Struct opensrdk_probability::nonparametric::elliptical_process::exact::ExactEllipticalProcessParams

pub struct ExactEllipticalProcessParams<K, T>where
    K: PositiveDefiniteKernel<T>,
    T: RandomVariable,{ /* private fields */ }

Trait Implementations

impl<K, T> Clone for ExactEllipticalProcessParams<K, T>where
K: PositiveDefiniteKernel<T> + Clone,
T: RandomVariable + Clone,

fn clone(&self) -> ExactEllipticalProcessParams<K, T>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<K, T> Debug for ExactEllipticalProcessParams<K, T>where
K: PositiveDefiniteKernel<T> + Debug,
T: RandomVariable + Debug,

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

Formats the value using the given formatter.

impl<K, T> EllipticalParams for ExactEllipticalProcessParams<K, T>where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,

fn mu(&self) -> &Vec<f64>

fn sigma_inv_mul(&self, v: Matrix) -> Result<Matrix, DistributionError>

fn lsigma_cols(&self) -> usize

fn sample(&self, z: Vec<f64>) -> Result<Vec<f64>, DistributionError>

fn x_mu(&self, x: &[f64]) -> Result<Vec<f64>, DistributionError>

impl<K, T> EllipticalProcessParams<K, T> for ExactEllipticalProcessParams<K, T>where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,

fn mahalanobis_squared(&self) -> f64

impl<K, T> GaussianProcessRegressor<K, T> for ExactEllipticalProcessParams<K, T>where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,

fn gp_predict_multivariate(
&self,
xs: &[T]
) -> Result<ExactEllipticalParams, DistributionError>

fn gp_predict(&self, xs: &T) -> Result<NormalParams, DistributionError>

impl<K, T> RandomVariable for ExactEllipticalProcessParams<K, T>where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,

type RestoreInfo = ()

fn transform_vec(&self) -> (Vec<f64>, Self::RestoreInfo)

fn len(&self) -> usize

fn restore(
v: &[f64],
info: &Self::RestoreInfo
) -> Result<Self, DistributionError>