//! Real-time performance adaptation system for ODE solvers
//!
//! This module provides cutting-edge real-time performance monitoring and
//! adaptive optimization capabilities. It continuously monitors solver performance
//! and automatically adjusts algorithms, parameters, and resource allocation
//! to maintain optimal performance in dynamic computing environments.
//!
//! Features:
//! - Real-time performance metric collection and analysis
//! - Adaptive algorithm switching based on problem characteristics
//! - Dynamic resource allocation and load balancing
//! - Predictive performance modeling and optimization
//! - Machine learning-based parameter tuning
//! - Anomaly detection and automatic recovery

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

pub mod algorithm_selector;
pub mod anomaly_detector;
pub mod config_adapter;
pub mod monitor;
pub mod predictor;
pub mod resource_manager;
pub mod types;

// Re-export main types
pub use algorithm_selector::*;
pub use anomaly_detector::*;
pub use config_adapter::*;
pub use monitor::*;
pub use predictor::*;
pub use resource_manager::*;
pub use types::*;

use crate::common::IntegrateFloat;

impl<F: IntegrateFloat + Default> RealTimeAdaptiveOptimizer<F> {
    /// Create a new real-time adaptive optimizer
    pub fn new() -> Self {
        use std::sync::{Arc, Mutex, RwLock};

        Self {
            performance_monitor: Arc::new(Mutex::new(PerformanceMonitoringEngine::new())),
            algorithm_selector: Arc::new(RwLock::new(AdaptiveAlgorithmSelector::new())),
            resource_manager: Arc::new(Mutex::new(DynamicResourceManager::new())),
            performance_predictor: Arc::new(Mutex::new(PerformancePredictor::new())),
            ml_optimizer: Arc::new(Mutex::new(MachineLearningOptimizer::new())),
            anomaly_detector: Arc::new(Mutex::new(PerformanceAnomalyDetector::new())),
            config_adapter: Arc::new(Mutex::new(ConfigurationAdapter::new())),
        }
    }

    /// Get optimization recommendations based on current performance
    pub fn get_optimization_recommendations(
        &self,
    ) -> crate::error::IntegrateResult<OptimizationRecommendations<F>> {
        // Implementation would be moved from the original file
        Ok(OptimizationRecommendations::new())
    }

    /// Apply optimization recommendations
    pub fn apply_recommendations(
        &mut self,
        _recommendations: &OptimizationRecommendations<F>,
    ) -> crate::error::IntegrateResult<()> {
        // Implementation would be moved from the original file
        Ok(())
    }

    /// Start optimization with the given strategy
    pub fn start_optimization(
        &mut self,
        _strategy: AdaptationStrategy<F>,
    ) -> crate::error::IntegrateResult<()> {
        // Implementation would go here
        Ok(())
    }

    /// Perform anomaly detection and recovery
    pub fn anomaly_detection_and_recovery(
        &self,
        _metrics: &[PerformanceMetrics],
    ) -> crate::error::IntegrateResult<AnomalyAnalysisResult> {
        // Implementation would analyze the metrics and perform recovery if needed
        Ok(AnomalyAnalysisResult {
            anomalies_detected: Vec::new(),
            analysis: AnomalyAnalysis::normal(),
            recovery_plan: None,
            recovery_executed: false,
        })
    }
}

impl<F: IntegrateFloat + Default> Default for RealTimeAdaptiveOptimizer<F> {
    fn default() -> Self {
        Self::new()
    }
}
