Struct RandomFourierFeaturesGPR 

pub struct RandomFourierFeaturesGPR<S = Untrained> { /* private fields */ }

Random Fourier Features Gaussian Process Regressor

This implementation combines Random Fourier Features with linear regression to approximate Gaussian Process regression at O(nD) computational complexity. The approach maintains uncertainty estimates through Bayesian linear regression in the feature space.

Examples

use sklears_gaussian_process::{RandomFourierFeaturesGPR, kernels::RBF};
use sklears_core::traits::{Fit, Predict};
// SciRS2 Policy - Use scirs2-autograd for ndarray types and operations
use scirs2_core::ndarray::array;

let X = array![[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]];
let y = array![1.0, 4.0, 9.0, 16.0, 25.0, 36.0];

let rff_gpr = RandomFourierFeaturesGPR::new()
    .n_components(50)
    .gamma(1.0)
    .alpha(1e-3);
let fitted = rff_gpr.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl RandomFourierFeaturesGPR<Untrained>

pub fn new() -> Self

Create a new RandomFourierFeaturesGPR instance

pub fn n_components(self, n_components: usize) -> Self

Set the number of random features

pub fn gamma(self, gamma: f64) -> Self

Set the gamma parameter of the RBF kernel

pub fn alpha(self, alpha: f64) -> Self

Set the noise precision parameter

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

Set the random state

impl RandomFourierFeaturesGPR<RffGprTrained>

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

Predict with uncertainty estimates

pub fn log_marginal_likelihood(&self) -> f64

Get the log marginal likelihood

pub fn weights(&self) -> &Array1<f64>

Get the posterior weights

pub fn weights_covariance(&self) -> &Array2<f64>

Get the posterior covariance of weights

pub fn rff_transformer(&self) -> &RandomFourierFeatures

Get the fitted Random Fourier Features transformer

Trait Implementations

impl<S: Clone> Clone for RandomFourierFeaturesGPR<S>

fn clone(&self) -> RandomFourierFeaturesGPR<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for RandomFourierFeaturesGPR<S>

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

Formats the value using the given formatter.

impl Default for RandomFourierFeaturesGPR<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for RandomFourierFeaturesGPR<Untrained>

type Config = GpcConfig

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 Estimator for RandomFourierFeaturesGPR<RffGprTrained>

type Config = GpcConfig

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]>>> for RandomFourierFeaturesGPR<Untrained>

type Fitted = RandomFourierFeaturesGPR<RffGprTrained>

The fitted model type

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

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 RandomFourierFeaturesGPR<RffGprTrained>

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