Quickstart

use watermill::quantile::Quantile;use watermill::stats::Univariate;let data:Vec<f64> =vec![9.,7.,3.,2.,6.,1.,8.,5.,4.];letmut running_median:Quantile<f64> =Quantile::new(0.5_f64).unwrap();for xin data.into_iter(){    running_median.update(x);// update the current statisticsprintln!("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();

use watermill::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);//       ^^^^^^^^^^}

use watermill::rolling::Rolling;use watermill::stats::Univariate;use watermill::variance::Variance;let data:Vec<f64> =vec![9.,7.,3.,2.,6.,1.,8.,5.,4.];letmut running_var:Variance<f64> =Variance::default();// We wrap `running_var` inside the `Rolling` struct.letmut rolling_var:Rolling<f64> =Rolling::new(&mut running_var,2).unwrap();for xin data.into_iter(){    rolling_var.update(x);}assert_eq!(rolling_var.get(),0.5);

Installation

[dependencies]watermill = "0.1.0"

Statistics available

Inspiration