Struct Archive 

pub struct Archive { /* private fields */ }

CVT quality-diversity archive: capacity niches, each holding the best solution found for it.

Implementations

impl Archive

pub fn try_new( dim: usize, qd_lb: &[f64], qd_ub: &[f64], capacity: usize, samples_per_niche: usize, rng: &mut Rng, ) -> Result<Self, &'static str>

Construct a validated CVT archive.

pub fn new( dim: usize, qd_lb: &[f64], qd_ub: &[f64], capacity: usize, samples_per_niche: usize, rng: &mut Rng, ) -> Self

Construct a CVT archive, panicking on invalid configuration. Prefer Archive::try_new for user-supplied inputs.

pub fn dim(&self) -> usize

pub fn qd_dim(&self) -> usize

pub fn capacity(&self) -> usize

pub fn occupied(&self) -> usize

pub fn seed_uniform(&mut self, lower: &[f64], upper: &[f64], rng: &mut Rng)

Seed all niche solutions with uniform random samples in [lower, upper] (never evaluated — they serve as the initial SBX/Iso parent pool, as the Python original documents).

pub fn index_of_niche(&self, d: &[f64]) -> usize

Index of the niche whose center is nearest the (encoded) descriptor.

pub fn set(&mut self, i: usize, y: f64, d: &[f64], x: &[f64])

Add a solution to niche i if it improves it.

pub fn update( &mut self, xs: &[Vec<f64>], fitness: &mut dyn QdFitness, ) -> (Vec<f64>, Vec<f64>)

Evaluate xs, add to the archive, and return (improvements, real_ys) where improvement = fitness - niche's previous fitness (negative is an improvement — the objective the Diversifier’s optimizer minimizes).

pub fn update_evaluated( &mut self, xs: &[Vec<f64>], evaluations: &[(f64, Vec<f64>)], ) -> Result<(Vec<f64>, Vec<f64>), &'static str>

Apply already evaluated (fitness, descriptor) values in input order. Keeping this step separate lets callers parallelize expensive objective functions without concurrently mutating the archive.

pub fn update_batch( &mut self, xs: &[Vec<f64>], fitness: &mut dyn QdBatchFitness, ) -> Result<(Vec<f64>, Vec<f64>), &'static str>

Evaluate and apply a complete batch. Evaluation may be parallel inside fitness; archive updates are deterministic and retain input order.

pub fn argsort(&mut self)

Re-sort niche indices ascending by fitness.

pub fn random_xs( &self, best_n: usize, chunk: usize, rng: &mut Rng, ) -> Vec<Vec<f64>>

Sample chunk solutions from the best best_n niches (by fitness).

pub fn random_x_one(&self, best_n: usize, rng: &mut Rng) -> (Vec<f64>, f64)

A random solution from the best best_n niches (with fitness).

pub fn best_y(&self) -> f64

pub fn qd_score(&self) -> f64

QD-score matching Python Archive.get_qd_score: for an all-positive archive, sum reciprocal fitness; otherwise sum the negated negative fitness values. Higher is better in both cases.

pub fn ys(&self) -> &[f64]

pub fn xs(&self) -> &[Vec<f64>]

pub fn descriptors(&self) -> &[Vec<f64>]

pub fn counts(&self) -> &[u64]

pub fn occupied_data(&self) -> Vec<(Vec<f64>, f64, Vec<f64>)>

Occupied (x, y, descriptor) triples.