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Implement WeightedIndex, SliceRandom::choose_weighted and SliceRandom…
…::choose_weighted_mut
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| // Copyright 2018 The Rust Project Developers. See the COPYRIGHT | ||
| // file at the top-level directory of this distribution and at | ||
| // https://rust-lang.org/COPYRIGHT. | ||
| // | ||
| // 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. | ||
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| use Rng; | ||
| use distributions::Distribution; | ||
| use distributions::uniform::{UniformSampler, SampleUniform, SampleBorrow}; | ||
| use ::core::cmp::PartialOrd; | ||
| use ::{Error, ErrorKind}; | ||
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| // Note that this whole module is only imported if feature="alloc" is enabled. | ||
| #[cfg(not(feature="std"))] use alloc::Vec; | ||
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| /// A distribution using weighted sampling to pick a discretely selected item. | ||
| /// | ||
| /// 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. | ||
| /// | ||
| /// # 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); | ||
| /// } | ||
| /// ``` | ||
| #[derive(Debug, Clone)] | ||
| pub struct WeightedIndex<X: SampleUniform + PartialOrd> { | ||
| cumulative_weights: Vec<X>, | ||
| weight_distribution: X::Sampler, | ||
| } | ||
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| 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. | ||
| /// | ||
| /// [`Distribution`]: trait.Distribution.html | ||
| /// [`Uniform<X>`]: struct.Uniform.html | ||
| pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, Error> | ||
| 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(Error::new(ErrorKind::Unexpected, "Empty iterator in WeightedIndex::new"))? | ||
| .borrow() | ||
| .clone(); | ||
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| let zero = <X as Default>::default(); | ||
| if total_weight < zero { | ||
| return Err(Error::new(ErrorKind::Unexpected, "Negative weight in WeightedIndex::new")); | ||
| } | ||
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| let mut weights = Vec::<X>::with_capacity(iter.size_hint().0); | ||
| for w in iter { | ||
| if *w.borrow() < zero { | ||
| return Err(Error::new(ErrorKind::Unexpected, "Negative weight in WeightedIndex::new")); | ||
| } | ||
| weights.push(total_weight.clone()); | ||
| total_weight += w.borrow(); | ||
| } | ||
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| if total_weight == zero { | ||
| return Err(Error::new(ErrorKind::Unexpected, "Total weight is zero in WeightedIndex::new")); | ||
| } | ||
| let distr = X::Sampler::new(zero, total_weight); | ||
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| Ok(WeightedIndex { cumulative_weights: weights, weight_distribution: distr }) | ||
| } | ||
| } | ||
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| 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() | ||
| } | ||
| } | ||
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| #[cfg(test)] | ||
| mod test { | ||
| use super::*; | ||
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| #[test] | ||
| fn test_weightedindex() { | ||
| let mut r = ::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; | ||
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| 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); | ||
| } | ||
| }; | ||
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| // 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); | ||
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| // 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); | ||
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| // 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); | ||
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| 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); | ||
| } | ||
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| assert!(WeightedIndex::new(&[10][0..0]).is_err()); | ||
| assert!(WeightedIndex::new(&[0]).is_err()); | ||
| assert!(WeightedIndex::new(&[10, 20, -1, 30]).is_err()); | ||
| assert!(WeightedIndex::new(&[-10, 20, 1, 30]).is_err()); | ||
| assert!(WeightedIndex::new(&[-10]).is_err()); | ||
| } | ||
| } |
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