Skip to main content

Kernel

augurs_changepoint::kernel

Trait Kernel 

Source 
pub trait Kernel:
    Debug
    + Clone
    + PartialEq {
    // Required methods
    fn n_parameters(&self) -> usize;
    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>;
    fn is_stationary(&self) -> bool;
    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>;
    fn parameters(
        &self,
    ) -> Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>;
    fn reparameterize(&self, params: &[f64]) -> Result<Self, KernelError>;
    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>;

    // Provided methods
    fn consume_parameters<I>(
        &self,
        params: I,
    ) -> Result<(Self, <I as IntoIterator>::IntoIter), KernelError>
       where I: IntoIterator<Item = f64> { ... }
    fn add<B>(self, other: B) -> AddKernel<Self, B>
       where B: Kernel { ... }
    fn mul<B>(self, other: B) -> ProductKernel<Self, B>
       where B: Kernel { ... }
}
Expand description

Kernel Function

Required Methods§

Source

fn n_parameters(&self) -> usize

Return the number of parameters used in this Kernel.

Source

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

Source

fn is_stationary(&self) -> bool

Reports if the given kernel function is stationary.

Source

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)

Source

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

Source

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

Source

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

Provided Methods§

Source

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

Source

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

Source

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

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§