Struct MultiViewCoTraining 

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

Multi-View Co-Training classifier for semi-supervised learning with multiple views

Multi-view co-training extends the traditional co-training algorithm to work with more than two views. Each view trains a classifier that can label examples for other views, creating a collaborative learning process.

Parameters

• views - Vector of feature indices for each view
• k_add - Number of examples to add per iteration per view
• max_iter - Maximum number of iterations
• confidence_threshold - Minimum confidence for pseudo-labeling
• selection_strategy - Strategy for selecting examples (“confidence” or “diversity”)
• verbose - Whether to print progress information

Examples

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


let X = array![[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
               [3.0, 4.0, 5.0, 6.0, 7.0, 8.0], [4.0, 5.0, 6.0, 7.0, 8.0, 9.0]];
let y = array![0, 1, -1, -1]; // -1 indicates unlabeled

let mvct = MultiViewCoTraining::new()
    .views(vec![vec![0, 1], vec![2, 3], vec![4, 5]])
    .k_add(1)
    .confidence_threshold(0.6)
    .max_iter(10);
let fitted = mvct.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl MultiViewCoTraining<Untrained>

pub fn new() -> Self

Create a new MultiViewCoTraining instance

pub fn views(self, views: Vec<Vec<usize>>) -> Self

Set the views (feature indices for each view)

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

Set the number of examples to add per iteration per view

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

Set the maximum number of iterations

pub fn confidence_threshold(self, threshold: f64) -> Self

Set the confidence threshold for pseudo-labeling

pub fn selection_strategy(self, strategy: String) -> Self

Set the selection strategy for pseudo-labeling

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

Set verbosity

Trait Implementations

impl<S: Clone> Clone for MultiViewCoTraining<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MultiViewCoTraining<S>

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

Formats the value using the given formatter.

impl Default for MultiViewCoTraining<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MultiViewCoTraining<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 MultiViewCoTraining<Untrained>

type Fitted = MultiViewCoTraining<MultiViewCoTrainingTrained>

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 MultiViewCoTraining<MultiViewCoTrainingTrained>

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