Struct LearnedHyperparameterTuner 

pub struct LearnedHyperparameterTuner { /* private fields */ }

Learned hyperparameter tuner with adaptive configuration

Implementations

impl LearnedHyperparameterTuner

pub fn new(config: LearnedOptimizationConfig) -> Self

Create new learned hyperparameter tuner

pub fn tune_hyperparameters<F>( &mut self, objective: F, initial_params: &ArrayView1<'_, f64>, problem: &OptimizationProblem, budget: f64, ) -> OptimizeResult<HyperparameterConfig>

where F: Fn(&ArrayView1<'_, f64>) -> f64,

Tune hyperparameters for optimization problem

pub fn get_tuning_stats(&self) -> &HyperparameterTuningStats

Get tuning statistics

Trait Implementations

impl Clone for LearnedHyperparameterTuner

fn clone(&self) -> LearnedHyperparameterTuner

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LearnedHyperparameterTuner

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

Formats the value using the given formatter.

impl LearnedOptimizer for LearnedHyperparameterTuner

fn meta_train(&mut self, training_tasks: &[TrainingTask]) -> OptimizeResult<()>

Meta-train the optimizer on a distribution of problems

fn adapt_to_problem( &mut self, problem: &OptimizationProblem, initial_params: &ArrayView1<'_, f64>, ) -> OptimizeResult<()>

Adapt to a new problem with few-shot learning

fn optimize<F>( &mut self, objective: F, initial_params: &ArrayView1<'_, f64>, ) -> OptimizeResult<OptimizeResults<f64>>

where F: Fn(&ArrayView1<'_, f64>) -> f64,

Optimize a function using learned knowledge

fn get_state(&self) -> &MetaOptimizerState

Get current meta-optimizer state

fn reset(&mut self)

Reset the optimizer