Struct ExpectationPropagationGaussianProcessClassifier 

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

Expectation Propagation Gaussian Process Classifier

This implementation uses Expectation Propagation (EP) for approximate inference in Gaussian Process Classification. EP provides a better approximation than the Laplace method by iteratively refining local approximations to the likelihood.

EP maintains a multivariate Gaussian approximation to the posterior by minimizing the KL divergence between the true posterior and the approximation at each data point.

Examples

use sklears_gaussian_process::{ExpectationPropagationGaussianProcessClassifier, 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]];
let y = array![0, 0, 1, 1];

let kernel = RBF::new(1.0);
let ep_gpc = ExpectationPropagationGaussianProcessClassifier::new()
    .kernel(Box::new(kernel));
let fitted = ep_gpc.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl ExpectationPropagationGaussianProcessClassifier<Untrained>

pub fn new() -> Self

Create a new ExpectationPropagationGaussianProcessClassifier instance

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

Set the kernel function

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

Set the maximum number of iterations

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

Set convergence tolerance

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

Set damping factor for updates

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

Set minimum variance threshold

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

Set verbosity

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

Set the random state

impl ExpectationPropagationGaussianProcessClassifier<EpGpcTrained>

pub fn log_marginal_likelihood(&self) -> f64

Get the log marginal likelihood

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

Get the unique classes

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

Get the posterior mean

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

Get the posterior covariance

pub fn n_iterations(&self) -> usize

Get the number of EP iterations used

Trait Implementations

impl<S: Clone> Clone for ExpectationPropagationGaussianProcessClassifier<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for ExpectationPropagationGaussianProcessClassifier<S>

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

Formats the value using the given formatter.

impl Default for ExpectationPropagationGaussianProcessClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for ExpectationPropagationGaussianProcessClassifier<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 ExpectationPropagationGaussianProcessClassifier<EpGpcTrained>

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

type Fitted = ExpectationPropagationGaussianProcessClassifier<EpGpcTrained>

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 ExpectationPropagationGaussianProcessClassifier<EpGpcTrained>

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; 2]>>> for ExpectationPropagationGaussianProcessClassifier<EpGpcTrained>

fn predict_proba(&self, X: &ArrayView2<'_, f64>) -> SklResult<Array2<f64>>

Predict class probabilities