Struct SparseSpectrumGprTrained 

pub struct SparseSpectrumGprTrained {
    pub config: SparseSpectrumGaussianProcessRegressor,
    pub spectral_points: Array2<f64>,
    pub spectral_weights: Array1<f64>,
    pub spectral_features: Array2<f64>,
    pub posterior_mean: Array1<f64>,
    pub posterior_covariance: Array2<f64>,
    pub X_train: Array2<f64>,
    pub y_train: Array1<f64>,
    pub spectral_density: Array1<f64>,
    pub log_marginal_likelihood: f64,
}

Trained sparse spectrum Gaussian process regressor

Fields

config: SparseSpectrumGaussianProcessRegressor

Original configuration

spectral_points: Array2<f64>

Selected spectral points (frequencies)

spectral_weights: Array1<f64>

Spectral weights

spectral_features: Array2<f64>

Training feature matrix in spectral space

posterior_mean: Array1<f64>

Posterior mean parameters

posterior_covariance: Array2<f64>

Posterior covariance matrix

X_train: Array2<f64>

Training inputs (for prediction)

y_train: Array1<f64>

Training targets

spectral_density: Array1<f64>

Spectral density estimates

log_marginal_likelihood: f64

Log marginal likelihood

Implementations

impl SparseSpectrumGprTrained

pub fn predict_with_uncertainty( &self, X: &ArrayView2<'_, f64>, ) -> SklResult<(Array1<f64>, Array1<f64>)>

Predict with uncertainty quantification

pub fn approximation_info(&self) -> SklResult<SpectralApproximationInfo>

Get spectral approximation quality information

pub fn log_marginal_likelihood(&self) -> f64

Get log marginal likelihood

Trait Implementations

impl Clone for SparseSpectrumGprTrained

fn clone(&self) -> SparseSpectrumGprTrained

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SparseSpectrumGprTrained

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

Formats the value using the given formatter.

impl Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<ViewRepr<&f64>, Dim<[usize; 1]>>, SparseSpectrumGprTrained> for SparseSpectrumGaussianProcessRegressor

type Fitted = SparseSpectrumGprTrained

The fitted model type

fn fit( self, X: &ArrayView2<'_, f64>, y: &ArrayView1<'_, f64>, ) -> SklResult<SparseSpectrumGprTrained>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for SparseSpectrumGprTrained

fn predict(&self, X: &ArrayView2<'_, f64>) -> SklResult<Array1<f64>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals