Struct AdaptiveNASSystem 

pub struct AdaptiveNASSystem { /* private fields */ }

Advanced Neural Architecture Search System for Optimization

Implementations

impl AdaptiveNASSystem

pub fn new(config: LearnedOptimizationConfig) -> Self

Create new adaptive NAS system

pub fn search_architectures( &mut self, training_problems: &[OptimizationProblem], ) -> OptimizeResult<Vec<OptimizationArchitecture>>

Search for optimal architectures for given problems

pub fn get_search_stats(&self) -> &NASSearchStats

Get search statistics

pub fn cache_architecture_for_problem( &mut self, problem_class: String, architecture: OptimizationArchitecture, )

Cache architecture for specific problem type

pub fn get_cached_architecture( &self, problem_class: &str, ) -> Option<&OptimizationArchitecture>

Retrieve cached architecture for problem type

Trait Implementations

impl Clone for AdaptiveNASSystem

fn clone(&self) -> AdaptiveNASSystem

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for AdaptiveNASSystem

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

Formats the value using the given formatter.

impl LearnedOptimizer for AdaptiveNASSystem

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