Struct ConsistencyTraining 

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

Consistency Training for Semi-Supervised Learning

Consistency training uses data augmentation and encourages the model to produce consistent predictions for augmented versions of the same input.

Implementations

impl ConsistencyTraining<Untrained>

pub fn new() -> Self

Create a new ConsistencyTraining instance

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

Set the hidden layer dimensions

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

Set the learning rate

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

Set the consistency loss weight

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

Set the data augmentation strength

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

Set the maximum number of epochs

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

Set the batch size

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

Set the convergence tolerance

Trait Implementations

impl<S: Clone> Clone for ConsistencyTraining<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for ConsistencyTraining<S>

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

Formats the value using the given formatter.

impl Default for ConsistencyTraining<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for ConsistencyTraining<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 ConsistencyTraining<Untrained>

type Fitted = ConsistencyTraining<ConsistencyTrainingTrained>

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 ConsistencyTraining<ConsistencyTrainingTrained>

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 ConsistencyTraining<ConsistencyTrainingTrained>

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

Predict class probabilities