Struct SquaredExponentialCorr 

pub struct SquaredExponentialCorr();

Squared exponential correlation models

Trait Implementations

impl Clone for SquaredExponentialCorr

fn clone(&self) -> SquaredExponentialCorr

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Float> CorrelationModel<F> for SquaredExponentialCorr

fn rval_from_distances( &self, d: &ArrayBase<impl Data<Elem = F>, Ix2>, theta: &ArrayBase<impl Data<Elem = F>, Ix1>, weights: &ArrayBase<impl Data<Elem = F>, Ix2>, ) -> Array2<F>

d h prod prod exp( - |theta_l * weight_j_l * d_j|^2 / 2 ) j=1 l=1

fn jac( &self, x: &ArrayBase<impl Data<Elem = F>, Ix1>, xtrain: &ArrayBase<impl Data<Elem = F>, Ix2>, theta: &ArrayBase<impl Data<Elem = F>, Ix1>, weights: &ArrayBase<impl Data<Elem = F>, Ix2>, ) -> Array2<F>

Compute gradients of r(x, x') at given x given a set of x' training samples, aka xtrain, theta parameters, and PLS weights. The returned jacobian matrix is dr/dx where r is the correlation function vector between x and xtrain (shape nt). Gradients are computed with respect to x and returned as a matrix of shape (nt, nx) where nt is the number of training samples (aka xtrain.nrows()) and nx is the dimension of x.

fn rval_with_jac( &self, x: &ArrayBase<impl Data<Elem = F>, Ix1>, xtrain: &ArrayBase<impl Data<Elem = F>, Ix2>, theta: &ArrayBase<impl Data<Elem = F>, Ix1>, weights: &ArrayBase<impl Data<Elem = F>, Ix2>, ) -> (Array2<F>, Array2<F>)

Compute both the correlation function matrix r(x, x') and its jacobian at given x given a set of xtrain training samples, theta parameters, and PLS weights. Used to avoid redundant computations when both correlation and jacobian are needed.

fn theta_influence_factors(&self) -> (F, F)

Returns the theta influence factors for the correlation model. See https://hal.science/hal-03812073v2/document

fn rval( &self, x: &ArrayBase<impl Data<Elem = F>, Ix1>, xtrain: &ArrayBase<impl Data<Elem = F>, Ix2>, theta: &ArrayBase<impl Data<Elem = F>, Ix1>, weights: &ArrayBase<impl Data<Elem = F>, Ix2>, ) -> Array2<F>

Compute correlation function r(x, x’) given x and a set of x' training samples, aka xtrain theta parameters, and PLS weights with:

impl Debug for SquaredExponentialCorr

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

Formats the value using the given formatter.

impl Default for SquaredExponentialCorr

fn default() -> SquaredExponentialCorr

Returns the “default value” for a type.

impl Display for SquaredExponentialCorr

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

Formats the value using the given formatter.

impl From<SquaredExponentialCorr> for String

fn from(_item: SquaredExponentialCorr) -> String

Converts to this type from the input type.

impl PartialEq for SquaredExponentialCorr

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

Tests for self and other values to be equal, and is used by ==.

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl TryFrom<String> for SquaredExponentialCorr

type Error = &'static str

The type returned in the event of a conversion error.

fn try_from(s: String) -> Result<Self, Self::Error>

Performs the conversion.

impl Copy for SquaredExponentialCorr

impl Eq for SquaredExponentialCorr

impl StructuralPartialEq for SquaredExponentialCorr