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mean.rs
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mean.rs
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use core::iter::Iterator;
#[cfg(any(feature = "std", feature = "libm"))]
use average::assert_almost_eq;
use average::{Estimate, MeanWithError, Merge};
#[test]
fn trivial() {
let mut a = MeanWithError::new();
assert_eq!(a.len(), 0);
assert!(a.mean().is_nan());
a.add(1.0);
assert_eq!(a.mean(), 1.0);
assert_eq!(a.len(), 1);
assert!(a.sample_variance().is_nan());
assert_eq!(a.population_variance(), 0.0);
assert_eq!(a.variance_of_mean(), 0.0);
#[cfg(any(feature = "std", feature = "libm"))]
assert_eq!(a.error(), 0.0);
a.add(1.0);
assert_eq!(a.mean(), 1.0);
assert_eq!(a.len(), 2);
assert_eq!(a.sample_variance(), 0.0);
assert_eq!(a.population_variance(), 0.0);
assert_eq!(a.variance_of_mean(), 0.0);
#[cfg(any(feature = "std", feature = "libm"))]
assert_eq!(a.error(), 0.0);
}
#[test]
fn simple() {
let a: MeanWithError = (1..6).map(f64::from).collect();
assert_eq!(a.mean(), 3.0);
assert_eq!(a.len(), 5);
assert_eq!(a.sample_variance(), 2.5);
#[cfg(any(feature = "std", feature = "libm"))]
assert_almost_eq!(a.error(), num_traits::Float::sqrt(0.5), 1e-16);
}
#[test]
fn simple_extend() {
let mut a = MeanWithError::new();
a.extend((1..6).map(f64::from));
assert_eq!(a.mean(), 3.0);
assert_eq!(a.len(), 5);
assert_eq!(a.sample_variance(), 2.5);
#[cfg(any(feature = "std", feature = "libm"))]
assert_almost_eq!(a.error(), num_traits::Float::sqrt(0.5), 1e-16);
}
#[cfg(feature = "serde1")]
#[test]
fn simple_serde() {
let a: MeanWithError = (1..6).map(f64::from).collect();
let b = serde_json::to_string(&a).unwrap();
assert_eq!(&b, "{\"avg\":{\"avg\":3.0,\"n\":5},\"sum_2\":10.0}");
let c: MeanWithError = serde_json::from_str(&b).unwrap();
assert_eq!(c.mean(), 3.0);
assert_eq!(c.len(), 5);
assert_eq!(c.sample_variance(), 2.5);
assert_eq!(c.variance_of_mean(), 0.5);
#[cfg(any(feature = "std", feature = "libm"))]
assert_almost_eq!(c.error(), f64::sqrt(0.5), 1e-16);
}
#[cfg(feature = "rayon")]
#[test]
fn simple_rayon() {
use rayon::iter::{IntoParallelIterator, ParallelIterator};
let a: MeanWithError = (1..6).into_par_iter().map(f64::from).collect();
assert_eq!(a.mean(), 3.0);
assert_eq!(a.len(), 5);
assert_eq!(a.sample_variance(), 2.5);
assert_eq!(a.variance_of_mean(), 0.5);
#[cfg(any(feature = "std", feature = "libm"))]
assert_almost_eq!(a.error(), f64::sqrt(0.5), 1e-16);
}
#[test]
fn numerically_unstable() {
// The naive algorithm fails for this example due to cancellation.
let big = 1e9;
let sample = &[big + 4., big + 7., big + 13., big + 16.];
let a: MeanWithError = sample.iter().collect();
assert_eq!(a.sample_variance(), 30.);
}
#[test]
fn merge() {
let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
for mid in 0..sequence.len() {
let (left, right) = sequence.split_at(mid);
let avg_total: MeanWithError = sequence.iter().collect();
let mut avg_left: MeanWithError = left.iter().collect();
let avg_right: MeanWithError = right.iter().collect();
avg_left.merge(&avg_right);
assert_eq!(avg_total.len(), avg_left.len());
assert_eq!(avg_total.mean(), avg_left.mean());
assert_eq!(avg_total.sample_variance(), avg_left.sample_variance());
}
}
#[test]
fn merge_empty() {
let mut left = MeanWithError::new();
let right = MeanWithError::new();
left.merge(&right);
assert_eq!(left.len(), 0);
left.add(1.);
left.add(1.);
assert_eq!(left.mean(), 1.);
assert_eq!(left.sample_variance(), 0.);
}