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/rand_distr/src/binomial.rs | 329 ---------------------------------------- 1 file changed, 329 deletions(-) delete mode 100644 rand/rand_distr/src/binomial.rs (limited to 'rand/rand_distr/src/binomial.rs') diff --git a/rand/rand_distr/src/binomial.rs b/rand/rand_distr/src/binomial.rs deleted file mode 100644 index 0e6bf9a..0000000 --- a/rand/rand_distr/src/binomial.rs +++ /dev/null @@ -1,329 +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. - -use rand::Rng; -use crate::{Distribution, Uniform}; - -/// 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`. -/// -/// # Example -/// -/// ``` -/// use rand_distr::{Binomial, Distribution}; -/// -/// let bin = Binomial::new(20, 0.3).unwrap(); -/// let v = bin.sample(&mut rand::thread_rng()); -/// println!("{} is from a binomial distribution", v); -/// ``` -#[derive(Clone, Copy, Debug)] -pub struct Binomial { - /// Number of trials. - n: u64, - /// Probability of success. - p: f64, -} - -/// Error type returned from `Binomial::new`. -#[derive(Clone, Copy, Debug, PartialEq, Eq)] -pub enum Error { - /// `p < 0` or `nan`. - ProbabilityTooSmall, - /// `p > 1`. - ProbabilityTooLarge, -} - -impl Binomial { - /// Construct a new `Binomial` with the given shape parameters `n` (number - /// of trials) and `p` (probability of success). - pub fn new(n: u64, p: f64) -> Result { - if !(p >= 0.0) { - return Err(Error::ProbabilityTooSmall); - } - if !(p <= 1.0) { - return Err(Error::ProbabilityTooLarge); - } - Ok(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 { - #[allow(clippy::many_single_char_names)] // Same names as in the reference. - 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 rand::Rng; - use crate::Distribution; - use super::Binomial; - - fn test_binomial_mean_and_variance(n: u64, p: f64, rng: &mut R) { - let binomial = Binomial::new(n, p).unwrap(); - - 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); - - let variance = - results.iter().map(|x| (x - mean) * (x - mean)).sum::() - / results.len() as f64; - assert!((variance - expected_variance).abs() < expected_variance / 10.0); - } - - #[test] - 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).unwrap()), 0); - assert_eq!(rng.sample(Binomial::new(20, 1.0).unwrap()), 20); - } - - #[test] - #[should_panic] - fn test_binomial_invalid_lambda_neg() { - Binomial::new(20, -10.0).unwrap(); - } -} -- cgit v1.2.1