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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);
}
}
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