From d0d9683df8398696147e7ee1fcffb2e4e957008c Mon Sep 17 00:00:00 2001 From: Daniel Mueller Date: Sat, 4 Apr 2020 14:39:19 -0700 Subject: Remove vendored dependencies While it appears that by now we actually can get successful builds without Cargo insisting on Internet access by virtue of using the --frozen flag, maintaining vendored dependencies is somewhat of a pain point. This state will also get worse with upcoming changes that replace argparse in favor of structopt and pull in a slew of new dependencies by doing so. Then there is also the repository structure aspect, which is non-standard due to the way we vendor dependencies and a potential source of confusion. In order to fix these problems, this change removes all the vendored dependencies we have. Delete subrepo argparse/:argparse Delete subrepo base32/:base32 Delete subrepo cc/:cc Delete subrepo cfg-if/:cfg-if Delete subrepo getrandom/:getrandom Delete subrepo lazy-static/:lazy-static Delete subrepo libc/:libc Delete subrepo nitrokey-sys/:nitrokey-sys Delete subrepo nitrokey/:nitrokey Delete subrepo rand/:rand --- rand/src/distributions/gamma.rs | 371 ---------------------------------------- 1 file changed, 371 deletions(-) delete mode 100644 rand/src/distributions/gamma.rs (limited to 'rand/src/distributions/gamma.rs') diff --git a/rand/src/distributions/gamma.rs b/rand/src/distributions/gamma.rs deleted file mode 100644 index b5a97f5..0000000 --- a/rand/src/distributions/gamma.rs +++ /dev/null @@ -1,371 +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::GammaRepr::*; -use self::ChiSquaredRepr::*; - -use crate::Rng; -use crate::distributions::normal::StandardNormal; -use crate::distributions::{Distribution, Exp, Open01}; - -/// 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 crate::distributions::Distribution; - use super::{Beta, ChiSquared, StudentT, FisherF}; - - 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.); - } -} -- cgit v1.2.1