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//! [Criterion]'s statistics library.
//!
//! [Criterion]: https://github.com/bheisler/criterion.rs
//!
//! **WARNING** This library is criterion's implementation detail and there no plans to stabilize
//! it. In other words, the API may break at any time without notice.
#[cfg(test)]
mod test;
pub mod bivariate;
pub mod tuple;
pub mod univariate;
mod float;
mod rand_util;
use std::mem;
use std::ops::Deref;
use crate::stats::float::Float;
use crate::stats::univariate::Sample;
/// The bootstrap distribution of some parameter
#[derive(Clone)]
pub struct Distribution<A>(Box<[A]>);
impl<A> Distribution<A>
where
A: Float,
{
/// Create a distribution from the given values
pub fn from(values: Box<[A]>) -> Distribution<A> {
Distribution(values)
}
/// Computes the confidence interval of the population parameter using percentiles
///
/// # Panics
///
/// Panics if the `confidence_level` is not in the `(0, 1)` range.
pub fn confidence_interval(&self, confidence_level: A) -> (A, A)
where
usize: cast::From<A, Output = Result<usize, cast::Error>>,
{
let _0 = A::cast(0);
let _1 = A::cast(1);
let _50 = A::cast(50);
assert!(confidence_level > _0 && confidence_level < _1);
let percentiles = self.percentiles();
// FIXME(privacy) this should use the `at_unchecked()` method
(
percentiles.at(_50 * (_1 - confidence_level)),
percentiles.at(_50 * (_1 + confidence_level)),
)
}
/// Computes the "likelihood" of seeing the value `t` or "more extreme" values in the
/// distribution.
pub fn p_value(&self, t: A, tails: &Tails) -> A {
use std::cmp;
let n = self.0.len();
let hits = self.0.iter().filter(|&&x| x < t).count();
let tails = A::cast(match *tails {
Tails::One => 1,
Tails::Two => 2,
});
A::cast(cmp::min(hits, n - hits)) / A::cast(n) * tails
}
}
impl<A> Deref for Distribution<A> {
type Target = Sample<A>;
fn deref(&self) -> &Sample<A> {
let slice: &[_] = &self.0;
unsafe { mem::transmute(slice) }
}
}
/// Number of tails for significance testing
pub enum Tails {
/// One tailed test
One,
/// Two tailed test
Two,
}
fn dot<A>(xs: &[A], ys: &[A]) -> A
where
A: Float,
{
xs.iter()
.zip(ys)
.fold(A::cast(0), |acc, (&x, &y)| acc + x * y)
}
fn sum<A>(xs: &[A]) -> A
where
A: Float,
{
use std::ops::Add;
xs.iter().cloned().fold(A::cast(0), Add::add)
}
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