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Remove const-generic size parameter from Dirichlet distribution #15

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Remove const-generic size parameter from Dirichlet distribution #15

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6 changes: 6 additions & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,12 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [0.6.0] - unreleased

### API changes
- `Dirichlet` no longer uses `const` generics, which means that its size is not required at compile time. Essentially a revert of #1292
- Add `Dirichlet::new_with_size` constructor

## [0.5.1]

### Testing
Expand Down
119 changes: 73 additions & 46 deletions src/dirichlet.rs
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Expand Up @@ -21,27 +21,24 @@ use alloc::{boxed::Box, vec, vec::Vec};

#[derive(Clone, Debug, PartialEq)]
#[cfg_attr(feature = "serde", serde_as)]
struct DirichletFromGamma<F, const N: usize>
struct DirichletFromGamma<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
samplers: [Gamma<F>; N],
samplers: Vec<Gamma<F>>,
}

/// Error type returned from [`DirchletFromGamma::new`].
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
enum DirichletFromGammaError {
/// Gamma::new(a, 1) failed.
GammmaNewFailed,

/// gamma_dists.try_into() failed (in theory, this should not happen).
GammaArrayCreationFailed,
}

impl<F, const N: usize> DirichletFromGamma<F, N>
impl<F> DirichletFromGamma<F>
where
F: Float,
StandardNormal: Distribution<F>,
Expand All @@ -53,30 +50,28 @@ where
/// This function is part of a private implementation detail.
/// It assumes that the input is correct, so no validation of alpha is done.
#[inline]
fn new(alpha: [F; N]) -> Result<DirichletFromGamma<F, N>, DirichletFromGammaError> {
fn new(alpha: &[F]) -> Result<DirichletFromGamma<F>, DirichletFromGammaError> {
let mut gamma_dists = Vec::new();
for a in alpha {
let dist =
Gamma::new(a, F::one()).map_err(|_| DirichletFromGammaError::GammmaNewFailed)?;
Gamma::new(*a, F::one()).map_err(|_| DirichletFromGammaError::GammmaNewFailed)?;
gamma_dists.push(dist);
}
Ok(DirichletFromGamma {
samplers: gamma_dists
.try_into()
.map_err(|_| DirichletFromGammaError::GammaArrayCreationFailed)?,
samplers: gamma_dists,
})
}
}

impl<F, const N: usize> Distribution<[F; N]> for DirichletFromGamma<F, N>
impl<F> Distribution<Vec<F>> for DirichletFromGamma<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [F; N] {
let mut samples = [F::zero(); N];
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<F> {
let mut samples = vec![F::zero(); self.samplers.len()];
let mut sum = F::zero();

for (s, g) in samples.iter_mut().zip(self.samplers.iter()) {
Expand All @@ -93,7 +88,7 @@ where

#[derive(Clone, Debug, PartialEq)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
struct DirichletFromBeta<F, const N: usize>
struct DirichletFromBeta<F>
where
F: Float,
StandardNormal: Distribution<F>,
Expand All @@ -110,7 +105,7 @@ enum DirichletFromBetaError {
BetaNewFailed,
}

impl<F, const N: usize> DirichletFromBeta<F, N>
impl<F> DirichletFromBeta<F>
where
F: Float,
StandardNormal: Distribution<F>,
Expand All @@ -122,15 +117,16 @@ where
/// This function is part of a private implementation detail.
/// It assumes that the input is correct, so no validation of alpha is done.
#[inline]
fn new(alpha: [F; N]) -> Result<DirichletFromBeta<F, N>, DirichletFromBetaError> {
fn new(alpha: &[F]) -> Result<DirichletFromBeta<F>, DirichletFromBetaError> {
// `alpha_rev_csum` is the reverse of the cumulative sum of the
// reverse of `alpha[1..]`. E.g. if `alpha = [a0, a1, a2, a3]`, then
// `alpha_rev_csum` is `[a1 + a2 + a3, a2 + a3, a3]`.
// Note that instances of DirichletFromBeta will always have N >= 2,
// so the subtractions of 1, 2 and 3 from N in the following are safe.
let mut alpha_rev_csum = vec![alpha[N - 1]; N - 1];
for k in 0..(N - 2) {
alpha_rev_csum[N - 3 - k] = alpha_rev_csum[N - 2 - k] + alpha[N - 2 - k];
let n = alpha.len();
let mut alpha_rev_csum = vec![alpha[n - 1]; n - 1];
for k in 0..(n - 2) {
alpha_rev_csum[n - 3 - k] = alpha_rev_csum[n - 2 - k] + alpha[n - 2 - k];
}

// Zip `alpha[..(N-1)]` and `alpha_rev_csum`; for the example
Expand All @@ -139,7 +135,7 @@ where
// Then pass each tuple to `Beta::new()` to create the `Beta`
// instances.
let mut beta_dists = Vec::new();
for (&a, &b) in alpha[..(N - 1)].iter().zip(alpha_rev_csum.iter()) {
for (&a, &b) in alpha[..(n - 1)].iter().zip(alpha_rev_csum.iter()) {
let dist = Beta::new(a, b).map_err(|_| DirichletFromBetaError::BetaNewFailed)?;
beta_dists.push(dist);
}
Expand All @@ -149,41 +145,42 @@ where
}
}

impl<F, const N: usize> Distribution<[F; N]> for DirichletFromBeta<F, N>
impl<F> Distribution<Vec<F>> for DirichletFromBeta<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [F; N] {
let mut samples = [F::zero(); N];
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<F> {
let n = self.samplers.len() + 1;
let mut samples = vec![F::zero(); n];
let mut acc = F::one();

for (s, beta) in samples.iter_mut().zip(self.samplers.iter()) {
let beta_sample = beta.sample(rng);
*s = acc * beta_sample;
acc = acc * (F::one() - beta_sample);
}
samples[N - 1] = acc;
samples[n - 1] = acc;
samples
}
}

#[derive(Clone, Debug, PartialEq)]
#[cfg_attr(feature = "serde", serde_as)]
enum DirichletRepr<F, const N: usize>
enum DirichletRepr<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
/// Dirichlet distribution that generates samples using the Gamma distribution.
FromGamma(DirichletFromGamma<F, N>),
FromGamma(DirichletFromGamma<F>),

/// Dirichlet distribution that generates samples using the Beta distribution.
FromBeta(DirichletFromBeta<F, N>),
FromBeta(DirichletFromBeta<F>),
}

/// The [Dirichlet distribution](https://en.wikipedia.org/wiki/Dirichlet_distribution) `Dirichlet(α₁, α₂, ..., αₖ)`.
Expand All @@ -210,20 +207,20 @@ where
/// use rand::prelude::*;
/// use rand_distr::Dirichlet;
///
/// let dirichlet = Dirichlet::new([1.0, 2.0, 3.0]).unwrap();
/// let dirichlet = Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap();
/// let samples = dirichlet.sample(&mut rand::rng());
/// println!("{:?} is from a Dirichlet([1.0, 2.0, 3.0]) distribution", samples);
/// println!("{:?} is from a Dirichlet(&[1.0, 2.0, 3.0]) distribution", samples);
/// ```
#[cfg_attr(feature = "serde", serde_as)]
#[derive(Clone, Debug, PartialEq)]
pub struct Dirichlet<F, const N: usize>
pub struct Dirichlet<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
repr: DirichletRepr<F, N>,
repr: DirichletRepr<F>,
}

/// Error type returned from [`Dirichlet::new`].
Expand Down Expand Up @@ -268,7 +265,7 @@ impl fmt::Display for Error {
#[cfg(feature = "std")]
impl std::error::Error for Error {}

impl<F, const N: usize> Dirichlet<F, N>
impl<F> Dirichlet<F>
where
F: Float,
StandardNormal: Distribution<F>,
Expand All @@ -280,8 +277,8 @@ where
/// Requires `alpha.len() >= 2`, and each value in `alpha` must be positive,
/// finite and not subnormal.
#[inline]
pub fn new(alpha: [F; N]) -> Result<Dirichlet<F, N>, Error> {
if N < 2 {
pub fn new(alpha: &[F]) -> Result<Dirichlet<F>, Error> {
if alpha.len() < 2 {
return Err(Error::AlphaTooShort);
}
for &ai in alpha.iter() {
Expand Down Expand Up @@ -313,16 +310,46 @@ where
})
}
}

/// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.
///
/// Requires `size >= 2`.
#[inline]
pub fn new_with_size(alpha: F, size: usize) -> Result<Dirichlet<F>, Error> {
if !(alpha > F::zero()) {
return Err(Error::AlphaTooSmall);
}
if size < 2 {
return Err(Error::SizeTooSmall);
}
if alpha <= NumCast::from(0.1).unwrap() {
// Use the Beta method when alpha is less than 0.1 This
// threshold provides a reasonable compromise between using the faster
// Gamma method for as wide a range as possible while ensuring that
// the probability of generating nans is negligibly small.
let dist = DirichletFromBeta::new(&vec![alpha; size])
.map_err(|_| Error::FailedToCreateBeta)?;
Ok(Dirichlet {
repr: DirichletRepr::FromBeta(dist),
})
} else {
let dist = DirichletFromGamma::new(&vec![alpha; size])
.map_err(|_| Error::FailedToCreateGamma)?;
Ok(Dirichlet {
repr: DirichletRepr::FromGamma(dist),
})
}
}
}

impl<F, const N: usize> Distribution<[F; N]> for Dirichlet<F, N>
impl<F> Distribution<Vec<F>> for Dirichlet<F>
where
F: Float,
StandardNormal: Distribution<F>,
Exp1: Distribution<F>,
Open01: Distribution<F>,
{
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [F; N] {
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<F> {
match &self.repr {
DirichletRepr::FromGamma(dirichlet) => dirichlet.sample(rng),
DirichletRepr::FromBeta(dirichlet) => dirichlet.sample(rng),
Expand All @@ -336,7 +363,7 @@ mod test {

#[test]
fn test_dirichlet() {
let d = Dirichlet::new([1.0, 2.0, 3.0]).unwrap();
let d = Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap();
let mut rng = crate::test::rng(221);
let samples = d.sample(&mut rng);
assert!(samples.into_iter().all(|x: f64| x > 0.0));
Expand All @@ -345,42 +372,42 @@ mod test {
#[test]
#[should_panic]
fn test_dirichlet_invalid_length() {
Dirichlet::new([0.5]).unwrap();
Dirichlet::new(&[0.5]).unwrap();
}

#[test]
#[should_panic]
fn test_dirichlet_alpha_zero() {
Dirichlet::new([0.1, 0.0, 0.3]).unwrap();
Dirichlet::new(&[0.1, 0.0, 0.3]).unwrap();
}

#[test]
#[should_panic]
fn test_dirichlet_alpha_negative() {
Dirichlet::new([0.1, -1.5, 0.3]).unwrap();
Dirichlet::new(&[0.1, -1.5, 0.3]).unwrap();
}

#[test]
#[should_panic]
fn test_dirichlet_alpha_nan() {
Dirichlet::new([0.5, f64::NAN, 0.25]).unwrap();
Dirichlet::new(&[0.5, f64::NAN, 0.25]).unwrap();
}

#[test]
#[should_panic]
fn test_dirichlet_alpha_subnormal() {
Dirichlet::new([0.5, 1.5e-321, 0.25]).unwrap();
Dirichlet::new(&[0.5, 1.5e-321, 0.25]).unwrap();
}

#[test]
#[should_panic]
fn test_dirichlet_alpha_inf() {
Dirichlet::new([0.5, f64::INFINITY, 0.25]).unwrap();
Dirichlet::new(&[0.5, f64::INFINITY, 0.25]).unwrap();
}

#[test]
fn dirichlet_distributions_can_be_compared() {
assert_eq!(Dirichlet::new([1.0, 2.0]), Dirichlet::new([1.0, 2.0]));
assert_eq!(Dirichlet::new(&[1.0, 2.0]), Dirichlet::new(&[1.0, 2.0]));
}

/// Check that the means of the components of n samples from
Expand All @@ -390,7 +417,7 @@ mod test {
/// This is a crude statistical test, but it will catch egregious
/// mistakes. It will also also fail if any samples contain nan.
fn check_dirichlet_means<const N: usize>(alpha: [f64; N], n: i32, rtol: f64, seed: u64) {
let d = Dirichlet::new(alpha).unwrap();
let d = Dirichlet::new(&alpha).unwrap();
let mut rng = crate::test::rng(seed);
let mut sums = [0.0; N];
for _ in 0..n {
Expand Down
6 changes: 3 additions & 3 deletions tests/value_stability.rs
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Expand Up @@ -502,11 +502,11 @@ fn weibull_stability() {
fn dirichlet_stability() {
let mut rng = get_rng(223);
assert_eq!(
rng.sample(Dirichlet::new([1.0, 2.0, 3.0]).unwrap()),
rng.sample(Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap()),
[0.12941567177708177, 0.4702121891675036, 0.4003721390554146]
);
assert_eq!(
rng.sample(Dirichlet::new([8.0; 5]).unwrap()),
rng.sample(Dirichlet::new(&[8.0; 5]).unwrap()),
[
0.17684200044809556,
0.29915953935953055,
Expand All @@ -517,7 +517,7 @@ fn dirichlet_stability() {
);
// Test stability for the case where all alphas are less than 0.1.
assert_eq!(
rng.sample(Dirichlet::new([0.05, 0.025, 0.075, 0.05]).unwrap()),
rng.sample(Dirichlet::new(&[0.05, 0.025, 0.075, 0.05]).unwrap()),
[
0.00027580456855692104,
2.296135759821706e-20,
Expand Down