1 | //! [Criterion]'s statistics library. |
2 | //! |
3 | //! [Criterion]: https://github.com/bheisler/criterion.rs |
4 | //! |
5 | //! **WARNING** This library is criterion's implementation detail and there no plans to stabilize |
6 | //! it. In other words, the API may break at any time without notice. |
7 | |
8 | #[cfg (test)] |
9 | mod test; |
10 | |
11 | pub mod bivariate; |
12 | pub mod tuple; |
13 | pub mod univariate; |
14 | |
15 | mod float; |
16 | mod rand_util; |
17 | |
18 | use std::mem; |
19 | use std::ops::Deref; |
20 | |
21 | use crate::stats::float::Float; |
22 | use crate::stats::univariate::Sample; |
23 | |
24 | /// The bootstrap distribution of some parameter |
25 | #[derive(Clone)] |
26 | pub struct Distribution<A>(Box<[A]>); |
27 | |
28 | impl<A> Distribution<A> |
29 | where |
30 | A: Float, |
31 | { |
32 | /// Create a distribution from the given values |
33 | pub fn from(values: Box<[A]>) -> Distribution<A> { |
34 | Distribution(values) |
35 | } |
36 | |
37 | /// Computes the confidence interval of the population parameter using percentiles |
38 | /// |
39 | /// # Panics |
40 | /// |
41 | /// Panics if the `confidence_level` is not in the `(0, 1)` range. |
42 | pub fn confidence_interval(&self, confidence_level: A) -> (A, A) |
43 | where |
44 | usize: cast::From<A, Output = Result<usize, cast::Error>>, |
45 | { |
46 | let _0 = A::cast(0); |
47 | let _1 = A::cast(1); |
48 | let _50 = A::cast(50); |
49 | |
50 | assert!(confidence_level > _0 && confidence_level < _1); |
51 | |
52 | let percentiles = self.percentiles(); |
53 | |
54 | // FIXME(privacy) this should use the `at_unchecked()` method |
55 | ( |
56 | percentiles.at(_50 * (_1 - confidence_level)), |
57 | percentiles.at(_50 * (_1 + confidence_level)), |
58 | ) |
59 | } |
60 | |
61 | /// Computes the "likelihood" of seeing the value `t` or "more extreme" values in the |
62 | /// distribution. |
63 | pub fn p_value(&self, t: A, tails: &Tails) -> A { |
64 | use std::cmp; |
65 | |
66 | let n = self.0.len(); |
67 | let hits = self.0.iter().filter(|&&x| x < t).count(); |
68 | |
69 | let tails = A::cast(match *tails { |
70 | Tails::One => 1, |
71 | Tails::Two => 2, |
72 | }); |
73 | |
74 | A::cast(cmp::min(hits, n - hits)) / A::cast(n) * tails |
75 | } |
76 | } |
77 | |
78 | impl<A> Deref for Distribution<A> { |
79 | type Target = Sample<A>; |
80 | |
81 | fn deref(&self) -> &Sample<A> { |
82 | let slice: &[_] = &self.0; |
83 | |
84 | unsafe { mem::transmute(slice) } |
85 | } |
86 | } |
87 | |
88 | /// Number of tails for significance testing |
89 | pub enum Tails { |
90 | /// One tailed test |
91 | One, |
92 | /// Two tailed test |
93 | Two, |
94 | } |
95 | |
96 | fn dot<A>(xs: &[A], ys: &[A]) -> A |
97 | where |
98 | A: Float, |
99 | { |
100 | xs.iter() |
101 | .zip(ys) |
102 | .fold(A::cast(0), |acc, (&x, &y)| acc + x * y) |
103 | } |
104 | |
105 | fn sum<A>(xs: &[A]) -> A |
106 | where |
107 | A: Float, |
108 | { |
109 | use std::ops::Add; |
110 | |
111 | xs.iter().cloned().fold(A::cast(0), Add::add) |
112 | } |
113 | |