// Concept drift detection and adaptation for streaming optimization
//
// This module provides various algorithms for detecting when the underlying
// data distribution changes (concept drift) and adapting the optimizer accordingly.

use scirs2_core::numeric::Float;
use std::collections::VecDeque;
use std::iter::Sum;
use std::time::{Duration, Instant};

#[allow(unused_imports)]
use crate::error::Result;

/// Types of concept drift detection algorithms
#[derive(Debug, Clone, Copy, PartialEq)]
#[allow(dead_code)]
pub enum DriftDetectionMethod {
    /// Page-Hinkley test for change detection
    PageHinkley,
    /// ADWIN (Adaptive Windowing) algorithm
    Adwin,
    /// Drift Detection Method (DDM)
    DriftDetectionMethod,
    /// Early Drift Detection Method (EDDM)
    EarlyDriftDetection,
    /// Statistical test-based detection
    StatisticalTest,
    /// Ensemble-based detection
    Ensemble,
}

/// Concept drift detector configuration
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct DriftDetectorConfig {
    /// Detection method to use
    pub method: DriftDetectionMethod,
    /// Minimum samples before detection
    pub min_samples: usize,
    /// Detection threshold
    pub threshold: f64,
    /// Window size for statistical methods
    pub window_size: usize,
    /// Alpha value for statistical tests
    pub alpha: f64,
    /// Warning threshold (before drift)
    pub warningthreshold: f64,
    /// Enable ensemble detection
    pub enable_ensemble: bool,
}

impl Default for DriftDetectorConfig {
    fn default() -> Self {
        Self {
            method: DriftDetectionMethod::PageHinkley,
            min_samples: 30,
            threshold: 3.0,
            window_size: 100,
            alpha: 0.005,
            warningthreshold: 2.0,
            enable_ensemble: false,
        }
    }
}

/// Concept drift detection result
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum DriftStatus {
    /// No drift detected
    Stable,
    /// Warning level - potential drift
    Warning,
    /// Drift detected
    Drift,
}

/// Drift detection event
#[derive(Debug, Clone)]
pub struct DriftEvent<A: Float + Send + Sync> {
    /// Timestamp of detection
    pub timestamp: Instant,
    /// Detection confidence (0.0 to 1.0)
    pub confidence: A,
    /// Type of drift detected
    pub drift_type: DriftType,
    /// Recommendation for adaptation
    pub adaptation_recommendation: AdaptationRecommendation,
}

/// Types of concept drift
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum DriftType {
    /// Sudden/abrupt drift
    Sudden,
    /// Gradual drift
    Gradual,
    /// Incremental drift
    Incremental,
    /// Recurring drift
    Recurring,
    /// Blip (temporary change)
    Blip,
}

/// Recommendations for adapting to drift
#[derive(Debug, Clone)]
pub enum AdaptationRecommendation {
    /// Reset optimizer state
    Reset,
    /// Increase learning rate
    IncreaseLearningRate { factor: f64 },
    /// Decrease learning rate
    DecreaseLearningRate { factor: f64 },
    /// Use different optimizer
    SwitchOptimizer { new_optimizer: String },
    /// Adjust window size
    AdjustWindow { new_size: usize },
    /// No adaptation needed
    NoAction,
}

/// Page-Hinkley drift detector
#[derive(Debug, Clone)]
pub struct PageHinkleyDetector<A: Float + Send + Sync> {
    /// Cumulative sum
    sum: A,
    /// Minimum cumulative sum seen
    min_sum: A,
    /// Detection threshold
    threshold: A,
    /// Warning threshold
    warningthreshold: A,
    /// Sample count
    sample_count: usize,
    /// Last drift time
    last_drift: Option<Instant>,
}

impl<A: Float + Send + Sync + Send + Sync> PageHinkleyDetector<A> {
    /// Create a new Page-Hinkley detector
    pub fn new(threshold: A, warningthreshold: A) -> Self {
        Self {
            sum: A::zero(),
            min_sum: A::zero(),
            threshold,
            warningthreshold,
            sample_count: 0,
            last_drift: None,
        }
    }

    /// Update detector with new loss value
    pub fn update(&mut self, loss: A) -> DriftStatus {
        self.sample_count += 1;

        // Update cumulative sum (assuming we want to detect increases in loss)
        let mean_loss = A::from(0.1).unwrap(); // Estimated mean under H0
        self.sum = self.sum + loss - mean_loss;

        // Update minimum
        if self.sum < self.min_sum {
            self.min_sum = self.sum;
        }

        // Compute test statistic
        let test_stat = self.sum - self.min_sum;

        if test_stat > self.threshold {
            self.last_drift = Some(Instant::now());
            self.reset();
            DriftStatus::Drift
        } else if test_stat > self.warningthreshold {
            DriftStatus::Warning
        } else {
            DriftStatus::Stable
        }
    }

    /// Reset detector state
    pub fn reset(&mut self) {
        self.sum = A::zero();
        self.min_sum = A::zero();
        self.sample_count = 0;
    }
}

/// ADWIN (Adaptive Windowing) drift detector
#[derive(Debug, Clone)]
pub struct AdwinDetector<A: Float + Send + Sync> {
    /// Window of recent values
    window: VecDeque<A>,
    /// Maximum window size
    max_windowsize: usize,
    /// Detection confidence level
    delta: A,
    /// Minimum window size for detection
    min_window_size: usize,
}

impl<A: Float + Sum + Send + Sync + Send + Sync> AdwinDetector<A> {
    /// Create a new ADWIN detector
    pub fn new(delta: A, max_windowsize: usize) -> Self {
        Self {
            window: VecDeque::new(),
            max_windowsize,
            delta,
            min_window_size: 10,
        }
    }

    /// Update detector with new value
    pub fn update(&mut self, value: A) -> DriftStatus {
        self.window.push_back(value);

        // Maintain window size
        if self.window.len() > self.max_windowsize {
            self.window.pop_front();
        }

        // Check for drift
        if self.window.len() >= self.min_window_size {
            if self.detect_change() {
                self.shrink_window();
                DriftStatus::Drift
            } else {
                DriftStatus::Stable
            }
        } else {
            DriftStatus::Stable
        }
    }

    /// Detect change using ADWIN algorithm
    fn detect_change(&self) -> bool {
        let n = self.window.len();
        if n < 2 {
            return false;
        }

        // Simplified ADWIN: check for significant difference between halves
        let mid = n / 2;

        let first_half: Vec<_> = self.window.iter().take(mid).cloned().collect();
        let second_half: Vec<_> = self.window.iter().skip(mid).cloned().collect();

        let mean1 = first_half.iter().cloned().sum::<A>() / A::from(first_half.len()).unwrap();
        let mean2 = second_half.iter().cloned().sum::<A>() / A::from(second_half.len()).unwrap();

        // Compute variance
        let var1 = first_half
            .iter()
            .map(|&x| {
                let diff = x - mean1;
                diff * diff
            })
            .sum::<A>()
            / A::from(first_half.len()).unwrap();

        let var2 = second_half
            .iter()
            .map(|&x| {
                let diff = x - mean2;
                diff * diff
            })
            .sum::<A>()
            / A::from(second_half.len()).unwrap();

        // Simplified change detection
        let diff = (mean1 - mean2).abs();
        let threshold = (var1 + var2 + A::from(0.01).unwrap()).sqrt();

        diff > threshold
    }

    /// Shrink window after drift detection
    fn shrink_window(&mut self) {
        let new_size = self.window.len() / 2;
        while self.window.len() > new_size {
            self.window.pop_front();
        }
    }
}

/// DDM (Drift Detection Method) detector
#[derive(Debug, Clone)]
pub struct DdmDetector<A: Float + Send + Sync> {
    /// Error rate
    error_rate: A,
    /// Standard deviation of error rate
    error_std: A,
    /// Minimum error rate + 2*std
    min_error_plus_2_std: A,
    /// Minimum error rate + 3*std
    min_error_plus_3_std: A,
    /// Sample count
    sample_count: usize,
    /// Error count
    error_count: usize,
}

impl<A: Float + Send + Sync + Send + Sync> DdmDetector<A> {
    /// Create a new DDM detector
    pub fn new() -> Self {
        Self {
            error_rate: A::zero(),
            error_std: A::one(),
            min_error_plus_2_std: A::from(f64::MAX).unwrap(),
            min_error_plus_3_std: A::from(f64::MAX).unwrap(),
            sample_count: 0,
            error_count: 0,
        }
    }

    /// Update with prediction result
    pub fn update(&mut self, iserror: bool) -> DriftStatus {
        self.sample_count += 1;
        if iserror {
            self.error_count += 1;
        }

        if self.sample_count < 30 {
            return DriftStatus::Stable;
        }

        // Update _error rate and standard deviation
        self.error_rate = A::from(self.error_count as f64 / self.sample_count as f64).unwrap();
        let p = self.error_rate;
        let n = A::from(self.sample_count as f64).unwrap();
        self.error_std = (p * (A::one() - p) / n).sqrt();

        let current_level = self.error_rate + A::from(2.0).unwrap() * self.error_std;

        // Update minimums
        if current_level < self.min_error_plus_2_std {
            self.min_error_plus_2_std = current_level;
            self.min_error_plus_3_std = self.error_rate + A::from(3.0).unwrap() * self.error_std;
        }

        // Check for drift
        if current_level > self.min_error_plus_3_std {
            self.reset();
            DriftStatus::Drift
        } else if current_level > self.min_error_plus_2_std {
            DriftStatus::Warning
        } else {
            DriftStatus::Stable
        }
    }

    /// Reset detector state
    pub fn reset(&mut self) {
        self.sample_count = 0;
        self.error_count = 0;
        self.error_rate = A::zero();
        self.error_std = A::one();
        self.min_error_plus_2_std = A::from(f64::MAX).unwrap();
        self.min_error_plus_3_std = A::from(f64::MAX).unwrap();
    }
}

impl<A: Float + Send + Sync + Send + Sync> Default for DdmDetector<A> {
    fn default() -> Self {
        Self::new()
    }
}

/// Comprehensive concept drift detector
pub struct ConceptDriftDetector<A: Float + Send + Sync> {
    /// Configuration
    config: DriftDetectorConfig,

    /// Page-Hinkley detector
    ph_detector: PageHinkleyDetector<A>,

    /// ADWIN detector
    adwin_detector: AdwinDetector<A>,

    /// DDM detector
    ddm_detector: DdmDetector<A>,

    /// Ensemble voting history
    ensemble_history: VecDeque<DriftStatus>,

    /// Drift events history
    drift_events: Vec<DriftEvent<A>>,

    /// Performance before/after drift
    performance_tracker: PerformanceDriftTracker<A>,
}

impl<A: Float + std::fmt::Debug + Sum + Send + Sync + Send + Sync> ConceptDriftDetector<A> {
    /// Create a new concept drift detector
    pub fn new(config: DriftDetectorConfig) -> Self {
        let threshold = A::from(config.threshold).unwrap();
        let warningthreshold = A::from(config.warningthreshold).unwrap();
        let delta = A::from(config.alpha).unwrap();

        Self {
            ph_detector: PageHinkleyDetector::new(threshold, warningthreshold),
            adwin_detector: AdwinDetector::new(delta, config.window_size),
            ddm_detector: DdmDetector::new(),
            ensemble_history: VecDeque::with_capacity(10),
            drift_events: Vec::new(),
            performance_tracker: PerformanceDriftTracker::new(),
            config,
        }
    }

    /// Update detector with new loss and prediction error
    pub fn update(&mut self, loss: A, is_predictionerror: bool) -> Result<DriftStatus> {
        let ph_status = self.ph_detector.update(loss);
        let adwin_status = self.adwin_detector.update(loss);
        let ddm_status = self.ddm_detector.update(is_predictionerror);

        let final_status = if self.config.enable_ensemble {
            self.ensemble_vote(ph_status, adwin_status, ddm_status)
        } else {
            match self.config.method {
                DriftDetectionMethod::PageHinkley => ph_status,
                DriftDetectionMethod::Adwin => adwin_status,
                DriftDetectionMethod::DriftDetectionMethod => ddm_status,
                _ => ddm_status, // Fallback for EarlyDriftDetection, StatisticalTest, Ensemble
            }
        };

        // Record drift event if detected
        if final_status == DriftStatus::Drift {
            let event = DriftEvent {
                timestamp: Instant::now(),
                confidence: A::from(0.8).unwrap(), // Simplified confidence
                drift_type: self.classify_drift_type(),
                adaptation_recommendation: self.generate_adaptation_recommendation(),
            };
            self.drift_events.push(event);
        }

        // Update performance tracking
        self.performance_tracker.update(loss, final_status);

        Ok(final_status)
    }

    /// Ensemble voting among detectors
    fn ensemble_vote(
        &mut self,
        ph: DriftStatus,
        adwin: DriftStatus,
        ddm: DriftStatus,
    ) -> DriftStatus {
        let votes = [ph, adwin, ddm];

        // Count votes
        let drift_votes = votes.iter().filter(|&&s| s == DriftStatus::Drift).count();
        let warning_votes = votes.iter().filter(|&&s| s == DriftStatus::Warning).count();

        if drift_votes >= 2 {
            DriftStatus::Drift
        } else if warning_votes >= 2 || drift_votes >= 1 {
            DriftStatus::Warning
        } else {
            DriftStatus::Stable
        }
    }

    /// Classify the type of drift based on recent history
    fn classify_drift_type(&self) -> DriftType {
        // Simplified classification based on recent drift events
        if self.drift_events.len() < 2 {
            return DriftType::Sudden;
        }

        let recent_events = self.drift_events.iter().rev().take(5);
        let time_intervals: Vec<_> = recent_events
            .map(|event| event.timestamp)
            .collect::<Vec<_>>()
            .windows(2)
            .map(|window| window[0].duration_since(window[1]))
            .collect();

        if time_intervals.iter().all(|&d| d < Duration::from_secs(60)) {
            DriftType::Sudden
        } else if time_intervals.len() > 2 {
            DriftType::Gradual
        } else {
            DriftType::Incremental
        }
    }

    /// Generate adaptation recommendation based on drift characteristics
    fn generate_adaptation_recommendation(&self) -> AdaptationRecommendation {
        let recent_performance = self.performance_tracker.get_recent_performance_change();

        if recent_performance > A::from(0.5).unwrap() {
            // Significant performance degradation
            AdaptationRecommendation::Reset
        } else if recent_performance > A::from(0.2).unwrap() {
            // Moderate degradation
            AdaptationRecommendation::IncreaseLearningRate { factor: 1.5 }
        } else if recent_performance < A::from(-0.1).unwrap() {
            // Performance improved (suspicious)
            AdaptationRecommendation::DecreaseLearningRate { factor: 0.8 }
        } else {
            AdaptationRecommendation::NoAction
        }
    }

    /// Get drift detection statistics
    pub fn get_statistics(&self) -> DriftStatistics<A> {
        DriftStatistics {
            total_drifts: self.drift_events.len(),
            recent_drift_rate: self.calculate_recent_drift_rate(),
            average_drift_confidence: self.calculate_average_confidence(),
            drift_types_distribution: self.calculate_drift_type_distribution(),
            time_since_last_drift: self.time_since_last_drift(),
        }
    }

    fn calculate_recent_drift_rate(&self) -> f64 {
        // Calculate drift rate in the last hour
        let one_hour_ago = Instant::now() - Duration::from_secs(3600);
        let recent_drifts = self
            .drift_events
            .iter()
            .filter(|event| event.timestamp > one_hour_ago)
            .count();
        recent_drifts as f64 / 3600.0 // Drifts per second
    }

    fn calculate_average_confidence(&self) -> Option<A> {
        if self.drift_events.is_empty() {
            None
        } else {
            let sum = self
                .drift_events
                .iter()
                .map(|event| event.confidence)
                .sum::<A>();
            Some(sum / A::from(self.drift_events.len()).unwrap())
        }
    }

    fn calculate_drift_type_distribution(&self) -> std::collections::HashMap<DriftType, usize> {
        let mut distribution = std::collections::HashMap::new();
        for event in &self.drift_events {
            *distribution.entry(event.drift_type).or_insert(0) += 1;
        }
        distribution
    }

    fn time_since_last_drift(&self) -> Option<Duration> {
        self.drift_events
            .last()
            .map(|event| event.timestamp.elapsed())
    }
}

/// Performance tracker for drift impact analysis
#[derive(Debug, Clone)]
struct PerformanceDriftTracker<A: Float + Send + Sync> {
    /// Performance history with drift annotations
    performance_history: VecDeque<(A, DriftStatus, Instant)>,
    /// Window size for analysis
    window_size: usize,
}

impl<A: Float + std::iter::Sum + Send + Sync + Send + Sync> PerformanceDriftTracker<A> {
    fn new() -> Self {
        Self {
            performance_history: VecDeque::new(),
            window_size: 100,
        }
    }

    fn update(&mut self, performance: A, driftstatus: DriftStatus) {
        self.performance_history
            .push_back((performance, driftstatus, Instant::now()));

        // Maintain window size
        if self.performance_history.len() > self.window_size {
            self.performance_history.pop_front();
        }
    }

    /// Get recent performance change (positive = degradation, negative = improvement)
    fn get_recent_performance_change(&self) -> A {
        if self.performance_history.len() < 10 {
            return A::zero();
        }

        let recent: Vec<_> = self.performance_history.iter().rev().take(10).collect();
        let older: Vec<_> = self
            .performance_history
            .iter()
            .rev()
            .skip(10)
            .take(10)
            .collect();

        if older.is_empty() {
            return A::zero();
        }

        let recent_avg =
            recent.iter().map(|(p, _, _)| *p).sum::<A>() / A::from(recent.len()).unwrap();
        let older_avg = older.iter().map(|(p, _, _)| *p).sum::<A>() / A::from(older.len()).unwrap();

        recent_avg - older_avg
    }
}

/// Drift detection statistics
#[derive(Debug, Clone)]
pub struct DriftStatistics<A: Float + Send + Sync> {
    /// Total number of drifts detected
    pub total_drifts: usize,
    /// Recent drift rate (drifts per second)
    pub recent_drift_rate: f64,
    /// Average confidence of drift detections
    pub average_drift_confidence: Option<A>,
    /// Distribution of drift types
    pub drift_types_distribution: std::collections::HashMap<DriftType, usize>,
    /// Time since last drift
    pub time_since_last_drift: Option<Duration>,
}

/// Advanced concept drift analysis and adaptation
pub mod advanced_drift_analysis {
    use super::*;
    use std::collections::HashMap;

    /// Advanced drift detector with machine learning-based detection
    #[derive(Debug)]
    pub struct AdvancedDriftDetector<A: Float + Send + Sync> {
        /// Base detector ensemble
        base_detectors: Vec<Box<dyn DriftDetectorTrait<A>>>,

        /// Drift pattern analyzer
        pattern_analyzer: DriftPatternAnalyzer<A>,

        /// Adaptive threshold manager
        threshold_manager: AdaptiveThresholdManager<A>,

        /// Context-aware drift detection
        context_detector: ContextAwareDriftDetector<A>,

        /// Performance impact analyzer
        impact_analyzer: DriftImpactAnalyzer<A>,

        /// Adaptation strategy selector
        adaptation_selector: AdaptationStrategySelector<A>,

        /// Historical drift database
        drift_database: DriftDatabase<A>,
    }

    /// Trait for all drift detectors
    pub trait DriftDetectorTrait<A: Float + Send + Sync>: std::fmt::Debug {
        fn update(&mut self, value: A) -> DriftStatus;
        fn reset(&mut self);
        fn get_confidence(&self) -> A;
    }

    /// Drift pattern analyzer for characterizing drift behavior
    #[derive(Debug)]
    pub struct DriftPatternAnalyzer<A: Float + Send + Sync> {
        /// Pattern history buffer
        pattern_buffer: VecDeque<PatternFeatures<A>>,

        /// Learned drift patterns
        known_patterns: HashMap<String, DriftPattern<A>>,

        /// Pattern matching threshold
        matching_threshold: A,

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

    /// Pattern features for drift characterization
    #[derive(Debug, Clone)]
    pub struct PatternFeatures<A: Float + Send + Sync> {
        /// Statistical moments
        pub mean: A,
        pub variance: A,
        pub skewness: A,
        pub kurtosis: A,

        /// Trend indicators
        pub trend_slope: A,
        pub trend_strength: A,

        /// Frequency domain features
        pub dominant_frequency: A,
        pub spectral_entropy: A,

        /// Temporal features
        pub temporal_locality: A,
        pub persistence: A,

        /// Complexity measures
        pub entropy: A,
        pub fractal_dimension: A,
    }

    /// Learned drift pattern
    #[derive(Debug, Clone)]
    pub struct DriftPattern<A: Float + Send + Sync> {
        /// Pattern identifier
        pub id: String,

        /// Characteristic features
        pub features: PatternFeatures<A>,

        /// Pattern type
        pub pattern_type: DriftType,

        /// Typical duration
        pub typical_duration: Duration,

        /// Optimal adaptation strategy
        pub optimal_adaptation: AdaptationRecommendation,

        /// Success rate of this pattern's adaptations
        pub adaptation_success_rate: A,

        /// Occurrence frequency
        pub occurrence_count: usize,
    }

    /// Feature extractor trait
    pub trait FeatureExtractor<A: Float + Send + Sync>: std::fmt::Debug {
        fn extract(&self, data: &[A]) -> A;
        fn name(&self) -> &str;
    }

    /// Adaptive threshold management
    #[derive(Debug)]
    pub struct AdaptiveThresholdManager<A: Float + Send + Sync> {
        /// Current thresholds for different detectors
        thresholds: HashMap<String, A>,

        /// Threshold adaptation history
        threshold_history: VecDeque<ThresholdUpdate<A>>,

        /// Performance feedback for threshold adjustment
        performance_feedback: VecDeque<PerformanceFeedback<A>>,

        /// Learning rate for threshold adaptation
        learning_rate: A,
    }

    /// Threshold update record
    #[derive(Debug, Clone)]
    pub struct ThresholdUpdate<A: Float + Send + Sync> {
        pub detector_name: String,
        pub old_threshold: A,
        pub new_threshold: A,
        pub timestamp: Instant,
        pub reason: String,
    }

    /// Performance feedback for threshold adjustment
    #[derive(Debug, Clone)]
    pub struct PerformanceFeedback<A: Float + Send + Sync> {
        pub true_positive_rate: A,
        pub false_positive_rate: A,
        pub detection_delay: Duration,
        pub adaptation_effectiveness: A,
        pub timestamp: Instant,
    }

    /// Context-aware drift detection
    #[derive(Debug)]
    pub struct ContextAwareDriftDetector<A: Float + Send + Sync> {
        /// Contextual features
        context_features: Vec<ContextFeature<A>>,

        /// Context-specific drift models
        context_models: HashMap<String, Box<dyn DriftDetectorTrait<A>>>,

        /// Current context state
        current_context: Option<String>,

        /// Context transition matrix
        transition_matrix: HashMap<(String, String), A>,
    }

    /// Contextual feature for drift detection
    #[derive(Debug, Clone)]
    pub struct ContextFeature<A: Float + Send + Sync> {
        pub name: String,
        pub value: A,
        pub importance_weight: A,
        pub temporal_stability: A,
    }

    /// Drift impact analyzer
    #[derive(Debug)]
    pub struct DriftImpactAnalyzer<A: Float + Send + Sync> {
        /// Impact metrics history
        impact_history: VecDeque<DriftImpact<A>>,

        /// Severity classifier
        severity_classifier: SeverityClassifier<A>,

        /// Recovery time predictor
        recovery_predictor: RecoveryTimePredictor<A>,

        /// Business impact estimator
        business_impact_estimator: BusinessImpactEstimator<A>,
    }

    /// Drift impact assessment
    #[derive(Debug, Clone)]
    pub struct DriftImpact<A: Float + Send + Sync> {
        /// Performance degradation magnitude
        pub performance_degradation: A,

        /// Affected metrics
        pub affected_metrics: Vec<String>,

        /// Estimated recovery time
        pub estimated_recovery_time: Duration,

        /// Confidence in impact assessment
        pub confidence: A,

        /// Business impact score
        pub business_impact_score: A,

        /// Urgency level
        pub urgency_level: UrgencyLevel,
    }

    /// Urgency levels for drift response
    #[derive(Debug, Clone, Copy, PartialEq)]
    pub enum UrgencyLevel {
        Low,
        Medium,
        High,
        Critical,
    }

    /// Adaptation strategy selector
    #[derive(Debug)]
    pub struct AdaptationStrategySelector<A: Float + Send + Sync> {
        /// Available adaptation strategies
        strategies: Vec<AdaptationStrategy<A>>,

        /// Strategy performance history
        strategy_performance: HashMap<String, StrategyPerformance<A>>,

        /// Multi-armed bandit for strategy selection
        bandit: EpsilonGreedyBandit<A>,

        /// Context-strategy mapping
        context_strategy_map: HashMap<String, Vec<String>>,
    }

    /// Adaptation strategy
    #[derive(Debug, Clone)]
    pub struct AdaptationStrategy<A: Float + Send + Sync> {
        /// Strategy identifier
        pub id: String,

        /// Strategy type
        pub strategy_type: AdaptationStrategyType,

        /// Parameters
        pub parameters: HashMap<String, A>,

        /// Applicability conditions
        pub applicability_conditions: Vec<ApplicabilityCondition<A>>,

        /// Expected effectiveness
        pub expected_effectiveness: A,

        /// Computational cost
        pub computational_cost: A,
    }

    /// Types of adaptation strategies
    #[derive(Debug, Clone, Copy)]
    pub enum AdaptationStrategyType {
        ParameterTuning,
        ModelReplacement,
        EnsembleReweighting,
        ArchitectureChange,
        DataAugmentation,
        FeatureSelection,
        Hybrid,
    }

    /// Conditions for strategy applicability
    #[derive(Debug, Clone)]
    pub struct ApplicabilityCondition<A: Float + Send + Sync> {
        pub feature_name: String,
        pub operator: ComparisonOperator,
        pub threshold: A,
        pub weight: A,
    }

    #[derive(Debug, Clone, Copy)]
    pub enum ComparisonOperator {
        GreaterThan,
        LessThan,
        Equal,
        NotEqual,
        GreaterEqual,
        LessEqual,
    }

    /// Strategy performance tracking
    #[derive(Debug, Clone)]
    pub struct StrategyPerformance<A: Float + Send + Sync> {
        pub success_rate: A,
        pub average_improvement: A,
        pub average_adaptation_time: Duration,
        pub stability_after_adaptation: A,
        pub usage_count: usize,
    }

    /// Epsilon-greedy bandit for strategy selection
    #[derive(Debug)]
    pub struct EpsilonGreedyBandit<A: Float + Send + Sync> {
        epsilon: A,
        action_values: HashMap<String, A>,
        action_counts: HashMap<String, usize>,
        total_trials: usize,
    }

    /// Historical drift database
    #[derive(Debug)]
    pub struct DriftDatabase<A: Float + Send + Sync> {
        /// Stored drift events
        drift_events: Vec<StoredDriftEvent<A>>,

        /// Pattern-outcome associations
        pattern_outcomes: HashMap<String, Vec<AdaptationOutcome<A>>>,

        /// Seasonal drift patterns
        seasonal_patterns: HashMap<String, SeasonalPattern<A>>,

        /// Similarity search index
        similarity_index: SimilarityIndex<A>,
    }

    /// Stored drift event for learning
    #[derive(Debug, Clone)]
    pub struct StoredDriftEvent<A: Float + Send + Sync> {
        pub features: PatternFeatures<A>,
        pub context: Vec<ContextFeature<A>>,
        pub applied_strategy: String,
        pub outcome: AdaptationOutcome<A>,
        pub timestamp: Instant,
    }

    /// Adaptation outcome for learning
    #[derive(Debug, Clone)]
    pub struct AdaptationOutcome<A: Float + Send + Sync> {
        pub success: bool,
        pub performance_improvement: A,
        pub adaptation_time: Duration,
        pub stability_period: Duration,
        pub side_effects: Vec<String>,
    }

    /// Seasonal drift pattern
    #[derive(Debug, Clone)]
    pub struct SeasonalPattern<A: Float + Send + Sync> {
        pub period: Duration,
        pub amplitude: A,
        pub phase_offset: Duration,
        pub pattern_strength: A,
        pub last_occurrence: Instant,
    }

    /// Similarity search for historical patterns
    #[derive(Debug)]
    pub struct SimilarityIndex<A: Float + Send + Sync> {
        /// Feature vectors for similarity search
        feature_vectors: Vec<(String, Vec<A>)>,

        /// Similarity threshold
        similarity_threshold: A,

        /// Distance metric
        distance_metric: DistanceMetric,
    }

    #[derive(Debug, Clone, Copy)]
    pub enum DistanceMetric {
        Euclidean,
        Manhattan,
        Cosine,
        Mahalanobis,
    }

    impl<
            A: Float + Default + Clone + std::fmt::Debug + std::iter::Sum + Send + Sync + Send + Sync,
        > AdvancedDriftDetector<A>
    {
        /// Create new advanced drift detector
        pub fn new(config: DriftDetectorConfig) -> Self {
            let base_detectors: Vec<Box<dyn DriftDetectorTrait<A>>> = vec![
                // Add base detectors here
            ];

            Self {
                base_detectors,
                pattern_analyzer: DriftPatternAnalyzer::new(),
                threshold_manager: AdaptiveThresholdManager::new(),
                context_detector: ContextAwareDriftDetector::new(),
                impact_analyzer: DriftImpactAnalyzer::new(),
                adaptation_selector: AdaptationStrategySelector::new(),
                drift_database: DriftDatabase::new(),
            }
        }

        /// Advanced drift detection with pattern analysis
        pub fn detect_drift_advanced(
            &mut self,
            value: A,
            context_features: &[ContextFeature<A>],
        ) -> Result<AdvancedDriftResult<A>> {
            // Update context
            self.context_detector.update_context(context_features);

            // Run base detectors
            let base_results: Vec<_> = self
                .base_detectors
                .iter_mut()
                .map(|detector| detector.update(value))
                .collect();

            // Analyze patterns
            let pattern_features = self.pattern_analyzer.extract_features(&[value])?;
            let matched_pattern = self.pattern_analyzer.match_pattern(&pattern_features);

            // Adaptive threshold adjustment
            self.threshold_manager
                .update_thresholds(&base_results, &pattern_features);

            // Combine results with confidence weighting
            let combined_result = self.combine_detection_results(&base_results, &matched_pattern);

            // Analyze impact if drift detected
            let impact = if combined_result.status == DriftStatus::Drift {
                Some(
                    self.impact_analyzer
                        .analyze_impact(&pattern_features, &matched_pattern)?,
                )
            } else {
                None
            };

            // Select adaptation strategy
            let adaptation_strategy = if let Some(ref impact) = impact {
                self.adaptation_selector.select_strategy(
                    &pattern_features,
                    impact,
                    &matched_pattern,
                )?
            } else {
                None
            };

            // Store in database for learning
            if combined_result.status == DriftStatus::Drift {
                self.drift_database.store_event(
                    &pattern_features,
                    context_features,
                    &adaptation_strategy,
                );
            }

            Ok(AdvancedDriftResult {
                status: combined_result.status,
                confidence: combined_result.confidence,
                matched_pattern,
                impact,
                recommended_strategy: adaptation_strategy,
                feature_importance: self.calculate_feature_importance(&pattern_features),
                prediction_horizon: self.estimate_drift_duration(&pattern_features),
            })
        }

        fn combine_detection_results(
            &self,
            base_results: &[DriftStatus],
            matched_pattern: &Option<DriftPattern<A>>,
        ) -> CombinedDetectionResult<A> {
            // Weighted voting based on detector confidence and _pattern matching
            let drift_votes = base_results
                .iter()
                .filter(|&&s| s == DriftStatus::Drift)
                .count();
            let warning_votes = base_results
                .iter()
                .filter(|&&s| s == DriftStatus::Warning)
                .count();

            // Pattern-based confidence adjustment
            let pattern_confidence = matched_pattern
                .as_ref()
                .map(|p| p.adaptation_success_rate)
                .unwrap_or(A::from(0.5).unwrap());

            let status = if drift_votes >= 2 {
                DriftStatus::Drift
            } else if warning_votes >= 2
                || (drift_votes >= 1 && pattern_confidence > A::from(0.7).unwrap())
            {
                DriftStatus::Warning
            } else {
                DriftStatus::Stable
            };

            let confidence = A::from(drift_votes as f64 / base_results.len() as f64).unwrap()
                * pattern_confidence;

            CombinedDetectionResult { status, confidence }
        }

        fn calculate_feature_importance(
            &self,
            features: &PatternFeatures<A>,
        ) -> HashMap<String, A> {
            // Simplified feature importance calculation
            let mut importance = HashMap::new();
            importance.insert("variance".to_string(), features.variance);
            importance.insert("trend_slope".to_string(), features.trend_slope.abs());
            importance.insert("entropy".to_string(), features.entropy);
            importance
        }

        fn estimate_drift_duration(&self, features: &PatternFeatures<A>) -> Duration {
            // Estimate how long the drift will last based on patterns
            let base_duration = Duration::from_secs(300); // 5 minutes base

            // Adjust based on trend strength and persistence
            let duration_multiplier = features.trend_strength * features.persistence;
            let adjustment = duration_multiplier.to_f64().unwrap_or(1.0);

            Duration::from_secs((base_duration.as_secs() as f64 * adjustment) as u64)
        }
    }

    /// Advanced drift detection result
    #[derive(Debug, Clone)]
    pub struct AdvancedDriftResult<A: Float + Send + Sync> {
        pub status: DriftStatus,
        pub confidence: A,
        pub matched_pattern: Option<DriftPattern<A>>,
        pub impact: Option<DriftImpact<A>>,
        pub recommended_strategy: Option<AdaptationStrategy<A>>,
        pub feature_importance: HashMap<String, A>,
        pub prediction_horizon: Duration,
    }

    #[derive(Debug, Clone)]
    struct CombinedDetectionResult<A: Float + Send + Sync> {
        status: DriftStatus,
        confidence: A,
    }

    // Implementation stubs for complex components

    impl<A: Float + std::iter::Sum + Send + Sync + Send + Sync> DriftPatternAnalyzer<A> {
        fn new() -> Self {
            Self {
                pattern_buffer: VecDeque::new(),
                known_patterns: HashMap::new(),
                matching_threshold: A::from(0.8).unwrap(),
                feature_extractors: Vec::new(),
            }
        }

        fn extract_features(&mut self, data: &[A]) -> Result<PatternFeatures<A>> {
            // Simplified feature extraction
            let mean = data.iter().cloned().sum::<A>() / A::from(data.len()).unwrap();
            let variance = data.iter().map(|&x| (x - mean) * (x - mean)).sum::<A>()
                / A::from(data.len()).unwrap();

            Ok(PatternFeatures {
                mean,
                variance,
                skewness: A::zero(), // Simplified
                kurtosis: A::zero(),
                trend_slope: A::zero(),
                trend_strength: A::zero(),
                dominant_frequency: A::zero(),
                spectral_entropy: A::zero(),
                temporal_locality: A::zero(),
                persistence: A::zero(),
                entropy: variance.ln().abs(), // Simplified entropy
                fractal_dimension: A::from(1.5).unwrap(), // Default
            })
        }

        fn match_pattern(&self, features: &PatternFeatures<A>) -> Option<DriftPattern<A>> {
            // Simplified pattern matching
            self.known_patterns
                .values()
                .find(|pattern| {
                    self.calculate_similarity(&pattern.features, features) > self.matching_threshold
                })
                .cloned()
        }

        fn calculate_similarity(&self, p1: &PatternFeatures<A>, p2: &PatternFeatures<A>) -> A {
            // Simplified similarity calculation
            let mean_diff = (p1.mean - p2.mean).abs();
            let var_diff = (p1.variance - p2.variance).abs();
            A::one() - (mean_diff + var_diff) / A::from(2.0).unwrap()
        }
    }

    impl<A: Float + Send + Sync + Send + Sync> AdaptiveThresholdManager<A> {
        fn new() -> Self {
            Self {
                thresholds: HashMap::new(),
                threshold_history: VecDeque::new(),
                performance_feedback: VecDeque::new(),
                learning_rate: A::from(0.01).unwrap(),
            }
        }

        fn update_thresholds(&mut self, results: &[DriftStatus], features: &PatternFeatures<A>) {
            // Simplified threshold adaptation
            for (i, result) in results.iter().enumerate() {
                let detector_name = format!("detector_{}", i);
                let current_threshold = self
                    .thresholds
                    .get(&detector_name)
                    .cloned()
                    .unwrap_or(A::from(1.0).unwrap());

                // Adjust threshold based on recent performance
                let adjustment = if *result == DriftStatus::Drift {
                    -self.learning_rate // Lower threshold if drift detected
                } else {
                    self.learning_rate * A::from(0.1).unwrap() // Slightly raise threshold
                };

                let new_threshold = current_threshold + adjustment;
                self.thresholds.insert(detector_name.clone(), new_threshold);

                self.threshold_history.push_back(ThresholdUpdate {
                    detector_name,
                    old_threshold: current_threshold,
                    new_threshold,
                    timestamp: Instant::now(),
                    reason: "Performance-based adjustment".to_string(),
                });
            }
        }
    }

    impl<A: Float + Send + Sync + Send + Sync> ContextAwareDriftDetector<A> {
        fn new() -> Self {
            Self {
                context_features: Vec::new(),
                context_models: HashMap::new(),
                current_context: None,
                transition_matrix: HashMap::new(),
            }
        }

        fn update_context(&mut self, features: &[ContextFeature<A>]) {
            self.context_features = features.to_vec();

            // Simplified context classification
            let context_id = if !features.is_empty() && features[0].value > A::from(0.5).unwrap() {
                "high_activity".to_string()
            } else {
                "low_activity".to_string()
            };

            self.current_context = Some(context_id);
        }
    }

    impl<A: Float + Send + Sync + Send + Sync> DriftImpactAnalyzer<A> {
        fn new() -> Self {
            Self {
                impact_history: VecDeque::new(),
                severity_classifier: SeverityClassifier::new(),
                recovery_predictor: RecoveryTimePredictor::new(),
                business_impact_estimator: BusinessImpactEstimator::new(),
            }
        }

        fn analyze_impact(
            &mut self,
            features: &PatternFeatures<A>,
            _pattern: &Option<DriftPattern<A>>,
        ) -> Result<DriftImpact<A>> {
            let performance_degradation = features.variance; // Simplified
            let urgency_level = if performance_degradation > A::from(1.0).unwrap() {
                UrgencyLevel::High
            } else {
                UrgencyLevel::Medium
            };

            Ok(DriftImpact {
                performance_degradation,
                affected_metrics: vec!["accuracy".to_string(), "loss".to_string()],
                estimated_recovery_time: Duration::from_secs(300),
                confidence: A::from(0.8).unwrap(),
                business_impact_score: performance_degradation,
                urgency_level,
            })
        }
    }

    impl<A: Float + Send + Sync + Send + Sync> AdaptationStrategySelector<A> {
        fn new() -> Self {
            Self {
                strategies: Vec::new(),
                strategy_performance: HashMap::new(),
                bandit: EpsilonGreedyBandit::new(A::from(0.1).unwrap()),
                context_strategy_map: HashMap::new(),
            }
        }

        fn select_strategy(
            &mut self,
            features: &PatternFeatures<A>,
            _impact: &DriftImpact<A>,
            _pattern: &Option<DriftPattern<A>>,
        ) -> Result<Option<AdaptationStrategy<A>>> {
            // Simplified strategy selection
            let strategy = AdaptationStrategy {
                id: "increase_lr".to_string(),
                strategy_type: AdaptationStrategyType::ParameterTuning,
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("learning_rate_factor".to_string(), A::from(1.5).unwrap());
                    params
                },
                applicability_conditions: Vec::new(),
                expected_effectiveness: A::from(0.7).unwrap(),
                computational_cost: A::from(0.1).unwrap(),
            };

            Ok(Some(strategy))
        }
    }

    impl<A: Float + Send + Sync + Send + Sync> DriftDatabase<A> {
        fn new() -> Self {
            Self {
                drift_events: Vec::new(),
                pattern_outcomes: HashMap::new(),
                seasonal_patterns: HashMap::new(),
                similarity_index: SimilarityIndex::new(),
            }
        }

        fn store_event(
            &mut self,
            features: &PatternFeatures<A>,
            context: &[ContextFeature<A>],
            strategy: &Option<AdaptationStrategy<A>>,
        ) {
            if let Some(strat) = strategy {
                let event = StoredDriftEvent {
                    features: features.clone(),
                    context: context.to_vec(),
                    applied_strategy: strat.id.clone(),
                    outcome: AdaptationOutcome {
                        success: true, // Simplified
                        performance_improvement: A::from(0.1).unwrap(),
                        adaptation_time: Duration::from_secs(60),
                        stability_period: Duration::from_secs(300),
                        side_effects: Vec::new(),
                    },
                    timestamp: Instant::now(),
                };

                self.drift_events.push(event);
            }
        }
    }

    impl<A: Float + Send + Sync + Send + Sync> SimilarityIndex<A> {
        fn new() -> Self {
            Self {
                feature_vectors: Vec::new(),
                similarity_threshold: A::from(0.8).unwrap(),
                distance_metric: DistanceMetric::Euclidean,
            }
        }
    }

    impl<A: Float + Send + Sync + Send + Sync> EpsilonGreedyBandit<A> {
        fn new(epsilon: A) -> Self {
            Self {
                epsilon,
                action_values: HashMap::new(),
                action_counts: HashMap::new(),
                total_trials: 0,
            }
        }
    }

    // Placeholder implementations for complex analyzers

    #[derive(Debug)]
    struct SeverityClassifier<A: Float + Send + Sync> {
        _phantom: std::marker::PhantomData<A>,
    }

    impl<A: Float + Send + Sync + Send + Sync> SeverityClassifier<A> {
        fn new() -> Self {
            Self {
                _phantom: std::marker::PhantomData,
            }
        }
    }

    #[derive(Debug)]
    struct RecoveryTimePredictor<A: Float + Send + Sync> {
        _phantom: std::marker::PhantomData<A>,
    }

    impl<A: Float + Send + Sync + Send + Sync> RecoveryTimePredictor<A> {
        fn new() -> Self {
            Self {
                _phantom: std::marker::PhantomData,
            }
        }
    }

    #[derive(Debug)]
    struct BusinessImpactEstimator<A: Float + Send + Sync> {
        _phantom: std::marker::PhantomData<A>,
    }

    impl<A: Float + Send + Sync + Send + Sync> BusinessImpactEstimator<A> {
        fn new() -> Self {
            Self {
                _phantom: std::marker::PhantomData,
            }
        }
    }
}

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

    #[test]
    fn test_page_hinkley_detector() {
        let mut detector = PageHinkleyDetector::new(3.0f64, 2.0f64);

        // Stable period
        for _ in 0..10 {
            let status = detector.update(0.1);
            assert_eq!(status, DriftStatus::Stable);
        }

        // Drift period
        for _ in 0..5 {
            let status = detector.update(0.5); // Higher loss
            if status == DriftStatus::Drift {
                break;
            }
        }
    }

    #[test]
    fn test_adwin_detector() {
        let mut detector = AdwinDetector::new(0.005f64, 100);

        // Add stable values
        for i in 0..20 {
            let value = 0.1 + (i as f64) * 0.001; // Slight trend
            detector.update(value);
        }

        // Add drift values
        for i in 0..10 {
            let value = 0.5 + (i as f64) * 0.01; // Clear change
            let status = detector.update(value);
            if status == DriftStatus::Drift {
                break;
            }
        }
    }

    #[test]
    fn test_ddm_detector() {
        let mut detector = DdmDetector::<f64>::new();

        // Stable period with low error rate
        for i in 0..50 {
            let iserror = i % 10 == 0; // 10% error rate
            detector.update(iserror);
        }

        // Period with high error rate
        for i in 0..20 {
            let iserror = i % 2 == 0; // 50% error rate
            let status = detector.update(iserror);
            if status == DriftStatus::Drift {
                break;
            }
        }
    }

    #[test]
    fn test_concept_drift_detector() {
        let config = DriftDetectorConfig::default();
        let mut detector = ConceptDriftDetector::new(config);

        // Simulate stable period
        for i in 0..30 {
            let loss = 0.1 + (i as f64) * 0.001;
            let iserror = i % 10 == 0;
            let status = detector.update(loss, iserror).unwrap();
            assert_ne!(status, DriftStatus::Drift); // Should be stable
        }

        // Simulate drift
        for i in 0..20 {
            let loss = 0.5 + (i as f64) * 0.01; // Much higher loss
            let iserror = i % 2 == 0; // Higher error rate
            let _status = detector.update(loss, iserror).unwrap();
        }

        let stats = detector.get_statistics();
        assert!(stats.total_drifts > 0 || stats.recent_drift_rate > 0.0);
    }

    #[test]
    fn test_drift_event() {
        let event = DriftEvent {
            timestamp: Instant::now(),
            confidence: 0.85f64,
            drift_type: DriftType::Sudden,
            adaptation_recommendation: AdaptationRecommendation::Reset,
        };

        assert_eq!(event.drift_type, DriftType::Sudden);
        assert!(event.confidence > 0.8);
    }
}
