| // Copyright 2018 Developers of the Rand project. |
| // Copyright 2017 The Rust Project Developers. |
| // |
| // 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. |
| |
| //! A distribution uniformly sampling numbers within a given range. |
| //! |
| //! [`Uniform`] is the standard distribution to sample uniformly from a range; |
| //! e.g. `Uniform::new_inclusive(1, 6)` can sample integers from 1 to 6, like a |
| //! standard die. [`Rng::gen_range`] supports any type supported by |
| //! [`Uniform`]. |
| //! |
| //! This distribution is provided with support for several primitive types |
| //! (all integer and floating-point types) as well as [`std::time::Duration`], |
| //! and supports extension to user-defined types via a type-specific *back-end* |
| //! implementation. |
| //! |
| //! The types [`UniformInt`], [`UniformFloat`] and [`UniformDuration`] are the |
| //! back-ends supporting sampling from primitive integer and floating-point |
| //! ranges as well as from [`std::time::Duration`]; these types do not normally |
| //! need to be used directly (unless implementing a derived back-end). |
| //! |
| //! # Example usage |
| //! |
| //! ``` |
| //! use rand::{Rng, thread_rng}; |
| //! use rand::distributions::Uniform; |
| //! |
| //! let mut rng = thread_rng(); |
| //! let side = Uniform::new(-10.0, 10.0); |
| //! |
| //! // sample between 1 and 10 points |
| //! for _ in 0..rng.gen_range(1, 11) { |
| //! // sample a point from the square with sides -10 - 10 in two dimensions |
| //! let (x, y) = (rng.sample(side), rng.sample(side)); |
| //! println!("Point: {}, {}", x, y); |
| //! } |
| //! ``` |
| //! |
| //! # Extending `Uniform` to support a custom type |
| //! |
| //! To extend [`Uniform`] to support your own types, write a back-end which |
| //! implements the [`UniformSampler`] trait, then implement the [`SampleUniform`] |
| //! helper trait to "register" your back-end. See the `MyF32` example below. |
| //! |
| //! At a minimum, the back-end needs to store any parameters needed for sampling |
| //! (e.g. the target range) and implement `new`, `new_inclusive` and `sample`. |
| //! Those methods should include an assert to check the range is valid (i.e. |
| //! `low < high`). The example below merely wraps another back-end. |
| //! |
| //! The `new`, `new_inclusive` and `sample_single` functions use arguments of |
| //! type SampleBorrow<X> in order to support passing in values by reference or |
| //! by value. In the implementation of these functions, you can choose to |
| //! simply use the reference returned by [`SampleBorrow::borrow`], or you can choose |
| //! to copy or clone the value, whatever is appropriate for your type. |
| //! |
| //! ``` |
| //! use rand::prelude::*; |
| //! use rand::distributions::uniform::{Uniform, SampleUniform, |
| //! UniformSampler, UniformFloat, SampleBorrow}; |
| //! |
| //! struct MyF32(f32); |
| //! |
| //! #[derive(Clone, Copy, Debug)] |
| //! struct UniformMyF32(UniformFloat<f32>); |
| //! |
| //! impl UniformSampler for UniformMyF32 { |
| //! type X = MyF32; |
| //! fn new<B1, B2>(low: B1, high: B2) -> Self |
| //! where B1: SampleBorrow<Self::X> + Sized, |
| //! B2: SampleBorrow<Self::X> + Sized |
| //! { |
| //! UniformMyF32(UniformFloat::<f32>::new(low.borrow().0, high.borrow().0)) |
| //! } |
| //! fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self |
| //! where B1: SampleBorrow<Self::X> + Sized, |
| //! B2: SampleBorrow<Self::X> + Sized |
| //! { |
| //! UniformSampler::new(low, high) |
| //! } |
| //! fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { |
| //! MyF32(self.0.sample(rng)) |
| //! } |
| //! } |
| //! |
| //! impl SampleUniform for MyF32 { |
| //! type Sampler = UniformMyF32; |
| //! } |
| //! |
| //! let (low, high) = (MyF32(17.0f32), MyF32(22.0f32)); |
| //! let uniform = Uniform::new(low, high); |
| //! let x = uniform.sample(&mut thread_rng()); |
| //! ``` |
| //! |
| //! [`SampleUniform`]: crate::distributions::uniform::SampleUniform |
| //! [`UniformSampler`]: crate::distributions::uniform::UniformSampler |
| //! [`UniformInt`]: crate::distributions::uniform::UniformInt |
| //! [`UniformFloat`]: crate::distributions::uniform::UniformFloat |
| //! [`UniformDuration`]: crate::distributions::uniform::UniformDuration |
| //! [`SampleBorrow::borrow`]: crate::distributions::uniform::SampleBorrow::borrow |
| |
| #[cfg(feature = "std")] |
| use std::time::Duration; |
| #[cfg(not(feature = "std"))] |
| use core::time::Duration; |
| |
| use crate::Rng; |
| use crate::distributions::Distribution; |
| use crate::distributions::float::IntoFloat; |
| use crate::distributions::utils::{WideningMultiply, FloatSIMDUtils, FloatAsSIMD, BoolAsSIMD}; |
| |
| #[cfg(not(feature = "std"))] |
| #[allow(unused_imports)] // rustc doesn't detect that this is actually used |
| use crate::distributions::utils::Float; |
| |
| |
| #[cfg(feature="simd_support")] |
| use packed_simd::*; |
| |
| /// Sample values uniformly between two bounds. |
| /// |
| /// [`Uniform::new`] and [`Uniform::new_inclusive`] construct a uniform |
| /// distribution sampling from the given range; these functions may do extra |
| /// work up front to make sampling of multiple values faster. |
| /// |
| /// When sampling from a constant range, many calculations can happen at |
| /// compile-time and all methods should be fast; for floating-point ranges and |
| /// the full range of integer types this should have comparable performance to |
| /// the `Standard` distribution. |
| /// |
| /// Steps are taken to avoid bias which might be present in naive |
| /// implementations; for example `rng.gen::<u8>() % 170` samples from the range |
| /// `[0, 169]` but is twice as likely to select numbers less than 85 than other |
| /// values. Further, the implementations here give more weight to the high-bits |
| /// generated by the RNG than the low bits, since with some RNGs the low-bits |
| /// are of lower quality than the high bits. |
| /// |
| /// Implementations must sample in `[low, high)` range for |
| /// `Uniform::new(low, high)`, i.e., excluding `high`. In particular care must |
| /// be taken to ensure that rounding never results values `< low` or `>= high`. |
| /// |
| /// # Example |
| /// |
| /// ``` |
| /// use rand::distributions::{Distribution, Uniform}; |
| /// |
| /// fn main() { |
| /// let between = Uniform::from(10..10000); |
| /// let mut rng = rand::thread_rng(); |
| /// let mut sum = 0; |
| /// for _ in 0..1000 { |
| /// sum += between.sample(&mut rng); |
| /// } |
| /// println!("{}", sum); |
| /// } |
| /// ``` |
| /// |
| /// [`new`]: Uniform::new |
| /// [`new_inclusive`]: Uniform::new_inclusive |
| #[derive(Clone, Copy, Debug)] |
| pub struct Uniform<X: SampleUniform>(X::Sampler); |
| |
| impl<X: SampleUniform> Uniform<X> { |
| /// Create a new `Uniform` instance which samples uniformly from the half |
| /// open range `[low, high)` (excluding `high`). Panics if `low >= high`. |
| pub fn new<B1, B2>(low: B1, high: B2) -> Uniform<X> |
| where B1: SampleBorrow<X> + Sized, |
| B2: SampleBorrow<X> + Sized |
| { |
| Uniform(X::Sampler::new(low, high)) |
| } |
| |
| /// Create a new `Uniform` instance which samples uniformly from the closed |
| /// range `[low, high]` (inclusive). Panics if `low > high`. |
| pub fn new_inclusive<B1, B2>(low: B1, high: B2) -> Uniform<X> |
| where B1: SampleBorrow<X> + Sized, |
| B2: SampleBorrow<X> + Sized |
| { |
| Uniform(X::Sampler::new_inclusive(low, high)) |
| } |
| } |
| |
| impl<X: SampleUniform> Distribution<X> for Uniform<X> { |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> X { |
| self.0.sample(rng) |
| } |
| } |
| |
| /// Helper trait for creating objects using the correct implementation of |
| /// [`UniformSampler`] for the sampling type. |
| /// |
| /// See the [module documentation] on how to implement [`Uniform`] range |
| /// sampling for a custom type. |
| /// |
| /// [module documentation]: crate::distributions::uniform |
| pub trait SampleUniform: Sized { |
| /// The `UniformSampler` implementation supporting type `X`. |
| type Sampler: UniformSampler<X = Self>; |
| } |
| |
| /// Helper trait handling actual uniform sampling. |
| /// |
| /// See the [module documentation] on how to implement [`Uniform`] range |
| /// sampling for a custom type. |
| /// |
| /// Implementation of [`sample_single`] is optional, and is only useful when |
| /// the implementation can be faster than `Self::new(low, high).sample(rng)`. |
| /// |
| /// [module documentation]: crate::distributions::uniform |
| /// [`sample_single`]: UniformSampler::sample_single |
| pub trait UniformSampler: Sized { |
| /// The type sampled by this implementation. |
| type X; |
| |
| /// Construct self, with inclusive lower bound and exclusive upper bound |
| /// `[low, high)`. |
| /// |
| /// Usually users should not call this directly but instead use |
| /// `Uniform::new`, which asserts that `low < high` before calling this. |
| fn new<B1, B2>(low: B1, high: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized; |
| |
| /// Construct self, with inclusive bounds `[low, high]`. |
| /// |
| /// Usually users should not call this directly but instead use |
| /// `Uniform::new_inclusive`, which asserts that `low <= high` before |
| /// calling this. |
| fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized; |
| |
| /// Sample a value. |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X; |
| |
| /// Sample a single value uniformly from a range with inclusive lower bound |
| /// and exclusive upper bound `[low, high)`. |
| /// |
| /// By default this is implemented using |
| /// `UniformSampler::new(low, high).sample(rng)`. However, for some types |
| /// more optimal implementations for single usage may be provided via this |
| /// method (which is the case for integers and floats). |
| /// Results may not be identical. |
| /// |
| /// Note that to use this method in a generic context, the type needs to be |
| /// retrieved via `SampleUniform::Sampler` as follows: |
| /// ``` |
| /// use rand::{thread_rng, distributions::uniform::{SampleUniform, UniformSampler}}; |
| /// # #[allow(unused)] |
| /// fn sample_from_range<T: SampleUniform>(lb: T, ub: T) -> T { |
| /// let mut rng = thread_rng(); |
| /// <T as SampleUniform>::Sampler::sample_single(lb, ub, &mut rng) |
| /// } |
| /// ``` |
| fn sample_single<R: Rng + ?Sized, B1, B2>(low: B1, high: B2, rng: &mut R) |
| -> Self::X |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let uniform: Self = UniformSampler::new(low, high); |
| uniform.sample(rng) |
| } |
| } |
| |
| impl<X: SampleUniform> From<::core::ops::Range<X>> for Uniform<X> { |
| fn from(r: ::core::ops::Range<X>) -> Uniform<X> { |
| Uniform::new(r.start, r.end) |
| } |
| } |
| |
| impl<X: SampleUniform> From<::core::ops::RangeInclusive<X>> for Uniform<X> { |
| fn from(r: ::core::ops::RangeInclusive<X>) -> Uniform<X> { |
| Uniform::new_inclusive(r.start(), r.end()) |
| } |
| } |
| |
| /// Helper trait similar to [`Borrow`] but implemented |
| /// only for SampleUniform and references to SampleUniform in |
| /// order to resolve ambiguity issues. |
| /// |
| /// [`Borrow`]: std::borrow::Borrow |
| pub trait SampleBorrow<Borrowed> { |
| /// Immutably borrows from an owned value. See [`Borrow::borrow`] |
| /// |
| /// [`Borrow::borrow`]: std::borrow::Borrow::borrow |
| fn borrow(&self) -> &Borrowed; |
| } |
| impl<Borrowed> SampleBorrow<Borrowed> for Borrowed where Borrowed: SampleUniform { |
| #[inline(always)] |
| fn borrow(&self) -> &Borrowed { self } |
| } |
| impl<'a, Borrowed> SampleBorrow<Borrowed> for &'a Borrowed where Borrowed: SampleUniform { |
| #[inline(always)] |
| fn borrow(&self) -> &Borrowed { *self } |
| } |
| |
| //////////////////////////////////////////////////////////////////////////////// |
| |
| // What follows are all back-ends. |
| |
| |
| /// The back-end implementing [`UniformSampler`] for integer types. |
| /// |
| /// Unless you are implementing [`UniformSampler`] for your own type, this type |
| /// should not be used directly, use [`Uniform`] instead. |
| /// |
| /// # Implementation notes |
| /// |
| /// For simplicity, we use the same generic struct `UniformInt<X>` for all |
| /// integer types `X`. This gives us only one field type, `X`; to store unsigned |
| /// values of this size, we take use the fact that these conversions are no-ops. |
| /// |
| /// For a closed range, the number of possible numbers we should generate is |
| /// `range = (high - low + 1)`. To avoid bias, we must ensure that the size of |
| /// our sample space, `zone`, is a multiple of `range`; other values must be |
| /// rejected (by replacing with a new random sample). |
| /// |
| /// As a special case, we use `range = 0` to represent the full range of the |
| /// result type (i.e. for `new_inclusive($ty::MIN, $ty::MAX)`). |
| /// |
| /// The optimum `zone` is the largest product of `range` which fits in our |
| /// (unsigned) target type. We calculate this by calculating how many numbers we |
| /// must reject: `reject = (MAX + 1) % range = (MAX - range + 1) % range`. Any (large) |
| /// product of `range` will suffice, thus in `sample_single` we multiply by a |
| /// power of 2 via bit-shifting (faster but may cause more rejections). |
| /// |
| /// The smallest integer PRNGs generate is `u32`. For 8- and 16-bit outputs we |
| /// use `u32` for our `zone` and samples (because it's not slower and because |
| /// it reduces the chance of having to reject a sample). In this case we cannot |
| /// store `zone` in the target type since it is too large, however we know |
| /// `ints_to_reject < range <= $unsigned::MAX`. |
| /// |
| /// An alternative to using a modulus is widening multiply: After a widening |
| /// multiply by `range`, the result is in the high word. Then comparing the low |
| /// word against `zone` makes sure our distribution is uniform. |
| #[derive(Clone, Copy, Debug)] |
| pub struct UniformInt<X> { |
| low: X, |
| range: X, |
| z: X, // either ints_to_reject or zone depending on implementation |
| } |
| |
| macro_rules! uniform_int_impl { |
| ($ty:ty, $unsigned:ident, $u_large:ident) => { |
| impl SampleUniform for $ty { |
| type Sampler = UniformInt<$ty>; |
| } |
| |
| impl UniformSampler for UniformInt<$ty> { |
| // We play free and fast with unsigned vs signed here |
| // (when $ty is signed), but that's fine, since the |
| // contract of this macro is for $ty and $unsigned to be |
| // "bit-equal", so casting between them is a no-op. |
| |
| type X = $ty; |
| |
| #[inline] // if the range is constant, this helps LLVM to do the |
| // calculations at compile-time. |
| fn new<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low < high, "Uniform::new called with `low >= high`"); |
| UniformSampler::new_inclusive(low, high - 1) |
| } |
| |
| #[inline] // if the range is constant, this helps LLVM to do the |
| // calculations at compile-time. |
| fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low <= high, |
| "Uniform::new_inclusive called with `low > high`"); |
| let unsigned_max = ::core::$u_large::MAX; |
| |
| let range = high.wrapping_sub(low).wrapping_add(1) as $unsigned; |
| let ints_to_reject = |
| if range > 0 { |
| let range = $u_large::from(range); |
| (unsigned_max - range + 1) % range |
| } else { |
| 0 |
| }; |
| |
| UniformInt { |
| low: low, |
| // These are really $unsigned values, but store as $ty: |
| range: range as $ty, |
| z: ints_to_reject as $unsigned as $ty |
| } |
| } |
| |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { |
| let range = self.range as $unsigned as $u_large; |
| if range > 0 { |
| let unsigned_max = ::core::$u_large::MAX; |
| let zone = unsigned_max - (self.z as $unsigned as $u_large); |
| loop { |
| let v: $u_large = rng.gen(); |
| let (hi, lo) = v.wmul(range); |
| if lo <= zone { |
| return self.low.wrapping_add(hi as $ty); |
| } |
| } |
| } else { |
| // Sample from the entire integer range. |
| rng.gen() |
| } |
| } |
| |
| fn sample_single<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) |
| -> Self::X |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low < high, |
| "UniformSampler::sample_single: low >= high"); |
| let range = high.wrapping_sub(low) as $unsigned as $u_large; |
| let zone = |
| if ::core::$unsigned::MAX <= ::core::u16::MAX as $unsigned { |
| // Using a modulus is faster than the approximation for |
| // i8 and i16. I suppose we trade the cost of one |
| // modulus for near-perfect branch prediction. |
| let unsigned_max: $u_large = ::core::$u_large::MAX; |
| let ints_to_reject = (unsigned_max - range + 1) % range; |
| unsigned_max - ints_to_reject |
| } else { |
| // conservative but fast approximation. `- 1` is necessary to allow the |
| // same comparison without bias. |
| (range << range.leading_zeros()).wrapping_sub(1) |
| }; |
| |
| loop { |
| let v: $u_large = rng.gen(); |
| let (hi, lo) = v.wmul(range); |
| if lo <= zone { |
| return low.wrapping_add(hi as $ty); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| uniform_int_impl! { i8, u8, u32 } |
| uniform_int_impl! { i16, u16, u32 } |
| uniform_int_impl! { i32, u32, u32 } |
| uniform_int_impl! { i64, u64, u64 } |
| #[cfg(not(target_os = "emscripten"))] |
| uniform_int_impl! { i128, u128, u128 } |
| uniform_int_impl! { isize, usize, usize } |
| uniform_int_impl! { u8, u8, u32 } |
| uniform_int_impl! { u16, u16, u32 } |
| uniform_int_impl! { u32, u32, u32 } |
| uniform_int_impl! { u64, u64, u64 } |
| uniform_int_impl! { usize, usize, usize } |
| #[cfg(not(target_os = "emscripten"))] |
| uniform_int_impl! { u128, u128, u128 } |
| |
| #[cfg(all(feature = "simd_support", feature = "nightly"))] |
| macro_rules! uniform_simd_int_impl { |
| ($ty:ident, $unsigned:ident, $u_scalar:ident) => { |
| // The "pick the largest zone that can fit in an `u32`" optimization |
| // is less useful here. Multiple lanes complicate things, we don't |
| // know the PRNG's minimal output size, and casting to a larger vector |
| // is generally a bad idea for SIMD performance. The user can still |
| // implement it manually. |
| |
| // TODO: look into `Uniform::<u32x4>::new(0u32, 100)` functionality |
| // perhaps `impl SampleUniform for $u_scalar`? |
| impl SampleUniform for $ty { |
| type Sampler = UniformInt<$ty>; |
| } |
| |
| impl UniformSampler for UniformInt<$ty> { |
| type X = $ty; |
| |
| #[inline] // if the range is constant, this helps LLVM to do the |
| // calculations at compile-time. |
| fn new<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low.lt(high).all(), "Uniform::new called with `low >= high`"); |
| UniformSampler::new_inclusive(low, high - 1) |
| } |
| |
| #[inline] // if the range is constant, this helps LLVM to do the |
| // calculations at compile-time. |
| fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low.le(high).all(), |
| "Uniform::new_inclusive called with `low > high`"); |
| let unsigned_max = ::core::$u_scalar::MAX; |
| |
| // NOTE: these may need to be replaced with explicitly |
| // wrapping operations if `packed_simd` changes |
| let range: $unsigned = ((high - low) + 1).cast(); |
| // `% 0` will panic at runtime. |
| let not_full_range = range.gt($unsigned::splat(0)); |
| // replacing 0 with `unsigned_max` allows a faster `select` |
| // with bitwise OR |
| let modulo = not_full_range.select(range, $unsigned::splat(unsigned_max)); |
| // wrapping addition |
| let ints_to_reject = (unsigned_max - range + 1) % modulo; |
| // When `range` is 0, `lo` of `v.wmul(range)` will always be |
| // zero which means only one sample is needed. |
| let zone = unsigned_max - ints_to_reject; |
| |
| UniformInt { |
| low: low, |
| // These are really $unsigned values, but store as $ty: |
| range: range.cast(), |
| z: zone.cast(), |
| } |
| } |
| |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { |
| let range: $unsigned = self.range.cast(); |
| let zone: $unsigned = self.z.cast(); |
| |
| // This might seem very slow, generating a whole new |
| // SIMD vector for every sample rejection. For most uses |
| // though, the chance of rejection is small and provides good |
| // general performance. With multiple lanes, that chance is |
| // multiplied. To mitigate this, we replace only the lanes of |
| // the vector which fail, iteratively reducing the chance of |
| // rejection. The replacement method does however add a little |
| // overhead. Benchmarking or calculating probabilities might |
| // reveal contexts where this replacement method is slower. |
| let mut v: $unsigned = rng.gen(); |
| loop { |
| let (hi, lo) = v.wmul(range); |
| let mask = lo.le(zone); |
| if mask.all() { |
| let hi: $ty = hi.cast(); |
| // wrapping addition |
| let result = self.low + hi; |
| // `select` here compiles to a blend operation |
| // When `range.eq(0).none()` the compare and blend |
| // operations are avoided. |
| let v: $ty = v.cast(); |
| return range.gt($unsigned::splat(0)).select(result, v); |
| } |
| // Replace only the failing lanes |
| v = mask.select(v, rng.gen()); |
| } |
| } |
| } |
| }; |
| |
| // bulk implementation |
| ($(($unsigned:ident, $signed:ident),)+ $u_scalar:ident) => { |
| $( |
| uniform_simd_int_impl!($unsigned, $unsigned, $u_scalar); |
| uniform_simd_int_impl!($signed, $unsigned, $u_scalar); |
| )+ |
| }; |
| } |
| |
| #[cfg(all(feature = "simd_support", feature = "nightly"))] |
| uniform_simd_int_impl! { |
| (u64x2, i64x2), |
| (u64x4, i64x4), |
| (u64x8, i64x8), |
| u64 |
| } |
| |
| #[cfg(all(feature = "simd_support", feature = "nightly"))] |
| uniform_simd_int_impl! { |
| (u32x2, i32x2), |
| (u32x4, i32x4), |
| (u32x8, i32x8), |
| (u32x16, i32x16), |
| u32 |
| } |
| |
| #[cfg(all(feature = "simd_support", feature = "nightly"))] |
| uniform_simd_int_impl! { |
| (u16x2, i16x2), |
| (u16x4, i16x4), |
| (u16x8, i16x8), |
| (u16x16, i16x16), |
| (u16x32, i16x32), |
| u16 |
| } |
| |
| #[cfg(all(feature = "simd_support", feature = "nightly"))] |
| uniform_simd_int_impl! { |
| (u8x2, i8x2), |
| (u8x4, i8x4), |
| (u8x8, i8x8), |
| (u8x16, i8x16), |
| (u8x32, i8x32), |
| (u8x64, i8x64), |
| u8 |
| } |
| |
| |
| /// The back-end implementing [`UniformSampler`] for floating-point types. |
| /// |
| /// Unless you are implementing [`UniformSampler`] for your own type, this type |
| /// should not be used directly, use [`Uniform`] instead. |
| /// |
| /// # Implementation notes |
| /// |
| /// Instead of generating a float in the `[0, 1)` range using [`Standard`], the |
| /// `UniformFloat` implementation converts the output of an PRNG itself. This |
| /// way one or two steps can be optimized out. |
| /// |
| /// The floats are first converted to a value in the `[1, 2)` interval using a |
| /// transmute-based method, and then mapped to the expected range with a |
| /// multiply and addition. Values produced this way have what equals 22 bits of |
| /// random digits for an `f32`, and 52 for an `f64`. |
| /// |
| /// [`new`]: UniformSampler::new |
| /// [`new_inclusive`]: UniformSampler::new_inclusive |
| /// [`Standard`]: crate::distributions::Standard |
| #[derive(Clone, Copy, Debug)] |
| pub struct UniformFloat<X> { |
| low: X, |
| scale: X, |
| } |
| |
| macro_rules! uniform_float_impl { |
| ($ty:ty, $uty:ident, $f_scalar:ident, $u_scalar:ident, $bits_to_discard:expr) => { |
| impl SampleUniform for $ty { |
| type Sampler = UniformFloat<$ty>; |
| } |
| |
| impl UniformSampler for UniformFloat<$ty> { |
| type X = $ty; |
| |
| fn new<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low.all_lt(high), |
| "Uniform::new called with `low >= high`"); |
| assert!(low.all_finite() && high.all_finite(), |
| "Uniform::new called with non-finite boundaries"); |
| let max_rand = <$ty>::splat((::core::$u_scalar::MAX >> $bits_to_discard) |
| .into_float_with_exponent(0) - 1.0); |
| |
| let mut scale = high - low; |
| |
| loop { |
| let mask = (scale * max_rand + low).ge_mask(high); |
| if mask.none() { |
| break; |
| } |
| scale = scale.decrease_masked(mask); |
| } |
| |
| debug_assert!(<$ty>::splat(0.0).all_le(scale)); |
| |
| UniformFloat { low, scale } |
| } |
| |
| fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low.all_le(high), |
| "Uniform::new_inclusive called with `low > high`"); |
| assert!(low.all_finite() && high.all_finite(), |
| "Uniform::new_inclusive called with non-finite boundaries"); |
| let max_rand = <$ty>::splat((::core::$u_scalar::MAX >> $bits_to_discard) |
| .into_float_with_exponent(0) - 1.0); |
| |
| let mut scale = (high - low) / max_rand; |
| |
| loop { |
| let mask = (scale * max_rand + low).gt_mask(high); |
| if mask.none() { |
| break; |
| } |
| scale = scale.decrease_masked(mask); |
| } |
| |
| debug_assert!(<$ty>::splat(0.0).all_le(scale)); |
| |
| UniformFloat { low, scale } |
| } |
| |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { |
| // Generate a value in the range [1, 2) |
| let value1_2 = (rng.gen::<$uty>() >> $bits_to_discard) |
| .into_float_with_exponent(0); |
| |
| // Get a value in the range [0, 1) in order to avoid |
| // overflowing into infinity when multiplying with scale |
| let value0_1 = value1_2 - 1.0; |
| |
| // We don't use `f64::mul_add`, because it is not available with |
| // `no_std`. Furthermore, it is slower for some targets (but |
| // faster for others). However, the order of multiplication and |
| // addition is important, because on some platforms (e.g. ARM) |
| // it will be optimized to a single (non-FMA) instruction. |
| value0_1 * self.scale + self.low |
| } |
| |
| #[inline] |
| fn sample_single<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) |
| -> Self::X |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low.all_lt(high), |
| "UniformSampler::sample_single: low >= high"); |
| let mut scale = high - low; |
| |
| loop { |
| // Generate a value in the range [1, 2) |
| let value1_2 = (rng.gen::<$uty>() >> $bits_to_discard) |
| .into_float_with_exponent(0); |
| |
| // Get a value in the range [0, 1) in order to avoid |
| // overflowing into infinity when multiplying with scale |
| let value0_1 = value1_2 - 1.0; |
| |
| // Doing multiply before addition allows some architectures |
| // to use a single instruction. |
| let res = value0_1 * scale + low; |
| |
| debug_assert!(low.all_le(res) || !scale.all_finite()); |
| if res.all_lt(high) { |
| return res; |
| } |
| |
| // This handles a number of edge cases. |
| // * `low` or `high` is NaN. In this case `scale` and |
| // `res` are going to end up as NaN. |
| // * `low` is negative infinity and `high` is finite. |
| // `scale` is going to be infinite and `res` will be |
| // NaN. |
| // * `high` is positive infinity and `low` is finite. |
| // `scale` is going to be infinite and `res` will |
| // be infinite or NaN (if value0_1 is 0). |
| // * `low` is negative infinity and `high` is positive |
| // infinity. `scale` will be infinite and `res` will |
| // be NaN. |
| // * `low` and `high` are finite, but `high - low` |
| // overflows to infinite. `scale` will be infinite |
| // and `res` will be infinite or NaN (if value0_1 is 0). |
| // So if `high` or `low` are non-finite, we are guaranteed |
| // to fail the `res < high` check above and end up here. |
| // |
| // While we technically should check for non-finite `low` |
| // and `high` before entering the loop, by doing the checks |
| // here instead, we allow the common case to avoid these |
| // checks. But we are still guaranteed that if `low` or |
| // `high` are non-finite we'll end up here and can do the |
| // appropriate checks. |
| // |
| // Likewise `high - low` overflowing to infinity is also |
| // rare, so handle it here after the common case. |
| let mask = !scale.finite_mask(); |
| if mask.any() { |
| assert!(low.all_finite() && high.all_finite(), |
| "Uniform::sample_single: low and high must be finite"); |
| scale = scale.decrease_masked(mask); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| uniform_float_impl! { f32, u32, f32, u32, 32 - 23 } |
| uniform_float_impl! { f64, u64, f64, u64, 64 - 52 } |
| |
| #[cfg(feature="simd_support")] |
| uniform_float_impl! { f32x2, u32x2, f32, u32, 32 - 23 } |
| #[cfg(feature="simd_support")] |
| uniform_float_impl! { f32x4, u32x4, f32, u32, 32 - 23 } |
| #[cfg(feature="simd_support")] |
| uniform_float_impl! { f32x8, u32x8, f32, u32, 32 - 23 } |
| #[cfg(feature="simd_support")] |
| uniform_float_impl! { f32x16, u32x16, f32, u32, 32 - 23 } |
| |
| #[cfg(feature="simd_support")] |
| uniform_float_impl! { f64x2, u64x2, f64, u64, 64 - 52 } |
| #[cfg(feature="simd_support")] |
| uniform_float_impl! { f64x4, u64x4, f64, u64, 64 - 52 } |
| #[cfg(feature="simd_support")] |
| uniform_float_impl! { f64x8, u64x8, f64, u64, 64 - 52 } |
| |
| |
| |
| /// The back-end implementing [`UniformSampler`] for `Duration`. |
| /// |
| /// Unless you are implementing [`UniformSampler`] for your own types, this type |
| /// should not be used directly, use [`Uniform`] instead. |
| #[derive(Clone, Copy, Debug)] |
| pub struct UniformDuration { |
| mode: UniformDurationMode, |
| offset: u32, |
| } |
| |
| #[derive(Debug, Copy, Clone)] |
| enum UniformDurationMode { |
| Small { |
| secs: u64, |
| nanos: Uniform<u32>, |
| }, |
| Medium { |
| nanos: Uniform<u64>, |
| }, |
| Large { |
| max_secs: u64, |
| max_nanos: u32, |
| secs: Uniform<u64>, |
| } |
| } |
| |
| impl SampleUniform for Duration { |
| type Sampler = UniformDuration; |
| } |
| |
| impl UniformSampler for UniformDuration { |
| type X = Duration; |
| |
| #[inline] |
| fn new<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low < high, "Uniform::new called with `low >= high`"); |
| UniformDuration::new_inclusive(low, high - Duration::new(0, 1)) |
| } |
| |
| #[inline] |
| fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| let low = *low_b.borrow(); |
| let high = *high_b.borrow(); |
| assert!(low <= high, "Uniform::new_inclusive called with `low > high`"); |
| |
| let low_s = low.as_secs(); |
| let low_n = low.subsec_nanos(); |
| let mut high_s = high.as_secs(); |
| let mut high_n = high.subsec_nanos(); |
| |
| if high_n < low_n { |
| high_s -= 1; |
| high_n += 1_000_000_000; |
| } |
| |
| let mode = if low_s == high_s { |
| UniformDurationMode::Small { |
| secs: low_s, |
| nanos: Uniform::new_inclusive(low_n, high_n), |
| } |
| } else { |
| let max = high_s |
| .checked_mul(1_000_000_000) |
| .and_then(|n| n.checked_add(u64::from(high_n))); |
| |
| if let Some(higher_bound) = max { |
| let lower_bound = low_s * 1_000_000_000 + u64::from(low_n); |
| UniformDurationMode::Medium { |
| nanos: Uniform::new_inclusive(lower_bound, higher_bound), |
| } |
| } else { |
| // An offset is applied to simplify generation of nanoseconds |
| let max_nanos = high_n - low_n; |
| UniformDurationMode::Large { |
| max_secs: high_s, |
| max_nanos, |
| secs: Uniform::new_inclusive(low_s, high_s), |
| } |
| } |
| }; |
| UniformDuration { |
| mode, |
| offset: low_n, |
| } |
| } |
| |
| #[inline] |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Duration { |
| match self.mode { |
| UniformDurationMode::Small { secs, nanos } => { |
| let n = nanos.sample(rng); |
| Duration::new(secs, n) |
| } |
| UniformDurationMode::Medium { nanos } => { |
| let nanos = nanos.sample(rng); |
| Duration::new(nanos / 1_000_000_000, (nanos % 1_000_000_000) as u32) |
| } |
| UniformDurationMode::Large { max_secs, max_nanos, secs } => { |
| // constant folding means this is at least as fast as `gen_range` |
| let nano_range = Uniform::new(0, 1_000_000_000); |
| loop { |
| let s = secs.sample(rng); |
| let n = nano_range.sample(rng); |
| if !(s == max_secs && n > max_nanos) { |
| let sum = n + self.offset; |
| break Duration::new(s, sum); |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| #[cfg(test)] |
| mod tests { |
| use super::*; |
| use crate::rngs::mock::StepRng; |
| |
| #[should_panic] |
| #[test] |
| fn test_uniform_bad_limits_equal_int() { |
| Uniform::new(10, 10); |
| } |
| |
| #[test] |
| fn test_uniform_good_limits_equal_int() { |
| let mut rng = crate::test::rng(804); |
| let dist = Uniform::new_inclusive(10, 10); |
| for _ in 0..20 { |
| assert_eq!(rng.sample(dist), 10); |
| } |
| } |
| |
| #[should_panic] |
| #[test] |
| fn test_uniform_bad_limits_flipped_int() { |
| Uniform::new(10, 5); |
| } |
| |
| #[test] |
| #[cfg(not(miri))] // Miri is too slow |
| fn test_integers() { |
| use core::{i8, i16, i32, i64, isize}; |
| use core::{u8, u16, u32, u64, usize}; |
| #[cfg(not(target_os = "emscripten"))] |
| use core::{i128, u128}; |
| |
| let mut rng = crate::test::rng(251); |
| macro_rules! t { |
| ($ty:ident, $v:expr, $le:expr, $lt:expr) => {{ |
| for &(low, high) in $v.iter() { |
| let my_uniform = Uniform::new(low, high); |
| for _ in 0..1000 { |
| let v: $ty = rng.sample(my_uniform); |
| assert!($le(low, v) && $lt(v, high)); |
| } |
| |
| let my_uniform = Uniform::new_inclusive(low, high); |
| for _ in 0..1000 { |
| let v: $ty = rng.sample(my_uniform); |
| assert!($le(low, v) && $le(v, high)); |
| } |
| |
| let my_uniform = Uniform::new(&low, high); |
| for _ in 0..1000 { |
| let v: $ty = rng.sample(my_uniform); |
| assert!($le(low, v) && $lt(v, high)); |
| } |
| |
| let my_uniform = Uniform::new_inclusive(&low, &high); |
| for _ in 0..1000 { |
| let v: $ty = rng.sample(my_uniform); |
| assert!($le(low, v) && $le(v, high)); |
| } |
| |
| for _ in 0..1000 { |
| let v: $ty = rng.gen_range(low, high); |
| assert!($le(low, v) && $lt(v, high)); |
| } |
| } |
| }}; |
| |
| // scalar bulk |
| ($($ty:ident),*) => {{ |
| $(t!( |
| $ty, |
| [(0, 10), (10, 127), ($ty::MIN, $ty::MAX)], |
| |x, y| x <= y, |
| |x, y| x < y |
| );)* |
| }}; |
| |
| // simd bulk |
| ($($ty:ident),* => $scalar:ident) => {{ |
| $(t!( |
| $ty, |
| [ |
| ($ty::splat(0), $ty::splat(10)), |
| ($ty::splat(10), $ty::splat(127)), |
| ($ty::splat($scalar::MIN), $ty::splat($scalar::MAX)), |
| ], |
| |x: $ty, y| x.le(y).all(), |
| |x: $ty, y| x.lt(y).all() |
| );)* |
| }}; |
| } |
| t!(i8, i16, i32, i64, isize, |
| u8, u16, u32, u64, usize); |
| #[cfg(not(target_os = "emscripten"))] |
| t!(i128, u128); |
| |
| #[cfg(all(feature = "simd_support", feature = "nightly"))] |
| { |
| t!(u8x2, u8x4, u8x8, u8x16, u8x32, u8x64 => u8); |
| t!(i8x2, i8x4, i8x8, i8x16, i8x32, i8x64 => i8); |
| t!(u16x2, u16x4, u16x8, u16x16, u16x32 => u16); |
| t!(i16x2, i16x4, i16x8, i16x16, i16x32 => i16); |
| t!(u32x2, u32x4, u32x8, u32x16 => u32); |
| t!(i32x2, i32x4, i32x8, i32x16 => i32); |
| t!(u64x2, u64x4, u64x8 => u64); |
| t!(i64x2, i64x4, i64x8 => i64); |
| } |
| } |
| |
| #[test] |
| #[cfg(not(miri))] // Miri is too slow |
| fn test_floats() { |
| let mut rng = crate::test::rng(252); |
| let mut zero_rng = StepRng::new(0, 0); |
| let mut max_rng = StepRng::new(0xffff_ffff_ffff_ffff, 0); |
| macro_rules! t { |
| ($ty:ty, $f_scalar:ident, $bits_shifted:expr) => {{ |
| let v: &[($f_scalar, $f_scalar)]= |
| &[(0.0, 100.0), |
| (-1e35, -1e25), |
| (1e-35, 1e-25), |
| (-1e35, 1e35), |
| (<$f_scalar>::from_bits(0), <$f_scalar>::from_bits(3)), |
| (-<$f_scalar>::from_bits(10), -<$f_scalar>::from_bits(1)), |
| (-<$f_scalar>::from_bits(5), 0.0), |
| (-<$f_scalar>::from_bits(7), -0.0), |
| (10.0, ::core::$f_scalar::MAX), |
| (-100.0, ::core::$f_scalar::MAX), |
| (-::core::$f_scalar::MAX / 5.0, ::core::$f_scalar::MAX), |
| (-::core::$f_scalar::MAX, ::core::$f_scalar::MAX / 5.0), |
| (-::core::$f_scalar::MAX * 0.8, ::core::$f_scalar::MAX * 0.7), |
| (-::core::$f_scalar::MAX, ::core::$f_scalar::MAX), |
| ]; |
| for &(low_scalar, high_scalar) in v.iter() { |
| for lane in 0..<$ty>::lanes() { |
| let low = <$ty>::splat(0.0 as $f_scalar).replace(lane, low_scalar); |
| let high = <$ty>::splat(1.0 as $f_scalar).replace(lane, high_scalar); |
| let my_uniform = Uniform::new(low, high); |
| let my_incl_uniform = Uniform::new_inclusive(low, high); |
| for _ in 0..100 { |
| let v = rng.sample(my_uniform).extract(lane); |
| assert!(low_scalar <= v && v < high_scalar); |
| let v = rng.sample(my_incl_uniform).extract(lane); |
| assert!(low_scalar <= v && v <= high_scalar); |
| let v = rng.gen_range(low, high).extract(lane); |
| assert!(low_scalar <= v && v < high_scalar); |
| } |
| |
| assert_eq!(rng.sample(Uniform::new_inclusive(low, low)).extract(lane), low_scalar); |
| |
| assert_eq!(zero_rng.sample(my_uniform).extract(lane), low_scalar); |
| assert_eq!(zero_rng.sample(my_incl_uniform).extract(lane), low_scalar); |
| assert_eq!(zero_rng.gen_range(low, high).extract(lane), low_scalar); |
| assert!(max_rng.sample(my_uniform).extract(lane) < high_scalar); |
| assert!(max_rng.sample(my_incl_uniform).extract(lane) <= high_scalar); |
| |
| // Don't run this test for really tiny differences between high and low |
| // since for those rounding might result in selecting high for a very |
| // long time. |
| if (high_scalar - low_scalar) > 0.0001 { |
| let mut lowering_max_rng = |
| StepRng::new(0xffff_ffff_ffff_ffff, |
| (-1i64 << $bits_shifted) as u64); |
| assert!(lowering_max_rng.gen_range(low, high).extract(lane) < high_scalar); |
| } |
| } |
| } |
| |
| assert_eq!(rng.sample(Uniform::new_inclusive(::core::$f_scalar::MAX, |
| ::core::$f_scalar::MAX)), |
| ::core::$f_scalar::MAX); |
| assert_eq!(rng.sample(Uniform::new_inclusive(-::core::$f_scalar::MAX, |
| -::core::$f_scalar::MAX)), |
| -::core::$f_scalar::MAX); |
| }} |
| } |
| |
| t!(f32, f32, 32 - 23); |
| t!(f64, f64, 64 - 52); |
| #[cfg(feature="simd_support")] |
| { |
| t!(f32x2, f32, 32 - 23); |
| t!(f32x4, f32, 32 - 23); |
| t!(f32x8, f32, 32 - 23); |
| t!(f32x16, f32, 32 - 23); |
| t!(f64x2, f64, 64 - 52); |
| t!(f64x4, f64, 64 - 52); |
| t!(f64x8, f64, 64 - 52); |
| } |
| } |
| |
| #[test] |
| #[cfg(all(feature="std", |
| not(target_arch = "wasm32"), |
| not(target_arch = "asmjs")))] |
| #[cfg(not(miri))] // Miri does not support catching panics |
| fn test_float_assertions() { |
| use std::panic::catch_unwind; |
| use super::SampleUniform; |
| fn range<T: SampleUniform>(low: T, high: T) { |
| let mut rng = crate::test::rng(253); |
| rng.gen_range(low, high); |
| } |
| |
| macro_rules! t { |
| ($ty:ident, $f_scalar:ident) => {{ |
| let v: &[($f_scalar, $f_scalar)] = |
| &[(::std::$f_scalar::NAN, 0.0), |
| (1.0, ::std::$f_scalar::NAN), |
| (::std::$f_scalar::NAN, ::std::$f_scalar::NAN), |
| (1.0, 0.5), |
| (::std::$f_scalar::MAX, -::std::$f_scalar::MAX), |
| (::std::$f_scalar::INFINITY, ::std::$f_scalar::INFINITY), |
| (::std::$f_scalar::NEG_INFINITY, ::std::$f_scalar::NEG_INFINITY), |
| (::std::$f_scalar::NEG_INFINITY, 5.0), |
| (5.0, ::std::$f_scalar::INFINITY), |
| (::std::$f_scalar::NAN, ::std::$f_scalar::INFINITY), |
| (::std::$f_scalar::NEG_INFINITY, ::std::$f_scalar::NAN), |
| (::std::$f_scalar::NEG_INFINITY, ::std::$f_scalar::INFINITY), |
| ]; |
| for &(low_scalar, high_scalar) in v.iter() { |
| for lane in 0..<$ty>::lanes() { |
| let low = <$ty>::splat(0.0 as $f_scalar).replace(lane, low_scalar); |
| let high = <$ty>::splat(1.0 as $f_scalar).replace(lane, high_scalar); |
| assert!(catch_unwind(|| range(low, high)).is_err()); |
| assert!(catch_unwind(|| Uniform::new(low, high)).is_err()); |
| assert!(catch_unwind(|| Uniform::new_inclusive(low, high)).is_err()); |
| assert!(catch_unwind(|| range(low, low)).is_err()); |
| assert!(catch_unwind(|| Uniform::new(low, low)).is_err()); |
| } |
| } |
| }} |
| } |
| |
| t!(f32, f32); |
| t!(f64, f64); |
| #[cfg(feature="simd_support")] |
| { |
| t!(f32x2, f32); |
| t!(f32x4, f32); |
| t!(f32x8, f32); |
| t!(f32x16, f32); |
| t!(f64x2, f64); |
| t!(f64x4, f64); |
| t!(f64x8, f64); |
| } |
| } |
| |
| |
| #[test] |
| #[cfg(not(miri))] // Miri is too slow |
| fn test_durations() { |
| #[cfg(feature = "std")] |
| use std::time::Duration; |
| #[cfg(not(feature = "std"))] |
| use core::time::Duration; |
| |
| let mut rng = crate::test::rng(253); |
| |
| let v = &[(Duration::new(10, 50000), Duration::new(100, 1234)), |
| (Duration::new(0, 100), Duration::new(1, 50)), |
| (Duration::new(0, 0), Duration::new(u64::max_value(), 999_999_999))]; |
| for &(low, high) in v.iter() { |
| let my_uniform = Uniform::new(low, high); |
| for _ in 0..1000 { |
| let v = rng.sample(my_uniform); |
| assert!(low <= v && v < high); |
| } |
| } |
| } |
| |
| #[test] |
| fn test_custom_uniform() { |
| use crate::distributions::uniform::{UniformSampler, UniformFloat, SampleUniform, SampleBorrow}; |
| #[derive(Clone, Copy, PartialEq, PartialOrd)] |
| struct MyF32 { |
| x: f32, |
| } |
| #[derive(Clone, Copy, Debug)] |
| struct UniformMyF32(UniformFloat<f32>); |
| impl UniformSampler for UniformMyF32 { |
| type X = MyF32; |
| fn new<B1, B2>(low: B1, high: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| UniformMyF32(UniformFloat::<f32>::new(low.borrow().x, high.borrow().x)) |
| } |
| fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self |
| where B1: SampleBorrow<Self::X> + Sized, |
| B2: SampleBorrow<Self::X> + Sized |
| { |
| UniformSampler::new(low, high) |
| } |
| fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X { |
| MyF32 { x: self.0.sample(rng) } |
| } |
| } |
| impl SampleUniform for MyF32 { |
| type Sampler = UniformMyF32; |
| } |
| |
| let (low, high) = (MyF32{ x: 17.0f32 }, MyF32{ x: 22.0f32 }); |
| let uniform = Uniform::new(low, high); |
| let mut rng = crate::test::rng(804); |
| for _ in 0..100 { |
| let x: MyF32 = rng.sample(uniform); |
| assert!(low <= x && x < high); |
| } |
| } |
| |
| #[test] |
| fn test_uniform_from_std_range() { |
| let r = Uniform::from(2u32..7); |
| assert_eq!(r.0.low, 2); |
| assert_eq!(r.0.range, 5); |
| let r = Uniform::from(2.0f64..7.0); |
| assert_eq!(r.0.low, 2.0); |
| assert_eq!(r.0.scale, 5.0); |
| } |
| |
| #[test] |
| fn test_uniform_from_std_range_inclusive() { |
| let r = Uniform::from(2u32..=6); |
| assert_eq!(r.0.low, 2); |
| assert_eq!(r.0.range, 5); |
| let r = Uniform::from(2.0f64..=7.0); |
| assert_eq!(r.0.low, 2.0); |
| assert!(r.0.scale > 5.0); |
| assert!(r.0.scale < 5.0 + 1e-14); |
| } |
| |
| #[test] |
| fn value_stability() { |
| fn test_samples<T: SampleUniform + Copy + core::fmt::Debug + PartialEq>( |
| lb: T, ub: T, expected_single: &[T], expected_multiple: &[T] |
| ) |
| where Uniform<T>: Distribution<T> |
| { |
| let mut rng = crate::test::rng(897); |
| let mut buf = [lb; 3]; |
| |
| for x in &mut buf { |
| *x = T::Sampler::sample_single(lb, ub, &mut rng); |
| } |
| assert_eq!(&buf, expected_single); |
| |
| let distr = Uniform::new(lb, ub); |
| for x in &mut buf { |
| *x = rng.sample(&distr); |
| } |
| assert_eq!(&buf, expected_multiple); |
| } |
| |
| // We test on a sub-set of types; possibly we should do more. |
| // TODO: SIMD types |
| |
| test_samples(11u8, 219, &[17, 66, 214], &[181, 93, 165]); |
| test_samples(11u32, 219, &[17, 66, 214], &[181, 93, 165]); |
| |
| test_samples(0f32, 1e-2f32, &[0.0003070104, 0.0026630748, 0.00979833], |
| &[0.008194133, 0.00398172, 0.007428536]); |
| test_samples(-1e10f64, 1e10f64, |
| &[-4673848682.871551, 6388267422.932352, 4857075081.198343], |
| &[1173375212.1808167, 1917642852.109581, 2365076174.3153973]); |
| |
| test_samples(Duration::new(2, 0), Duration::new(4, 0), |
| &[Duration::new(2,532615131), Duration::new(3,638826742), Duration::new(3,485707508)], &[Duration::new(3,117337521), Duration::new(3,191764285), Duration::new(3,236507617)]); |
| } |
| } |
