Struct EvolutionarySMC 

pub struct EvolutionarySMC<G, F>
where
    G: EvolutionaryGenome,
    F: Fitness<Genome = G, Value = f64>,
{ /* private fields */ }

Sequential Monte Carlo for evolutionary inference

SMC treats evolution as a sequence of distributions that progressively concentrate on high-fitness solutions.

Implementations

impl<G, F> EvolutionarySMC<G, F>

where G: EvolutionaryGenome + Clone, F: Fitness<Genome = G, Value = f64> + Clone,

pub fn new(fitness: F, bounds: MultiBounds, num_particles: usize) -> Self

Create a new SMC sampler

pub fn with_schedule(self, schedule: Vec<f64>) -> Self

Set a custom temperature schedule

pub fn initialize<R: Rng>(&self, rng: &mut R) -> Vec<Particle<G>>

Initialize particles from prior

pub fn resample<R: Rng>( particles: &[Particle<G>], rng: &mut R, ) -> Vec<Particle<G>>

Resample particles using systematic resampling

pub fn run<R: Rng>(&self, rng: &mut R) -> Vec<Particle<G>>

Run the SMC sampler

pub fn best_particle(particles: &[Particle<G>]) -> Option<&Particle<G>>

Get the best particle

pub fn posterior_mean(particles: &[Particle<G>]) -> Option<Trace>

Compute posterior mean (weighted average of traces)