u-numflow

Domain-agnostic mathematical primitives in Rust

Overview

u-numflow provides foundational mathematical, statistical, and probabilistic building blocks. Entirely domain-agnostic with no external dependencies beyond rand.

Modules

Design Philosophy

• Numerical stability first — Welford's algorithm for variance, Neumaier summation for accumulation
• Reproducibility — Seeded RNG support for deterministic experiments
• Property-based testing — Mathematical invariants verified via proptest

Quick Start

[dependencies]
u-numflow = "0.10"

use u_numflow::distributions::Pert;
use u_numflow::random::{create_rng, shuffle};
use u_numflow::stats::WelfordAccumulator;

// Online statistics with numerical stability (Welford)
let mut stats = WelfordAccumulator::new();
for x in [1.0, 2.0, 3.0, 4.0, 5.0] {
    stats.update(x);
}
assert_eq!(stats.mean(), Some(3.0));

// PERT distribution: moments, quantiles and reproducible samples
use u_numflow::distributions::Sample;
let pert = Pert::new(1.0, 4.0, 7.0).unwrap();
assert_eq!(pert.mean(), 4.0);
let p90 = pert.quantile(0.9).unwrap();
assert!(p90 > 4.0 && p90 < 7.0);
let draws = pert.sample_n(1000, 7); // same (n, seed), same draws
assert!(draws.iter().all(|&x| (1.0..=7.0).contains(&x)));

// Seeded shuffling for reproducibility
let mut rng = create_rng(42);
let mut items = vec![1, 2, 3, 4, 5];
shuffle(&mut items, &mut rng);

// Box-Cox transformation (non-normal data normalization)
use u_numflow::transforms::{box_cox, estimate_lambda};
let data = [1.0, 2.0, 4.0, 8.0, 16.0];
let fit = estimate_lambda(&data, -2.0, 2.0).unwrap(); // MLE via golden-section
let transformed = box_cox(&data, fit.lambda).unwrap();
assert_eq!(transformed.len(), data.len());

// Discrete Fourier transform of any length
use u_numflow::fourier::rfft;
let signal: Vec<f64> = (0..30).map(|j| (2.0 * std::f64::consts::PI * 3.0 * j as f64 / 30.0).sin()).collect();
let spectrum = rfft(&signal);           // 30 complex bins; bin 3 carries the energy
assert!(spectrum[3].norm() > 14.0);

// Interval sets: overlapping stops are down-time once, not twice
use u_numflow::collections::IntervalSet;
let planned = IntervalSet::from_intervals([(0.0, 24.0)]).unwrap();
let unplanned = IntervalSet::from_intervals([(10.0, 30.0)]).unwrap();
let week = IntervalSet::from_intervals([(0.0, 168.0)]).unwrap();
let up = week.difference(&planned.union(&unplanned));
assert_eq!(up.measure(), 138.0);

IntervalSet<T> works over f32, f64 and every primitive integer type. Its measure has the type of a distance — the unsigned type of the same width for integers (as i64::abs_diff returns u64), so it cannot overflow. A reversed interval (start > end) or a NaN/infinite bound is refused with its position, never swapped or dropped.

Build & Test

cargo build
cargo test

Dependencies

• rand 0.10 — Random number generation
• proptest 1.4 — Property-based testing (dev only)

License

MIT License — see LICENSE.

.NET (NuGet)

dotnet add package UNumflow

The distributions (CDF, quantile, moments, seeded sampling) and interval sets, backed by this crate through its C ABI (the ffi feature) — see bindings/csharp/README.md. Refusals carry the same code and fields as the WebAssembly binding's.

npm (WebAssembly)

npm install @iyulab/u-numflow

The package resolves per environment via a conditional exports map:

A browser without a bundler is not supported: the package loads its .wasm file with an ES module import, which browsers refuse (application/wasm is not a module script type), so <script type="module"> from a CDN fails, and CDN re-bundling services fail on the same import. Use a bundler or Node.

Exported functions: mean, std_dev, variance, normal_cdf, normal_sf (upper tail P(Z > x), computed directly so tail probabilities keep ~15 significant digits), box_cox, estimate_lambda (returns { lambda, at_bound } — at_bound is true when the likelihood was still rising at an end of the search range, so lambda is that limit), and rfft(data) -> Float64Array — the DFT of a real sequence of any length, interleaved as [re0, im0, re1, im1, …] (bins k and n − k are conjugates, so 0..=n/2 describes the spectrum).

Every data argument is a number[] or a Float64Array, read exactly as sent: an element that is not a number (null, undefined, a string) throws malformed_input and a NaN or ±Infinity throws value_not_finite, each with the element's index — mean([1, null, 3]) throws rather than averaging the null as 0. mean of no values throws empty_input; std_dev and variance of fewer than 2 throw insufficient_data.

Distribution functions for critical values and p-values — each returns a number and throws (an Error naming the argument — see Errors below) when an argument is outside the domain, rather than returning NaN:

p must lie strictly between 0 and 1 and every degrees-of-freedom argument must be a finite number > 0 (fractional values are allowed).

const { t_distribution_quantile } = require("@iyulab/u-numflow");
t_distribution_quantile(0.975, 10); // 2.2281…

Any of the crate's distributions by specification — distribution is an object { kind, ...parameters }, declared as the union type DistributionSpec:

const { distribution_quantile, distribution_sample } = require("@iyulab/u-numflow");
distribution_quantile({ kind: "chi_squared", k: 4 }, 0.95);              // 9.4877…
const lives = distribution_sample({ kind: "weibull", shape: 2, scale: 100 }, 1000, 42);
console.log(lives.length);                                                // 1000

Interval sets — each argument is a [start, end][] of half-open intervals, and every result is in normal form (sorted, disjoint, touching pieces merged, empty ones dropped):

const { interval_union, interval_difference, interval_measure } = require("@iyulab/u-numflow");
const down = interval_union([[0, 24]], [[10, 30]]);       // [[0, 30]] — the overlap counts once
const up = interval_difference([[0, 168]], down);          // [[30, 168]]
console.log(interval_measure(up));                         // 138

Errors. A refusal throws an Error whose message is readable text and which carries a code naming the reason, next to the values behind it:

const { t_distribution_quantile } = require("@iyulab/u-numflow");
try {
  t_distribution_quantile(1.5, 10);
} catch (err) {
  console.log(err.code, err.parameter, err.min, err.max, err.got); // parameter_out_of_range p 0 1 1.5
}

TypeScript

Every exported function declares its parameter and return types, and the declarations are generated from the same structs the binding reads and serialises, so they cannot drift from what it actually accepts and returns:

export function mean(data: number[] | Float64Array): number;
export function estimate_lambda(data: number[] | Float64Array, lambda_min: number, lambda_max: number): LambdaEstimateDto;

An absent optional value is declared T | undefined, which is what the binding sends. Nothing needs an as cast -- and a wrong assumption about a result's shape is a compile error rather than something that fails at run time.

A data parameter is declared number[] | Float64Array and read by the binding itself rather than copied into a typed array by the generated glue, so a value the declaration does not allow is refused where it sits instead of being converted. The publishing workflow keeps every declaration free of any.

Related

• u-metaheur — Metaheuristic optimization (GA, SA, ALNS, CP)
• u-geometry — Computational geometry
• u-schedule — Scheduling framework
• u-nesting — 2D/3D nesting and bin packing