Struct MultiTaskDiscriminantLearning 

pub struct MultiTaskDiscriminantLearning { /* private fields */ }

Multi-task discriminant learning estimator

Implementations

impl MultiTaskDiscriminantLearning

pub fn new() -> Self

Create a new multi-task discriminant learning estimator

pub fn n_shared_components(self, n_components: Option<usize>) -> Self

Set number of shared discriminant components

pub fn n_task_components(self, n_components: Option<usize>) -> Self

Set number of task-specific components

pub fn sharing_penalty(self, penalty: Float) -> Self

Set sharing penalty (regularization for shared components)

pub fn task_penalty(self, penalty: Float) -> Self

Set task penalty (regularization for task-specific components)

pub fn base_discriminant(self, discriminant_type: &str) -> Self

Set base discriminant type

pub fn task_weighting(self, weighting: &str) -> Self

Set task weighting strategy

pub fn normalize_weights(self, normalize: bool) -> Self

Set whether to normalize task weights

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

Set maximum iterations

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

Set convergence tolerance

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

Set warm start option

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

Set random state

Trait Implementations

impl Clone for MultiTaskDiscriminantLearning

fn clone(&self) -> MultiTaskDiscriminantLearning

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MultiTaskDiscriminantLearning

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

Formats the value using the given formatter.

impl Default for MultiTaskDiscriminantLearning

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MultiTaskDiscriminantLearning

type Config = MultiTaskDiscriminantLearningConfig

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<Vec<Task>, ()> for MultiTaskDiscriminantLearning

type Fitted = TrainedMultiTaskDiscriminantLearning

The fitted model type

fn fit(self, tasks: &Vec<Task>, _y: &()) -> Result<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