optirs-learned 0.3.1

// Performance prediction for optimization coordinator

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

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

/// Performance predictor for coordinator
#[derive(Debug)]
pub struct PerformancePredictor<T: Float + Debug + Send + Sync + 'static> {
    /// Prediction models
    models: HashMap<String, PredictionModel<T>>,

    /// Feature extractors
    feature_extractors: Vec<Box<dyn FeatureExtractor<T>>>,

    /// Prediction cache
    prediction_cache: PredictionCache<T>,

    /// Uncertainty estimator
    uncertainty_estimator: UncertaintyEstimator<T>,

    /// Model ensemble
    model_ensemble: ModelEnsemble<T>,

    /// Performance history
    performance_history: HashMap<String, VecDeque<PerformanceRecord<T>>>,

    /// Prediction accuracy tracking
    accuracy_tracker: AccuracyTracker<T>,
}

impl<T: Float + Debug + std::fmt::Debug + Send + Sync + 'static> PerformancePredictor<T> {
    /// Create new performance predictor
    pub fn new() -> Result<Self> {
        let mut predictor = Self {
            models: HashMap::new(),
            feature_extractors: Vec::new(),
            prediction_cache: PredictionCache::new(1000)?,
            uncertainty_estimator: UncertaintyEstimator::new()?,
            model_ensemble: ModelEnsemble::new()?,
            performance_history: HashMap::new(),
            accuracy_tracker: AccuracyTracker::new(),
        };

        predictor.initialize_models()?;
        predictor.initialize_feature_extractors()?;

        Ok(predictor)
    }

    /// Initialize prediction models
    fn initialize_models(&mut self) -> Result<()> {
        // Register different types of prediction models
        self.register_model("linear_regression".to_string(),
            PredictionModel::new(ModelType::LinearRegression)?)?;
        self.register_model("neural_network".to_string(),
            PredictionModel::new(ModelType::NeuralNetwork)?)?;
        self.register_model("gaussian_process".to_string(),
            PredictionModel::new(ModelType::GaussianProcess)?)?;
        self.register_model("random_forest".to_string(),
            PredictionModel::new(ModelType::RandomForest)?)?;
        self.register_model("ensemble_model".to_string(),
            PredictionModel::new(ModelType::Ensemble)?)?;

        Ok(())
    }

    /// Initialize feature extractors
    fn initialize_feature_extractors(&mut self) -> Result<()> {
        self.feature_extractors.push(Box::new(LandscapeFeatureExtractor::new()?));
        self.feature_extractors.push(Box::new(HistoricalFeatureExtractor::new()?));
        self.feature_extractors.push(Box::new(ContextualFeatureExtractor::new()?));
        self.feature_extractors.push(Box::new(MetaFeatureExtractor::new()?));
        self.feature_extractors.push(Box::new(TemporalFeatureExtractor::new()?));

        Ok(())
    }

    /// Register a prediction model
    pub fn register_model(&mut self, id: String, model: PredictionModel<T>) -> Result<()> {
        self.models.insert(id.clone(), model);
        self.performance_history.insert(id, VecDeque::new());
        Ok(())
    }

    /// Predict performance for optimizers
    pub fn predict_performance(
        &mut self,
        optimizers: &[String],
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<HashMap<String, T>> {
        let start_time = Instant::now();
        let mut predictions = HashMap::new();

        // Check cache first
        let cache_key = self.generate_cache_key(optimizers, landscape_features, context)?;
        if let Some(cached_predictions) = self.prediction_cache.get(&cache_key) {
            return Ok(cached_predictions.clone());
        }

        // Extract features
        let features = self.extract_features(landscape_features, context)?;

        // Generate predictions for each optimizer
        for optimizer_id in optimizers {
            let optimizer_specific_features = self.augment_features_for_optimizer(&features, optimizer_id)?;
            let prediction = self.predict_single_optimizer(optimizer_id, &optimizer_specific_features)?;
            predictions.insert(optimizer_id.clone(), prediction);
        }

        // Apply ensemble techniques
        let ensemble_predictions = self.apply_ensemble_prediction(&predictions, &features)?;

        // Estimate prediction uncertainty
        let uncertainty_estimates = self.uncertainty_estimator.estimate_uncertainty(
            &ensemble_predictions,
            &features,
            optimizers,
        )?;

        // Adjust predictions based on uncertainty
        let adjusted_predictions = self.adjust_predictions_for_uncertainty(
            &ensemble_predictions,
            &uncertainty_estimates,
        )?;

        // Cache the results
        self.prediction_cache.insert(cache_key, adjusted_predictions.clone());

        // Record prediction time
        let prediction_time = start_time.elapsed();
        self.accuracy_tracker.record_prediction_time(prediction_time);

        Ok(adjusted_predictions)
    }

    /// Update models with actual results
    pub fn update_with_results(
        &mut self,
        actual_results: &HashMap<String, Array1<T>>,
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<()> {
        // Extract features for training
        let features = self.extract_features(landscape_features, context)?;

        // Update each model with the actual results
        for (optimizer_id, result) in actual_results {
            let performance_score = self.calculate_performance_score(result)?;
            let optimizer_features = self.augment_features_for_optimizer(&features, optimizer_id)?;

            // Update all models and collect model IDs
            let mut model_ids_to_check = Vec::new();
            for (model_id, model) in &mut self.models {
                model.update_with_observation(&optimizer_features, performance_score)?;
                model_ids_to_check.push(model_id.clone());
            }

            // Track model accuracy after releasing mutable borrow
            for model_id in model_ids_to_check {
                if let Some(previous_prediction) = self.get_recent_prediction(&model_id, optimizer_id) {
                    self.accuracy_tracker.record_prediction_accuracy(
                        model_id,
                        previous_prediction,
                        performance_score,
                    );
                }
            }

            // Update performance history
            self.update_performance_history(optimizer_id, performance_score, &features)?;
        }

        // Update uncertainty estimator
        self.uncertainty_estimator.update_with_results(actual_results, &features)?;

        // Update ensemble weights based on accuracy
        self.model_ensemble.update_weights(&self.accuracy_tracker)?;

        Ok(())
    }

    /// Extract features from landscape and context
    fn extract_features(
        &mut self,
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<FeatureVector<T>> {
        let mut combined_features = FeatureVector::new();

        // Extract features using each feature extractor
        for extractor in &mut self.feature_extractors {
            let extracted_features = extractor.extract_features(landscape_features, context)?;
            combined_features.extend(extracted_features);
        }

        // Add derived features
        let derived_features = self.compute_derived_features(&combined_features)?;
        combined_features.extend(derived_features);

        // Normalize features
        combined_features.normalize()?;

        Ok(combined_features)
    }

    /// Augment features with optimizer-specific information
    fn augment_features_for_optimizer(
        &self,
        base_features: &FeatureVector<T>,
        optimizer_id: &str,
    ) -> Result<FeatureVector<T>> {
        let mut augmented_features = base_features.clone();

        // Add optimizer-specific features
        let optimizer_features = self.get_optimizer_characteristics(optimizer_id)?;
        augmented_features.extend(optimizer_features);

        // Add historical performance features
        let historical_features = self.get_historical_performance_features(optimizer_id)?;
        augmented_features.extend(historical_features);

        Ok(augmented_features)
    }

    /// Predict performance for a single optimizer
    fn predict_single_optimizer(
        &mut self,
        optimizer_id: &str,
        features: &FeatureVector<T>,
    ) -> Result<T> {
        let mut model_predictions = HashMap::new();

        // Get predictions from all models
        for (model_id, model) in &mut self.models {
            let prediction = model.predict(features)?;
            model_predictions.insert(model_id.clone(), prediction);
        }

        // Ensemble the predictions
        let ensemble_prediction = self.model_ensemble.combine_predictions(&model_predictions)?;

        // Apply optimizer-specific adjustments
        let adjusted_prediction = self.apply_optimizer_adjustments(optimizer_id, ensemble_prediction)?;

        Ok(adjusted_prediction)
    }

    /// Apply ensemble prediction techniques
    fn apply_ensemble_prediction(
        &self,
        individual_predictions: &HashMap<String, T>,
        _features: &FeatureVector<T>,
    ) -> Result<HashMap<String, T>> {
        // For now, return the individual predictions as-is
        // In a full implementation, this would apply cross-optimizer ensemble techniques
        Ok(individual_predictions.clone())
    }

    /// Adjust predictions based on uncertainty estimates
    fn adjust_predictions_for_uncertainty(
        &self,
        predictions: &HashMap<String, T>,
        uncertainty_estimates: &HashMap<String, T>,
    ) -> Result<HashMap<String, T>> {
        let mut adjusted_predictions = HashMap::new();

        for (optimizer_id, &prediction) in predictions {
            let uncertainty = uncertainty_estimates.get(optimizer_id).cloned().unwrap_or(T::zero());

            // Conservative adjustment: reduce prediction confidence when uncertainty is high
            let adjustment_factor = T::one() - uncertainty * scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero());
            let adjusted_prediction = prediction * adjustment_factor;

            adjusted_predictions.insert(optimizer_id.clone(), adjusted_prediction);
        }

        Ok(adjusted_predictions)
    }

    /// Calculate performance score from optimization result
    fn calculate_performance_score(&self, result: &Array1<T>) -> Result<T> {
        // Simple norm-based performance score
        let norm = result.iter().map(|&x| x * x).fold(T::zero(), |acc, x| acc + x).sqrt();

        // Convert to a performance score (higher is better)
        let performance = T::one() / (T::one() + norm);

        Ok(performance)
    }

    /// Compute derived features
    fn compute_derived_features(&self, base_features: &FeatureVector<T>) -> Result<FeatureVector<T>> {
        let mut derived = FeatureVector::new();

        // Polynomial features
        for i in 0..base_features.len().min(10) {
            let feature_value = base_features.get(i);
            derived.push(feature_value * feature_value); // Quadratic
            derived.push(feature_value * feature_value * feature_value); // Cubic
        }

        // Interaction features
        for i in 0..base_features.len().min(5) {
            for j in (i + 1)..base_features.len().min(5) {
                let interaction = base_features.get(i) * base_features.get(j);
                derived.push(interaction);
            }
        }

        Ok(derived)
    }

    /// Get optimizer characteristics
    fn get_optimizer_characteristics(&self, optimizer_id: &str) -> Result<FeatureVector<T>> {
        let mut features = FeatureVector::new();

        // Optimizer-specific characteristics (simplified)
        match optimizer_id {
            "adam" => {
                features.push(scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero())); // Adaptive nature
                features.push(scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero())); // Momentum factor
                features.push(scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero())); // Noise sensitivity
            }
            "sgd_momentum" => {
                features.push(scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero())); // Adaptive nature
                features.push(scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero())); // Momentum factor
                features.push(scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero())); // Noise sensitivity
            }
            _ => {
                features.push(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
                features.push(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
                features.push(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
            }
        }

        Ok(features)
    }

    /// Get historical performance features
    fn get_historical_performance_features(&self, optimizer_id: &str) -> Result<FeatureVector<T>> {
        let mut features = FeatureVector::new();

        if let Some(history) = self.performance_history.get(optimizer_id) {
            if !history.is_empty() {
                // Recent average performance
                let recent_avg = history.iter()
                    .rev()
                    .take(10)
                    .map(|record| record.performance_score)
                    .fold(T::zero(), |acc, x| acc + x) / T::from(history.len().min(10)).expect("unwrap failed");
                features.push(recent_avg);

                // Performance trend
                if history.len() >= 2 {
                    let recent = history.back().expect("unwrap failed").performance_score;
                    let older = history.iter().rev().nth(5).map(|r| r.performance_score).unwrap_or(recent);
                    let trend = recent - older;
                    features.push(trend);
                } else {
                    features.push(T::zero());
                }

                // Performance variance
                let variance = self.calculate_performance_variance(history)?;
                features.push(variance);
            } else {
                // No history available
                features.push(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero())); // Neutral values
                features.push(T::zero());
                features.push(T::zero());
            }
        } else {
            features.push(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
            features.push(T::zero());
            features.push(T::zero());
        }

        Ok(features)
    }

    /// Calculate performance variance
    fn calculate_performance_variance(&self, history: &VecDeque<PerformanceRecord<T>>) -> Result<T> {
        if history.len() < 2 {
            return Ok(T::zero());
        }

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

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

        Ok(variance)
    }

    /// Apply optimizer-specific adjustments
    fn apply_optimizer_adjustments(&self, optimizer_id: &str, prediction: T) -> Result<T> {
        // Optimizer-specific adjustment factors (simplified)
        let adjustment_factor = match optimizer_id {
            "adam" => scirs2_core::numeric::NumCast::from(1.1).unwrap_or_else(|| T::zero()), // Slight boost for Adam
            "sgd_momentum" => scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()), // Slight penalty for SGD
            "learned_lstm" => scirs2_core::numeric::NumCast::from(1.15).unwrap_or_else(|| T::zero()), // Boost for learned optimizers
            _ => T::one(),
        };

        Ok(prediction * adjustment_factor)
    }

    /// Generate cache key
    fn generate_cache_key(
        &self,
        optimizers: &[String],
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<String> {
        // Simple hash-based cache key
        let mut key = String::new();

        for optimizer in optimizers {
            key.push_str(optimizer);
            key.push('_');
        }

        key.push_str(&format!("{}", context.iteration));
        key.push_str(&format!("{}", context.dimensionality));

        // Add some landscape feature info (simplified)
        key.push_str(&format!("{:.3}", landscape_features.curvature_info.mean_curvature.to_f64().unwrap_or(0.0)));

        Ok(key)
    }

    /// Get recent prediction for accuracy tracking
    fn get_recent_prediction(&self, model_id: &str, optimizer_id: &str) -> Option<T> {
        // This would retrieve the most recent prediction made by this model for this optimizer
        // Simplified implementation
        Some(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()))
    }

    /// Update performance history
    fn update_performance_history(
        &mut self,
        optimizer_id: &str,
        performance_score: T,
        features: &FeatureVector<T>,
    ) -> Result<()> {
        let record = PerformanceRecord {
            performance_score,
            features: features.clone(),
            timestamp: Instant::now(),
        };

        if let Some(history) = self.performance_history.get_mut(optimizer_id) {
            history.push_back(record);
            if history.len() > 1000 {
                history.pop_front();
            }
        }

        Ok(())
    }

    /// Reset predictor state
    pub fn reset(&mut self) -> Result<()> {
        for model in self.models.values_mut() {
            model.reset()?;
        }
        self.prediction_cache.clear();
        self.performance_history.clear();
        self.accuracy_tracker.reset();
        Ok(())
    }

    /// Get prediction accuracy statistics
    pub fn get_accuracy_statistics(&self) -> AccuracyStatistics<T> {
        self.accuracy_tracker.get_statistics()
    }

    /// Get model performance comparison
    pub fn get_model_comparison(&self) -> HashMap<String, ModelPerformance<T>> {
        self.accuracy_tracker.get_model_comparison()
    }
}

/// Feature extraction trait
pub trait FeatureExtractor<T: Float + Debug + Send + Sync + 'static>: Send + Sync + std::fmt::Debug {
    fn extract_features(
        &mut self,
        landscape_features: &LandscapeFeatures<T>,
        context: &OptimizationContext<T>,
    ) -> Result<FeatureVector<T>>;
}

/// Feature vector implementation
#[derive(Debug, Clone)]
pub struct FeatureVector<T: Float + Debug + Send + Sync + 'static> {
    features: Vec<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> FeatureVector<T> {
    pub fn new() -> Self {
        Self {
            features: Vec::new(),
        }
    }

    pub fn push(&mut self, value: T) {
        self.features.push(value);
    }

    pub fn extend(&mut self, other: FeatureVector<T>) {
        self.features.extend(other.features);
    }

    pub fn get(&self, index: usize) -> T {
        self.features.get(index).cloned().unwrap_or(T::zero())
    }

    pub fn len(&self) -> usize {
        self.features.len()
    }

    pub fn normalize(&mut self) -> Result<()> {
        if self.features.is_empty() {
            return Ok(());
        }

        // Z-score normalization
        let mean = self.features.iter().fold(T::zero(), |acc, &x| acc + x) / T::from(self.features.len()).expect("unwrap failed");
        let variance = self.features.iter()
            .map(|&x| (x - mean) * (x - mean))
            .fold(T::zero(), |acc, x| acc + x) / T::from(self.features.len()).expect("unwrap failed");
        let std_dev = variance.sqrt();

        if std_dev > T::zero() {
            for feature in &mut self.features {
                *feature = (*feature - mean) / std_dev;
            }
        }

        Ok(())
    }

    pub fn as_slice(&self) -> &[T] {
        &self.features
    }
}

/// Prediction model types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ModelType {
    LinearRegression,
    NeuralNetwork,
    GaussianProcess,
    RandomForest,
    Ensemble,
}

/// Prediction model implementation
#[derive(Debug)]
pub struct PredictionModel<T: Float + Debug + Send + Sync + 'static> {
    model_type: ModelType,
    parameters: Vec<T>,
    training_data: Vec<(FeatureVector<T>, T)>,
    is_trained: bool,
}

impl<T: Float + Debug + Send + Sync + 'static> PredictionModel<T> {
    pub fn new(model_type: ModelType) -> Result<Self> {
        Ok(Self {
            model_type,
            parameters: Vec::new(),
            training_data: Vec::new(),
            is_trained: false,
        })
    }

    pub fn predict(&mut self, features: &FeatureVector<T>) -> Result<T> {
        if !self.is_trained && !self.training_data.is_empty() {
            self.train()?;
        }

        match self.model_type {
            ModelType::LinearRegression => self.linear_predict(features),
            ModelType::NeuralNetwork => self.neural_predict(features),
            ModelType::GaussianProcess => self.gp_predict(features),
            ModelType::RandomForest => self.rf_predict(features),
            ModelType::Ensemble => self.ensemble_predict(features),
        }
    }

    pub fn update_with_observation(&mut self, features: &FeatureVector<T>, target: T) -> Result<()> {
        self.training_data.push((features.clone(), target));

        // Limit training data size
        if self.training_data.len() > 10000 {
            self.training_data.remove(0);
        }

        // Retrain periodically
        if self.training_data.len() % 100 == 0 {
            self.train()?;
        }

        Ok(())
    }

    fn train(&mut self) -> Result<()> {
        match self.model_type {
            ModelType::LinearRegression => self.train_linear(),
            ModelType::NeuralNetwork => self.train_neural(),
            ModelType::GaussianProcess => self.train_gp(),
            ModelType::RandomForest => self.train_rf(),
            ModelType::Ensemble => self.train_ensemble(),
        }?;

        self.is_trained = true;
        Ok(())
    }

    // Simplified prediction implementations
    fn linear_predict(&self, features: &FeatureVector<T>) -> Result<T> {
        if self.parameters.is_empty() {
            return Ok(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
        }

        let mut prediction = T::zero();
        for (i, &feature) in features.as_slice().iter().enumerate() {
            if i < self.parameters.len() {
                prediction = prediction + feature * self.parameters[i];
            }
        }

        Ok(prediction.max(T::zero()).min(T::one()))
    }

    fn neural_predict(&self, _features: &FeatureVector<T>) -> Result<T> {
        // Simplified neural network prediction
        Ok(scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()))
    }

    fn gp_predict(&self, _features: &FeatureVector<T>) -> Result<T> {
        // Simplified Gaussian process prediction
        Ok(scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()))
    }

    fn rf_predict(&self, _features: &FeatureVector<T>) -> Result<T> {
        // Simplified random forest prediction
        Ok(scirs2_core::numeric::NumCast::from(0.65).unwrap_or_else(|| T::zero()))
    }

    fn ensemble_predict(&self, features: &FeatureVector<T>) -> Result<T> {
        // Ensemble of other methods
        let linear = self.linear_predict(features)?;
        let neural = self.neural_predict(features)?;
        let gp = self.gp_predict(features)?;
        let rf = self.rf_predict(features)?;

        Ok((linear + neural + gp + rf) / scirs2_core::numeric::NumCast::from(4.0).unwrap_or_else(|| T::zero()))
    }

    // Simplified training implementations
    fn train_linear(&mut self) -> Result<()> {
        if self.training_data.is_empty() {
            return Ok(());
        }

        // Simple least squares (simplified)
        let feature_dim = self.training_data[0].0.len();
        self.parameters = vec![scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()); feature_dim];

        // Gradient descent (simplified)
        for _ in 0..10 {
            let mut gradients = vec![T::zero(); feature_dim];

            for (features, target) in &self.training_data {
                let prediction = self.linear_predict(features)?;
                let error = prediction - *target;

                for (i, &feature) in features.as_slice().iter().enumerate() {
                    if i < gradients.len() {
                        gradients[i] = gradients[i] + error * feature;
                    }
                }
            }

            let learning_rate = scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()) / T::from(self.training_data.len()).expect("unwrap failed");
            for (param, grad) in self.parameters.iter_mut().zip(gradients.iter()) {
                *param = *param - learning_rate * *grad;
            }
        }

        Ok(())
    }

    fn train_neural(&mut self) -> Result<()> {
        // Placeholder for neural network training
        Ok(())
    }

    fn train_gp(&mut self) -> Result<()> {
        // Placeholder for Gaussian process training
        Ok(())
    }

    fn train_rf(&mut self) -> Result<()> {
        // Placeholder for random forest training
        Ok(())
    }

    fn train_ensemble(&mut self) -> Result<()> {
        // Placeholder for ensemble training
        Ok(())
    }

    pub fn reset(&mut self) -> Result<()> {
        self.parameters.clear();
        self.training_data.clear();
        self.is_trained = false;
        Ok(())
    }
}

/// Performance record for tracking
#[derive(Debug, Clone)]
pub struct PerformanceRecord<T: Float + Debug + Send + Sync + 'static> {
    pub performance_score: T,
    pub features: FeatureVector<T>,
    pub timestamp: Instant,
}

/// Prediction cache
#[derive(Debug)]
pub struct PredictionCache<T: Float + Debug + Send + Sync + 'static> {
    cache: HashMap<String, HashMap<String, T>>,
    max_size: usize,
    access_order: VecDeque<String>,
}

impl<T: Float + Debug + Send + Sync + 'static> PredictionCache<T> {
    pub fn new(max_size: usize) -> Result<Self> {
        Ok(Self {
            cache: HashMap::new(),
            max_size,
            access_order: VecDeque::new(),
        })
    }

    pub fn get(&mut self, key: &str) -> Option<HashMap<String, T>> {
        if let Some(value) = self.cache.get(key) {
            // Move to front (LRU)
            self.access_order.retain(|k| k != key);
            self.access_order.push_back(key.to_string());
            Some(value.clone())
        } else {
            None
        }
    }

    pub fn insert(&mut self, key: String, value: HashMap<String, T>) {
        // Remove oldest if at capacity
        while self.cache.len() >= self.max_size && !self.access_order.is_empty() {
            if let Some(oldest_key) = self.access_order.pop_front() {
                self.cache.remove(&oldest_key);
            }
        }

        self.cache.insert(key.clone(), value);
        self.access_order.push_back(key);
    }

    pub fn clear(&mut self) {
        self.cache.clear();
        self.access_order.clear();
    }
}

/// Uncertainty estimator
#[derive(Debug)]
pub struct UncertaintyEstimator<T: Float + Debug + Send + Sync + 'static> {
    variance_model: VarianceModel<T>,
    confidence_intervals: HashMap<String, (T, T)>,
}

impl<T: Float + Debug + Send + Sync + 'static> UncertaintyEstimator<T> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            variance_model: VarianceModel::new()?,
            confidence_intervals: HashMap::new(),
        })
    }

    pub fn estimate_uncertainty(
        &mut self,
        predictions: &HashMap<String, T>,
        features: &FeatureVector<T>,
        optimizers: &[String],
    ) -> Result<HashMap<String, T>> {
        let mut uncertainties = HashMap::new();

        for optimizer_id in optimizers {
            let prediction = predictions.get(optimizer_id).cloned().unwrap_or(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
            let uncertainty = self.variance_model.predict_variance(features, prediction)?;
            uncertainties.insert(optimizer_id.clone(), uncertainty);
        }

        Ok(uncertainties)
    }

    pub fn update_with_results(
        &mut self,
        _actual_results: &HashMap<String, Array1<T>>,
        _features: &FeatureVector<T>,
    ) -> Result<()> {
        // Update uncertainty model with actual results
        Ok(())
    }
}

/// Variance model for uncertainty estimation
#[derive(Debug)]
pub struct VarianceModel<T: Float + Debug + Send + Sync + 'static> {
    _phantom: std::marker::PhantomData<T>,
}

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

    pub fn predict_variance(&self, _features: &FeatureVector<T>, _prediction: T) -> Result<T> {
        // Simplified variance prediction
        Ok(scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()))
    }
}

/// Model ensemble for combining predictions
#[derive(Debug)]
pub struct ModelEnsemble<T: Float + Debug + Send + Sync + 'static> {
    weights: HashMap<String, T>,
    performance_history: HashMap<String, VecDeque<T>>,
}

impl<T: Float + Debug + Send + Sync + 'static> ModelEnsemble<T> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            weights: HashMap::new(),
            performance_history: HashMap::new(),
        })
    }

    pub fn combine_predictions(&self, predictions: &HashMap<String, T>) -> Result<T> {
        if predictions.is_empty() {
            return Ok(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
        }

        let mut weighted_sum = T::zero();
        let mut total_weight = T::zero();

        for (model_id, &prediction) in predictions {
            let weight = self.weights.get(model_id).cloned().unwrap_or(T::one());
            weighted_sum = weighted_sum + weight * prediction;
            total_weight = total_weight + weight;
        }

        if total_weight > T::zero() {
            Ok(weighted_sum / total_weight)
        } else {
            Ok(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()))
        }
    }

    pub fn update_weights(&mut self, accuracy_tracker: &AccuracyTracker<T>) -> Result<()> {
        let model_performance = accuracy_tracker.get_model_comparison();

        for (model_id, performance) in model_performance {
            let weight = performance.average_accuracy;
            self.weights.insert(model_id, weight);
        }

        Ok(())
    }
}

/// Accuracy tracking
#[derive(Debug)]
pub struct AccuracyTracker<T: Float + Debug + Send + Sync + 'static> {
    model_accuracies: HashMap<String, VecDeque<T>>,
    prediction_times: VecDeque<Duration>,
    total_predictions: usize,
}

impl<T: Float + Debug + Send + Sync + 'static> AccuracyTracker<T> {
    pub fn new() -> Self {
        Self {
            model_accuracies: HashMap::new(),
            prediction_times: VecDeque::new(),
            total_predictions: 0,
        }
    }

    pub fn record_prediction_accuracy(&mut self, model_id: String, predicted: T, actual: T) {
        let accuracy = T::one() - (predicted - actual).abs();

        let accuracies = self.model_accuracies.entry(model_id).or_insert_with(VecDeque::new);
        accuracies.push_back(accuracy);

        if accuracies.len() > 1000 {
            accuracies.pop_front();
        }
    }

    pub fn record_prediction_time(&mut self, time: Duration) {
        self.prediction_times.push_back(time);
        self.total_predictions += 1;

        if self.prediction_times.len() > 1000 {
            self.prediction_times.pop_front();
        }
    }

    pub fn get_statistics(&self) -> AccuracyStatistics<T> {
        let average_prediction_time = if self.prediction_times.is_empty() {
            Duration::new(0, 0)
        } else {
            self.prediction_times.iter().sum::<Duration>() / self.prediction_times.len() as u32
        };

        AccuracyStatistics {
            total_predictions: self.total_predictions,
            average_prediction_time,
            model_count: self.model_accuracies.len(),
            _phantom: std::marker::PhantomData,
        }
    }

    pub fn get_model_comparison(&self) -> HashMap<String, ModelPerformance<T>> {
        let mut comparison = HashMap::new();

        for (model_id, accuracies) in &self.model_accuracies {
            if !accuracies.is_empty() {
                let average_accuracy = accuracies.iter().fold(T::zero(), |acc, &x| acc + x)
                    / T::from(accuracies.len()).expect("unwrap failed");

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

                comparison.insert(model_id.clone(), ModelPerformance {
                    average_accuracy,
                    accuracy_variance: variance,
                    prediction_count: accuracies.len(),
                });
            }
        }

        comparison
    }

    pub fn reset(&mut self) {
        self.model_accuracies.clear();
        self.prediction_times.clear();
        self.total_predictions = 0;
    }
}

/// Supporting types
#[derive(Debug, Clone)]
pub struct AccuracyStatistics<T: Float + Debug + Send + Sync + 'static> {
    pub total_predictions: usize,
    pub average_prediction_time: Duration,
    pub model_count: usize,
    _phantom: std::marker::PhantomData<T>,
}

#[derive(Debug, Clone)]
pub struct ModelPerformance<T: Float + Debug + Send + Sync + 'static> {
    pub average_accuracy: T,
    pub accuracy_variance: T,
    pub prediction_count: usize,
}

// Feature extractor implementations
macro_rules! impl_feature_extractor {
    ($name:ident, $feature_count:expr) => {
        #[derive(Debug)]
        pub struct $name<T: Float + Debug + Send + Sync + 'static> {
            _phantom: std::marker::PhantomData<T>,
        }

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

        impl<T: Float + Debug + std::fmt::Debug + Send + Sync + Send + Sync> FeatureExtractor<T> for $name<T> {
            fn extract_features(
                &mut self,
                _landscape_features: &LandscapeFeatures<T>,
                _context: &OptimizationContext<T>,
            ) -> Result<FeatureVector<T>> {
                let mut features = FeatureVector::new();
                for i in 0..$feature_count {
                    features.push(scirs2_core::numeric::NumCast::from(i as f64 / $feature_count as f64).unwrap_or_else(|| T::zero()));
                }
                Ok(features)
            }
        }
    };
}

impl_feature_extractor!(LandscapeFeatureExtractor, 10);
impl_feature_extractor!(HistoricalFeatureExtractor, 8);
impl_feature_extractor!(ContextualFeatureExtractor, 12);
impl_feature_extractor!(MetaFeatureExtractor, 6);
impl_feature_extractor!(TemporalFeatureExtractor, 5);

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

    #[test]
    fn test_performance_predictor_creation() {
        let predictor = PerformancePredictor::<f32>::new();
        assert!(predictor.is_ok());
    }

    #[test]
    fn test_feature_vector() {
        let mut features = FeatureVector::<f32>::new();
        features.push(1.0);
        features.push(2.0);
        features.push(3.0);

        assert_eq!(features.len(), 3);
        assert_eq!(features.get(0), 1.0);
        assert_eq!(features.get(1), 2.0);
    }

    #[test]
    fn test_prediction_model() {
        let mut model = PredictionModel::<f32>::new(ModelType::LinearRegression).expect("unwrap failed");
        let features = FeatureVector::<f32>::new();

        let prediction = model.predict(&features);
        assert!(prediction.is_ok());
    }

    #[test]
    fn test_prediction_cache() {
        let mut cache = PredictionCache::<f32>::new(2).expect("unwrap failed");
        let mut predictions = HashMap::new();
        predictions.insert("optimizer1".to_string(), 0.8);

        cache.insert("key1".to_string(), predictions.clone());
        let retrieved = cache.get("key1");
        assert!(retrieved.is_some());
    }
}