| // Copyright 2018 Developers of the Rand project. |
| // |
| // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or |
| // https://www.apache.org/licenses/LICENSE-2.0> or the MIT license |
| // <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your |
| // option. This file may not be copied, modified, or distributed |
| // except according to those terms. |
| |
| //! Math helper functions |
| |
| use crate::ziggurat_tables; |
| use rand::distributions::hidden_export::IntoFloat; |
| use rand::Rng; |
| |
| /// 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. |
| pub(crate) fn log_gamma<F: num_traits::Float>(x: F) -> F { |
| // precalculated 6 coefficients for the first 6 terms of the series |
| let coefficients: [F; 6] = [ |
| F::from(76.18009172947146).unwrap(), |
| F::from(-86.50532032941677).unwrap(), |
| F::from(24.01409824083091).unwrap(), |
| F::from(-1.231739572450155).unwrap(), |
| F::from(0.1208650973866179e-2).unwrap(), |
| F::from(-0.5395239384953e-5).unwrap(), |
| ]; |
| |
| // (x+0.5)*ln(x+g+0.5)-(x+g+0.5) |
| let tmp = x + F::from(5.5).unwrap(); |
| let log = (x + F::from(0.5).unwrap()) * tmp.ln() - tmp; |
| |
| // the first few terms of the series for Ag(x) |
| let mut a = F::from(1.000000000190015).unwrap(); |
| let mut denom = x; |
| for &coeff in &coefficients { |
| denom = denom + F::one(); |
| a = a + (coeff / denom); |
| } |
| |
| // get everything together |
| // a is Ag(x) |
| // 2.5066... is sqrt(2pi) |
| log + (F::from(2.5066282746310005).unwrap() * 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. |
| #[inline(always)] |
| pub(crate) fn ziggurat<R: Rng + ?Sized, P, Z>( |
| 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, |
| { |
| 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 subtract to get [-1,1) |
| // We can't convert to an open range directly, that would require |
| // subtracting `3.0 - EPSILON`, which is not representable. |
| // It is possible with an extra step, but an open range does not |
| // seem necessary 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 subtract 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::<f64>() < pdf(x) { |
| return x; |
| } |
| } |
| } |