e4345f5046
This allows to write
let k: Kurtosis = a.iter().collect();
instead of
let k: Kurtosis = a.iter().map(|x| *x).collect();
but breaks type inference for code like
let m: Min = (1..6).map(Into::into).collect();
where
let m: Min = (1..6).map(f64::from).collect();
has to be used instead.
Fixes #8.
76 lines
2.3 KiB
Rust
76 lines
2.3 KiB
Rust
#![cfg_attr(feature = "cargo-clippy", allow(float_cmp, map_clone))]
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#[macro_use] extern crate average;
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extern crate core;
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#[cfg(feature = "serde")]
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extern crate serde_json;
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use core::iter::Iterator;
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use average::{MeanWithError, Estimate, Merge};
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#[test]
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fn trivial() {
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let mut a = MeanWithError::new();
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assert_eq!(a.len(), 0);
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a.add(1.0);
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assert_eq!(a.mean(), 1.0);
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assert_eq!(a.len(), 1);
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assert_eq!(a.sample_variance(), 0.0);
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assert_eq!(a.population_variance(), 0.0);
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assert_eq!(a.error(), 0.0);
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a.add(1.0);
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assert_eq!(a.mean(), 1.0);
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assert_eq!(a.len(), 2);
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assert_eq!(a.sample_variance(), 0.0);
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assert_eq!(a.population_variance(), 0.0);
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assert_eq!(a.error(), 0.0);
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}
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#[test]
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fn simple() {
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let a: MeanWithError = (1..6).map(f64::from).collect();
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assert_eq!(a.mean(), 3.0);
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assert_eq!(a.len(), 5);
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assert_eq!(a.sample_variance(), 2.5);
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assert_almost_eq!(a.error(), f64::sqrt(0.5), 1e-16);
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}
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#[cfg(feature = "serde")]
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#[test]
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fn simple_serde() {
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let a: MeanWithError = (1..6).map(f64::from).collect();
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let b = serde_json::to_string(&a).unwrap();
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assert_eq!(&b, "{\"avg\":{\"avg\":3.0,\"n\":5},\"sum_2\":10.0}");
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let c: MeanWithError = serde_json::from_str(&b).unwrap();
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assert_eq!(c.mean(), 3.0);
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assert_eq!(c.len(), 5);
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assert_eq!(c.sample_variance(), 2.5);
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assert_almost_eq!(c.error(), f64::sqrt(0.5), 1e-16);
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}
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#[test]
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fn numerically_unstable() {
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// The naive algorithm fails for this example due to cancelation.
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let big = 1e9;
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let sample = &[big + 4., big + 7., big + 13., big + 16.];
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let a: MeanWithError = sample.iter().collect();
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assert_eq!(a.sample_variance(), 30.);
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}
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#[test]
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fn merge() {
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let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
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for mid in 0..sequence.len() {
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let (left, right) = sequence.split_at(mid);
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let avg_total: MeanWithError = sequence.iter().collect();
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let mut avg_left: MeanWithError = left.iter().collect();
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let avg_right: MeanWithError = right.iter().collect();
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avg_left.merge(&avg_right);
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assert_eq!(avg_total.len(), avg_left.len());
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assert_eq!(avg_total.mean(), avg_left.mean());
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assert_eq!(avg_total.sample_variance(), avg_left.sample_variance());
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}
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}
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