Enum SurrogateModelType 

pub enum SurrogateModelType {
    KernelRegression,
    GaussianProcess,
    RandomForest,
    NeuralNetwork,
    Polynomial,
}

Type of surrogate model

Variants

KernelRegression

Nadaraya-Watson kernel regression — the surrogate this crate actually implements (see surrogate::KernelSurrogate). It needs no linear algebra and no kernel-hyperparameter fitting, which is why it is the default.

GaussianProcess

Gaussian Process. Not implemented in this module; kept because it is part of the public type and callers may record their own model choice. HyperparameterOptimizer never fits one.

RandomForest

Random Forest. Not implemented here; see SurrogateModelType::GaussianProcess.

NeuralNetwork

Neural Network. Not implemented here; see SurrogateModelType::GaussianProcess.

Polynomial

Polynomial regression. Not implemented here; see SurrogateModelType::GaussianProcess.

Trait Implementations

impl Clone for SurrogateModelType

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Copy for SurrogateModelType

impl Debug for SurrogateModelType

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

Formats the value using the given formatter.

impl Eq for SurrogateModelType

impl PartialEq for SurrogateModelType

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

Equality operator ==.

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

Inequality operator !=.

impl StructuralPartialEq for SurrogateModelType