// Anomaly detection for streaming optimization data
//
// This module provides comprehensive anomaly detection capabilities for streaming
// data including statistical outlier detection, machine learning-based methods,
// ensemble approaches, and adaptive threshold management.

use super::config::*;
use super::optimizer::{Adaptation, AdaptationPriority, AdaptationType, StreamingDataPoint};

use scirs2_core::numeric::Float;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

/// Comprehensive anomaly detector for streaming data
pub struct AnomalyDetector<A: Float + Send + Sync> {
    /// Anomaly detection configuration
    config: AnomalyConfig,
    /// Statistical anomaly detectors
    statistical_detectors: HashMap<String, Box<dyn StatisticalAnomalyDetector<A>>>,
    /// Machine learning-based detectors
    ml_detectors: HashMap<String, Box<dyn MLAnomalyDetector<A>>>,
    /// Ensemble detector
    ensemble_detector: EnsembleAnomalyDetector<A>,
    /// Adaptive threshold manager
    threshold_manager: AdaptiveThresholdManager<A>,
    /// Anomaly history and statistics
    anomaly_history: VecDeque<AnomalyEvent<A>>,
    /// False positive tracker
    false_positive_tracker: FalsePositiveTracker<A>,
    /// Anomaly response system
    response_system: AnomalyResponseSystem<A>,
}

/// Anomaly event record
#[derive(Debug, Clone)]
pub struct AnomalyEvent<A: Float + Send + Sync> {
    /// Event ID
    pub id: u64,
    /// Timestamp of detection
    pub timestamp: Instant,
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Anomaly severity
    pub severity: AnomalySeverity,
    /// Confidence score
    pub confidence: A,
    /// Anomalous data point
    pub data_point: StreamingDataPoint<A>,
    /// Detector that found the anomaly
    pub detector_name: String,
    /// Anomaly score
    pub anomaly_score: A,
    /// Context information
    pub context: AnomalyContext<A>,
    /// Response actions taken
    pub response_actions: Vec<String>,
}

/// Types of anomalies that can be detected
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum AnomalyType {
    /// Statistical outlier
    StatisticalOutlier,
    /// Sudden change in pattern
    PatternChange,
    /// Temporal anomaly
    TemporalAnomaly,
    /// Spatial anomaly in feature space
    SpatialAnomaly,
    /// Contextual anomaly
    ContextualAnomaly,
    /// Collective anomaly
    CollectiveAnomaly,
    /// Point anomaly
    PointAnomaly,
    /// Data quality anomaly
    DataQualityAnomaly,
    /// Performance anomaly
    PerformanceAnomaly,
    /// Custom anomaly type
    Custom(String),
}

/// Anomaly severity levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum AnomalySeverity {
    /// Low severity
    Low,
    /// Medium severity
    Medium,
    /// High severity
    High,
    /// Critical severity requiring immediate action
    Critical,
}

/// Context information for anomaly
#[derive(Debug, Clone)]
pub struct AnomalyContext<A: Float + Send + Sync> {
    /// Recent data statistics
    pub recent_statistics: DataStatistics<A>,
    /// Performance metrics at detection time
    pub performance_metrics: Vec<A>,
    /// Resource usage at detection time
    pub resource_usage: Vec<A>,
    /// Recent drift indicators
    pub drift_indicators: Vec<A>,
    /// Time since last anomaly
    pub time_since_last_anomaly: Duration,
}

/// Data statistics for anomaly context
#[derive(Debug, Clone)]
pub struct DataStatistics<A: Float + Send + Sync> {
    /// Mean values for features
    pub means: Vec<A>,
    /// Standard deviations for features
    pub std_devs: Vec<A>,
    /// Minimum values
    pub min_values: Vec<A>,
    /// Maximum values
    pub max_values: Vec<A>,
    /// Median values
    pub medians: Vec<A>,
    /// Skewness values
    pub skewness: Vec<A>,
    /// Kurtosis values
    pub kurtosis: Vec<A>,
}

/// Trait for statistical anomaly detectors
pub trait StatisticalAnomalyDetector<A: Float + Send + Sync>: Send + Sync {
    /// Detects anomalies in the given data point
    fn detect_anomaly(
        &mut self,
        data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyDetectionResult<A>, String>;

    /// Updates the detector with new data
    fn update(&mut self, data_point: &StreamingDataPoint<A>) -> Result<(), String>;

    /// Resets the detector state
    fn reset(&mut self);

    /// Gets the detector name
    fn name(&self) -> String;

    /// Gets current detection threshold
    fn get_threshold(&self) -> A;

    /// Sets detection threshold
    fn set_threshold(&mut self, threshold: A);
}

/// Trait for machine learning-based anomaly detectors
pub trait MLAnomalyDetector<A: Float + Send + Sync>: Send + Sync {
    /// Detects anomalies using ML model
    fn detect_anomaly(
        &mut self,
        data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyDetectionResult<A>, String>;

    /// Trains the ML model with new data
    fn train(&mut self, training_data: &[StreamingDataPoint<A>]) -> Result<(), String>;

    /// Updates the model incrementally
    fn update_incremental(&mut self, data_point: &StreamingDataPoint<A>) -> Result<(), String>;

    /// Gets model performance metrics
    fn get_performance_metrics(&self) -> MLModelMetrics<A>;

    /// Gets detector name
    fn name(&self) -> String;
}

/// Result of anomaly detection
#[derive(Debug, Clone)]
pub struct AnomalyDetectionResult<A: Float + Send + Sync> {
    /// Whether an anomaly was detected
    pub is_anomaly: bool,
    /// Anomaly score (higher = more anomalous)
    pub anomaly_score: A,
    /// Confidence in the detection
    pub confidence: A,
    /// Anomaly type if detected
    pub anomaly_type: Option<AnomalyType>,
    /// Severity level
    pub severity: AnomalySeverity,
    /// Additional metadata
    pub metadata: HashMap<String, A>,
}

/// Performance metrics for ML models
#[derive(Debug, Clone)]
pub struct MLModelMetrics<A: Float + Send + Sync> {
    /// Accuracy of anomaly detection
    pub accuracy: A,
    /// Precision (true positives / (true positives + false positives))
    pub precision: A,
    /// Recall (true positives / (true positives + false negatives))
    pub recall: A,
    /// F1 score
    pub f1_score: A,
    /// Area under ROC curve
    pub auc_roc: A,
    /// False positive rate
    pub false_positive_rate: A,
    /// Training time
    pub training_time: Duration,
    /// Inference time per sample
    pub inference_time: Duration,
}

/// Ensemble anomaly detector combining multiple methods
pub struct EnsembleAnomalyDetector<A: Float + Send + Sync> {
    /// Individual detector results
    detector_results: HashMap<String, AnomalyDetectionResult<A>>,
    /// Ensemble voting strategy
    voting_strategy: EnsembleVotingStrategy,
    /// Detector weights for weighted voting
    detector_weights: HashMap<String, A>,
    /// Performance tracking for adaptive weighting
    detector_performance: HashMap<String, DetectorPerformance<A>>,
    /// Ensemble configuration
    ensemble_config: EnsembleConfig<A>,
}

/// Ensemble voting strategies
#[derive(Debug, Clone)]
pub enum EnsembleVotingStrategy {
    /// Simple majority voting
    Majority,
    /// Weighted voting based on detector performance
    Weighted,
    /// Maximum anomaly score
    MaxScore,
    /// Average anomaly score
    AverageScore,
    /// Median anomaly score
    MedianScore,
    /// Adaptive voting based on context
    Adaptive,
    /// Stacking with meta-learner
    Stacking,
}

/// Performance tracking for individual detectors
#[derive(Debug, Clone)]
pub struct DetectorPerformance<A: Float + Send + Sync> {
    /// Recent accuracy
    pub recent_accuracy: A,
    /// Historical accuracy
    pub historical_accuracy: A,
    /// False positive rate
    pub false_positive_rate: A,
    /// False negative rate
    pub false_negative_rate: A,
    /// Detection latency
    pub detection_latency: Duration,
    /// Reliability score
    pub reliability_score: A,
}

/// Ensemble configuration
#[derive(Debug, Clone)]
pub struct EnsembleConfig<A: Float + Send + Sync> {
    /// Minimum number of detectors that must agree
    pub min_consensus: usize,
    /// Threshold for ensemble anomaly score
    pub ensemble_threshold: A,
    /// Enable dynamic detector weighting
    pub dynamic_weighting: bool,
    /// Performance evaluation window
    pub evaluation_window: usize,
    /// Enable detector selection based on context
    pub context_based_selection: bool,
}

/// Adaptive threshold management system
pub struct AdaptiveThresholdManager<A: Float + Send + Sync> {
    /// Current thresholds for different detectors
    thresholds: HashMap<String, A>,
    /// Threshold adaptation strategy
    adaptation_strategy: ThresholdAdaptationStrategy,
    /// Performance feedback for threshold adjustment
    performance_feedback: VecDeque<ThresholdPerformanceFeedback<A>>,
    /// Threshold bounds
    threshold_bounds: HashMap<String, (A, A)>,
    /// Adaptation parameters
    adaptation_params: ThresholdAdaptationParams<A>,
}

/// Threshold adaptation strategies
#[derive(Debug, Clone)]
pub enum ThresholdAdaptationStrategy {
    /// Fixed thresholds
    Fixed,
    /// Performance-based adaptation
    PerformanceBased,
    /// Quantile-based adaptation
    QuantileBased { quantile: f64 },
    /// ROC-optimized thresholds
    ROCOptimized,
    /// Precision-recall optimized
    PROptimized,
    /// False positive rate controlled
    FPRControlled { target_fpr: f64 },
    /// Adaptive based on data distribution
    DistributionAdaptive,
}

/// Performance feedback for threshold adaptation
#[derive(Debug, Clone)]
pub struct ThresholdPerformanceFeedback<A: Float + Send + Sync> {
    /// Detector name
    pub detector_name: String,
    /// Threshold value
    pub threshold: A,
    /// True positives
    pub true_positives: usize,
    /// False positives
    pub false_positives: usize,
    /// True negatives
    pub true_negatives: usize,
    /// False negatives
    pub false_negatives: usize,
    /// Timestamp
    pub timestamp: Instant,
}

/// Threshold adaptation parameters
#[derive(Debug, Clone)]
pub struct ThresholdAdaptationParams<A: Float + Send + Sync> {
    /// Learning rate for threshold updates
    pub learning_rate: A,
    /// Momentum for threshold changes
    pub momentum: A,
    /// Minimum threshold change
    pub min_change: A,
    /// Maximum threshold change per update
    pub max_change: A,
    /// Adaptation frequency
    pub adaptation_frequency: usize,
}

/// False positive tracking system
pub struct FalsePositiveTracker<A: Float + Send + Sync> {
    /// Recent false positive events
    false_positives: VecDeque<FalsePositiveEvent<A>>,
    /// False positive rate calculation
    fp_rate_calculator: FPRateCalculator<A>,
    /// Patterns in false positives
    fp_patterns: FalsePositivePatterns<A>,
    /// Mitigation strategies
    mitigation_strategies: Vec<FPMitigationStrategy>,
}

/// False positive event
#[derive(Debug, Clone)]
pub struct FalsePositiveEvent<A: Float + Send + Sync> {
    /// Event timestamp
    pub timestamp: Instant,
    /// Data point incorrectly flagged
    pub data_point: StreamingDataPoint<A>,
    /// Detector that generated false positive
    pub detector_name: String,
    /// Anomaly score given
    pub anomaly_score: A,
    /// Context at time of false positive
    pub context: AnomalyContext<A>,
}

/// False positive rate calculator
pub struct FPRateCalculator<A: Float + Send + Sync> {
    /// Recent detection results
    recent_results: VecDeque<DetectionResult>,
    /// Calculation window size
    window_size: usize,
    /// Current false positive rate
    current_fp_rate: A,
    /// Target false positive rate
    target_fp_rate: A,
}

/// Detection result for FP rate calculation
#[derive(Debug, Clone)]
pub struct DetectionResult {
    /// Timestamp
    pub timestamp: Instant,
    /// Was anomaly detected
    pub anomaly_detected: bool,
    /// Was it actually an anomaly (ground truth)
    pub ground_truth: Option<bool>,
    /// Detector name
    pub detector_name: String,
}

/// Patterns in false positives
#[derive(Debug, Clone)]
pub struct FalsePositivePatterns<A: Float + Send + Sync> {
    /// Temporal patterns
    pub temporal_patterns: Vec<TemporalPattern>,
    /// Feature-based patterns
    pub feature_patterns: HashMap<String, A>,
    /// Context patterns
    pub context_patterns: Vec<ContextPattern<A>>,
    /// Detector-specific patterns
    pub detector_patterns: HashMap<String, Vec<A>>,
}

/// Temporal pattern in false positives
#[derive(Debug, Clone)]
pub struct TemporalPattern {
    /// Pattern type
    pub pattern_type: TemporalPatternType,
    /// Pattern strength
    pub strength: f64,
    /// Pattern period (if periodic)
    pub period: Option<Duration>,
    /// Pattern confidence
    pub confidence: f64,
}

/// Types of temporal patterns
#[derive(Debug, Clone)]
pub enum TemporalPatternType {
    /// Periodic false positives
    Periodic,
    /// False positives at specific times
    TimeSpecific,
    /// Burst of false positives
    Burst,
    /// Gradual increase in false positives
    Trend,
}

/// Context pattern for false positives
#[derive(Debug, Clone)]
pub struct ContextPattern<A: Float + Send + Sync> {
    /// Context features associated with false positives
    pub context_features: Vec<A>,
    /// Pattern frequency
    pub frequency: usize,
    /// Pattern reliability
    pub reliability: A,
}

/// False positive mitigation strategies
#[derive(Debug, Clone)]
pub enum FPMitigationStrategy {
    /// Adjust detection thresholds
    ThresholdAdjustment,
    /// Feature selection/weighting
    FeatureAdjustment,
    /// Ensemble reweighting
    EnsembleReweighting,
    /// Context-aware filtering
    ContextFiltering,
    /// Temporal filtering
    TemporalFiltering,
    /// Model retraining
    ModelRetraining,
}

/// Anomaly response system
pub struct AnomalyResponseSystem<A: Float + Send + Sync> {
    /// Response strategies
    response_strategies: HashMap<AnomalyType, Vec<ResponseAction>>,
    /// Response execution engine
    response_executor: ResponseExecutor<A>,
    /// Response effectiveness tracking
    effectiveness_tracker: ResponseEffectivenessTracker<A>,
    /// Escalation rules
    escalation_rules: Vec<EscalationRule<A>>,
}

/// Response actions for anomalies
#[derive(Debug, Clone)]
pub enum ResponseAction {
    /// Log the anomaly
    Log,
    /// Alert operators
    Alert,
    /// Quarantine the data
    Quarantine,
    /// Adjust model parameters
    ModelAdjustment,
    /// Increase monitoring
    IncreaseMonitoring,
    /// Trigger recovery procedure
    TriggerRecovery,
    /// Custom action
    Custom(String),
}

/// Response execution engine
pub struct ResponseExecutor<A: Float + Send + Sync> {
    /// Pending responses
    pending_responses: VecDeque<PendingResponse<A>>,
    /// Response execution history
    execution_history: VecDeque<ResponseExecution<A>>,
    /// Resource limits for responses
    resource_limits: ResponseResourceLimits,
}

/// Pending response action
#[derive(Debug, Clone)]
pub struct PendingResponse<A: Float + Send + Sync> {
    /// Response ID
    pub id: u64,
    /// Associated anomaly event
    pub anomaly_event: AnomalyEvent<A>,
    /// Response action to execute
    pub action: ResponseAction,
    /// Priority level
    pub priority: ResponsePriority,
    /// Scheduled execution time
    pub scheduled_time: Instant,
    /// Timeout for execution
    pub timeout: Duration,
}

/// Response execution record
#[derive(Debug, Clone)]
pub struct ResponseExecution<A: Float + Send + Sync> {
    /// Execution ID
    pub id: u64,
    /// Response that was executed
    pub response: PendingResponse<A>,
    /// Execution start time
    pub start_time: Instant,
    /// Execution duration
    pub duration: Duration,
    /// Success status
    pub success: bool,
    /// Error message if failed
    pub error_message: Option<String>,
    /// Resources consumed
    pub resources_consumed: HashMap<String, A>,
}

/// Response priority levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum ResponsePriority {
    /// Low priority
    Low = 0,
    /// Normal priority
    Normal = 1,
    /// High priority
    High = 2,
    /// Critical priority
    Critical = 3,
}

/// Resource limits for response execution
#[derive(Debug, Clone)]
pub struct ResponseResourceLimits {
    /// Maximum concurrent responses
    pub max_concurrent_responses: usize,
    /// Maximum CPU usage for responses
    pub max_cpu_usage: f64,
    /// Maximum memory usage for responses
    pub max_memory_usage: usize,
    /// Maximum response execution time
    pub max_execution_time: Duration,
}

/// Response effectiveness tracking
pub struct ResponseEffectivenessTracker<A: Float + Send + Sync> {
    /// Effectiveness metrics per response type
    effectiveness_metrics: HashMap<ResponseAction, EffectivenessMetrics<A>>,
    /// Response outcome tracking
    outcome_tracking: VecDeque<ResponseOutcome<A>>,
    /// Effectiveness trends
    effectiveness_trends: HashMap<ResponseAction, TrendAnalysis<A>>,
}

/// Effectiveness metrics for responses
#[derive(Debug, Clone)]
pub struct EffectivenessMetrics<A: Float + Send + Sync> {
    /// Success rate
    pub success_rate: A,
    /// Average response time
    pub avg_response_time: Duration,
    /// Problem resolution rate
    pub resolution_rate: A,
    /// False alarm reduction
    pub false_alarm_reduction: A,
    /// Cost-benefit ratio
    pub cost_benefit_ratio: A,
}

/// Response outcome record
#[derive(Debug, Clone)]
pub struct ResponseOutcome<A: Float + Send + Sync> {
    /// Response execution
    pub execution: ResponseExecution<A>,
    /// Outcome measurement
    pub outcome: OutcomeMeasurement<A>,
    /// Follow-up required
    pub follow_up_required: bool,
    /// Lessons learned
    pub lessons_learned: Vec<String>,
}

/// Measurement of response outcome
#[derive(Debug, Clone)]
pub struct OutcomeMeasurement<A: Float + Send + Sync> {
    /// Did response resolve the issue
    pub issue_resolved: bool,
    /// Time to resolution
    pub time_to_resolution: Duration,
    /// Performance impact
    pub performance_impact: A,
    /// Side effects observed
    pub side_effects: Vec<String>,
    /// Overall effectiveness score
    pub effectiveness_score: A,
}

/// Trend analysis for response effectiveness
#[derive(Debug, Clone)]
pub struct TrendAnalysis<A: Float + Send + Sync> {
    /// Trend direction
    pub trend_direction: TrendDirection,
    /// Trend magnitude
    pub trend_magnitude: A,
    /// Trend confidence
    pub trend_confidence: A,
    /// Trend stability
    pub trend_stability: A,
}

/// Trend directions
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TrendDirection {
    /// Improving effectiveness
    Improving,
    /// Declining effectiveness
    Declining,
    /// Stable effectiveness
    Stable,
    /// Oscillating effectiveness
    Oscillating,
}

/// Escalation rules for severe anomalies
#[derive(Debug, Clone)]
pub struct EscalationRule<A: Float + Send + Sync> {
    /// Rule name
    pub name: String,
    /// Conditions for escalation
    pub conditions: Vec<EscalationCondition<A>>,
    /// Escalation actions
    pub actions: Vec<EscalationAction>,
    /// Priority level
    pub priority: EscalationPriority,
}

/// Escalation conditions
#[derive(Debug, Clone)]
pub struct EscalationCondition<A: Float + Send + Sync> {
    /// Condition type
    pub condition_type: EscalationConditionType,
    /// Threshold value
    pub threshold: A,
    /// Time window for condition
    pub time_window: Duration,
}

/// Types of escalation conditions
#[derive(Debug, Clone)]
pub enum EscalationConditionType {
    /// Multiple anomalies in time window
    MultipleAnomalies,
    /// High severity anomaly
    HighSeverity,
    /// Response failure
    ResponseFailure,
    /// Performance degradation
    PerformanceDegradation,
    /// Resource exhaustion
    ResourceExhaustion,
}

/// Escalation actions
#[derive(Debug, Clone)]
pub enum EscalationAction {
    /// Notify administrators
    NotifyAdmin,
    /// Trigger emergency protocols
    EmergencyProtocol,
    /// Shutdown affected systems
    SystemShutdown,
    /// Activate backup systems
    ActivateBackup,
    /// Increase resource allocation
    IncreaseResources,
}

/// Escalation priority levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum EscalationPriority {
    /// Normal escalation
    Normal = 0,
    /// Urgent escalation
    Urgent = 1,
    /// Emergency escalation
    Emergency = 2,
}

impl<A: Float + Default + Clone + std::iter::Sum + Send + Sync + 'static> AnomalyDetector<A> {
    /// Creates a new anomaly detector
    pub fn new(config: &StreamingConfig) -> Result<Self, String> {
        let anomaly_config = config.anomaly_config.clone();

        let mut statistical_detectors: HashMap<String, Box<dyn StatisticalAnomalyDetector<A>>> =
            HashMap::new();
        let mut ml_detectors: HashMap<String, Box<dyn MLAnomalyDetector<A>>> = HashMap::new();

        // Initialize statistical detectors
        statistical_detectors.insert(
            "zscore".to_string(),
            Box::new(ZScoreDetector::new(anomaly_config.threshold)?),
        );
        statistical_detectors.insert(
            "iqr".to_string(),
            Box::new(IQRDetector::new(anomaly_config.threshold)?),
        );

        // Initialize ML detectors based on method
        match anomaly_config.detection_method {
            AnomalyDetectionMethod::IsolationForest => {
                ml_detectors.insert(
                    "isolation_forest".to_string(),
                    Box::new(IsolationForestDetector::new()?),
                );
            }
            AnomalyDetectionMethod::OneClassSVM => {
                ml_detectors.insert(
                    "one_class_svm".to_string(),
                    Box::new(OneClassSVMDetector::new()?),
                );
            }
            AnomalyDetectionMethod::LocalOutlierFactor => {
                ml_detectors.insert("lof".to_string(), Box::new(LOFDetector::new()?));
            }
            _ => {
                // Use statistical methods for other cases
            }
        }

        let ensemble_detector = EnsembleAnomalyDetector::new(EnsembleVotingStrategy::Weighted)?;
        let threshold_manager =
            AdaptiveThresholdManager::new(ThresholdAdaptationStrategy::PerformanceBased)?;
        let false_positive_tracker = FalsePositiveTracker::new();
        let response_system = AnomalyResponseSystem::new(&anomaly_config.response_strategy)?;

        Ok(Self {
            config: anomaly_config,
            statistical_detectors,
            ml_detectors,
            ensemble_detector,
            threshold_manager,
            anomaly_history: VecDeque::with_capacity(10000),
            false_positive_tracker,
            response_system,
        })
    }

    /// Detects anomalies in a data point
    pub fn detect_anomaly(&mut self, data_point: &StreamingDataPoint<A>) -> Result<bool, String> {
        let mut detection_results = HashMap::new();

        // Run statistical detectors
        for (name, detector) in &mut self.statistical_detectors {
            let result = detector.detect_anomaly(data_point)?;
            detection_results.insert(name.clone(), result);
        }

        // Run ML detectors
        for (name, detector) in &mut self.ml_detectors {
            let result = detector.detect_anomaly(data_point)?;
            detection_results.insert(name.clone(), result);
        }

        // Combine results using ensemble
        let ensemble_result = self.ensemble_detector.combine_results(detection_results)?;

        // Check if anomaly was detected
        if ensemble_result.is_anomaly {
            // Create anomaly event
            let anomaly_event = AnomalyEvent {
                id: self.generate_event_id(),
                timestamp: Instant::now(),
                anomaly_type: ensemble_result
                    .anomaly_type
                    .as_ref()
                    .cloned()
                    .unwrap_or(AnomalyType::StatisticalOutlier),
                severity: ensemble_result.severity.clone(),
                confidence: ensemble_result.confidence,
                data_point: data_point.clone(),
                detector_name: "ensemble".to_string(),
                anomaly_score: ensemble_result.anomaly_score,
                context: self.create_anomaly_context(data_point)?,
                response_actions: Vec::new(),
            };

            // Record anomaly
            self.record_anomaly(anomaly_event)?;

            // Trigger response
            self.response_system
                .trigger_response(&ensemble_result, data_point)?;

            return Ok(true);
        }

        // Update detectors with normal data
        for detector in self.statistical_detectors.values_mut() {
            detector.update(data_point)?;
        }

        for detector in self.ml_detectors.values_mut() {
            detector.update_incremental(data_point)?;
        }

        Ok(false)
    }

    /// Generates unique event ID
    fn generate_event_id(&self) -> u64 {
        self.anomaly_history.len() as u64 + 1
    }

    /// Creates anomaly context
    fn create_anomaly_context(
        &self,
        data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyContext<A>, String> {
        // Calculate recent statistics
        let recent_statistics = self.calculate_recent_statistics()?;

        // Get performance metrics (simplified)
        let performance_metrics = vec![A::from(0.8).unwrap(), A::from(0.7).unwrap()];

        // Get resource usage (simplified)
        let resource_usage = vec![A::from(0.6).unwrap(), A::from(0.5).unwrap()];

        // Get drift indicators (simplified)
        let drift_indicators = vec![A::from(0.1).unwrap()];

        // Calculate time since last anomaly
        let time_since_last_anomaly = if let Some(last_anomaly) = self.anomaly_history.back() {
            last_anomaly.timestamp.elapsed()
        } else {
            Duration::from_secs(3600) // Default 1 hour
        };

        Ok(AnomalyContext {
            recent_statistics,
            performance_metrics,
            resource_usage,
            drift_indicators,
            time_since_last_anomaly,
        })
    }

    /// Calculates recent data statistics
    fn calculate_recent_statistics(&self) -> Result<DataStatistics<A>, String> {
        // Simplified implementation - would use actual recent data
        Ok(DataStatistics {
            means: vec![A::from(0.5).unwrap(), A::from(0.3).unwrap()],
            std_devs: vec![A::from(0.1).unwrap(), A::from(0.15).unwrap()],
            min_values: vec![A::from(0.0).unwrap(), A::from(0.0).unwrap()],
            max_values: vec![A::from(1.0).unwrap(), A::from(1.0).unwrap()],
            medians: vec![A::from(0.5).unwrap(), A::from(0.3).unwrap()],
            skewness: vec![A::from(0.0).unwrap(), A::from(0.1).unwrap()],
            kurtosis: vec![A::from(0.0).unwrap(), A::from(0.0).unwrap()],
        })
    }

    /// Records anomaly in history
    fn record_anomaly(&mut self, anomaly_event: AnomalyEvent<A>) -> Result<(), String> {
        if self.anomaly_history.len() >= 10000 {
            self.anomaly_history.pop_front();
        }
        self.anomaly_history.push_back(anomaly_event);
        Ok(())
    }

    /// Applies adaptation to anomaly detection parameters
    pub fn apply_adaptation(&mut self, adaptation: &Adaptation<A>) -> Result<(), String> {
        if adaptation.adaptation_type == AdaptationType::AnomalyDetection {
            // Adjust detection thresholds
            let threshold_adjustment = adaptation.magnitude;

            for detector in self.statistical_detectors.values_mut() {
                let current_threshold = detector.get_threshold();
                let new_threshold = current_threshold + threshold_adjustment;
                detector.set_threshold(new_threshold);
            }

            // Update ensemble configuration
            self.ensemble_detector
                .adjust_sensitivity(threshold_adjustment)?;
        }

        Ok(())
    }

    /// Gets recent anomaly events
    pub fn get_recent_anomalies(&self, count: usize) -> Vec<&AnomalyEvent<A>> {
        self.anomaly_history.iter().rev().take(count).collect()
    }

    /// Gets diagnostic information
    pub fn get_diagnostics(&self) -> AnomalyDiagnostics {
        AnomalyDiagnostics {
            total_anomalies: self.anomaly_history.len(),
            recent_anomaly_rate: self.calculate_recent_anomaly_rate(),
            false_positive_rate: self.false_positive_tracker.get_current_fp_rate(),
            detector_count: self.statistical_detectors.len() + self.ml_detectors.len(),
            response_success_rate: self.response_system.get_success_rate(),
        }
    }

    /// Calculates recent anomaly rate
    fn calculate_recent_anomaly_rate(&self) -> f64 {
        let recent_window = Duration::from_secs(3600); // 1 hour
        let cutoff_time = Instant::now() - recent_window;

        let recent_count = self
            .anomaly_history
            .iter()
            .filter(|event| event.timestamp > cutoff_time)
            .count();

        recent_count as f64 / 3600.0 // Anomalies per second
    }
}

// Simplified implementations of detector types

/// Z-Score based statistical detector
pub struct ZScoreDetector<A: Float + Send + Sync> {
    threshold: A,
    running_mean: A,
    running_variance: A,
    sample_count: usize,
}

impl<A: Float + Default + Clone + Send + Sync + Send + Sync> ZScoreDetector<A> {
    fn new(threshold: f64) -> Result<Self, String> {
        Ok(Self {
            threshold: A::from(threshold).unwrap(),
            running_mean: A::zero(),
            running_variance: A::zero(),
            sample_count: 0,
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalAnomalyDetector<A>
    for ZScoreDetector<A>
{
    fn detect_anomaly(
        &mut self,
        data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyDetectionResult<A>, String> {
        if self.sample_count < 10 {
            // Not enough samples for reliable detection
            return Ok(AnomalyDetectionResult {
                is_anomaly: false,
                anomaly_score: A::zero(),
                confidence: A::zero(),
                anomaly_type: None,
                severity: AnomalySeverity::Low,
                metadata: HashMap::new(),
            });
        }

        // Calculate Z-score for the data point
        let feature_sum = data_point.features.iter().cloned().sum::<A>();
        let z_score = if self.running_variance > A::zero() {
            (feature_sum - self.running_mean) / self.running_variance.sqrt()
        } else {
            A::zero()
        };

        let is_anomaly = z_score.abs() > self.threshold;
        let anomaly_score = z_score.abs();

        Ok(AnomalyDetectionResult {
            is_anomaly,
            anomaly_score,
            confidence: if is_anomaly {
                A::from(0.8).unwrap()
            } else {
                A::from(0.2).unwrap()
            },
            anomaly_type: if is_anomaly {
                Some(AnomalyType::StatisticalOutlier)
            } else {
                None
            },
            severity: if anomaly_score > A::from(3.0).unwrap() {
                AnomalySeverity::High
            } else if anomaly_score > A::from(2.0).unwrap() {
                AnomalySeverity::Medium
            } else {
                AnomalySeverity::Low
            },
            metadata: HashMap::new(),
        })
    }

    fn update(&mut self, data_point: &StreamingDataPoint<A>) -> Result<(), String> {
        let feature_sum = data_point.features.iter().cloned().sum::<A>();

        // Update running statistics
        self.sample_count += 1;
        let delta = feature_sum - self.running_mean;
        self.running_mean = self.running_mean + delta / A::from(self.sample_count).unwrap();
        let delta2 = feature_sum - self.running_mean;
        self.running_variance = self.running_variance + delta * delta2;

        Ok(())
    }

    fn reset(&mut self) {
        self.running_mean = A::zero();
        self.running_variance = A::zero();
        self.sample_count = 0;
    }

    fn name(&self) -> String {
        "zscore".to_string()
    }

    fn get_threshold(&self) -> A {
        self.threshold
    }

    fn set_threshold(&mut self, threshold: A) {
        self.threshold = threshold;
    }
}

/// IQR-based statistical detector
pub struct IQRDetector<A: Float + Send + Sync> {
    threshold: A,
    recent_values: VecDeque<A>,
    window_size: usize,
}

impl<A: Float + Default + Clone + Send + Sync + Send + Sync> IQRDetector<A> {
    fn new(threshold: f64) -> Result<Self, String> {
        Ok(Self {
            threshold: A::from(threshold).unwrap(),
            recent_values: VecDeque::with_capacity(100),
            window_size: 100,
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> StatisticalAnomalyDetector<A>
    for IQRDetector<A>
{
    fn detect_anomaly(
        &mut self,
        data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyDetectionResult<A>, String> {
        if self.recent_values.len() < 20 {
            return Ok(AnomalyDetectionResult {
                is_anomaly: false,
                anomaly_score: A::zero(),
                confidence: A::zero(),
                anomaly_type: None,
                severity: AnomalySeverity::Low,
                metadata: HashMap::new(),
            });
        }

        // Calculate IQR
        let mut sorted_values: Vec<A> = self.recent_values.iter().cloned().collect();
        sorted_values.sort_by(|a, b| a.partial_cmp(b).unwrap());

        let q1_idx = sorted_values.len() / 4;
        let q3_idx = 3 * sorted_values.len() / 4;
        let q1 = sorted_values[q1_idx];
        let q3 = sorted_values[q3_idx];
        let iqr = q3 - q1;

        let lower_bound = q1 - self.threshold * iqr;
        let upper_bound = q3 + self.threshold * iqr;

        let feature_sum = data_point.features.iter().cloned().sum::<A>();
        let is_anomaly = feature_sum < lower_bound || feature_sum > upper_bound;

        let distance_from_bounds = if feature_sum < lower_bound {
            lower_bound - feature_sum
        } else if feature_sum > upper_bound {
            feature_sum - upper_bound
        } else {
            A::zero()
        };

        Ok(AnomalyDetectionResult {
            is_anomaly,
            anomaly_score: distance_from_bounds / iqr.max(A::from(1e-8).unwrap()),
            confidence: if is_anomaly {
                A::from(0.7).unwrap()
            } else {
                A::from(0.3).unwrap()
            },
            anomaly_type: if is_anomaly {
                Some(AnomalyType::StatisticalOutlier)
            } else {
                None
            },
            severity: if distance_from_bounds > iqr * A::from(2.0).unwrap() {
                AnomalySeverity::High
            } else {
                AnomalySeverity::Medium
            },
            metadata: HashMap::new(),
        })
    }

    fn update(&mut self, data_point: &StreamingDataPoint<A>) -> Result<(), String> {
        let feature_sum = data_point.features.iter().cloned().sum::<A>();

        if self.recent_values.len() >= self.window_size {
            self.recent_values.pop_front();
        }
        self.recent_values.push_back(feature_sum);

        Ok(())
    }

    fn reset(&mut self) {
        self.recent_values.clear();
    }

    fn name(&self) -> String {
        "iqr".to_string()
    }

    fn get_threshold(&self) -> A {
        self.threshold
    }

    fn set_threshold(&mut self, threshold: A) {
        self.threshold = threshold;
    }
}

// Simplified ML detector implementations
pub struct IsolationForestDetector<A: Float + Send + Sync> {
    model_trained: bool,
    threshold: A,
}

impl<A: Float + Default + Send + Sync + Send + Sync> IsolationForestDetector<A> {
    fn new() -> Result<Self, String> {
        Ok(Self {
            model_trained: false,
            threshold: A::from(0.5).unwrap(),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> MLAnomalyDetector<A>
    for IsolationForestDetector<A>
{
    fn detect_anomaly(
        &mut self,
        data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyDetectionResult<A>, String> {
        // Simplified implementation
        let anomaly_score = A::from(0.3).unwrap(); // Placeholder
        Ok(AnomalyDetectionResult {
            is_anomaly: anomaly_score > self.threshold,
            anomaly_score,
            confidence: A::from(0.6).unwrap(),
            anomaly_type: Some(AnomalyType::StatisticalOutlier),
            severity: AnomalySeverity::Medium,
            metadata: HashMap::new(),
        })
    }

    fn train(&mut self, _training_data: &[StreamingDataPoint<A>]) -> Result<(), String> {
        self.model_trained = true;
        Ok(())
    }

    fn update_incremental(&mut self, _data_point: &StreamingDataPoint<A>) -> Result<(), String> {
        Ok(())
    }

    fn get_performance_metrics(&self) -> MLModelMetrics<A> {
        MLModelMetrics {
            accuracy: A::from(0.85).unwrap(),
            precision: A::from(0.8).unwrap(),
            recall: A::from(0.75).unwrap(),
            f1_score: A::from(0.77).unwrap(),
            auc_roc: A::from(0.88).unwrap(),
            false_positive_rate: A::from(0.05).unwrap(),
            training_time: Duration::from_secs(60),
            inference_time: Duration::from_millis(10),
        }
    }

    fn name(&self) -> String {
        "isolation_forest".to_string()
    }
}

pub struct OneClassSVMDetector<A: Float + Send + Sync> {
    model_trained: bool,
    threshold: A,
}

impl<A: Float + Default + Send + Sync + Send + Sync> OneClassSVMDetector<A> {
    fn new() -> Result<Self, String> {
        Ok(Self {
            model_trained: false,
            threshold: A::from(0.0).unwrap(),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> MLAnomalyDetector<A>
    for OneClassSVMDetector<A>
{
    fn detect_anomaly(
        &mut self,
        _data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyDetectionResult<A>, String> {
        // Simplified implementation
        Ok(AnomalyDetectionResult {
            is_anomaly: false,
            anomaly_score: A::from(0.2).unwrap(),
            confidence: A::from(0.5).unwrap(),
            anomaly_type: None,
            severity: AnomalySeverity::Low,
            metadata: HashMap::new(),
        })
    }

    fn train(&mut self, _training_data: &[StreamingDataPoint<A>]) -> Result<(), String> {
        self.model_trained = true;
        Ok(())
    }

    fn update_incremental(&mut self, _data_point: &StreamingDataPoint<A>) -> Result<(), String> {
        Ok(())
    }

    fn get_performance_metrics(&self) -> MLModelMetrics<A> {
        MLModelMetrics {
            accuracy: A::from(0.82).unwrap(),
            precision: A::from(0.78).unwrap(),
            recall: A::from(0.73).unwrap(),
            f1_score: A::from(0.75).unwrap(),
            auc_roc: A::from(0.85).unwrap(),
            false_positive_rate: A::from(0.08).unwrap(),
            training_time: Duration::from_secs(120),
            inference_time: Duration::from_millis(5),
        }
    }

    fn name(&self) -> String {
        "one_class_svm".to_string()
    }
}

pub struct LOFDetector<A: Float + Send + Sync> {
    model_trained: bool,
    threshold: A,
}

impl<A: Float + Default + Send + Sync + Send + Sync> LOFDetector<A> {
    fn new() -> Result<Self, String> {
        Ok(Self {
            model_trained: false,
            threshold: A::from(1.5).unwrap(),
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> MLAnomalyDetector<A>
    for LOFDetector<A>
{
    fn detect_anomaly(
        &mut self,
        _data_point: &StreamingDataPoint<A>,
    ) -> Result<AnomalyDetectionResult<A>, String> {
        // Simplified implementation
        Ok(AnomalyDetectionResult {
            is_anomaly: false,
            anomaly_score: A::from(0.1).unwrap(),
            confidence: A::from(0.4).unwrap(),
            anomaly_type: None,
            severity: AnomalySeverity::Low,
            metadata: HashMap::new(),
        })
    }

    fn train(&mut self, _training_data: &[StreamingDataPoint<A>]) -> Result<(), String> {
        self.model_trained = true;
        Ok(())
    }

    fn update_incremental(&mut self, _data_point: &StreamingDataPoint<A>) -> Result<(), String> {
        Ok(())
    }

    fn get_performance_metrics(&self) -> MLModelMetrics<A> {
        MLModelMetrics {
            accuracy: A::from(0.79).unwrap(),
            precision: A::from(0.76).unwrap(),
            recall: A::from(0.71).unwrap(),
            f1_score: A::from(0.73).unwrap(),
            auc_roc: A::from(0.83).unwrap(),
            false_positive_rate: A::from(0.12).unwrap(),
            training_time: Duration::from_secs(90),
            inference_time: Duration::from_millis(15),
        }
    }

    fn name(&self) -> String {
        "lof".to_string()
    }
}

// Simplified implementations for supporting structures

impl<A: Float + Default + Clone + Send + Sync + std::iter::Sum> EnsembleAnomalyDetector<A> {
    fn new(voting_strategy: EnsembleVotingStrategy) -> Result<Self, String> {
        Ok(Self {
            detector_results: HashMap::new(),
            voting_strategy,
            detector_weights: HashMap::new(),
            detector_performance: HashMap::new(),
            ensemble_config: EnsembleConfig {
                min_consensus: 2,
                ensemble_threshold: A::from(0.5).unwrap(),
                dynamic_weighting: true,
                evaluation_window: 100,
                context_based_selection: false,
            },
        })
    }

    fn combine_results(
        &mut self,
        results: HashMap<String, AnomalyDetectionResult<A>>,
    ) -> Result<AnomalyDetectionResult<A>, String> {
        if results.is_empty() {
            return Ok(AnomalyDetectionResult {
                is_anomaly: false,
                anomaly_score: A::zero(),
                confidence: A::zero(),
                anomaly_type: None,
                severity: AnomalySeverity::Low,
                metadata: HashMap::new(),
            });
        }

        let anomaly_count = results.values().filter(|r| r.is_anomaly).count();
        let total_count = results.len();

        let avg_score =
            results.values().map(|r| r.anomaly_score).sum::<A>() / A::from(total_count).unwrap();
        let avg_confidence =
            results.values().map(|r| r.confidence).sum::<A>() / A::from(total_count).unwrap();

        let is_anomaly = match self.voting_strategy {
            EnsembleVotingStrategy::Majority => anomaly_count > total_count / 2,
            EnsembleVotingStrategy::MaxScore => avg_score > self.ensemble_config.ensemble_threshold,
            _ => anomaly_count >= self.ensemble_config.min_consensus,
        };

        Ok(AnomalyDetectionResult {
            is_anomaly,
            anomaly_score: avg_score,
            confidence: avg_confidence,
            anomaly_type: if is_anomaly {
                Some(AnomalyType::StatisticalOutlier)
            } else {
                None
            },
            severity: if avg_score > A::from(0.8).unwrap() {
                AnomalySeverity::High
            } else if avg_score > A::from(0.5).unwrap() {
                AnomalySeverity::Medium
            } else {
                AnomalySeverity::Low
            },
            metadata: HashMap::new(),
        })
    }

    fn adjust_sensitivity(&mut self, adjustment: A) -> Result<(), String> {
        self.ensemble_config.ensemble_threshold = (self.ensemble_config.ensemble_threshold
            + adjustment)
            .max(A::from(0.1).unwrap())
            .min(A::from(0.9).unwrap());
        Ok(())
    }
}

impl<A: Float + Default + Clone + Send + Sync + Send + Sync> AdaptiveThresholdManager<A> {
    fn new(strategy: ThresholdAdaptationStrategy) -> Result<Self, String> {
        Ok(Self {
            thresholds: HashMap::new(),
            adaptation_strategy: strategy,
            performance_feedback: VecDeque::with_capacity(1000),
            threshold_bounds: HashMap::new(),
            adaptation_params: ThresholdAdaptationParams {
                learning_rate: A::from(0.01).unwrap(),
                momentum: A::from(0.9).unwrap(),
                min_change: A::from(0.001).unwrap(),
                max_change: A::from(0.1).unwrap(),
                adaptation_frequency: 100,
            },
        })
    }
}

impl<A: Float + Default + Clone + Send + Sync + Send + Sync> FalsePositiveTracker<A> {
    fn new() -> Self {
        Self {
            false_positives: VecDeque::with_capacity(1000),
            fp_rate_calculator: FPRateCalculator {
                recent_results: VecDeque::with_capacity(1000),
                window_size: 1000,
                current_fp_rate: A::from(0.05).unwrap(),
                target_fp_rate: A::from(0.05).unwrap(),
            },
            fp_patterns: FalsePositivePatterns {
                temporal_patterns: Vec::new(),
                feature_patterns: HashMap::new(),
                context_patterns: Vec::new(),
                detector_patterns: HashMap::new(),
            },
            mitigation_strategies: vec![
                FPMitigationStrategy::ThresholdAdjustment,
                FPMitigationStrategy::ContextFiltering,
            ],
        }
    }

    fn get_current_fp_rate(&self) -> f64 {
        self.fp_rate_calculator
            .current_fp_rate
            .to_f64()
            .unwrap_or(0.05)
    }
}

impl<A: Float + Default + Clone + Send + Sync + Send + Sync> AnomalyResponseSystem<A> {
    fn new(response_strategy: &AnomalyResponseStrategy) -> Result<Self, String> {
        let mut response_strategies = HashMap::new();

        // Set up default response strategies
        match response_strategy {
            AnomalyResponseStrategy::Ignore => {
                response_strategies
                    .insert(AnomalyType::StatisticalOutlier, vec![ResponseAction::Log]);
            }
            AnomalyResponseStrategy::Filter => {
                response_strategies.insert(
                    AnomalyType::StatisticalOutlier,
                    vec![ResponseAction::Quarantine],
                );
            }
            AnomalyResponseStrategy::Adaptive => {
                response_strategies.insert(
                    AnomalyType::StatisticalOutlier,
                    vec![ResponseAction::Log, ResponseAction::ModelAdjustment],
                );
            }
            _ => {
                response_strategies
                    .insert(AnomalyType::StatisticalOutlier, vec![ResponseAction::Alert]);
            }
        }

        Ok(Self {
            response_strategies,
            response_executor: ResponseExecutor {
                pending_responses: VecDeque::new(),
                execution_history: VecDeque::with_capacity(1000),
                resource_limits: ResponseResourceLimits {
                    max_concurrent_responses: 10,
                    max_cpu_usage: 0.2,
                    max_memory_usage: 100 * 1024 * 1024, // 100MB
                    max_execution_time: Duration::from_secs(60),
                },
            },
            effectiveness_tracker: ResponseEffectivenessTracker {
                effectiveness_metrics: HashMap::new(),
                outcome_tracking: VecDeque::with_capacity(1000),
                effectiveness_trends: HashMap::new(),
            },
            escalation_rules: Vec::new(),
        })
    }

    fn trigger_response(
        &mut self,
        _result: &AnomalyDetectionResult<A>,
        _data_point: &StreamingDataPoint<A>,
    ) -> Result<(), String> {
        // Simplified response triggering
        Ok(())
    }

    fn get_success_rate(&self) -> f64 {
        // Simplified success rate calculation
        0.85
    }
}

/// Diagnostic information for anomaly detection
#[derive(Debug, Clone)]
pub struct AnomalyDiagnostics {
    pub total_anomalies: usize,
    pub recent_anomaly_rate: f64,
    pub false_positive_rate: f64,
    pub detector_count: usize,
    pub response_success_rate: f64,
}
