test] - fn norm() { - let mut rng = crate::test::rng(1); - let dist = UnitSphereSurface::new(); - for _ in 0..1000 { - let x = dist.sample(&mut rng); - assert_almost_eq!(x[0] * x[0] + x[1] * x[1] + x[2] * x[2], 1., 1e-15); - } - } - - #[test] - fn value_stability() { - let mut rng = crate::test::rng(2); - let expected = [ - [0.03247542860231647, -0.7830477442152738, 0.6211131755296027], - [-0.09978440840914075, 0.9706650829833128, -0.21875184231323952], - [0.2735582468624679, 0.9435374242279655, -0.1868234852870203], - ]; - let samples = [ - UnitSphereSurface.sample(&mut rng), - UnitSphereSurface.sample(&mut rng), - UnitSphereSurface.sample(&mut rng), - ]; - assert_eq!(samples, expected); - } -} diff --git a/src/distributions/utils.rs b/src/distributions/utils.rs index 2d36b022658..057d7813c38 100644 --- a/src/distributions/utils.rs +++ b/src/distributions/utils.rs @@ -8,8 +8,6 @@ //! Math helper functions -#[cfg(feature = "std")] use crate::distributions::ziggurat_tables; -#[cfg(feature = "std")] use crate::Rng; #[cfg(feature = "simd_support")] use packed_simd::*; @@ -435,113 +433,3 @@ macro_rules! simd_impl { #[cfg(feature="simd_support")] simd_impl! { f64x2, f64, m64x2, u64x2 } #[cfg(feature="simd_support")] simd_impl! { f64x4, f64, m64x4, u64x4 } #[cfg(feature="simd_support")] simd_impl! { f64x8, f64, m64x8, u64x8 } - -/// Calculates ln(gamma(x)) (natural logarithm of the gamma -/// function) using the Lanczos approximation. -/// -/// The approximation expresses the gamma function as: -/// `gamma(z+1) = sqrt(2*pi)*(z+g+0.5)^(z+0.5)*exp(-z-g-0.5)*Ag(z)` -/// `g` is an arbitrary constant; we use the approximation with `g=5`. -/// -/// Noting that `gamma(z+1) = z*gamma(z)` and applying `ln` to both sides: -/// `ln(gamma(z)) = (z+0.5)*ln(z+g+0.5)-(z+g+0.5) + ln(sqrt(2*pi)*Ag(z)/z)` -/// -/// `Ag(z)` is an infinite series with coefficients that can be calculated -/// ahead of time - we use just the first 6 terms, which is good enough -/// for most purposes. -#[cfg(feature = "std")] -pub fn log_gamma(x: f64) -> f64 { - // precalculated 6 coefficients for the first 6 terms of the series - let coefficients: [f64; 6] = [ - 76.18009172947146, - -86.50532032941677, - 24.01409824083091, - -1.231739572450155, - 0.1208650973866179e-2, - -0.5395239384953e-5, - ]; - - // (x+0.5)*ln(x+g+0.5)-(x+g+0.5) - let tmp = x + 5.5; - let log = (x + 0.5) * tmp.ln() - tmp; - - // the first few terms of the series for Ag(x) - let mut a = 1.000000000190015; - let mut denom = x; - for coeff in &coefficients { - denom += 1.0; - a += coeff / denom; - } - - // get everything together - // a is Ag(x) - // 2.5066... is sqrt(2pi) - log + (2.5066282746310005 * a / x).ln() -} - -/// Sample a random number using the Ziggurat method (specifically the -/// ZIGNOR variant from Doornik 2005). Most of the arguments are -/// directly from the paper: -/// -/// * `rng`: source of randomness -/// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0. -/// * `X`: the $x_i$ abscissae. -/// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$) -/// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$ -/// * `pdf`: the probability density function -/// * `zero_case`: manual sampling from the tail when we chose the -/// bottom box (i.e. i == 0) - -// the perf improvement (25-50%) is definitely worth the extra code -// size from force-inlining. -#[cfg(feature = "std")] -#[inline(always)] -pub fn ziggurat( - rng: &mut R, - symmetric: bool, - x_tab: ziggurat_tables::ZigTable, - f_tab: ziggurat_tables::ZigTable, - mut pdf: P, - mut zero_case: Z -) -> f64 -where - P: FnMut(f64) -> f64, - Z: FnMut(&mut R, f64) -> f64, -{ - use crate::distributions::float::IntoFloat; - loop { - // As an optimisation we re-implement the conversion to a f64. - // From the remaining 12 most significant bits we use 8 to construct `i`. - // This saves us generating a whole extra random number, while the added - // precision of using 64 bits for f64 does not buy us much. - let bits = rng.next_u64(); - let i = bits as usize & 0xff; - - let u = if symmetric { - // Convert to a value in the range [2,4) and substract to get [-1,1) - // We can't convert to an open range directly, that would require - // substracting `3.0 - EPSILON`, which is not representable. - // It is possible with an extra step, but an open range does not - // seem neccesary for the ziggurat algorithm anyway. - (bits >> 12).into_float_with_exponent(1) - 3.0 - } else { - // Convert to a value in the range [1,2) and substract to get (0,1) - (bits >> 12).into_float_with_exponent(0) - (1.0 - ::core::f64::EPSILON / 2.0) - }; - let x = u * x_tab[i]; - - let test_x = if symmetric { x.abs() } else { x }; - - // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i]) - if test_x < x_tab[i + 1] { - return x; - } - if i == 0 { - return zero_case(rng, u); - } - // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1 - if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen::() < pdf(x) { - return x; - } - } -} diff --git a/src/distributions/weibull.rs b/src/distributions/weibull.rs deleted file mode 100644 index ffbc93b0156..00000000000 --- a/src/distributions/weibull.rs +++ /dev/null @@ -1,67 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The Weibull distribution. -#![allow(deprecated)] - -use crate::distributions::{Distribution, OpenClosed01}; -use crate::Rng; - -/// Samples floating-point numbers according to the Weibull distribution -#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")] -#[derive(Clone, Copy, Debug)] -pub struct Weibull { - inv_shape: f64, - scale: f64, -} - -impl Weibull { - /// Construct a new `Weibull` distribution with given `scale` and `shape`. - /// - /// # Panics - /// - /// `scale` and `shape` have to be non-zero and positive. - pub fn new(scale: f64, shape: f64) -> Weibull { - assert!((scale > 0.) & (shape > 0.)); - Weibull { - inv_shape: 1. / shape, - scale, - } - } -} - -impl Distribution for Weibull { - fn sample(&self, rng: &mut R) -> f64 { - let x: f64 = rng.sample(OpenClosed01); - self.scale * (-x.ln()).powf(self.inv_shape) - } -} - -#[cfg(test)] -mod tests { - use super::Weibull; - use crate::distributions::Distribution; - - #[test] - #[should_panic] - fn invalid() { - Weibull::new(0., 0.); - } - - #[test] - fn sample() { - let scale = 1.0; - let shape = 2.0; - let d = Weibull::new(scale, shape); - let mut rng = crate::test::rng(1); - for _ in 0..1000 { - let r = d.sample(&mut rng); - assert!(r >= 0.); - } - } -} diff --git a/src/distributions/ziggurat_tables.rs b/src/distributions/ziggurat_tables.rs deleted file mode 100644 index f830a601bdd..00000000000 --- a/src/distributions/ziggurat_tables.rs +++ /dev/null @@ -1,283 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// Copyright 2013 The Rust Project Developers. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -// Tables for distributions which are sampled using the ziggurat -// algorithm. Autogenerated by `ziggurat_tables.py`. - -pub type ZigTable = &'static [f64; 257]; -pub const ZIG_NORM_R: f64 = 3.654152885361008796; -#[rustfmt::skip] -pub static ZIG_NORM_X: [f64; 257] = - [3.910757959537090045, 3.654152885361008796, 3.449278298560964462, 3.320244733839166074, - 3.224575052047029100, 3.147889289517149969, 3.083526132001233044, 3.027837791768635434, - 2.978603279880844834, 2.934366867207854224, 2.894121053612348060, 2.857138730872132548, - 2.822877396825325125, 2.790921174000785765, 2.760944005278822555, 2.732685359042827056, - 2.705933656121858100, 2.680514643284522158, 2.656283037575502437, 2.633116393630324570, - 2.610910518487548515, 2.589575986706995181, 2.569035452680536569, 2.549221550323460761, - 2.530075232158516929, 2.511544441625342294, 2.493583041269680667, 2.476149939669143318, - 2.459208374333311298, 2.442725318198956774, 2.426670984935725972, 2.411018413899685520, - 2.395743119780480601, 2.380822795170626005, 2.366237056715818632, 2.351967227377659952, - 2.337996148795031370, 2.324308018869623016, 2.310888250599850036, 2.297723348901329565, - 2.284800802722946056, 2.272108990226823888, 2.259637095172217780, 2.247375032945807760, - 2.235313384928327984, 2.223443340090905718, 2.211756642882544366, 2.200245546609647995, - 2.188902771624720689, 2.177721467738641614, 2.166695180352645966, 2.155817819875063268, - 2.145083634046203613, 2.134487182844320152, 2.124023315687815661, 2.113687150684933957, - 2.103474055713146829, 2.093379631137050279, 2.083399693996551783, 2.073530263516978778, - 2.063767547809956415, 2.054107931648864849, 2.044547965215732788, 2.035084353727808715, - 2.025713947862032960, 2.016433734904371722, 2.007240830558684852, 1.998132471356564244, - 1.989106007615571325, 1.980158896898598364, 1.971288697931769640, 1.962493064942461896, - 1.953769742382734043, 1.945116560006753925, 1.936531428273758904, 1.928012334050718257, - 1.919557336591228847, 1.911164563769282232, 1.902832208548446369, 1.894558525668710081, - 1.886341828534776388, 1.878180486290977669, 1.870072921069236838, 1.862017605397632281, - 1.854013059758148119, 1.846057850283119750, 1.838150586580728607, 1.830289919680666566, - 1.822474540091783224, 1.814703175964167636, 1.806974591348693426, 1.799287584547580199, - 1.791640986550010028, 1.784033659547276329, 1.776464495522344977, 1.768932414909077933, - 1.761436365316706665, 1.753975320315455111, 1.746548278279492994, 1.739154261283669012, - 1.731792314050707216, 1.724461502945775715, 1.717160915015540690, 1.709889657069006086, - 1.702646854797613907, 1.695431651932238548, 1.688243209434858727, 1.681080704722823338, - 1.673943330923760353, 1.666830296159286684, 1.659740822855789499, 1.652674147080648526, - 1.645629517902360339, 1.638606196773111146, 1.631603456932422036, 1.624620582830568427, - 1.617656869570534228, 1.610711622367333673, 1.603784156023583041, 1.596873794420261339, - 1.589979870021648534, 1.583101723393471438, 1.576238702733332886, 1.569390163412534456, - 1.562555467528439657, 1.555733983466554893, 1.548925085471535512, 1.542128153226347553, - 1.535342571438843118, 1.528567729435024614, 1.521803020758293101, 1.515047842773992404, - 1.508301596278571965, 1.501563685112706548, 1.494833515777718391, 1.488110497054654369, - 1.481394039625375747, 1.474683555695025516, 1.467978458615230908, 1.461278162507407830, - 1.454582081885523293, 1.447889631277669675, 1.441200224845798017, 1.434513276002946425, - 1.427828197027290358, 1.421144398672323117, 1.414461289772464658, 1.407778276843371534, - 1.401094763676202559, 1.394410150925071257, 1.387723835686884621, 1.381035211072741964, - 1.374343665770030531, 1.367648583594317957, 1.360949343030101844, 1.354245316759430606, - 1.347535871177359290, 1.340820365893152122, 1.334098153216083604, 1.327368577624624679, - 1.320630975217730096, 1.313884673146868964, 1.307128989027353860, 1.300363230327433728, - 1.293586693733517645, 1.286798664489786415, 1.279998415710333237, 1.273185207661843732, - 1.266358287014688333, 1.259516886060144225, 1.252660221891297887, 1.245787495544997903, - 1.238897891102027415, 1.231990574742445110, 1.225064693752808020, 1.218119375481726552, - 1.211153726239911244, 1.204166830140560140, 1.197157747875585931, 1.190125515422801650, - 1.183069142678760732, 1.175987612011489825, 1.168879876726833800, 1.161744859441574240, - 1.154581450355851802, 1.147388505416733873, 1.140164844363995789, 1.132909248648336975, - 1.125620459211294389, 1.118297174115062909, 1.110938046009249502, 1.103541679420268151, - 1.096106627847603487, 1.088631390649514197, 1.081114409698889389, 1.073554065787871714, - 1.065948674757506653, 1.058296483326006454, 1.050595664586207123, 1.042844313139370538, - 1.035040439828605274, 1.027181966030751292, 1.019266717460529215, 1.011292417434978441, - 1.003256679539591412, 0.995156999629943084, 0.986990747093846266, 0.978755155288937750, - 0.970447311058864615, 0.962064143217605250, 0.953602409875572654, 0.945058684462571130, - 0.936429340280896860, 0.927710533396234771, 0.918898183643734989, 0.909987953490768997, - 0.900975224455174528, 0.891855070726792376, 0.882622229578910122, 0.873271068082494550, - 0.863795545546826915, 0.854189171001560554, 0.844444954902423661, 0.834555354079518752, - 0.824512208745288633, 0.814306670128064347, 0.803929116982664893, 0.793369058833152785, - 0.782615023299588763, 0.771654424216739354, 0.760473406422083165, 0.749056662009581653, - 0.737387211425838629, 0.725446140901303549, 0.713212285182022732, 0.700661841097584448, - 0.687767892786257717, 0.674499822827436479, 0.660822574234205984, 0.646695714884388928, - 0.632072236375024632, 0.616896989996235545, 0.601104617743940417, 0.584616766093722262, - 0.567338257040473026, 0.549151702313026790, 0.529909720646495108, 0.509423329585933393, - 0.487443966121754335, 0.463634336771763245, 0.437518402186662658, 0.408389134588000746, - 0.375121332850465727, 0.335737519180459465, 0.286174591747260509, 0.215241895913273806, - 0.000000000000000000]; -#[rustfmt::skip] -pub static ZIG_NORM_F: [f64; 257] = - [0.000477467764586655, 0.001260285930498598, 0.002609072746106363, 0.004037972593371872, - 0.005522403299264754, 0.007050875471392110, 0.008616582769422917, 0.010214971439731100, - 0.011842757857943104, 0.013497450601780807, 0.015177088307982072, 0.016880083152595839, - 0.018605121275783350, 0.020351096230109354, 0.022117062707379922, 0.023902203305873237, - 0.025705804008632656, 0.027527235669693315, 0.029365939758230111, 0.031221417192023690, - 0.033093219458688698, 0.034980941461833073, 0.036884215688691151, 0.038802707404656918, - 0.040736110656078753, 0.042684144916619378, 0.044646552251446536, 0.046623094902089664, - 0.048613553216035145, 0.050617723861121788, 0.052635418276973649, 0.054666461325077916, - 0.056710690106399467, 0.058767952921137984, 0.060838108349751806, 0.062921024437977854, - 0.065016577971470438, 0.067124653828023989, 0.069245144397250269, 0.071377949059141965, - 0.073522973714240991, 0.075680130359194964, 0.077849336702372207, 0.080030515814947509, - 0.082223595813495684, 0.084428509570654661, 0.086645194450867782, 0.088873592068594229, - 0.091113648066700734, 0.093365311913026619, 0.095628536713353335, 0.097903279039215627, - 0.100189498769172020, 0.102487158942306270, 0.104796225622867056, 0.107116667775072880, - 0.109448457147210021, 0.111791568164245583, 0.114145977828255210, 0.116511665626037014, - 0.118888613443345698, 0.121276805485235437, 0.123676228202051403, 0.126086870220650349, - 0.128508722280473636, 0.130941777174128166, 0.133386029692162844, 0.135841476571757352, - 0.138308116449064322, 0.140785949814968309, 0.143274978974047118, 0.145775208006537926, - 0.148286642733128721, 0.150809290682410169, 0.153343161060837674, 0.155888264725064563, - 0.158444614156520225, 0.161012223438117663, 0.163591108232982951, 0.166181285765110071, - 0.168782774801850333, 0.171395595638155623, 0.174019770082499359, 0.176655321444406654, - 0.179302274523530397, 0.181960655600216487, 0.184630492427504539, 0.187311814224516926, - 0.190004651671193070, 0.192709036904328807, 0.195425003514885592, 0.198152586546538112, - 0.200891822495431333, 0.203642749311121501, 0.206405406398679298, 0.209179834621935651, - 0.211966076307852941, 0.214764175252008499, 0.217574176725178370, 0.220396127481011589, - 0.223230075764789593, 0.226076071323264877, 0.228934165415577484, 0.231804410825248525, - 0.234686861873252689, 0.237581574432173676, 0.240488605941449107, 0.243408015423711988, - 0.246339863502238771, 0.249284212419516704, 0.252241126056943765, 0.255210669955677150, - 0.258192911338648023, 0.261187919133763713, 0.264195763998317568, 0.267216518344631837, - 0.270250256366959984, 0.273297054069675804, 0.276356989296781264, 0.279430141762765316, - 0.282516593084849388, 0.285616426816658109, 0.288729728483353931, 0.291856585618280984, - 0.294997087801162572, 0.298151326697901342, 0.301319396102034120, 0.304501391977896274, - 0.307697412505553769, 0.310907558127563710, 0.314131931597630143, 0.317370638031222396, - 0.320623784958230129, 0.323891482377732021, 0.327173842814958593, 0.330470981380537099, - 0.333783015832108509, 0.337110066638412809, 0.340452257045945450, 0.343809713148291340, - 0.347182563958251478, 0.350570941482881204, 0.353974980801569250, 0.357394820147290515, - 0.360830600991175754, 0.364282468130549597, 0.367750569780596226, 0.371235057669821344, - 0.374736087139491414, 0.378253817247238111, 0.381788410875031348, 0.385340034841733958, - 0.388908860020464597, 0.392495061461010764, 0.396098818517547080, 0.399720314981931668, - 0.403359739222868885, 0.407017284331247953, 0.410693148271983222, 0.414387534042706784, - 0.418100649839684591, 0.421832709231353298, 0.425583931339900579, 0.429354541031341519, - 0.433144769114574058, 0.436954852549929273, 0.440785034667769915, 0.444635565397727750, - 0.448506701509214067, 0.452398706863882505, 0.456311852680773566, 0.460246417814923481, - 0.464202689050278838, 0.468180961407822172, 0.472181538469883255, 0.476204732721683788, - 0.480250865911249714, 0.484320269428911598, 0.488413284707712059, 0.492530263646148658, - 0.496671569054796314, 0.500837575128482149, 0.505028667945828791, 0.509245245998136142, - 0.513487720749743026, 0.517756517232200619, 0.522052074674794864, 0.526374847174186700, - 0.530725304406193921, 0.535103932383019565, 0.539511234259544614, 0.543947731192649941, - 0.548413963257921133, 0.552910490428519918, 0.557437893621486324, 0.561996775817277916, - 0.566587763258951771, 0.571211506738074970, 0.575868682975210544, 0.580559996103683473, - 0.585286179266300333, 0.590047996335791969, 0.594846243770991268, 0.599681752622167719, - 0.604555390700549533, 0.609468064928895381, 0.614420723892076803, 0.619414360609039205, - 0.624450015550274240, 0.629528779928128279, 0.634651799290960050, 0.639820277456438991, - 0.645035480824251883, 0.650298743114294586, 0.655611470583224665, 0.660975147780241357, - 0.666391343912380640, 0.671861719900766374, 0.677388036222513090, 0.682972161648791376, - 0.688616083008527058, 0.694321916130032579, 0.700091918140490099, 0.705928501336797409, - 0.711834248882358467, 0.717811932634901395, 0.723864533472881599, 0.729995264565802437, - 0.736207598131266683, 0.742505296344636245, 0.748892447223726720, 0.755373506511754500, - 0.761953346841546475, 0.768637315803334831, 0.775431304986138326, 0.782341832659861902, - 0.789376143571198563, 0.796542330428254619, 0.803849483176389490, 0.811307874318219935, - 0.818929191609414797, 0.826726833952094231, 0.834716292992930375, 0.842915653118441077, - 0.851346258465123684, 0.860033621203008636, 0.869008688043793165, 0.878309655816146839, - 0.887984660763399880, 0.898095921906304051, 0.908726440060562912, 0.919991505048360247, - 0.932060075968990209, 0.945198953453078028, 0.959879091812415930, 0.977101701282731328, - 1.000000000000000000]; -pub const ZIG_EXP_R: f64 = 7.697117470131050077; -#[rustfmt::skip] -pub static ZIG_EXP_X: [f64; 257] = - [8.697117470131052741, 7.697117470131050077, 6.941033629377212577, 6.478378493832569696, - 6.144164665772472667, 5.882144315795399869, 5.666410167454033697, 5.482890627526062488, - 5.323090505754398016, 5.181487281301500047, 5.054288489981304089, 4.938777085901250530, - 4.832939741025112035, 4.735242996601741083, 4.644491885420085175, 4.559737061707351380, - 4.480211746528421912, 4.405287693473573185, 4.334443680317273007, 4.267242480277365857, - 4.203313713735184365, 4.142340865664051464, 4.084051310408297830, 4.028208544647936762, - 3.974606066673788796, 3.923062500135489739, 3.873417670399509127, 3.825529418522336744, - 3.779270992411667862, 3.734528894039797375, 3.691201090237418825, 3.649195515760853770, - 3.608428813128909507, 3.568825265648337020, 3.530315889129343354, 3.492837654774059608, - 3.456332821132760191, 3.420748357251119920, 3.386035442460300970, 3.352149030900109405, - 3.319047470970748037, 3.286692171599068679, 3.255047308570449882, 3.224079565286264160, - 3.193757903212240290, 3.164053358025972873, 3.134938858084440394, 3.106389062339824481, - 3.078380215254090224, 3.050890016615455114, 3.023897504455676621, 2.997382949516130601, - 2.971327759921089662, 2.945714394895045718, 2.920526286512740821, 2.895747768600141825, - 2.871364012015536371, 2.847360965635188812, 2.823725302450035279, 2.800444370250737780, - 2.777506146439756574, 2.754899196562344610, 2.732612636194700073, 2.710636095867928752, - 2.688959688741803689, 2.667573980773266573, 2.646469963151809157, 2.625639026797788489, - 2.605072938740835564, 2.584763820214140750, 2.564704126316905253, 2.544886627111869970, - 2.525304390037828028, 2.505950763528594027, 2.486819361740209455, 2.467904050297364815, - 2.449198932978249754, 2.430698339264419694, 2.412396812688870629, 2.394289099921457886, - 2.376370140536140596, 2.358635057409337321, 2.341079147703034380, 2.323697874390196372, - 2.306486858283579799, 2.289441870532269441, 2.272558825553154804, 2.255833774367219213, - 2.239262898312909034, 2.222842503111036816, 2.206569013257663858, 2.190438966723220027, - 2.174449009937774679, 2.158595893043885994, 2.142876465399842001, 2.127287671317368289, - 2.111826546019042183, 2.096490211801715020, 2.081275874393225145, 2.066180819490575526, - 2.051202409468584786, 2.036338080248769611, 2.021585338318926173, 2.006941757894518563, - 1.992404978213576650, 1.977972700957360441, 1.963642687789548313, 1.949412758007184943, - 1.935280786297051359, 1.921244700591528076, 1.907302480018387536, 1.893452152939308242, - 1.879691795072211180, 1.866019527692827973, 1.852433515911175554, 1.838931967018879954, - 1.825513128903519799, 1.812175288526390649, 1.798916770460290859, 1.785735935484126014, - 1.772631179231305643, 1.759600930889074766, 1.746643651946074405, 1.733757834985571566, - 1.720942002521935299, 1.708194705878057773, 1.695514524101537912, 1.682900062917553896, - 1.670349953716452118, 1.657862852574172763, 1.645437439303723659, 1.633072416535991334, - 1.620766508828257901, 1.608518461798858379, 1.596327041286483395, 1.584191032532688892, - 1.572109239386229707, 1.560080483527888084, 1.548103603714513499, 1.536177455041032092, - 1.524300908219226258, 1.512472848872117082, 1.500692176842816750, 1.488957805516746058, - 1.477268661156133867, 1.465623682245745352, 1.454021818848793446, 1.442462031972012504, - 1.430943292938879674, 1.419464582769983219, 1.408024891569535697, 1.396623217917042137, - 1.385258568263121992, 1.373929956328490576, 1.362636402505086775, 1.351376933258335189, - 1.340150580529504643, 1.328956381137116560, 1.317793376176324749, 1.306660610415174117, - 1.295557131686601027, 1.284481990275012642, 1.273434238296241139, 1.262412929069615330, - 1.251417116480852521, 1.240445854334406572, 1.229498195693849105, 1.218573192208790124, - 1.207669893426761121, 1.196787346088403092, 1.185924593404202199, 1.175080674310911677, - 1.164254622705678921, 1.153445466655774743, 1.142652227581672841, 1.131873919411078511, - 1.121109547701330200, 1.110358108727411031, 1.099618588532597308, 1.088889961938546813, - 1.078171191511372307, 1.067461226479967662, 1.056759001602551429, 1.046063435977044209, - 1.035373431790528542, 1.024687873002617211, 1.014005623957096480, 1.003325527915696735, - 0.992646405507275897, 0.981967053085062602, 0.971286240983903260, 0.960602711668666509, - 0.949915177764075969, 0.939222319955262286, 0.928522784747210395, 0.917815182070044311, - 0.907098082715690257, 0.896370015589889935, 0.885629464761751528, 0.874874866291025066, - 0.864104604811004484, 0.853317009842373353, 0.842510351810368485, 0.831682837734273206, - 0.820832606554411814, 0.809957724057418282, 0.799056177355487174, 0.788125868869492430, - 0.777164609759129710, 0.766170112735434672, 0.755139984181982249, 0.744071715500508102, - 0.732962673584365398, 0.721810090308756203, 0.710611050909655040, 0.699362481103231959, - 0.688061132773747808, 0.676703568029522584, 0.665286141392677943, 0.653804979847664947, - 0.642255960424536365, 0.630634684933490286, 0.618936451394876075, 0.607156221620300030, - 0.595288584291502887, 0.583327712748769489, 0.571267316532588332, 0.559100585511540626, - 0.546820125163310577, 0.534417881237165604, 0.521885051592135052, 0.509211982443654398, - 0.496388045518671162, 0.483401491653461857, 0.470239275082169006, 0.456886840931420235, - 0.443327866073552401, 0.429543940225410703, 0.415514169600356364, 0.401214678896277765, - 0.386617977941119573, 0.371692145329917234, 0.356399760258393816, 0.340696481064849122, - 0.324529117016909452, 0.307832954674932158, 0.290527955491230394, 0.272513185478464703, - 0.253658363385912022, 0.233790483059674731, 0.212671510630966620, 0.189958689622431842, - 0.165127622564187282, 0.137304980940012589, 0.104838507565818778, 0.063852163815001570, - 0.000000000000000000]; -#[rustfmt::skip] -pub static ZIG_EXP_F: [f64; 257] = - [0.000167066692307963, 0.000454134353841497, 0.000967269282327174, 0.001536299780301573, - 0.002145967743718907, 0.002788798793574076, 0.003460264777836904, 0.004157295120833797, - 0.004877655983542396, 0.005619642207205489, 0.006381905937319183, 0.007163353183634991, - 0.007963077438017043, 0.008780314985808977, 0.009614413642502212, 0.010464810181029981, - 0.011331013597834600, 0.012212592426255378, 0.013109164931254991, 0.014020391403181943, - 0.014945968011691148, 0.015885621839973156, 0.016839106826039941, 0.017806200410911355, - 0.018786700744696024, 0.019780424338009740, 0.020787204072578114, 0.021806887504283581, - 0.022839335406385240, 0.023884420511558174, 0.024942026419731787, 0.026012046645134221, - 0.027094383780955803, 0.028188948763978646, 0.029295660224637411, 0.030414443910466622, - 0.031545232172893622, 0.032687963508959555, 0.033842582150874358, 0.035009037697397431, - 0.036187284781931443, 0.037377282772959382, 0.038578995503074871, 0.039792391023374139, - 0.041017441380414840, 0.042254122413316254, 0.043502413568888197, 0.044762297732943289, - 0.046033761076175184, 0.047316792913181561, 0.048611385573379504, 0.049917534282706379, - 0.051235237055126281, 0.052564494593071685, 0.053905310196046080, 0.055257689676697030, - 0.056621641283742870, 0.057997175631200659, 0.059384305633420280, 0.060783046445479660, - 0.062193415408541036, 0.063615431999807376, 0.065049117786753805, 0.066494496385339816, - 0.067951593421936643, 0.069420436498728783, 0.070901055162371843, 0.072393480875708752, - 0.073897746992364746, 0.075413888734058410, 0.076941943170480517, 0.078481949201606435, - 0.080033947542319905, 0.081597980709237419, 0.083174093009632397, 0.084762330532368146, - 0.086362741140756927, 0.087975374467270231, 0.089600281910032886, 0.091237516631040197, - 0.092887133556043569, 0.094549189376055873, 0.096223742550432825, 0.097910853311492213, - 0.099610583670637132, 0.101322997425953631, 0.103048160171257702, 0.104786139306570145, - 0.106537004050001632, 0.108300825451033755, 0.110077676405185357, 0.111867631670056283, - 0.113670767882744286, 0.115487163578633506, 0.117316899211555525, 0.119160057175327641, - 0.121016721826674792, 0.122886979509545108, 0.124770918580830933, 0.126668629437510671, - 0.128580204545228199, 0.130505738468330773, 0.132445327901387494, 0.134399071702213602, - 0.136367070926428829, 0.138349428863580176, 0.140346251074862399, 0.142357645432472146, - 0.144383722160634720, 0.146424593878344889, 0.148480375643866735, 0.150551185001039839, - 0.152637142027442801, 0.154738369384468027, 0.156854992369365148, 0.158987138969314129, - 0.161134939917591952, 0.163298528751901734, 0.165478041874935922, 0.167673618617250081, - 0.169885401302527550, 0.172113535315319977, 0.174358169171353411, 0.176619454590494829, - 0.178897546572478278, 0.181192603475496261, 0.183504787097767436, 0.185834262762197083, - 0.188181199404254262, 0.190545769663195363, 0.192928149976771296, 0.195328520679563189, - 0.197747066105098818, 0.200183974691911210, 0.202639439093708962, 0.205113656293837654, - 0.207606827724221982, 0.210119159388988230, 0.212650861992978224, 0.215202151075378628, - 0.217773247148700472, 0.220364375843359439, 0.222975768058120111, 0.225607660116683956, - 0.228260293930716618, 0.230933917169627356, 0.233628783437433291, 0.236345152457059560, - 0.239083290262449094, 0.241843469398877131, 0.244625969131892024, 0.247431075665327543, - 0.250259082368862240, 0.253110290015629402, 0.255985007030415324, 0.258883549749016173, - 0.261806242689362922, 0.264753418835062149, 0.267725419932044739, 0.270722596799059967, - 0.273745309652802915, 0.276793928448517301, 0.279868833236972869, 0.282970414538780746, - 0.286099073737076826, 0.289255223489677693, 0.292439288161892630, 0.295651704281261252, - 0.298892921015581847, 0.302163400675693528, 0.305463619244590256, 0.308794066934560185, - 0.312155248774179606, 0.315547685227128949, 0.318971912844957239, 0.322428484956089223, - 0.325917972393556354, 0.329440964264136438, 0.332998068761809096, 0.336589914028677717, - 0.340217149066780189, 0.343880444704502575, 0.347580494621637148, 0.351318016437483449, - 0.355093752866787626, 0.358908472948750001, 0.362762973354817997, 0.366658079781514379, - 0.370594648435146223, 0.374573567615902381, 0.378595759409581067, 0.382662181496010056, - 0.386773829084137932, 0.390931736984797384, 0.395136981833290435, 0.399390684475231350, - 0.403694012530530555, 0.408048183152032673, 0.412454465997161457, 0.416914186433003209, - 0.421428728997616908, 0.425999541143034677, 0.430628137288459167, 0.435316103215636907, - 0.440065100842354173, 0.444876873414548846, 0.449753251162755330, 0.454696157474615836, - 0.459707615642138023, 0.464789756250426511, 0.469944825283960310, 0.475175193037377708, - 0.480483363930454543, 0.485871987341885248, 0.491343869594032867, 0.496901987241549881, - 0.502549501841348056, 0.508289776410643213, 0.514126393814748894, 0.520063177368233931, - 0.526104213983620062, 0.532253880263043655, 0.538516872002862246, 0.544898237672440056, - 0.551403416540641733, 0.558038282262587892, 0.564809192912400615, 0.571723048664826150, - 0.578787358602845359, 0.586010318477268366, 0.593400901691733762, 0.600968966365232560, - 0.608725382079622346, 0.616682180915207878, 0.624852738703666200, 0.633251994214366398, - 0.641896716427266423, 0.650805833414571433, 0.660000841079000145, 0.669506316731925177, - 0.679350572264765806, 0.689566496117078431, 0.700192655082788606, 0.711274760805076456, - 0.722867659593572465, 0.735038092431424039, 0.747868621985195658, 0.761463388849896838, - 0.775956852040116218, 0.791527636972496285, 0.808421651523009044, 0.826993296643051101, - 0.847785500623990496, 0.871704332381204705, 0.900469929925747703, 0.938143680862176477, - 1.000000000000000000]; From ca755824acd69a564d5f842cf3c8ff8136245f17 Mon Sep 17 00:00:00 2001 From: Diggory Hardy Date: Mon, 9 Mar 2020 15:28:11 +0000 Subject: [PATCH 5/6] Remove deprecated EntropyRng --- src/rngs/entropy.rs | 76 --------------------------------------------- src/rngs/mod.rs | 5 --- 2 files changed, 81 deletions(-) delete mode 100644 src/rngs/entropy.rs diff --git a/src/rngs/entropy.rs b/src/rngs/entropy.rs deleted file mode 100644 index 9ad0d71e0cd..00000000000 --- a/src/rngs/entropy.rs +++ /dev/null @@ -1,76 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 or the MIT license -// , at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! Entropy generator, or wrapper around external generators - -#![allow(deprecated)] // whole module is deprecated - -use crate::rngs::OsRng; -use rand_core::{CryptoRng, Error, RngCore}; - -/// An interface returning random data from external source(s), provided -/// specifically for securely seeding algorithmic generators (PRNGs). -/// -/// This is deprecated. It is suggested you use [`rngs::OsRng`] instead. -/// -/// [`rngs::OsRng`]: crate::rngs::OsRng -#[derive(Debug)] -#[deprecated(since = "0.7.0", note = "use rngs::OsRng instead")] -pub struct EntropyRng { - source: OsRng, -} - -impl EntropyRng { - /// Create a new `EntropyRng`. - /// - /// This method will do no system calls or other initialization routines, - /// those are done on first use. This is done to make `new` infallible, - /// and `try_fill_bytes` the only place to report errors. - pub fn new() -> Self { - EntropyRng { source: OsRng } - } -} - -impl Default for EntropyRng { - fn default() -> Self { - EntropyRng::new() - } -} - -impl RngCore for EntropyRng { - fn next_u32(&mut self) -> u32 { - self.source.next_u32() - } - - fn next_u64(&mut self) -> u64 { - self.source.next_u64() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - self.source.fill_bytes(dest) - } - - fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> { - self.source.try_fill_bytes(dest) - } -} - -impl CryptoRng for EntropyRng {} - - -#[cfg(test)] -mod test { - use super::*; - - #[test] - fn test_entropy() { - let mut rng = EntropyRng::new(); - let n = (rng.next_u32() ^ rng.next_u32()).count_ones(); - assert!(n >= 2); // p(failure) approx 1e-7 - } -} diff --git a/src/rngs/mod.rs b/src/rngs/mod.rs index fc0f8cbc2ca..52001d45e03 100644 --- a/src/rngs/mod.rs +++ b/src/rngs/mod.rs @@ -98,17 +98,12 @@ pub mod adapter; -#[cfg(feature = "std")] mod entropy; pub mod mock; // Public so we don't export `StepRng` directly, making it a bit // more clear it is intended for testing. #[cfg(feature = "small_rng")] mod small; #[cfg(feature = "std_rng")] mod std; #[cfg(all(feature = "std", feature = "std_rng"))] pub(crate) mod thread; -#[allow(deprecated)] -#[cfg(feature = "std")] -pub use self::entropy::EntropyRng; - #[cfg(feature = "small_rng")] pub use self::small::SmallRng; #[cfg(feature = "std_rng")] pub use self::std::StdRng; #[cfg(all(feature = "std", feature = "std_rng"))] pub use self::thread::ThreadRng; From e96cc6fed51876e0262fded9f905bc966f245090 Mon Sep 17 00:00:00 2001 From: Diggory Hardy Date: Tue, 10 Mar 2020 12:14:36 +0000 Subject: [PATCH 6/6] Cargo.toml doc: address review --- Cargo.toml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Cargo.toml b/Cargo.toml index 4a5e4456829..e0581952329 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -26,8 +26,8 @@ default = ["std", "std_rng"] nightly = ["simd_support"] # enables all features requiring nightly rust serde1 = [] # does nothing, deprecated -# Option: without "std" rand uses libcore; this option enables functionality -# expected to be available on a standard platform. +# Option (enabled by default): without "std" rand uses libcore; this option +# enables functionality expected to be available on a standard platform. std = ["rand_core/std", "rand_chacha/std", "alloc", "getrandom", "libc"] # Option: "alloc" enables support for Vec and Box when not using "std" @@ -36,10 +36,10 @@ alloc = ["rand_core/alloc"] # Option: use getrandom package for seeding getrandom = ["rand_core/getrandom"] -# Option: experimental SIMD support +# Option (requires nightly): experimental SIMD support simd_support = ["packed_simd"] -# Option: enable StdRng (enabled by default) +# Option (enabled by default): enable StdRng std_rng = ["rand_chacha", "rand_hc"] # Option: enable SmallRng