Crate big_o

Infers asymptotic computational complexity.

big_o helps to estimate computational complexity of algorithms by inspecting measurement data (eg. execution time, memory consumption, etc). Users are expected to provide measurement data, big_o will try to fit a set of complexity models and return the best fit.

Example

// f(x) = gain * x ^ 2 + offset
let data = vec![(1., 1.), (2., 4.), (3., 9.), (4., 16.)];

let (complexity, _all) = big_o::infer_complexity(data).unwrap();

assert_eq!(complexity.name, big_o::Name::Quadratic);
assert_eq!(complexity.notation, "O(n^2)");
assert_approx_eq!(complexity.params.gain.unwrap(), 1.0, 1e-6);
assert_approx_eq!(complexity.params.offset.unwrap(), 0.0, 1e-6);
assert!(complexity.rank < big_o::complexity("O(n^3)").unwrap().rank);

Structs

Complexity

A structure to describe asymptotic computational complexity

Params

A structure to hold function parameters

Enums

Error

Name

Names of asymptotic computational complexities.

Functions

complexity

Creates Complexity from string.

infer_complexity

Infers complexity of given data points, returns the best and all the fitted complexities.