1 files changed, 0 insertions, 170 deletions
diff --git a/rand/src/distributions/normal.rs b/rand/src/distributions/normal.rs
deleted file mode 100644
index 7808baf..0000000
--- a/rand/src/distributions/normal.rs
+++ /dev/null
@@ -1,170 +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 normal and derived distributions.
-#![allow(deprecated)]
-
-use crate::Rng;
-use crate::distributions::{ziggurat_tables, Distribution, Open01};
-use crate::distributions::utils::ziggurat;
-
-/// Samples floating-point numbers according to the normal distribution
-/// `N(0, 1)` (a.k.a. a standard normal, or Gaussian). This is equivalent to
-/// `Normal::new(0.0, 1.0)` but faster.
-///
-/// See `Normal` for the general normal distribution.
-///
-/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method.
-///
-/// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
-///       Generate Normal Random Samples*](
-///       https://www.doornik.com/research/ziggurat.pdf).
-///       Nuffield College, Oxford
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct StandardNormal;
-
-impl Distribution<f64> for StandardNormal {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        #[inline]
-        fn pdf(x: f64) -> f64 {
-            (-x*x/2.0).exp()
-        }
-        #[inline]
-        fn zero_case<R: Rng + ?Sized>(rng: &mut R, u: f64) -> f64 {
-            // compute a random number in the tail by hand
-
-            // strange initial conditions, because the loop is not
-            // do-while, so the condition should be true on the first
-            // run, they get overwritten anyway (0 < 1, so these are
-            // good).
-            let mut x = 1.0f64;
-            let mut y = 0.0f64;
-
-            while -2.0 * y < x * x {
-                let x_: f64 = rng.sample(Open01);
-                let y_: f64 = rng.sample(Open01);
-
-                x = x_.ln() / ziggurat_tables::ZIG_NORM_R;
-                y = y_.ln();
-            }
-
-            if u < 0.0 { x - ziggurat_tables::ZIG_NORM_R } else { ziggurat_tables::ZIG_NORM_R - x }
-        }
-
-        ziggurat(rng, true, // this is symmetric
-                 &ziggurat_tables::ZIG_NORM_X,
-                 &ziggurat_tables::ZIG_NORM_F,
-                 pdf, zero_case)
-    }
-}
-
-/// The normal distribution `N(mean, std_dev**2)`.
-///
-/// This uses the ZIGNOR variant of the Ziggurat method, see [`StandardNormal`]
-/// for more details.
-/// 
-/// Note that [`StandardNormal`] is an optimised implementation for mean 0, and
-/// standard deviation 1.
-///
-/// [`StandardNormal`]: crate::distributions::StandardNormal
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct Normal {
-    mean: f64,
-    std_dev: f64,
-}
-
-impl Normal {
-    /// Construct a new `Normal` distribution with the given mean and
-    /// standard deviation.
-    ///
-    /// # Panics
-    ///
-    /// Panics if `std_dev < 0`.
-    #[inline]
-    pub fn new(mean: f64, std_dev: f64) -> Normal {
-        assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0");
-        Normal {
-            mean,
-            std_dev
-        }
-    }
-}
-impl Distribution<f64> for Normal {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let n = rng.sample(StandardNormal);
-        self.mean + self.std_dev * n
-    }
-}
-
-
-/// The log-normal distribution `ln N(mean, std_dev**2)`.
-///
-/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, std_dev**2)`
-/// distributed.
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct LogNormal {
-    norm: Normal
-}
-
-impl LogNormal {
-    /// Construct a new `LogNormal` distribution with the given mean
-    /// and standard deviation.
-    ///
-    /// # Panics
-    ///
-    /// Panics if `std_dev < 0`.
-    #[inline]
-    pub fn new(mean: f64, std_dev: f64) -> LogNormal {
-        assert!(std_dev >= 0.0, "LogNormal::new called with `std_dev` < 0");
-        LogNormal { norm: Normal::new(mean, std_dev) }
-    }
-}
-impl Distribution<f64> for LogNormal {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        self.norm.sample(rng).exp()
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use crate::distributions::Distribution;
-    use super::{Normal, LogNormal};
-
-    #[test]
-    fn test_normal() {
-        let norm = Normal::new(10.0, 10.0);
-        let mut rng = crate::test::rng(210);
-        for _ in 0..1000 {
-            norm.sample(&mut rng);
-        }
-    }
-    #[test]
-    #[should_panic]
-    fn test_normal_invalid_sd() {
-        Normal::new(10.0, -1.0);
-    }
-
-
-    #[test]
-    fn test_log_normal() {
-        let lnorm = LogNormal::new(10.0, 10.0);
-        let mut rng = crate::test::rng(211);
-        for _ in 0..1000 {
-            lnorm.sample(&mut rng);
-        }
-    }
-    #[test]
-    #[should_panic]
-    fn test_log_normal_invalid_sd() {
-        LogNormal::new(10.0, -1.0);
-    }
-}