Struct GaussianProcessSemiSupervised 

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

Gaussian Process Semi-Supervised Learning

This method uses Gaussian processes to perform semi-supervised learning by treating the labeled samples as observed function values and inferring the function values at unlabeled points through GP inference.

Parameters

• kernel - Kernel function (‘rbf’, ‘linear’, ‘polynomial’)
• length_scale - Length scale parameter for RBF kernel
• noise_level - Noise level (variance) for observations
• alpha - Regularization parameter
• n_restarts_optimizer - Number of restarts for hyperparameter optimization
• random_state - Random seed for reproducibility

Examples

use sklears_semi_supervised::GaussianProcessSemiSupervised;
use sklears_core::traits::{Predict, Fit};


let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0]];
let y = array![0, 1, -1, -1]; // -1 indicates unlabeled

let gp = GaussianProcessSemiSupervised::new()
    .kernel("rbf".to_string())
    .length_scale(1.0)
    .noise_level(0.1);
let fitted = gp.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl GaussianProcessSemiSupervised<Untrained>

pub fn new() -> Self

Create a new GaussianProcessSemiSupervised instance

pub fn kernel(self, kernel: String) -> Self

Set the kernel function

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

Set the length scale parameter

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

Set the noise level

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

Set the alpha regularization parameter

pub fn n_restarts_optimizer(self, n_restarts: usize) -> Self

Set the number of optimizer restarts

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

Set the random state

Trait Implementations

impl<S: Clone> Clone for GaussianProcessSemiSupervised<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for GaussianProcessSemiSupervised<S>

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

Formats the value using the given formatter.

impl Default for GaussianProcessSemiSupervised<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for GaussianProcessSemiSupervised<Untrained>

type Config = ()

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 GaussianProcessSemiSupervised<Untrained>

type Fitted = GaussianProcessSemiSupervised<GaussianProcessTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, 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 GaussianProcessSemiSupervised<GaussianProcessTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> 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 GaussianProcessSemiSupervised<GaussianProcessTrained>

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

Predict class probabilities