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#![deny(missing_docs)]
#![deny(warnings)]
#![cfg_attr(clippy, allow(used_underscore_binding))]
extern crate cast;
extern crate num_traits;
extern crate num_cpus;
extern crate rand;
extern crate thread_scoped;
#[cfg(test)] #[macro_use] extern crate approx;
#[cfg(test)] #[macro_use] extern crate quickcheck;
#[cfg(test)] extern crate itertools;
#[cfg(test)] mod test;
pub mod bivariate;
pub mod tuple;
pub mod univariate;
mod float;
use std::mem;
use std::ops::Deref;
use float::Float;
use univariate::Sample;
pub struct Distribution<A>(Box<[A]>);
impl<A> Distribution<A> where A: Float {
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();
(
percentiles.at(_50 * (_1 - confidence_level)),
percentiles.at(_50 * (_1 + confidence_level)),
)
}
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)
}
}
}
pub enum Tails {
One,
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)
}