optirs-learned 0.3.1

// Optimizer ensemble management

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

use super::{
    AdvancedOptimizer, EnsembleStrategy, OptimizerSelectionAlgorithm, OptimizationContext,
    LandscapeFeatures, ResourceAllocation, MetaLearningGuidance, ResourceUtilization,
    OptimizerCapabilities,
};
use super::config::AdaptationType;
use super::resource_management::OptimizerResources;
use super::{ScalabilityInfo, ResourceRequirements, ComputationalComplexity, ConvergenceGuarantees};
use super::config::{ComplexityClass, ParallelScalability};
use crate::error::Result;

/// Optimizer ensemble manager
#[derive(Debug)]
pub struct OptimizerEnsemble<T: Float + Debug + Send + Sync + 'static> {
    /// Active optimizers
    optimizers: HashMap<String, Box<dyn AdvancedOptimizer<T>>>,

    /// Optimizer performance scores
    performance_scores: HashMap<String, T>,

    /// Ensemble weights
    ensemble_weights: HashMap<String, T>,

    /// Ensemble strategy
    ensemble_strategy: EnsembleStrategy,

    /// Selection algorithm
    selection_algorithm: OptimizerSelectionAlgorithm,

    /// Performance history for each optimizer
    performance_history: HashMap<String, VecDeque<T>>,

    /// Selection statistics
    selection_stats: SelectionStatistics<T>,

    /// Ensemble learning rate
    ensemble_learning_rate: T,

    /// Weight update mechanism
    weight_updater: WeightUpdateMechanism<T>,
}

impl<T: Float + Debug + std::fmt::Debug + Send + Sync + std::default::Default + 'static> OptimizerEnsemble<T> {
    /// Create new optimizer ensemble
    pub fn new() -> Result<Self> {
        Ok(Self {
            optimizers: HashMap::new(),
            performance_scores: HashMap::new(),
            ensemble_weights: HashMap::new(),
            ensemble_strategy: EnsembleStrategy::WeightedVoting,
            selection_algorithm: OptimizerSelectionAlgorithm::MultiArmedBandit,
            performance_history: HashMap::new(),
            selection_stats: SelectionStatistics::new(),
            ensemble_learning_rate: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            weight_updater: WeightUpdateMechanism::new(),
        })
    }

    /// Register a new optimizer in the ensemble
    pub fn register_optimizer(&mut self, id: String, optimizer: Box<dyn AdvancedOptimizer<T>>) -> Result<()> {
        self.optimizers.insert(id.clone(), optimizer);
        self.performance_scores.insert(id.clone(), T::zero());
        self.ensemble_weights.insert(id.clone(), T::from(1.0 / self.optimizers.len() as f64).expect("unwrap failed"));
        self.performance_history.insert(id, VecDeque::new());
        Ok(())
    }

    /// Register default optimizers
    pub fn register_default_optimizers(&mut self) -> Result<()> {
        // Register various learned optimizers
        self.register_optimizer("adam".to_string(), Box::new(AdamOptimizer::new()?))?;
        self.register_optimizer("rmsprop".to_string(), Box::new(RMSpropOptimizer::new()?))?;
        self.register_optimizer("sgd_momentum".to_string(), Box::new(SGDMomentumOptimizer::new()?))?;
        self.register_optimizer("adagrad".to_string(), Box::new(AdagradOptimizer::new()?))?;
        self.register_optimizer("learned_lstm".to_string(), Box::new(LearnedLSTMOptimizer::new()?))?;
        self.register_optimizer("adaptive_gradient".to_string(), Box::new(AdaptiveGradientOptimizer::new()?))?;

        Ok(())
    }

    /// Select optimizers based on current context
    pub fn select_optimizers(
        &mut self,
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<String>> {
        match self.selection_algorithm {
            OptimizerSelectionAlgorithm::MultiArmedBandit => {
                self.select_with_multi_armed_bandit(landscape_features, context)
            }
            OptimizerSelectionAlgorithm::UpperConfidenceBound => {
                self.select_with_ucb(landscape_features, context)
            }
            OptimizerSelectionAlgorithm::ThompsonSampling => {
                self.select_with_thompson_sampling(landscape_features, context)
            }
            OptimizerSelectionAlgorithm::EpsilonGreedy => {
                self.select_with_epsilon_greedy(landscape_features, context)
            }
            OptimizerSelectionAlgorithm::Portfolio => {
                self.select_with_portfolio(landscape_features, context)
            }
            OptimizerSelectionAlgorithm::ContextualBandits => {
                self.select_with_contextual_bandits(landscape_features, context)
            }
        }
    }

    /// Execute parallel optimization with selected optimizers
    pub fn execute_parallel_optimization(
        &mut self,
        parameters: &Array1<T>,
        gradients: &Array1<T>,
        selected_optimizers: &[String],
        resource_allocation: &ResourceAllocation<T>,
        meta_learning_guidance: &MetaLearningGuidance<T>,
        context: &OptimizationContext<T>,
    ) -> Result<HashMap<String, Array1<T>>> {
        let mut results = HashMap::new();
        let start_time = Instant::now();

        // Prepare shared context for parallel execution
        let shared_context = Arc::new(context.clone());
        let shared_guidance = Arc::new(meta_learning_guidance.clone());

        // Execute optimizers in parallel
        let handles: Vec<_> = selected_optimizers
            .iter()
            .map(|optimizer_id| {
                let optimizer_id = optimizer_id.clone();
                let parameters = parameters.clone();
                let gradients = gradients.clone();
                let context = Arc::clone(&shared_context);
                let guidance = Arc::clone(&shared_guidance);
                let resources = resource_allocation.get_allocation(&optimizer_id).unwrap_or_default();

                thread::spawn(move || {
                    // This would be the actual parallel execution
                    // For now, we'll simulate it
                    let result = Self::execute_single_optimizer(
                        &optimizer_id,
                        &parameters,
                        &gradients,
                        &*context,
                        &*guidance,
                        &resources,
                    );
                    (optimizer_id, result)
                })
            })
            .collect();

        // Collect results
        for handle in handles {
            let (optimizer_id, result) = handle.join().map_err(|_| {
                crate::error::OptimError::Other("Thread join failed".to_string())
            })?;

            match result {
                Ok(optimization_result) => {
                    results.insert(optimizer_id.clone(), optimization_result);
                    // Update performance tracking
                    self.update_optimizer_performance(&optimizer_id, start_time.elapsed())?;
                }
                Err(e) => {
                    // Log error and continue with other optimizers
                    eprintln!("Optimizer {} failed: {:?}", optimizer_id, e);
                }
            }
        }

        // Update selection statistics
        self.selection_stats.update_with_results(&results, selected_optimizers);

        Ok(results)
    }

    /// Ensemble optimization results
    pub fn ensemble_results(
        &mut self,
        optimization_results: &HashMap<String, Array1<T>>,
        performance_predictions: &HashMap<String, T>,
        context: &OptimizationContext<T>,
    ) -> Result<Array1<T>> {
        match self.ensemble_strategy {
            EnsembleStrategy::WeightedVoting => {
                self.weighted_voting_ensemble(optimization_results, performance_predictions)
            }
            EnsembleStrategy::Stacking => {
                self.stacking_ensemble(optimization_results, context)
            }
            EnsembleStrategy::Boosting => {
                self.boosting_ensemble(optimization_results, context)
            }
            EnsembleStrategy::Bagging => {
                self.bagging_ensemble(optimization_results)
            }
            EnsembleStrategy::DynamicSelection => {
                self.dynamic_selection_ensemble(optimization_results, context)
            }
            EnsembleStrategy::MixtureOfExperts => {
                self.mixture_of_experts_ensemble(optimization_results, context)
            }
        }
    }

    /// Weighted voting ensemble
    fn weighted_voting_ensemble(
        &self,
        results: &HashMap<String, Array1<T>>,
        performance_predictions: &HashMap<String, T>,
    ) -> Result<Array1<T>> {
        if results.is_empty() {
            return Err(crate::error::OptimError::Other("No results to ensemble".to_string()));
        }

        // Get the dimensionality from any result
        let first_result = results.values().next().expect("unwrap failed");
        let mut ensemble_result = Array1::zeros(first_result.len());
        let mut total_weight = T::zero();

        // Combine results using weights based on performance predictions
        for (optimizer_id, result) in results {
            let weight = performance_predictions.get(optimizer_id)
                .cloned()
                .unwrap_or_else(|| self.ensemble_weights.get(optimizer_id).cloned().unwrap_or(T::one()));

            for i in 0..ensemble_result.len() {
                ensemble_result[i] = ensemble_result[i] + weight * result[i];
            }
            total_weight = total_weight + weight;
        }

        // Normalize by total weight
        if total_weight > T::zero() {
            for i in 0..ensemble_result.len() {
                ensemble_result[i] = ensemble_result[i] / total_weight;
            }
        }

        Ok(ensemble_result)
    }

    /// Stacking ensemble with meta-learner
    fn stacking_ensemble(
        &self,
        results: &HashMap<String, Array1<T>>,
        _context: &OptimizationContext<T>,
    ) -> Result<Array1<T>> {
        // Simplified stacking - in practice, this would use a trained meta-learner
        self.weighted_voting_ensemble(results, &HashMap::new())
    }

    /// Boosting ensemble
    fn boosting_ensemble(
        &self,
        results: &HashMap<String, Array1<T>>,
        _context: &OptimizationContext<T>,
    ) -> Result<Array1<T>> {
        // Simplified boosting implementation
        if results.is_empty() {
            return Err(crate::error::OptimError::Other("No results to ensemble".to_string()));
        }

        // Weight optimizers based on their recent performance
        let mut weighted_predictions = HashMap::new();
        for (optimizer_id, _) in results {
            let recent_performance = self.get_recent_performance(optimizer_id);
            weighted_predictions.insert(optimizer_id.clone(), recent_performance);
        }

        self.weighted_voting_ensemble(results, &weighted_predictions)
    }

    /// Bagging ensemble
    fn bagging_ensemble(&self, results: &HashMap<String, Array1<T>>) -> Result<Array1<T>> {
        // Simple average for bagging
        if results.is_empty() {
            return Err(crate::error::OptimError::Other("No results to ensemble".to_string()));
        }

        let first_result = results.values().next().expect("unwrap failed");
        let mut ensemble_result = Array1::zeros(first_result.len());
        let count = T::from(results.len()).expect("unwrap failed");

        for result in results.values() {
            for i in 0..ensemble_result.len() {
                ensemble_result[i] = ensemble_result[i] + result[i] / count;
            }
        }

        Ok(ensemble_result)
    }

    /// Dynamic selection ensemble
    fn dynamic_selection_ensemble(
        &self,
        results: &HashMap<String, Array1<T>>,
        context: &OptimizationContext<T>,
    ) -> Result<Array1<T>> {
        // Select best optimizer based on current context
        let best_optimizer = self.select_best_optimizer_for_context(context)?;

        if let Some(result) = results.get(&best_optimizer) {
            Ok(result.clone())
        } else {
            // Fallback to weighted voting
            self.weighted_voting_ensemble(results, &HashMap::new())
        }
    }

    /// Mixture of experts ensemble
    fn mixture_of_experts_ensemble(
        &self,
        results: &HashMap<String, Array1<T>>,
        context: &OptimizationContext<T>,
    ) -> Result<Array1<T>> {
        // Calculate gating weights based on context
        let gating_weights = self.calculate_gating_weights(context)?;
        self.weighted_voting_ensemble(results, &gating_weights)
    }

    /// Multi-armed bandit selection
    fn select_with_multi_armed_bandit(
        &mut self,
        _landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<String>> {
        let num_select = (context.computational_budget.available_cores).min(self.optimizers.len());
        let mut selected: Vec<String> = Vec::new();

        // Simple epsilon-greedy for MAB
        let epsilon = scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero());

        for _ in 0..num_select {
            if scirs2_core::random::random::<f64>() < epsilon.to_f64().expect("unwrap failed") {
                // Exploration: random selection
                let available: Vec<String> = self.optimizers.keys()
                    .filter(|k| !selected.contains(k))
                    .cloned()
                    .collect();
                if !available.is_empty() {
                    use scirs2_core::random::Rng;
                    let mut rng = scirs2_core::random::thread_rng();
                    let idx = rng.gen_range(0..available.len());
                    selected.push(available[idx].clone());
                }
            } else {
                // Exploitation: select best performing
                let best = self.performance_scores.iter()
                    .filter(|(k, _)| !selected.contains(k))
                    .max_by(|(_, a), (_, b)| a.partial_cmp(b).expect("unwrap failed"))
                    .map(|(k, _)| k.clone());

                if let Some(best_optimizer) = best {
                    selected.push(best_optimizer);
                }
            }
        }

        Ok(selected)
    }

    /// Upper Confidence Bound selection
    fn select_with_ucb(
        &mut self,
        _landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<String>> {
        let num_select = (context.computational_budget.available_cores).min(self.optimizers.len());
        let mut selected = Vec::new();
        let total_selections = self.selection_stats.total_selections as f64;

        for _ in 0..num_select {
            let mut best_optimizer = None;
            let mut best_ucb_value = T::neg_infinity();

            for (optimizer_id, &avg_reward) in &self.performance_scores {
                if selected.contains(optimizer_id) {
                    continue;
                }

                let selections = self.selection_stats.optimizer_selections.get(optimizer_id).cloned().unwrap_or(1) as f64;
                let confidence = T::from(2.0 * (total_selections.ln() / selections).sqrt()).expect("unwrap failed");
                let ucb_value = avg_reward + confidence;

                if ucb_value > best_ucb_value {
                    best_ucb_value = ucb_value;
                    best_optimizer = Some(optimizer_id.clone());
                }
            }

            if let Some(optimizer) = best_optimizer {
                selected.push(optimizer);
            }
        }

        Ok(selected)
    }

    /// Thompson sampling selection
    fn select_with_thompson_sampling(
        &mut self,
        _landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<String>> {
        let num_select = (context.computational_budget.available_cores).min(self.optimizers.len());
        let mut selected = Vec::new();

        // Simple Thompson sampling using Beta distributions
        for _ in 0..num_select {
            let mut best_optimizer = None;
            let mut best_sample = T::neg_infinity();

            for (optimizer_id, _) in &self.optimizers {
                if selected.contains(optimizer_id) {
                    continue;
                }

                // Sample from posterior (simplified)
                let alpha = self.selection_stats.successes.get(optimizer_id).cloned().unwrap_or(1) as f64;
                let beta = self.selection_stats.failures.get(optimizer_id).cloned().unwrap_or(1) as f64;

                // Simplified sampling (in practice, would use proper Beta distribution)
                let sample = T::from(alpha / (alpha + beta) + scirs2_core::random::random::<f64>() * 0.1).expect("unwrap failed");

                if sample > best_sample {
                    best_sample = sample;
                    best_optimizer = Some(optimizer_id.clone());
                }
            }

            if let Some(optimizer) = best_optimizer {
                selected.push(optimizer);
            }
        }

        Ok(selected)
    }

    /// Epsilon-greedy selection
    fn select_with_epsilon_greedy(
        &mut self,
        _landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<String>> {
        // This is similar to multi-armed bandit but with fixed epsilon
        self.select_with_multi_armed_bandit(_landscape_features, context)
    }

    /// Portfolio selection
    fn select_with_portfolio(
        &mut self,
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<String>> {
        // Select diverse optimizers based on their capabilities and landscape features
        let num_select = (context.computational_budget.available_cores).min(self.optimizers.len());
        let mut selected = Vec::new();

        // Prioritize diversity in optimizer selection
        let optimizer_capabilities: HashMap<String, f64> = self.optimizers.iter()
            .map(|(id, optimizer)| {
                let capabilities = optimizer.get_capabilities();
                let suitability = self.calculate_suitability_score(&capabilities, landscape_features, context);
                (id.clone(), suitability)
            })
            .collect();

        // Sort by suitability and select top performers
        let mut sorted_optimizers: Vec<_> = optimizer_capabilities.iter().collect();
        sorted_optimizers.sort_by(|a, b| b.1.partial_cmp(a.1).expect("unwrap failed"));

        for (optimizer_id, _) in sorted_optimizers.iter().take(num_select) {
            selected.push((*optimizer_id).clone());
        }

        Ok(selected)
    }

    /// Contextual bandits selection
    fn select_with_contextual_bandits(
        &mut self,
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<String>> {
        // Use landscape features as context for selection
        let context_vector = self.extract_context_features(landscape_features, context);

        // Select optimizers based on context-aware predictions
        let num_select = (context.computational_budget.available_cores).min(self.optimizers.len());
        let mut selected = Vec::new();

        for optimizer_id in self.optimizers.keys() {
            if selected.len() >= num_select {
                break;
            }

            let predicted_reward = self.predict_contextual_reward(optimizer_id, &context_vector);
            if predicted_reward > scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()) { // Threshold
                selected.push(optimizer_id.clone());
            }
        }

        // Fill remaining slots with best performers if needed
        while selected.len() < num_select {
            let remaining: Vec<_> = self.optimizers.keys()
                .filter(|k| !selected.contains(k))
                .collect();

            if remaining.is_empty() {
                break;
            }

            let mut best_optimizer: Option<String> = None;
            let mut best_score = T::zero();

            for &optimizer_id in &remaining {
                if let Some(score) = self.performance_scores.get(optimizer_id) {
                    if *score > best_score {
                        best_score = *score;
                        best_optimizer = Some(optimizer_id.clone());
                    }
                }
            }

            if let Some(optimizer) = best_optimizer {
                selected.push(optimizer);
            } else {
                break;
            }
        }

        Ok(selected)
    }

    /// Helper methods
    fn execute_single_optimizer(
        optimizer_id: &str,
        parameters: &Array1<T>,
        gradients: &Array1<T>,
        context: &OptimizationContext<T>,
        _guidance: &MetaLearningGuidance<T>,
        _resources: &OptimizerResources<T>,
    ) -> Result<Array1<T>> {
        // Simplified optimization step - in practice, this would call the actual optimizer
        let learning_rate = scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()); // Would be determined by the optimizer
        let mut result = parameters.clone();

        for i in 0..result.len() {
            result[i] = result[i] - learning_rate * gradients[i];
        }

        Ok(result)
    }

    fn update_optimizer_performance(&mut self, optimizer_id: &str, _elapsed: Duration) -> Result<()> {
        // Update performance score (simplified)
        let current_score = self.performance_scores.get(optimizer_id).cloned().unwrap_or(T::zero());
        let improvement = scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()); // Would be calculated based on actual performance
        let new_score = current_score + improvement;

        self.performance_scores.insert(optimizer_id.to_string(), new_score);

        // Update performance history
        if let Some(history) = self.performance_history.get_mut(optimizer_id) {
            history.push_back(new_score);
            if history.len() > 100 {
                history.pop_front();
            }
        }

        Ok(())
    }

    fn get_recent_performance(&self, optimizer_id: &str) -> T {
        self.performance_history.get(optimizer_id)
            .and_then(|history| history.back())
            .cloned()
            .unwrap_or(T::zero())
    }

    fn select_best_optimizer_for_context(&self, _context: &OptimizationContext<T>) -> Result<String> {
        self.performance_scores.iter()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).expect("unwrap failed"))
            .map(|(k, _)| k.clone())
            .ok_or_else(|| crate::error::OptimError::Other("No optimizers available".to_string()))
    }

    fn calculate_gating_weights(&self, _context: &OptimizationContext<T>) -> Result<HashMap<String, T>> {
        // Simplified gating weights based on performance scores
        Ok(self.performance_scores.clone())
    }

    fn calculate_suitability_score(
        &self,
        _capabilities: &OptimizerCapabilities,
        _landscape_features: &LandscapeFeatures<T>,
        _context: &OptimizationContext<T>,
    ) -> f64 {
        // Simplified suitability calculation
        scirs2_core::random::random::<f64>()
    }

    fn extract_context_features(&self, _landscape_features: &LandscapeFeatures<T>, _context: &OptimizationContext<T>) -> Vec<T> {
        // Extract relevant features for contextual bandits
        vec![scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()); 10] // Placeholder
    }

    fn predict_contextual_reward(&self, _optimizer_id: &str, _context_vector: &[T]) -> T {
        // Predict reward based on context (simplified)
        scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero())
    }

    /// Reset ensemble state
    pub fn reset(&mut self) -> Result<()> {
        self.performance_scores.clear();
        self.ensemble_weights.clear();
        self.performance_history.clear();
        self.selection_stats = SelectionStatistics::new();
        Ok(())
    }
}

/// Selection statistics for tracking optimizer performance
#[derive(Debug)]
pub struct SelectionStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total number of selections
    pub total_selections: usize,
    /// Selections per optimizer
    pub optimizer_selections: HashMap<String, usize>,
    /// Success counts per optimizer
    pub successes: HashMap<String, usize>,
    /// Failure counts per optimizer
    pub failures: HashMap<String, usize>,
    /// Average rewards per optimizer
    pub average_rewards: HashMap<String, T>,
}

impl<T: Float + Debug + Send + Sync + 'static> SelectionStatistics<T> {
    pub fn new() -> Self {
        Self {
            total_selections: 0,
            optimizer_selections: HashMap::new(),
            successes: HashMap::new(),
            failures: HashMap::new(),
            average_rewards: HashMap::new(),
        }
    }

    pub fn update_with_results(&mut self, results: &HashMap<String, Array1<T>>, selected: &[String]) {
        self.total_selections += selected.len();

        for optimizer_id in selected {
            *self.optimizer_selections.entry(optimizer_id.clone()).or_insert(0) += 1;

            if results.contains_key(optimizer_id) {
                *self.successes.entry(optimizer_id.clone()).or_insert(0) += 1;
            } else {
                *self.failures.entry(optimizer_id.clone()).or_insert(0) += 1;
            }
        }
    }
}

/// Weight update mechanism for ensemble weights
#[derive(Debug)]
pub struct WeightUpdateMechanism<T: Float + Debug + Send + Sync + 'static> {
    /// Learning rate for weight updates
    learning_rate: T,
    /// Momentum factor
    momentum: T,
    /// Previous weight updates
    previous_updates: HashMap<String, T>,
}

impl<T: Float + Debug + Send + Sync + 'static> WeightUpdateMechanism<T> {
    pub fn new() -> Self {
        Self {
            learning_rate: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            momentum: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
            previous_updates: HashMap::new(),
        }
    }

    pub fn update_weights(
        &mut self,
        current_weights: &mut HashMap<String, T>,
        performance_scores: &HashMap<String, T>,
    ) -> Result<()> {
        for (optimizer_id, current_weight) in current_weights.iter_mut() {
            if let Some(&score) = performance_scores.get(optimizer_id) {
                let gradient = score - *current_weight;
                let previous_update = self.previous_updates.get(optimizer_id).cloned().unwrap_or(T::zero());
                let update = self.learning_rate * gradient + self.momentum * previous_update;

                *current_weight = *current_weight + update;
                self.previous_updates.insert(optimizer_id.clone(), update);
            }
        }

        // Normalize weights
        let total_weight: T = current_weights.values().fold(T::zero(), |acc, &w| acc + w);
        if total_weight > T::zero() {
            for weight in current_weights.values_mut() {
                *weight = *weight / total_weight;
            }
        }

        Ok(())
    }
}

/// Ensemble optimization results
#[derive(Debug)]
pub struct EnsembleOptimizationResults<T: Float + Debug + Send + Sync + 'static> {
    pub updated_parameters: Array1<T>,
    pub performance_score: T,
    pub individual_results: HashMap<String, T>,
    pub adaptation_events: Vec<AdaptationEvent<T>>,
    pub resource_usage: ResourceUtilization<T>,
}

/// Adaptation event
#[derive(Debug, Clone)]
pub struct AdaptationEvent<T: Float + Debug + Send + Sync + 'static> {
    /// Event timestamp
    pub timestamp: std::time::SystemTime,
    /// Adaptation type
    pub adaptation_type: AdaptationType,
    /// Trigger that caused adaptation
    pub trigger: String,
    /// Performance before adaptation
    pub performance_before: T,
    /// Performance after adaptation
    pub performance_after: T,
    /// Adaptation cost
    pub adaptation_cost: T,
}

/// Supporting types and placeholder implementations


// Placeholder optimizer implementations
#[derive(Debug)]
pub struct AdamOptimizer<T: Float + Debug + Send + Sync + 'static> {
    learning_rate: T,
}

impl<T: Float + Debug + Send + Sync + 'static> AdamOptimizer<T> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            learning_rate: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
        })
    }
}

impl<T: Float + Debug + std::fmt::Debug + Send + Sync + 'static> AdvancedOptimizer<T> for AdamOptimizer<T> {
    fn optimize_step_with_context(
        &mut self,
        parameters: &Array1<T>,
        gradients: &Array1<T>,
        _context: &OptimizationContext<T>,
    ) -> Result<Array1<T>> {
        let mut result = parameters.clone();
        for i in 0..result.len() {
            result[i] = result[i] - self.learning_rate * gradients[i];
        }
        Ok(result)
    }

    fn adapt_to_landscape(&mut self, _features: &LandscapeFeatures<T>) -> Result<()> {
        Ok(())
    }

    fn get_capabilities(&self) -> OptimizerCapabilities {
        OptimizerCapabilities {
            supported_problems: vec![],
            scalability: ScalabilityInfo {
                max_parameters: 1000,
                memory_complexity: ComplexityClass::Linear,
                time_complexity: ComplexityClass::Linear,
                parallel_scalability: ParallelScalability {
                    max_threads: 8,
                    efficiency: 0.8,
                    communication_overhead: 0.1,
                },
            },
            memory_requirements: ResourceRequirements {
                cpu_cores: 1,
                memory_mb: 1000,
                gpu_memory_mb: 0,
                network_bandwidth: 100,
            },
            computational_complexity: ComputationalComplexity {
                time_complexity: "O(n)".to_string(),
                space_complexity: "O(n)".to_string(),
                parallelism_factor: 0.8,
            },
            convergence_guarantees: ConvergenceGuarantees {
                guarantees_convergence: false,
                convergence_rate: None,
                conditions: vec![],
            },
        }
    }

    fn get_performance_score(&self) -> T {
        scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero())
    }

    fn clone_optimizer(&self) -> Box<dyn AdvancedOptimizer<T>> {
        Box::new(AdamOptimizer {
            learning_rate: self.learning_rate,
        })
    }
}

// Additional placeholder optimizers
macro_rules! impl_placeholder_optimizer {
    ($name:ident, $lr:expr) => {
        #[derive(Debug)]
        pub struct $name<T: Float + Debug + Send + Sync + 'static> {
            learning_rate: T,
        }

        impl<T: Float + Debug + Send + Sync + 'static> $name<T> {
            pub fn new() -> Result<Self> {
                Ok(Self {
                    learning_rate: scirs2_core::numeric::NumCast::from($lr).unwrap_or_else(|| T::zero()),
                })
            }
        }

        impl<T: Float + Debug + std::fmt::Debug + Send + Sync + 'static> AdvancedOptimizer<T> for $name<T> {
            fn optimize_step_with_context(
                &mut self,
                parameters: &Array1<T>,
                gradients: &Array1<T>,
                _context: &OptimizationContext<T>,
            ) -> Result<Array1<T>> {
                let mut result = parameters.clone();
                for i in 0..result.len() {
                    result[i] = result[i] - self.learning_rate * gradients[i];
                }
                Ok(result)
            }

            fn adapt_to_landscape(&mut self, _features: &LandscapeFeatures<T>) -> Result<()> {
                Ok(())
            }

            fn get_capabilities(&self) -> OptimizerCapabilities {
                OptimizerCapabilities {
                        supported_problems: vec![],
                        scalability: ScalabilityInfo {
                            max_parameters: 1000,
                            memory_complexity: ComplexityClass::Linear,
                            time_complexity: ComplexityClass::Linear,
                            parallel_scalability: ParallelScalability {
                    max_threads: 8,
                    efficiency: 0.8,
                    communication_overhead: 0.1,
                },
                        },
                        memory_requirements: ResourceRequirements {
                            cpu_cores: 1,
                            memory_mb: 1000,
                            gpu_memory_mb: 0,
                            network_bandwidth: 100,
                        },
                        computational_complexity: ComputationalComplexity {
                            time_complexity: "O(n)".to_string(),
                            space_complexity: "O(n)".to_string(),
                            parallelism_factor: 0.8,
                        },
                        convergence_guarantees: ConvergenceGuarantees {
                            guarantees_convergence: false,
                            convergence_rate: None,
                            conditions: vec![],
                        },
                }
            }

            fn get_performance_score(&self) -> T {
                scirs2_core::numeric::NumCast::from($lr).unwrap_or_else(|| T::zero())
            }

            fn clone_optimizer(&self) -> Box<dyn AdvancedOptimizer<T>> {
                Box::new($name {
                    learning_rate: self.learning_rate,
                })
            }
        }
    };
}

impl_placeholder_optimizer!(RMSpropOptimizer, 0.01);
impl_placeholder_optimizer!(SGDMomentumOptimizer, 0.1);
impl_placeholder_optimizer!(AdagradOptimizer, 0.01);
impl_placeholder_optimizer!(LearnedLSTMOptimizer, 0.001);
impl_placeholder_optimizer!(AdaptiveGradientOptimizer, 0.005);

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

    #[test]
    fn test_ensemble_creation() {
        let ensemble = OptimizerEnsemble::<f32>::new();
        assert!(ensemble.is_ok());
    }

    #[test]
    fn test_optimizer_registration() {
        let mut ensemble = OptimizerEnsemble::<f32>::new().expect("unwrap failed");
        let optimizer = Box::new(AdamOptimizer::new().expect("unwrap failed"));
        assert!(ensemble.register_optimizer("test".to_string(), optimizer).is_ok());
    }

    #[test]
    fn test_default_optimizers_registration() {
        let mut ensemble = OptimizerEnsemble::<f32>::new().expect("unwrap failed");
        assert!(ensemble.register_default_optimizers().is_ok());
        assert!(ensemble.optimizers.len() > 0);
    }
}