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John Doty 2024-03-08 11:03:01 -08:00
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third-party/vendor/rand/src/distributions/float.rs vendored Normal file
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// 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.
//! Basic floating-point number distributions
use crate::distributions::utils::FloatSIMDUtils;
use crate::distributions::{Distribution, Standard};
use crate::Rng;
use core::mem;
#[cfg(feature = "simd_support")] use packed_simd::*;
#[cfg(feature = "serde1")]
use serde::{Serialize, Deserialize};
/// A distribution to sample floating point numbers uniformly in the half-open
/// interval `(0, 1]`, i.e. including 1 but not 0.
///
/// All values that can be generated are of the form `n * ε/2`. For `f32`
/// the 24 most significant random bits of a `u32` are used and for `f64` the
/// 53 most significant bits of a `u64` are used. The conversion uses the
/// multiplicative method.
///
/// See also: [`Standard`] which samples from `[0, 1)`, [`Open01`]
/// which samples from `(0, 1)` and [`Uniform`] which samples from arbitrary
/// ranges.
///
/// # Example
/// ```
/// use rand::{thread_rng, Rng};
/// use rand::distributions::OpenClosed01;
///
/// let val: f32 = thread_rng().sample(OpenClosed01);
/// println!("f32 from (0, 1): {}", val);
/// ```
///
/// [`Standard`]: crate::distributions::Standard
/// [`Open01`]: crate::distributions::Open01
/// [`Uniform`]: crate::distributions::uniform::Uniform
#[derive(Clone, Copy, Debug)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct OpenClosed01;
/// A distribution to sample floating point numbers uniformly in the open
/// interval `(0, 1)`, i.e. not including either endpoint.
///
/// All values that can be generated are of the form `n * ε + ε/2`. For `f32`
/// the 23 most significant random bits of an `u32` are used, for `f64` 52 from
/// an `u64`. The conversion uses a transmute-based method.
///
/// See also: [`Standard`] which samples from `[0, 1)`, [`OpenClosed01`]
/// which samples from `(0, 1]` and [`Uniform`] which samples from arbitrary
/// ranges.
///
/// # Example
/// ```
/// use rand::{thread_rng, Rng};
/// use rand::distributions::Open01;
///
/// let val: f32 = thread_rng().sample(Open01);
/// println!("f32 from (0, 1): {}", val);
/// ```
///
/// [`Standard`]: crate::distributions::Standard
/// [`OpenClosed01`]: crate::distributions::OpenClosed01
/// [`Uniform`]: crate::distributions::uniform::Uniform
#[derive(Clone, Copy, Debug)]
#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
pub struct Open01;
// This trait is needed by both this lib and rand_distr hence is a hidden export
#[doc(hidden)]
pub trait IntoFloat {
type F;
/// Helper method to combine the fraction and a constant exponent into a
/// float.
///
/// Only the least significant bits of `self` may be set, 23 for `f32` and
/// 52 for `f64`.
/// The resulting value will fall in a range that depends on the exponent.
/// As an example the range with exponent 0 will be
/// [2<sup>0</sup>..2<sup>1</sup>), which is [1..2).
fn into_float_with_exponent(self, exponent: i32) -> Self::F;
}
macro_rules! float_impls {
($ty:ident, $uty:ident, $f_scalar:ident, $u_scalar:ty,
$fraction_bits:expr, $exponent_bias:expr) => {
impl IntoFloat for $uty {
type F = $ty;
#[inline(always)]
fn into_float_with_exponent(self, exponent: i32) -> $ty {
// The exponent is encoded using an offset-binary representation
let exponent_bits: $u_scalar =
(($exponent_bias + exponent) as $u_scalar) << $fraction_bits;
$ty::from_bits(self | exponent_bits)
}
}
impl Distribution<$ty> for Standard {
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
// Multiply-based method; 24/53 random bits; [0, 1) interval.
// We use the most significant bits because for simple RNGs
// those are usually more random.
let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
let precision = $fraction_bits + 1;
let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
let value: $uty = rng.gen();
let value = value >> (float_size - precision);
scale * $ty::cast_from_int(value)
}
}
impl Distribution<$ty> for OpenClosed01 {
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
// Multiply-based method; 24/53 random bits; (0, 1] interval.
// We use the most significant bits because for simple RNGs
// those are usually more random.
let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
let precision = $fraction_bits + 1;
let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
let value: $uty = rng.gen();
let value = value >> (float_size - precision);
// Add 1 to shift up; will not overflow because of right-shift:
scale * $ty::cast_from_int(value + 1)
}
}
impl Distribution<$ty> for Open01 {
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
// Transmute-based method; 23/52 random bits; (0, 1) interval.
// We use the most significant bits because for simple RNGs
// those are usually more random.
use core::$f_scalar::EPSILON;
let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
let value: $uty = rng.gen();
let fraction = value >> (float_size - $fraction_bits);
fraction.into_float_with_exponent(0) - (1.0 - EPSILON / 2.0)
}
}
}
}
float_impls! { f32, u32, f32, u32, 23, 127 }
float_impls! { f64, u64, f64, u64, 52, 1023 }
#[cfg(feature = "simd_support")]
float_impls! { f32x2, u32x2, f32, u32, 23, 127 }
#[cfg(feature = "simd_support")]
float_impls! { f32x4, u32x4, f32, u32, 23, 127 }
#[cfg(feature = "simd_support")]
float_impls! { f32x8, u32x8, f32, u32, 23, 127 }
#[cfg(feature = "simd_support")]
float_impls! { f32x16, u32x16, f32, u32, 23, 127 }
#[cfg(feature = "simd_support")]
float_impls! { f64x2, u64x2, f64, u64, 52, 1023 }
#[cfg(feature = "simd_support")]
float_impls! { f64x4, u64x4, f64, u64, 52, 1023 }
#[cfg(feature = "simd_support")]
float_impls! { f64x8, u64x8, f64, u64, 52, 1023 }
#[cfg(test)]
mod tests {
use super::*;
use crate::rngs::mock::StepRng;
const EPSILON32: f32 = ::core::f32::EPSILON;
const EPSILON64: f64 = ::core::f64::EPSILON;
macro_rules! test_f32 {
($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
#[test]
fn $fnn() {
// Standard
let mut zeros = StepRng::new(0, 0);
assert_eq!(zeros.gen::<$ty>(), $ZERO);
let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
let mut max = StepRng::new(!0, 0);
assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);
// OpenClosed01
let mut zeros = StepRng::new(0, 0);
assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
let mut max = StepRng::new(!0, 0);
assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);
// Open01
let mut zeros = StepRng::new(0, 0);
assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
let mut one = StepRng::new(1 << 9 | 1 << (9 + 32), 0);
assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
let mut max = StepRng::new(!0, 0);
assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
}
};
}
test_f32! { f32_edge_cases, f32, 0.0, EPSILON32 }
#[cfg(feature = "simd_support")]
test_f32! { f32x2_edge_cases, f32x2, f32x2::splat(0.0), f32x2::splat(EPSILON32) }
#[cfg(feature = "simd_support")]
test_f32! { f32x4_edge_cases, f32x4, f32x4::splat(0.0), f32x4::splat(EPSILON32) }
#[cfg(feature = "simd_support")]
test_f32! { f32x8_edge_cases, f32x8, f32x8::splat(0.0), f32x8::splat(EPSILON32) }
#[cfg(feature = "simd_support")]
test_f32! { f32x16_edge_cases, f32x16, f32x16::splat(0.0), f32x16::splat(EPSILON32) }
macro_rules! test_f64 {
($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
#[test]
fn $fnn() {
// Standard
let mut zeros = StepRng::new(0, 0);
assert_eq!(zeros.gen::<$ty>(), $ZERO);
let mut one = StepRng::new(1 << 11, 0);
assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
let mut max = StepRng::new(!0, 0);
assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);
// OpenClosed01
let mut zeros = StepRng::new(0, 0);
assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
let mut one = StepRng::new(1 << 11, 0);
assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
let mut max = StepRng::new(!0, 0);
assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);
// Open01
let mut zeros = StepRng::new(0, 0);
assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
let mut one = StepRng::new(1 << 12, 0);
assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
let mut max = StepRng::new(!0, 0);
assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
}
};
}
test_f64! { f64_edge_cases, f64, 0.0, EPSILON64 }
#[cfg(feature = "simd_support")]
test_f64! { f64x2_edge_cases, f64x2, f64x2::splat(0.0), f64x2::splat(EPSILON64) }
#[cfg(feature = "simd_support")]
test_f64! { f64x4_edge_cases, f64x4, f64x4::splat(0.0), f64x4::splat(EPSILON64) }
#[cfg(feature = "simd_support")]
test_f64! { f64x8_edge_cases, f64x8, f64x8::splat(0.0), f64x8::splat(EPSILON64) }
#[test]
fn value_stability() {
fn test_samples<T: Copy + core::fmt::Debug + PartialEq, D: Distribution<T>>(
distr: &D, zero: T, expected: &[T],
) {
let mut rng = crate::test::rng(0x6f44f5646c2a7334);
let mut buf = [zero; 3];
for x in &mut buf {
*x = rng.sample(&distr);
}
assert_eq!(&buf, expected);
}
test_samples(&Standard, 0f32, &[0.0035963655, 0.7346052, 0.09778172]);
test_samples(&Standard, 0f64, &[
0.7346051961657583,
0.20298547462974248,
0.8166436635290655,
]);
test_samples(&OpenClosed01, 0f32, &[0.003596425, 0.73460525, 0.09778178]);
test_samples(&OpenClosed01, 0f64, &[
0.7346051961657584,
0.2029854746297426,
0.8166436635290656,
]);
test_samples(&Open01, 0f32, &[0.0035963655, 0.73460525, 0.09778172]);
test_samples(&Open01, 0f64, &[
0.7346051961657584,
0.20298547462974248,
0.8166436635290656,
]);
#[cfg(feature = "simd_support")]
{
// We only test a sub-set of types here. Values are identical to
// non-SIMD types; we assume this pattern continues across all
// SIMD types.
test_samples(&Standard, f32x2::new(0.0, 0.0), &[
f32x2::new(0.0035963655, 0.7346052),
f32x2::new(0.09778172, 0.20298547),
f32x2::new(0.34296435, 0.81664366),
]);
test_samples(&Standard, f64x2::new(0.0, 0.0), &[
f64x2::new(0.7346051961657583, 0.20298547462974248),
f64x2::new(0.8166436635290655, 0.7423708925400552),
f64x2::new(0.16387782224016323, 0.9087068770169618),
]);
}
}
}