Struct KnowledgeGradient 

pub struct KnowledgeGradient { /* private fields */ }

Knowledge Gradient acquisition function.

Measures the expected improvement in the estimated optimal value after observing a new point. This is a one-step lookahead policy.

KG(x) = E[ min_{x’} mu_{n+1}(x’) | x_n+1 = x ] - min_{x’} mu_n(x’)

We approximate this by discretising the inner minimisation over a finite set of reference points.

Implementations

impl KnowledgeGradient

pub fn new(reference_points: Array2<f64>, n_fantasies: usize, seed: u64) -> Self

Create a new KG acquisition function.

• reference_points - Candidate points for inner min (typically the training data).
• n_fantasies - Number of Monte Carlo samples (default 20).
• seed - Random seed for reproducibility.

Trait Implementations

impl AcquisitionFn for KnowledgeGradient

fn evaluate( &self, x: &ArrayView1<'_, f64>, surrogate: &GpSurrogate, ) -> OptimizeResult<f64>

Evaluate the acquisition function at a single point.

fn name(&self) -> &str

Name of the acquisition function.

fn evaluate_batch( &self, x: &Array2<f64>, surrogate: &GpSurrogate, ) -> OptimizeResult<Array1<f64>>

Evaluate the acquisition function at multiple points (batch).

impl Clone for KnowledgeGradient

fn clone(&self) -> KnowledgeGradient

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for KnowledgeGradient

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.