oxirs-arq 0.2.4

//! Anomaly detection

use serde::{Deserialize, Serialize};

    High,
    Critical,
}

/// Ensemble anomaly detection
#[derive(Debug, Clone)]
pub struct EnsembleAnomalyDetector {
    pub detectors: Vec<AnomalyDetectorType>,
    pub voting_strategy: VotingStrategy,
    pub confidence_weights: HashMap<AnomalyDetectorType, f64>,
}

/// Types of anomaly detectors
#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub enum AnomalyDetectorType {
    Statistical,
    IsolationForest,
    OneClassSvm,
    Autoencoder,
    Lstm,
    RuleBased,
}

/// Voting strategies for ensemble
#[derive(Debug, Clone)]
pub enum VotingStrategy {
    Majority,
    Weighted,
    Unanimous,
    Threshold(f64),
}

/// Resource usage tracking and optimization
#[derive(Debug, Clone)]
pub struct ResourceUsageTracker {
    /// Memory tracking
    memory_tracker: MemoryUsageTracker,
    /// CPU tracking
    cpu_tracker: CpuUsageTracker,
    /// I/O tracking
    io_tracker: IoUsageTracker,
    /// Network tracking
    network_tracker: NetworkUsageTracker,
    /// Cache tracking
    cache_tracker: CacheUsageTracker,
}

/// Memory usage tracking
#[derive(Debug, Clone)]
pub struct MemoryUsageTracker {
    pub current_usage: usize,
    pub peak_usage: usize,
    pub allocation_rate: f64,
    pub deallocation_rate: f64,
    pub fragmentation_level: f64,
    pub gc_activity: GcActivity,
}

/// Garbage collection activity
#[derive(Debug, Clone)]
pub struct GcActivity {
    pub minor_gc_count: usize,
    pub major_gc_count: usize,
    pub gc_time: Duration,
    pub gc_efficiency: f64,
}

/// CPU usage tracking
#[derive(Debug, Clone)]
pub struct CpuUsageTracker {
    pub current_usage: f64,
    pub per_core_usage: Vec<f64>,
    pub context_switches: usize,
    pub instruction_rate: f64,
    pub cache_misses: usize,
}

/// I/O usage tracking
#[derive(Debug, Clone)]
pub struct IoUsageTracker {
    pub read_bytes: usize,
    pub write_bytes: usize,
    pub read_operations: usize,
    pub write_operations: usize,
    pub latency_distribution: Histogram,
    pub throughput: f64,
}

/// Network usage tracking
#[derive(Debug, Clone)]
pub struct NetworkUsageTracker {
    pub bytes_sent: usize,
    pub bytes_received: usize,
    pub connections_active: usize,
    pub connections_total: usize,
    pub latency: Duration,
    pub bandwidth_utilization: f64,
}

/// Cache usage tracking
#[derive(Debug, Clone)]
pub struct CacheUsageTracker {
    pub hit_rate: f64,
    pub miss_rate: f64,
    pub eviction_rate: f64,
    pub cache_size: usize,
    pub cache_utilization: f64,
    pub cache_levels: HashMap<String, CacheLevelStats>,
}

/// Cache level statistics
#[derive(Debug, Clone)]
pub struct CacheLevelStats {
    pub level_name: String,
    pub hit_rate: f64,
    pub size: usize,
    pub utilization: f64,
    pub access_time: Duration,
}

/// Optimizer feedback system
#[derive(Debug, Clone)]
pub struct OptimizerFeedback {
    /// Optimization effectiveness tracking
    effectiveness_tracker: OptimizationEffectivenessTracker,
    /// Adaptive optimization parameters
    adaptive_parameters: AdaptiveOptimizationParameters,
    /// Learning-based optimization
    learning_optimizer: LearningBasedOptimizer,
    /// Feedback loop controller
    feedback_controller: FeedbackLoopController,
}

/// Optimization effectiveness tracking
#[derive(Debug, Clone)]
pub struct OptimizationEffectivenessTracker {
    pub optimization_history: Vec<OptimizationRecord>,
    pub effectiveness_metrics: EffectivenessMetrics,
    pub regression_detector: RegressionDetector,
}

/// Optimization record
#[derive(Debug, Clone)]
pub struct OptimizationRecord {
    pub timestamp: SystemTime,
    pub optimization_type: OptimizationType,
    pub before_metrics: PerformanceMetrics,
    pub after_metrics: PerformanceMetrics,
    pub improvement: f64,
    pub side_effects: Vec<SideEffect>,
}

/// Performance metrics
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub execution_time: Duration,
    pub memory_usage: usize,
    pub cpu_usage: f64,
    pub throughput: f64,
    pub error_rate: f64,
}

/// Side effects of optimizations
#[derive(Debug, Clone)]
pub struct SideEffect {
    pub effect_type: SideEffectType,
    pub magnitude: f64,
    pub description: String,
}

/// Types of side effects
#[derive(Debug, Clone)]
pub enum SideEffectType {
    MemoryIncrease,
    CpuIncrease,
    LatencyIncrease,
    AccuracyDecrease,
    ComplexityIncrease,
    MaintenabilityDecrease,
}

/// Effectiveness metrics
#[derive(Debug, Clone)]
pub struct EffectivenessMetrics {
    pub success_rate: f64,
    pub average_improvement: f64,
    pub regression_rate: f64,
    pub stability_score: f64,
}

/// Regression detection in optimizations
#[derive(Debug, Clone)]
pub struct RegressionDetector {
    pub baseline_metrics: PerformanceMetrics,
    pub regression_threshold: f64,
    pub detection_window: Duration,
    pub regression_alerts: Vec<RegressionAlert>,
}

/// Regression alert
#[derive(Debug, Clone)]
pub struct RegressionAlert {
    pub metric_name: String,
    pub baseline_value: f64,
    pub current_value: f64,
    pub regression_percentage: f64,
    pub detected_at: SystemTime,
}

/// Adaptive optimization parameters

impl AnomalyDetector {
    pub fn new() -> Self {
        Self {
            statistical_detector: StatisticalAnomalyDetector::new(),
            ml_detector: MlAnomalyDetector::new(),
            rule_based_detector: RuleBasedAnomalyDetector::new(),
            ensemble_detector: EnsembleAnomalyDetector::new(),
        }
    }

    pub fn detect_anomalies(&mut self, algebra: &Algebra, execution_time: Duration, memory_usage: usize) -> Result<()> {
        // Implementation would detect anomalies
        Ok(())
    }

    pub fn detect(&self, algebra: &Algebra) -> Result<Vec<Anomaly>> {
        // Implementation would detect anomalies and return them
        Ok(Vec::new())
    }
}

impl StatisticalAnomalyDetector {
    pub fn new() -> Self {
        Self {
            z_score_threshold: 3.0,
            iqr_multiplier: 1.5,
            moving_average_window: 100,
            seasonal_decomposition: true,
        }
    }
}

impl MlAnomalyDetector {
    pub fn new() -> Self {
        Self {
            isolation_forest: IsolationForest::new(),
            one_class_svm: OneClassSvm::new(),
            autoencoder: Autoencoder::new(),
            lstm_detector: LstmDetector::new(),
        }
    }
}

impl IsolationForest {
    pub fn new() -> Self {
        Self {
            num_trees: 100,
            contamination_rate: 0.1,
            trees: Vec::new(),
        }
    }
}

impl OneClassSvm {
    pub fn new() -> Self {
        Self {
            nu: 0.05,
            gamma: 0.1,
            support_vectors: Vec::new(),
            decision_function: Vec::new(),
        }
    }
}

impl Autoencoder {
    pub fn new() -> Self {
        Self {
            encoder_layers: Vec::new(),
            decoder_layers: Vec::new(),
            reconstruction_threshold: 0.1,
        }
    }
}

impl LstmDetector {
    pub fn new() -> Self {
        Self {
            lstm_layers: Vec::new(),
            sequence_length: 50,
            prediction_threshold: 0.1,
        }
    }
}

impl RuleBasedAnomalyDetector {
    pub fn new() -> Self {
        Self {
            rules: Vec::new(),
            rule_priorities: HashMap::new(),
        }
    }
}

impl EnsembleAnomalyDetector {
    pub fn new() -> Self {
        Self {
            detectors: Vec::new(),
            voting_strategy: VotingStrategy::Majority,
            confidence_weights: HashMap::new(),
        }
    }
}