optirs-learned 0.3.1

// Meta-learning orchestrator for coordination logic

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::{Duration, Instant, SystemTime};

use super::{
    OptimizationContext, LandscapeFeatures, MetaLearningSchedule, PerformanceSnapshot,
};
use crate::error::Result;

/// Meta-learning orchestrator
#[derive(Debug)]
pub struct MetaLearningOrchestrator<T: Float + Debug + Send + Sync + 'static> {
    /// Meta-learning strategies
    strategies: Vec<Box<dyn MetaLearningStrategy<T>>>,

    /// Strategy performance history
    strategy_performance: HashMap<String, VecDeque<T>>,

    /// Current meta-task
    current_meta_task: Option<MetaTask<T>>,

    /// Meta-learning schedule
    schedule: MetaLearningSchedule,

    /// Task distribution analyzer
    task_analyzer: TaskDistributionAnalyzer<T>,

    /// Meta-learning state
    meta_state: MetaLearningState<T>,

    /// Episode counter
    episode_count: usize,

    /// Meta-learning history
    meta_history: VecDeque<MetaLearningEpisode<T>>,
}

impl<T: Float + Debug + std::fmt::Debug + Send + Sync + 'static> MetaLearningOrchestrator<T> {
    /// Create new meta-learning orchestrator
    pub fn new() -> Result<Self> {
        Ok(Self {
            strategies: Vec::new(),
            strategy_performance: HashMap::new(),
            current_meta_task: None,
            schedule: MetaLearningSchedule::default(),
            task_analyzer: TaskDistributionAnalyzer::new(),
            meta_state: MetaLearningState::new(),
            episode_count: 0,
            meta_history: VecDeque::new(),
        })
    }

    /// Initialize meta-learning strategies
    pub fn initialize_strategies(&mut self) -> Result<()> {
        // Register different meta-learning strategies
        self.register_strategy(Box::new(MAMLStrategy::new()?))?;
        self.register_strategy(Box::new(ReptileStrategy::new()?))?;
        self.register_strategy(Box::new(ProtoNetStrategy::new()?))?;
        self.register_strategy(Box::new(MetaSGDStrategy::new()?))?;
        self.register_strategy(Box::new(LEARNStrategy::new()?))?;

        Ok(())
    }

    /// Register a meta-learning strategy
    pub fn register_strategy(&mut self, strategy: Box<dyn MetaLearningStrategy<T>>) -> Result<()> {
        let strategy_id = strategy.get_id();
        self.strategy_performance.insert(strategy_id, VecDeque::new());
        self.strategies.push(strategy);
        Ok(())
    }

    /// Orchestrate meta-learning across optimizers
    pub fn orchestrate_meta_learning(
        &mut self,
        selected_optimizers: &[String],
        landscape_features: &LandscapeFeatures<T>,
        performance_predictions: &HashMap<String, T>,
    ) -> Result<MetaLearningGuidance<T>> {
        let start_time = Instant::now();

        // Phase 1: Task Analysis and Characterization
        let task_characteristics = self.task_analyzer.analyze_task(
            landscape_features,
            &self.meta_state.current_context,
        )?;

        // Phase 2: Meta-Task Generation
        let meta_task = self.generate_meta_task(
            selected_optimizers,
            &task_characteristics,
            landscape_features,
        )?;

        // Phase 3: Strategy Selection and Coordination
        // Clone meta_state to avoid borrowing conflicts
        let meta_state_copy = self.meta_state.clone();
        let selected_strategy = self.select_meta_learning_strategy(&meta_task)?;
        let strategy_guidance = selected_strategy.generate_guidance(
            &meta_task,
            &meta_state_copy,
            performance_predictions,
        )?;

        // Phase 4: Multi-Strategy Ensemble (if enabled)
        let ensemble_guidance = if self.meta_state.enable_strategy_ensemble {
            self.ensemble_strategy_guidance(&meta_task, performance_predictions)?
        } else {
            strategy_guidance
        };

        // Phase 5: Meta-Learning Update
        if self.should_update_meta_learning()? {
            self.perform_meta_update(&meta_task, &ensemble_guidance)?;
        }

        // Phase 6: History and State Update
        self.update_meta_learning_history(&meta_task, &ensemble_guidance, start_time.elapsed())?;

        // Phase 7: Generate Final Guidance
        let final_guidance = MetaLearningGuidance {
            strategy_guidance: ensemble_guidance,
            adaptation_instructions: self.generate_adaptation_instructions(&meta_task)?,
            learning_rate_adjustments: self.calculate_learning_rate_adjustments(
                selected_optimizers,
                &task_characteristics,
            )?,
            exploration_parameters: self.calculate_exploration_parameters(&meta_task)?,
            meta_features: self.extract_meta_features(&task_characteristics)?,
            confidence_scores: self.calculate_confidence_scores(selected_optimizers)?,
            transfer_learning_info: self.generate_transfer_learning_info(&meta_task)?,
            temporal_coordination: self.generate_temporal_coordination(&meta_task)?,
        };

        Ok(final_guidance)
    }

    /// Generate meta-task from current context
    fn generate_meta_task(
        &mut self,
        optimizers: &[String],
        task_characteristics: &TaskCharacteristics<T>,
        landscape_features: &LandscapeFeatures<T>,
    ) -> Result<MetaTask<T>> {
        let meta_task = MetaTask {
            task_id: format!("meta_task_{}", self.episode_count),
            task_type: self.classify_task_type(task_characteristics)?,
            participating_optimizers: optimizers.to_vec(),
            task_characteristics: task_characteristics.clone(),
            landscape_context: landscape_features.clone(),
            difficulty_level: self.assess_task_difficulty(task_characteristics)?,
            expected_duration: self.estimate_task_duration(task_characteristics)?,
            resource_requirements: self.calculate_resource_requirements(optimizers)?,
            success_criteria: self.define_success_criteria(task_characteristics)?,
            meta_objective: self.determine_meta_objective(task_characteristics)?,
            temporal_context: TemporalContext {
                episode_number: self.episode_count,
                time_since_start: self.meta_state.session_start_time.elapsed(),
                recent_performance_trend: self.calculate_recent_trend()?,
            },
        };

        self.current_meta_task = Some(meta_task.clone());
        Ok(meta_task)
    }

    /// Select appropriate meta-learning strategy
    fn select_meta_learning_strategy(&mut self, meta_task: &MetaTask<T>) -> Result<&mut dyn MetaLearningStrategy<T>> {
        // Strategy selection based on task characteristics and performance history
        let mut best_strategy_idx = 0;
        let mut best_score = T::neg_infinity();

        for (idx, strategy) in self.strategies.iter().enumerate() {
            let suitability_score = self.calculate_strategy_suitability(strategy.as_ref(), meta_task)?;
            let performance_score = self.get_strategy_performance_score(strategy.get_id())?;
            let combined_score = suitability_score * scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()) + performance_score * scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero());

            if combined_score > best_score {
                best_score = combined_score;
                best_strategy_idx = idx;
            }
        }

        Ok(self.strategies[best_strategy_idx].as_mut())
    }

    /// Ensemble multiple strategy guidances
    fn ensemble_strategy_guidance(
        &mut self,
        meta_task: &MetaTask<T>,
        performance_predictions: &HashMap<String, T>,
    ) -> Result<StrategyGuidance<T>> {
        let mut strategy_outputs = Vec::new();
        let mut strategy_weights = Vec::new();

        // Collect guidance from multiple strategies
        let mut strategy_ids = Vec::new();
        for strategy in &mut self.strategies {
            let guidance = strategy.generate_guidance(meta_task, &self.meta_state, performance_predictions)?;
            let strategy_id = strategy.get_id();

            strategy_outputs.push(guidance);
            strategy_ids.push(strategy_id);
        }

        // Get weights after releasing mutable borrow
        for strategy_id in strategy_ids {
            let weight = self.get_strategy_performance_score(strategy_id)?;
            strategy_weights.push(weight);
        }

        // Ensemble the guidance
        self.combine_strategy_guidances(&strategy_outputs, &strategy_weights)
    }

    /// Combine multiple strategy guidances
    fn combine_strategy_guidances(
        &self,
        guidances: &[StrategyGuidance<T>],
        weights: &[T],
    ) -> Result<StrategyGuidance<T>> {
        if guidances.is_empty() {
            return Err(crate::error::OptimError::Other("No guidances to combine".to_string()));
        }

        // Normalize weights
        let total_weight: T = weights.iter().fold(T::zero(), |acc, &w| acc + w);
        let normalized_weights: Vec<T> = weights.iter().map(|&w| w / total_weight).collect();

        // Combine guidances using weighted average
        let first_guidance = &guidances[0];
        let mut combined_guidance = StrategyGuidance {
            optimizer_priorities: HashMap::new(),
            learning_rate_scaling: HashMap::new(),
            exploration_factors: HashMap::new(),
            coordination_matrix: first_guidance.coordination_matrix.clone(),
            meta_parameters: HashMap::new(),
            adaptation_signals: Vec::new(),
        };

        // Combine each component
        for (guidance, &weight) in guidances.iter().zip(&normalized_weights) {
            // Combine optimizer priorities
            for (optimizer, &priority) in &guidance.optimizer_priorities {
                let current = combined_guidance.optimizer_priorities.get(optimizer).cloned().unwrap_or(T::zero());
                combined_guidance.optimizer_priorities.insert(optimizer.clone(), current + weight * priority);
            }

            // Combine learning rate scaling
            for (optimizer, &scaling) in &guidance.learning_rate_scaling {
                let current = combined_guidance.learning_rate_scaling.get(optimizer).cloned().unwrap_or(T::one());
                combined_guidance.learning_rate_scaling.insert(optimizer.clone(), current + weight * (scaling - T::one()));
            }

            // Combine exploration factors
            for (optimizer, &factor) in &guidance.exploration_factors {
                let current = combined_guidance.exploration_factors.get(optimizer).cloned().unwrap_or(T::zero());
                combined_guidance.exploration_factors.insert(optimizer.clone(), current + weight * factor);
            }

            // Combine meta parameters
            for (param, &value) in &guidance.meta_parameters {
                let current = combined_guidance.meta_parameters.get(param).cloned().unwrap_or(T::zero());
                combined_guidance.meta_parameters.insert(param.clone(), current + weight * value);
            }
        }

        Ok(combined_guidance)
    }

    /// Perform meta-learning update
    fn perform_meta_update(&mut self, meta_task: &MetaTask<T>, guidance: &StrategyGuidance<T>) -> Result<()> {
        // Update meta-learning parameters based on task performance
        for strategy in &mut self.strategies {
            strategy.update_from_experience(meta_task, guidance, &self.meta_state)?;
        }

        // Update meta-state
        self.meta_state.update_with_task(meta_task, guidance)?;

        // Update task analyzer
        self.task_analyzer.update_with_task_result(meta_task, guidance)?;

        Ok(())
    }

    /// Check if meta-learning should be updated
    fn should_update_meta_learning(&self) -> Result<bool> {
        match &self.schedule {
            MetaLearningSchedule::Fixed { episodes_per_update } => {
                Ok(self.episode_count % episodes_per_update == 0)
            }
            MetaLearningSchedule::Adaptive { performance_threshold, min_episodes, max_episodes } => {
                if self.episode_count < *min_episodes {
                    Ok(false)
                } else if self.episode_count >= *max_episodes {
                    Ok(true)
                } else {
                    let recent_performance = self.calculate_recent_performance()?;
                    Ok(recent_performance < *performance_threshold)
                }
            }
            MetaLearningSchedule::EventDriven { trigger_events: _ } => {
                // Check for trigger events
                Ok(self.meta_state.has_trigger_events())
            }
        }
    }

    /// Update meta-learning history
    fn update_meta_learning_history(
        &mut self,
        meta_task: &MetaTask<T>,
        guidance: &StrategyGuidance<T>,
        duration: Duration,
    ) -> Result<()> {
        let episode = MetaLearningEpisode {
            episode_id: self.episode_count,
            meta_task: meta_task.clone(),
            guidance_used: guidance.clone(),
            performance_achieved: self.calculate_episode_performance(meta_task, guidance)?,
            duration,
            timestamp: SystemTime::now(),
        };

        self.meta_history.push_back(episode);
        if self.meta_history.len() > 1000 {
            self.meta_history.pop_front();
        }

        self.episode_count += 1;
        Ok(())
    }

    /// Generate adaptation instructions
    fn generate_adaptation_instructions(&self, meta_task: &MetaTask<T>) -> Result<AdaptationInstructions<T>> {
        Ok(AdaptationInstructions {
            parameter_adjustments: self.calculate_parameter_adjustments(meta_task)?,
            strategy_switches: self.calculate_strategy_switches(meta_task)?,
            resource_reallocations: self.calculate_resource_reallocations(meta_task)?,
            coordination_updates: self.calculate_coordination_updates(meta_task)?,
        })
    }

    /// Calculate learning rate adjustments
    fn calculate_learning_rate_adjustments(
        &self,
        optimizers: &[String],
        task_characteristics: &TaskCharacteristics<T>,
    ) -> Result<HashMap<String, T>> {
        let mut adjustments = HashMap::new();
        let difficulty_level = self.assess_task_difficulty(task_characteristics)?;

        for optimizer in optimizers {
            let base_adjustment = match difficulty_level {
                DifficultyLevel::Easy => scirs2_core::numeric::NumCast::from(1.2).unwrap_or_else(|| T::zero()),
                DifficultyLevel::Medium => T::one(),
                DifficultyLevel::Hard => scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
                DifficultyLevel::Extreme => scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
            };

            // Additional adjustments based on optimizer-specific characteristics
            let optimizer_specific = self.get_optimizer_specific_adjustment(optimizer)?;
            adjustments.insert(optimizer.clone(), base_adjustment * optimizer_specific);
        }

        Ok(adjustments)
    }

    /// Calculate exploration parameters
    fn calculate_exploration_parameters(&self, meta_task: &MetaTask<T>) -> Result<ExplorationParameters<T>> {
        Ok(ExplorationParameters {
            exploration_rate: match meta_task.difficulty_level {
                DifficultyLevel::Easy => scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
                DifficultyLevel::Medium => scirs2_core::numeric::NumCast::from(0.2).unwrap_or_else(|| T::zero()),
                DifficultyLevel::Hard => scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()),
                DifficultyLevel::Extreme => scirs2_core::numeric::NumCast::from(0.4).unwrap_or_else(|| T::zero()),
            },
            exploration_decay: scirs2_core::numeric::NumCast::from(0.99).unwrap_or_else(|| T::zero()),
            exploration_strategy: ExplorationStrategy::EpsilonGreedy,
            adaptive_exploration: true,
        })
    }

    /// Extract meta-features
    fn extract_meta_features(&self, task_characteristics: &TaskCharacteristics<T>) -> Result<MetaFeatures<T>> {
        Ok(MetaFeatures {
            task_complexity: task_characteristics.complexity_score,
            convergence_history: self.get_convergence_history()?,
            performance_variance: self.calculate_performance_variance()?,
            adaptation_responsiveness: self.calculate_adaptation_responsiveness()?,
            transfer_potential: self.assess_transfer_potential(task_characteristics)?,
        })
    }

    /// Calculate confidence scores
    fn calculate_confidence_scores(&self, optimizers: &[String]) -> Result<HashMap<String, T>> {
        let mut scores = HashMap::new();

        for optimizer in optimizers {
            let historical_performance = self.get_optimizer_historical_performance(optimizer)?;
            let confidence = self.calculate_confidence_from_history(&historical_performance)?;
            scores.insert(optimizer.clone(), confidence);
        }

        Ok(scores)
    }

    /// Generate transfer learning information
    fn generate_transfer_learning_info(&self, meta_task: &MetaTask<T>) -> Result<TransferLearningInfo<T>> {
        Ok(TransferLearningInfo {
            source_tasks: self.find_similar_tasks(meta_task)?,
            transferable_knowledge: self.extract_transferable_knowledge(meta_task)?,
            transfer_strength: self.calculate_transfer_strength(meta_task)?,
            negative_transfer_risk: self.assess_negative_transfer_risk(meta_task)?,
        })
    }

    /// Generate temporal coordination
    fn generate_temporal_coordination(&self, meta_task: &MetaTask<T>) -> Result<TemporalCoordination<T>> {
        Ok(TemporalCoordination {
            synchronization_points: self.identify_synchronization_points(meta_task)?,
            coordination_schedule: self.create_coordination_schedule(meta_task)?,
            temporal_dependencies: self.analyze_temporal_dependencies(meta_task)?,
            coordination_strength: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
        })
    }

    /// Helper methods with simplified implementations
    fn classify_task_type(&self, _characteristics: &TaskCharacteristics<T>) -> Result<TaskType> {
        Ok(TaskType::StandardOptimization)
    }

    fn assess_task_difficulty(&self, characteristics: &TaskCharacteristics<T>) -> Result<DifficultyLevel> {
        if characteristics.complexity_score > scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()) {
            Ok(DifficultyLevel::Extreme)
        } else if characteristics.complexity_score > scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()) {
            Ok(DifficultyLevel::Hard)
        } else if characteristics.complexity_score > scirs2_core::numeric::NumCast::from(0.4).unwrap_or_else(|| T::zero()) {
            Ok(DifficultyLevel::Medium)
        } else {
            Ok(DifficultyLevel::Easy)
        }
    }

    fn estimate_task_duration(&self, _characteristics: &TaskCharacteristics<T>) -> Result<Duration> {
        Ok(Duration::from_secs(60))
    }

    fn calculate_resource_requirements(&self, optimizers: &[String]) -> Result<ResourceRequirements> {
        Ok(ResourceRequirements {
            cpu_cores: optimizers.len(),
            memory_mb: optimizers.len() * 100,
            gpu_memory_mb: 0,
            network_bandwidth: 0,
        })
    }

    fn define_success_criteria(&self, characteristics: &TaskCharacteristics<T>) -> Result<SuccessCriteria<T>> {
        Ok(SuccessCriteria {
            target_performance: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
            convergence_threshold: characteristics.complexity_score / scirs2_core::numeric::NumCast::from(10.0).unwrap_or_else(|| T::zero()),
            max_iterations: 1000,
            stability_requirement: scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()),
        })
    }

    fn determine_meta_objective(&self, _characteristics: &TaskCharacteristics<T>) -> Result<MetaObjective> {
        Ok(MetaObjective::PerformanceMaximization)
    }

    fn calculate_recent_trend(&self) -> Result<T> {
        if self.meta_history.len() < 2 {
            return Ok(T::zero());
        }

        let recent_performances: Vec<_> = self.meta_history
            .iter()
            .rev()
            .take(5)
            .map(|episode| episode.performance_achieved)
            .collect();

        let first = recent_performances.last().expect("unwrap failed");
        let last = recent_performances.first().expect("unwrap failed");

        Ok(*last - *first)
    }

    fn calculate_strategy_suitability(&self, _strategy: &dyn MetaLearningStrategy<T>, _task: &MetaTask<T>) -> Result<T> {
        Ok(scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()))
    }

    fn get_strategy_performance_score(&self, strategy_id: String) -> Result<T> {
        Ok(self.strategy_performance.get(&strategy_id)
            .and_then(|history| history.back())
            .cloned()
            .unwrap_or_else(|| scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero())))
    }

    fn calculate_recent_performance(&self) -> Result<f64> {
        if self.meta_history.is_empty() {
            return Ok(0.0);
        }

        let recent_avg = self.meta_history
            .iter()
            .rev()
            .take(10)
            .map(|episode| episode.performance_achieved.to_f64().unwrap_or(0.0))
            .sum::<f64>() / 10.0;

        Ok(recent_avg)
    }

    fn calculate_episode_performance(&self, _task: &MetaTask<T>, _guidance: &StrategyGuidance<T>) -> Result<T> {
        Ok(scirs2_core::numeric::NumCast::from(0.75).unwrap_or_else(|| T::zero())) // Placeholder
    }

    fn calculate_parameter_adjustments(&self, _task: &MetaTask<T>) -> Result<HashMap<String, T>> {
        Ok(HashMap::new())
    }

    fn calculate_strategy_switches(&self, _task: &MetaTask<T>) -> Result<Vec<StrategySwitch>> {
        Ok(Vec::new())
    }

    fn calculate_resource_reallocations(&self, _task: &MetaTask<T>) -> Result<Vec<ResourceReallocation>> {
        Ok(Vec::new())
    }

    fn calculate_coordination_updates(&self, _task: &MetaTask<T>) -> Result<CoordinationUpdates> {
        Ok(CoordinationUpdates::default())
    }

    fn get_optimizer_specific_adjustment(&self, _optimizer: &str) -> Result<T> {
        Ok(T::one())
    }

    fn get_convergence_history(&self) -> Result<Vec<T>> {
        Ok(self.meta_history.iter()
            .map(|episode| episode.performance_achieved)
            .collect())
    }

    fn calculate_performance_variance(&self) -> Result<T> {
        let performances: Vec<_> = self.meta_history.iter()
            .map(|episode| episode.performance_achieved)
            .collect();

        if performances.len() < 2 {
            return Ok(T::zero());
        }

        let mean = performances.iter().fold(T::zero(), |acc, &x| acc + x) / T::from(performances.len()).expect("unwrap failed");
        let variance = performances.iter()
            .map(|&x| (x - mean) * (x - mean))
            .fold(T::zero(), |acc, x| acc + x) / T::from(performances.len()).expect("unwrap failed");

        Ok(variance)
    }

    fn calculate_adaptation_responsiveness(&self) -> Result<T> {
        Ok(scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero())) // Placeholder
    }

    fn assess_transfer_potential(&self, _characteristics: &TaskCharacteristics<T>) -> Result<T> {
        Ok(scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero())) // Placeholder
    }

    fn get_optimizer_historical_performance(&self, _optimizer: &str) -> Result<Vec<T>> {
        Ok(vec![scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()); 10]) // Placeholder
    }

    fn calculate_confidence_from_history(&self, history: &[T]) -> Result<T> {
        if history.is_empty() {
            return Ok(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
        }

        let mean = history.iter().fold(T::zero(), |acc, &x| acc + x) / T::from(history.len()).expect("unwrap failed");
        let variance = history.iter()
            .map(|&x| (x - mean) * (x - mean))
            .fold(T::zero(), |acc, x| acc + x) / T::from(history.len()).expect("unwrap failed");

        // High confidence with high mean and low variance
        Ok(mean * (T::one() / (T::one() + variance)))
    }

    fn find_similar_tasks(&self, _task: &MetaTask<T>) -> Result<Vec<String>> {
        Ok(vec!["similar_task_1".to_string(), "similar_task_2".to_string()])
    }

    fn extract_transferable_knowledge(&self, _task: &MetaTask<T>) -> Result<HashMap<String, T>> {
        Ok(HashMap::new())
    }

    fn calculate_transfer_strength(&self, _task: &MetaTask<T>) -> Result<T> {
        Ok(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()))
    }

    fn assess_negative_transfer_risk(&self, _task: &MetaTask<T>) -> Result<T> {
        Ok(scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()))
    }

    fn identify_synchronization_points(&self, _task: &MetaTask<T>) -> Result<Vec<SynchronizationPoint>> {
        Ok(Vec::new())
    }

    fn create_coordination_schedule(&self, _task: &MetaTask<T>) -> Result<CoordinationSchedule> {
        Ok(CoordinationSchedule::default())
    }

    fn analyze_temporal_dependencies(&self, _task: &MetaTask<T>) -> Result<TemporalDependencies> {
        Ok(TemporalDependencies::default())
    }

    /// Reset orchestrator state
    pub fn reset(&mut self) -> Result<()> {
        self.strategies.clear();
        self.strategy_performance.clear();
        self.current_meta_task = None;
        self.meta_state = MetaLearningState::new();
        self.episode_count = 0;
        self.meta_history.clear();
        Ok(())
    }
}

/// Supporting types and data structures

/// Meta-learning strategy trait
pub trait MetaLearningStrategy<T: Float + Debug + Send + Sync + 'static>: Send + Sync + std::fmt::Debug {
    fn get_id(&self) -> String;
    fn generate_guidance(
        &mut self,
        meta_task: &MetaTask<T>,
        meta_state: &MetaLearningState<T>,
        performance_predictions: &HashMap<String, T>,
    ) -> Result<StrategyGuidance<T>>;
    fn update_from_experience(
        &mut self,
        meta_task: &MetaTask<T>,
        guidance: &StrategyGuidance<T>,
        meta_state: &MetaLearningState<T>,
    ) -> Result<()>;
}

/// Meta-task definition
#[derive(Debug, Clone)]
pub struct MetaTask<T: Float + Debug + Send + Sync + 'static> {
    pub task_id: String,
    pub task_type: TaskType,
    pub participating_optimizers: Vec<String>,
    pub task_characteristics: TaskCharacteristics<T>,
    pub landscape_context: LandscapeFeatures<T>,
    pub difficulty_level: DifficultyLevel,
    pub expected_duration: Duration,
    pub resource_requirements: ResourceRequirements,
    pub success_criteria: SuccessCriteria<T>,
    pub meta_objective: MetaObjective,
    pub temporal_context: TemporalContext<T>,
}

/// Task characteristics
#[derive(Debug, Clone)]
pub struct TaskCharacteristics<T: Float + Debug + Send + Sync + 'static> {
    pub complexity_score: T,
    pub dimensionality: usize,
    pub noise_level: T,
    pub multimodality: T,
    pub conditioning: T,
    pub separability: T,
    pub smoothness: T,
}

/// Task distribution analyzer
#[derive(Debug)]
pub struct TaskDistributionAnalyzer<T: Float + Debug + Send + Sync + 'static> {
    pub task_history: VecDeque<TaskCharacteristics<T>>,
    pub distribution_model: DistributionModel<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> TaskDistributionAnalyzer<T> {
    pub fn new() -> Self {
        Self {
            task_history: VecDeque::new(),
            distribution_model: DistributionModel::new(),
        }
    }

    pub fn analyze_task(
        &mut self,
        landscape_features: &LandscapeFeatures<T>,
        _context: &OptimizationContext<T>,
    ) -> Result<TaskCharacteristics<T>> {
        // Extract task characteristics from landscape features
        let characteristics = TaskCharacteristics {
            complexity_score: scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()), // Would be computed from landscape features
            dimensionality: 100,
            noise_level: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
            multimodality: scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()),
            conditioning: scirs2_core::numeric::NumCast::from(0.4).unwrap_or_else(|| T::zero()),
            separability: scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()),
            smoothness: scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
        };

        self.task_history.push_back(characteristics.clone());
        if self.task_history.len() > 1000 {
            self.task_history.pop_front();
        }

        Ok(characteristics)
    }

    pub fn update_with_task_result(&mut self, _task: &MetaTask<T>, _guidance: &StrategyGuidance<T>) -> Result<()> {
        // Update distribution model with task results
        Ok(())
    }
}

/// Distribution model for task analysis
#[derive(Debug)]
pub struct DistributionModel<T: Float + Debug + Send + Sync + 'static> {
    _phantom: std::marker::PhantomData<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> DistributionModel<T> {
    pub fn new() -> Self {
        Self {
            _phantom: std::marker::PhantomData,
        }
    }
}

/// Meta-learning state
#[derive(Debug, Clone)]
pub struct MetaLearningState<T: Float + Debug + Send + Sync + 'static> {
    pub current_context: OptimizationContext<T>,
    pub enable_strategy_ensemble: bool,
    pub session_start_time: Instant,
    pub adaptation_history: VecDeque<AdaptationEvent<T>>,
    pub performance_tracking: PerformanceTracking<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> MetaLearningState<T> {
    pub fn new() -> Self {
        Self {
            current_context: OptimizationContext::default(),
            enable_strategy_ensemble: true,
            session_start_time: Instant::now(),
            adaptation_history: VecDeque::new(),
            performance_tracking: PerformanceTracking::new(),
        }
    }

    pub fn update_with_task(&mut self, _task: &MetaTask<T>, _guidance: &StrategyGuidance<T>) -> Result<()> {
        Ok(())
    }

    pub fn has_trigger_events(&self) -> bool {
        false // Placeholder
    }
}

/// Strategy guidance
#[derive(Debug, Clone)]
pub struct StrategyGuidance<T: Float + Debug + Send + Sync + 'static> {
    pub optimizer_priorities: HashMap<String, T>,
    pub learning_rate_scaling: HashMap<String, T>,
    pub exploration_factors: HashMap<String, T>,
    pub coordination_matrix: Array2<T>,
    pub meta_parameters: HashMap<String, T>,
    pub adaptation_signals: Vec<AdaptationSignal<T>>,
}

/// Meta-learning guidance
#[derive(Debug, Clone)]
pub struct MetaLearningGuidance<T: Float + Debug + Send + Sync + 'static> {
    pub strategy_guidance: StrategyGuidance<T>,
    pub adaptation_instructions: AdaptationInstructions<T>,
    pub learning_rate_adjustments: HashMap<String, T>,
    pub exploration_parameters: ExplorationParameters<T>,
    pub meta_features: MetaFeatures<T>,
    pub confidence_scores: HashMap<String, T>,
    pub transfer_learning_info: TransferLearningInfo<T>,
    pub temporal_coordination: TemporalCoordination<T>,
}

/// Additional supporting types with default implementations
#[derive(Debug, Clone)]
pub struct MetaLearningEpisode<T: Float + Debug + Send + Sync + 'static> {
    pub episode_id: usize,
    pub meta_task: MetaTask<T>,
    pub guidance_used: StrategyGuidance<T>,
    pub performance_achieved: T,
    pub duration: Duration,
    pub timestamp: SystemTime,
}

#[derive(Debug, Clone)]
pub struct TemporalContext<T: Float + Debug + Send + Sync + 'static> {
    pub episode_number: usize,
    pub time_since_start: Duration,
    pub recent_performance_trend: T,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TaskType {
    StandardOptimization,
    MultiObjective,
    ConstrainedOptimization,
    OnlineOptimization,
    MetaOptimization,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum DifficultyLevel {
    Easy,
    Medium,
    Hard,
    Extreme,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MetaObjective {
    PerformanceMaximization,
    EfficiencyOptimization,
    RobustnessImprovement,
    GeneralizationEnhancement,
}

#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    pub cpu_cores: usize,
    pub memory_mb: usize,
    pub gpu_memory_mb: usize,
    pub network_bandwidth: usize,
}

#[derive(Debug, Clone)]
pub struct SuccessCriteria<T: Float + Debug + Send + Sync + 'static> {
    pub target_performance: T,
    pub convergence_threshold: T,
    pub max_iterations: usize,
    pub stability_requirement: T,
}

// Placeholder implementations for strategy types
macro_rules! impl_strategy {
    ($name:ident, $id:expr) => {
        #[derive(Debug)]
        pub struct $name<T: Float + Debug + Send + Sync + 'static> {
            _phantom: std::marker::PhantomData<T>,
        }

        impl<T: Float + Debug + std::fmt::Debug + Send + Sync + Send + Sync> $name<T> {
            pub fn new() -> Result<Self> {
                Ok(Self {
                    _phantom: std::marker::PhantomData,
                })
            }
        }

        impl<T: Float + Debug + std::fmt::Debug + Send + Sync + Send + Sync> MetaLearningStrategy<T> for $name<T> {
            fn get_id(&self) -> String {
                $id.to_string()
            }

            fn generate_guidance(
                &mut self,
                meta_task: &MetaTask<T>,
                _meta_state: &MetaLearningState<T>,
                _performance_predictions: &HashMap<String, T>,
            ) -> Result<StrategyGuidance<T>> {
                Ok(StrategyGuidance {
                    optimizer_priorities: meta_task.participating_optimizers.iter()
                        .map(|id| (id.clone(), scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero())))
                        .collect(),
                    learning_rate_scaling: HashMap::new(),
                    exploration_factors: HashMap::new(),
                    coordination_matrix: Array2::eye(meta_task.participating_optimizers.len()),
                    meta_parameters: HashMap::new(),
                    adaptation_signals: Vec::new(),
                })
            }

            fn update_from_experience(
                &mut self,
                _meta_task: &MetaTask<T>,
                _guidance: &StrategyGuidance<T>,
                _meta_state: &MetaLearningState<T>,
            ) -> Result<()> {
                Ok(())
            }
        }
    };
}

impl_strategy!(MAMLStrategy, "maml");
impl_strategy!(ReptileStrategy, "reptile");
impl_strategy!(ProtoNetStrategy, "protonet");
impl_strategy!(MetaSGDStrategy, "meta_sgd");
impl_strategy!(LEARNStrategy, "learn");

// Additional supporting types
#[derive(Debug, Clone)]
pub struct AdaptationInstructions<T: Float + Debug + Send + Sync + 'static> {
    pub parameter_adjustments: HashMap<String, T>,
    pub strategy_switches: Vec<StrategySwitch>,
    pub resource_reallocations: Vec<ResourceReallocation>,
    pub coordination_updates: CoordinationUpdates,
}

#[derive(Debug, Clone)]
pub struct ExplorationParameters<T: Float + Debug + Send + Sync + 'static> {
    pub exploration_rate: T,
    pub exploration_decay: T,
    pub exploration_strategy: ExplorationStrategy,
    pub adaptive_exploration: bool,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ExplorationStrategy {
    EpsilonGreedy,
    Softmax,
    UCB,
    ThompsonSampling,
}

#[derive(Debug, Clone)]
pub struct MetaFeatures<T: Float + Debug + Send + Sync + 'static> {
    pub task_complexity: T,
    pub convergence_history: Vec<T>,
    pub performance_variance: T,
    pub adaptation_responsiveness: T,
    pub transfer_potential: T,
}

#[derive(Debug, Clone)]
pub struct TransferLearningInfo<T: Float + Debug + Send + Sync + 'static> {
    pub source_tasks: Vec<String>,
    pub transferable_knowledge: HashMap<String, T>,
    pub transfer_strength: T,
    pub negative_transfer_risk: T,
}

#[derive(Debug, Clone)]
pub struct TemporalCoordination<T: Float + Debug + Send + Sync + 'static> {
    pub synchronization_points: Vec<SynchronizationPoint>,
    pub coordination_schedule: CoordinationSchedule,
    pub temporal_dependencies: TemporalDependencies,
    pub coordination_strength: T,
}

// Default implementations for remaining types
#[derive(Debug, Clone, Default)]
pub struct StrategySwitch {
    pub from_strategy: String,
    pub to_strategy: String,
    pub trigger_condition: String,
}

#[derive(Debug, Clone, Default)]
pub struct ResourceReallocation {
    pub resource_type: String,
    pub from_optimizer: String,
    pub to_optimizer: String,
    pub amount: f64,
}

#[derive(Debug, Clone, Default)]
pub struct CoordinationUpdates {
    pub coordination_matrix_updates: Vec<(usize, usize, f64)>,
    pub synchronization_changes: Vec<String>,
}

#[derive(Debug, Clone, Default)]
pub struct SynchronizationPoint {
    pub name: String,
    pub time_offset: Duration,
    pub participants: Vec<String>,
}

#[derive(Debug, Clone, Default)]
pub struct CoordinationSchedule {
    pub intervals: Vec<Duration>,
    pub coordination_types: Vec<String>,
}

#[derive(Debug, Clone, Default)]
pub struct TemporalDependencies {
    pub dependencies: Vec<(String, String)>,
    pub dependency_strength: Vec<f64>,
}

#[derive(Debug, Clone)]
pub struct AdaptationEvent<T: Float + Debug + Send + Sync + 'static> {
    pub event_type: String,
    pub timestamp: Instant,
    pub parameters_changed: HashMap<String, T>,
}

#[derive(Debug, Clone)]
pub struct AdaptationSignal<T: Float + Debug + Send + Sync + 'static> {
    pub signal_type: String,
    pub strength: T,
    pub target_optimizers: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct PerformanceTracking<T: Float + Debug + Send + Sync + 'static> {
    pub episode_performances: VecDeque<T>,
    pub strategy_performances: HashMap<String, VecDeque<T>>,
}

impl<T: Float + Debug + Send + Sync + 'static> PerformanceTracking<T> {
    pub fn new() -> Self {
        Self {
            episode_performances: VecDeque::new(),
            strategy_performances: HashMap::new(),
        }
    }
}

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

    #[test]
    fn test_orchestrator_creation() {
        let orchestrator = MetaLearningOrchestrator::<f32>::new();
        assert!(orchestrator.is_ok());
    }

    #[test]
    fn test_strategy_initialization() {
        let mut orchestrator = MetaLearningOrchestrator::<f32>::new().expect("unwrap failed");
        assert!(orchestrator.initialize_strategies().is_ok());
        assert!(orchestrator.strategies.len() > 0);
    }

    #[test]
    fn test_task_characteristics() {
        let characteristics = TaskCharacteristics {
            complexity_score: 0.5,
            dimensionality: 100,
            noise_level: 0.1,
            multimodality: 0.3,
            conditioning: 0.4,
            separability: 0.6,
            smoothness: 0.7,
        };

        assert_eq!(characteristics.dimensionality, 100);
        assert!(characteristics.complexity_score > 0.0);
    }
}