Struct rv::process::gaussian::kernel::ExpSineSquaredKernel

pub struct ExpSineSquaredKernel { /* private fields */ }

Exp Sine^2 Kernel k(x_i, x_j) = exp(-2 (sin(pi / periodicity * d(x_i, x_j)) / length_scale) ^ 2)

Implementations

impl ExpSineSquaredKernel

pub fn new(length_scale: f64, periodicity: f64) -> Result<Self, KernelError>

Create a new ExpSineSquaredKernel

pub fn new_unchecked(length_scale: f64, periodicity: f64) -> Self

Create a new ExpSineSquaredKernel without checking the parameters

Trait Implementations

impl<B> Add<B> for ExpSineSquaredKernel

where B: Kernel,

type Output = AddKernel<ExpSineSquaredKernel, B>

The resulting type after applying the + operator.

fn add(self, rhs: B) -> Self::Output

Performs the + operation.

impl Clone for ExpSineSquaredKernel

fn clone(&self) -> ExpSineSquaredKernel

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for ExpSineSquaredKernel

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for ExpSineSquaredKernel

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Kernel for ExpSineSquaredKernel

fn diag<R, C, S>(&self, x: &Matrix<f64, R, C, S>) -> DVector<f64>

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

Returns the diagnal of the kernel(x, x)

fn parameters(&self) -> DVector<f64>

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

fn reparameterize(&self, params: &[f64]) -> Result<Self, 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<(DMatrix<f64>, 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 covariance<R1, R2, C1, C2, S1, S2>( &self, x1: &Matrix<f64, R1, C1, S1>, x2: &Matrix<f64, R2, C2, S2> ) -> DMatrix<f64>

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 consume_parameters<I: IntoIterator<Item = f64>>( &self, params: I ) -> Result<(Self, I::IntoIter), KernelError>

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

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

impl<B> Mul<B> for ExpSineSquaredKernel

where B: Kernel,

type Output = ProductKernel<ExpSineSquaredKernel, B>

The resulting type after applying the * operator.

fn mul(self, rhs: B) -> Self::Output

Performs the * operation.

impl PartialEq for ExpSineSquaredKernel

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

This method tests for self and other values to be equal, and is used by ==.

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

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

impl Serialize for ExpSineSquaredKernel

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for ExpSineSquaredKernel