scirs2_optimize/learned_optimizers/

adaptive_nas_system.rs

//! Adaptive Neural Architecture Search (NAS) System for Optimization
//!
//! This module implements sophisticated neural architecture search algorithms
//! that adaptively design optimization strategies based on problem characteristics.
//! The system can discover and evolve optimization architectures automatically.

use super::{
    ActivationType, LearnedOptimizationConfig, LearnedOptimizer, MetaOptimizerState,
    OptimizationProblem, TrainingTask,
};
use crate::error::OptimizeResult;
use crate::result::OptimizeResults;
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1};
use scirs2_core::random::Rng;
use statrs::statistics::Statistics;
use std::collections::HashMap;

/// Advanced Neural Architecture Search System for Optimization
#[derive(Debug, Clone)]
pub struct AdaptiveNASSystem {
    /// Configuration
    config: LearnedOptimizationConfig,
    /// Current architecture population
    architecture_population: Vec<OptimizationArchitecture>,
    /// Architecture performance history
    performance_history: HashMap<ArchitectureId, Vec<f64>>,
    /// Search controller network
    controller: ArchitectureController,
    /// Meta-optimizer state
    meta_state: MetaOptimizerState,
    /// Problem-specific architecture cache
    architecture_cache: HashMap<String, OptimizationArchitecture>,
    /// Search statistics
    search_stats: NASSearchStats,
    /// Current generation
    generation: usize,
}

/// Unique identifier for architectures
type ArchitectureId = String;

/// Architecture for optimization algorithms
#[derive(Debug, Clone)]
pub struct OptimizationArchitecture {
    /// Unique identifier
    pub id: ArchitectureId,
    /// Layer configuration
    pub layers: Vec<LayerConfig>,
    /// Connection pattern
    pub connections: Vec<Connection>,
    /// Activation functions
    pub activations: Vec<ActivationType>,
    /// Skip connections
    pub skip_connections: Vec<SkipConnection>,
    /// Optimization-specific components
    pub optimizer_components: Vec<OptimizerComponent>,
    /// Architecture complexity score
    pub complexity: f64,
    /// Performance metrics
    pub performance_metrics: ArchitectureMetrics,
}

/// Layer configuration in the architecture
#[derive(Debug, Clone)]
pub struct LayerConfig {
    /// Layer type
    pub layer_type: LayerType,
    /// Number of units/neurons
    pub units: usize,
    /// Dropout rate
    pub dropout: f64,
    /// Normalization type
    pub normalization: NormalizationType,
    /// Additional parameters
    pub parameters: HashMap<String, f64>,
}

/// Types of layers in optimization architectures
#[derive(Debug, Clone)]
pub enum LayerType {
    /// Dense/fully connected layer
    Dense,
    /// Convolutional layer (for structured problems)
    Convolution { kernel_size: usize, stride: usize },
    /// Attention layer
    Attention { num_heads: usize },
    /// LSTM layer
    LSTM { hidden_size: usize },
    /// GRU layer
    GRU { hidden_size: usize },
    /// Transformer block
    Transformer { num_heads: usize, ff_dim: usize },
    /// Graph neural network layer
    GraphNN { aggregation: String },
    /// Memory-augmented layer
    Memory { memory_size: usize },
}

/// Types of normalization
#[derive(Debug, Clone)]
pub enum NormalizationType {
    None,
    BatchNorm,
    LayerNorm,
    GroupNorm { groups: usize },
    InstanceNorm,
}

/// Connection between layers
#[derive(Debug, Clone)]
pub struct Connection {
    /// Source layer index
    pub from: usize,
    /// Target layer index
    pub to: usize,
    /// Connection weight
    pub weight: f64,
    /// Connection type
    pub connection_type: ConnectionType,
}

/// Types of connections
#[derive(Debug, Clone)]
pub enum ConnectionType {
    /// Standard feedforward
    Forward,
    /// Residual connection
    Residual,
    /// Dense connection (from all previous layers)
    Dense,
    /// Attention-based connection
    Attention,
}

/// Skip connection configuration
#[derive(Debug, Clone)]
pub struct SkipConnection {
    /// Source layer
    pub source: usize,
    /// Target layer
    pub target: usize,
    /// Skip type
    pub skip_type: SkipType,
}

/// Types of skip connections
#[derive(Debug, Clone)]
pub enum SkipType {
    /// Simple addition
    Add,
    /// Concatenation
    Concat,
    /// Gated connection
    Gated { gate_size: usize },
    /// Highway connection
    Highway,
}

/// Optimizer-specific components
#[derive(Debug, Clone)]
pub enum OptimizerComponent {
    /// Momentum component
    Momentum { decay: f64 },
    /// Adaptive learning rate
    AdaptiveLR {
        adaptation_rate: f64,
        min_lr: f64,
        max_lr: f64,
    },
    /// Second-order approximation
    SecondOrder {
        hessian_approximation: HessianApprox,
        regularization: f64,
    },
    /// Trust region component
    TrustRegion {
        initial_radius: f64,
        max_radius: f64,
        shrink_factor: f64,
        expand_factor: f64,
    },
    /// Line search component
    LineSearch {
        method: LineSearchMethod,
        max_nit: usize,
    },
    /// Regularization component
    Regularization {
        l1_weight: f64,
        l2_weight: f64,
        elastic_net_ratio: f64,
    },
}

/// Hessian approximation methods
#[derive(Debug, Clone)]
pub enum HessianApprox {
    BFGS,
    LBFGS { memory_size: usize },
    SR1,
    DFP,
    DiagonalApprox,
}

/// Line search methods
#[derive(Debug, Clone)]
pub enum LineSearchMethod {
    Backtracking,
    StrongWolfe,
    MoreThuente,
    Armijo,
    Exact,
}

/// Performance metrics for architectures
#[derive(Debug, Clone)]
pub struct ArchitectureMetrics {
    /// Average convergence rate
    pub convergence_rate: f64,
    /// Success rate on test problems
    pub success_rate: f64,
    /// Average function evaluations
    pub avg_evaluations: f64,
    /// Robustness score
    pub robustness: f64,
    /// Transfer learning capability
    pub transfer_score: f64,
    /// Computational efficiency
    pub efficiency: f64,
}

impl Default for ArchitectureMetrics {
    fn default() -> Self {
        Self {
            convergence_rate: 0.0,
            success_rate: 0.0,
            avg_evaluations: 0.0,
            robustness: 0.0,
            transfer_score: 0.0,
            efficiency: 0.0,
        }
    }
}

/// Controller for generating architectures
#[derive(Debug, Clone)]
pub struct ArchitectureController {
    /// LSTM-based controller network
    lstm_weights: Array3<f64>,
    /// Embedding layer for architecture components
    embedding_layer: Array2<f64>,
    /// Output layer for architecture decisions
    output_layer: Array2<f64>,
    /// Controller state
    controller_state: Array1<f64>,
    /// Vocabulary for architecture components
    vocabulary: ArchitectureVocabulary,
}

/// Vocabulary for architecture search
#[derive(Debug, Clone)]
pub struct ArchitectureVocabulary {
    /// Layer types mapping
    pub layer_types: HashMap<String, usize>,
    /// Activation functions mapping
    pub activations: HashMap<String, usize>,
    /// Optimizer components mapping
    pub components: HashMap<String, usize>,
    /// Total vocabulary size
    pub vocab_size: usize,
}

/// Search statistics
#[derive(Debug, Clone)]
pub struct NASSearchStats {
    /// Number of architectures evaluated
    pub architectures_evaluated: usize,
    /// Best performance found
    pub best_performance: f64,
    /// Search efficiency
    pub search_efficiency: f64,
    /// Diversity of population
    pub population_diversity: f64,
    /// Convergence indicators
    pub convergence_indicators: Vec<f64>,
}

impl Default for NASSearchStats {
    fn default() -> Self {
        Self {
            architectures_evaluated: 0,
            best_performance: f64::NEG_INFINITY,
            search_efficiency: 0.0,
            population_diversity: 1.0,
            convergence_indicators: Vec::new(),
        }
    }
}

impl AdaptiveNASSystem {
    /// Create new adaptive NAS system
    pub fn new(config: LearnedOptimizationConfig) -> Self {
        let vocabulary = ArchitectureVocabulary::new();
        let controller = ArchitectureController::new(&vocabulary, config.hidden_size);
        let hidden_size = config.hidden_size;

        Self {
            config,
            architecture_population: Vec::new(),
            performance_history: HashMap::new(),
            controller,
            meta_state: MetaOptimizerState {
                meta_params: Array1::zeros(100),
                network_weights: Array2::zeros((hidden_size, hidden_size)),
                performance_history: Vec::new(),
                adaptation_stats: super::AdaptationStatistics::default(),
                episode: 0,
            },
            architecture_cache: HashMap::new(),
            search_stats: NASSearchStats::default(),
            generation: 0,
        }
    }

    /// Search for optimal architectures for given problems
    pub fn search_architectures(
        &mut self,
        training_problems: &[OptimizationProblem],
    ) -> OptimizeResult<Vec<OptimizationArchitecture>> {
        // Initialize population if empty
        if self.architecture_population.is_empty() {
            self.initialize_population()?;
        }

        for generation in 0..self.config.meta_training_episodes {
            self.generation = generation;

            // Evaluate current population
            self.evaluate_population(training_problems)?;

            // Update controller based on performance
            self.update_controller()?;

            // Generate new architectures
            let new_architectures = self.generate_new_architectures()?;

            // Select best architectures for next generation
            self.select_next_generation(new_architectures)?;

            // Update search statistics
            self.update_search_stats()?;

            // Check convergence
            if self.check_convergence() {
                break;
            }
        }

        Ok(self.get_best_architectures())
    }

    /// Initialize population with diverse architectures
    fn initialize_population(&mut self) -> OptimizeResult<()> {
        for _ in 0..self.config.batch_size {
            let architecture = self.generate_random_architecture()?;
            self.architecture_population.push(architecture);
        }
        Ok(())
    }

    /// Generate a random architecture
    fn generate_random_architecture(&self) -> OptimizeResult<OptimizationArchitecture> {
        let num_layers = 2 + (scirs2_core::random::rng().random_range(0..8)); // 2-10 layers
        let mut layers = Vec::new();
        let mut connections = Vec::new();
        let mut activations = Vec::new();
        let mut optimizer_components = Vec::new();

        // Generate layers
        for i in 0..num_layers {
            let layer_type = self.sample_layer_type();
            let units = 16 + (scirs2_core::random::rng().random_range(0..256)); // 16-272 units

            layers.push(LayerConfig {
                layer_type,
                units,
                dropout: scirs2_core::random::rng().random_range(0.0..0.5),
                normalization: self.sample_normalization(),
                parameters: HashMap::new(),
            });

            activations.push(self.sample_activation());

            // Add connections (skip first layer)
            if i > 0 {
                connections.push(Connection {
                    from: i - 1,
                    to: i,
                    weight: 1.0,
                    connection_type: ConnectionType::Forward,
                });

                // Add skip connections with some probability
                if i > 1 && scirs2_core::random::rng().random_range(0.0..1.0) < 0.3 {
                    let skip_source = scirs2_core::random::rng().random_range(0..i);
                    connections.push(Connection {
                        from: skip_source,
                        to: i,
                        weight: 0.5,
                        connection_type: ConnectionType::Residual,
                    });
                }
            }
        }

        // Generate optimizer components
        for _ in 0..(1 + scirs2_core::random::rng().random_range(0..4)) {
            optimizer_components.push(self.sample_optimizer_component());
        }

        let id = format!(
            "arch_{}",
            scirs2_core::random::rng().random_range(0..u64::MAX)
        );

        Ok(OptimizationArchitecture {
            id,
            layers,
            connections,
            activations,
            skip_connections: Vec::new(),
            optimizer_components,
            complexity: 0.0,
            performance_metrics: ArchitectureMetrics::default(),
        })
    }

    fn sample_layer_type(&self) -> LayerType {
        match scirs2_core::random::rng().random_range(0..8) {
            0 => LayerType::Dense,
            1 => LayerType::Attention {
                num_heads: 2 + scirs2_core::random::rng().random_range(0..6),
            },
            2 => LayerType::LSTM {
                hidden_size: 32 + scirs2_core::random::rng().random_range(0..128),
            },
            3 => LayerType::GRU {
                hidden_size: 32 + scirs2_core::random::rng().random_range(0..128),
            },
            4 => LayerType::Transformer {
                num_heads: 2 + scirs2_core::random::rng().random_range(0..6),
                ff_dim: 64 + scirs2_core::random::rng().random_range(0..256),
            },
            5 => LayerType::Memory {
                memory_size: 16 + scirs2_core::random::rng().random_range(0..64),
            },
            6 => LayerType::Convolution {
                kernel_size: 1 + scirs2_core::random::rng().random_range(0..5),
                stride: 1 + scirs2_core::random::rng().random_range(0..3),
            },
            _ => LayerType::GraphNN {
                aggregation: "mean".to_string(),
            },
        }
    }

    fn sample_normalization(&self) -> NormalizationType {
        match scirs2_core::random::rng().random_range(0..5) {
            0 => NormalizationType::None,
            1 => NormalizationType::BatchNorm,
            2 => NormalizationType::LayerNorm,
            3 => NormalizationType::GroupNorm {
                groups: 2 + scirs2_core::random::rng().random_range(0..6),
            },
            _ => NormalizationType::InstanceNorm,
        }
    }

    fn sample_activation(&self) -> ActivationType {
        match scirs2_core::random::rng().random_range(0..5) {
            0 => ActivationType::ReLU,
            1 => ActivationType::GELU,
            2 => ActivationType::Swish,
            3 => ActivationType::Tanh,
            _ => ActivationType::LeakyReLU,
        }
    }

    fn sample_optimizer_component(&self) -> OptimizerComponent {
        match scirs2_core::random::rng().random_range(0..6) {
            0 => OptimizerComponent::Momentum {
                decay: 0.8 + scirs2_core::random::rng().random_range(0.0..0.19),
            },
            1 => OptimizerComponent::AdaptiveLR {
                adaptation_rate: 0.001 + scirs2_core::random::rng().random_range(0.0..0.009),
                min_lr: 1e-8,
                max_lr: 1.0,
            },
            2 => OptimizerComponent::SecondOrder {
                hessian_approximation: HessianApprox::LBFGS {
                    memory_size: 5 + scirs2_core::random::rng().random_range(0..15),
                },
                regularization: 1e-6 + scirs2_core::random::rng().random_range(0.0..1e-3),
            },
            3 => OptimizerComponent::TrustRegion {
                initial_radius: 0.1 + scirs2_core::random::rng().random_range(0.0..0.9),
                max_radius: 10.0,
                shrink_factor: 0.25,
                expand_factor: 2.0,
            },
            4 => OptimizerComponent::LineSearch {
                method: LineSearchMethod::StrongWolfe,
                max_nit: 10 + scirs2_core::random::rng().random_range(0..20),
            },
            _ => OptimizerComponent::Regularization {
                l1_weight: scirs2_core::random::rng().random_range(0.0..0.01),
                l2_weight: scirs2_core::random::rng().random_range(0.0..0.01),
                elastic_net_ratio: scirs2_core::random::rng().random_range(0.0..1.0),
            },
        }
    }

    /// Evaluate population on training problems
    fn evaluate_population(
        &mut self,
        training_problems: &[OptimizationProblem],
    ) -> OptimizeResult<()> {
        // First evaluate all architectures
        let scores: Vec<_> = self
            .architecture_population
            .iter()
            .map(|architecture| {
                let mut total_score = 0.0;
                let mut num_evaluated = 0;

                for problem in training_problems.iter().take(5) {
                    // Limit for efficiency
                    if let Ok(score) = self.evaluate_architecture_on_problem(architecture, problem)
                    {
                        total_score += score;
                        num_evaluated += 1;
                    }
                }

                if num_evaluated > 0 {
                    Some(total_score / num_evaluated as f64)
                } else {
                    None
                }
            })
            .collect();

        // Now update architectures with their scores
        for (architecture, score) in self.architecture_population.iter_mut().zip(scores.iter()) {
            if let Some(avg_score) = score {
                architecture.performance_metrics.convergence_rate = *avg_score;

                // Update performance history
                self.performance_history
                    .entry(architecture.id.clone())
                    .or_default()
                    .push(*avg_score);
            }
        }

        Ok(())
    }

    /// Evaluate single architecture on a problem
    fn evaluate_architecture_on_problem(
        &self,
        architecture: &OptimizationArchitecture,
        problem: &OptimizationProblem,
    ) -> OptimizeResult<f64> {
        // Simplified evaluation - in practice would build and test the actual architecture
        let complexity_penalty = architecture.complexity * 0.01;
        let num_components = architecture.optimizer_components.len() as f64;
        let num_layers = architecture.layers.len() as f64;

        // Heuristic scoring based on architecture properties
        let base_score = (num_components * 0.1 + num_layers * 0.05).min(1.0);
        let final_score = base_score - complexity_penalty;

        Ok(final_score.max(0.0))
    }

    /// Update controller network
    fn update_controller(&mut self) -> OptimizeResult<()> {
        // Collect performance feedback
        let mut rewards = Vec::new();
        for arch in &self.architecture_population {
            rewards.push(arch.performance_metrics.convergence_rate);
        }

        if rewards.is_empty() {
            return Ok(());
        }

        // Update controller using REINFORCE-like algorithm
        let baseline = rewards.iter().sum::<f64>() / rewards.len() as f64;

        for (i, &reward) in rewards.iter().enumerate() {
            let advantage = reward - baseline;

            // Update controller weights (simplified)
            let lstm_len = self.controller.lstm_weights.len();
            if i < lstm_len {
                let shape = self.controller.lstm_weights.shape();
                let dims = (shape[0], shape[1], shape[2]);
                for j in 0..dims.1 {
                    for k in 0..dims.2 {
                        let learning_rate = self.config.meta_learning_rate;
                        let idx = (i % lstm_len, j, k);
                        self.controller.lstm_weights[idx] += learning_rate * advantage * 0.01;
                    }
                }
            }
        }

        Ok(())
    }

    /// Generate new architectures using controller
    fn generate_new_architectures(&mut self) -> OptimizeResult<Vec<OptimizationArchitecture>> {
        let mut new_architectures = Vec::new();

        for _ in 0..self.config.batch_size / 2 {
            // Generate architecture using controller
            let architecture = self.controller_generate_architecture()?;
            new_architectures.push(architecture);

            // Also add mutated versions of best architectures
            if !self.architecture_population.is_empty() {
                let best_idx = self.get_best_architecture_index();
                let mutated = self.mutate_architecture(&self.architecture_population[best_idx])?;
                new_architectures.push(mutated);
            }
        }

        Ok(new_architectures)
    }

    /// Generate architecture using controller network
    fn controller_generate_architecture(&mut self) -> OptimizeResult<OptimizationArchitecture> {
        // Simplified architecture generation using controller
        // In practice, this would use the LSTM controller to generate architecture sequences

        let mut architecture = self.generate_random_architecture()?;

        // Modify based on controller state
        let controller_influence = self.controller.controller_state.view().mean();

        // Adjust architecture complexity based on controller
        if controller_influence > 0.5 {
            // Increase complexity
            if architecture.layers.len() < 10 {
                architecture.layers.push(LayerConfig {
                    layer_type: LayerType::Dense,
                    units: 64,
                    dropout: 0.1,
                    normalization: NormalizationType::LayerNorm,
                    parameters: HashMap::new(),
                });
            }
        } else {
            // Reduce complexity
            if architecture.layers.len() > 2 {
                architecture.layers.pop();
            }
        }

        Ok(architecture)
    }

    /// Mutate an existing architecture
    fn mutate_architecture(
        &self,
        base_arch: &OptimizationArchitecture,
    ) -> OptimizeResult<OptimizationArchitecture> {
        let mut mutated = base_arch.clone();
        mutated.id = format!(
            "mutated_{}",
            scirs2_core::random::rng().random_range(0..u64::MAX)
        );

        // Mutate with some probability
        if scirs2_core::random::rng().random_range(0.0..1.0) < 0.3 {
            // Mutate layer count
            if scirs2_core::random::rng().random_range(0.0..1.0) < 0.5 && mutated.layers.len() < 12
            {
                mutated.layers.push(LayerConfig {
                    layer_type: self.sample_layer_type(),
                    units: 32 + scirs2_core::random::rng().random_range(0..128),
                    dropout: scirs2_core::random::rng().random_range(0.0..0.5),
                    normalization: self.sample_normalization(),
                    parameters: HashMap::new(),
                });
            } else if mutated.layers.len() > 2 {
                mutated.layers.pop();
            }
        }

        // Mutate activations
        for activation in &mut mutated.activations {
            if scirs2_core::random::rng().random_range(0.0..1.0) < 0.2 {
                *activation = self.sample_activation();
            }
        }

        // Mutate optimizer components
        if scirs2_core::random::rng().random_range(0.0..1.0) < 0.4 {
            if scirs2_core::random::rng().random_range(0.0..1.0) < 0.5
                && mutated.optimizer_components.len() < 6
            {
                mutated
                    .optimizer_components
                    .push(self.sample_optimizer_component());
            } else if !mutated.optimizer_components.is_empty() {
                let idx =
                    scirs2_core::random::rng().random_range(0..mutated.optimizer_components.len());
                mutated.optimizer_components.remove(idx);
            }
        }

        Ok(mutated)
    }

    /// Select best architectures for next generation
    fn select_next_generation(
        &mut self,
        mut new_architectures: Vec<OptimizationArchitecture>,
    ) -> OptimizeResult<()> {
        // Combine current population with new architectures
        self.architecture_population.append(&mut new_architectures);

        // Sort by performance
        self.architecture_population.sort_by(|a, b| {
            b.performance_metrics
                .convergence_rate
                .partial_cmp(&a.performance_metrics.convergence_rate)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Keep only the best architectures
        self.architecture_population
            .truncate(self.config.batch_size);

        Ok(())
    }

    /// Update search statistics
    fn update_search_stats(&mut self) -> OptimizeResult<()> {
        self.search_stats.architectures_evaluated += self.architecture_population.len();

        if let Some(best_arch) = self.architecture_population.first() {
            let best_performance = best_arch.performance_metrics.convergence_rate;
            if best_performance > self.search_stats.best_performance {
                self.search_stats.best_performance = best_performance;
            }
        }

        // Compute population diversity
        let performances: Vec<f64> = self
            .architecture_population
            .iter()
            .map(|a| a.performance_metrics.convergence_rate)
            .collect();

        if performances.len() > 1 {
            let mean = performances.iter().sum::<f64>() / performances.len() as f64;
            let variance = performances
                .iter()
                .map(|&p| (p - mean).powi(2))
                .sum::<f64>()
                / performances.len() as f64;
            self.search_stats.population_diversity = variance.sqrt();
        }

        self.search_stats
            .convergence_indicators
            .push(self.search_stats.best_performance);

        Ok(())
    }

    /// Check if search has converged
    fn check_convergence(&self) -> bool {
        if self.search_stats.convergence_indicators.len() < 10 {
            return false;
        }

        // Check if improvement has stagnated
        let recent_improvements: Vec<f64> = self
            .search_stats
            .convergence_indicators
            .windows(2)
            .map(|w| w[1] - w[0])
            .collect();

        let avg_improvement =
            recent_improvements.iter().sum::<f64>() / recent_improvements.len() as f64;
        avg_improvement < 1e-6
    }

    /// Get best architectures from current population
    fn get_best_architectures(&self) -> Vec<OptimizationArchitecture> {
        self.architecture_population.clone()
    }

    fn get_best_architecture_index(&self) -> usize {
        self.architecture_population
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| {
                a.performance_metrics
                    .convergence_rate
                    .partial_cmp(&b.performance_metrics.convergence_rate)
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
            .map(|(i, _)| i)
            .unwrap_or(0)
    }

    /// Get search statistics
    pub fn get_search_stats(&self) -> &NASSearchStats {
        &self.search_stats
    }

    /// Cache architecture for specific problem type
    pub fn cache_architecture_for_problem(
        &mut self,
        problem_class: String,
        architecture: OptimizationArchitecture,
    ) {
        self.architecture_cache.insert(problem_class, architecture);
    }

    /// Retrieve cached architecture for problem type
    pub fn get_cached_architecture(
        &self,
        problem_class: &str,
    ) -> Option<&OptimizationArchitecture> {
        self.architecture_cache.get(problem_class)
    }
}

impl ArchitectureController {
    /// Create new architecture controller
    pub fn new(vocabulary: &ArchitectureVocabulary, hidden_size: usize) -> Self {
        Self {
            lstm_weights: Array3::from_shape_fn((4, hidden_size, hidden_size), |_| {
                (scirs2_core::random::rng().random_range(0.0..1.0) - 0.5) * 0.1
            }),
            embedding_layer: Array2::from_shape_fn((hidden_size, vocabulary.vocab_size), |_| {
                (scirs2_core::random::rng().random_range(0.0..1.0) - 0.5) * 0.1
            }),
            output_layer: Array2::from_shape_fn((vocabulary.vocab_size, hidden_size), |_| {
                (scirs2_core::random::rng().random_range(0.0..1.0) - 0.5) * 0.1
            }),
            controller_state: Array1::zeros(hidden_size),
            vocabulary: vocabulary.clone(),
        }
    }
}

impl Default for ArchitectureVocabulary {
    fn default() -> Self {
        Self::new()
    }
}

impl ArchitectureVocabulary {
    /// Create new architecture vocabulary
    pub fn new() -> Self {
        let mut layer_types = HashMap::new();
        layer_types.insert("dense".to_string(), 0);
        layer_types.insert("conv".to_string(), 1);
        layer_types.insert("attention".to_string(), 2);
        layer_types.insert("lstm".to_string(), 3);
        layer_types.insert("gru".to_string(), 4);
        layer_types.insert("transformer".to_string(), 5);
        layer_types.insert("graph".to_string(), 6);
        layer_types.insert("memory".to_string(), 7);

        let mut activations = HashMap::new();
        activations.insert("relu".to_string(), 8);
        activations.insert("gelu".to_string(), 9);
        activations.insert("swish".to_string(), 10);
        activations.insert("tanh".to_string(), 11);
        activations.insert("leaky_relu".to_string(), 12);

        let mut components = HashMap::new();
        components.insert("momentum".to_string(), 13);
        components.insert("adaptive_lr".to_string(), 14);
        components.insert("second_order".to_string(), 15);
        components.insert("trust_region".to_string(), 16);
        components.insert("line_search".to_string(), 17);
        components.insert("regularization".to_string(), 18);

        Self {
            layer_types,
            activations,
            components,
            vocab_size: 19,
        }
    }
}

impl LearnedOptimizer for AdaptiveNASSystem {
    fn meta_train(&mut self, training_tasks: &[TrainingTask]) -> OptimizeResult<()> {
        let problems: Vec<OptimizationProblem> = training_tasks
            .iter()
            .map(|task| task.problem.clone())
            .collect();

        self.search_architectures(&problems)?;
        Ok(())
    }

    fn adapt_to_problem(
        &mut self,
        problem: &OptimizationProblem,
        initial_params: &ArrayView1<f64>,
    ) -> OptimizeResult<()> {
        // Check if we have a cached architecture for this problem type
        if let Some(cached_arch) = self.get_cached_architecture(&problem.problem_class) {
            // Use cached architecture - no adaptation needed
            return Ok(());
        }

        // Generate specialized architecture for this problem
        let specialized_arch = self.generate_random_architecture()?;
        self.cache_architecture_for_problem(problem.problem_class.clone(), specialized_arch);

        Ok(())
    }

    fn optimize<F>(
        &mut self,
        objective: F,
        initial_params: &ArrayView1<f64>,
    ) -> OptimizeResult<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        // Use best architecture to optimize
        if self.architecture_population.is_empty() {
            self.initialize_population()?;
        }

        let best_idx = self.get_best_architecture_index();
        let best_arch = &self.architecture_population[best_idx];

        // Simplified optimization using best architecture
        let mut current_params = initial_params.to_owned();
        let mut best_value = objective(initial_params);
        let mut iterations = 0;

        for iter in 0..1000 {
            iterations = iter;

            // Apply optimization step based on architecture
            let step_size = self.compute_step_size(best_arch, iter);
            let direction = self.compute_search_direction(&objective, &current_params, best_arch);

            // Update parameters
            for i in 0..current_params.len() {
                current_params[i] -= step_size * direction[i];
            }

            let current_value = objective(&current_params.view());

            if current_value < best_value {
                best_value = current_value;
            }

            // Check convergence
            if step_size < 1e-8 {
                break;
            }
        }

        Ok(OptimizeResults::<f64> {
            x: current_params,
            fun: best_value,
            success: true,
            nit: iterations,
            message: format!(
                "NAS optimization completed using architecture: {}",
                best_arch.id
            ),
            jac: None,
            hess: None,
            constr: None,
            nfev: iterations * best_arch.layers.len(), // Architecture depth affects evaluations
            njev: 0,
            nhev: 0,
            maxcv: 0,
            status: 0,
        })
    }

    fn get_state(&self) -> &MetaOptimizerState {
        &self.meta_state
    }

    fn reset(&mut self) {
        self.architecture_population.clear();
        self.performance_history.clear();
        self.search_stats = NASSearchStats::default();
        self.generation = 0;
    }
}

impl AdaptiveNASSystem {
    fn compute_step_size(&self, architecture: &OptimizationArchitecture, iteration: usize) -> f64 {
        let mut step_size = 0.01;

        // Adapt step size based on architecture components
        for component in &architecture.optimizer_components {
            match component {
                OptimizerComponent::AdaptiveLR {
                    adaptation_rate,
                    min_lr,
                    max_lr,
                } => {
                    step_size *= 1.0 + adaptation_rate * (iteration as f64).cos();
                    step_size = step_size.max(*min_lr).min(*max_lr);
                }
                OptimizerComponent::TrustRegion { initial_radius, .. } => {
                    step_size = step_size.min(*initial_radius);
                }
                _ => {}
            }
        }

        step_size / (1.0 + iteration as f64 * 0.001)
    }

    fn compute_search_direction<F>(
        &self,
        objective: &F,
        params: &Array1<f64>,
        architecture: &OptimizationArchitecture,
    ) -> Array1<f64>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let mut direction = Array1::zeros(params.len());

        // Compute gradient (finite differences)
        let h = 1e-6;
        let f0 = objective(&params.view());

        for i in 0..params.len() {
            let mut params_plus = params.clone();
            params_plus[i] += h;
            let f_plus = objective(&params_plus.view());
            direction[i] = (f_plus - f0) / h;
        }

        // Apply architecture-specific modifications
        for component in &architecture.optimizer_components {
            match component {
                OptimizerComponent::Momentum { decay } => {
                    // Simple momentum approximation
                    direction *= 1.0 - decay;
                }
                OptimizerComponent::Regularization {
                    l1_weight,
                    l2_weight,
                    ..
                } => {
                    // Add regularization
                    for i in 0..direction.len() {
                        direction[i] += l1_weight * params[i].signum() + l2_weight * params[i];
                    }
                }
                _ => {}
            }
        }

        direction
    }
}

/// Convenience function for NAS-based optimization
#[allow(dead_code)]
pub fn nas_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    config: Option<LearnedOptimizationConfig>,
) -> super::OptimizeResult<OptimizeResults<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let config = config.unwrap_or_default();
    let mut nas_system = AdaptiveNASSystem::new(config);
    nas_system.optimize(objective, initial_params)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_nas_system_creation() {
        let config = LearnedOptimizationConfig::default();
        let nas_system = AdaptiveNASSystem::new(config);

        assert_eq!(nas_system.generation, 0);
        assert!(nas_system.architecture_population.is_empty());
    }

    #[test]
    fn test_architecture_generation() {
        let config = LearnedOptimizationConfig::default();
        let nas_system = AdaptiveNASSystem::new(config);

        let architecture = nas_system.generate_random_architecture().unwrap();

        assert!(!architecture.layers.is_empty());
        assert!(!architecture.activations.is_empty());
        assert!(!architecture.optimizer_components.is_empty());
    }

    #[test]
    fn test_vocabulary_creation() {
        let vocab = ArchitectureVocabulary::new();

        assert!(vocab.layer_types.contains_key("dense"));
        assert!(vocab.activations.contains_key("relu"));
        assert!(vocab.components.contains_key("momentum"));
        assert_eq!(vocab.vocab_size, 19);
    }

    #[test]
    fn test_architecture_mutation() {
        let config = LearnedOptimizationConfig::default();
        let nas_system = AdaptiveNASSystem::new(config);

        let base_arch = nas_system.generate_random_architecture().unwrap();
        let mutated = nas_system.mutate_architecture(&base_arch).unwrap();

        assert_ne!(base_arch.id, mutated.id);
    }

    #[test]
    fn test_nas_optimization() {
        let objective = |x: &ArrayView1<f64>| x[0].powi(2) + x[1].powi(2);
        let initial = Array1::from(vec![2.0, 2.0]);

        let config = LearnedOptimizationConfig {
            meta_training_episodes: 5,
            inner_steps: 10,
            ..Default::default()
        };

        let result = nas_optimize(objective, &initial.view(), Some(config)).unwrap();

        assert!(result.fun >= 0.0);
        assert_eq!(result.x.len(), 2);
        assert!(result.success);
    }
}

#[allow(dead_code)]
pub fn placeholder() {
    // Placeholder function to prevent unused module warnings
}