Struct SparseSpectrumGaussianProcessRegressor 

pub struct SparseSpectrumGaussianProcessRegressor {
    pub kernel: Box<dyn Kernel>,
    pub num_spectral_points: usize,
    pub spectral_density_threshold: f64,
    pub selection_method: SpectralSelectionMethod,
    pub random_state: Option<u64>,
    pub noise_variance: f64,
    pub optimize_spectral_points: bool,
    pub spectral_learning_rate: f64,
    pub max_optimization_iterations: usize,
}

Sparse Spectrum Gaussian Process Regressor

Uses spectral approximation to scale Gaussian processes to large datasets by approximating the kernel using a sparse set of spectral points.

Example

use sklears_gaussian_process::{SparseSpectrumGaussianProcessRegressor, kernels::RBF};
use sklears_core::prelude::*;
// SciRS2 Policy - Use scirs2-autograd for ndarray types and operations

let kernel = Box::new(RBF::new(1.0));
let model = SparseSpectrumGaussianProcessRegressor::new(kernel)
    .num_spectral_points(50)
    .spectral_density_threshold(1e-6);

let X = Array2::from_shape_vec((10, 2), (0..20).map(|x| x as f64).collect()).unwrap();
let y = Array1::from_vec((0..10).map(|x| (x as f64).sin()).collect());

let trained_model = model.fit(&X.view(), &y.view()).unwrap();
let predictions = trained_model.predict(&X.view()).unwrap();

Fields

kernel: Box<dyn Kernel>

Base kernel to approximate

num_spectral_points: usize

Number of spectral points to use

spectral_density_threshold: f64

Threshold for spectral density selection

selection_method: SpectralSelectionMethod

Method for selecting spectral points

random_state: Option<u64>

Random state for reproducible results

noise_variance: f64

Noise variance parameter

optimize_spectral_points: bool

Whether to optimize spectral points during training

spectral_learning_rate: f64

Learning rate for spectral point optimization

max_optimization_iterations: usize

Maximum iterations for optimization

Implementations

impl SparseSpectrumGaussianProcessRegressor

pub fn new(kernel: Box<dyn Kernel>) -> Self

Create a new sparse spectrum Gaussian process regressor

pub fn num_spectral_points(self, num_points: usize) -> Self

Set the number of spectral points

pub fn spectral_density_threshold(self, threshold: f64) -> Self

Set the spectral density threshold

pub fn selection_method(self, method: SpectralSelectionMethod) -> Self

Set the spectral selection method

pub fn noise_variance(self, variance: f64) -> Self

Set the noise variance

pub fn optimize_spectral_points(self, optimize: bool) -> Self

Set whether to optimize spectral points

pub fn random_state(self, seed: Option<u64>) -> Self

Set random state for reproducible results

pub fn compute_approximation_info( &self, spectral_points: &Array2<f64>, spectral_weights: &Array1<f64>, ) -> SklResult<SpectralApproximationInfo>

Compute spectral approximation quality metrics

Trait Implementations

impl Clone for SparseSpectrumGaussianProcessRegressor

fn clone(&self) -> SparseSpectrumGaussianProcessRegressor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SparseSpectrumGaussianProcessRegressor

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

Formats the value using the given formatter.

impl Default for SparseSpectrumGaussianProcessRegressor

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for SparseSpectrumGaussianProcessRegressor

type Config = SparseSpectrumGaussianProcessRegressor

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

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