Struct WhiteKernel 

pub struct WhiteKernel { /* private fields */ }

White Noise Kernel

Implementations

impl WhiteKernel

pub fn new(noise_level: f64) -> Result<WhiteKernel, KernelError>

Create a new WhiteKernel with the given level of noise

pub fn new_unchecked(noise_level: f64) -> WhiteKernel

Create a new WhiteKernel without check the parameters

Trait Implementations

impl<B> Add<B> for WhiteKernel

where B: Kernel,

type Output = AddKernel<WhiteKernel, B>

The resulting type after applying the + operator.

fn add(self, rhs: B) -> <WhiteKernel as Add<B>>::Output

Performs the + operation.

impl Clone for WhiteKernel

fn clone(&self) -> WhiteKernel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for WhiteKernel

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

Formats the value using the given formatter.

impl Kernel for WhiteKernel

fn covariance<R1, R2, C1, C2, S1, S2>( &self, x1: &Matrix<f64, R1, C1, S1>, x2: &Matrix<f64, R2, C2, S2>, ) -> Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>

where R1: Dim, R2: Dim, C1: Dim, C2: Dim, S1: Storage<f64, R1, C1>, S2: Storage<f64, R2, C2>, ShapeConstraint: SameNumberOfColumns<C1, C2>,

Returns the covariance matrix for two equal sized vectors

fn is_stationary(&self) -> bool

Reports if the given kernel function is stationary.

fn diag<R, C, S>( &self, x: &Matrix<f64, R, C, S>, ) -> Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>

where R: Dim, C: Dim, S: Storage<f64, R, C>,

Returns the diagonal of the kernel(x, x)

fn parameters( &self, ) -> Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>

Return the corresponding parameter vector The parameters here are in a log-scale

fn reparameterize(&self, param_vec: &[f64]) -> Result<WhiteKernel, KernelError>

Create a new kernel of the given type from the provided parameters. The parameters here are in a log-scale

fn covariance_with_gradient<R, C, S>( &self, x: &Matrix<f64, R, C, S>, ) -> Result<(Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>, CovGrad), CovGradError>

where R: Dim, C: Dim, S: Storage<f64, R, C>,

Covariance and Gradient with the log-scaled hyper-parameters

fn n_parameters(&self) -> usize

Return the number of parameters used in this Kernel.

fn consume_parameters<I>( &self, params: I, ) -> Result<(Self, <I as IntoIterator>::IntoIter), KernelError>

where I: IntoIterator<Item = f64>,

Takes a sequence of parameters and consumes only the ones it needs to create itself. The parameters here are in a log-scale

fn add<B>(self, other: B) -> AddKernel<Self, B>

where B: Kernel,

fn mul<B>(self, other: B) -> ProductKernel<Self, B>

where B: Kernel,

impl<B> Mul<B> for WhiteKernel

where B: Kernel,

type Output = ProductKernel<WhiteKernel, B>

The resulting type after applying the * operator.

fn mul(self, rhs: B) -> <WhiteKernel as Mul<B>>::Output

Performs the * operation.

impl PartialEq for WhiteKernel

fn eq(&self, other: &WhiteKernel) -> 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 StructuralPartialEq for WhiteKernel