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use distribution;
use source::Source;
#[derive(Clone, Copy, Debug)]
pub struct Uniform {
a: f64,
b: f64,
}
impl Uniform {
#[inline]
pub fn new(a: f64, b: f64) -> Self {
should!(a < b);
Uniform { a: a, b: b }
}
#[inline(always)]
pub fn a(&self) -> f64 {
self.a
}
#[inline(always)]
pub fn b(&self) -> f64 {
self.b
}
}
impl Default for Uniform {
#[inline]
fn default() -> Self {
Uniform::new(0.0, 1.0)
}
}
impl distribution::Continuous for Uniform {
#[inline]
fn density(&self, x: f64) -> f64 {
if x < self.a || x > self.b {
0.0
} else {
1.0 / (self.b - self.a)
}
}
}
impl distribution::Distribution for Uniform {
type Value = f64;
#[inline]
fn distribution(&self, x: f64) -> f64 {
if x <= self.a {
0.0
} else if x >= self.b {
1.0
} else {
(x - self.a) / (self.b - self.a)
}
}
}
impl distribution::Entropy for Uniform {
#[inline]
fn entropy(&self) -> f64 {
(self.b - self.a).ln()
}
}
impl distribution::Inverse for Uniform {
#[inline]
fn inverse(&self, p: f64) -> f64 {
should!(0.0 <= p && p <= 1.0);
self.a + (self.b - self.a) * p
}
}
impl distribution::Kurtosis for Uniform {
#[inline]
fn kurtosis(&self) -> f64 {
-1.2
}
}
impl distribution::Mean for Uniform {
#[inline]
fn mean(&self) -> f64 {
(self.a + self.b) / 2.0
}
}
impl distribution::Median for Uniform {
#[inline]
fn median(&self) -> f64 {
use distribution::Mean;
self.mean()
}
}
impl distribution::Sample for Uniform {
#[inline]
fn sample<S>(&self, source: &mut S) -> f64
where
S: Source,
{
self.a + (self.b - self.a) * source.read::<f64>()
}
}
impl distribution::Skewness for Uniform {
#[inline]
fn skewness(&self) -> f64 {
0.0
}
}
impl distribution::Variance for Uniform {
#[inline]
fn variance(&self) -> f64 {
(self.b - self.a).powi(2) / 12.0
}
}
#[cfg(test)]
mod tests {
use prelude::*;
macro_rules! new(
($a:expr, $b:expr) => (Uniform::new($a, $b));
);
#[test]
fn distribution() {
let d = new!(-1.0, 1.0);
let x = vec![-1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5];
let p = vec![0.0, 0.0, 0.25, 0.5, 0.75, 1.0, 1.0];
assert_eq!(
&x.iter().map(|&x| d.distribution(x)).collect::<Vec<_>>(),
&p
);
}
#[test]
fn density() {
let d = new!(-1.0, 1.0);
let x = vec![-1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5];
let p = vec![0.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.0];
assert_eq!(&x.iter().map(|&x| d.density(x)).collect::<Vec<_>>(), &p);
}
#[test]
fn entropy() {
use std::f64::consts::E;
assert_eq!(new!(0.0, E).entropy(), 1.0);
}
#[test]
fn inverse() {
let d = new!(-1.0, 1.0);
let x = vec![-1.0, -0.5, 0.0, 0.5, 1.0];
let p = vec![0.0, 0.25, 0.5, 0.75, 1.0];
assert_eq!(&p.iter().map(|&p| d.inverse(p)).collect::<Vec<_>>(), &x);
}
#[test]
fn kurtosis() {
assert_eq!(new!(0.0, 2.0).kurtosis(), -1.2);
}
#[test]
fn mean() {
assert_eq!(new!(0.0, 2.0).mean(), 1.0);
}
#[test]
fn median() {
assert_eq!(new!(0.0, 2.0).median(), 1.0);
}
#[test]
fn sample() {
for x in Independent(&new!(7.0, 42.0), &mut source::default()).take(100) {
assert!(7.0 <= x && x <= 42.0);
}
}
#[test]
fn skewness() {
assert_eq!(new!(0.0, 2.0).skewness(), 0.0);
}
#[test]
fn variance() {
assert_eq!(new!(0.0, 12.0).variance(), 12.0);
}
}