Struct LabelPropagation 

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

Label Propagation classifier

Label propagation is a graph-based semi-supervised learning algorithm that propagates labels through a neighborhood graph. It uses the graph Laplacian to propagate labels from labeled to unlabeled points.

Parameters

• kernel - Kernel function (‘knn’ or ‘rbf’)
• gamma - Parameter for RBF kernel
• n_neighbors - Number of neighbors for KNN kernel
• alpha - Clamping factor (0.0 = hard clamping, 1.0 = soft clamping)
• max_iter - Maximum number of iterations
• tol - Convergence tolerance

Examples

use scirs2_core::array;
use sklears_semi_supervised::LabelPropagation;
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 lp = LabelPropagation::new()
    .kernel("rbf".to_string())
    .gamma(20.0);
let fitted = lp.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl LabelPropagation<Untrained>

pub fn new() -> Self

Create a new LabelPropagation instance

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

Set the kernel function

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

Set the gamma parameter for RBF kernel

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

Set the number of neighbors for KNN kernel

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

Set the clamping factor

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

Trait Implementations

impl<S: Clone> Clone for LabelPropagation<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for LabelPropagation<S>

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

Formats the value using the given formatter.

impl Default for LabelPropagation<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for LabelPropagation<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 LabelPropagation<Untrained>

type Fitted = LabelPropagation<LabelPropagationTrained>

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 LabelPropagation<LabelPropagationTrained>

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 LabelPropagation<LabelPropagationTrained>

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

Predict class probabilities