| // 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 <LICENSE-APACHE or |
| // http://www.apache.org/licenses/LICENSE-2.0> or the MIT license |
| // <LICENSE-MIT or http://opensource.org/licenses/MIT>, 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<f64> = 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); |
| } |
| } |
| |
