1 files changed, 0 insertions, 154 deletions
diff --git a/rand/rand_distr/src/dirichlet.rs b/rand/rand_distr/src/dirichlet.rs
deleted file mode 100644
index 71cf73c..0000000
--- a/rand/rand_distr/src/dirichlet.rs
+++ /dev/null
@@ -1,154 +0,0 @@
-// Copyright 2018 Developers of the Rand project.
-// Copyright 2013 The Rust Project Developers.
-//
-// 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.
-
-//! The dirichlet distribution.
-
-use rand::Rng;
-use crate::{Distribution, Gamma, StandardNormal, Exp1, Open01};
-use crate::utils::Float;
-
-/// The dirichelet distribution `Dirichlet(alpha)`.
-///
-/// The Dirichlet distribution is a family of continuous multivariate
-/// probability distributions parameterized by a vector alpha of positive reals.
-/// It is a multivariate generalization of the beta distribution.
-///
-/// # Example
-///
-/// ```
-/// use rand::prelude::*;
-/// use rand_distr::Dirichlet;
-///
-/// let dirichlet = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
-/// let samples = dirichlet.sample(&mut rand::thread_rng());
-/// println!("{:?} is from a Dirichlet([1.0, 2.0, 3.0]) distribution", samples);
-/// ```
-#[derive(Clone, Debug)]
-pub struct Dirichlet<N> {
-    /// Concentration parameters (alpha)
-    alpha: Vec<N>,
-}
-
-/// Error type returned from `Dirchlet::new`.
-#[derive(Clone, Copy, Debug, PartialEq, Eq)]
-pub enum Error {
-    /// `alpha.len() < 2`.
-    AlphaTooShort,
-    /// `alpha <= 0.0` or `nan`.
-    AlphaTooSmall,
-    /// `size < 2`.
-    SizeTooSmall,
-}
-
-impl<N: Float> Dirichlet<N>
-where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
-{
-    /// Construct a new `Dirichlet` with the given alpha parameter `alpha`.
-    ///
-    /// Requires `alpha.len() >= 2`.
-    #[inline]
-    pub fn new<V: Into<Vec<N>>>(alpha: V) -> Result<Dirichlet<N>, Error> {
-        let a = alpha.into();
-        if a.len() < 2 {
-            return Err(Error::AlphaTooShort);
-        }
-        for &ai in &a {
-            if !(ai > N::from(0.0)) {
-                return Err(Error::AlphaTooSmall);
-            }
-        }
-
-        Ok(Dirichlet { alpha: a })
-    }
-
-    /// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.
-    ///
-    /// Requires `size >= 2`.
-    #[inline]
-    pub fn new_with_size(alpha: N, size: usize) -> Result<Dirichlet<N>, Error> {
-        if !(alpha > N::from(0.0)) {
-            return Err(Error::AlphaTooSmall);
-        }
-        if size < 2 {
-            return Err(Error::SizeTooSmall);
-        }
-        Ok(Dirichlet {
-            alpha: vec![alpha; size],
-        })
-    }
-}
-
-impl<N: Float> Distribution<Vec<N>> for Dirichlet<N>
-where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
-{
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<N> {
-        let n = self.alpha.len();
-        let mut samples = vec![N::from(0.0); n];
-        let mut sum = N::from(0.0);
-
-        for (s, &a) in samples.iter_mut().zip(self.alpha.iter()) {
-            let g = Gamma::new(a, N::from(1.0)).unwrap();
-            *s = g.sample(rng);
-            sum += *s;
-        }
-        let invacc = N::from(1.0) / sum;
-        for s in samples.iter_mut() {
-            *s *= invacc;
-        }
-        samples
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use super::Dirichlet;
-    use crate::Distribution;
-
-    #[test]
-    fn test_dirichlet() {
-        let d = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
-        let mut rng = crate::test::rng(221);
-        let samples = d.sample(&mut rng);
-        let _: Vec<f64> = samples
-            .into_iter()
-            .map(|x| {
-                assert!(x > 0.0);
-                x
-            })
-            .collect();
-    }
-
-    #[test]
-    fn test_dirichlet_with_param() {
-        let alpha = 0.5f64;
-        let size = 2;
-        let d = Dirichlet::new_with_size(alpha, size).unwrap();
-        let mut rng = crate::test::rng(221);
-        let samples = d.sample(&mut rng);
-        let _: Vec<f64> = samples
-            .into_iter()
-            .map(|x| {
-                assert!(x > 0.0);
-                x
-            })
-            .collect();
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_dirichlet_invalid_length() {
-        Dirichlet::new_with_size(0.5f64, 1).unwrap();
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_dirichlet_invalid_alpha() {
-        Dirichlet::new_with_size(0.0f64, 2).unwrap();
-    }
-}