Crates.io | rolling-stats |
lib.rs | rolling-stats |
version | 0.7.0 |
source | src |
created_at | 2019-02-13 22:32:39.449165 |
updated_at | 2023-06-04 22:21:22.464275 |
description | Rolling statistics calculations (min/max/mean/std_dev) over arbitrary floating point numbers based on Welford's Online Algorithm |
homepage | |
repository | https://github.com/ryankurte/rust-rolling-stats |
max_upload_size | |
id | 114630 |
size | 21,158 |
The rolling-stats
library offers rolling statistics calculations (minimum, maximum, mean, standard
deviation) over arbitrary floating point numbers. It uses Welford's Online Algorithm for these
computations. This crate is no_std
compatible.
For more information on the algorithm, visit Algorithms for calculating variance on Wikipedia.
Below is an example of using rust-rolling-stats
in a single-threaded context:
use rolling_stats::Stats;
use rand_distr::{Distribution, Normal};
use rand::SeedableRng;
type T = f64;
const MEAN: T = 0.0;
const STD_DEV: T = 1.0;
const NUM_SAMPLES: usize = 10_000;
const SEED: u64 = 42;
let mut stats: Stats<T> = Stats::new();
let mut rng = rand::rngs::StdRng::seed_from_u64(SEED); // Seed the RNG for reproducibility
let normal = Normal::<T>::new(MEAN, STD_DEV).unwrap();
// Generate random data
let random_data: Vec<T> = (0..NUM_SAMPLES).map(|_x| normal.sample(&mut rng)).collect();
// Update the stats one by one
random_data.iter().for_each(|v| stats.update(*v));
// Print the stats
println!("{}", stats);
// Output: (avg: 0.00, std_dev: 1.00, min: -3.53, max: 4.11, count: 10000)
This example showcases the usage of rust-rolling-stats
in a multi-threaded context with the help
of the rayon
crate:
use rolling_stats::Stats;
use rand_distr::{Distribution, Normal};
use rand::SeedableRng;
use rayon::prelude::*;
type T = f64;
const MEAN: T = 0.0;
const STD_DEV: T = 1.0;
const NUM_SAMPLES: usize = 500_000;
const SEED: u64 = 42;
const CHUNK_SIZE: usize = 1000;
let mut stats: Stats<T> = Stats::new();
let mut rng = rand::rngs::StdRng::seed_from_u64(SEED); // Seed the RNG for reproducibility
let normal = Normal::<T>::new(MEAN, STD_DEV).unwrap();
// Generate random data
let random_data: Vec<T> = (0..NUM_SAMPLES).map(|_x| normal.sample(&mut rng)).collect();
// Update the stats in parallel. New stats objects are created for each chunk of data.
let stats: Vec<Stats<T>> = random_data
.par_chunks(CHUNK_SIZE) // Multi-threaded parallelization via Rayon
.map(|chunk| {
let mut s: Stats<T> = Stats::new();
chunk.iter().for_each(|v| s.update(*v));
s
})
.collect();
// Check if there's more than one stat object
assert!(stats.len() > 1);
// Accumulate the stats using the reduce method
let merged_stats = stats.into_iter().reduce(|acc, s| acc.merge(&s)).unwrap();
// Print the stats
println!("{}", merged_stats);
// Output: (avg: -0.00, std_dev: 1.00, min: -4.53, max: 4.57, count: 500000)
The following feature flags are available:
serde
: Enables serialization and deserialization of the Stats
struct via the serde
crate.The rolling-stats
library is dual-licensed under the MIT and Apache License 2.0. By opening a pull
request, you are implicitly agreeing to these licensing terms.