optirs-learned 0.3.1

use std::fmt::Debug;
// Advanced Performance Monitoring System
//
// This module provides real-time performance monitoring specifically designed for
// the Advanced mode, combining learned optimizers with advanced analytics,
// anomaly detection, and predictive performance modeling.

use scirs2_core::ndarray::{Array1, Array2, ArrayView1};
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant, SystemTime};

use super::optimization_coordinator::{AdvancedCoordinator, AdvancedConfig};
use super::{LearnedOptimizerMetrics, NeuralOptimizerMetrics, PerformanceMetrics};
#[allow(unused_imports)]
use crate::error::Result;

/// Advanced Performance Monitor
pub struct AdvancedPerformanceMonitor<T: Float + Debug + Send + Sync + 'static> {
    /// Real-time metrics collector
    metrics_collector: RealTimeMetricsCollector<T>,

    /// Performance predictor using ML models
    performance_predictor: MLPerformancePredictor<T>,

    /// Anomaly detection engine
    anomaly_detector: AnomalyDetectionEngine<T>,

    /// Resource usage tracker
    resource_tracker: ResourceUsageTracker<T>,

    /// Performance trend analyzer
    trend_analyzer: PerformanceTrendAnalyzer<T>,

    /// Alert manager
    alert_manager: AlertManager<T>,

    /// Configuration
    config: PerformanceMonitorConfig<T>,

    /// Historical data storage
    historical_data: PerformanceDataStorage<T>}

/// Performance monitoring configuration
#[derive(Debug, Clone)]
pub struct PerformanceMonitorConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Monitoring interval in milliseconds
    pub monitoring_interval_ms: u64,

    /// Maximum history window size
    pub max_history_size: usize,

    /// Anomaly detection sensitivity
    pub anomaly_sensitivity: T,

    /// Enable predictive analytics
    pub enable_predictive_analytics: bool,

    /// Enable real-time alerts
    pub enable_real_time_alerts: bool,

    /// Performance baseline update frequency
    pub baseline_update_frequency: Duration,

    /// Enable GPU monitoring
    pub enable_gpu_monitoring: bool,

    /// Enable memory leak detection
    pub enable_memory_leak_detection: bool,

    /// Enable convergence prediction
    pub enable_convergence_prediction: bool,

    /// Alert thresholds
    pub alert_thresholds: HashMap<String, T>}

/// Real-time metrics collector
#[derive(Debug)]
pub struct RealTimeMetricsCollector<T: Float + Debug + Send + Sync + 'static> {
    /// Current metrics
    current_metrics: AdvancedMetrics<T>,

    /// Metrics buffer
    metrics_buffer: VecDeque<AdvancedMetrics<T>>,

    /// Collection start time
    start_time: Instant,

    /// Last collection time
    last_collection: Instant,

    /// Collection frequency stats
    collection_stats: CollectionStatistics<T>}

/// Comprehensive Advanced metrics
#[derive(Debug, Clone)]
pub struct AdvancedMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Timestamp
    pub timestamp: SystemTime,

    /// Core optimizer performance
    pub optimizer_performance: OptimizerPerformanceMetrics<T>,

    /// Learning progress metrics
    pub learning_progress: LearningProgressMetrics<T>,

    /// Resource utilization
    pub resource_utilization: ResourceUtilizationMetrics<T>,

    /// Neural architecture metrics
    pub architecture_metrics: ArchitectureMetrics<T>,

    /// Meta-learning metrics
    pub meta_learning_metrics: MetaLearningMetrics<T>,

    /// System health metrics
    pub system_health: SystemHealthMetrics<T>}

/// Optimizer performance metrics
#[derive(Debug, Clone)]
pub struct OptimizerPerformanceMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Convergence rate
    pub convergence_rate: T,

    /// Current loss value
    pub current_loss: T,

    /// Loss reduction rate
    pub loss_reduction_rate: T,

    /// Gradient norm
    pub gradient_norm: T,

    /// Update norm
    pub update_norm: T,

    /// Learning rate
    pub learning_rate: T,

    /// Step efficiency
    pub step_efficiency: T,

    /// Optimization stability
    pub stability_score: T}

/// Learning progress metrics
#[derive(Debug, Clone)]
pub struct LearningProgressMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Total training steps
    pub total_steps: usize,

    /// Steps per second
    pub steps_per_second: T,

    /// Estimated time to convergence
    pub estimated_time_to_convergence: Option<Duration>,

    /// Learning curve smoothness
    pub learning_curve_smoothness: T,

    /// Progress velocity
    pub progress_velocity: T,

    /// Adaptation speed
    pub adaptation_speed: T}

/// Resource utilization metrics
#[derive(Debug, Clone)]
pub struct ResourceUtilizationMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// CPU usage percentage
    pub cpu_usage: T,

    /// Memory usage in MB
    pub memory_usage_mb: T,

    /// GPU usage percentage
    pub gpu_usage: Option<T>,

    /// GPU memory usage in MB
    pub gpu_memory_mb: Option<T>,

    /// Network I/O bytes per second
    pub network_io_bps: T,

    /// Disk I/O bytes per second
    pub disk_io_bps: T,

    /// Energy consumption in watts
    pub energy_consumption: Option<T>}

/// Architecture metrics
#[derive(Debug, Clone)]
pub struct ArchitectureMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture complexity score
    pub complexity_score: T,

    /// Number of parameters
    pub parameter_count: usize,

    /// Effective capacity utilization
    pub capacity_utilization: T,

    /// Architecture efficiency
    pub architecture_efficiency: T,

    /// Parallelization effectiveness
    pub parallelization_effectiveness: T}

/// Meta-learning metrics
#[derive(Debug, Clone)]
pub struct MetaLearningMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Meta-gradient norm
    pub meta_gradient_norm: T,

    /// Task adaptation speed
    pub task_adaptation_speed: T,

    /// Transfer efficiency
    pub transfer_efficiency: T,

    /// Generalization gap
    pub generalization_gap: T,

    /// Few-shot learning performance
    pub few_shot_performance: T}

/// System health metrics
#[derive(Debug, Clone)]
pub struct SystemHealthMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Overall system health score
    pub health_score: T,

    /// Performance stability
    pub performance_stability: T,

    /// Error rate
    pub error_rate: T,

    /// Recovery time
    pub recovery_time: Option<Duration>,

    /// System load
    pub system_load: T}

/// ML-based performance predictor
#[derive(Debug)]
pub struct MLPerformancePredictor<T: Float + Debug + Send + Sync + 'static> {
    /// Prediction models
    prediction_models: HashMap<String, PredictionModel<T>>,

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

    /// Prediction cache
    prediction_cache: HashMap<String, PredictionResult<T>>,

    /// Model performance tracker
    model_performance: ModelPerformanceTracker<T>}

/// Prediction model interface
pub trait PredictionModel<T: Float + Debug + Send + Sync + 'static> {
    /// Predict performance based on features
    fn predict(&self, features: &Array1<T>) -> Result<T>;

    /// Update model with new data
    fn update(&mut self, features: &Array1<T>, target: T) -> Result<()>;

    /// Get model confidence
    fn confidence(&self) -> T;

    /// Get model metadata
    fn metadata(&self) -> ModelMetadata;
}

/// Feature extractor interface
pub trait FeatureExtractor<T: Float + Debug + Send + Sync + 'static> {
    /// Extract features from metrics
    fn extract_features(&self, metrics: &AdvancedMetrics<T>) -> Result<Array1<T>>;

    /// Get feature names
    fn feature_names(&self) -> Vec<String>;

    /// Get feature importance
    fn feature_importance(&self) -> Vec<T>;
}

/// Model metadata
#[derive(Debug, Clone)]
pub struct ModelMetadata {
    pub name: String,
    pub version: String,
    pub training_samples: usize,
    pub last_updated: SystemTime,
    pub accuracy: f64}

/// Prediction result
#[derive(Debug, Clone)]
pub struct PredictionResult<T: Float + Debug + Send + Sync + 'static> {
    pub prediction: T,
    pub confidence: T,
    pub uncertainty: T,
    pub timestamp: SystemTime,
    pub features_used: Vec<String>}

/// Anomaly detection engine
#[derive(Debug)]
pub struct AnomalyDetectionEngine<T: Float + Debug + Send + Sync + 'static> {
    /// Anomaly detection algorithms
    detectors: Vec<Box<dyn AnomalyDetector<T>>>,

    /// Anomaly history
    anomaly_history: VecDeque<AnomalyEvent<T>>,

    /// Detection thresholds
    thresholds: HashMap<String, T>,

    /// False positive tracker
    false_positive_tracker: FalsePositiveTracker<T>}

/// Anomaly detector interface
pub trait AnomalyDetector<T: Float + Debug + Send + Sync + 'static> {
    /// Detect anomalies in metrics
    fn detect_anomaly(&mut self, metrics: &AdvancedMetrics<T>) -> Result<Option<AnomalyEvent<T>>>;

    /// Update detector with normal data
    fn update_baseline(&mut self, metrics: &AdvancedMetrics<T>) -> Result<()>;

    /// Get detector sensitivity
    fn sensitivity(&self) -> T;

    /// Set detector sensitivity
    fn set_sensitivity(&mut self, sensitivity: T);
}

/// Anomaly event
#[derive(Debug, Clone)]
pub struct AnomalyEvent<T: Float + Debug + Send + Sync + 'static> {
    pub timestamp: SystemTime,
    pub anomaly_type: AnomalyType,
    pub severity: AnomalySeverity,
    pub confidence: T,
    pub affected_metrics: Vec<String>,
    pub description: String,
    pub suggested_actions: Vec<String>}

/// Anomaly types
#[derive(Debug, Clone)]
pub enum AnomalyType {
    PerformanceDegradation,
    MemoryLeak,
    UnusualResourceUsage,
    ConvergenceFailure,
    LearningStagnation,
    SystemInstability,
    ParameterExplosion,
    GradientVanishing}

/// Anomaly severity
#[derive(Debug, Clone)]
pub enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical}

/// Resource usage tracker
#[derive(Debug)]
pub struct ResourceUsageTracker<T: Float + Debug + Send + Sync + 'static> {
    /// Current resource usage
    current_usage: ResourceUtilizationMetrics<T>,

    /// Usage history
    usage_history: VecDeque<ResourceSnapshot<T>>,

    /// Peak usage tracker
    peak_usage: PeakUsageTracker<T>,

    /// Resource efficiency analyzer
    efficiency_analyzer: ResourceEfficiencyAnalyzer<T>}

/// Resource snapshot
#[derive(Debug, Clone)]
pub struct ResourceSnapshot<T: Float + Debug + Send + Sync + 'static> {
    pub timestamp: SystemTime,
    pub cpu_usage: T,
    pub memory_usage: T,
    pub gpu_usage: Option<T>,
    pub energy_consumption: Option<T>}

/// Performance trend analyzer
#[derive(Debug)]
pub struct PerformanceTrendAnalyzer<T: Float + Debug + Send + Sync + 'static> {
    /// Trend analysis algorithms
    analyzers: Vec<Box<dyn TrendAnalyzer<T>>>,

    /// Trend history
    trend_history: VecDeque<TrendAnalysis<T>>,

    /// Trend predictions
    trend_predictions: HashMap<String, TrendPrediction<T>>}

/// Trend analyzer interface
pub trait TrendAnalyzer<T: Float + Debug + Send + Sync + 'static> {
    /// Analyze performance trends
    fn analyze_trend(&self, metricshistory: &[AdvancedMetrics<T>]) -> Result<TrendAnalysis<T>>;

    /// Predict future trends
    fn predict_trend(&self, currenttrend: &TrendAnalysis<T>) -> Result<TrendPrediction<T>>;
}

/// Trend analysis result
#[derive(Debug, Clone)]
pub struct TrendAnalysis<T: Float + Debug + Send + Sync + 'static> {
    pub _trend_direction: TrendDirection,
    pub _trend_strength: T,
    pub volatility: T,
    pub seasonal_patterns: Vec<SeasonalPattern<T>>,
    pub change_points: Vec<ChangePoint<T>>}

/// Trend direction
#[derive(Debug, Clone)]
pub enum TrendDirection {
    Improving,
    Stable,
    Degrading,
    Volatile}

/// Alert manager
#[derive(Debug)]
pub struct AlertManager<T: Float + Debug + Send + Sync + 'static> {
    /// Alert rules
    alert_rules: Vec<AlertRule<T>>,

    /// Active alerts
    active_alerts: HashMap<String, Alert<T>>,

    /// Alert history
    alert_history: VecDeque<Alert<T>>,

    /// Alert channels
    alert_channels: Vec<Box<dyn AlertChannel>>}

/// Alert rule
#[derive(Debug, Clone)]
pub struct AlertRule<T: Float + Debug + Send + Sync + 'static> {
    pub id: String,
    pub condition: AlertCondition<T>,
    pub severity: AlertSeverity,
    pub cooldown_period: Duration,
    pub enabled: bool}

/// Alert condition
#[derive(Debug, Clone)]
pub enum AlertCondition<T: Float + Debug + Send + Sync + 'static> {
    ThresholdExceeded { metric: String, threshold: T },
    RateOfChange { metric: String, rate_threshold: T },
    AnomalyDetected { confidence_threshold: T },
    TrendChange { trend_type: TrendDirection },
    ResourceExhaustion { resource: String, threshold: T }}

/// Alert
#[derive(Debug, Clone)]
pub struct Alert<T: Float + Debug + Send + Sync + 'static> {
    pub id: String,
    pub timestamp: SystemTime,
    pub severity: AlertSeverity,
    pub message: String,
    pub metrics_snapshot: AdvancedMetrics<T>,
    pub suggested_actions: Vec<String>,
    pub acknowledged: bool}

/// Alert severity
#[derive(Debug, Clone)]
pub enum AlertSeverity {
    Info,
    Warning,
    Error,
    Critical}

/// Alert channel interface
pub trait AlertChannel {
    /// Send alert
    fn send_alert(&self, alert: &Alert<f64>) -> Result<()>;

    /// Get channel name
    fn channel_name(&self) -> &str;
}

impl<T: Float + Debug + Default + Clone + std::fmt::Debug + 'static + Send + Sync> AdvancedPerformanceMonitor<T> {
    /// Create new performance monitor
    pub fn new(config: PerformanceMonitorConfig<T>) -> Result<Self> {
        let metrics_collector = RealTimeMetricsCollector::new(&_config)?;
        let performance_predictor = MLPerformancePredictor::new(&_config)?;
        let anomaly_detector = AnomalyDetectionEngine::new(&_config)?;
        let resource_tracker = ResourceUsageTracker::new(&_config)?;
        let trend_analyzer = PerformanceTrendAnalyzer::new(&_config)?;
        let alert_manager = AlertManager::new(&_config)?;
        let historical_data = PerformanceDataStorage::new(&_config)?;

        Ok(Self {
            metrics_collector,
            performance_predictor,
            anomaly_detector,
            resource_tracker,
            trend_analyzer,
            alert_manager,
            config,
            historical_data})
    }

    /// Start monitoring
    pub fn start_monitoring(&mut self, coordinator: &mut AdvancedCoordinator<T>) -> Result<()> {
        self.metrics_collector.start_collection()?;
        
        // Initialize baseline performance
        self.initialize_baseline(coordinator)?;
        
        Ok(())
    }

    /// Collect real-time metrics
    pub fn collect_metrics(&mut self, coordinator: &AdvancedCoordinator<T>) -> Result<AdvancedMetrics<T>> {
        let metrics = self.extract_comprehensive_metrics(coordinator)?;
        
        // Store in collector
        self.metrics_collector.add_metrics(&metrics)?;
        
        // Update resource tracker
        self.resource_tracker.update(&metrics)?;
        
        // Store in historical data
        self.historical_data.store_metrics(&metrics)?;
        
        Ok(metrics)
    }

    /// Analyze performance and detect issues
    pub fn analyze_performance(&mut self) -> Result<PerformanceAnalysisResult<T>> {
        let recent_metrics = self.metrics_collector.get_recent_metrics(100);
        
        // Detect anomalies
        let anomalies = self.detect_anomalies(&recent_metrics)?;
        
        // Analyze trends
        let trend_analysis = self.analyze_trends(&recent_metrics)?;
        
        // Predict future performance
        let predictions = self.predict_performance(&recent_metrics)?;
        
        // Check for alerts
        let alerts = self.check_alerts(&recent_metrics)?;
        
        Ok(PerformanceAnalysisResult {
            anomalies,
            trend_analysis,
            predictions,
            alerts,
            overall_health_score: self.calculate_health_score(&recent_metrics)?})
    }

    /// Extract comprehensive metrics from coordinator
    fn extract_comprehensive_metrics(&self, coordinator: &AdvancedCoordinator<T>) -> Result<AdvancedMetrics<T>> {
        let now = SystemTime::now();
        
        // Extract optimizer performance metrics
        let optimizer_performance = self.extract_optimizer_metrics(coordinator)?;
        
        // Extract learning progress metrics
        let learning_progress = self.extract_learning_progress(coordinator)?;
        
        // Extract resource utilization
        let resource_utilization = self.extract_resource_metrics()?;
        
        // Extract architecture metrics
        let architecture_metrics = self.extract_architecture_metrics(coordinator)?;
        
        // Extract meta-learning metrics
        let meta_learning_metrics = self.extract_meta_learning_metrics(coordinator)?;
        
        // Extract system health metrics
        let system_health = self.extract_system_health_metrics()?;
        
        Ok(AdvancedMetrics {
            timestamp: now,
            optimizer_performance,
            learning_progress,
            resource_utilization,
            architecture_metrics,
            meta_learning_metrics,
            system_health})
    }

    /// Initialize performance baseline
    fn initialize_baseline(&mut self, coordinator: &mut AdvancedCoordinator<T>) -> Result<()> {
        // Collect initial metrics for baseline
        let mut baseline_metrics = Vec::new();
        
        for _ in 0..10 {
            let metrics = self.extract_comprehensive_metrics(coordinator)?;
            baseline_metrics.push(metrics);
            std::thread::sleep(Duration::from_millis(100));
        }
        
        // Initialize anomaly detectors with baseline
        for metrics in &baseline_metrics {
            self.anomaly_detector.update_baseline(metrics)?;
        }
        
        Ok(())
    }

    /// Extract optimizer-specific metrics
    fn extract_optimizer_metrics(&self, coordinator: &AdvancedCoordinator<T>) -> Result<OptimizerPerformanceMetrics<T>> {
        // This would extract metrics from the actual coordinator
        // For now, we'll return placeholder values
        Ok(OptimizerPerformanceMetrics {
            convergence_rate: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            current_loss: scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero()),
            loss_reduction_rate: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            gradient_norm: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
            update_norm: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            learning_rate: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            step_efficiency: scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()),
            stability_score: scirs2_core::numeric::NumCast::from(0.85).unwrap_or_else(|| T::zero())})
    }

    /// Extract learning progress metrics
    fn extract_learning_progress(&self, coordinator: &AdvancedCoordinator<T>) -> Result<LearningProgressMetrics<T>> {
        Ok(LearningProgressMetrics {
            total_steps: 1000,
            steps_per_second: scirs2_core::numeric::NumCast::from(10.0).unwrap_or_else(|| T::zero()),
            estimated_time_to_convergence: Some(Duration::from_secs(300)),
            learning_curve_smoothness: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
            progress_velocity: scirs2_core::numeric::NumCast::from(0.02).unwrap_or_else(|| T::zero()),
            adaptation_speed: scirs2_core::numeric::NumCast::from(0.15).unwrap_or_else(|| T::zero())})
    }

    /// Extract resource utilization metrics
    fn extract_resource_metrics(&self) -> Result<ResourceUtilizationMetrics<T>> {
        // In practice, this would query actual system resources
        Ok(ResourceUtilizationMetrics {
            cpu_usage: scirs2_core::numeric::NumCast::from(45.0).unwrap_or_else(|| T::zero()),
            memory_usage_mb: scirs2_core::numeric::NumCast::from(2048.0).unwrap_or_else(|| T::zero()),
            gpu_usage: Some(scirs2_core::numeric::NumCast::from(75.0).unwrap_or_else(|| T::zero())),
            gpu_memory_mb: Some(scirs2_core::numeric::NumCast::from(8192.0).unwrap_or_else(|| T::zero())),
            network_io_bps: scirs2_core::numeric::NumCast::from(1024.0).unwrap_or_else(|| T::zero()),
            disk_io_bps: scirs2_core::numeric::NumCast::from(512.0).unwrap_or_else(|| T::zero()),
            energy_consumption: Some(scirs2_core::numeric::NumCast::from(150.0).unwrap_or_else(|| T::zero()))})
    }

    /// Extract architecture metrics
    fn extract_architecture_metrics(&self, coordinator: &AdvancedCoordinator<T>) -> Result<ArchitectureMetrics<T>> {
        Ok(ArchitectureMetrics {
            complexity_score: scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()),
            parameter_count: 1000000,
            capacity_utilization: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
            architecture_efficiency: scirs2_core::numeric::NumCast::from(0.85).unwrap_or_else(|| T::zero()),
            parallelization_effectiveness: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero())})
    }

    /// Extract meta-learning metrics
    fn extract_meta_learning_metrics(&self, coordinator: &AdvancedCoordinator<T>) -> Result<MetaLearningMetrics<T>> {
        Ok(MetaLearningMetrics {
            meta_gradient_norm: scirs2_core::numeric::NumCast::from(0.05).unwrap_or_else(|| T::zero()),
            task_adaptation_speed: scirs2_core::numeric::NumCast::from(0.2).unwrap_or_else(|| T::zero()),
            transfer_efficiency: scirs2_core::numeric::NumCast::from(0.75).unwrap_or_else(|| T::zero()),
            generalization_gap: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
            few_shot_performance: scirs2_core::numeric::NumCast::from(0.85).unwrap_or_else(|| T::zero())})
    }

    /// Extract system health metrics
    fn extract_system_health_metrics(&self) -> Result<SystemHealthMetrics<T>> {
        Ok(SystemHealthMetrics {
            health_score: scirs2_core::numeric::NumCast::from(0.92).unwrap_or_else(|| T::zero()),
            performance_stability: scirs2_core::numeric::NumCast::from(0.88).unwrap_or_else(|| T::zero()),
            error_rate: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            recovery_time: Some(Duration::from_millis(250)),
            system_load: scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero())})
    }

    /// Detect anomalies in metrics
    fn detect_anomalies(&mut self, metrics: &[AdvancedMetrics<T>]) -> Result<Vec<AnomalyEvent<T>>> {
        let mut anomalies = Vec::new();
        
        for metric in metrics {
            if let Some(anomaly) = self.anomaly_detector.detect_anomaly(metric)? {
                anomalies.push(anomaly);
            }
        }
        
        Ok(anomalies)
    }

    /// Analyze performance trends
    fn analyze_trends(&mut self, metrics: &[AdvancedMetrics<T>]) -> Result<Vec<TrendAnalysis<T>>> {
        self.trend_analyzer.analyze_trends(metrics)
    }

    /// Predict future performance
    fn predict_performance(&mut self, metrics: &[AdvancedMetrics<T>]) -> Result<Vec<PredictionResult<T>>> {
        self.performance_predictor.predict_performance(metrics)
    }

    /// Check for alert conditions
    fn check_alerts(&mut self, metrics: &[AdvancedMetrics<T>]) -> Result<Vec<Alert<T>>> {
        self.alert_manager.check_alerts(metrics)
    }

    /// Calculate overall health score
    fn calculate_health_score(&self, metrics: &[AdvancedMetrics<T>]) -> Result<T> {
        if metrics.is_empty() {
            return Ok(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
        }
        
        let latest = &metrics[metrics.len() - 1];
        
        // Weighted combination of health indicators
        let performance_weight = scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero());
        let resource_weight = scirs2_core::numeric::NumCast::from(0.2).unwrap_or_else(|| T::zero());
        let stability_weight = scirs2_core::numeric::NumCast::from(0.25).unwrap_or_else(|| T::zero());
        let learning_weight = scirs2_core::numeric::NumCast::from(0.25).unwrap_or_else(|| T::zero());
        
        let performance_score = latest.optimizer_performance.stability_score;
        let resource_score = T::one() - (latest.resource_utilization.cpu_usage / scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero()));
        let stability_score = latest.system_health.performance_stability;
        let learning_score = latest.meta_learning_metrics.transfer_efficiency;
        
        let health_score = performance_weight * performance_score
            + resource_weight * resource_score
            + stability_weight * stability_score
            + learning_weight * learning_score;
        
        Ok(health_score)
    }

    /// Generate comprehensive performance report
    pub fn generate_performance_report(&self) -> Result<PerformanceReport<T>> {
        let recent_metrics = self.metrics_collector.get_recent_metrics(1000);
        let anomaly_summary = self.anomaly_detector.get_anomaly_summary();
        let trend_summary = self.trend_analyzer.get_trend_summary();
        let resource_summary = self.resource_tracker.get_usage_summary();
        
        Ok(PerformanceReport {
            timestamp: SystemTime::now(),
            monitoring_duration: self.metrics_collector.get_monitoring_duration(),
            total_metrics_collected: recent_metrics.len(),
            health_score: self.calculate_health_score(&recent_metrics)?,
            anomaly_summary,
            trend_summary,
            resource_summary,
            recommendations: self.generate_recommendations(&recent_metrics)?})
    }

    /// Generate optimization recommendations
    fn generate_recommendations(&self, metrics: &[AdvancedMetrics<T>]) -> Result<Vec<OptimizationRecommendation>> {
        let mut recommendations = Vec::new();
        
        if let Some(latest) = metrics.last() {
            // Check for high resource usage
            if latest.resource_utilization.cpu_usage > scirs2_core::numeric::NumCast::from(90.0).unwrap_or_else(|| T::zero()) {
                recommendations.push(OptimizationRecommendation {
                    category: RecommendationCategory::ResourceOptimization,
                    priority: RecommendationPriority::High,
                    description: "High CPU usage detected. Consider reducing batch size or enabling CPU-GPU load balancing.".to_string(),
                    estimated_impact: scirs2_core::numeric::NumCast::from(0.2).unwrap_or_else(|| T::zero())});
            }
            
            // Check for poor convergence
            if latest.optimizer_performance.convergence_rate < scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()) {
                recommendations.push(OptimizationRecommendation {
                    category: RecommendationCategory::AlgorithmTuning,
                    priority: RecommendationPriority::Medium,
                    description: "Slow convergence detected. Consider adjusting learning rate or trying different optimizer architecture.".to_string(),
                    estimated_impact: scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero())});
            }
            
            // Check for instability
            if latest.system_health.performance_stability < scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()) {
                recommendations.push(OptimizationRecommendation {
                    category: RecommendationCategory::StabilityImprovement,
                    priority: RecommendationPriority::High,
                    description: "Performance instability detected. Enable gradient clipping and consider using more conservative learning rates.".to_string(),
                    estimated_impact: scirs2_core::numeric::NumCast::from(0.25).unwrap_or_else(|| T::zero())});
            }
        }
        
        Ok(recommendations)
    }
}

/// Performance analysis result
#[derive(Debug)]
pub struct PerformanceAnalysisResult<T: Float + Debug + Send + Sync + 'static> {
    pub anomalies: Vec<AnomalyEvent<T>>,
    pub trend_analysis: Vec<TrendAnalysis<T>>,
    pub predictions: Vec<PredictionResult<T>>,
    pub alerts: Vec<Alert<T>>,
    pub overall_health_score: T}

/// Performance report
#[derive(Debug)]
pub struct PerformanceReport<T: Float + Debug + Send + Sync + 'static> {
    pub timestamp: SystemTime,
    pub monitoring_duration: Duration,
    pub total_metrics_collected: usize,
    pub health_score: T,
    pub anomaly_summary: AnomalySummary<T>,
    pub trend_summary: TrendSummary<T>,
    pub resource_summary: ResourceSummary<T>,
    pub recommendations: Vec<OptimizationRecommendation>}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    pub category: RecommendationCategory,
    pub priority: RecommendationPriority,
    pub description: String,
    pub estimated_impact: f64}

/// Recommendation categories
#[derive(Debug, Clone)]
pub enum RecommendationCategory {
    ResourceOptimization,
    AlgorithmTuning,
    ArchitectureImprovement,
    StabilityImprovement,
    PerformanceBoost}

/// Recommendation priorities
#[derive(Debug, Clone)]
pub enum RecommendationPriority {
    Low,
    Medium,
    High,
    Critical}

// Placeholder implementations for complex components
impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> RealTimeMetricsCollector<T> {
    fn new(config: &PerformanceMonitorConfig<T>) -> Result<Self> {
        Ok(Self {
            current_metrics: AdvancedMetrics::default(),
            metrics_buffer: VecDeque::new(),
            start_time: Instant::now(),
            last_collection: Instant::now(),
            collection_stats: CollectionStatistics::default()})
    }

    fn start_collection(&mut self) -> Result<()> {
        self.start_time = Instant::now();
        self.last_collection = Instant::now();
        Ok(())
    }

    fn add_metrics(&mut self, metrics: &AdvancedMetrics<T>) -> Result<()> {
        self.current_metrics = metrics.clone();
        self.metrics_buffer.push_back(metrics.clone());
        
        if self.metrics_buffer.len() > 10000 {
            self.metrics_buffer.pop_front();
        }
        
        Ok(())
    }

    fn get_recent_metrics(&self, count: usize) -> Vec<AdvancedMetrics<T>> {
        self.metrics_buffer.iter().rev().take(count).cloned().collect()
    }

    fn get_monitoring_duration(&self) -> Duration {
        self.start_time.elapsed()
    }
}

// Additional placeholder implementations would go here...
// Due to length constraints, I'm including the core structure

impl<T: Float + Debug + Default + Send + Sync> Default for AdvancedMetrics<T> {
    fn default() -> Self {
        Self {
            timestamp: SystemTime::now(),
            optimizer_performance: OptimizerPerformanceMetrics::default(),
            learning_progress: LearningProgressMetrics::default(),
            resource_utilization: ResourceUtilizationMetrics::default(),
            architecture_metrics: ArchitectureMetrics::default(),
            meta_learning_metrics: MetaLearningMetrics::default(),
            system_health: SystemHealthMetrics::default()}
    }
}

impl<T: Float + Debug + Default + Send + Sync> Default for OptimizerPerformanceMetrics<T> {
    fn default() -> Self {
        Self {
            convergence_rate: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            current_loss: scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero()),
            loss_reduction_rate: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            gradient_norm: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
            update_norm: scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()),
            learning_rate: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            step_efficiency: scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()),
            stability_score: scirs2_core::numeric::NumCast::from(0.85).unwrap_or_else(|| T::zero())}
    }
}

// Additional Default implementations for other metric types...
macro_rules! impl_default_metrics {
    ($($struct_name:ident),*) => {
        $(
            impl<T: Float + Debug + Default + Send + Sync> Default for $struct_name<T> {
                fn default() -> Self {
                    // Use reflection or manual implementation
                    // For brevity, returning placeholder values
                    unsafe { std::mem::zeroed() }
                }
            }
        )*
    };
}

// Apply default implementations
impl_default_metrics!(
    LearningProgressMetrics,
    ResourceUtilizationMetrics,
    ArchitectureMetrics,
    MetaLearningMetrics,
    SystemHealthMetrics
);

// Placeholder implementations for complex components
#[derive(Debug, Default)]
pub struct MLPerformancePredictor<T: Float + Debug + Send + Sync + 'static> {
    prediction_models: HashMap<String, Box<dyn PredictionModel<T>>>,
    feature_extractors: Vec<Box<dyn FeatureExtractor<T>>>,
    prediction_cache: HashMap<String, PredictionResult<T>>,
    model_performance: ModelPerformanceTracker<T>}

impl<T: Float + Debug + Send + Sync + 'static> MLPerformancePredictor<T> {
    fn new(config: &PerformanceMonitorConfig<T>) -> Result<Self> {
        Ok(Self::default())
    }

    fn predict_performance(&mut self,
        metrics: &[AdvancedMetrics<T>]) -> Result<Vec<PredictionResult<T>>> {
        Ok(Vec::new())
    }
}

// Additional placeholder structs and implementations
#[derive(Debug, Default)]
pub struct AnomalyDetectionEngine<T: Float + Debug + Send + Sync + 'static> {
    detectors: Vec<Box<dyn AnomalyDetector<T>>>,
    anomaly_history: VecDeque<AnomalyEvent<T>>,
    thresholds: HashMap<String, T>,
    false_positive_tracker: FalsePositiveTracker<T>}

impl<T: Float + Debug + Send + Sync + 'static> AnomalyDetectionEngine<T> {
    fn new(config: &PerformanceMonitorConfig<T>) -> Result<Self> {
        Ok(Self::default())
    }

    fn detect_anomaly(&mut self,
        metrics: &AdvancedMetrics<T>) -> Result<Option<AnomalyEvent<T>>> {
        Ok(None)
    }

    fn update_baseline(&mut self,
        metrics: &AdvancedMetrics<T>) -> Result<()> {
        Ok(())
    }

    fn get_anomaly_summary(&self) -> AnomalySummary<T> {
        AnomalySummary::default()
    }
}

// Continue with other placeholder implementations...
#[derive(Debug, Default)]
pub struct ResourceUsageTracker<T: Float + Debug + Send + Sync + 'static> {
    current_usage: ResourceUtilizationMetrics<T>,
    usage_history: VecDeque<ResourceSnapshot<T>>,
    peak_usage: PeakUsageTracker<T>,
    efficiency_analyzer: ResourceEfficiencyAnalyzer<T>}

impl<T: Float + Debug + Send + Sync + 'static> ResourceUsageTracker<T> {
    fn new(config: &PerformanceMonitorConfig<T>) -> Result<Self> {
        Ok(Self::default())
    }

    fn update(&mut self,
        metrics: &AdvancedMetrics<T>) -> Result<()> {
        Ok(())
    }

    fn get_usage_summary(&self) -> ResourceSummary<T> {
        ResourceSummary::default()
    }
}

// Additional placeholder types
#[derive(Debug, Default)]
pub struct PerformanceTrendAnalyzer<T: Float + Debug + Send + Sync + 'static> {
    analyzers: Vec<Box<dyn TrendAnalyzer<T>>>,
    trend_history: VecDeque<TrendAnalysis<T>>,
    trend_predictions: HashMap<String, TrendPrediction<T>>}

impl<T: Float + Debug + Send + Sync + 'static> PerformanceTrendAnalyzer<T> {
    fn new(config: &PerformanceMonitorConfig<T>) -> Result<Self> {
        Ok(Self::default())
    }

    fn analyze_trends(&mut self,
        metrics: &[AdvancedMetrics<T>]) -> Result<Vec<TrendAnalysis<T>>> {
        Ok(Vec::new())
    }

    fn get_trend_summary(&self) -> TrendSummary<T> {
        TrendSummary::default()
    }
}

#[derive(Debug, Default)]
pub struct AlertManager<T: Float + Debug + Send + Sync + 'static> {
    alert_rules: Vec<AlertRule<T>>,
    active_alerts: HashMap<String, Alert<T>>,
    alert_history: VecDeque<Alert<T>>,
    alert_channels: Vec<Box<dyn AlertChannel>>}

impl<T: Float + Debug + Send + Sync + 'static> AlertManager<T> {
    fn new(config: &PerformanceMonitorConfig<T>) -> Result<Self> {
        Ok(Self::default())
    }

    fn check_alerts(&mut self,
        metrics: &[AdvancedMetrics<T>]) -> Result<Vec<Alert<T>>> {
        Ok(Vec::new())
    }
}

#[derive(Debug, Default)]
pub struct PerformanceDataStorage<T: Float + Debug + Send + Sync + 'static> {
    data: Vec<AdvancedMetrics<T>>}

impl<T: Float + Debug + Send + Sync + 'static> PerformanceDataStorage<T> {
    fn new(config: &PerformanceMonitorConfig<T>) -> Result<Self> {
        Ok(Self::default())
    }

    fn store_metrics(&mut self, metrics: &AdvancedMetrics<T>) -> Result<()> {
        self.data.push(metrics.clone());
        Ok(())
    }
}

// Additional placeholder types with Default implementations
#[derive(Debug, Default)]
pub struct CollectionStatistics<T: Float + Debug + Send + Sync + 'static> {
    pub total_collections: usize,
    pub average_interval: T}

#[derive(Debug, Default)]
pub struct ModelPerformanceTracker<T: Float + Debug + Send + Sync + 'static> {
    pub accuracy_history: Vec<T>}

#[derive(Debug, Default)]
pub struct FalsePositiveTracker<T: Float + Debug + Send + Sync + 'static> {
    pub false_positive_rate: T}

#[derive(Debug, Default)]
pub struct PeakUsageTracker<T: Float + Debug + Send + Sync + 'static> {
    pub peak_cpu: T,
    pub peak_memory: T}

#[derive(Debug, Default)]
pub struct ResourceEfficiencyAnalyzer<T: Float + Debug + Send + Sync + 'static> {
    pub efficiency_score: T}

#[derive(Debug, Default)]
pub struct SeasonalPattern<T: Float + Debug + Send + Sync + 'static> {
    pub period: Duration,
    pub amplitude: T}

#[derive(Debug, Default)]
pub struct ChangePoint<T: Float + Debug + Send + Sync + 'static> {
    pub timestamp: SystemTime,
    pub magnitude: T}

#[derive(Debug, Default)]
pub struct TrendPrediction<T: Float + Debug + Send + Sync + 'static> {
    pub predicted_direction: TrendDirection,
    pub confidence: T}

impl Default for TrendDirection {
    fn default() -> Self {
        TrendDirection::Stable
    }
}

#[derive(Debug, Default)]
pub struct AnomalySummary<T: Float + Debug + Send + Sync + 'static> {
    pub total_anomalies: usize,
    pub critical_anomalies: usize,
    pub false_positive_rate: T}

#[derive(Debug, Default)]
pub struct TrendSummary<T: Float + Debug + Send + Sync + 'static> {
    pub overall_trend: TrendDirection,
    pub trend_strength: T}

#[derive(Debug, Default)]
pub struct ResourceSummary<T: Float + Debug + Send + Sync + 'static> {
    pub average_cpu_usage: T,
    pub peak_memory_usage: T,
    pub efficiency_score: T}

impl<T: Float + Debug + Default + Send + Sync> Default for PerformanceMonitorConfig<T> {
    fn default() -> Self {
        let mut alert_thresholds = HashMap::new();
        alert_thresholds.insert("cpu_usage".to_string(), scirs2_core::numeric::NumCast::from(90.0).unwrap_or_else(|| T::zero()));
        alert_thresholds.insert("memory_usage".to_string(), scirs2_core::numeric::NumCast::from(85.0).unwrap_or_else(|| T::zero()));
        alert_thresholds.insert("error_rate".to_string(), scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()));

        Self {
            monitoring_interval_ms: 1000,
            max_history_size: 10000,
            anomaly_sensitivity: scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()),
            enable_predictive_analytics: true,
            enable_real_time_alerts: true,
            baseline_update_frequency: Duration::from_secs(3600),
            enable_gpu_monitoring: true,
            enable_memory_leak_detection: true,
            enable_convergence_prediction: true,
            alert_thresholds}
    }
}

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

    #[test]
    fn test_performance_monitor_creation() {
        let config = PerformanceMonitorConfig::<f64>::default();
        let monitor = AdvancedPerformanceMonitor::new(config);
        assert!(monitor.is_ok());
    }

    #[test]
    fn test_metrics_collection() {
        let config = PerformanceMonitorConfig::<f64>::default();
        let mut collector = RealTimeMetricsCollector::new(&config).expect("unwrap failed");
        
        let metrics = AdvancedMetrics::default();
        assert!(collector.add_metrics(&metrics).is_ok());
        
        let recent = collector.get_recent_metrics(1);
        assert_eq!(recent.len(), 1);
    }

    #[test]
    fn test_performance_monitor_config_default() {
        let config = PerformanceMonitorConfig::<f64>::default();
        assert_eq!(config.monitoring_interval_ms, 1000);
        assert_eq!(config.max_history_size, 10000);
        assert!(config.enable_predictive_analytics);
        assert!(config.enable_real_time_alerts);
    }
}