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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.
-
-//! The Bernoulli distribution.
-
-use crate::distributions::Distribution;
-use crate::Rng;
-use core::{fmt, u64};
-
-#[cfg(feature = "serde1")]
-use serde::{Serialize, Deserialize};
-/// The Bernoulli distribution.
-///
-/// This is a special case of the Binomial distribution where `n = 1`.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Bernoulli, Distribution};
-///
-/// let d = Bernoulli::new(0.3).unwrap();
-/// let v = d.sample(&mut rand::thread_rng());
-/// println!("{} is from a Bernoulli distribution", v);
-/// ```
-///
-/// # Precision
-///
-/// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`),
-/// so only probabilities that are multiples of 2<sup>-64</sup> can be
-/// represented.
-#[derive(Clone, Copy, Debug, PartialEq)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-pub struct Bernoulli {
-    /// Probability of success, relative to the maximal integer.
-    p_int: u64,
-}
-
-// To sample from the Bernoulli distribution we use a method that compares a
-// random `u64` value `v < (p * 2^64)`.
-//
-// If `p == 1.0`, the integer `v` to compare against can not represented as a
-// `u64`. We manually set it to `u64::MAX` instead (2^64 - 1 instead of 2^64).
-// Note that  value of `p < 1.0` can never result in `u64::MAX`, because an
-// `f64` only has 53 bits of precision, and the next largest value of `p` will
-// result in `2^64 - 2048`.
-//
-// Also there is a 100% theoretical concern: if someone consistently wants to
-// generate `true` using the Bernoulli distribution (i.e. by using a probability
-// of `1.0`), just using `u64::MAX` is not enough. On average it would return
-// false once every 2^64 iterations. Some people apparently care about this
-// case.
-//
-// That is why we special-case `u64::MAX` to always return `true`, without using
-// the RNG, and pay the performance price for all uses that *are* reasonable.
-// Luckily, if `new()` and `sample` are close, the compiler can optimize out the
-// extra check.
-const ALWAYS_TRUE: u64 = u64::MAX;
-
-// This is just `2.0.powi(64)`, but written this way because it is not available
-// in `no_std` mode.
-const SCALE: f64 = 2.0 * (1u64 << 63) as f64;
-
-/// Error type returned from `Bernoulli::new`.
-#[derive(Clone, Copy, Debug, PartialEq, Eq)]
-pub enum BernoulliError {
-    /// `p < 0` or `p > 1`.
-    InvalidProbability,
-}
-
-impl fmt::Display for BernoulliError {
-    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
-        f.write_str(match self {
-            BernoulliError::InvalidProbability => "p is outside [0, 1] in Bernoulli distribution",
-        })
-    }
-}
-
-#[cfg(feature = "std")]
-impl ::std::error::Error for BernoulliError {}
-
-impl Bernoulli {
-    /// Construct a new `Bernoulli` with the given probability of success `p`.
-    ///
-    /// # Precision
-    ///
-    /// For `p = 1.0`, the resulting distribution will always generate true.
-    /// For `p = 0.0`, the resulting distribution will always generate false.
-    ///
-    /// This method is accurate for any input `p` in the range `[0, 1]` which is
-    /// a multiple of 2<sup>-64</sup>. (Note that not all multiples of
-    /// 2<sup>-64</sup> in `[0, 1]` can be represented as a `f64`.)
-    #[inline]
-    pub fn new(p: f64) -> Result<Bernoulli, BernoulliError> {
-        if !(0.0..1.0).contains(&p) {
-            if p == 1.0 {
-                return Ok(Bernoulli { p_int: ALWAYS_TRUE });
-            }
-            return Err(BernoulliError::InvalidProbability);
-        }
-        Ok(Bernoulli {
-            p_int: (p * SCALE) as u64,
-        })
-    }
-
-    /// Construct a new `Bernoulli` with the probability of success of
-    /// `numerator`-in-`denominator`. I.e. `new_ratio(2, 3)` will return
-    /// a `Bernoulli` with a 2-in-3 chance, or about 67%, of returning `true`.
-    ///
-    /// return `true`. If `numerator == 0` it will always return `false`.
-    /// For `numerator > denominator` and `denominator == 0`, this returns an
-    /// error. Otherwise, for `numerator == denominator`, samples are always
-    /// true; for `numerator == 0` samples are always false.
-    #[inline]
-    pub fn from_ratio(numerator: u32, denominator: u32) -> Result<Bernoulli, BernoulliError> {
-        if numerator > denominator || denominator == 0 {
-            return Err(BernoulliError::InvalidProbability);
-        }
-        if numerator == denominator {
-            return Ok(Bernoulli { p_int: ALWAYS_TRUE });
-        }
-        let p_int = ((f64::from(numerator) / f64::from(denominator)) * SCALE) as u64;
-        Ok(Bernoulli { p_int })
-    }
-}
-
-impl Distribution<bool> for Bernoulli {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
-        // Make sure to always return true for p = 1.0.
-        if self.p_int == ALWAYS_TRUE {
-            return true;
-        }
-        let v: u64 = rng.gen();
-        v < self.p_int
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use super::Bernoulli;
-    use crate::distributions::Distribution;
-    use crate::Rng;
-
-    #[test]
-    #[cfg(feature="serde1")]
-    fn test_serializing_deserializing_bernoulli() {
-        let coin_flip = Bernoulli::new(0.5).unwrap();
-        let de_coin_flip : Bernoulli = bincode::deserialize(&bincode::serialize(&coin_flip).unwrap()).unwrap();
-
-        assert_eq!(coin_flip.p_int, de_coin_flip.p_int);
-    }
-
-    #[test]
-    fn test_trivial() {
-        // We prefer to be explicit here.
-        #![allow(clippy::bool_assert_comparison)]
-
-        let mut r = crate::test::rng(1);
-        let always_false = Bernoulli::new(0.0).unwrap();
-        let always_true = Bernoulli::new(1.0).unwrap();
-        for _ in 0..5 {
-            assert_eq!(r.sample::<bool, _>(&always_false), false);
-            assert_eq!(r.sample::<bool, _>(&always_true), true);
-            assert_eq!(Distribution::<bool>::sample(&always_false, &mut r), false);
-            assert_eq!(Distribution::<bool>::sample(&always_true, &mut r), true);
-        }
-    }
-
-    #[test]
-    #[cfg_attr(miri, ignore)] // Miri is too slow
-    fn test_average() {
-        const P: f64 = 0.3;
-        const NUM: u32 = 3;
-        const DENOM: u32 = 10;
-        let d1 = Bernoulli::new(P).unwrap();
-        let d2 = Bernoulli::from_ratio(NUM, DENOM).unwrap();
-        const N: u32 = 100_000;
-
-        let mut sum1: u32 = 0;
-        let mut sum2: u32 = 0;
-        let mut rng = crate::test::rng(2);
-        for _ in 0..N {
-            if d1.sample(&mut rng) {
-                sum1 += 1;
-            }
-            if d2.sample(&mut rng) {
-                sum2 += 1;
-            }
-        }
-        let avg1 = (sum1 as f64) / (N as f64);
-        assert!((avg1 - P).abs() < 5e-3);
-
-        let avg2 = (sum2 as f64) / (N as f64);
-        assert!((avg2 - (NUM as f64) / (DENOM as f64)).abs() < 5e-3);
-    }
-
-    #[test]
-    fn value_stability() {
-        let mut rng = crate::test::rng(3);
-        let distr = Bernoulli::new(0.4532).unwrap();
-        let mut buf = [false; 10];
-        for x in &mut buf {
-            *x = rng.sample(&distr);
-        }
-        assert_eq!(buf, [
-            true, false, false, true, false, false, true, true, true, true
-        ]);
-    }
-
-    #[test]
-    fn bernoulli_distributions_can_be_compared() {
-        assert_eq!(Bernoulli::new(1.0), Bernoulli::new(1.0));
-    }
-}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/distribution.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/distribution.rs.html deleted file mode 100644 index afc8925..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/distribution.rs.html +++ /dev/null @@ -1,547 +0,0 @@ -distribution.rs - source
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-
// Copyright 2018 Developers of the Rand project.
-// Copyright 2013-2017 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.
-
-//! Distribution trait and associates
-
-use crate::Rng;
-use core::iter;
-#[cfg(feature = "alloc")]
-use alloc::string::String;
-
-/// Types (distributions) that can be used to create a random instance of `T`.
-///
-/// It is possible to sample from a distribution through both the
-/// `Distribution` and [`Rng`] traits, via `distr.sample(&mut rng)` and
-/// `rng.sample(distr)`. They also both offer the [`sample_iter`] method, which
-/// produces an iterator that samples from the distribution.
-///
-/// All implementations are expected to be immutable; this has the significant
-/// advantage of not needing to consider thread safety, and for most
-/// distributions efficient state-less sampling algorithms are available.
-///
-/// Implementations are typically expected to be portable with reproducible
-/// results when used with a PRNG with fixed seed; see the
-/// [portability chapter](https://rust-random.github.io/book/portability.html)
-/// of The Rust Rand Book. In some cases this does not apply, e.g. the `usize`
-/// type requires different sampling on 32-bit and 64-bit machines.
-///
-/// [`sample_iter`]: Distribution::sample_iter
-pub trait Distribution<T> {
-    /// Generate a random value of `T`, using `rng` as the source of randomness.
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T;
-
-    /// Create an iterator that generates random values of `T`, using `rng` as
-    /// the source of randomness.
-    ///
-    /// Note that this function takes `self` by value. This works since
-    /// `Distribution<T>` is impl'd for `&D` where `D: Distribution<T>`,
-    /// however borrowing is not automatic hence `distr.sample_iter(...)` may
-    /// need to be replaced with `(&distr).sample_iter(...)` to borrow or
-    /// `(&*distr).sample_iter(...)` to reborrow an existing reference.
-    ///
-    /// # Example
-    ///
-    /// ```
-    /// use rand::thread_rng;
-    /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard};
-    ///
-    /// let mut rng = thread_rng();
-    ///
-    /// // Vec of 16 x f32:
-    /// let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect();
-    ///
-    /// // String:
-    /// let s: String = Alphanumeric
-    ///     .sample_iter(&mut rng)
-    ///     .take(7)
-    ///     .map(char::from)
-    ///     .collect();
-    ///
-    /// // Dice-rolling:
-    /// let die_range = Uniform::new_inclusive(1, 6);
-    /// let mut roll_die = die_range.sample_iter(&mut rng);
-    /// while roll_die.next().unwrap() != 6 {
-    ///     println!("Not a 6; rolling again!");
-    /// }
-    /// ```
-    fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>
-    where
-        R: Rng,
-        Self: Sized,
-    {
-        DistIter {
-            distr: self,
-            rng,
-            phantom: ::core::marker::PhantomData,
-        }
-    }
-
-    /// Create a distribution of values of 'S' by mapping the output of `Self`
-    /// through the closure `F`
-    ///
-    /// # Example
-    ///
-    /// ```
-    /// use rand::thread_rng;
-    /// use rand::distributions::{Distribution, Uniform};
-    ///
-    /// let mut rng = thread_rng();
-    ///
-    /// let die = Uniform::new_inclusive(1, 6);
-    /// let even_number = die.map(|num| num % 2 == 0);
-    /// while !even_number.sample(&mut rng) {
-    ///     println!("Still odd; rolling again!");
-    /// }
-    /// ```
-    fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>
-    where
-        F: Fn(T) -> S,
-        Self: Sized,
-    {
-        DistMap {
-            distr: self,
-            func,
-            phantom: ::core::marker::PhantomData,
-        }
-    }
-}
-
-impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T {
-        (*self).sample(rng)
-    }
-}
-
-/// An iterator that generates random values of `T` with distribution `D`,
-/// using `R` as the source of randomness.
-///
-/// This `struct` is created by the [`sample_iter`] method on [`Distribution`].
-/// See its documentation for more.
-///
-/// [`sample_iter`]: Distribution::sample_iter
-#[derive(Debug)]
-pub struct DistIter<D, R, T> {
-    distr: D,
-    rng: R,
-    phantom: ::core::marker::PhantomData<T>,
-}
-
-impl<D, R, T> Iterator for DistIter<D, R, T>
-where
-    D: Distribution<T>,
-    R: Rng,
-{
-    type Item = T;
-
-    #[inline(always)]
-    fn next(&mut self) -> Option<T> {
-        // Here, self.rng may be a reference, but we must take &mut anyway.
-        // Even if sample could take an R: Rng by value, we would need to do this
-        // since Rng is not copyable and we cannot enforce that this is "reborrowable".
-        Some(self.distr.sample(&mut self.rng))
-    }
-
-    fn size_hint(&self) -> (usize, Option<usize>) {
-        (usize::max_value(), None)
-    }
-}
-
-impl<D, R, T> iter::FusedIterator for DistIter<D, R, T>
-where
-    D: Distribution<T>,
-    R: Rng,
-{
-}
-
-#[cfg(features = "nightly")]
-impl<D, R, T> iter::TrustedLen for DistIter<D, R, T>
-where
-    D: Distribution<T>,
-    R: Rng,
-{
-}
-
-/// A distribution of values of type `S` derived from the distribution `D`
-/// by mapping its output of type `T` through the closure `F`.
-///
-/// This `struct` is created by the [`Distribution::map`] method.
-/// See its documentation for more.
-#[derive(Debug)]
-pub struct DistMap<D, F, T, S> {
-    distr: D,
-    func: F,
-    phantom: ::core::marker::PhantomData<fn(T) -> S>,
-}
-
-impl<D, F, T, S> Distribution<S> for DistMap<D, F, T, S>
-where
-    D: Distribution<T>,
-    F: Fn(T) -> S,
-{
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> S {
-        (self.func)(self.distr.sample(rng))
-    }
-}
-
-/// `String` sampler
-///
-/// Sampling a `String` of random characters is not quite the same as collecting
-/// a sequence of chars. This trait contains some helpers.
-#[cfg(feature = "alloc")]
-pub trait DistString {
-    /// Append `len` random chars to `string`
-    fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, string: &mut String, len: usize);
-
-    /// Generate a `String` of `len` random chars
-    #[inline]
-    fn sample_string<R: Rng + ?Sized>(&self, rng: &mut R, len: usize) -> String {
-        let mut s = String::new();
-        self.append_string(rng, &mut s, len);
-        s
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use crate::distributions::{Distribution, Uniform};
-    use crate::Rng;
-
-    #[test]
-    fn test_distributions_iter() {
-        use crate::distributions::Open01;
-        let mut rng = crate::test::rng(210);
-        let distr = Open01;
-        let mut iter = Distribution::<f32>::sample_iter(distr, &mut rng);
-        let mut sum: f32 = 0.;
-        for _ in 0..100 {
-            sum += iter.next().unwrap();
-        }
-        assert!(0. < sum && sum < 100.);
-    }
-
-    #[test]
-    fn test_distributions_map() {
-        let dist = Uniform::new_inclusive(0, 5).map(|val| val + 15);
-
-        let mut rng = crate::test::rng(212);
-        let val = dist.sample(&mut rng);
-        assert!((15..=20).contains(&val));
-    }
-
-    #[test]
-    fn test_make_an_iter() {
-        fn ten_dice_rolls_other_than_five<R: Rng>(
-            rng: &mut R,
-        ) -> impl Iterator<Item = i32> + '_ {
-            Uniform::new_inclusive(1, 6)
-                .sample_iter(rng)
-                .filter(|x| *x != 5)
-                .take(10)
-        }
-
-        let mut rng = crate::test::rng(211);
-        let mut count = 0;
-        for val in ten_dice_rolls_other_than_five(&mut rng) {
-            assert!((1..=6).contains(&val) && val != 5);
-            count += 1;
-        }
-        assert_eq!(count, 10);
-    }
-
-    #[test]
-    #[cfg(feature = "alloc")]
-    fn test_dist_string() {
-        use core::str;
-        use crate::distributions::{Alphanumeric, DistString, Standard};
-        let mut rng = crate::test::rng(213);
-
-        let s1 = Alphanumeric.sample_string(&mut rng, 20);
-        assert_eq!(s1.len(), 20);
-        assert_eq!(str::from_utf8(s1.as_bytes()), Ok(s1.as_str()));
-
-        let s2 = Standard.sample_string(&mut rng, 20);
-        assert_eq!(s2.chars().count(), 20);
-        assert_eq!(str::from_utf8(s2.as_bytes()), Ok(s2.as_str()));
-    }
-}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/float.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/float.rs.html deleted file mode 100644 index cba1f5b..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/float.rs.html +++ /dev/null @@ -1,627 +0,0 @@ -float.rs - source
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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),
-            ]);
-        }
-    }
-}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/integer.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/integer.rs.html deleted file mode 100644 index 0fbac64..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/integer.rs.html +++ /dev/null @@ -1,551 +0,0 @@ -integer.rs - source
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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.
-
-//! The implementations of the `Standard` distribution for integer types.
-
-use crate::distributions::{Distribution, Standard};
-use crate::Rng;
-#[cfg(all(target_arch = "x86", feature = "simd_support"))]
-use core::arch::x86::{__m128i, __m256i};
-#[cfg(all(target_arch = "x86_64", feature = "simd_support"))]
-use core::arch::x86_64::{__m128i, __m256i};
-use core::num::{NonZeroU16, NonZeroU32, NonZeroU64, NonZeroU8, NonZeroUsize,
-    NonZeroU128};
-#[cfg(feature = "simd_support")] use packed_simd::*;
-
-impl Distribution<u8> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u8 {
-        rng.next_u32() as u8
-    }
-}
-
-impl Distribution<u16> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u16 {
-        rng.next_u32() as u16
-    }
-}
-
-impl Distribution<u32> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u32 {
-        rng.next_u32()
-    }
-}
-
-impl Distribution<u64> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
-        rng.next_u64()
-    }
-}
-
-impl Distribution<u128> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u128 {
-        // Use LE; we explicitly generate one value before the next.
-        let x = u128::from(rng.next_u64());
-        let y = u128::from(rng.next_u64());
-        (y << 64) | x
-    }
-}
-
-impl Distribution<usize> for Standard {
-    #[inline]
-    #[cfg(any(target_pointer_width = "32", target_pointer_width = "16"))]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
-        rng.next_u32() as usize
-    }
-
-    #[inline]
-    #[cfg(target_pointer_width = "64")]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
-        rng.next_u64() as usize
-    }
-}
-
-macro_rules! impl_int_from_uint {
-    ($ty:ty, $uty:ty) => {
-        impl Distribution<$ty> for Standard {
-            #[inline]
-            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
-                rng.gen::<$uty>() as $ty
-            }
-        }
-    };
-}
-
-impl_int_from_uint! { i8, u8 }
-impl_int_from_uint! { i16, u16 }
-impl_int_from_uint! { i32, u32 }
-impl_int_from_uint! { i64, u64 }
-impl_int_from_uint! { i128, u128 }
-impl_int_from_uint! { isize, usize }
-
-macro_rules! impl_nzint {
-    ($ty:ty, $new:path) => {
-        impl Distribution<$ty> for Standard {
-            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
-                loop {
-                    if let Some(nz) = $new(rng.gen()) {
-                        break nz;
-                    }
-                }
-            }
-        }
-    };
-}
-
-impl_nzint!(NonZeroU8, NonZeroU8::new);
-impl_nzint!(NonZeroU16, NonZeroU16::new);
-impl_nzint!(NonZeroU32, NonZeroU32::new);
-impl_nzint!(NonZeroU64, NonZeroU64::new);
-impl_nzint!(NonZeroU128, NonZeroU128::new);
-impl_nzint!(NonZeroUsize, NonZeroUsize::new);
-
-#[cfg(feature = "simd_support")]
-macro_rules! simd_impl {
-    ($(($intrinsic:ident, $vec:ty),)+) => {$(
-        impl Distribution<$intrinsic> for Standard {
-            #[inline]
-            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $intrinsic {
-                $intrinsic::from_bits(rng.gen::<$vec>())
-            }
-        }
-    )+};
-
-    ($bits:expr,) => {};
-    ($bits:expr, $ty:ty, $($ty_more:ty,)*) => {
-        simd_impl!($bits, $($ty_more,)*);
-
-        impl Distribution<$ty> for Standard {
-            #[inline]
-            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
-                let mut vec: $ty = Default::default();
-                unsafe {
-                    let ptr = &mut vec;
-                    let b_ptr = &mut *(ptr as *mut $ty as *mut [u8; $bits/8]);
-                    rng.fill_bytes(b_ptr);
-                }
-                vec.to_le()
-            }
-        }
-    };
-}
-
-#[cfg(feature = "simd_support")]
-simd_impl!(16, u8x2, i8x2,);
-#[cfg(feature = "simd_support")]
-simd_impl!(32, u8x4, i8x4, u16x2, i16x2,);
-#[cfg(feature = "simd_support")]
-simd_impl!(64, u8x8, i8x8, u16x4, i16x4, u32x2, i32x2,);
-#[cfg(feature = "simd_support")]
-simd_impl!(128, u8x16, i8x16, u16x8, i16x8, u32x4, i32x4, u64x2, i64x2,);
-#[cfg(feature = "simd_support")]
-simd_impl!(256, u8x32, i8x32, u16x16, i16x16, u32x8, i32x8, u64x4, i64x4,);
-#[cfg(feature = "simd_support")]
-simd_impl!(512, u8x64, i8x64, u16x32, i16x32, u32x16, i32x16, u64x8, i64x8,);
-#[cfg(all(
-    feature = "simd_support",
-    any(target_arch = "x86", target_arch = "x86_64")
-))]
-simd_impl!((__m128i, u8x16), (__m256i, u8x32),);
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn test_integers() {
-        let mut rng = crate::test::rng(806);
-
-        rng.sample::<isize, _>(Standard);
-        rng.sample::<i8, _>(Standard);
-        rng.sample::<i16, _>(Standard);
-        rng.sample::<i32, _>(Standard);
-        rng.sample::<i64, _>(Standard);
-        rng.sample::<i128, _>(Standard);
-
-        rng.sample::<usize, _>(Standard);
-        rng.sample::<u8, _>(Standard);
-        rng.sample::<u16, _>(Standard);
-        rng.sample::<u32, _>(Standard);
-        rng.sample::<u64, _>(Standard);
-        rng.sample::<u128, _>(Standard);
-    }
-
-    #[test]
-    fn value_stability() {
-        fn test_samples<T: Copy + core::fmt::Debug + PartialEq>(zero: T, expected: &[T])
-        where Standard: Distribution<T> {
-            let mut rng = crate::test::rng(807);
-            let mut buf = [zero; 3];
-            for x in &mut buf {
-                *x = rng.sample(Standard);
-            }
-            assert_eq!(&buf, expected);
-        }
-
-        test_samples(0u8, &[9, 247, 111]);
-        test_samples(0u16, &[32265, 42999, 38255]);
-        test_samples(0u32, &[2220326409, 2575017975, 2018088303]);
-        test_samples(0u64, &[
-            11059617991457472009,
-            16096616328739788143,
-            1487364411147516184,
-        ]);
-        test_samples(0u128, &[
-            296930161868957086625409848350820761097,
-            145644820879247630242265036535529306392,
-            111087889832015897993126088499035356354,
-        ]);
-        #[cfg(any(target_pointer_width = "32", target_pointer_width = "16"))]
-        test_samples(0usize, &[2220326409, 2575017975, 2018088303]);
-        #[cfg(target_pointer_width = "64")]
-        test_samples(0usize, &[
-            11059617991457472009,
-            16096616328739788143,
-            1487364411147516184,
-        ]);
-
-        test_samples(0i8, &[9, -9, 111]);
-        // Skip further i* types: they are simple reinterpretation of u* samples
-
-        #[cfg(feature = "simd_support")]
-        {
-            // We only test a sub-set of types here and make assumptions about the rest.
-
-            test_samples(u8x2::default(), &[
-                u8x2::new(9, 126),
-                u8x2::new(247, 167),
-                u8x2::new(111, 149),
-            ]);
-            test_samples(u8x4::default(), &[
-                u8x4::new(9, 126, 87, 132),
-                u8x4::new(247, 167, 123, 153),
-                u8x4::new(111, 149, 73, 120),
-            ]);
-            test_samples(u8x8::default(), &[
-                u8x8::new(9, 126, 87, 132, 247, 167, 123, 153),
-                u8x8::new(111, 149, 73, 120, 68, 171, 98, 223),
-                u8x8::new(24, 121, 1, 50, 13, 46, 164, 20),
-            ]);
-
-            test_samples(i64x8::default(), &[
-                i64x8::new(
-                    -7387126082252079607,
-                    -2350127744969763473,
-                    1487364411147516184,
-                    7895421560427121838,
-                    602190064936008898,
-                    6022086574635100741,
-                    -5080089175222015595,
-                    -4066367846667249123,
-                ),
-                i64x8::new(
-                    9180885022207963908,
-                    3095981199532211089,
-                    6586075293021332726,
-                    419343203796414657,
-                    3186951873057035255,
-                    5287129228749947252,
-                    444726432079249540,
-                    -1587028029513790706,
-                ),
-                i64x8::new(
-                    6075236523189346388,
-                    1351763722368165432,
-                    -6192309979959753740,
-                    -7697775502176768592,
-                    -4482022114172078123,
-                    7522501477800909500,
-                    -1837258847956201231,
-                    -586926753024886735,
-                ),
-            ]);
-        }
-    }
-}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/mod.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/mod.rs.html deleted file mode 100644 index 8e93c46..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/mod.rs.html +++ /dev/null @@ -1,439 +0,0 @@ -mod.rs - source
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-
// Copyright 2018 Developers of the Rand project.
-// Copyright 2013-2017 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.
-
-//! Generating random samples from probability distributions
-//!
-//! This module is the home of the [`Distribution`] trait and several of its
-//! implementations. It is the workhorse behind some of the convenient
-//! functionality of the [`Rng`] trait, e.g. [`Rng::gen`] and of course
-//! [`Rng::sample`].
-//!
-//! Abstractly, a [probability distribution] describes the probability of
-//! occurrence of each value in its sample space.
-//!
-//! More concretely, an implementation of `Distribution<T>` for type `X` is an
-//! algorithm for choosing values from the sample space (a subset of `T`)
-//! according to the distribution `X` represents, using an external source of
-//! randomness (an RNG supplied to the `sample` function).
-//!
-//! A type `X` may implement `Distribution<T>` for multiple types `T`.
-//! Any type implementing [`Distribution`] is stateless (i.e. immutable),
-//! but it may have internal parameters set at construction time (for example,
-//! [`Uniform`] allows specification of its sample space as a range within `T`).
-//!
-//!
-//! # The `Standard` distribution
-//!
-//! The [`Standard`] distribution is important to mention. This is the
-//! distribution used by [`Rng::gen`] and represents the "default" way to
-//! produce a random value for many different types, including most primitive
-//! types, tuples, arrays, and a few derived types. See the documentation of
-//! [`Standard`] for more details.
-//!
-//! Implementing `Distribution<T>` for [`Standard`] for user types `T` makes it
-//! possible to generate type `T` with [`Rng::gen`], and by extension also
-//! with the [`random`] function.
-//!
-//! ## Random characters
-//!
-//! [`Alphanumeric`] is a simple distribution to sample random letters and
-//! numbers of the `char` type; in contrast [`Standard`] may sample any valid
-//! `char`.
-//!
-//!
-//! # Uniform numeric ranges
-//!
-//! The [`Uniform`] distribution is more flexible than [`Standard`], but also
-//! more specialised: it supports fewer target types, but allows the sample
-//! space to be specified as an arbitrary range within its target type `T`.
-//! Both [`Standard`] and [`Uniform`] are in some sense uniform distributions.
-//!
-//! Values may be sampled from this distribution using [`Rng::sample(Range)`] or
-//! by creating a distribution object with [`Uniform::new`],
-//! [`Uniform::new_inclusive`] or `From<Range>`. When the range limits are not
-//! known at compile time it is typically faster to reuse an existing
-//! `Uniform` object than to call [`Rng::sample(Range)`].
-//!
-//! User types `T` may also implement `Distribution<T>` for [`Uniform`],
-//! although this is less straightforward than for [`Standard`] (see the
-//! documentation in the [`uniform`] module). Doing so enables generation of
-//! values of type `T` with  [`Rng::sample(Range)`].
-//!
-//! ## Open and half-open ranges
-//!
-//! There are surprisingly many ways to uniformly generate random floats. A
-//! range between 0 and 1 is standard, but the exact bounds (open vs closed)
-//! and accuracy differ. In addition to the [`Standard`] distribution Rand offers
-//! [`Open01`] and [`OpenClosed01`]. See "Floating point implementation" section of
-//! [`Standard`] documentation for more details.
-//!
-//! # Non-uniform sampling
-//!
-//! Sampling a simple true/false outcome with a given probability has a name:
-//! the [`Bernoulli`] distribution (this is used by [`Rng::gen_bool`]).
-//!
-//! For weighted sampling from a sequence of discrete values, use the
-//! [`WeightedIndex`] distribution.
-//!
-//! This crate no longer includes other non-uniform distributions; instead
-//! it is recommended that you use either [`rand_distr`] or [`statrs`].
-//!
-//!
-//! [probability distribution]: https://en.wikipedia.org/wiki/Probability_distribution
-//! [`rand_distr`]: https://crates.io/crates/rand_distr
-//! [`statrs`]: https://crates.io/crates/statrs
-
-//! [`random`]: crate::random
-//! [`rand_distr`]: https://crates.io/crates/rand_distr
-//! [`statrs`]: https://crates.io/crates/statrs
-
-mod bernoulli;
-mod distribution;
-mod float;
-mod integer;
-mod other;
-mod slice;
-mod utils;
-#[cfg(feature = "alloc")]
-mod weighted_index;
-
-#[doc(hidden)]
-pub mod hidden_export {
-    pub use super::float::IntoFloat; // used by rand_distr
-}
-pub mod uniform;
-#[deprecated(
-    since = "0.8.0",
-    note = "use rand::distributions::{WeightedIndex, WeightedError} instead"
-)]
-#[cfg(feature = "alloc")]
-#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
-pub mod weighted;
-
-pub use self::bernoulli::{Bernoulli, BernoulliError};
-pub use self::distribution::{Distribution, DistIter, DistMap};
-#[cfg(feature = "alloc")]
-pub use self::distribution::DistString;
-pub use self::float::{Open01, OpenClosed01};
-pub use self::other::Alphanumeric;
-pub use self::slice::Slice;
-#[doc(inline)]
-pub use self::uniform::Uniform;
-#[cfg(feature = "alloc")]
-pub use self::weighted_index::{WeightedError, WeightedIndex};
-
-#[allow(unused)]
-use crate::Rng;
-
-/// A generic random value distribution, implemented for many primitive types.
-/// Usually generates values with a numerically uniform distribution, and with a
-/// range appropriate to the type.
-///
-/// ## Provided implementations
-///
-/// Assuming the provided `Rng` is well-behaved, these implementations
-/// generate values with the following ranges and distributions:
-///
-/// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed
-///   over all values of the type.
-/// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all
-///   code points in the range `0...0x10_FFFF`, except for the range
-///   `0xD800...0xDFFF` (the surrogate code points). This includes
-///   unassigned/reserved code points.
-/// * `bool`: Generates `false` or `true`, each with probability 0.5.
-/// * Floating point types (`f32` and `f64`): Uniformly distributed in the
-///   half-open range `[0, 1)`. See notes below.
-/// * Wrapping integers (`Wrapping<T>`), besides the type identical to their
-///   normal integer variants.
-///
-/// The `Standard` distribution also supports generation of the following
-/// compound types where all component types are supported:
-///
-/// *   Tuples (up to 12 elements): each element is generated sequentially.
-/// *   Arrays (up to 32 elements): each element is generated sequentially;
-///     see also [`Rng::fill`] which supports arbitrary array length for integer
-///     and float types and tends to be faster for `u32` and smaller types.
-///     When using `rustc` ≥ 1.51, enable the `min_const_gen` feature to support
-///     arrays larger than 32 elements.
-///     Note that [`Rng::fill`] and `Standard`'s array support are *not* equivalent:
-///     the former is optimised for integer types (using fewer RNG calls for
-///     element types smaller than the RNG word size), while the latter supports
-///     any element type supported by `Standard`.
-/// *   `Option<T>` first generates a `bool`, and if true generates and returns
-///     `Some(value)` where `value: T`, otherwise returning `None`.
-///
-/// ## Custom implementations
-///
-/// The [`Standard`] distribution may be implemented for user types as follows:
-///
-/// ```
-/// # #![allow(dead_code)]
-/// use rand::Rng;
-/// use rand::distributions::{Distribution, Standard};
-///
-/// struct MyF32 {
-///     x: f32,
-/// }
-///
-/// impl Distribution<MyF32> for Standard {
-///     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> MyF32 {
-///         MyF32 { x: rng.gen() }
-///     }
-/// }
-/// ```
-///
-/// ## Example usage
-/// ```
-/// use rand::prelude::*;
-/// use rand::distributions::Standard;
-///
-/// let val: f32 = StdRng::from_entropy().sample(Standard);
-/// println!("f32 from [0, 1): {}", val);
-/// ```
-///
-/// # Floating point implementation
-/// The floating point implementations for `Standard` generate a random value in
-/// the half-open interval `[0, 1)`, i.e. including 0 but not 1.
-///
-/// 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: `(rng.gen::<$uty>() >> N) as $ty * (ε/2)`.
-///
-/// See also: [`Open01`] which samples from `(0, 1)`, [`OpenClosed01`] which
-/// samples from `(0, 1]` and `Rng::gen_range(0..1)` which also samples from
-/// `[0, 1)`. Note that `Open01` uses transmute-based methods which yield 1 bit
-/// less precision but may perform faster on some architectures (on modern Intel
-/// CPUs all methods have approximately equal performance).
-///
-/// [`Uniform`]: uniform::Uniform
-#[derive(Clone, Copy, Debug)]
-#[cfg_attr(feature = "serde1", derive(serde::Serialize, serde::Deserialize))]
-pub struct Standard;
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/other.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/other.rs.html deleted file mode 100644 index 415f1da..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/other.rs.html +++ /dev/null @@ -1,733 +0,0 @@ -other.rs - source
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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.
-
-//! The implementations of the `Standard` distribution for other built-in types.
-
-use core::char;
-use core::num::Wrapping;
-#[cfg(feature = "alloc")]
-use alloc::string::String;
-
-use crate::distributions::{Distribution, Standard, Uniform};
-#[cfg(feature = "alloc")]
-use crate::distributions::DistString;
-use crate::Rng;
-
-#[cfg(feature = "serde1")]
-use serde::{Serialize, Deserialize};
-#[cfg(feature = "min_const_gen")]
-use core::mem::{self, MaybeUninit};
-
-
-// ----- Sampling distributions -----
-
-/// Sample a `u8`, uniformly distributed over ASCII letters and numbers:
-/// a-z, A-Z and 0-9.
-///
-/// # Example
-///
-/// ```
-/// use rand::{Rng, thread_rng};
-/// use rand::distributions::Alphanumeric;
-///
-/// let mut rng = thread_rng();
-/// let chars: String = (0..7).map(|_| rng.sample(Alphanumeric) as char).collect();
-/// println!("Random chars: {}", chars);
-/// ```
-///
-/// The [`DistString`] trait provides an easier method of generating
-/// a random `String`, and offers more efficient allocation:
-/// ```
-/// use rand::distributions::{Alphanumeric, DistString};
-/// let string = Alphanumeric.sample_string(&mut rand::thread_rng(), 16);
-/// println!("Random string: {}", string);
-/// ```
-///
-/// # Passwords
-///
-/// Users sometimes ask whether it is safe to use a string of random characters
-/// as a password. In principle, all RNGs in Rand implementing `CryptoRng` are
-/// suitable as a source of randomness for generating passwords (if they are
-/// properly seeded), but it is more conservative to only use randomness
-/// directly from the operating system via the `getrandom` crate, or the
-/// corresponding bindings of a crypto library.
-///
-/// When generating passwords or keys, it is important to consider the threat
-/// model and in some cases the memorability of the password. This is out of
-/// scope of the Rand project, and therefore we defer to the following
-/// references:
-///
-/// - [Wikipedia article on Password Strength](https://en.wikipedia.org/wiki/Password_strength)
-/// - [Diceware for generating memorable passwords](https://en.wikipedia.org/wiki/Diceware)
-#[derive(Debug, Clone, Copy)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-pub struct Alphanumeric;
-
-
-// ----- Implementations of distributions -----
-
-impl Distribution<char> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> char {
-        // A valid `char` is either in the interval `[0, 0xD800)` or
-        // `(0xDFFF, 0x11_0000)`. All `char`s must therefore be in
-        // `[0, 0x11_0000)` but not in the "gap" `[0xD800, 0xDFFF]` which is
-        // reserved for surrogates. This is the size of that gap.
-        const GAP_SIZE: u32 = 0xDFFF - 0xD800 + 1;
-
-        // Uniform::new(0, 0x11_0000 - GAP_SIZE) can also be used but it
-        // seemed slower.
-        let range = Uniform::new(GAP_SIZE, 0x11_0000);
-
-        let mut n = range.sample(rng);
-        if n <= 0xDFFF {
-            n -= GAP_SIZE;
-        }
-        unsafe { char::from_u32_unchecked(n) }
-    }
-}
-
-/// Note: the `String` is potentially left with excess capacity; optionally the
-/// user may call `string.shrink_to_fit()` afterwards.
-#[cfg(feature = "alloc")]
-impl DistString for Standard {
-    fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, s: &mut String, len: usize) {
-        // A char is encoded with at most four bytes, thus this reservation is
-        // guaranteed to be sufficient. We do not shrink_to_fit afterwards so
-        // that repeated usage on the same `String` buffer does not reallocate.
-        s.reserve(4 * len);
-        s.extend(Distribution::<char>::sample_iter(self, rng).take(len));
-    }
-}
-
-impl Distribution<u8> for Alphanumeric {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u8 {
-        const RANGE: u32 = 26 + 26 + 10;
-        const GEN_ASCII_STR_CHARSET: &[u8] = b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
-                abcdefghijklmnopqrstuvwxyz\
-                0123456789";
-        // We can pick from 62 characters. This is so close to a power of 2, 64,
-        // that we can do better than `Uniform`. Use a simple bitshift and
-        // rejection sampling. We do not use a bitmask, because for small RNGs
-        // the most significant bits are usually of higher quality.
-        loop {
-            let var = rng.next_u32() >> (32 - 6);
-            if var < RANGE {
-                return GEN_ASCII_STR_CHARSET[var as usize];
-            }
-        }
-    }
-}
-
-#[cfg(feature = "alloc")]
-impl DistString for Alphanumeric {
-    fn append_string<R: Rng + ?Sized>(&self, rng: &mut R, string: &mut String, len: usize) {
-        unsafe {
-            let v = string.as_mut_vec();
-            v.extend(self.sample_iter(rng).take(len));
-        }
-    }
-}
-
-impl Distribution<bool> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
-        // We can compare against an arbitrary bit of an u32 to get a bool.
-        // Because the least significant bits of a lower quality RNG can have
-        // simple patterns, we compare against the most significant bit. This is
-        // easiest done using a sign test.
-        (rng.next_u32() as i32) < 0
-    }
-}
-
-macro_rules! tuple_impl {
-    // use variables to indicate the arity of the tuple
-    ($($tyvar:ident),* ) => {
-        // the trailing commas are for the 1 tuple
-        impl< $( $tyvar ),* >
-            Distribution<( $( $tyvar ),* , )>
-            for Standard
-            where $( Standard: Distribution<$tyvar> ),*
-        {
-            #[inline]
-            fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> ( $( $tyvar ),* , ) {
-                (
-                    // use the $tyvar's to get the appropriate number of
-                    // repeats (they're not actually needed)
-                    $(
-                        _rng.gen::<$tyvar>()
-                    ),*
-                    ,
-                )
-            }
-        }
-    }
-}
-
-impl Distribution<()> for Standard {
-    #[allow(clippy::unused_unit)]
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, _: &mut R) -> () {
-        ()
-    }
-}
-tuple_impl! {A}
-tuple_impl! {A, B}
-tuple_impl! {A, B, C}
-tuple_impl! {A, B, C, D}
-tuple_impl! {A, B, C, D, E}
-tuple_impl! {A, B, C, D, E, F}
-tuple_impl! {A, B, C, D, E, F, G}
-tuple_impl! {A, B, C, D, E, F, G, H}
-tuple_impl! {A, B, C, D, E, F, G, H, I}
-tuple_impl! {A, B, C, D, E, F, G, H, I, J}
-tuple_impl! {A, B, C, D, E, F, G, H, I, J, K}
-tuple_impl! {A, B, C, D, E, F, G, H, I, J, K, L}
-
-#[cfg(feature = "min_const_gen")]
-#[cfg_attr(doc_cfg, doc(cfg(feature = "min_const_gen")))]
-impl<T, const N: usize> Distribution<[T; N]> for Standard
-where Standard: Distribution<T>
-{
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; N] {
-        let mut buff: [MaybeUninit<T>; N] = unsafe { MaybeUninit::uninit().assume_init() };
-
-        for elem in &mut buff {
-            *elem = MaybeUninit::new(_rng.gen());
-        }
-
-        unsafe { mem::transmute_copy::<_, _>(&buff) }
-    }
-}
-
-#[cfg(not(feature = "min_const_gen"))]
-macro_rules! array_impl {
-    // recursive, given at least one type parameter:
-    {$n:expr, $t:ident, $($ts:ident,)*} => {
-        array_impl!{($n - 1), $($ts,)*}
-
-        impl<T> Distribution<[T; $n]> for Standard where Standard: Distribution<T> {
-            #[inline]
-            fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; $n] {
-                [_rng.gen::<$t>(), $(_rng.gen::<$ts>()),*]
-            }
-        }
-    };
-    // empty case:
-    {$n:expr,} => {
-        impl<T> Distribution<[T; $n]> for Standard {
-            fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; $n] { [] }
-        }
-    };
-}
-
-#[cfg(not(feature = "min_const_gen"))]
-array_impl! {32, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T,}
-
-impl<T> Distribution<Option<T>> for Standard
-where Standard: Distribution<T>
-{
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Option<T> {
-        // UFCS is needed here: https://github.com/rust-lang/rust/issues/24066
-        if rng.gen::<bool>() {
-            Some(rng.gen())
-        } else {
-            None
-        }
-    }
-}
-
-impl<T> Distribution<Wrapping<T>> for Standard
-where Standard: Distribution<T>
-{
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Wrapping<T> {
-        Wrapping(rng.gen())
-    }
-}
-
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-    use crate::RngCore;
-    #[cfg(feature = "alloc")] use alloc::string::String;
-
-    #[test]
-    fn test_misc() {
-        let rng: &mut dyn RngCore = &mut crate::test::rng(820);
-
-        rng.sample::<char, _>(Standard);
-        rng.sample::<bool, _>(Standard);
-    }
-
-    #[cfg(feature = "alloc")]
-    #[test]
-    fn test_chars() {
-        use core::iter;
-        let mut rng = crate::test::rng(805);
-
-        // Test by generating a relatively large number of chars, so we also
-        // take the rejection sampling path.
-        let word: String = iter::repeat(())
-            .map(|()| rng.gen::<char>())
-            .take(1000)
-            .collect();
-        assert!(!word.is_empty());
-    }
-
-    #[test]
-    fn test_alphanumeric() {
-        let mut rng = crate::test::rng(806);
-
-        // Test by generating a relatively large number of chars, so we also
-        // take the rejection sampling path.
-        let mut incorrect = false;
-        for _ in 0..100 {
-            let c: char = rng.sample(Alphanumeric).into();
-            incorrect |= !(('0'..='9').contains(&c) ||
-                           ('A'..='Z').contains(&c) ||
-                           ('a'..='z').contains(&c) );
-        }
-        assert!(!incorrect);
-    }
-
-    #[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(807);
-            let mut buf = [zero; 5];
-            for x in &mut buf {
-                *x = rng.sample(&distr);
-            }
-            assert_eq!(&buf, expected);
-        }
-
-        test_samples(&Standard, 'a', &[
-            '\u{8cdac}',
-            '\u{a346a}',
-            '\u{80120}',
-            '\u{ed692}',
-            '\u{35888}',
-        ]);
-        test_samples(&Alphanumeric, 0, &[104, 109, 101, 51, 77]);
-        test_samples(&Standard, false, &[true, true, false, true, false]);
-        test_samples(&Standard, None as Option<bool>, &[
-            Some(true),
-            None,
-            Some(false),
-            None,
-            Some(false),
-        ]);
-        test_samples(&Standard, Wrapping(0i32), &[
-            Wrapping(-2074640887),
-            Wrapping(-1719949321),
-            Wrapping(2018088303),
-            Wrapping(-547181756),
-            Wrapping(838957336),
-        ]);
-
-        // We test only sub-sets of tuple and array impls
-        test_samples(&Standard, (), &[(), (), (), (), ()]);
-        test_samples(&Standard, (false,), &[
-            (true,),
-            (true,),
-            (false,),
-            (true,),
-            (false,),
-        ]);
-        test_samples(&Standard, (false, false), &[
-            (true, true),
-            (false, true),
-            (false, false),
-            (true, false),
-            (false, false),
-        ]);
-
-        test_samples(&Standard, [0u8; 0], &[[], [], [], [], []]);
-        test_samples(&Standard, [0u8; 3], &[
-            [9, 247, 111],
-            [68, 24, 13],
-            [174, 19, 194],
-            [172, 69, 213],
-            [149, 207, 29],
-        ]);
-    }
-}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/slice.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/slice.rs.html deleted file mode 100644 index 2f43484..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/slice.rs.html +++ /dev/null @@ -1,237 +0,0 @@ -slice.rs - source
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// Copyright 2021 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.
-
-use crate::distributions::{Distribution, Uniform};
-
-/// A distribution to sample items uniformly from a slice.
-///
-/// [`Slice::new`] constructs a distribution referencing a slice and uniformly
-/// samples references from the items in the slice. It may do extra work up
-/// front to make sampling of multiple values faster; if only one sample from
-/// the slice is required, [`SliceRandom::choose`] can be more efficient.
-///
-/// Steps are taken to avoid bias which might be present in naive
-/// implementations; for example `slice[rng.gen() % slice.len()]` samples from
-/// the slice, but may be more likely to select numbers in the low range than
-/// other values.
-///
-/// This distribution samples with replacement; each sample is independent.
-/// Sampling without replacement requires state to be retained, and therefore
-/// cannot be handled by a distribution; you should instead consider methods
-/// on [`SliceRandom`], such as [`SliceRandom::choose_multiple`].
-///
-/// # Example
-///
-/// ```
-/// use rand::Rng;
-/// use rand::distributions::Slice;
-///
-/// let vowels = ['a', 'e', 'i', 'o', 'u'];
-/// let vowels_dist = Slice::new(&vowels).unwrap();
-/// let rng = rand::thread_rng();
-///
-/// // build a string of 10 vowels
-/// let vowel_string: String = rng
-///     .sample_iter(&vowels_dist)
-///     .take(10)
-///     .collect();
-///
-/// println!("{}", vowel_string);
-/// assert_eq!(vowel_string.len(), 10);
-/// assert!(vowel_string.chars().all(|c| vowels.contains(&c)));
-/// ```
-///
-/// For a single sample, [`SliceRandom::choose`][crate::seq::SliceRandom::choose]
-/// may be preferred:
-///
-/// ```
-/// use rand::seq::SliceRandom;
-///
-/// let vowels = ['a', 'e', 'i', 'o', 'u'];
-/// let mut rng = rand::thread_rng();
-///
-/// println!("{}", vowels.choose(&mut rng).unwrap())
-/// ```
-///
-/// [`SliceRandom`]: crate::seq::SliceRandom
-/// [`SliceRandom::choose`]: crate::seq::SliceRandom::choose
-/// [`SliceRandom::choose_multiple`]: crate::seq::SliceRandom::choose_multiple
-#[derive(Debug, Clone, Copy)]
-pub struct Slice<'a, T> {
-    slice: &'a [T],
-    range: Uniform<usize>,
-}
-
-impl<'a, T> Slice<'a, T> {
-    /// Create a new `Slice` instance which samples uniformly from the slice.
-    /// Returns `Err` if the slice is empty.
-    pub fn new(slice: &'a [T]) -> Result<Self, EmptySlice> {
-        match slice.len() {
-            0 => Err(EmptySlice),
-            len => Ok(Self {
-                slice,
-                range: Uniform::new(0, len),
-            }),
-        }
-    }
-}
-
-impl<'a, T> Distribution<&'a T> for Slice<'a, T> {
-    fn sample<R: crate::Rng + ?Sized>(&self, rng: &mut R) -> &'a T {
-        let idx = self.range.sample(rng);
-
-        debug_assert!(
-            idx < self.slice.len(),
-            "Uniform::new(0, {}) somehow returned {}",
-            self.slice.len(),
-            idx
-        );
-
-        // Safety: at construction time, it was ensured that the slice was
-        // non-empty, and that the `Uniform` range produces values in range
-        // for the slice
-        unsafe { self.slice.get_unchecked(idx) }
-    }
-}
-
-/// Error type indicating that a [`Slice`] distribution was improperly
-/// constructed with an empty slice.
-#[derive(Debug, Clone, Copy)]
-pub struct EmptySlice;
-
-impl core::fmt::Display for EmptySlice {
-    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
-        write!(
-            f,
-            "Tried to create a `distributions::Slice` with an empty slice"
-        )
-    }
-}
-
-#[cfg(feature = "std")]
-impl std::error::Error for EmptySlice {}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/uniform.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/uniform.rs.html deleted file mode 100644 index 72d0ba1..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/uniform.rs.html +++ /dev/null @@ -1,3319 +0,0 @@ -uniform.rs - source
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-
// Copyright 2018-2020 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..=10) {
-//!     // 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
-//!     {
-//!         UniformMyF32(UniformFloat::<f32>::new_inclusive(
-//!             low.borrow().0,
-//!             high.borrow().0,
-//!         ))
-//!     }
-//!     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
-
-use core::time::Duration;
-use core::ops::{Range, RangeInclusive};
-
-use crate::distributions::float::IntoFloat;
-use crate::distributions::utils::{BoolAsSIMD, FloatAsSIMD, FloatSIMDUtils, WideningMultiply};
-use crate::distributions::Distribution;
-use crate::{Rng, RngCore};
-
-#[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::*;
-
-#[cfg(feature = "serde1")]
-use serde::{Serialize, Deserialize};
-
-/// 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. If only one sample
-/// from the range is required, [`Rng::gen_range`] can be more efficient.
-///
-/// 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};
-///
-/// 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);
-/// ```
-///
-/// For a single sample, [`Rng::gen_range`] may be preferred:
-///
-/// ```
-/// use rand::Rng;
-///
-/// let mut rng = rand::thread_rng();
-/// println!("{}", rng.gen_range(0..10));
-/// ```
-///
-/// [`new`]: Uniform::new
-/// [`new_inclusive`]: Uniform::new_inclusive
-/// [`Rng::gen_range`]: Rng::gen_range
-#[derive(Clone, Copy, Debug, PartialEq)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-#[cfg_attr(feature = "serde1", serde(bound(serialize = "X::Sampler: Serialize")))]
-#[cfg_attr(feature = "serde1", serde(bound(deserialize = "X::Sampler: Deserialize<'de>")))]
-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)
-    }
-
-    /// Sample a single value uniformly from a range with inclusive lower bound
-    /// and inclusive upper bound `[low, high]`.
-    ///
-    /// By default this is implemented using
-    /// `UniformSampler::new_inclusive(low, high).sample(rng)`. However, for
-    /// some types more optimal implementations for single usage may be provided
-    /// via this method.
-    /// Results may not be identical.
-    fn sample_single_inclusive<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_inclusive(low, high);
-        uniform.sample(rng)
-    }
-}
-
-impl<X: SampleUniform> From<Range<X>> for Uniform<X> {
-    fn from(r: ::core::ops::Range<X>) -> Uniform<X> {
-        Uniform::new(r.start, r.end)
-    }
-}
-
-impl<X: SampleUniform> From<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
-    }
-}
-
-/// Range that supports generating a single sample efficiently.
-///
-/// Any type implementing this trait can be used to specify the sampled range
-/// for `Rng::gen_range`.
-pub trait SampleRange<T> {
-    /// Generate a sample from the given range.
-    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T;
-
-    /// Check whether the range is empty.
-    fn is_empty(&self) -> bool;
-}
-
-impl<T: SampleUniform + PartialOrd> SampleRange<T> for Range<T> {
-    #[inline]
-    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T {
-        T::Sampler::sample_single(self.start, self.end, rng)
-    }
-
-    #[inline]
-    fn is_empty(&self) -> bool {
-        !(self.start < self.end)
-    }
-}
-
-impl<T: SampleUniform + PartialOrd> SampleRange<T> for RangeInclusive<T> {
-    #[inline]
-    fn sample_single<R: RngCore + ?Sized>(self, rng: &mut R) -> T {
-        T::Sampler::sample_single_inclusive(self.start(), self.end(), rng)
-    }
-
-    #[inline]
-    fn is_empty(&self) -> bool {
-        !(self.start() <= self.end())
-    }
-}
-
-
-////////////////////////////////////////////////////////////////////////////////
-
-// 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 <= $unsigned::MAX`.
-///
-/// An alternative to using a modulus is widening multiply: After a widening
-/// multiply by `range`, the result is in the high word. Then comparing the low
-/// word against `zone` makes sure our distribution is uniform.
-#[derive(Clone, Copy, Debug, PartialEq)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-pub struct UniformInt<X> {
-    low: X,
-    range: X,
-    z: X, // either ints_to_reject or zone depending on implementation
-}
-
-macro_rules! uniform_int_impl {
-    ($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,
-                    // These are really $unsigned values, but store as $ty:
-                    range: range as $ty,
-                    z: ints_to_reject as $unsigned as $ty,
-                }
-            }
-
-            #[inline]
-            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()
-                }
-            }
-
-            #[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 < high, "UniformSampler::sample_single: low >= high");
-                Self::sample_single_inclusive(low, high - 1, rng)
-            }
-
-            #[inline]
-            fn sample_single_inclusive<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_inclusive: low > high");
-                let range = high.wrapping_sub(low).wrapping_add(1) as $unsigned as $u_large;
-                // If the above resulted in wrap-around to 0, the range is $ty::MIN..=$ty::MAX,
-                // and any integer will do.
-                if range == 0 {
-                    return rng.gen();
-                }
-
-                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 $ty);
-                    }
-                }
-            }
-        }
-    };
-}
-
-uniform_int_impl! { i8, u8, u32 }
-uniform_int_impl! { i16, u16, u32 }
-uniform_int_impl! { i32, u32, u32 }
-uniform_int_impl! { i64, u64, u64 }
-uniform_int_impl! { i128, u128, u128 }
-uniform_int_impl! { isize, usize, usize }
-uniform_int_impl! { u8, u8, u32 }
-uniform_int_impl! { u16, u16, u32 }
-uniform_int_impl! { u32, u32, u32 }
-uniform_int_impl! { u64, u64, u64 }
-uniform_int_impl! { usize, usize, usize }
-uniform_int_impl! { u128, u128, u128 }
-
-#[cfg(feature = "simd_support")]
-macro_rules! uniform_simd_int_impl {
-    ($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,
-                    // 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(feature = "simd_support")]
-uniform_simd_int_impl! {
-    (u64x2, i64x2),
-    (u64x4, i64x4),
-    (u64x8, i64x8),
-    u64
-}
-
-#[cfg(feature = "simd_support")]
-uniform_simd_int_impl! {
-    (u32x2, i32x2),
-    (u32x4, i32x4),
-    (u32x8, i32x8),
-    (u32x16, i32x16),
-    u32
-}
-
-#[cfg(feature = "simd_support")]
-uniform_simd_int_impl! {
-    (u16x2, i16x2),
-    (u16x4, i16x4),
-    (u16x8, i16x8),
-    (u16x16, i16x16),
-    (u16x32, i16x32),
-    u16
-}
-
-#[cfg(feature = "simd_support")]
-uniform_simd_int_impl! {
-    (u8x2, i8x2),
-    (u8x4, i8x4),
-    (u8x8, i8x8),
-    (u8x16, i8x16),
-    (u8x32, i8x32),
-    (u8x64, i8x64),
-    u8
-}
-
-impl SampleUniform for char {
-    type Sampler = UniformChar;
-}
-
-/// The back-end implementing [`UniformSampler`] for `char`.
-///
-/// Unless you are implementing [`UniformSampler`] for your own type, this type
-/// should not be used directly, use [`Uniform`] instead.
-///
-/// This differs from integer range sampling since the range `0xD800..=0xDFFF`
-/// are used for surrogate pairs in UCS and UTF-16, and consequently are not
-/// valid Unicode code points. We must therefore avoid sampling values in this
-/// range.
-#[derive(Clone, Copy, Debug)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-pub struct UniformChar {
-    sampler: UniformInt<u32>,
-}
-
-/// UTF-16 surrogate range start
-const CHAR_SURROGATE_START: u32 = 0xD800;
-/// UTF-16 surrogate range size
-const CHAR_SURROGATE_LEN: u32 = 0xE000 - CHAR_SURROGATE_START;
-
-/// Convert `char` to compressed `u32`
-fn char_to_comp_u32(c: char) -> u32 {
-    match c as u32 {
-        c if c >= CHAR_SURROGATE_START => c - CHAR_SURROGATE_LEN,
-        c => c,
-    }
-}
-
-impl UniformSampler for UniformChar {
-    type X = char;
-
-    #[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 = char_to_comp_u32(*low_b.borrow());
-        let high = char_to_comp_u32(*high_b.borrow());
-        let sampler = UniformInt::<u32>::new(low, high);
-        UniformChar { sampler }
-    }
-
-    #[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 = char_to_comp_u32(*low_b.borrow());
-        let high = char_to_comp_u32(*high_b.borrow());
-        let sampler = UniformInt::<u32>::new_inclusive(low, high);
-        UniformChar { sampler }
-    }
-
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
-        let mut x = self.sampler.sample(rng);
-        if x >= CHAR_SURROGATE_START {
-            x += CHAR_SURROGATE_LEN;
-        }
-        // SAFETY: x must not be in surrogate range or greater than char::MAX.
-        // This relies on range constructors which accept char arguments.
-        // Validity of input char values is assumed.
-        unsafe { core::char::from_u32_unchecked(x) }
-    }
-}
-
-/// 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 23 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, PartialEq)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-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 = $ty;
-
-            fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
-            where
-                B1: SampleBorrow<Self::X> + Sized,
-                B2: SampleBorrow<Self::X> + Sized,
-            {
-                let low = *low_b.borrow();
-                let high = *high_b.borrow();
-                debug_assert!(
-                    low.all_finite(),
-                    "Uniform::new called with `low` non-finite."
-                );
-                debug_assert!(
-                    high.all_finite(),
-                    "Uniform::new called with `high` non-finite."
-                );
-                assert!(low.all_lt(high), "Uniform::new called with `low >= high`");
-                let max_rand = <$ty>::splat(
-                    (::core::$u_scalar::MAX >> $bits_to_discard).into_float_with_exponent(0) - 1.0,
-                );
-
-                let mut scale = high - low;
-                assert!(scale.all_finite(), "Uniform::new: range overflow");
-
-                loop {
-                    let mask = (scale * max_rand + low).ge_mask(high);
-                    if mask.none() {
-                        break;
-                    }
-                    scale = scale.decrease_masked(mask);
-                }
-
-                debug_assert!(<$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();
-                debug_assert!(
-                    low.all_finite(),
-                    "Uniform::new_inclusive called with `low` non-finite."
-                );
-                debug_assert!(
-                    high.all_finite(),
-                    "Uniform::new_inclusive called with `high` non-finite."
-                );
-                assert!(
-                    low.all_le(high),
-                    "Uniform::new_inclusive called with `low > high`"
-                );
-                let max_rand = <$ty>::splat(
-                    (::core::$u_scalar::MAX >> $bits_to_discard).into_float_with_exponent(0) - 1.0,
-                );
-
-                let mut scale = (high - low) / max_rand;
-                assert!(scale.all_finite(), "Uniform::new_inclusive: range overflow");
-
-                loop {
-                    let mask = (scale * max_rand + low).gt_mask(high);
-                    if mask.none() {
-                        break;
-                    }
-                    scale = scale.decrease_masked(mask);
-                }
-
-                debug_assert!(<$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>() >> $bits_to_discard).into_float_with_exponent(0);
-
-                // Get a value in the range [0, 1) in order to avoid
-                // overflowing into infinity when multiplying with scale
-                let value0_1 = value1_2 - 1.0;
-
-                // We don't use `f64::mul_add`, because it is not available with
-                // `no_std`. Furthermore, it is slower for some targets (but
-                // faster for others). However, the order of multiplication and
-                // addition is important, because on some platforms (e.g. ARM)
-                // it will be optimized to a single (non-FMA) instruction.
-                value0_1 * self.scale + self.low
-            }
-
-            #[inline]
-            fn sample_single<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) -> Self::X
-            where
-                B1: SampleBorrow<Self::X> + Sized,
-                B2: SampleBorrow<Self::X> + Sized,
-            {
-                let low = *low_b.borrow();
-                let high = *high_b.borrow();
-                debug_assert!(
-                    low.all_finite(),
-                    "UniformSampler::sample_single called with `low` non-finite."
-                );
-                debug_assert!(
-                    high.all_finite(),
-                    "UniformSampler::sample_single called with `high` non-finite."
-                );
-                assert!(
-                    low.all_lt(high),
-                    "UniformSampler::sample_single: low >= high"
-                );
-                let mut scale = high - low;
-                assert!(scale.all_finite(), "UniformSampler::sample_single: range overflow");
-
-                loop {
-                    // Generate a value in the range [1, 2)
-                    let value1_2 =
-                        (rng.gen::<$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)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-pub struct UniformDuration {
-    mode: UniformDurationMode,
-    offset: u32,
-}
-
-#[derive(Debug, Copy, Clone)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-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 `Rng::sample(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;
-
-    #[test]
-    #[cfg(feature = "serde1")]
-    fn test_serialization_uniform_duration() {
-        let distr = UniformDuration::new(Duration::from_secs(10), Duration::from_secs(60));
-        let de_distr: UniformDuration = bincode::deserialize(&bincode::serialize(&distr).unwrap()).unwrap();
-        assert_eq!(
-            distr.offset, de_distr.offset
-        );
-        match (distr.mode, de_distr.mode) {
-            (UniformDurationMode::Small {secs: a_secs, nanos: a_nanos}, UniformDurationMode::Small {secs, nanos}) => {
-                assert_eq!(a_secs, secs);
-
-                assert_eq!(a_nanos.0.low, nanos.0.low);
-                assert_eq!(a_nanos.0.range, nanos.0.range);
-                assert_eq!(a_nanos.0.z, nanos.0.z);
-            }
-            (UniformDurationMode::Medium {nanos: a_nanos} , UniformDurationMode::Medium {nanos}) => {
-                assert_eq!(a_nanos.0.low, nanos.0.low);
-                assert_eq!(a_nanos.0.range, nanos.0.range);
-                assert_eq!(a_nanos.0.z, nanos.0.z);
-            }
-            (UniformDurationMode::Large {max_secs:a_max_secs, max_nanos:a_max_nanos, secs:a_secs}, UniformDurationMode::Large {max_secs, max_nanos, secs} ) => {
-                assert_eq!(a_max_secs, max_secs);
-                assert_eq!(a_max_nanos, max_nanos);
-
-                assert_eq!(a_secs.0.low, secs.0.low);
-                assert_eq!(a_secs.0.range, secs.0.range);
-                assert_eq!(a_secs.0.z, secs.0.z);
-            }
-            _ => panic!("`UniformDurationMode` was not serialized/deserialized correctly")
-        }
-    }
-    
-    #[test]
-    #[cfg(feature = "serde1")]
-    fn test_uniform_serialization() {
-        let unit_box: Uniform<i32>  = Uniform::new(-1, 1);
-        let de_unit_box: Uniform<i32> = bincode::deserialize(&bincode::serialize(&unit_box).unwrap()).unwrap();
-
-        assert_eq!(unit_box.0.low, de_unit_box.0.low);
-        assert_eq!(unit_box.0.range, de_unit_box.0.range);
-        assert_eq!(unit_box.0.z, de_unit_box.0.z);
-
-        let unit_box: Uniform<f32> = Uniform::new(-1., 1.);
-        let de_unit_box: Uniform<f32> = bincode::deserialize(&bincode::serialize(&unit_box).unwrap()).unwrap();
-
-        assert_eq!(unit_box.0.low, de_unit_box.0.low);
-        assert_eq!(unit_box.0.scale, de_unit_box.0.scale);
-    }
-
-    #[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_attr(miri, ignore)] // Miri is too slow
-    fn test_integers() {
-        use core::{i128, u128};
-        use core::{i16, i32, i64, i8, isize};
-        use core::{u16, u32, u64, u8, usize};
-
-        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 as SampleUniform>::Sampler::sample_single(low, high, &mut rng);
-                        assert!($le(low, v) && $lt(v, high));
-                    }
-
-                    for _ in 0..1000 {
-                        let v = <$ty as SampleUniform>::Sampler::sample_single_inclusive(low, high, &mut rng);
-                        assert!($le(low, v) && $le(v, high));
-                    }
-                }
-            }};
-
-            // scalar bulk
-            ($($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: $ty, y| x.lt(y).all()
-                );)*
-            }};
-        }
-        t!(i8, i16, i32, i64, isize, u8, u16, u32, u64, usize, i128, u128);
-
-        #[cfg(feature = "simd_support")]
-        {
-            t!(u8x2, u8x4, u8x8, u8x16, u8x32, u8x64 => u8);
-            t!(i8x2, i8x4, i8x8, i8x16, i8x32, i8x64 => i8);
-            t!(u16x2, u16x4, u16x8, u16x16, u16x32 => u16);
-            t!(i16x2, i16x4, i16x8, i16x16, i16x32 => i16);
-            t!(u32x2, u32x4, u32x8, u32x16 => u32);
-            t!(i32x2, i32x4, i32x8, i32x16 => i32);
-            t!(u64x2, u64x4, u64x8 => u64);
-            t!(i64x2, i64x4, i64x8 => i64);
-        }
-    }
-
-    #[test]
-    #[cfg_attr(miri, ignore)] // Miri is too slow
-    fn test_char() {
-        let mut rng = crate::test::rng(891);
-        let mut max = core::char::from_u32(0).unwrap();
-        for _ in 0..100 {
-            let c = rng.gen_range('A'..='Z');
-            assert!(('A'..='Z').contains(&c));
-            max = max.max(c);
-        }
-        assert_eq!(max, 'Z');
-        let d = Uniform::new(
-            core::char::from_u32(0xD7F0).unwrap(),
-            core::char::from_u32(0xE010).unwrap(),
-        );
-        for _ in 0..100 {
-            let c = d.sample(&mut rng);
-            assert!((c as u32) < 0xD800 || (c as u32) > 0xDFFF);
-        }
-    }
-
-    #[test]
-    #[cfg_attr(miri, ignore)] // Miri is too slow
-    fn test_floats() {
-        let mut rng = crate::test::rng(252);
-        let mut zero_rng = StepRng::new(0, 0);
-        let mut max_rng = StepRng::new(0xffff_ffff_ffff_ffff, 0);
-        macro_rules! t {
-            ($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),
-                    (-<$f_scalar>::from_bits(7), -0.0),
-                    (0.1 * ::core::$f_scalar::MAX, ::core::$f_scalar::MAX),
-                    (-::core::$f_scalar::MAX * 0.2, ::core::$f_scalar::MAX * 0.7),
-                ];
-                for &(low_scalar, high_scalar) in v.iter() {
-                    for lane in 0..<$ty>::lanes() {
-                        let low = <$ty>::splat(0.0 as $f_scalar).replace(lane, low_scalar);
-                        let high = <$ty>::splat(1.0 as $f_scalar).replace(lane, high_scalar);
-                        let my_uniform = Uniform::new(low, high);
-                        let my_incl_uniform = Uniform::new_inclusive(low, high);
-                        for _ in 0..100 {
-                            let v = rng.sample(my_uniform).extract(lane);
-                            assert!(low_scalar <= v && v < high_scalar);
-                            let v = rng.sample(my_incl_uniform).extract(lane);
-                            assert!(low_scalar <= v && v <= high_scalar);
-                            let v = <$ty as SampleUniform>::Sampler
-                                ::sample_single(low, high, &mut rng).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!(<$ty as SampleUniform>::Sampler
-                            ::sample_single(low, high, &mut zero_rng)
-                            .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 << $bits_shifted) as u64,
-                            );
-                            assert!(
-                                <$ty as SampleUniform>::Sampler
-                                    ::sample_single(low, high, &mut lowering_max_rng)
-                                    .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]
-    #[should_panic]
-    fn test_float_overflow() {
-        let _ = Uniform::from(::core::f64::MIN..::core::f64::MAX);
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_float_overflow_single() {
-        let mut rng = crate::test::rng(252);
-        rng.gen_range(::core::f64::MIN..::core::f64::MAX);
-    }
-
-    #[test]
-    #[cfg(all(
-        feature = "std",
-        not(target_arch = "wasm32"),
-        not(target_arch = "asmjs")
-    ))]
-    fn test_float_assertions() {
-        use super::SampleUniform;
-        use std::panic::catch_unwind;
-        fn range<T: SampleUniform>(low: T, high: T) {
-            let mut rng = crate::test::rng(253);
-            T::Sampler::sample_single(low, high, &mut rng);
-        }
-
-        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_attr(miri, ignore)] // Miri is too slow
-    fn test_durations() {
-        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::{
-            SampleBorrow, SampleUniform, UniformFloat, UniformSampler,
-        };
-        #[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),
-            ],
-        );
-    }
-
-    #[test]
-    fn uniform_distributions_can_be_compared() {
-        assert_eq!(Uniform::new(1.0, 2.0), Uniform::new(1.0, 2.0));
-
-        // To cover UniformInt
-        assert_eq!(Uniform::new(1 as u32, 2 as u32), Uniform::new(1 as u32, 2 as u32));
-    }
-}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/utils.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/utils.rs.html deleted file mode 100644 index 118f6fa..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/utils.rs.html +++ /dev/null @@ -1,861 +0,0 @@ -utils.rs - source
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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.
-
-//! Math helper functions
-
-#[cfg(feature = "simd_support")] use packed_simd::*;
-
-
-pub(crate) 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: $ty = tmp.cast();
-                    (hi, lo)
-                }
-            }
-        )+
-    };
-}
-wmul_impl! { u8, u16, 8 }
-wmul_impl! { u16, u32, 16 }
-wmul_impl! { u32, u64, 32 }
-wmul_impl! { u64, u128, 64 }
-
-// This code is a translation of the __mulddi3 function in LLVM's
-// compiler-rt. It is an optimised variant of the common method
-// `(a + b) * (c + d) = ac + ad + bc + bd`.
-//
-// For some reason LLVM can optimise the C version very well, but
-// keeps shuffling registers in this Rust translation.
-macro_rules! wmul_impl_large {
-    ($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 >> $half);
-
-                    (high, low)
-                }
-            }
-        )+
-    };
-}
-wmul_impl_large! { u128, 64 }
-
-macro_rules! wmul_impl_usize {
-    ($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 $ty);
-                (high as usize, low as usize)
-            }
-        }
-    };
-}
-#[cfg(target_pointer_width = "16")]
-wmul_impl_usize! { u16 }
-#[cfg(target_pointer_width = "32")]
-wmul_impl_usize! { u32 }
-#[cfg(target_pointer_width = "64")]
-wmul_impl_usize! { u64 }
-
-#[cfg(feature = "simd_support")]
-mod simd_wmul {
-    use super::*;
-    #[cfg(target_arch = "x86")] use core::arch::x86::*;
-    #[cfg(target_arch = "x86_64")] use core::arch::x86_64::*;
-
-    wmul_impl! {
-        (u8x2, u16x2),
-        (u8x4, u16x4),
-        (u8x8, u16x8),
-        (u8x16, u16x16),
-        (u8x32, u16x32),,
-        8
-    }
-
-    wmul_impl! { (u16x2, u32x2),, 16 }
-    wmul_impl! { (u16x4, u32x4),, 16 }
-    #[cfg(not(target_feature = "sse2"))]
-    wmul_impl! { (u16x8, u32x8),, 16 }
-    #[cfg(not(target_feature = "avx2"))]
-    wmul_impl! { (u16x16, u32x16),, 16 }
-
-    // 16-bit lane widths allow use of the x86 `mulhi` instructions, which
-    // means `wmul` can be implemented with only two instructions.
-    #[allow(unused_macros)]
-    macro_rules! wmul_impl_16 {
-        ($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! { 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 }
-}
-
-/// 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))]
-// False positive: We are following `std` here.
-#[allow(clippy::wrong_self_convention)]
-pub(crate) trait Float: Sized {
-    fn is_nan(self) -> bool;
-    fn is_infinite(self) -> bool;
-    fn is_finite(self) -> bool;
-}
-
-/// 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 $ty {
-            #[inline]
-            fn is_nan(self) -> bool {
-                self != self
-            }
-
-            #[inline]
-            fn is_infinite(self) -> bool {
-                self == ::core::$ty::INFINITY || self == ::core::$ty::NEG_INFINITY
-            }
-
-            #[inline]
-            fn is_finite(self) -> bool {
-                !(self.is_nan() || self.is_infinite())
-            }
-        }
-
-        impl FloatSIMDUtils for $ty {
-            type Mask = bool;
-            type UInt = $uty;
-
-            #[inline(always)]
-            fn all_lt(self, other: Self) -> bool {
-                self < other
-            }
-
-            #[inline(always)]
-            fn all_le(self, other: Self) -> bool {
-                self <= other
-            }
-
-            #[inline(always)]
-            fn all_finite(self) -> bool {
-                self.is_finite()
-            }
-
-            #[inline(always)]
-            fn finite_mask(self) -> Self::Mask {
-                self.is_finite()
-            }
-
-            #[inline(always)]
-            fn gt_mask(self, other: Self) -> Self::Mask {
-                self > other
-            }
-
-            #[inline(always)]
-            fn ge_mask(self, other: Self) -> Self::Mask {
-                self >= other
-            }
-
-            #[inline(always)]
-            fn decrease_masked(self, mask: Self::Mask) -> Self {
-                debug_assert!(mask, "At least one lane must be set");
-                <$ty>::from_bits(self.to_bits() - 1)
-            }
-
-            #[inline]
-            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;
-            type UInt = $uty;
-
-            #[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))
-            }
-
-            #[inline]
-            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 }
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/weighted.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/weighted.rs.html deleted file mode 100644 index 7ae8d0d..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/weighted.rs.html +++ /dev/null @@ -1,97 +0,0 @@ -weighted.rs - source
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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.
-
-//! Weighted index sampling
-//!
-//! This module is deprecated. Use [`crate::distributions::WeightedIndex`] and
-//! [`crate::distributions::WeightedError`] instead.
-
-pub use super::{WeightedIndex, WeightedError};
-
-#[allow(missing_docs)]
-#[deprecated(since = "0.8.0", note = "moved to rand_distr crate")]
-pub mod alias_method {
-    // This module exists to provide a deprecation warning which minimises
-    // compile errors, but still fails to compile if ever used.
-    use core::marker::PhantomData;
-    use alloc::vec::Vec;
-    use super::WeightedError;
-
-    #[derive(Debug)]
-    pub struct WeightedIndex<W: Weight> {
-        _phantom: PhantomData<W>,
-    }
-    impl<W: Weight> WeightedIndex<W> {
-        pub fn new(_weights: Vec<W>) -> Result<Self, WeightedError> {
-            Err(WeightedError::NoItem)
-        }
-    }
-
-    pub trait Weight {}
-    macro_rules! impl_weight {
-        () => {};
-        ($T:ident, $($more:ident,)*) => {
-            impl Weight for $T {}
-            impl_weight!($($more,)*);
-        };
-    }
-    impl_weight!(f64, f32,);
-    impl_weight!(u8, u16, u32, u64, usize,);
-    impl_weight!(i8, i16, i32, i64, isize,);
-    impl_weight!(u128, i128,);
-}
-
-
\ No newline at end of file diff --git a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/weighted_index.rs.html b/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/weighted_index.rs.html deleted file mode 100644 index 7439a5b..0000000 --- a/rust/theBook/chapter-2-guessing-game/guessing_game/target/doc/src/rand/distributions/weighted_index.rs.html +++ /dev/null @@ -1,919 +0,0 @@ -weighted_index.rs - source
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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.
-
-//! Weighted index sampling
-
-use crate::distributions::uniform::{SampleBorrow, SampleUniform, UniformSampler};
-use crate::distributions::Distribution;
-use crate::Rng;
-use core::cmp::PartialOrd;
-use core::fmt;
-
-// Note that this whole module is only imported if feature="alloc" is enabled.
-use alloc::vec::Vec;
-
-#[cfg(feature = "serde1")]
-use serde::{Serialize, Deserialize};
-
-/// A distribution using weighted sampling of discrete items
-///
-/// Sampling a `WeightedIndex` distribution returns the index of a randomly
-/// selected element from the iterator used when the `WeightedIndex` was
-/// created. The chance of a given element being picked is proportional to the
-/// value of the element. The weights can use any type `X` for which an
-/// implementation of [`Uniform<X>`] exists.
-///
-/// # Performance
-///
-/// Time complexity of sampling from `WeightedIndex` is `O(log N)` where
-/// `N` is the number of weights. As an alternative,
-/// [`rand_distr::weighted_alias`](https://docs.rs/rand_distr/*/rand_distr/weighted_alias/index.html)
-/// supports `O(1)` sampling, but with much higher initialisation cost.
-///
-/// A `WeightedIndex<X>` contains a `Vec<X>` and a [`Uniform<X>`] and so its
-/// size is the sum of the size of those objects, possibly plus some alignment.
-///
-/// Creating a `WeightedIndex<X>` will allocate enough space to hold `N - 1`
-/// weights of type `X`, where `N` is the number of weights. However, since
-/// `Vec` doesn't guarantee a particular growth strategy, additional memory
-/// might be allocated but not used. Since the `WeightedIndex` object also
-/// contains, this might cause additional allocations, though for primitive
-/// types, [`Uniform<X>`] doesn't allocate any memory.
-///
-/// Sampling from `WeightedIndex` will result in a single call to
-/// `Uniform<X>::sample` (method of the [`Distribution`] trait), which typically
-/// will request a single value from the underlying [`RngCore`], though the
-/// exact number depends on the implementation of `Uniform<X>::sample`.
-///
-/// # Example
-///
-/// ```
-/// use rand::prelude::*;
-/// use rand::distributions::WeightedIndex;
-///
-/// let choices = ['a', 'b', 'c'];
-/// let weights = [2,   1,   1];
-/// let dist = WeightedIndex::new(&weights).unwrap();
-/// let mut rng = thread_rng();
-/// for _ in 0..100 {
-///     // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
-///     println!("{}", choices[dist.sample(&mut rng)]);
-/// }
-///
-/// let items = [('a', 0), ('b', 3), ('c', 7)];
-/// let dist2 = WeightedIndex::new(items.iter().map(|item| item.1)).unwrap();
-/// for _ in 0..100 {
-///     // 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
-///     println!("{}", items[dist2.sample(&mut rng)].0);
-/// }
-/// ```
-///
-/// [`Uniform<X>`]: crate::distributions::Uniform
-/// [`RngCore`]: crate::RngCore
-#[derive(Debug, Clone, PartialEq)]
-#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
-#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
-pub struct WeightedIndex<X: SampleUniform + PartialOrd> {
-    cumulative_weights: Vec<X>,
-    total_weight: X,
-    weight_distribution: X::Sampler,
-}
-
-impl<X: SampleUniform + PartialOrd> WeightedIndex<X> {
-    /// Creates a new a `WeightedIndex` [`Distribution`] using the values
-    /// in `weights`. The weights can use any type `X` for which an
-    /// implementation of [`Uniform<X>`] exists.
-    ///
-    /// Returns an error if the iterator is empty, if any weight is `< 0`, or
-    /// if its total value is 0.
-    ///
-    /// [`Uniform<X>`]: crate::distributions::uniform::Uniform
-    pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>
-    where
-        I: IntoIterator,
-        I::Item: SampleBorrow<X>,
-        X: for<'a> ::core::ops::AddAssign<&'a X> + Clone + Default,
-    {
-        let mut iter = weights.into_iter();
-        let mut total_weight: X = iter.next().ok_or(WeightedError::NoItem)?.borrow().clone();
-
-        let zero = <X as Default>::default();
-        if !(total_weight >= zero) {
-            return Err(WeightedError::InvalidWeight);
-        }
-
-        let mut weights = Vec::<X>::with_capacity(iter.size_hint().0);
-        for w in iter {
-            // Note that `!(w >= x)` is not equivalent to `w < x` for partially
-            // ordered types due to NaNs which are equal to nothing.
-            if !(w.borrow() >= &zero) {
-                return Err(WeightedError::InvalidWeight);
-            }
-            weights.push(total_weight.clone());
-            total_weight += w.borrow();
-        }
-
-        if total_weight == zero {
-            return Err(WeightedError::AllWeightsZero);
-        }
-        let distr = X::Sampler::new(zero, total_weight.clone());
-
-        Ok(WeightedIndex {
-            cumulative_weights: weights,
-            total_weight,
-            weight_distribution: distr,
-        })
-    }
-
-    /// Update a subset of weights, without changing the number of weights.
-    ///
-    /// `new_weights` must be sorted by the index.
-    ///
-    /// Using this method instead of `new` might be more efficient if only a small number of
-    /// weights is modified. No allocations are performed, unless the weight type `X` uses
-    /// allocation internally.
-    ///
-    /// In case of error, `self` is not modified.
-    pub fn update_weights(&mut self, new_weights: &[(usize, &X)]) -> Result<(), WeightedError>
-    where X: for<'a> ::core::ops::AddAssign<&'a X>
-            + for<'a> ::core::ops::SubAssign<&'a X>
-            + Clone
-            + Default {
-        if new_weights.is_empty() {
-            return Ok(());
-        }
-
-        let zero = <X as Default>::default();
-
-        let mut total_weight = self.total_weight.clone();
-
-        // Check for errors first, so we don't modify `self` in case something
-        // goes wrong.
-        let mut prev_i = None;
-        for &(i, w) in new_weights {
-            if let Some(old_i) = prev_i {
-                if old_i >= i {
-                    return Err(WeightedError::InvalidWeight);
-                }
-            }
-            if !(*w >= zero) {
-                return Err(WeightedError::InvalidWeight);
-            }
-            if i > self.cumulative_weights.len() {
-                return Err(WeightedError::TooMany);
-            }
-
-            let mut old_w = if i < self.cumulative_weights.len() {
-                self.cumulative_weights[i].clone()
-            } else {
-                self.total_weight.clone()
-            };
-            if i > 0 {
-                old_w -= &self.cumulative_weights[i - 1];
-            }
-
-            total_weight -= &old_w;
-            total_weight += w;
-            prev_i = Some(i);
-        }
-        if total_weight <= zero {
-            return Err(WeightedError::AllWeightsZero);
-        }
-
-        // Update the weights. Because we checked all the preconditions in the
-        // previous loop, this should never panic.
-        let mut iter = new_weights.iter();
-
-        let mut prev_weight = zero.clone();
-        let mut next_new_weight = iter.next();
-        let &(first_new_index, _) = next_new_weight.unwrap();
-        let mut cumulative_weight = if first_new_index > 0 {
-            self.cumulative_weights[first_new_index - 1].clone()
-        } else {
-            zero.clone()
-        };
-        for i in first_new_index..self.cumulative_weights.len() {
-            match next_new_weight {
-                Some(&(j, w)) if i == j => {
-                    cumulative_weight += w;
-                    next_new_weight = iter.next();
-                }
-                _ => {
-                    let mut tmp = self.cumulative_weights[i].clone();
-                    tmp -= &prev_weight; // We know this is positive.
-                    cumulative_weight += &tmp;
-                }
-            }
-            prev_weight = cumulative_weight.clone();
-            core::mem::swap(&mut prev_weight, &mut self.cumulative_weights[i]);
-        }
-
-        self.total_weight = total_weight;
-        self.weight_distribution = X::Sampler::new(zero, self.total_weight.clone());
-
-        Ok(())
-    }
-}
-
-impl<X> Distribution<usize> for WeightedIndex<X>
-where X: SampleUniform + PartialOrd
-{
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
-        use ::core::cmp::Ordering;
-        let chosen_weight = self.weight_distribution.sample(rng);
-        // Find the first item which has a weight *higher* than the chosen weight.
-        self.cumulative_weights
-            .binary_search_by(|w| {
-                if *w <= chosen_weight {
-                    Ordering::Less
-                } else {
-                    Ordering::Greater
-                }
-            })
-            .unwrap_err()
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use super::*;
-
-    #[cfg(feature = "serde1")]
-    #[test]
-    fn test_weightedindex_serde1() {
-        let weighted_index = WeightedIndex::new(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).unwrap();
-
-        let ser_weighted_index = bincode::serialize(&weighted_index).unwrap();
-        let de_weighted_index: WeightedIndex<i32> =
-            bincode::deserialize(&ser_weighted_index).unwrap();
-
-        assert_eq!(
-            de_weighted_index.cumulative_weights,
-            weighted_index.cumulative_weights
-        );
-        assert_eq!(de_weighted_index.total_weight, weighted_index.total_weight);
-    }
-
-    #[test]
-    fn test_accepting_nan(){
-        assert_eq!(
-            WeightedIndex::new(&[core::f32::NAN, 0.5]).unwrap_err(),
-            WeightedError::InvalidWeight,
-        );
-        assert_eq!(
-            WeightedIndex::new(&[core::f32::NAN]).unwrap_err(),
-            WeightedError::InvalidWeight,
-        );
-        assert_eq!(
-            WeightedIndex::new(&[0.5, core::f32::NAN]).unwrap_err(),
-            WeightedError::InvalidWeight,
-        );
-
-        assert_eq!(
-            WeightedIndex::new(&[0.5, 7.0])
-                .unwrap()
-                .update_weights(&[(0, &core::f32::NAN)])
-                .unwrap_err(),
-            WeightedError::InvalidWeight,
-        )
-    }
-
-
-    #[test]
-    #[cfg_attr(miri, ignore)] // Miri is too slow
-    fn test_weightedindex() {
-        let mut r = crate::test::rng(700);
-        const N_REPS: u32 = 5000;
-        let weights = [1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7];
-        let total_weight = weights.iter().sum::<u32>() as f32;
-
-        let verify = |result: [i32; 14]| {
-            for (i, count) in result.iter().enumerate() {
-                let exp = (weights[i] * N_REPS) as f32 / total_weight;
-                let mut err = (*count as f32 - exp).abs();
-                if err != 0.0 {
-                    err /= exp;
-                }
-                assert!(err <= 0.25);
-            }
-        };
-
-        // WeightedIndex from vec
-        let mut chosen = [0i32; 14];
-        let distr = WeightedIndex::new(weights.to_vec()).unwrap();
-        for _ in 0..N_REPS {
-            chosen[distr.sample(&mut r)] += 1;
-        }
-        verify(chosen);
-
-        // WeightedIndex from slice
-        chosen = [0i32; 14];
-        let distr = WeightedIndex::new(&weights[..]).unwrap();
-        for _ in 0..N_REPS {
-            chosen[distr.sample(&mut r)] += 1;
-        }
-        verify(chosen);
-
-        // WeightedIndex from iterator
-        chosen = [0i32; 14];
-        let distr = WeightedIndex::new(weights.iter()).unwrap();
-        for _ in 0..N_REPS {
-            chosen[distr.sample(&mut r)] += 1;
-        }
-        verify(chosen);
-
-        for _ in 0..5 {
-            assert_eq!(WeightedIndex::new(&[0, 1]).unwrap().sample(&mut r), 1);
-            assert_eq!(WeightedIndex::new(&[1, 0]).unwrap().sample(&mut r), 0);
-            assert_eq!(
-                WeightedIndex::new(&[0, 0, 0, 0, 10, 0])
-                    .unwrap()
-                    .sample(&mut r),
-                4
-            );
-        }
-
-        assert_eq!(
-            WeightedIndex::new(&[10][0..0]).unwrap_err(),
-            WeightedError::NoItem
-        );
-        assert_eq!(
-            WeightedIndex::new(&[0]).unwrap_err(),
-            WeightedError::AllWeightsZero
-        );
-        assert_eq!(
-            WeightedIndex::new(&[10, 20, -1, 30]).unwrap_err(),
-            WeightedError::InvalidWeight
-        );
-        assert_eq!(
-            WeightedIndex::new(&[-10, 20, 1, 30]).unwrap_err(),
-            WeightedError::InvalidWeight
-        );
-        assert_eq!(
-            WeightedIndex::new(&[-10]).unwrap_err(),
-            WeightedError::InvalidWeight
-        );
-    }
-
-    #[test]
-    fn test_update_weights() {
-        let data = [
-            (
-                &[10u32, 2, 3, 4][..],
-                &[(1, &100), (2, &4)][..], // positive change
-                &[10, 100, 4, 4][..],
-            ),
-            (
-                &[1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7][..],
-                &[(2, &1), (5, &1), (13, &100)][..], // negative change and last element
-                &[1u32, 2, 1, 0, 5, 1, 7, 1, 2, 3, 4, 5, 6, 100][..],
-            ),
-        ];
-
-        for (weights, update, expected_weights) in data.iter() {
-            let total_weight = weights.iter().sum::<u32>();
-            let mut distr = WeightedIndex::new(weights.to_vec()).unwrap();
-            assert_eq!(distr.total_weight, total_weight);
-
-            distr.update_weights(update).unwrap();
-            let expected_total_weight = expected_weights.iter().sum::<u32>();
-            let expected_distr = WeightedIndex::new(expected_weights.to_vec()).unwrap();
-            assert_eq!(distr.total_weight, expected_total_weight);
-            assert_eq!(distr.total_weight, expected_distr.total_weight);
-            assert_eq!(distr.cumulative_weights, expected_distr.cumulative_weights);
-        }
-    }
-
-    #[test]
-    fn value_stability() {
-        fn test_samples<X: SampleUniform + PartialOrd, I>(
-            weights: I, buf: &mut [usize], expected: &[usize],
-        ) where
-            I: IntoIterator,
-            I::Item: SampleBorrow<X>,
-            X: for<'a> ::core::ops::AddAssign<&'a X> + Clone + Default,
-        {
-            assert_eq!(buf.len(), expected.len());
-            let distr = WeightedIndex::new(weights).unwrap();
-            let mut rng = crate::test::rng(701);
-            for r in buf.iter_mut() {
-                *r = rng.sample(&distr);
-            }
-            assert_eq!(buf, expected);
-        }
-
-        let mut buf = [0; 10];
-        test_samples(&[1i32, 1, 1, 1, 1, 1, 1, 1, 1], &mut buf, &[
-            0, 6, 2, 6, 3, 4, 7, 8, 2, 5,
-        ]);
-        test_samples(&[0.7f32, 0.1, 0.1, 0.1], &mut buf, &[
-            0, 0, 0, 1, 0, 0, 2, 3, 0, 0,
-        ]);
-        test_samples(&[1.0f64, 0.999, 0.998, 0.997], &mut buf, &[
-            2, 2, 1, 3, 2, 1, 3, 3, 2, 1,
-        ]);
-    }
-
-    #[test]
-    fn weighted_index_distributions_can_be_compared() {
-        assert_eq!(WeightedIndex::new(&[1, 2]), WeightedIndex::new(&[1, 2]));
-    }
-}
-
-/// Error type returned from `WeightedIndex::new`.
-#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub enum WeightedError {
-    /// The provided weight collection contains no items.
-    NoItem,
-
-    /// A weight is either less than zero, greater than the supported maximum,
-    /// NaN, or otherwise invalid.
-    InvalidWeight,
-
-    /// All items in the provided weight collection are zero.
-    AllWeightsZero,
-
-    /// Too many weights are provided (length greater than `u32::MAX`)
-    TooMany,
-}
-
-#[cfg(feature = "std")]
-impl std::error::Error for WeightedError {}
-
-impl fmt::Display for WeightedError {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        f.write_str(match *self {
-            WeightedError::NoItem => "No weights provided in distribution",
-            WeightedError::InvalidWeight => "A weight is invalid in distribution",
-            WeightedError::AllWeightsZero => "All weights are zero in distribution",
-            WeightedError::TooMany => "Too many weights (hit u32::MAX) in distribution",
-        })
-    }
-}
-
-
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