Struct ContinualLearning 

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

Continual Learning for Multi-Task Learning

Implements continual learning where new tasks are learned sequentially without forgetting previously learned tasks using elastic weight consolidation.

Examples

use sklears_multioutput::transfer_learning::ContinualLearning;
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0]];
let y = array![[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];

let continual = ContinualLearning::new()
    .importance_weight(1000.0)
    .learning_rate(0.01);

Implementations

impl ContinualLearning<Untrained>

pub fn new() -> Self

Create a new ContinualLearning instance

pub fn importance_weight(self, weight: Float) -> Self

Set the importance weight for preventing forgetting

pub fn learning_rate(self, lr: Float) -> Self

Set the learning rate

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

Set the maximum number of iterations

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

Set the random state for reproducibility

pub fn fit( &self, tasks_X: &[ArrayView2<'_, Float>], tasks_y: &[ArrayView2<'_, Float>], ) -> SklResult<ContinualLearning<ContinualLearningTrained>>

Fit the continual learning model

impl ContinualLearning<ContinualLearningTrained>

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

Predict using the continual learning model

pub fn task_weights(&self) -> &[Array2<Float>]

Get the task weights

pub fn fisher_information(&self) -> &Array2<Float>

Get the Fisher information matrix

Trait Implementations

impl<S: Clone> Clone for ContinualLearning<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for ContinualLearning<S>

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

Formats the value using the given formatter.

impl Default for ContinualLearning<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for ContinualLearning<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 Estimator for ContinualLearning<ContinualLearningTrained>

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