From d036b627b02942513d968d239dfa5f99f036321e Mon Sep 17 00:00:00 2001 From: Jeff Vander Stoep Date: Thu, 17 Dec 2020 19:59:02 +0100 Subject: Initial import of bencher v0.1.5 Test: n/a Change-Id: I53222b48d3c04c9c7b630f06beba26e459125c68 --- stats.rs | 878 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 878 insertions(+) create mode 100644 stats.rs (limited to 'stats.rs') diff --git a/stats.rs b/stats.rs new file mode 100644 index 0000000..d07633e --- /dev/null +++ b/stats.rs @@ -0,0 +1,878 @@ +// Copyright 2012 The Rust Project Developers. See the COPYRIGHT +// file at the top-level directory of this distribution and at +// http://rust-lang.org/COPYRIGHT. +// +// Licensed under the Apache License, Version 2.0 or the MIT license +// , at your +// option. This file may not be copied, modified, or distributed +// except according to those terms. + +#![allow(missing_docs)] +#![allow(deprecated)] // Float + +use std::cmp::Ordering::{self, Equal, Greater, Less}; +use std::mem; + +fn local_cmp(x: f64, y: f64) -> Ordering { + // arbitrarily decide that NaNs are larger than everything. + if y.is_nan() { + Less + } else if x.is_nan() { + Greater + } else if x < y { + Less + } else if x == y { + Equal + } else { + Greater + } +} + +fn local_sort(v: &mut [f64]) { + v.sort_by(|x: &f64, y: &f64| local_cmp(*x, *y)); +} + +/// Trait that provides simple descriptive statistics on a univariate set of numeric samples. +pub trait Stats { + /// Sum of the samples. + /// + /// Note: this method sacrifices performance at the altar of accuracy + /// Depends on IEEE-754 arithmetic guarantees. See proof of correctness at: + /// ["Adaptive Precision Floating-Point Arithmetic and Fast Robust Geometric Predicates"] + /// (http://www.cs.cmu.edu/~quake-papers/robust-arithmetic.ps) + fn sum(&self) -> f64; + + /// Minimum value of the samples. + fn min(&self) -> f64; + + /// Maximum value of the samples. + fn max(&self) -> f64; + + /// Arithmetic mean (average) of the samples: sum divided by sample-count. + /// + /// See: https://en.wikipedia.org/wiki/Arithmetic_mean + fn mean(&self) -> f64; + + /// Median of the samples: value separating the lower half of the samples from the higher half. + /// Equal to `self.percentile(50.0)`. + /// + /// See: https://en.wikipedia.org/wiki/Median + fn median(&self) -> f64; + + /// Variance of the samples: bias-corrected mean of the squares of the differences of each + /// sample from the sample mean. Note that this calculates the _sample variance_ rather than the + /// population variance, which is assumed to be unknown. It therefore corrects the `(n-1)/n` + /// bias that would appear if we calculated a population variance, by dividing by `(n-1)` rather + /// than `n`. + /// + /// See: https://en.wikipedia.org/wiki/Variance + fn var(&self) -> f64; + + /// Standard deviation: the square root of the sample variance. + /// + /// Note: this is not a robust statistic for non-normal distributions. Prefer the + /// `median_abs_dev` for unknown distributions. + /// + /// See: https://en.wikipedia.org/wiki/Standard_deviation + fn std_dev(&self) -> f64; + + /// Standard deviation as a percent of the mean value. See `std_dev` and `mean`. + /// + /// Note: this is not a robust statistic for non-normal distributions. Prefer the + /// `median_abs_dev_pct` for unknown distributions. + fn std_dev_pct(&self) -> f64; + + /// Scaled median of the absolute deviations of each sample from the sample median. This is a + /// robust (distribution-agnostic) estimator of sample variability. Use this in preference to + /// `std_dev` if you cannot assume your sample is normally distributed. Note that this is scaled + /// by the constant `1.4826` to allow its use as a consistent estimator for the standard + /// deviation. + /// + /// See: http://en.wikipedia.org/wiki/Median_absolute_deviation + fn median_abs_dev(&self) -> f64; + + /// Median absolute deviation as a percent of the median. See `median_abs_dev` and `median`. + fn median_abs_dev_pct(&self) -> f64; + + /// Percentile: the value below which `pct` percent of the values in `self` fall. For example, + /// percentile(95.0) will return the value `v` such that 95% of the samples `s` in `self` + /// satisfy `s <= v`. + /// + /// Calculated by linear interpolation between closest ranks. + /// + /// See: http://en.wikipedia.org/wiki/Percentile + fn percentile(&self, pct: f64) -> f64; + + /// Quartiles of the sample: three values that divide the sample into four equal groups, each + /// with 1/4 of the data. The middle value is the median. See `median` and `percentile`. This + /// function may calculate the 3 quartiles more efficiently than 3 calls to `percentile`, but + /// is otherwise equivalent. + /// + /// See also: https://en.wikipedia.org/wiki/Quartile + fn quartiles(&self) -> (f64, f64, f64); + + /// Inter-quartile range: the difference between the 25th percentile (1st quartile) and the 75th + /// percentile (3rd quartile). See `quartiles`. + /// + /// See also: https://en.wikipedia.org/wiki/Interquartile_range + fn iqr(&self) -> f64; +} + +/// Extracted collection of all the summary statistics of a sample set. +#[derive(Clone, PartialEq)] +#[allow(missing_docs)] +pub struct Summary { + pub sum: f64, + pub min: f64, + pub max: f64, + pub mean: f64, + pub median: f64, + pub var: f64, + pub std_dev: f64, + pub std_dev_pct: f64, + pub median_abs_dev: f64, + pub median_abs_dev_pct: f64, + pub quartiles: (f64, f64, f64), + pub iqr: f64, +} + +impl Summary { + /// Construct a new summary of a sample set. + pub fn new(samples: &[f64]) -> Summary { + Summary { + sum: samples.sum(), + min: samples.min(), + max: samples.max(), + mean: samples.mean(), + median: samples.median(), + var: samples.var(), + std_dev: samples.std_dev(), + std_dev_pct: samples.std_dev_pct(), + median_abs_dev: samples.median_abs_dev(), + median_abs_dev_pct: samples.median_abs_dev_pct(), + quartiles: samples.quartiles(), + iqr: samples.iqr(), + } + } +} + +impl Stats for [f64] { + // FIXME #11059 handle NaN, inf and overflow + fn sum(&self) -> f64 { + let mut partials = vec![]; + + for &x in self { + let mut x = x; + let mut j = 0; + // This inner loop applies `hi`/`lo` summation to each + // partial so that the list of partial sums remains exact. + for i in 0..partials.len() { + let mut y: f64 = partials[i]; + if x.abs() < y.abs() { + mem::swap(&mut x, &mut y); + } + // Rounded `x+y` is stored in `hi` with round-off stored in + // `lo`. Together `hi+lo` are exactly equal to `x+y`. + let hi = x + y; + let lo = y - (hi - x); + if lo != 0.0 { + partials[j] = lo; + j += 1; + } + x = hi; + } + if j >= partials.len() { + partials.push(x); + } else { + partials[j] = x; + partials.truncate(j + 1); + } + } + let zero: f64 = 0.0; + partials.iter().fold(zero, |p, q| p + *q) + } + + fn min(&self) -> f64 { + assert!(!self.is_empty()); + self.iter().fold(self[0], |p, q| p.min(*q)) + } + + fn max(&self) -> f64 { + assert!(!self.is_empty()); + self.iter().fold(self[0], |p, q| p.max(*q)) + } + + fn mean(&self) -> f64 { + assert!(!self.is_empty()); + self.sum() / (self.len() as f64) + } + + fn median(&self) -> f64 { + self.percentile(50 as f64) + } + + fn var(&self) -> f64 { + if self.len() < 2 { + 0.0 + } else { + let mean = self.mean(); + let mut v: f64 = 0.0; + for s in self { + let x = *s - mean; + v += x * x; + } + // NB: this is _supposed to be_ len-1, not len. If you + // change it back to len, you will be calculating a + // population variance, not a sample variance. + let denom = (self.len() - 1) as f64; + v / denom + } + } + + fn std_dev(&self) -> f64 { + self.var().sqrt() + } + + fn std_dev_pct(&self) -> f64 { + let hundred = 100 as f64; + (self.std_dev() / self.mean()) * hundred + } + + fn median_abs_dev(&self) -> f64 { + let med = self.median(); + let abs_devs: Vec = self.iter().map(|&v| (med - v).abs()).collect(); + // This constant is derived by smarter statistics brains than me, but it is + // consistent with how R and other packages treat the MAD. + let number = 1.4826; + abs_devs.median() * number + } + + fn median_abs_dev_pct(&self) -> f64 { + let hundred = 100 as f64; + (self.median_abs_dev() / self.median()) * hundred + } + + fn percentile(&self, pct: f64) -> f64 { + let mut tmp = self.to_vec(); + local_sort(&mut tmp); + percentile_of_sorted(&tmp, pct) + } + + fn quartiles(&self) -> (f64, f64, f64) { + let mut tmp = self.to_vec(); + local_sort(&mut tmp); + let first = 25f64; + let a = percentile_of_sorted(&tmp, first); + let secound = 50f64; + let b = percentile_of_sorted(&tmp, secound); + let third = 75f64; + let c = percentile_of_sorted(&tmp, third); + (a, b, c) + } + + fn iqr(&self) -> f64 { + let (a, _, c) = self.quartiles(); + c - a + } +} + + +// Helper function: extract a value representing the `pct` percentile of a sorted sample-set, using +// linear interpolation. If samples are not sorted, return nonsensical value. +fn percentile_of_sorted(sorted_samples: &[f64], pct: f64) -> f64 { + assert!(!sorted_samples.is_empty()); + if sorted_samples.len() == 1 { + return sorted_samples[0]; + } + let zero: f64 = 0.0; + assert!(zero <= pct); + let hundred = 100f64; + assert!(pct <= hundred); + if pct == hundred { + return sorted_samples[sorted_samples.len() - 1]; + } + let length = (sorted_samples.len() - 1) as f64; + let rank = (pct / hundred) * length; + let lrank = rank.floor(); + let d = rank - lrank; + let n = lrank as usize; + let lo = sorted_samples[n]; + let hi = sorted_samples[n + 1]; + lo + (hi - lo) * d +} + + +/// Winsorize a set of samples, replacing values above the `100-pct` percentile +/// and below the `pct` percentile with those percentiles themselves. This is a +/// way of minimizing the effect of outliers, at the cost of biasing the sample. +/// It differs from trimming in that it does not change the number of samples, +/// just changes the values of those that are outliers. +/// +/// See: http://en.wikipedia.org/wiki/Winsorising +pub fn winsorize(samples: &mut [f64], pct: f64) { + let mut tmp = samples.to_vec(); + local_sort(&mut tmp); + let lo = percentile_of_sorted(&tmp, pct); + let hundred = 100 as f64; + let hi = percentile_of_sorted(&tmp, hundred - pct); + for samp in samples { + if *samp > hi { + *samp = hi + } else if *samp < lo { + *samp = lo + } + } +} + +// Test vectors generated from R, using the script src/etc/stat-test-vectors.r. + +#[cfg(test)] +mod tests { + use stats::Stats; + use stats::Summary; + use std::f64; + use std::io::prelude::*; + use std::io; + + macro_rules! assert_approx_eq { + ($a:expr, $b:expr) => ({ + let (a, b) = (&$a, &$b); + assert!((*a - *b).abs() < 1.0e-6, + "{} is not approximately equal to {}", *a, *b); + }) + } + + fn check(samples: &[f64], summ: &Summary) { + + let summ2 = Summary::new(samples); + + let mut w = io::sink(); + let w = &mut w; + (write!(w, "\n")).unwrap(); + + assert_eq!(summ.sum, summ2.sum); + assert_eq!(summ.min, summ2.min); + assert_eq!(summ.max, summ2.max); + assert_eq!(summ.mean, summ2.mean); + assert_eq!(summ.median, summ2.median); + + // We needed a few more digits to get exact equality on these + // but they're within float epsilon, which is 1.0e-6. + assert_approx_eq!(summ.var, summ2.var); + assert_approx_eq!(summ.std_dev, summ2.std_dev); + assert_approx_eq!(summ.std_dev_pct, summ2.std_dev_pct); + assert_approx_eq!(summ.median_abs_dev, summ2.median_abs_dev); + assert_approx_eq!(summ.median_abs_dev_pct, summ2.median_abs_dev_pct); + + assert_eq!(summ.quartiles, summ2.quartiles); + assert_eq!(summ.iqr, summ2.iqr); + } + + #[test] + fn test_min_max_nan() { + let xs = &[1.0, 2.0, f64::NAN, 3.0, 4.0]; + let summary = Summary::new(xs); + assert_eq!(summary.min, 1.0); + assert_eq!(summary.max, 4.0); + } + + #[test] + fn test_norm2() { + let val = &[958.0000000000, 924.0000000000]; + let summ = &Summary { + sum: 1882.0000000000, + min: 924.0000000000, + max: 958.0000000000, + mean: 941.0000000000, + median: 941.0000000000, + var: 578.0000000000, + std_dev: 24.0416305603, + std_dev_pct: 2.5549022912, + median_abs_dev: 25.2042000000, + median_abs_dev_pct: 2.6784484591, + quartiles: (932.5000000000, 941.0000000000, 949.5000000000), + iqr: 17.0000000000, + }; + check(val, summ); + } + #[test] + fn test_norm10narrow() { + let val = &[966.0000000000, + 985.0000000000, + 1110.0000000000, + 848.0000000000, + 821.0000000000, + 975.0000000000, + 962.0000000000, + 1157.0000000000, + 1217.0000000000, + 955.0000000000]; + let summ = &Summary { + sum: 9996.0000000000, + min: 821.0000000000, + max: 1217.0000000000, + mean: 999.6000000000, + median: 970.5000000000, + var: 16050.7111111111, + std_dev: 126.6914010938, + std_dev_pct: 12.6742097933, + median_abs_dev: 102.2994000000, + median_abs_dev_pct: 10.5408964451, + quartiles: (956.7500000000, 970.5000000000, 1078.7500000000), + iqr: 122.0000000000, + }; + check(val, summ); + } + #[test] + fn test_norm10medium() { + let val = &[954.0000000000, + 1064.0000000000, + 855.0000000000, + 1000.0000000000, + 743.0000000000, + 1084.0000000000, + 704.0000000000, + 1023.0000000000, + 357.0000000000, + 869.0000000000]; + let summ = &Summary { + sum: 8653.0000000000, + min: 357.0000000000, + max: 1084.0000000000, + mean: 865.3000000000, + median: 911.5000000000, + var: 48628.4555555556, + std_dev: 220.5186059170, + std_dev_pct: 25.4846418487, + median_abs_dev: 195.7032000000, + median_abs_dev_pct: 21.4704552935, + quartiles: (771.0000000000, 911.5000000000, 1017.2500000000), + iqr: 246.2500000000, + }; + check(val, summ); + } + #[test] + fn test_norm10wide() { + let val = &[505.0000000000, + 497.0000000000, + 1591.0000000000, + 887.0000000000, + 1026.0000000000, + 136.0000000000, + 1580.0000000000, + 940.0000000000, + 754.0000000000, + 1433.0000000000]; + let summ = &Summary { + sum: 9349.0000000000, + min: 136.0000000000, + max: 1591.0000000000, + mean: 934.9000000000, + median: 913.5000000000, + var: 239208.9888888889, + std_dev: 489.0899599142, + std_dev_pct: 52.3146817750, + median_abs_dev: 611.5725000000, + median_abs_dev_pct: 66.9482758621, + quartiles: (567.2500000000, 913.5000000000, 1331.2500000000), + iqr: 764.0000000000, + }; + check(val, summ); + } + #[test] + fn test_norm25verynarrow() { + let val = &[991.0000000000, + 1018.0000000000, + 998.0000000000, + 1013.0000000000, + 974.0000000000, + 1007.0000000000, + 1014.0000000000, + 999.0000000000, + 1011.0000000000, + 978.0000000000, + 985.0000000000, + 999.0000000000, + 983.0000000000, + 982.0000000000, + 1015.0000000000, + 1002.0000000000, + 977.0000000000, + 948.0000000000, + 1040.0000000000, + 974.0000000000, + 996.0000000000, + 989.0000000000, + 1015.0000000000, + 994.0000000000, + 1024.0000000000]; + let summ = &Summary { + sum: 24926.0000000000, + min: 948.0000000000, + max: 1040.0000000000, + mean: 997.0400000000, + median: 998.0000000000, + var: 393.2066666667, + std_dev: 19.8294393937, + std_dev_pct: 1.9888308788, + median_abs_dev: 22.2390000000, + median_abs_dev_pct: 2.2283567134, + quartiles: (983.0000000000, 998.0000000000, 1013.0000000000), + iqr: 30.0000000000, + }; + check(val, summ); + } + #[test] + fn test_exp10a() { + let val = &[23.0000000000, + 11.0000000000, + 2.0000000000, + 57.0000000000, + 4.0000000000, + 12.0000000000, + 5.0000000000, + 29.0000000000, + 3.0000000000, + 21.0000000000]; + let summ = &Summary { + sum: 167.0000000000, + min: 2.0000000000, + max: 57.0000000000, + mean: 16.7000000000, + median: 11.5000000000, + var: 287.7888888889, + std_dev: 16.9643416875, + std_dev_pct: 101.5828843560, + median_abs_dev: 13.3434000000, + median_abs_dev_pct: 116.0295652174, + quartiles: (4.2500000000, 11.5000000000, 22.5000000000), + iqr: 18.2500000000, + }; + check(val, summ); + } + #[test] + fn test_exp10b() { + let val = &[24.0000000000, + 17.0000000000, + 6.0000000000, + 38.0000000000, + 25.0000000000, + 7.0000000000, + 51.0000000000, + 2.0000000000, + 61.0000000000, + 32.0000000000]; + let summ = &Summary { + sum: 263.0000000000, + min: 2.0000000000, + max: 61.0000000000, + mean: 26.3000000000, + median: 24.5000000000, + var: 383.5666666667, + std_dev: 19.5848580967, + std_dev_pct: 74.4671410520, + median_abs_dev: 22.9803000000, + median_abs_dev_pct: 93.7971428571, + quartiles: (9.5000000000, 24.5000000000, 36.5000000000), + iqr: 27.0000000000, + }; + check(val, summ); + } + #[test] + fn test_exp10c() { + let val = &[71.0000000000, + 2.0000000000, + 32.0000000000, + 1.0000000000, + 6.0000000000, + 28.0000000000, + 13.0000000000, + 37.0000000000, + 16.0000000000, + 36.0000000000]; + let summ = &Summary { + sum: 242.0000000000, + min: 1.0000000000, + max: 71.0000000000, + mean: 24.2000000000, + median: 22.0000000000, + var: 458.1777777778, + std_dev: 21.4050876611, + std_dev_pct: 88.4507754589, + median_abs_dev: 21.4977000000, + median_abs_dev_pct: 97.7168181818, + quartiles: (7.7500000000, 22.0000000000, 35.0000000000), + iqr: 27.2500000000, + }; + check(val, summ); + } + #[test] + fn test_exp25() { + let val = &[3.0000000000, + 24.0000000000, + 1.0000000000, + 19.0000000000, + 7.0000000000, + 5.0000000000, + 30.0000000000, + 39.0000000000, + 31.0000000000, + 13.0000000000, + 25.0000000000, + 48.0000000000, + 1.0000000000, + 6.0000000000, + 42.0000000000, + 63.0000000000, + 2.0000000000, + 12.0000000000, + 108.0000000000, + 26.0000000000, + 1.0000000000, + 7.0000000000, + 44.0000000000, + 25.0000000000, + 11.0000000000]; + let summ = &Summary { + sum: 593.0000000000, + min: 1.0000000000, + max: 108.0000000000, + mean: 23.7200000000, + median: 19.0000000000, + var: 601.0433333333, + std_dev: 24.5161851301, + std_dev_pct: 103.3565983562, + median_abs_dev: 19.2738000000, + median_abs_dev_pct: 101.4410526316, + quartiles: (6.0000000000, 19.0000000000, 31.0000000000), + iqr: 25.0000000000, + }; + check(val, summ); + } + #[test] + fn test_binom25() { + let val = &[18.0000000000, + 17.0000000000, + 27.0000000000, + 15.0000000000, + 21.0000000000, + 25.0000000000, + 17.0000000000, + 24.0000000000, + 25.0000000000, + 24.0000000000, + 26.0000000000, + 26.0000000000, + 23.0000000000, + 15.0000000000, + 23.0000000000, + 17.0000000000, + 18.0000000000, + 18.0000000000, + 21.0000000000, + 16.0000000000, + 15.0000000000, + 31.0000000000, + 20.0000000000, + 17.0000000000, + 15.0000000000]; + let summ = &Summary { + sum: 514.0000000000, + min: 15.0000000000, + max: 31.0000000000, + mean: 20.5600000000, + median: 20.0000000000, + var: 20.8400000000, + std_dev: 4.5650848842, + std_dev_pct: 22.2037202539, + median_abs_dev: 5.9304000000, + median_abs_dev_pct: 29.6520000000, + quartiles: (17.0000000000, 20.0000000000, 24.0000000000), + iqr: 7.0000000000, + }; + check(val, summ); + } + #[test] + fn test_pois25lambda30() { + let val = &[27.0000000000, + 33.0000000000, + 34.0000000000, + 34.0000000000, + 24.0000000000, + 39.0000000000, + 28.0000000000, + 27.0000000000, + 31.0000000000, + 28.0000000000, + 38.0000000000, + 21.0000000000, + 33.0000000000, + 36.0000000000, + 29.0000000000, + 37.0000000000, + 32.0000000000, + 34.0000000000, + 31.0000000000, + 39.0000000000, + 25.0000000000, + 31.0000000000, + 32.0000000000, + 40.0000000000, + 24.0000000000]; + let summ = &Summary { + sum: 787.0000000000, + min: 21.0000000000, + max: 40.0000000000, + mean: 31.4800000000, + median: 32.0000000000, + var: 26.5933333333, + std_dev: 5.1568724372, + std_dev_pct: 16.3814245145, + median_abs_dev: 5.9304000000, + median_abs_dev_pct: 18.5325000000, + quartiles: (28.0000000000, 32.0000000000, 34.0000000000), + iqr: 6.0000000000, + }; + check(val, summ); + } + #[test] + fn test_pois25lambda40() { + let val = &[42.0000000000, + 50.0000000000, + 42.0000000000, + 46.0000000000, + 34.0000000000, + 45.0000000000, + 34.0000000000, + 49.0000000000, + 39.0000000000, + 28.0000000000, + 40.0000000000, + 35.0000000000, + 37.0000000000, + 39.0000000000, + 46.0000000000, + 44.0000000000, + 32.0000000000, + 45.0000000000, + 42.0000000000, + 37.0000000000, + 48.0000000000, + 42.0000000000, + 33.0000000000, + 42.0000000000, + 48.0000000000]; + let summ = &Summary { + sum: 1019.0000000000, + min: 28.0000000000, + max: 50.0000000000, + mean: 40.7600000000, + median: 42.0000000000, + var: 34.4400000000, + std_dev: 5.8685603004, + std_dev_pct: 14.3978417577, + median_abs_dev: 5.9304000000, + median_abs_dev_pct: 14.1200000000, + quartiles: (37.0000000000, 42.0000000000, 45.0000000000), + iqr: 8.0000000000, + }; + check(val, summ); + } + #[test] + fn test_pois25lambda50() { + let val = &[45.0000000000, + 43.0000000000, + 44.0000000000, + 61.0000000000, + 51.0000000000, + 53.0000000000, + 59.0000000000, + 52.0000000000, + 49.0000000000, + 51.0000000000, + 51.0000000000, + 50.0000000000, + 49.0000000000, + 56.0000000000, + 42.0000000000, + 52.0000000000, + 51.0000000000, + 43.0000000000, + 48.0000000000, + 48.0000000000, + 50.0000000000, + 42.0000000000, + 43.0000000000, + 42.0000000000, + 60.0000000000]; + let summ = &Summary { + sum: 1235.0000000000, + min: 42.0000000000, + max: 61.0000000000, + mean: 49.4000000000, + median: 50.0000000000, + var: 31.6666666667, + std_dev: 5.6273143387, + std_dev_pct: 11.3913245723, + median_abs_dev: 4.4478000000, + median_abs_dev_pct: 8.8956000000, + quartiles: (44.0000000000, 50.0000000000, 52.0000000000), + iqr: 8.0000000000, + }; + check(val, summ); + } + #[test] + fn test_unif25() { + let val = &[99.0000000000, + 55.0000000000, + 92.0000000000, + 79.0000000000, + 14.0000000000, + 2.0000000000, + 33.0000000000, + 49.0000000000, + 3.0000000000, + 32.0000000000, + 84.0000000000, + 59.0000000000, + 22.0000000000, + 86.0000000000, + 76.0000000000, + 31.0000000000, + 29.0000000000, + 11.0000000000, + 41.0000000000, + 53.0000000000, + 45.0000000000, + 44.0000000000, + 98.0000000000, + 98.0000000000, + 7.0000000000]; + let summ = &Summary { + sum: 1242.0000000000, + min: 2.0000000000, + max: 99.0000000000, + mean: 49.6800000000, + median: 45.0000000000, + var: 1015.6433333333, + std_dev: 31.8691595957, + std_dev_pct: 64.1488719719, + median_abs_dev: 45.9606000000, + median_abs_dev_pct: 102.1346666667, + quartiles: (29.0000000000, 45.0000000000, 79.0000000000), + iqr: 50.0000000000, + }; + check(val, summ); + } + + #[test] + fn test_sum_f64s() { + assert_eq!([0.5f64, 3.2321f64, 1.5678f64].sum(), 5.2999); + } + #[test] + fn test_sum_f64_between_ints_that_sum_to_0() { + assert_eq!([1e30f64, 1.2f64, -1e30f64].sum(), 1.2); + } +} + -- cgit v1.2.3