Struct RobustGraphLearning 

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

Robust Graph Learning for Semi-Supervised Learning

This method learns a robust graph structure that is resistant to outliers and noise in the data. It uses robust distance metrics and regularization to learn a clean graph structure.

Parameters

• lambda_sparse - Sparsity regularization parameter
• lambda_robust - Robustness regularization parameter
• max_iter - Maximum number of iterations
• tol - Convergence tolerance
• robust_metric - Robust distance metric (“l1”, “huber”, “tukey”)

Examples

use scirs2_core::array;
use sklears_semi_supervised::RobustGraphLearning;
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 rgl = RobustGraphLearning::new()
    .lambda_sparse(0.1)
    .robust_metric("huber".to_string());
let fitted = rgl.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl RobustGraphLearning<Untrained>

pub fn new() -> Self

Create a new RobustGraphLearning instance

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

Set the sparsity regularization parameter

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

Set the robustness regularization parameter

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

Set the maximum number of iterations

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

Set the convergence tolerance

pub fn robust_metric(self, metric: String) -> Self

Set the robust distance metric

pub fn huber_delta(self, delta: f64) -> Self

Set the Huber delta parameter

pub fn tukey_c(self, c: f64) -> Self

Set the Tukey c parameter

Trait Implementations

impl<S: Clone> Clone for RobustGraphLearning<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for RobustGraphLearning<S>

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

Formats the value using the given formatter.

impl Default for RobustGraphLearning<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for RobustGraphLearning<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 RobustGraphLearning<Untrained>

type Fitted = RobustGraphLearning<RobustGraphLearningTrained>

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 RobustGraphLearning<RobustGraphLearningTrained>

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 RobustGraphLearning<RobustGraphLearningTrained>

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

Predict class probabilities