8 releases
0.2.6 | Sep 10, 2022 |
---|---|
0.2.5 | Sep 9, 2022 |
0.2.4 | Aug 27, 2022 |
0.1.0 | Jul 23, 2022 |
#652 in Math
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74KB
1.5K
SLoC
Online statistics in Rust 🦀
online-statistics
is crate 🦀 for Blazingly fast, generic and serializable online statistics.
Quickstart
Let's compute the online median and then serialize it:
use online_statistics::quantile::Quantile;
use online_statistics::stats::Univariate;
let data: Vec<f64> = vec![9., 7., 3., 2., 6., 1., 8., 5., 4.];
let mut running_median: Quantile<f64> = Quantile::new(0.5_f64).unwrap();
for x in data.into_iter() {
running_median.update(x); // update the current statistics
println!("The actual median value is: {}", running_median.get());
}
assert_eq!(running_median.get(), 5.0);
// Convert the statistic to a JSON string.
let serialized = serde_json::to_string(&running_median).unwrap();
// Convert the JSON string back to a statistic.
let deserialized: Quantile<f64> = serde_json::from_str(&serialized).unwrap();
Now let's compute the online sum using the iterators:
use online_statistics::iter::IterStatisticsExtend;
let data: Vec<f64> = vec![1., 2., 3.];
let vec_true: Vec<f64> = vec![1., 3., 6.];
for (d, t) in data.into_iter().online_sum().zip(vec_true.into_iter()) {
assert_eq!(d, t); // ^^^^^^^^^^
}
You can also compute rolling statistics; in the following example let's compute the rolling sum on 2 previous data:
use online_statistics::rolling::Rolling;
use online_statistics::stats::Univariate;
use online_statistics::variance::Variance;
let data: Vec<f64> = vec![9., 7., 3., 2., 6., 1., 8., 5., 4.];
let mut running_var: Variance<f64> = Variance::default();
// We wrap `running_var` inside the `Rolling` struct.
let mut rolling_var: Rolling<f64> = Rolling::new(&mut running_var, 2).unwrap();
for x in data.into_iter() {
rolling_var.update(x);
}
assert_eq!(rolling_var.get(), 0.5);
Installation
Add the following line to your cargo.toml
:
[dependencies]
online-statistics = "0.2.6"
Statistics available
Statistics | Rollable ? |
---|---|
Mean | ✅ |
Variance | ✅ |
Sum | ✅ |
Min | ✅ |
Max | ✅ |
Count | ❌ |
Quantile | ✅ |
Peak to peak | ✅ |
Exponentially weighted mean | ❌ |
Exponentially weighted variance | ❌ |
Interquartile range | ✅ |
Kurtosis | ❌ |
Skewness | ❌ |
Covariance | ❌ |
Inspiration
The stats
module of the river
library in Python
greatly inspired this crate.
Dependencies
~1.4–2.4MB
~51K SLoC