Struct VariationalGaussianProcessClassifier 

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

Sparse variational Gaussian Process classifier with a Bernoulli-logit likelihood (SVGP-C).

Examples

let X = array![[-2.0, -2.0], [-2.5, -1.5], [2.0, 2.0], [2.5, 1.5]];
let y = array![0, 0, 1, 1];

let kernel = RBF::new(1.5);
let vgpc = VariationalGaussianProcessClassifier::new()
    .kernel(Box::new(kernel))
    .n_inducing(4);
let fitted = vgpc.fit(&X.view(), &y.view()).expect("fit should succeed with valid training data");
let predictions = fitted.predict(&X.view()).expect("predict should succeed on trained model");

Implementations

impl VariationalGaussianProcessClassifier<Untrained>

pub fn new() -> Self

Create a new VariationalGaussianProcessClassifier instance.

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

Set the kernel function.

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

Set the number of inducing points.

pub fn inducing_init(self, inducing_init: InducingPointInit) -> Self

Set the inducing point initialization method.

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

Set the maximum number of gradient-ascent iterations.

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

Set the learning rate.

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

Set the convergence tolerance.

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

Set the diagonal jitter added to K_mm.

pub fn optimize_kernel(self, optimize_kernel: bool) -> Self

Set whether kernel hyperparameters are optimized alongside m/S.

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

Set the random state.

impl VariationalGaussianProcessClassifier<VariationalGpcTrained>

pub fn elbo_history(&self) -> &[f64]

Get the ELBO history recorded during optimization.

pub fn elbo(&self) -> f64

Get the final ELBO value.

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

Get the inducing points.

pub fn classes(&self) -> &Array1<i32>

Get the sorted unique class labels seen during training.

Trait Implementations

impl<S: Clone> Clone for VariationalGaussianProcessClassifier<S>

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for VariationalGaussianProcessClassifier<S>

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

Formats the value using the given formatter.

impl Default for VariationalGaussianProcessClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for VariationalGaussianProcessClassifier<Untrained>

type Config = VariationalGpcConfig

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 VariationalGaussianProcessClassifier<VariationalGpcTrained>

type Config = VariationalGpcConfig

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<&i32>, Dim<[usize; 1]>>> for VariationalGaussianProcessClassifier<Untrained>

type Fitted = VariationalGaussianProcessClassifier<VariationalGpcTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, f64>, y: &ArrayView1<'_, i32>, ) -> 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<i32>, Dim<[usize; 1]>>> for VariationalGaussianProcessClassifier<VariationalGpcTrained>

fn predict(&self, X: &ArrayView2<'_, f64>) -> SklResult<Array1<i32>>

Make predictions on the provided data

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

Make predictions with confidence intervals

impl PredictProba<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for VariationalGaussianProcessClassifier<VariationalGpcTrained>

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

Predict P(y = classes()[1] | x), estimated by Gauss-Hermite integration of sigmoid over the latent posterior q(f*) (a more calibrated estimate than evaluating sigmoid at the posterior mean alone).