2 // file at the top-level directory of this distribution and at
3 // http://rust-lang.org/COPYRIGHT.
5 // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
6 // http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
7 // <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
17 fn local_cmp(x: f64, y: f64) -> Ordering {  in local_cmp()
41     /// Depends on IEEE-754 arithmetic guarantees. See proof of correctness at:
42     /// ["Adaptive Precision Floating-Point Arithmetic and Fast Robust Geometric Predicates"]
43     /// (http://www.cs.cmu.edu/~quake-papers/robust-arithmetic.ps)
44     fn sum(&self) -> f64;  in sum()
47     fn min(&self) -> f64;  in min()
50     fn max(&self) -> f64;  in max()
52     /// Arithmetic mean (average) of the samples: sum divided by sample-count.
55     fn mean(&self) -> f64;  in mean()
61     fn median(&self) -> f64;  in median()
63     /// Variance of the samples: bias-corrected mean of the squares of the differences of each
65     /// population variance, which is assumed to be unknown. It therefore corrects the `(n-1)/n`
66     /// bias that would appear if we calculated a population variance, by dividing by `(n-1)` rather
70     fn var(&self) -> f64;  in var()
74     /// Note: this is not a robust statistic for non-normal distributions. Prefer the
78     fn std_dev(&self) -> f64;  in std_dev()
82     /// Note: this is not a robust statistic for non-normal distributions. Prefer the
84     fn std_dev_pct(&self) -> f64;  in std_dev_pct()
87     /// robust (distribution-agnostic) estimator of sample variability. Use this in preference to
93     fn median_abs_dev(&self) -> f64;  in median_abs_dev()
96     fn median_abs_dev_pct(&self) -> f64;  in median_abs_dev_pct()
105     fn percentile(&self, pct: f64) -> f64;  in percentile()
113     fn quartiles(&self) -> (f64, f64, f64);  in quartiles()
115     /// Inter-quartile range: the difference between the 25th percentile (1st quartile) and the 75th
119     fn iqr(&self) -> f64;  in iqr()
142     pub fn new(samples: &[f64]) -> Summary {  in new()
162     fn sum(&self) -> f64 {  in sum()
175                 // Rounded `x+y` is stored in `hi` with round-off stored in  in sum()
178                 let lo = y - (hi - x);  in sum()
196     fn min(&self) -> f64 {  in min()
201     fn max(&self) -> f64 {  in max()
206     fn mean(&self) -> f64 {  in mean()
211     fn median(&self) -> f64 {  in median()
215     fn var(&self) -> f64 {  in var()
222                 let x = *s - mean;  in var()
225             // NB: this is _supposed to be_ len-1, not len. If you  in var()
228             let denom = (self.len() - 1) as f64;  in var()
233     fn std_dev(&self) -> f64 {  in std_dev()
237     fn std_dev_pct(&self) -> f64 {  in std_dev_pct()
242     fn median_abs_dev(&self) -> f64 {  in median_abs_dev()
244         let abs_devs: Vec<f64> = self.iter().map(|&v| (med - v).abs()).collect();  in median_abs_dev()
251     fn median_abs_dev_pct(&self) -> f64 {  in median_abs_dev_pct()
256     fn percentile(&self, pct: f64) -> f64 {  in percentile()
262     fn quartiles(&self) -> (f64, f64, f64) {  in quartiles()
274     fn iqr(&self) -> f64 {  in iqr()
276         c - a  in iqr()
281 // Helper function: extract a value representing the `pct` percentile of a sorted sample-set, using
283 fn percentile_of_sorted(sorted_samples: &[f64], pct: f64) -> f64 {  in percentile_of_sorted()
293         return sorted_samples[sorted_samples.len() - 1];  in percentile_of_sorted()
295     let length = (sorted_samples.len() - 1) as f64;  in percentile_of_sorted()
298     let d = rank - lrank;  in percentile_of_sorted()
302     lo + (hi - lo) * d  in percentile_of_sorted()
306 /// Winsorize a set of samples, replacing values above the `100-pct` percentile
318     let hi = percentile_of_sorted(&tmp, hundred - pct);  in winsorize()
328 // Test vectors generated from R, using the script src/etc/stat-test-vectors.r.
341             assert!((*a - *b).abs() < 1.0e-6,
361         // but they're within float epsilon, which is 1.0e-6.  in check()
875         assert_eq!([1e30f64, 1.2f64, -1e30f64].sum(), 1.2);  in test_sum_f64_between_ints_that_sum_to_0()