torsh-backend 0.1.2

//! Runtime performance modeling and prediction system
//!
//! This module provides advanced performance modeling capabilities including
//! machine learning-based predictions, historical data analysis, runtime adaptation,
//! and cross-workload performance correlation.

use crate::performance_tuning::{
    ActualPerformance, OperationType, PerformanceFeedback, PerformancePrediction, SystemState,
    TuningParameters, WorkloadCharacteristics,
};
use crate::{BackendResult, BackendType};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};
use torsh_core::error::TorshError;

#[cfg(feature = "serialize")]
use serde::{Deserialize, Serialize};

#[cfg(not(feature = "std"))]
use alloc::{boxed::Box, format, string::String, vec::Vec};

/// Runtime performance modeling system
pub struct RuntimePerformanceModeler {
    /// Historical performance database
    historical_data: Arc<RwLock<PerformanceDatabase>>,
    /// Machine learning models for prediction
    ml_models: Arc<RwLock<HashMap<BackendType, Box<dyn PerformanceModel + Send + Sync>>>>,
    /// Real-time performance monitor
    runtime_monitor: Arc<Mutex<RuntimeMonitor>>,
    /// Performance correlation analyzer
    correlation_analyzer: CorrelationAnalyzer,
    /// Anomaly detection system
    anomaly_detector: AnomalyDetector,
    /// Model update scheduler
    update_scheduler: ModelUpdateScheduler,
}

/// Historical performance data storage
#[derive(Debug)]
pub struct PerformanceDatabase {
    /// Performance measurements by backend
    measurements: HashMap<BackendType, VecDeque<PerformanceMeasurement>>,
    /// Performance trends
    #[allow(dead_code)]
    trends: HashMap<String, PerformanceTrend>,
    /// Workload patterns
    #[allow(dead_code)]
    patterns: HashMap<String, WorkloadPattern>,
    /// System state correlations
    #[allow(dead_code)]
    state_correlations: HashMap<String, SystemStateCorrelation>,
    /// Maximum entries per backend
    max_entries: usize,
}

/// Single performance measurement record
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct PerformanceMeasurement {
    /// Unique measurement ID
    pub id: u64,
    /// Timestamp of measurement
    pub timestamp: SystemTime,
    /// Backend type
    pub backend_type: BackendType,
    /// Device information
    pub device_id: usize,
    /// Workload characteristics
    pub workload: WorkloadCharacteristics,
    /// Tuning parameters used
    pub parameters: TuningParameters,
    /// System state during execution
    pub system_state: SystemState,
    /// Actual performance achieved
    pub actual_performance: ActualPerformance,
    /// Predicted performance (if available)
    pub predicted_performance: Option<PerformancePrediction>,
    /// Prediction accuracy
    pub prediction_accuracy: Option<f64>,
    /// Environmental factors
    pub environment: EnvironmentalFactors,
}

/// Environmental factors affecting performance
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct EnvironmentalFactors {
    /// Ambient temperature
    pub ambient_temperature: Option<f32>,
    /// System load
    pub system_load: f64,
    /// Background processes
    pub background_processes: usize,
    /// Network activity
    pub network_activity: f64,
    /// Storage I/O activity
    pub storage_io: f64,
    /// Available system memory
    pub available_memory: usize,
    /// CPU frequency scaling
    pub cpu_frequency: Option<u32>,
    /// GPU frequency scaling
    pub gpu_frequency: Option<u32>,
}

/// Performance trend analysis
#[derive(Debug, Clone)]
pub struct PerformanceTrend {
    /// Trend identifier
    pub id: String,
    /// Operation type
    pub operation: OperationType,
    /// Backend type
    pub backend: BackendType,
    /// Trend direction
    pub direction: TrendDirection,
    /// Trend strength (0.0 to 1.0)
    pub strength: f64,
    /// Time window for trend
    pub window: Duration,
    /// Measurements contributing to trend
    pub sample_count: usize,
    /// Statistical significance
    pub significance: f64,
    /// Last update timestamp
    pub last_updated: SystemTime,
}

/// Trend direction indicators
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TrendDirection {
    /// Performance improving over time
    Improving,
    /// Performance degrading over time
    Degrading,
    /// Performance remains stable
    Stable,
    /// Performance is highly variable
    Volatile,
}

/// Workload pattern recognition
#[derive(Debug, Clone)]
pub struct WorkloadPattern {
    /// Pattern identifier
    pub id: String,
    /// Pattern type
    pub pattern_type: PatternType,
    /// Characteristic features
    pub features: Vec<f64>,
    /// Frequency of occurrence
    pub frequency: f64,
    /// Average performance characteristics
    pub avg_performance: PerformanceCharacteristics,
    /// Performance variance
    pub variance: f64,
    /// Optimal parameters for this pattern
    pub optimal_parameters: TuningParameters,
    /// Confidence in pattern recognition
    pub confidence: f64,
}

/// Types of workload patterns
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PatternType {
    /// Compute-intensive pattern
    ComputeIntensive,
    /// Memory-bandwidth bound pattern
    MemoryBound,
    /// Cache-friendly access pattern
    CacheFriendly,
    /// Random access pattern
    RandomAccess,
    /// Streaming pattern
    Streaming,
    /// Burst pattern
    Burst,
    /// Periodic pattern
    Periodic,
    /// Custom pattern
    Custom,
}

/// Performance characteristics summary
#[derive(Debug, Clone)]
pub struct PerformanceCharacteristics {
    /// Average execution time
    pub avg_execution_time: Duration,
    /// Throughput (ops/sec)
    pub throughput: f64,
    /// Memory usage
    pub memory_usage: usize,
    /// Cache efficiency
    pub cache_efficiency: f64,
    /// Power consumption
    pub power_consumption: f32,
    /// Thermal impact
    pub thermal_impact: f32,
}

/// System state correlation analysis
#[derive(Debug, Clone)]
pub struct SystemStateCorrelation {
    /// Correlation identifier
    pub id: String,
    /// Correlation coefficient
    pub coefficient: f64,
    /// P-value for statistical significance
    pub p_value: f64,
    /// Sample size
    pub sample_size: usize,
    /// Correlation type
    pub correlation_type: CorrelationType,
}

/// Types of correlations
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CorrelationType {
    /// Positive correlation
    Positive,
    /// Negative correlation
    Negative,
    /// No correlation
    None,
    /// Non-linear correlation
    NonLinear,
}

/// Machine learning model interface for performance prediction
pub trait PerformanceModel: std::fmt::Debug + Send + Sync {
    /// Train the model with historical data
    fn train(&mut self, data: &[PerformanceMeasurement]) -> BackendResult<ModelTrainingResult>;

    /// Predict performance for given inputs
    fn predict(
        &self,
        workload: &WorkloadCharacteristics,
        parameters: &TuningParameters,
        system_state: &SystemState,
        environment: &EnvironmentalFactors,
    ) -> BackendResult<PerformancePrediction>;

    /// Update model with new feedback
    fn update(&mut self, feedback: &PerformanceFeedback) -> BackendResult<()>;

    /// Get model accuracy metrics
    fn get_accuracy_metrics(&self) -> BackendResult<ModelAccuracy>;

    /// Get model complexity
    fn get_complexity(&self) -> ModelComplexity;

    /// Check if model needs retraining
    fn needs_retraining(&self) -> bool;
}

/// Model training results
#[derive(Debug, Clone)]
pub struct ModelTrainingResult {
    /// Training accuracy
    pub training_accuracy: f64,
    /// Validation accuracy
    pub validation_accuracy: f64,
    /// Training time
    pub training_time: Duration,
    /// Model size (bytes)
    pub model_size: usize,
    /// Feature importance scores
    pub feature_importance: Vec<FeatureImportance>,
    /// Cross-validation score
    pub cv_score: Option<f64>,
}

/// Feature importance for interpretability
#[derive(Debug, Clone)]
pub struct FeatureImportance {
    /// Feature name
    pub name: String,
    /// Importance score (0.0 to 1.0)
    pub importance: f64,
    /// Feature type
    pub feature_type: FeatureType,
}

/// Types of features used in modeling
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FeatureType {
    /// Workload feature
    Workload,
    /// System state feature
    System,
    /// Environmental feature
    Environmental,
    /// Historical feature
    Historical,
    /// Derived feature
    Derived,
}

/// Model accuracy metrics
#[derive(Debug, Clone)]
pub struct ModelAccuracy {
    /// Mean absolute error
    pub mae: f64,
    /// Root mean square error
    pub rmse: f64,
    /// R-squared score
    pub r2_score: f64,
    /// Mean absolute percentage error
    pub mape: f64,
    /// Prediction confidence interval coverage
    pub confidence_coverage: f64,
}

/// Model complexity indicators
#[derive(Debug, Clone)]
pub struct ModelComplexity {
    /// Number of parameters
    pub parameter_count: usize,
    /// Memory usage
    pub memory_usage: usize,
    /// Inference time
    pub inference_time: Duration,
    /// Training time complexity
    pub training_complexity: ComplexityClass,
}

/// Algorithmic complexity classes
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ComplexityClass {
    /// O(1) - Constant time
    Constant,
    /// O(log n) - Logarithmic time
    Logarithmic,
    /// O(n) - Linear time
    Linear,
    /// O(n log n) - Linearithmic time
    Linearithmic,
    /// O(n²) - Quadratic time
    Quadratic,
    /// O(n³) - Cubic time
    Cubic,
    /// O(2^n) - Exponential time
    Exponential,
}

/// Real-time performance monitoring
#[derive(Debug)]
pub struct RuntimeMonitor {
    /// Current monitoring state
    #[allow(dead_code)]
    monitoring_active: bool,
    /// Performance samples buffer
    sample_buffer: VecDeque<PerformanceSample>,
    /// Sampling rate (samples per second)
    #[allow(dead_code)]
    sampling_rate: f64,
    /// Buffer size limit
    buffer_size_limit: usize,
    /// Real-time statistics
    realtime_stats: RealtimeStatistics,
    /// Alert thresholds
    #[allow(dead_code)]
    alert_thresholds: AlertThresholds,
}

/// Single performance sample
#[derive(Debug, Clone)]
pub struct PerformanceSample {
    /// Sample timestamp
    pub timestamp: Instant,
    /// Execution time
    pub execution_time: Duration,
    /// Throughput
    pub throughput: f64,
    /// Memory usage
    pub memory_usage: usize,
    /// CPU utilization
    pub cpu_utilization: f64,
    /// GPU utilization
    pub gpu_utilization: Option<f64>,
    /// Power consumption
    pub power_consumption: f32,
    /// Temperature
    pub temperature: f32,
}

/// Real-time performance statistics
#[derive(Debug, Clone)]
pub struct RealtimeStatistics {
    /// Moving average execution time
    pub avg_execution_time: Duration,
    /// Moving average throughput
    pub avg_throughput: f64,
    /// Performance variance
    pub variance: f64,
    /// Trend indicator
    pub trend: TrendDirection,
    /// Anomaly count in current window
    pub anomaly_count: usize,
    /// Statistics window size
    pub window_size: usize,
}

/// Performance alert thresholds
#[derive(Debug, Clone)]
pub struct AlertThresholds {
    /// Maximum execution time
    pub max_execution_time: Duration,
    /// Minimum throughput
    pub min_throughput: f64,
    /// Maximum memory usage
    pub max_memory_usage: usize,
    /// Maximum temperature
    pub max_temperature: f32,
    /// Performance degradation threshold
    pub degradation_threshold: f64,
}

/// Performance correlation analyzer
#[derive(Debug)]
pub struct CorrelationAnalyzer {
    /// Correlation cache
    #[allow(dead_code)]
    correlation_cache: HashMap<String, CorrelationResult>,
    /// Analysis configuration
    #[allow(dead_code)]
    config: CorrelationConfig,
}

/// Correlation analysis result
#[derive(Debug, Clone)]
pub struct CorrelationResult {
    /// Variables being correlated
    pub variables: (String, String),
    /// Correlation coefficient
    pub coefficient: f64,
    /// Statistical significance
    pub p_value: f64,
    /// Confidence interval
    pub confidence_interval: (f64, f64),
    /// Sample size
    pub sample_size: usize,
    /// Analysis timestamp
    pub timestamp: SystemTime,
}

/// Correlation analysis configuration
#[derive(Debug, Clone)]
pub struct CorrelationConfig {
    /// Minimum sample size for analysis
    pub min_sample_size: usize,
    /// Significance threshold
    pub significance_threshold: f64,
    /// Correlation strength threshold
    pub strength_threshold: f64,
    /// Analysis window duration
    pub analysis_window: Duration,
}

/// Performance anomaly detection system
#[derive(Debug)]
pub struct AnomalyDetector {
    /// Detection models
    #[allow(dead_code)]
    detection_models: HashMap<BackendType, Box<dyn AnomalyDetectionModel + Send + Sync>>,
    /// Anomaly history
    #[allow(dead_code)]
    anomaly_history: VecDeque<PerformanceAnomaly>,
    /// Detection configuration
    #[allow(dead_code)]
    config: AnomalyDetectionConfig,
}

/// Anomaly detection model interface
pub trait AnomalyDetectionModel: std::fmt::Debug + Send + Sync {
    /// Train anomaly detection model
    fn train(&mut self, normal_data: &[PerformanceMeasurement]) -> BackendResult<()>;

    /// Detect anomalies in new data
    fn detect(&self, measurement: &PerformanceMeasurement)
        -> BackendResult<AnomalyDetectionResult>;

    /// Update model with feedback
    fn update(
        &mut self,
        measurement: &PerformanceMeasurement,
        is_anomaly: bool,
    ) -> BackendResult<()>;

    /// Get detection statistics
    fn get_statistics(&self) -> AnomalyDetectionStatistics;
}

/// Anomaly detection result
#[derive(Debug, Clone)]
pub struct AnomalyDetectionResult {
    /// Whether an anomaly was detected
    pub is_anomaly: bool,
    /// Anomaly score (0.0 to 1.0)
    pub anomaly_score: f64,
    /// Confidence in detection
    pub confidence: f64,
    /// Contributing factors
    pub factors: Vec<AnomalyFactor>,
}

/// Factor contributing to anomaly detection
#[derive(Debug, Clone)]
pub struct AnomalyFactor {
    /// Factor name
    pub name: String,
    /// Contribution score
    pub contribution: f64,
    /// Expected vs actual value
    pub expected_value: f64,
    pub actual_value: f64,
}

/// Performance anomaly record
#[derive(Debug, Clone)]
pub struct PerformanceAnomaly {
    /// Anomaly ID
    pub id: u64,
    /// Detection timestamp
    pub timestamp: SystemTime,
    /// Backend type
    pub backend_type: BackendType,
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Severity level
    pub severity: AnomalySeverity,
    /// Anomaly score
    pub score: f64,
    /// Description
    pub description: String,
    /// Measurement that triggered detection
    pub measurement: PerformanceMeasurement,
    /// Suggested remediation
    pub remediation: Vec<String>,
}

/// Types of performance anomalies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AnomalyType {
    /// Execution time anomaly
    ExecutionTime,
    /// Throughput anomaly
    Throughput,
    /// Memory usage anomaly
    Memory,
    /// Power consumption anomaly
    Power,
    /// Temperature anomaly
    Temperature,
    /// Cache efficiency anomaly
    Cache,
    /// Combined anomaly
    Combined,
}

/// Anomaly severity levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum AnomalySeverity {
    /// Low severity - minor deviation
    Low,
    /// Medium severity - noticeable impact
    Medium,
    /// High severity - significant performance impact
    High,
    /// Critical severity - system may be unstable
    Critical,
}

/// Anomaly detection configuration
#[derive(Debug, Clone)]
pub struct AnomalyDetectionConfig {
    /// Detection sensitivity (0.0 to 1.0)
    pub sensitivity: f64,
    /// False positive tolerance
    pub false_positive_rate: f64,
    /// Detection window size
    pub detection_window: Duration,
    /// Minimum confidence threshold
    pub confidence_threshold: f64,
}

/// Anomaly detection statistics
#[derive(Debug, Clone)]
pub struct AnomalyDetectionStatistics {
    /// Total detections
    pub total_detections: usize,
    /// True positives
    pub true_positives: usize,
    /// False positives
    pub false_positives: usize,
    /// True negatives
    pub true_negatives: usize,
    /// False negatives
    pub false_negatives: usize,
    /// Precision
    pub precision: f64,
    /// Recall
    pub recall: f64,
    /// F1 score
    pub f1_score: f64,
}

/// Model update scheduling system
#[derive(Debug)]
pub struct ModelUpdateScheduler {
    /// Update schedule configuration
    #[allow(dead_code)]
    config: UpdateScheduleConfig,
    /// Last update timestamps
    #[allow(dead_code)]
    last_updates: HashMap<BackendType, SystemTime>,
    /// Pending updates
    #[allow(dead_code)]
    pending_updates: Vec<UpdateRequest>,
    /// Update statistics
    #[allow(dead_code)]
    update_stats: UpdateStatistics,
}

/// Update schedule configuration
#[derive(Debug, Clone)]
pub struct UpdateScheduleConfig {
    /// Minimum time between updates
    pub min_update_interval: Duration,
    /// Maximum time between updates
    pub max_update_interval: Duration,
    /// Performance threshold for triggering update
    pub performance_threshold: f64,
    /// Data accumulation threshold
    pub data_threshold: usize,
}

/// Model update request
#[derive(Debug, Clone)]
pub struct UpdateRequest {
    /// Backend type
    pub backend_type: BackendType,
    /// Update priority
    pub priority: UpdatePriority,
    /// Update type
    pub update_type: UpdateType,
    /// Request timestamp
    pub timestamp: SystemTime,
    /// Reason for update
    pub reason: String,
}

/// Update priority levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum UpdatePriority {
    /// Low priority - can be deferred
    Low,
    /// Normal priority - standard schedule
    Normal,
    /// High priority - should be expedited
    High,
    /// Critical priority - immediate update needed
    Critical,
}

/// Types of model updates
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum UpdateType {
    /// Incremental update with new data
    Incremental,
    /// Full retraining from scratch
    FullRetrain,
    /// Parameter tuning only
    ParameterTuning,
    /// Architecture modification
    Architecture,
}

/// Update statistics
#[derive(Debug, Clone)]
pub struct UpdateStatistics {
    /// Total updates performed
    pub total_updates: usize,
    /// Average update time
    pub avg_update_time: Duration,
    /// Update success rate
    pub success_rate: f64,
    /// Performance improvement from updates
    pub avg_improvement: f64,
}

impl Default for RuntimePerformanceModeler {
    fn default() -> Self {
        Self::new().expect("Failed to create runtime performance modeler")
    }
}

impl RuntimePerformanceModeler {
    /// Create a new runtime performance modeler
    pub fn new() -> BackendResult<Self> {
        let historical_data = Arc::new(RwLock::new(PerformanceDatabase::new(10000)?));
        let ml_models = Arc::new(RwLock::new(HashMap::new()));
        let runtime_monitor = Arc::new(Mutex::new(RuntimeMonitor::new()));
        let correlation_analyzer = CorrelationAnalyzer::new();
        let anomaly_detector = AnomalyDetector::new()?;
        let update_scheduler = ModelUpdateScheduler::new();

        Ok(Self {
            historical_data,
            ml_models,
            runtime_monitor,
            correlation_analyzer,
            anomaly_detector,
            update_scheduler,
        })
    }

    /// Initialize ML models for all backends
    pub fn initialize_models(&self) -> BackendResult<()> {
        let mut models = self.ml_models.write().map_err(|_| {
            TorshError::BackendError("Failed to acquire ML models lock".to_string())
        })?;

        // Initialize models for each backend
        models.insert(BackendType::Cpu, Box::new(LinearRegressionModel::new()));
        models.insert(BackendType::Cuda, Box::new(LinearRegressionModel::new()));
        models.insert(BackendType::Metal, Box::new(LinearRegressionModel::new()));
        models.insert(BackendType::WebGpu, Box::new(LinearRegressionModel::new()));

        Ok(())
    }

    /// Record a new performance measurement
    pub fn record_measurement(&self, measurement: PerformanceMeasurement) -> BackendResult<()> {
        // Add to historical database
        {
            let mut db = self.historical_data.write().map_err(|_| {
                TorshError::BackendError("Failed to acquire database lock".to_string())
            })?;
            db.add_measurement(measurement.clone())?;
        }

        // Check for anomalies
        let anomaly_result = self.anomaly_detector.detect(&measurement)?;
        if anomaly_result.is_anomaly {
            self.handle_anomaly(measurement.clone(), anomaly_result)?;
        }

        // Update real-time monitor
        {
            let mut monitor = self.runtime_monitor.lock().map_err(|_| {
                TorshError::BackendError("Failed to acquire monitor lock".to_string())
            })?;
            monitor.add_sample(&measurement)?;
        }

        // Check if models need updating
        self.check_model_updates(measurement.backend_type)?;

        Ok(())
    }

    /// Predict performance for given inputs
    pub fn predict_performance(
        &self,
        backend_type: BackendType,
        workload: &WorkloadCharacteristics,
        parameters: &TuningParameters,
        system_state: &SystemState,
        environment: &EnvironmentalFactors,
    ) -> BackendResult<PerformancePrediction> {
        let models = self.ml_models.read().map_err(|_| {
            TorshError::BackendError("Failed to acquire ML models lock".to_string())
        })?;

        let model = models.get(&backend_type).ok_or_else(|| {
            TorshError::BackendError(format!("No model for backend {:?}", backend_type))
        })?;

        model.predict(workload, parameters, system_state, environment)
    }

    /// Get performance trends for a backend
    pub fn get_performance_trends(
        &self,
        backend_type: BackendType,
    ) -> BackendResult<Vec<PerformanceTrend>> {
        let db = self
            .historical_data
            .read()
            .map_err(|_| TorshError::BackendError("Failed to acquire database lock".to_string()))?;

        Ok(db.get_trends_for_backend(backend_type))
    }

    /// Analyze correlations between system factors and performance
    pub fn analyze_correlations(
        &self,
        backend_type: BackendType,
    ) -> BackendResult<Vec<CorrelationResult>> {
        let db = self
            .historical_data
            .read()
            .map_err(|_| TorshError::BackendError("Failed to acquire database lock".to_string()))?;

        let measurements = db.get_measurements_for_backend(backend_type);
        self.correlation_analyzer.analyze(&measurements)
    }

    /// Get recent anomalies
    pub fn get_recent_anomalies(
        &self,
        since: SystemTime,
    ) -> BackendResult<Vec<PerformanceAnomaly>> {
        self.anomaly_detector.get_anomalies_since(since)
    }

    /// Get model accuracy metrics
    pub fn get_model_accuracy(&self, backend_type: BackendType) -> BackendResult<ModelAccuracy> {
        let models = self.ml_models.read().map_err(|_| {
            TorshError::BackendError("Failed to acquire ML models lock".to_string())
        })?;

        let model = models.get(&backend_type).ok_or_else(|| {
            TorshError::BackendError(format!("No model for backend {:?}", backend_type))
        })?;

        model.get_accuracy_metrics()
    }

    /// Trigger manual model update
    pub fn update_model(&self, backend_type: BackendType) -> BackendResult<ModelTrainingResult> {
        let historical_data = {
            let db = self.historical_data.read().map_err(|_| {
                TorshError::BackendError("Failed to acquire database lock".to_string())
            })?;
            db.get_measurements_for_backend(backend_type)
        };

        let mut models = self.ml_models.write().map_err(|_| {
            TorshError::BackendError("Failed to acquire ML models lock".to_string())
        })?;

        let model = models.get_mut(&backend_type).ok_or_else(|| {
            TorshError::BackendError(format!("No model for backend {:?}", backend_type))
        })?;

        model.train(&historical_data)
    }

    /// Get comprehensive performance report
    pub fn generate_performance_report(
        &self,
        backend_type: BackendType,
    ) -> BackendResult<PerformanceReport> {
        let trends = self.get_performance_trends(backend_type)?;
        let correlations = self.analyze_correlations(backend_type)?;
        let accuracy = self.get_model_accuracy(backend_type)?;
        let anomalies = self.get_recent_anomalies(
            SystemTime::now() - Duration::from_secs(24 * 3600), // Last 24 hours
        )?;

        let db = self
            .historical_data
            .read()
            .map_err(|_| TorshError::BackendError("Failed to acquire database lock".to_string()))?;
        let measurements = db.get_measurements_for_backend(backend_type);

        Ok(PerformanceReport {
            backend_type,
            measurement_count: measurements.len(),
            trends,
            correlations,
            model_accuracy: accuracy,
            recent_anomalies: anomalies,
            generated_at: SystemTime::now(),
        })
    }

    // Private helper methods
    fn handle_anomaly(
        &self,
        measurement: PerformanceMeasurement,
        result: AnomalyDetectionResult,
    ) -> BackendResult<()> {
        let anomaly = PerformanceAnomaly {
            id: self.generate_anomaly_id(),
            timestamp: SystemTime::now(),
            backend_type: measurement.backend_type,
            anomaly_type: AnomalyType::Combined, // Simplified for now
            severity: self.determine_severity(result.anomaly_score),
            score: result.anomaly_score,
            description: format!(
                "Performance anomaly detected with score {:.3}",
                result.anomaly_score
            ),
            measurement,
            remediation: vec![
                "Review system state".to_string(),
                "Check for thermal throttling".to_string(),
            ],
        };

        self.anomaly_detector.add_anomaly(anomaly)?;
        Ok(())
    }

    fn check_model_updates(&self, backend_type: BackendType) -> BackendResult<()> {
        self.update_scheduler.check_update_needed(backend_type)
    }

    fn generate_anomaly_id(&self) -> u64 {
        // Simplified ID generation
        SystemTime::now()
            .duration_since(SystemTime::UNIX_EPOCH)
            .unwrap_or_default()
            .as_nanos() as u64
    }

    fn determine_severity(&self, score: f64) -> AnomalySeverity {
        if score > 0.9 {
            AnomalySeverity::Critical
        } else if score > 0.7 {
            AnomalySeverity::High
        } else if score > 0.5 {
            AnomalySeverity::Medium
        } else {
            AnomalySeverity::Low
        }
    }
}

/// Comprehensive performance report
#[derive(Debug, Clone)]
pub struct PerformanceReport {
    /// Backend type
    pub backend_type: BackendType,
    /// Number of measurements in database
    pub measurement_count: usize,
    /// Performance trends
    pub trends: Vec<PerformanceTrend>,
    /// Correlation analysis results
    pub correlations: Vec<CorrelationResult>,
    /// Model accuracy metrics
    pub model_accuracy: ModelAccuracy,
    /// Recent anomalies
    pub recent_anomalies: Vec<PerformanceAnomaly>,
    /// Report generation timestamp
    pub generated_at: SystemTime,
}

// Implementation stubs for concrete types

/// Simple linear regression model implementation
#[derive(Debug)]
struct LinearRegressionModel {
    weights: Vec<f64>,
    bias: f64,
    trained: bool,
    accuracy: ModelAccuracy,
}

impl LinearRegressionModel {
    fn new() -> Self {
        Self {
            weights: Vec::new(),
            bias: 0.0,
            trained: false,
            accuracy: ModelAccuracy {
                mae: 0.0,
                rmse: 0.0,
                r2_score: 0.0,
                mape: 0.0,
                confidence_coverage: 0.0,
            },
        }
    }
}

impl PerformanceModel for LinearRegressionModel {
    fn train(&mut self, data: &[PerformanceMeasurement]) -> BackendResult<ModelTrainingResult> {
        // Simplified linear regression training
        if data.is_empty() {
            return Err(TorshError::BackendError(
                "No training data provided".to_string(),
            ));
        }

        // Initialize weights for basic features
        self.weights = vec![0.1; 10]; // Simplified feature count
        self.bias = 0.0;
        self.trained = true;

        // Update accuracy metrics
        self.accuracy = ModelAccuracy {
            mae: 0.05,
            rmse: 0.08,
            r2_score: 0.85,
            mape: 0.03,
            confidence_coverage: 0.9,
        };

        Ok(ModelTrainingResult {
            training_accuracy: 0.85,
            validation_accuracy: 0.82,
            training_time: Duration::from_millis(100),
            model_size: self.weights.len() * 8 + 8, // Rough size estimate
            feature_importance: vec![FeatureImportance {
                name: "data_size".to_string(),
                importance: 0.8,
                feature_type: FeatureType::Workload,
            }],
            cv_score: Some(0.83),
        })
    }

    fn predict(
        &self,
        workload: &WorkloadCharacteristics,
        _parameters: &TuningParameters,
        _system_state: &SystemState,
        _environment: &EnvironmentalFactors,
    ) -> BackendResult<PerformancePrediction> {
        if !self.trained {
            return Err(TorshError::BackendError("Model not trained".to_string()));
        }

        // Simplified prediction based on data size
        let execution_time = Duration::from_nanos((workload.data_size as f64 / 1e6) as u64);

        Ok(PerformancePrediction {
            execution_time,
            throughput: workload.data_size as f64 / execution_time.as_secs_f64(),
            memory_usage: workload.data_size,
            power_consumption: 50.0,
            cache_efficiency: 0.8,
            thermal_impact: 5.0,
            confidence_interval: (0.8, 1.2),
        })
    }

    fn update(&mut self, _feedback: &PerformanceFeedback) -> BackendResult<()> {
        // Simplified online learning update
        Ok(())
    }

    fn get_accuracy_metrics(&self) -> BackendResult<ModelAccuracy> {
        Ok(self.accuracy.clone())
    }

    fn get_complexity(&self) -> ModelComplexity {
        ModelComplexity {
            parameter_count: self.weights.len() + 1,
            memory_usage: (self.weights.len() + 1) * 8,
            inference_time: Duration::from_micros(10),
            training_complexity: ComplexityClass::Linear,
        }
    }

    fn needs_retraining(&self) -> bool {
        !self.trained || self.accuracy.r2_score < 0.8
    }
}

// Implementation stubs for other components
impl PerformanceDatabase {
    fn new(max_entries: usize) -> BackendResult<Self> {
        Ok(Self {
            measurements: HashMap::new(),
            trends: HashMap::new(),
            patterns: HashMap::new(),
            state_correlations: HashMap::new(),
            max_entries,
        })
    }

    fn add_measurement(&mut self, measurement: PerformanceMeasurement) -> BackendResult<()> {
        let backend_measurements = self
            .measurements
            .entry(measurement.backend_type)
            .or_insert_with(VecDeque::new);

        backend_measurements.push_back(measurement);

        // Maintain size limit
        if backend_measurements.len() > self.max_entries {
            backend_measurements.pop_front();
        }

        Ok(())
    }

    fn get_measurements_for_backend(
        &self,
        backend_type: BackendType,
    ) -> Vec<PerformanceMeasurement> {
        self.measurements
            .get(&backend_type)
            .map(|deque| deque.iter().cloned().collect())
            .unwrap_or_default()
    }

    fn get_trends_for_backend(&self, _backend_type: BackendType) -> Vec<PerformanceTrend> {
        // Simplified trend analysis
        Vec::new()
    }
}

impl RuntimeMonitor {
    fn new() -> Self {
        Self {
            monitoring_active: false,
            sample_buffer: VecDeque::new(),
            sampling_rate: 10.0, // 10 samples per second
            buffer_size_limit: 1000,
            realtime_stats: RealtimeStatistics {
                avg_execution_time: Duration::from_millis(100),
                avg_throughput: 1000.0,
                variance: 0.1,
                trend: TrendDirection::Stable,
                anomaly_count: 0,
                window_size: 100,
            },
            alert_thresholds: AlertThresholds {
                max_execution_time: Duration::from_secs(10),
                min_throughput: 100.0,
                max_memory_usage: 1024 * 1024 * 1024,
                max_temperature: 85.0,
                degradation_threshold: 0.3,
            },
        }
    }

    fn add_sample(&mut self, measurement: &PerformanceMeasurement) -> BackendResult<()> {
        let sample = PerformanceSample {
            timestamp: Instant::now(),
            execution_time: measurement.actual_performance.execution_time,
            throughput: measurement.actual_performance.throughput,
            memory_usage: measurement.actual_performance.memory_usage_peak,
            cpu_utilization: measurement.actual_performance.cpu_utilization,
            gpu_utilization: None, // Would be extracted from system state
            power_consumption: measurement.actual_performance.power_consumption_avg,
            temperature: 65.0, // Would be extracted from system state
        };

        self.sample_buffer.push_back(sample);

        if self.sample_buffer.len() > self.buffer_size_limit {
            self.sample_buffer.pop_front();
        }

        self.update_realtime_stats()?;
        Ok(())
    }

    fn update_realtime_stats(&mut self) -> BackendResult<()> {
        if self.sample_buffer.is_empty() {
            return Ok(());
        }

        let window_size = self
            .realtime_stats
            .window_size
            .min(self.sample_buffer.len());
        let recent_samples: Vec<_> = self.sample_buffer.iter().rev().take(window_size).collect();

        // Calculate moving averages
        let avg_execution_time = recent_samples
            .iter()
            .map(|s| s.execution_time.as_nanos() as f64)
            .sum::<f64>()
            / recent_samples.len() as f64;

        self.realtime_stats.avg_execution_time = Duration::from_nanos(avg_execution_time as u64);

        self.realtime_stats.avg_throughput =
            recent_samples.iter().map(|s| s.throughput).sum::<f64>() / recent_samples.len() as f64;

        Ok(())
    }
}

impl CorrelationAnalyzer {
    fn new() -> Self {
        Self {
            correlation_cache: HashMap::new(),
            config: CorrelationConfig {
                min_sample_size: 30,
                significance_threshold: 0.05,
                strength_threshold: 0.3,
                analysis_window: Duration::from_secs(24 * 3600),
            },
        }
    }

    fn analyze(
        &self,
        _measurements: &[PerformanceMeasurement],
    ) -> BackendResult<Vec<CorrelationResult>> {
        // Simplified correlation analysis
        Ok(Vec::new())
    }
}

impl AnomalyDetector {
    fn new() -> BackendResult<Self> {
        Ok(Self {
            detection_models: HashMap::new(),
            anomaly_history: VecDeque::new(),
            config: AnomalyDetectionConfig {
                sensitivity: 0.8,
                false_positive_rate: 0.05,
                detection_window: Duration::from_secs(300),
                confidence_threshold: 0.7,
            },
        })
    }

    fn detect(
        &self,
        measurement: &PerformanceMeasurement,
    ) -> BackendResult<AnomalyDetectionResult> {
        // Simplified anomaly detection
        let score = if measurement.actual_performance.execution_time > Duration::from_secs(5) {
            0.8 // High anomaly score for slow operations
        } else {
            0.1 // Low anomaly score for normal operations
        };

        Ok(AnomalyDetectionResult {
            is_anomaly: score > 0.5,
            anomaly_score: score,
            confidence: 0.9,
            factors: vec![],
        })
    }

    fn add_anomaly(&self, _anomaly: PerformanceAnomaly) -> BackendResult<()> {
        // Add to anomaly history
        Ok(())
    }

    fn get_anomalies_since(&self, _since: SystemTime) -> BackendResult<Vec<PerformanceAnomaly>> {
        // Return recent anomalies
        Ok(Vec::new())
    }
}

impl ModelUpdateScheduler {
    fn new() -> Self {
        Self {
            config: UpdateScheduleConfig {
                min_update_interval: Duration::from_secs(3600), // 1 hour
                max_update_interval: Duration::from_secs(24 * 3600), // 24 hours
                performance_threshold: 0.1,
                data_threshold: 100,
            },
            last_updates: HashMap::new(),
            pending_updates: Vec::new(),
            update_stats: UpdateStatistics {
                total_updates: 0,
                avg_update_time: Duration::from_secs(60),
                success_rate: 0.95,
                avg_improvement: 0.15,
            },
        }
    }

    fn check_update_needed(&self, _backend_type: BackendType) -> BackendResult<()> {
        // Check if update is needed based on schedule
        Ok(())
    }
}

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

    #[test]
    fn test_performance_modeler_creation() {
        let modeler = RuntimePerformanceModeler::new().unwrap();
        assert!(modeler.initialize_models().is_ok());
    }

    #[test]
    fn test_linear_regression_model() {
        let mut model = LinearRegressionModel::new();
        assert!(!model.trained);

        // Create dummy training data
        let workload = WorkloadCharacteristics {
            operation_type: OperationType::MatrixMultiply,
            data_size: 1024,
            data_shape: vec![32, 32],
            data_type: DataType::F32,
            access_pattern: AccessPattern::Sequential,
            compute_intensity: 0.8,
            memory_bandwidth_requirement: 0.6,
            parallelization_potential: 0.9,
            cache_locality: 0.7,
            branch_predictability: 0.95,
            vectorization_potential: 0.85,
        };

        let measurement = PerformanceMeasurement {
            id: 1,
            timestamp: SystemTime::now(),
            backend_type: BackendType::Cpu,
            device_id: 0,
            workload,
            parameters: TuningParameters {
                thread_count: 4,
                vector_width: 256,
                block_size: Some(64),
                tile_size: None,
                unroll_factor: 4,
                scheduling_strategy: SchedulingStrategy::Static,
                memory_allocation_strategy: MemoryAllocationStrategy::Default,
                optimization_level: OptimizationLevel::Optimized,
                backend_specific: HashMap::new(),
            },
            system_state: SystemState {
                cpu_utilization: 0.5,
                memory_utilization: 0.4,
                thermal_state: ThermalState {
                    cpu_temperature: 65.0,
                    gpu_temperature: None,
                    thermal_throttling_active: false,
                    cooling_efficiency: 0.8,
                },
                power_state: PowerState {
                    power_limit: None,
                    current_power_draw: 50.0,
                    battery_level: None,
                    power_efficiency_mode: PowerEfficiencyMode::Balanced,
                },
                concurrent_workloads: 2,
                available_memory_bandwidth: 0.7,
                cache_pressure: 0.4,
                numa_topology: NumaTopologyState {
                    node_count: 1,
                    current_node: 0,
                    memory_distribution: vec![1.0],
                    cross_node_traffic: 0.0,
                },
            },
            actual_performance: ActualPerformance {
                execution_time: Duration::from_millis(100),
                throughput: 1000.0,
                memory_usage_peak: 1024,
                power_consumption_avg: 50.0,
                cache_hit_ratio: 0.85,
                thermal_increase: 2.0,
                cpu_utilization: 0.6,
            },
            predicted_performance: None,
            prediction_accuracy: None,
            environment: EnvironmentalFactors {
                ambient_temperature: Some(22.0),
                system_load: 0.3,
                background_processes: 50,
                network_activity: 0.1,
                storage_io: 0.2,
                available_memory: 8 * 1024 * 1024 * 1024,
                cpu_frequency: Some(3200),
                gpu_frequency: None,
            },
        };

        let training_data = vec![measurement];
        let result = model.train(&training_data).unwrap();

        assert!(model.trained);
        assert!(result.training_accuracy > 0.0);
        assert!(result.model_size > 0);
    }

    #[test]
    fn test_performance_database() {
        let mut db = PerformanceDatabase::new(100).unwrap();

        let measurement = create_test_measurement();
        db.add_measurement(measurement.clone()).unwrap();

        let measurements = db.get_measurements_for_backend(BackendType::Cpu);
        assert_eq!(measurements.len(), 1);
        assert_eq!(measurements[0].id, measurement.id);
    }

    #[test]
    fn test_runtime_monitor() {
        let mut monitor = RuntimeMonitor::new();
        let measurement = create_test_measurement();

        monitor.add_sample(&measurement).unwrap();
        assert!(!monitor.sample_buffer.is_empty());
    }

    #[test]
    fn test_anomaly_detection() {
        let detector = AnomalyDetector::new().unwrap();
        let measurement = create_test_measurement();

        let result = detector.detect(&measurement).unwrap();
        assert!(result.confidence > 0.0);
        assert!(result.anomaly_score >= 0.0 && result.anomaly_score <= 1.0);
    }

    fn create_test_measurement() -> PerformanceMeasurement {
        PerformanceMeasurement {
            id: 1,
            timestamp: SystemTime::now(),
            backend_type: BackendType::Cpu,
            device_id: 0,
            workload: WorkloadCharacteristics {
                operation_type: OperationType::ElementWise,
                data_size: 1000,
                data_shape: vec![100, 10],
                data_type: DataType::F32,
                access_pattern: AccessPattern::Sequential,
                compute_intensity: 0.5,
                memory_bandwidth_requirement: 0.3,
                parallelization_potential: 0.7,
                cache_locality: 0.8,
                branch_predictability: 0.9,
                vectorization_potential: 0.6,
            },
            parameters: TuningParameters {
                thread_count: 4,
                vector_width: 256,
                block_size: Some(64),
                tile_size: None,
                unroll_factor: 2,
                scheduling_strategy: SchedulingStrategy::Dynamic,
                memory_allocation_strategy: MemoryAllocationStrategy::Default,
                optimization_level: OptimizationLevel::Default,
                backend_specific: HashMap::new(),
            },
            system_state: SystemState {
                cpu_utilization: 0.5,
                memory_utilization: 0.6,
                thermal_state: ThermalState {
                    cpu_temperature: 65.0,
                    gpu_temperature: None,
                    thermal_throttling_active: false,
                    cooling_efficiency: 0.8,
                },
                power_state: PowerState {
                    power_limit: None,
                    current_power_draw: 50.0,
                    battery_level: None,
                    power_efficiency_mode: PowerEfficiencyMode::Balanced,
                },
                concurrent_workloads: 2,
                available_memory_bandwidth: 0.7,
                cache_pressure: 0.4,
                numa_topology: NumaTopologyState {
                    node_count: 1,
                    current_node: 0,
                    memory_distribution: vec![1.0],
                    cross_node_traffic: 0.0,
                },
            },
            actual_performance: ActualPerformance {
                execution_time: Duration::from_millis(50),
                throughput: 2000.0,
                memory_usage_peak: 1000,
                power_consumption_avg: 45.0,
                cache_hit_ratio: 0.9,
                thermal_increase: 1.0,
                cpu_utilization: 0.55,
            },
            predicted_performance: None,
            prediction_accuracy: None,
            environment: EnvironmentalFactors {
                ambient_temperature: Some(22.0),
                system_load: 0.3,
                background_processes: 50,
                network_activity: 0.1,
                storage_io: 0.2,
                available_memory: 8 * 1024 * 1024 * 1024,
                cpu_frequency: Some(3200),
                gpu_frequency: None,
            },
        }
    }
}