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Diffstat (limited to 'rand/src/distributions/weighted/mod.rs')
| -rw-r--r-- | rand/src/distributions/weighted/mod.rs | 363 |
1 files changed, 0 insertions, 363 deletions
diff --git a/rand/src/distributions/weighted/mod.rs b/rand/src/distributions/weighted/mod.rs deleted file mode 100644 index 2711637..0000000 --- a/rand/src/distributions/weighted/mod.rs +++ /dev/null @@ -1,363 +0,0 @@ -// 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 provides two implementations for sampling indices: -//! -//! * [`WeightedIndex`] allows `O(log N)` sampling -//! * [`alias_method::WeightedIndex`] allows `O(1)` sampling, but with -//! much greater set-up cost -//! -//! [`alias_method::WeightedIndex`]: alias_method/struct.WeightedIndex.html - -pub mod alias_method; - -use crate::Rng; -use crate::distributions::Distribution; -use crate::distributions::uniform::{UniformSampler, SampleUniform, SampleBorrow}; -use core::cmp::PartialOrd; -use core::fmt; - -// Note that this whole module is only imported if feature="alloc" is enabled. -#[cfg(not(feature="std"))] use crate::alloc::vec::Vec; - -/// 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. -/// -/// # Performance -/// -/// 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. -/// -/// Time complexity of sampling from `WeightedIndex` is `O(log N)` where -/// `N` is the number of weights. -/// -/// 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 implementaiton 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::Uniform -/// [`RngCore`]: crate::RngCore -#[derive(Debug, Clone)] -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 { - 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() + 1 { - 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::*; - - #[test] - #[cfg(not(miri))] // 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.into_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); - } - } -} - -/// Error type returned from `WeightedIndex::new`. -#[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 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, -} - -impl WeightedError { - fn msg(&self) -> &str { - match *self { - WeightedError::NoItem => "No weights provided.", - WeightedError::InvalidWeight => "A weight is invalid.", - WeightedError::AllWeightsZero => "All weights are zero.", - WeightedError::TooMany => "Too many weights (hit u32::MAX)", - } - } -} - -#[cfg(feature="std")] -impl ::std::error::Error for WeightedError { - fn description(&self) -> &str { - self.msg() - } - fn cause(&self) -> Option<&dyn (::std::error::Error)> { - None - } -} - -impl fmt::Display for WeightedError { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - write!(f, "{}", self.msg()) - } -} |