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// Copyright (C) 2017-2018 Baidu, Inc. All Rights Reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions // are met: // // * Redistributions of source code must retain the above copyright // notice, this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in // the documentation and/or other materials provided with the // distribution. // * Neither the name of Baidu, Inc., nor the names of its // contributors may be used to endorse or promote products derived // from this software without specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT // OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. //! Generating numbers between two others. // this is surprisingly complicated to be both generic & correct use std::num::Wrapping as w; use Rng; use distributions::{Sample, IndependentSample}; /// Sample values uniformly between two bounds. /// /// This gives a uniform distribution (assuming the RNG used to sample /// it is itself uniform & the `SampleRange` implementation for the /// given type is correct), even for edge cases like `low = 0u8`, /// `high = 170u8`, for which a naive modulo operation would return /// numbers less than 85 with double the probability to those greater /// than 85. /// /// Types should attempt to sample in `[low, high)`, i.e., not /// including `high`, but this may be very difficult. All the /// primitive integer types satisfy this property, and the float types /// normally satisfy it, but rounding may mean `high` can occur. /// /// # Example /// /// ```rust /// use sgx_rand::distributions::{IndependentSample, Range}; /// /// fn main() { /// let between = Range::new(10, 10000); /// let mut rng = sgx_rand::thread_rng(); /// let mut sum = 0; /// for _ in 0..1000 { /// sum += between.ind_sample(&mut rng); /// } /// println!("{}", sum); /// } /// ``` #[derive(Clone, Copy, Debug)] pub struct Range<X> { low: X, range: X, accept_zone: X } impl<X: SampleRange + PartialOrd> Range<X> { /// Create a new `Range` instance that samples uniformly from /// `[low, high)`. Panics if `low >= high`. pub fn new(low: X, high: X) -> Range<X> { assert!(low < high, "Range::new called with `low >= high`"); SampleRange::construct_range(low, high) } } impl<Sup: SampleRange> Sample<Sup> for Range<Sup> { #[inline] fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) } } impl<Sup: SampleRange> IndependentSample<Sup> for Range<Sup> { fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup { SampleRange::sample_range(self, rng) } } /// The helper trait for types that have a sensible way to sample /// uniformly between two values. This should not be used directly, /// and is only to facilitate `Range`. pub trait SampleRange : Sized { /// Construct the `Range` object that `sample_range` /// requires. This should not ever be called directly, only via /// `Range::new`, which will check that `low < high`, so this /// function doesn't have to repeat the check. fn construct_range(low: Self, high: Self) -> Range<Self>; /// Sample a value from the given `Range` with the given `Rng` as /// a source of randomness. fn sample_range<R: Rng>(r: &Range<Self>, rng: &mut R) -> Self; } macro_rules! integer_impl { ($ty:ty, $unsigned:ident) => { impl SampleRange for $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 & a // bijection. #[inline] fn construct_range(low: $ty, high: $ty) -> Range<$ty> { let range = (w(high as $unsigned) - w(low as $unsigned)).0; let unsigned_max: $unsigned = ::std::$unsigned::MAX; // this is the largest number that fits into $unsigned // that `range` divides evenly, so, if we've sampled // `n` uniformly from this region, then `n % range` is // uniform in [0, range) let zone = unsigned_max - unsigned_max % range; Range { low: low, range: range as $ty, accept_zone: zone as $ty } } #[inline] fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty { loop { // rejection sample let v = rng.gen::<$unsigned>(); // until we find something that fits into the // region which r.range evenly divides (this will // be uniformly distributed) if v < r.accept_zone as $unsigned { // and return it, with some adjustments return (w(r.low) + w((v % r.range as $unsigned) as $ty)).0; } } } } } } integer_impl! { i8, u8 } integer_impl! { i16, u16 } integer_impl! { i32, u32 } integer_impl! { i64, u64 } integer_impl! { isize, usize } integer_impl! { u8, u8 } integer_impl! { u16, u16 } integer_impl! { u32, u32 } integer_impl! { u64, u64 } integer_impl! { usize, usize } macro_rules! float_impl { ($ty:ty) => { impl SampleRange for $ty { fn construct_range(low: $ty, high: $ty) -> Range<$ty> { Range { low: low, range: high - low, accept_zone: 0.0 // unused } } fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty { r.low + r.range * rng.gen::<$ty>() } } } } float_impl! { f32 } float_impl! { f64 }