Struct MetaLearningKernelSelector 

pub struct MetaLearningKernelSelector<State = Untrained> { /* private fields */ }

Meta-Learning Kernel Selector

Automatically selects the best kernel approximation method based on dataset characteristics and historical performance.

Examples

use sklears_kernel_approximation::meta_learning_kernels::{MetaLearningKernelSelector, MetaLearningConfig};
use scirs2_core::ndarray::array;
use sklears_core::traits::{Fit, Transform};

let config = MetaLearningConfig::default();
let selector = MetaLearningKernelSelector::new(config);

let X = array![[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]];
let fitted = selector.fit(&X, &()).unwrap();
let features = fitted.transform(&X).unwrap();

Implementations

impl MetaLearningKernelSelector<Untrained>

pub fn new(config: MetaLearningConfig) -> Self

Create a new meta-learning kernel selector

pub fn with_components(n_components: usize) -> Self

Create with default configuration

pub fn add_task_history(self, task: TaskMetadata) -> Self

Add historical task data for meta-learning

pub fn strategy(self, strategy: MetaLearningStrategy) -> Self

Set meta-learning strategy

impl MetaLearningKernelSelector<Trained>

pub fn selected_kernel(&self) -> MetaKernelType

Get the selected kernel type

pub fn selected_hyperparameters(&self) -> &HashMap<String, Float>

Get the selected hyperparameters

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

Get kernel weights

pub fn kernel_offset(&self) -> &Array1<Float>

Get kernel offset

Trait Implementations

impl<State: Clone> Clone for MetaLearningKernelSelector<State>

fn clone(&self) -> MetaLearningKernelSelector<State>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for MetaLearningKernelSelector<State>

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

Formats the value using the given formatter.

impl Estimator for MetaLearningKernelSelector<Untrained>

type Config = MetaLearningConfig

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<OwnedRepr<f64>, Dim<[usize; 2]>>, ()> for MetaLearningKernelSelector<Untrained>

type Fitted = MetaLearningKernelSelector<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, _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

impl Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MetaLearningKernelSelector<Trained>

fn transform(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Transform the input data