Struct opensrdk_probability::nonparametric::elliptical_process::sparse::SparseEllipticalProcessParams
source · [−]pub struct SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable, { /* private fields */ }Trait Implementations
sourceimpl<K: Clone, T: Clone> Clone for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
impl<K: Clone, T: Clone> Clone for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
sourcefn clone(&self) -> SparseEllipticalProcessParams<K, T>
fn clone(&self) -> SparseEllipticalProcessParams<K, T>
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source. Read more
sourceimpl<K: Debug, T: Debug> Debug for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
impl<K: Debug, T: Debug> Debug for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
sourceimpl<K, T> EllipticalParams for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
impl<K, T> EllipticalParams for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
sourceimpl<K, T> EllipticalProcessParams<K, T> for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
impl<K, T> EllipticalProcessParams<K, T> for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
fn mahalanobis_squared(&self) -> f64
sourceimpl<K, T> GaussianProcessRegressor<K, T> for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
impl<K, T> GaussianProcessRegressor<K, T> for SparseEllipticalProcessParams<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>
sourceimpl<K, T> RandomVariable for SparseEllipticalProcessParams<K, T> where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
impl<K, T> RandomVariable for SparseEllipticalProcessParams<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>
Auto Trait Implementations
impl<K, T> RefUnwindSafe for SparseEllipticalProcessParams<K, T> where
K: RefUnwindSafe,
T: RefUnwindSafe,
impl<K, T> Send for SparseEllipticalProcessParams<K, T>
impl<K, T> Sync for SparseEllipticalProcessParams<K, T>
impl<K, T> Unpin for SparseEllipticalProcessParams<K, T> where
K: Unpin,
T: Unpin,
impl<K, T> UnwindSafe for SparseEllipticalProcessParams<K, T> where
K: UnwindSafe,
T: UnwindSafe,
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<K, T, GPR> CauchyProcessRegressor<K, T> for GPR where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
GPR: GaussianProcessRegressor<K, T>,
impl<K, T, GPR> CauchyProcessRegressor<K, T> for GPR where
K: PositiveDefiniteKernel<T>,
T: RandomVariable,
GPR: GaussianProcessRegressor<K, T>,
fn cp_predict_multivariate(
&self,
xs: &[T]
) -> Result<ExactMultivariateStudentTParams, DistributionError>
fn cp_predict(&self, xs: &T) -> Result<StudentTParams, DistributionError>
impl<T> Pointable for T
impl<T> Pointable for T
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
🔬 This is a nightly-only experimental API. (
toowned_clone_into)Uses borrowed data to replace owned data, usually by cloning. Read more