Struct rv::process::gaussian::GaussianProcessPrediction

pub struct GaussianProcessPrediction<K>
where
    K: Kernel,
{ /* private fields */ }

Structure for making GP preditions

Implementations

impl<K> GaussianProcessPrediction<K>

where K: Kernel,

pub fn cov(&self) -> &DMatrix<f64>

Return the covariance of the posterior

pub fn std(&self) -> DVector<f64>

Return the standard deviation of posterior.

pub fn dist(&self) -> &MvGaussian

Return the MV Gaussian distribution which shows the predicted values

pub fn draw<RNG: Rng>(&self, rng: &mut RNG) -> DVector<f64>

Draw a single value from the corresponding MV Gaussian

pub fn sample<R: Rng>(&self, size: usize, rng: &mut R) -> Vec<DVector<f64>>

Return a number of samples from the MV Gaussian

Trait Implementations

impl<K> Mean<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>> for GaussianProcessPrediction<K>

where K: Kernel,

fn mean(&self) -> Option<DVector<f64>>

Returns None if the mean is undefined

impl<K> Rv<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>> for GaussianProcessPrediction<K>

where K: Kernel,

fn ln_f(&self, x: &DVector<f64>) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> DVector<f64>

Single draw from the Rv

fn f(&self, x: &X) -> f64

Probability function

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl<K> Variance<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>> for GaussianProcessPrediction<K>

where K: Kernel,

fn variance(&self) -> Option<DVector<f64>>

Returns None if the variance is undefined