blob: c3d0ff51344fef1ffbe466548e76b14889783809 [file] [log] [blame]
//! Kernels
use crate::stats::float::Float;
/// Kernel function
pub trait Kernel<A>: Copy + Sync
where
A: Float,
{
/// Apply the kernel function to the given x-value.
fn evaluate(&self, x: A) -> A;
}
/// Gaussian kernel
#[derive(Clone, Copy)]
pub struct Gaussian;
impl<A> Kernel<A> for Gaussian
where
A: Float,
{
fn evaluate(&self, x: A) -> A {
use std::f32::consts::PI;
(x.powi(2).exp() * A::cast(2. * PI)).sqrt().recip()
}
}
#[cfg(test)]
macro_rules! test {
($ty:ident) => {
mod $ty {
mod gaussian {
use approx::relative_eq;
use quickcheck::quickcheck;
use quickcheck::TestResult;
use crate::stats::univariate::kde::kernel::{Gaussian, Kernel};
quickcheck! {
fn symmetric(x: $ty) -> bool {
x.is_nan() || relative_eq!(Gaussian.evaluate(-x), Gaussian.evaluate(x))
}
}
// Any [a b] integral should be in the range [0 1]
quickcheck! {
fn integral(a: $ty, b: $ty) -> TestResult {
let a = a.sin().abs(); // map the value to [0 1]
let b = b.sin().abs(); // map the value to [0 1]
const DX: $ty = 1e-3;
if a > b {
TestResult::discard()
} else {
let mut acc = 0.;
let mut x = a;
let mut y = Gaussian.evaluate(a);
while x < b {
acc += DX * y / 2.;
x += DX;
y = Gaussian.evaluate(x);
acc += DX * y / 2.;
}
TestResult::from_bool(
(acc > 0. || relative_eq!(acc, 0.)) &&
(acc < 1. || relative_eq!(acc, 1.)))
}
}
}
}
}
};
}
#[cfg(test)]
mod test {
test!(f32);
test!(f64);
}