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diff --git a/rand/src/distributions/cauchy.rs b/rand/src/distributions/cauchy.rs
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+// Copyright 2018 Developers of the Rand project.
+// Copyright 2016-2017 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 Cauchy distribution.
+
+use Rng;
+use distributions::Distribution;
+use std::f64::consts::PI;
+
+/// The Cauchy distribution `Cauchy(median, scale)`.
+///
+/// This distribution has a density function:
+/// `f(x) = 1 / (pi * scale * (1 + ((x - median) / scale)^2))`
+///
+/// # Example
+///
+/// ```
+/// use rand::distributions::{Cauchy, Distribution};
+///
+/// let cau = Cauchy::new(2.0, 5.0);
+/// let v = cau.sample(&mut rand::thread_rng());
+/// println!("{} is from a Cauchy(2, 5) distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Cauchy {
+    median: f64,
+    scale: f64
+}
+
+impl Cauchy {
+    /// Construct a new `Cauchy` with the given shape parameters
+    /// `median` the peak location and `scale` the scale factor.
+    /// Panics if `scale <= 0`.
+    pub fn new(median: f64, scale: f64) -> Cauchy {
+        assert!(scale > 0.0, "Cauchy::new called with scale factor <= 0");
+        Cauchy {
+            median,
+            scale
+        }
+    }
+}
+
+impl Distribution<f64> for Cauchy {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        // sample from [0, 1)
+        let x = rng.gen::<f64>();
+        // get standard cauchy random number
+        // note that π/2 is not exactly representable, even if x=0.5 the result is finite
+        let comp_dev = (PI * x).tan();
+        // shift and scale according to parameters
+        let result = self.median + self.scale * comp_dev;
+        result
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use distributions::Distribution;
+    use super::Cauchy;
+
+    fn median(mut numbers: &mut [f64]) -> f64 {
+        sort(&mut numbers);
+        let mid = numbers.len() / 2;
+        numbers[mid]
+    }
+
+    fn sort(numbers: &mut [f64]) {
+        numbers.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    }
+
+    #[test]
+    fn test_cauchy_median() {
+        let cauchy = Cauchy::new(10.0, 5.0);
+        let mut rng = ::test::rng(123);
+        let mut numbers: [f64; 1000] = [0.0; 1000];
+        for i in 0..1000 {
+            numbers[i] = cauchy.sample(&mut rng);
+        }
+        let median = median(&mut numbers);
+        println!("Cauchy median: {}", median);
+        assert!((median - 10.0).abs() < 0.5); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    fn test_cauchy_mean() {
+        let cauchy = Cauchy::new(10.0, 5.0);
+        let mut rng = ::test::rng(123);
+        let mut sum = 0.0;
+        for _ in 0..1000 {
+            sum += cauchy.sample(&mut rng);
+        }
+        let mean = sum / 1000.0;
+        println!("Cauchy mean: {}", mean);
+        // for a Cauchy distribution the mean should not converge
+        assert!((mean - 10.0).abs() > 0.5); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_cauchy_invalid_scale_zero() {
+        Cauchy::new(0.0, 0.0);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_cauchy_invalid_scale_neg() {
+        Cauchy::new(0.0, -10.0);
+    }
+}