Struct MAML 

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

Model-Agnostic Meta-Learning (MAML) for Few-Shot Learning

MAML learns good initialization parameters that can be quickly adapted to new tasks with just a few gradient steps. The key insight is to optimize for parameters that lead to fast learning on new tasks.

The algorithm uses a two-level optimization: the inner loop adapts to individual tasks, while the outer loop optimizes the initialization for good adaptation across tasks.

Implementations

impl MAML<Untrained>

pub fn new() -> Self

Create a new MAML instance

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

Set the inner loop learning rate

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

Set the outer loop learning rate

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

Set the number of inner loop steps

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

Set the number of meta-training episodes

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

Set the hidden layer dimensions

Trait Implementations

impl<S: Clone> Clone for MAML<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MAML<S>

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

Formats the value using the given formatter.

impl Default for MAML<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MAML<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 MAML<Untrained>

type Fitted = MAML<MAMLTrained>

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 MAML<MAMLTrained>

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 MAML<MAMLTrained>

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

Predict class probabilities