| // Copyright (c) 2017 Baidu, Inc. All Rights Reserved. |
| // |
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| |
| //! Sampling from random distributions. |
| //! |
| //! This is a generalization of `Rand` to allow parameters to control the |
| //! exact properties of the generated values, e.g. the mean and standard |
| //! deviation of a normal distribution. The `Sample` trait is the most |
| //! general, and allows for generating values that change some state |
| //! internally. The `IndependentSample` trait is for generating values |
| //! that do not need to record state. |
| |
| use std::marker; |
| |
| use {Rng, Rand}; |
| |
| pub use self::range::Range; |
| pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT}; |
| pub use self::normal::{Normal, LogNormal}; |
| pub use self::exponential::Exp; |
| |
| pub mod range; |
| pub mod gamma; |
| pub mod normal; |
| pub mod exponential; |
| |
| /// Types that can be used to create a random instance of `Support`. |
| pub trait Sample<Support> { |
| /// Generate a random value of `Support`, using `rng` as the |
| /// source of randomness. |
| fn sample<R: Rng>(&mut self, rng: &mut R) -> Support; |
| } |
| |
| /// `Sample`s that do not require keeping track of state. |
| /// |
| /// Since no state is recorded, each sample is (statistically) |
| /// independent of all others, assuming the `Rng` used has this |
| /// property. |
| // FIXME maybe having this separate is overkill (the only reason is to |
| // take &self rather than &mut self)? or maybe this should be the |
| // trait called `Sample` and the other should be `DependentSample`. |
| pub trait IndependentSample<Support>: Sample<Support> { |
| /// Generate a random value. |
| fn ind_sample<R: Rng>(&self, &mut R) -> Support; |
| } |
| |
| /// A wrapper for generating types that implement `Rand` via the |
| /// `Sample` & `IndependentSample` traits. |
| #[derive(Debug)] |
| pub struct RandSample<Sup> { |
| _marker: marker::PhantomData<fn() -> Sup>, |
| } |
| |
| impl<Sup> Copy for RandSample<Sup> {} |
| impl<Sup> Clone for RandSample<Sup> { |
| fn clone(&self) -> Self { *self } |
| } |
| |
| impl<Sup: Rand> Sample<Sup> for RandSample<Sup> { |
| fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) } |
| } |
| |
| impl<Sup: Rand> IndependentSample<Sup> for RandSample<Sup> { |
| fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup { |
| rng.gen() |
| } |
| } |
| |
| impl<Sup> RandSample<Sup> { |
| pub fn new() -> RandSample<Sup> { |
| RandSample { _marker: marker::PhantomData } |
| } |
| } |
| |
| /// A value with a particular weight for use with `WeightedChoice`. |
| #[derive(Copy, Clone, Debug)] |
| pub struct Weighted<T> { |
| /// The numerical weight of this item |
| pub weight: u32, |
| /// The actual item which is being weighted |
| pub item: T, |
| } |
| |
| /// A distribution that selects from a finite collection of weighted items. |
| /// |
| /// Each item has an associated weight that influences how likely it |
| /// is to be chosen: higher weight is more likely. |
| /// |
| /// The `Clone` restriction is a limitation of the `Sample` and |
| /// `IndependentSample` traits. Note that `&T` is (cheaply) `Clone` for |
| /// all `T`, as is `u32`, so one can store references or indices into |
| /// another vector. |
| /// |
| /// # Example |
| /// |
| /// ```rust |
| /// use sgx_rand::distributions::{Weighted, WeightedChoice, IndependentSample}; |
| /// |
| /// let mut items = vec!(Weighted { weight: 2, item: 'a' }, |
| /// Weighted { weight: 4, item: 'b' }, |
| /// Weighted { weight: 1, item: 'c' }); |
| /// let wc = WeightedChoice::new(&mut items); |
| /// let mut rng = sgx_rand::thread_rng(); |
| /// for _ in 0..16 { |
| /// // on average prints 'a' 4 times, 'b' 8 and 'c' twice. |
| /// println!("{}", wc.ind_sample(&mut rng)); |
| /// } |
| /// ``` |
| #[derive(Debug)] |
| pub struct WeightedChoice<'a, T:'a> { |
| items: &'a mut [Weighted<T>], |
| weight_range: Range<u32> |
| } |
| |
| impl<'a, T: Clone> WeightedChoice<'a, T> { |
| /// Create a new `WeightedChoice`. |
| /// |
| /// Panics if: |
| /// - `v` is empty |
| /// - the total weight is 0 |
| /// - the total weight is larger than a `u32` can contain. |
| pub fn new(items: &'a mut [Weighted<T>]) -> WeightedChoice<'a, T> { |
| // strictly speaking, this is subsumed by the total weight == 0 case |
| assert!(!items.is_empty(), "WeightedChoice::new called with no items"); |
| |
| let mut running_total: u32 = 0; |
| |
| // we convert the list from individual weights to cumulative |
| // weights so we can binary search. This *could* drop elements |
| // with weight == 0 as an optimisation. |
| for item in items.iter_mut() { |
| running_total = match running_total.checked_add(item.weight) { |
| Some(n) => n, |
| None => panic!("WeightedChoice::new called with a total weight \ |
| larger than a u32 can contain") |
| }; |
| |
| item.weight = running_total; |
| } |
| assert!(running_total != 0, "WeightedChoice::new called with a total weight of 0"); |
| |
| WeightedChoice { |
| items: items, |
| // we're likely to be generating numbers in this range |
| // relatively often, so might as well cache it |
| weight_range: Range::new(0, running_total) |
| } |
| } |
| } |
| |
| impl<'a, T: Clone> Sample<T> for WeightedChoice<'a, T> { |
| fn sample<R: Rng>(&mut self, rng: &mut R) -> T { self.ind_sample(rng) } |
| } |
| |
| impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T> { |
| fn ind_sample<R: Rng>(&self, rng: &mut R) -> T { |
| // we want to find the first element that has cumulative |
| // weight > sample_weight, which we do by binary since the |
| // cumulative weights of self.items are sorted. |
| |
| // choose a weight in [0, total_weight) |
| let sample_weight = self.weight_range.ind_sample(rng); |
| |
| // short circuit when it's the first item |
| if sample_weight < self.items[0].weight { |
| return self.items[0].item.clone(); |
| } |
| |
| let mut idx = 0; |
| let mut modifier = self.items.len(); |
| |
| // now we know that every possibility has an element to the |
| // left, so we can just search for the last element that has |
| // cumulative weight <= sample_weight, then the next one will |
| // be "it". (Note that this greatest element will never be the |
| // last element of the vector, since sample_weight is chosen |
| // in [0, total_weight) and the cumulative weight of the last |
| // one is exactly the total weight.) |
| while modifier > 1 { |
| let i = idx + modifier / 2; |
| if self.items[i].weight <= sample_weight { |
| // we're small, so look to the right, but allow this |
| // exact element still. |
| idx = i; |
| // we need the `/ 2` to round up otherwise we'll drop |
| // the trailing elements when `modifier` is odd. |
| modifier += 1; |
| } else { |
| // otherwise we're too big, so go left. (i.e. do |
| // nothing) |
| } |
| modifier /= 2; |
| } |
| return self.items[idx + 1].item.clone(); |
| } |
| } |
| |
| mod ziggurat_tables; |
| |
| /// Sample a random number using the Ziggurat method (specifically the |
| /// ZIGNOR variant from Doornik 2005). Most of the arguments are |
| /// directly from the paper: |
| /// |
| /// * `rng`: source of randomness |
| /// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0. |
| /// * `X`: the $x_i$ abscissae. |
| /// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$) |
| /// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$ |
| /// * `pdf`: the probability density function |
| /// * `zero_case`: manual sampling from the tail when we chose the |
| /// bottom box (i.e. i == 0) |
| |
| // the perf improvement (25-50%) is definitely worth the extra code |
| // size from force-inlining. |
| #[inline(always)] |
| fn ziggurat<R: Rng, P, Z>( |
| rng: &mut R, |
| symmetric: bool, |
| x_tab: ziggurat_tables::ZigTable, |
| f_tab: ziggurat_tables::ZigTable, |
| mut pdf: P, |
| mut zero_case: Z) |
| -> f64 where P: FnMut(f64) -> f64, Z: FnMut(&mut R, f64) -> f64 { |
| const SCALE: f64 = (1u64 << 53) as f64; |
| loop { |
| // reimplement the f64 generation as an optimisation suggested |
| // by the Doornik paper: we have a lot of precision-space |
| // (i.e. there are 11 bits of the 64 of a u64 to use after |
| // creating a f64), so we might as well reuse some to save |
| // generating a whole extra random number. (Seems to be 15% |
| // faster.) |
| // |
| // This unfortunately misses out on the benefits of direct |
| // floating point generation if an RNG like dSMFT is |
| // used. (That is, such RNGs create floats directly, highly |
| // efficiently and overload next_f32/f64, so by not calling it |
| // this may be slower than it would be otherwise.) |
| // FIXME: investigate/optimise for the above. |
| let bits: u64 = rng.gen(); |
| let i = (bits & 0xff) as usize; |
| let f = (bits >> 11) as f64 / SCALE; |
| |
| // u is either U(-1, 1) or U(0, 1) depending on if this is a |
| // symmetric distribution or not. |
| let u = if symmetric {2.0 * f - 1.0} else {f}; |
| let x = u * x_tab[i]; |
| |
| let test_x = if symmetric { x.abs() } else {x}; |
| |
| // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i]) |
| if test_x < x_tab[i + 1] { |
| return x; |
| } |
| if i == 0 { |
| return zero_case(rng, u); |
| } |
| // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1 |
| if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen::<f64>() < pdf(x) { |
| return x; |
| } |
| } |
| } |