// 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
#[cfg(feature="simd_support")]
use packed_simd::*;
#[cfg(feature="std")]
use distributions::ziggurat_tables;
#[cfg(feature="std")]
use Rng;
pub trait WideningMultiply<RHS = Self> {
type Output;
fn wmul(self, x: RHS) -> Self::Output;
}
macro_rules! wmul_impl {
($ty:ty, $wide:ty, $shift:expr) => {
impl WideningMultiply for $ty {
type Output = ($ty, $ty);
#[inline(always)]
fn wmul(self, x: $ty) -> Self::Output {
let tmp = (self as $wide) * (x as $wide);
((tmp >> $shift) as $ty, tmp as $ty)
}
}
};
// simd bulk implementation
($(($ty:ident, $wide:ident),)+, $shift:expr) => {
$(
impl WideningMultiply for $ty {
type Output = ($ty, $ty);
#[inline(always)]
fn wmul(self, x: $ty) -> Self::Output {
// For supported vectors, this should compile to a couple
// supported multiply & swizzle instructions (no actual
// casting).
// TODO: optimize
let y: $wide = self.cast();
let x: $wide = x.cast();
let tmp = y * x;
let hi: $ty = (tmp >> $shift).cast();
let lo: $ty = tmp.cast();
(hi, lo)
}
}
)+
};
}
wmul_impl! { u8, u16, 8 }
wmul_impl! { u16, u32, 16 }
wmul_impl! { u32, u64, 32 }
#[cfg(all(rustc_1_26, not(target_os = "emscripten")))]
wmul_impl! { u64, u128, 64 }
// This code is a translation of the __mulddi3 function in LLVM's
// compiler-rt. It is an optimised variant of the common method
// `(a + b) * (c + d) = ac + ad + bc + bd`.
//
// For some reason LLVM can optimise the C version very well, but
// keeps shuffling registers in this Rust translation.
macro_rules! wmul_impl_large {
($ty:ty, $half:expr) => {
impl WideningMultiply for $ty {
type Output = ($ty, $ty);
#[inline(always)]
fn wmul(self, b: $ty) -> Self::Output {
const LOWER_MASK: $ty = !0 >> $half;
let mut low = (self & LOWER_MASK).wrapping_mul(b & LOWER_MASK);
let mut t = low >> $half;
low &= LOWER_MASK;
t += (self >> $half).wrapping_mul(b & LOWER_MASK);
low += (t & LOWER_MASK) << $half;
let mut high = t >> $half;
t = low >> $half;
low &= LOWER_MASK;
t += (b >> $half).wrapping_mul(self & LOWER_MASK);
low += (t & LOWER_MASK) << $half;
high += t >> $half;
high += (self >> $half).wrapping_mul(b >> $half);
(high, low)
}
}
};
// simd bulk implementation
(($($ty:ty,)+) $scalar:ty, $half:expr) => {
$(
impl WideningMultiply for $ty {
type Output = ($ty, $ty);
#[inline(always)]
fn wmul(self, b: $ty) -> Self::Output {
// needs wrapping multiplication
const LOWER_MASK: $scalar = !0 >> $half;
let mut low = (self & LOWER_MASK) * (b & LOWER_MASK);
let mut t = low >> $half;
low &= LOWER_MASK;
t += (self >> $half) * (b & LOWER_MASK);
low += (t & LOWER_MASK) << $half;
let mut high = t >> $half;
t = low >> $half;
low &= LOWER_MASK;
t += (b >> $half) * (self & LOWER_MASK);
low += (t & LOWER_MASK) << $half;
high += t >> $half;
high += (self >> $half) * (b >> $half);
(high, low)
}
}
)+
};
}
#[cfg(not(all(rustc_1_26, not(target_os = "emscripten"))))]
wmul_impl_large! { u64, 32 }
#[cfg(all(rustc_1_26, not(target_os = "emscripten")))]
wmul_impl_large! { u128, 64 }
macro_rules! wmul_impl_usize {
($ty:ty) => {
impl WideningMultiply for usize {
type Output = (usize, usize);
#[inline(always)]
fn wmul(self, x: usize) -> Self::Output {
let (high, low) = (self as $ty).wmul(x as $ty);
(high as usize, low as usize)
}
}
}
}
#[cfg(target_pointer_width = "32")]
wmul_impl_usize! { u32 }
#[cfg(target_pointer_width = "64")]
wmul_impl_usize! { u64 }
#[cfg(all(feature = "simd_support", feature = "nightly"))]
mod simd_wmul {
#[cfg(target_arch = "x86")]
use core::arch::x86::*;
#[cfg(target_arch = "x86_64")]
use core::arch::x86_64::*;
use super::*;
wmul_impl! {
(u8x2, u16x2),
(u8x4, u16x4),
(u8x8, u16x8),
(u8x16, u16x16),
(u8x32, u16x32),,
8
}
wmul_impl! { (u16x2, u32x2),, 16 }
#[cfg(not(target_feature = "sse2"))]
wmul_impl! { (u16x4, u32x4),, 16 }
#[cfg(not(target_feature = "sse4.2"))]
wmul_impl! { (u16x8, u32x8),, 16 }
#[cfg(not(target_feature = "avx2"))]
wmul_impl! { (u16x16, u32x16),, 16 }
// 16-bit lane widths allow use of the x86 `mulhi` instructions, which
// means `wmul` can be implemented with only two instructions.
#[allow(unused_macros)]
macro_rules! wmul_impl_16 {
($ty:ident, $intrinsic:ident, $mulhi:ident, $mullo:ident) => {
impl WideningMultiply for $ty {
type Output = ($ty, $ty);
#[inline(always)]
fn wmul(self, x: $ty) -> Self::Output {
let b = $intrinsic::from_bits(x);
let a = $intrinsic::from_bits(self);
let hi = $ty::from_bits(unsafe { $mulhi(a, b) });
let lo = $ty::from_bits(unsafe { $mullo(a, b) });
(hi, lo)
}
}
};
}
#[cfg(target_feature = "sse2")]
wmul_impl_16! { u16x4, __m64, _mm_mulhi_pu16, _mm_mullo_pi16 }
#[cfg(target_feature = "sse4.2")]
wmul_impl_16! { u16x8, __m128i, _mm_mulhi_epu16, _mm_mullo_epi16 }
#[cfg(target_feature = "avx2")]
wmul_impl_16! { u16x16, __m256i, _mm256_mulhi_epu16, _mm256_mullo_epi16 }
// FIXME: there are no `__m512i` types in stdsimd yet, so `wmul::<u16x32>`
// cannot use the same implementation.
wmul_impl! {
(u32x2, u64x2),
(u32x4, u64x4),
(u32x8, u64x8),,
32
}
// TODO: optimize, this seems to seriously slow things down
wmul_impl_large! { (u8x64,) u8, 4 }
wmul_impl_large! { (u16x32,) u16, 8 }
wmul_impl_large! { (u32x16,) u32, 16 }
wmul_impl_large! { (u64x2, u64x4, u64x8,) u64, 32 }
}
#[cfg(all(feature = "simd_support", feature = "nightly"))]
pub use self::simd_wmul::*;
/// Helper trait when dealing with scalar and SIMD floating point types.
pub(crate) trait FloatSIMDUtils {
// `PartialOrd` for vectors compares lexicographically. We want to compare all
// the individual SIMD lanes instead, and get the combined result over all
// lanes. This is possible using something like `a.lt(b).all()`, but we
// implement it as a trait so we can write the same code for `f32` and `f64`.
// Only the comparison functions we need are implemented.
fn all_lt(self, other: Self) -> bool;
fn all_le(self, other: Self) -> bool;
fn all_finite(self) -> bool;
type Mask;
fn finite_mask(self) -> Self::Mask;
fn gt_mask(self, other: Self) -> Self::Mask;
fn ge_mask(self, other: Self) -> Self::Mask;
// Decrease all lanes where the mask is `true` to the next lower value
// representable by the floating-point type. At least one of the lanes
// must be set.
fn decrease_masked(self, mask: Self::Mask) -> Self;
// Convert from int value. Conversion is done while retaining the numerical
// value, not by retaining the binary representation.
type UInt;
fn cast_from_int(i: Self::UInt) -> Self;
}
/// Implement functions available in std builds but missing from core primitives
#[cfg(not(std))]
pub(crate) trait Float : Sized {
type Bits;
fn is_nan(self) -> bool;
fn is_infinite(self) -> bool;
fn is_finite(self) -> bool;
fn to_bits(self) -> Self::Bits;
fn from_bits(v: Self::Bits) -> Self;
}
/// Implement functions on f32/f64 to give them APIs similar to SIMD types
pub(crate) trait FloatAsSIMD : Sized {
#[inline(always)]
fn lanes() -> usize { 1 }
#[inline(always)]
fn splat(scalar: Self) -> Self { scalar }
#[inline(always)]
fn extract(self, index: usize) -> Self { debug_assert_eq!(index, 0); self }
#[inline(always)]
fn replace(self, index: usize, new_value: Self) -> Self { debug_assert_eq!(index, 0); new_value }
}
pub(crate) trait BoolAsSIMD : Sized {
fn any(self) -> bool;
fn all(self) -> bool;
fn none(self) -> bool;
}
impl BoolAsSIMD for bool {
#[inline(always)]
fn any(self) -> bool { self }
#[inline(always)]
fn all(self) -> bool { self }
#[inline(always)]
fn none(self) -> bool { !self }
}
macro_rules! scalar_float_impl {
($ty:ident, $uty:ident) => {
#[cfg(not(std))]
impl Float for $ty {
type Bits = $uty;
#[inline]
fn is_nan(self) -> bool {
self != self
}
#[inline]
fn is_infinite(self) -> bool {
self == ::core::$ty::INFINITY || self == ::core::$ty::NEG_INFINITY
}
#[inline]
fn is_finite(self) -> bool {
!(self.is_nan() || self.is_infinite())
}
#[inline]
fn to_bits(self) -> Self::Bits {
unsafe { ::core::mem::transmute(self) }
}
#[inline]
fn from_bits(v: Self::Bits) -> Self {
// It turns out the safety issues with sNaN were overblown! Hooray!
unsafe { ::core::mem::transmute(v) }
}
}
impl FloatSIMDUtils for $ty {
type Mask = bool;
#[inline(always)]
fn all_lt(self, other: Self) -> bool { self < other }
#[inline(always)]
fn all_le(self, other: Self) -> bool { self <= other }
#[inline(always)]
fn all_finite(self) -> bool { self.is_finite() }
#[inline(always)]
fn finite_mask(self) -> Self::Mask { self.is_finite() }
#[inline(always)]
fn gt_mask(self, other: Self) -> Self::Mask { self > other }
#[inline(always)]
fn ge_mask(self, other: Self) -> Self::Mask { self >= other }
#[inline(always)]
fn decrease_masked(self, mask: Self::Mask) -> Self {
debug_assert!(mask, "At least one lane must be set");
<$ty>::from_bits(self.to_bits() - 1)
}
type UInt = $uty;
fn cast_from_int(i: Self::UInt) -> Self { i as $ty }
}
impl FloatAsSIMD for $ty {}
}
}
scalar_float_impl!(f32, u32);
scalar_float_impl!(f64, u64);
#[cfg(feature="simd_support")]
macro_rules! simd_impl {
($ty:ident, $f_scalar:ident, $mty:ident, $uty:ident) => {
impl FloatSIMDUtils for $ty {
type Mask = $mty;
#[inline(always)]
fn all_lt(self, other: Self) -> bool { self.lt(other).all() }
#[inline(always)]
fn all_le(self, other: Self) -> bool { self.le(other).all() }
#[inline(always)]
fn all_finite(self) -> bool { self.finite_mask().all() }
#[inline(always)]
fn finite_mask(self) -> Self::Mask {
// This can possibly be done faster by checking bit patterns
let neg_inf = $ty::splat(::core::$f_scalar::NEG_INFINITY);
let pos_inf = $ty::splat(::core::$f_scalar::INFINITY);
self.gt(neg_inf) & self.lt(pos_inf)
}
#[inline(always)]
fn gt_mask(self, other: Self) -> Self::Mask { self.gt(other) }
#[inline(always)]
fn ge_mask(self, other: Self) -> Self::Mask { self.ge(other) }
#[inline(always)]
fn decrease_masked(self, mask: Self::Mask) -> Self {
// Casting a mask into ints will produce all bits set for
// true, and 0 for false. Adding that to the binary
// representation of a float means subtracting one from
// the binary representation, resulting in the next lower
// value representable by $ty. This works even when the
// current value is infinity.
debug_assert!(mask.any(), "At least one lane must be set");
<$ty>::from_bits(<$uty>::from_bits(self) + <$uty>::from_bits(mask))
}
type UInt = $uty;
fn cast_from_int(i: Self::UInt) -> Self { i.cast() }
}
}
}
#[cfg(feature="simd_support")] simd_impl! { f32x2, f32, m32x2, u32x2 }
#[cfg(feature="simd_support")] simd_impl! { f32x4, f32, m32x4, u32x4 }
#[cfg(feature="simd_support")] simd_impl! { f32x8, f32, m32x8, u32x8 }
#[cfg(feature="simd_support")] simd_impl! { f32x16, f32, m32x16, u32x16 }
#[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<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 {
use 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::<f64>() < pdf(x) {
return x;
}
}
}