Struct ActiveDesignStep 

pub struct ActiveDesignStep { /* private fields */ }

One function evaluation selected by the active design.

Implementations

impl ActiveDesignStep

pub const fn point(self) -> f64

Selected evaluation point.

Examples found in repository

examples/active_quadrature.rs (line 42)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let kernel = RbfKernel::new(1.0, 1.0)?;
    let measure = GaussianMeasure::new(0.0, 1.0)?;
    let quadrature = BayesianQuadrature::new(kernel, measure, 1.0e-10);
    let active = ActiveBayesianQuadrature::new(quadrature);

    // Start from three evaluations; allow up to six more from a fixed candidate grid,
    // stopping early once the posterior variance of the integral drops below 1e-6.
    let initial_nodes = [-1.0, 0.0, 1.0];
    let initial_values: Vec<f64> = initial_nodes.iter().copied().map(integrand).collect();
    let candidates: Vec<f64> = (0..=23).map(|i| -2.875 + 0.25 * f64::from(i)).collect();

    let initial = quadrature.posterior(&initial_nodes, &initial_values)?;
    println!("initial posterior variance = {:.3e}", initial.variance());

    let result = active.run(
        &initial_nodes,
        &initial_values,
        &candidates,
        6,
        1.0e-6,
        integrand,
    )?;

    for step in result.steps() {
        println!(
            "x = {:+.3}  predicted reduction = {:.3e}  posterior variance = {:.3e}",
            step.point(),
            step.predicted_variance_reduction(),
            step.posterior_variance(),
        );
    }

    let exact = (1.0_f64 / 3.0).sqrt() * (-0.25_f64 / 3.0).exp();
    let posterior = result.posterior();
    println!("stopped because: {:?}", result.termination());
    println!(
        "E[I | y] = {:.6} ± {:.3e}   (exact {exact:.6})",
        posterior.mean(),
        posterior.standard_deviation(),
    );
    Ok(())
}

pub const fn value(self) -> f64

Observed function value at the selected point.

pub const fn predicted_variance_reduction(self) -> f64

Predicted variance reduction before the point was evaluated.

Examples found in repository

examples/active_quadrature.rs (line 43)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let kernel = RbfKernel::new(1.0, 1.0)?;
    let measure = GaussianMeasure::new(0.0, 1.0)?;
    let quadrature = BayesianQuadrature::new(kernel, measure, 1.0e-10);
    let active = ActiveBayesianQuadrature::new(quadrature);

    // Start from three evaluations; allow up to six more from a fixed candidate grid,
    // stopping early once the posterior variance of the integral drops below 1e-6.
    let initial_nodes = [-1.0, 0.0, 1.0];
    let initial_values: Vec<f64> = initial_nodes.iter().copied().map(integrand).collect();
    let candidates: Vec<f64> = (0..=23).map(|i| -2.875 + 0.25 * f64::from(i)).collect();

    let initial = quadrature.posterior(&initial_nodes, &initial_values)?;
    println!("initial posterior variance = {:.3e}", initial.variance());

    let result = active.run(
        &initial_nodes,
        &initial_values,
        &candidates,
        6,
        1.0e-6,
        integrand,
    )?;

    for step in result.steps() {
        println!(
            "x = {:+.3}  predicted reduction = {:.3e}  posterior variance = {:.3e}",
            step.point(),
            step.predicted_variance_reduction(),
            step.posterior_variance(),
        );
    }

    let exact = (1.0_f64 / 3.0).sqrt() * (-0.25_f64 / 3.0).exp();
    let posterior = result.posterior();
    println!("stopped because: {:?}", result.termination());
    println!(
        "E[I | y] = {:.6} ± {:.3e}   (exact {exact:.6})",
        posterior.mean(),
        posterior.standard_deviation(),
    );
    Ok(())
}

pub const fn posterior_variance(self) -> f64

Posterior integral variance after adding this observation.

Examples found in repository

examples/active_quadrature.rs (line 44)

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let kernel = RbfKernel::new(1.0, 1.0)?;
    let measure = GaussianMeasure::new(0.0, 1.0)?;
    let quadrature = BayesianQuadrature::new(kernel, measure, 1.0e-10);
    let active = ActiveBayesianQuadrature::new(quadrature);

    // Start from three evaluations; allow up to six more from a fixed candidate grid,
    // stopping early once the posterior variance of the integral drops below 1e-6.
    let initial_nodes = [-1.0, 0.0, 1.0];
    let initial_values: Vec<f64> = initial_nodes.iter().copied().map(integrand).collect();
    let candidates: Vec<f64> = (0..=23).map(|i| -2.875 + 0.25 * f64::from(i)).collect();

    let initial = quadrature.posterior(&initial_nodes, &initial_values)?;
    println!("initial posterior variance = {:.3e}", initial.variance());

    let result = active.run(
        &initial_nodes,
        &initial_values,
        &candidates,
        6,
        1.0e-6,
        integrand,
    )?;

    for step in result.steps() {
        println!(
            "x = {:+.3}  predicted reduction = {:.3e}  posterior variance = {:.3e}",
            step.point(),
            step.predicted_variance_reduction(),
            step.posterior_variance(),
        );
    }

    let exact = (1.0_f64 / 3.0).sqrt() * (-0.25_f64 / 3.0).exp();
    let posterior = result.posterior();
    println!("stopped because: {:?}", result.termination());
    println!(
        "E[I | y] = {:.6} ± {:.3e}   (exact {exact:.6})",
        posterior.mean(),
        posterior.standard_deviation(),
    );
    Ok(())
}

Trait Implementations

impl Clone for ActiveDesignStep

fn clone(&self) -> ActiveDesignStep

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Copy for ActiveDesignStep

impl Debug for ActiveDesignStep

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

Formats the value using the given formatter.

impl PartialEq for ActiveDesignStep

fn eq(&self, other: &ActiveDesignStep) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for ActiveDesignStep