crates/rand/src/distributions/float.rs - platform/external/rust/android-crates-io - Git at Google

 // 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),
             ]);
         }
     }
 }
	// 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),
	]);
	}
	}
	}