//! Optimization engine with ML capabilities for SciRS2 integration
//!
//! This module provides machine learning models, predictive allocation,
//! automated optimization, and anomaly detection capabilities.

use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Advanced SciRS2 optimization features
#[derive(Debug)]
pub struct AdvancedScirS2Features {
    /// Machine learning models for optimization
    pub ml_models: HashMap<String, MLModel>,

    /// Predictive allocation engine
    pub predictive_engine: PredictiveAllocationEngine,

    /// Automated optimization system
    pub auto_optimization: AutoOptimizationSystem,

    /// Performance anomaly detection
    pub anomaly_detector: AnomalyDetector,
}

/// Machine learning model for optimization
#[derive(Debug, Clone)]
pub struct MLModel {
    /// Model identifier
    pub model_id: String,

    /// Model type (e.g., "linear_regression", "neural_network", "decision_tree")
    pub model_type: String,

    /// Model accuracy (0.0 to 1.0)
    pub accuracy: f64,

    /// Last training timestamp
    pub last_training: Instant,

    /// Training data size
    pub training_samples: usize,

    /// Model parameters
    pub parameters: ModelParameters,

    /// Model state
    pub state: ModelState,
}

/// Model parameters (simplified representation)
#[derive(Debug, Clone)]
pub struct ModelParameters {
    /// Feature weights
    pub weights: Vec<f64>,

    /// Bias term
    pub bias: f64,

    /// Learning rate
    pub learning_rate: f64,

    /// Regularization parameter
    pub regularization: f64,
}

/// Model state
#[derive(Debug, Clone, PartialEq)]
pub enum ModelState {
    Untrained,
    Training,
    Trained,
    Deprecated,
}

/// Predictive allocation engine
#[derive(Debug)]
pub struct PredictiveAllocationEngine {
    /// Whether prediction is enabled
    pub enabled: bool,

    /// Prediction horizon
    pub prediction_horizon: Duration,

    /// Model accuracy
    pub model_accuracy: f64,

    /// Prediction history
    prediction_history: Vec<AllocationPrediction>,

    /// Feature extractors
    feature_extractors: Vec<FeatureExtractor>,

    /// Prediction cache
    prediction_cache: HashMap<String, CachedPrediction>,
}

/// Allocation prediction
#[derive(Debug, Clone)]
pub struct AllocationPrediction {
    /// Prediction timestamp
    pub timestamp: Instant,

    /// Predicted allocation size
    pub size: usize,

    /// Prediction confidence (0.0 to 1.0)
    pub confidence: f64,

    /// Predicted allocator
    pub allocator: String,

    /// Prediction features
    pub features: Vec<f64>,

    /// Actual outcome (if available)
    pub actual_outcome: Option<AllocationOutcome>,
}

/// Actual allocation outcome for prediction validation
#[derive(Debug, Clone)]
pub struct AllocationOutcome {
    /// Actual size allocated
    pub actual_size: usize,

    /// Actual allocator used
    pub actual_allocator: String,

    /// Allocation timestamp
    pub timestamp: Instant,

    /// Success status
    pub success: bool,
}

/// Feature extractor for ML models
#[derive(Debug, Clone)]
pub struct FeatureExtractor {
    /// Extractor name
    pub name: String,

    /// Feature dimensions
    pub dimensions: usize,

    /// Extractor type
    pub extractor_type: FeatureType,

    /// Last extraction timestamp
    pub last_extraction: Instant,
}

/// Types of features for ML models
#[derive(Debug, Clone)]
pub enum FeatureType {
    /// Time-based features (hour, day, etc.)
    Temporal,
    /// Statistical features (mean, variance, etc.)
    Statistical,
    /// Pattern-based features (frequency, cycles, etc.)
    Pattern,
    /// System state features (memory usage, CPU, etc.)
    SystemState,
}

/// Cached prediction
#[derive(Debug, Clone)]
pub struct CachedPrediction {
    /// Prediction result
    pub prediction: AllocationPrediction,

    /// Cache timestamp
    pub cached_at: Instant,

    /// Cache expiry
    pub expires_at: Instant,

    /// Hit count
    pub hit_count: u32,
}

/// Automated optimization system
#[derive(Debug)]
pub struct AutoOptimizationSystem {
    /// Whether auto-optimization is enabled
    pub enabled: bool,

    /// Optimization queue
    pub optimization_queue: Vec<OptimizationTask>,

    /// Last optimization run
    pub last_optimization: Option<Instant>,

    /// Optimization strategies
    strategies: Vec<OptimizationStrategy>,

    /// Optimization history
    history: Vec<OptimizationResult>,
}

/// Optimization task
#[derive(Debug, Clone)]
pub struct OptimizationTask {
    /// Task identifier
    pub task_id: String,

    /// Task type
    pub task_type: String,

    /// Priority level
    pub priority: u32,

    /// Estimated benefit (0.0 to 1.0)
    pub estimated_benefit: f64,

    /// Created timestamp
    pub created_at: Instant,

    /// Target component
    pub target_component: String,

    /// Task parameters
    pub parameters: HashMap<String, String>,

    /// Dependencies
    pub dependencies: Vec<String>,
}

/// Optimization strategy
#[derive(Debug, Clone)]
pub struct OptimizationStrategy {
    /// Strategy name
    pub name: String,

    /// Strategy type
    pub strategy_type: StrategyType,

    /// Applicability conditions
    pub conditions: Vec<OptimizationCondition>,

    /// Expected impact
    pub expected_impact: f64,

    /// Implementation complexity
    pub complexity: OptimizationComplexity,
}

/// Strategy types
#[derive(Debug, Clone)]
pub enum StrategyType {
    /// Improve cache performance
    CacheOptimization,
    /// Reduce memory fragmentation
    FragmentationReduction,
    /// Optimize allocation patterns
    AllocationOptimization,
    /// Improve thread contention
    ContentionReduction,
    /// Dynamic parameter tuning
    ParameterTuning,
}

/// Optimization conditions
#[derive(Debug, Clone)]
pub struct OptimizationCondition {
    /// Condition description
    pub description: String,

    /// Metric name to check
    pub metric: String,

    /// Threshold value
    pub threshold: f64,

    /// Comparison operator
    pub operator: ComparisonOperator,
}

/// Comparison operators for conditions
#[derive(Debug, Clone)]
pub enum ComparisonOperator {
    GreaterThan,
    LessThan,
    Equal,
    NotEqual,
    Between(f64, f64),
}

/// Optimization complexity levels
#[derive(Debug, Clone, PartialEq, PartialOrd)]
pub enum OptimizationComplexity {
    Low,
    Medium,
    High,
    Expert,
}

/// Optimization result
#[derive(Debug, Clone)]
pub struct OptimizationResult {
    /// Result timestamp
    pub timestamp: Instant,

    /// Task that was optimized
    pub task_id: String,

    /// Optimization type
    pub optimization_type: String,

    /// Success status
    pub success: bool,

    /// Performance improvement achieved
    pub performance_improvement: f64,

    /// Optimization duration
    pub duration: Duration,

    /// Resource cost
    pub resource_cost: f64,

    /// Error message (if failed)
    pub error_message: Option<String>,
}

/// Performance anomaly detection system
#[derive(Debug)]
pub struct AnomalyDetector {
    /// Whether detection is enabled
    pub enabled: bool,

    /// Detection sensitivity (0.0 to 1.0)
    pub sensitivity: f64,

    /// Number of anomalies detected
    pub detected_anomalies: u64,

    /// Anomaly detection models
    detection_models: Vec<AnomalyModel>,

    /// Anomaly history
    anomaly_history: Vec<DetectedAnomaly>,

    /// Baseline metrics for comparison
    baseline_metrics: HashMap<String, BaselineMetric>,
}

/// Anomaly detection model
#[derive(Debug, Clone)]
pub struct AnomalyModel {
    /// Model name
    pub name: String,

    /// Model type
    pub model_type: AnomalyModelType,

    /// Detection threshold
    pub threshold: f64,

    /// Model parameters
    pub parameters: AnomalyModelParameters,

    /// Last update timestamp
    pub last_update: Instant,
}

/// Types of anomaly detection models
#[derive(Debug, Clone)]
pub enum AnomalyModelType {
    /// Statistical outlier detection
    StatisticalOutlier,
    /// Isolation forest
    IsolationForest,
    /// Local outlier factor
    LocalOutlierFactor,
    /// One-class SVM
    OneClassSVM,
    /// Autoencoder-based
    Autoencoder,
}

/// Anomaly model parameters
#[derive(Debug, Clone)]
pub struct AnomalyModelParameters {
    /// Window size for analysis
    pub window_size: usize,

    /// Model-specific parameters
    pub custom_params: HashMap<String, f64>,
}

/// Detected anomaly
#[derive(Debug, Clone)]
pub struct DetectedAnomaly {
    /// Detection timestamp
    pub timestamp: Instant,

    /// Anomaly type
    pub anomaly_type: AnomalyType,

    /// Affected metric
    pub metric_name: String,

    /// Anomaly score (higher = more anomalous)
    pub score: f64,

    /// Expected value
    pub expected_value: f64,

    /// Actual value
    pub actual_value: f64,

    /// Confidence in detection
    pub confidence: f64,

    /// Suggested action
    pub suggested_action: Option<String>,
}

/// Types of anomalies
#[derive(Debug, Clone)]
pub enum AnomalyType {
    /// Value significantly higher than expected
    HighOutlier,
    /// Value significantly lower than expected
    LowOutlier,
    /// Unusual pattern detected
    PatternAnomaly,
    /// Sudden change in behavior
    BehaviorChange,
    /// Performance degradation
    PerformanceDegradation,
}

/// Baseline metric for anomaly detection
#[derive(Debug, Clone)]
pub struct BaselineMetric {
    /// Metric name
    pub name: String,

    /// Historical mean
    pub mean: f64,

    /// Historical standard deviation
    pub std_dev: f64,

    /// Minimum observed value
    pub min_value: f64,

    /// Maximum observed value
    pub max_value: f64,

    /// Sample count
    pub sample_count: usize,

    /// Last update timestamp
    pub last_update: Instant,
}

/// SciRS2 optimization engine
#[derive(Debug)]
pub struct ScirS2OptimizationEngine {
    /// Optimization history
    pub optimization_history: Vec<OptimizationResult>,

    /// Currently active optimizations
    pub active_optimizations: HashMap<String, ActiveOptimization>,

    /// Optimization metrics
    pub optimization_metrics: OptimizationMetrics,

    /// Configuration
    pub config: OptimizationConfig,

    /// Performance baselines
    pub baselines: HashMap<String, PerformanceBaseline>,
}

/// Active optimization
#[derive(Debug, Clone)]
pub struct ActiveOptimization {
    /// Optimization ID
    pub id: String,

    /// Start time
    pub start_time: Instant,

    /// Target component
    pub target: String,

    /// Expected completion time
    pub expected_completion: Instant,

    /// Current progress (0.0 to 1.0)
    pub progress: f64,

    /// Optimization strategy
    pub strategy: OptimizationStrategy,

    /// Intermediate results
    pub intermediate_results: Vec<IntermediateResult>,
}

/// Intermediate optimization result
#[derive(Debug, Clone)]
pub struct IntermediateResult {
    /// Result timestamp
    pub timestamp: Instant,

    /// Progress at this point
    pub progress: f64,

    /// Metrics at this point
    pub metrics: HashMap<String, f64>,

    /// Status message
    pub status: String,
}

/// Optimization metrics
#[derive(Debug, Clone)]
pub struct OptimizationMetrics {
    /// Total optimizations attempted
    pub total_optimizations: u64,

    /// Successful optimizations
    pub successful_optimizations: u64,

    /// Average improvement achieved
    pub average_improvement: f64,

    /// Optimization efficiency
    pub optimization_efficiency: f64,

    /// Total time spent optimizing
    pub total_optimization_time: Duration,
}

/// Optimization configuration
#[derive(Debug, Clone)]
pub struct OptimizationConfig {
    /// Enable automated optimization
    pub auto_optimize: bool,

    /// Optimization aggressiveness (0.0 to 1.0)
    pub aggressiveness: f64,

    /// Maximum concurrent optimizations
    pub max_concurrent: usize,

    /// Optimization timeout
    pub timeout: Duration,

    /// Minimum improvement threshold
    pub min_improvement: f64,
}

/// Performance baseline
#[derive(Debug, Clone)]
pub struct PerformanceBaseline {
    /// Baseline name
    pub name: String,

    /// Baseline metrics
    pub metrics: HashMap<String, f64>,

    /// Establishment timestamp
    pub established_at: Instant,

    /// Confidence in baseline (0.0 to 1.0)
    pub confidence: f64,
}

impl AdvancedScirS2Features {
    /// Create new advanced features
    pub fn new() -> Self {
        Self {
            ml_models: HashMap::new(),
            predictive_engine: PredictiveAllocationEngine::new(),
            auto_optimization: AutoOptimizationSystem::new(),
            anomaly_detector: AnomalyDetector::new(),
        }
    }

    /// Initialize machine learning models
    pub fn initialize_ml_models(&mut self) {
        // Create default allocation prediction model
        let allocation_model = MLModel {
            model_id: "allocation_predictor".to_string(),
            model_type: "neural_network".to_string(),
            accuracy: 0.0,
            last_training: Instant::now(),
            training_samples: 0,
            parameters: ModelParameters {
                weights: vec![0.0; 10], // 10-dimensional feature space
                bias: 0.0,
                learning_rate: 0.01,
                regularization: 0.001,
            },
            state: ModelState::Untrained,
        };

        self.ml_models
            .insert("allocation_predictor".to_string(), allocation_model);

        // Create performance optimization model
        let performance_model = MLModel {
            model_id: "performance_optimizer".to_string(),
            model_type: "decision_tree".to_string(),
            accuracy: 0.0,
            last_training: Instant::now(),
            training_samples: 0,
            parameters: ModelParameters {
                weights: vec![0.0; 5],
                bias: 0.0,
                learning_rate: 0.05,
                regularization: 0.0,
            },
            state: ModelState::Untrained,
        };

        self.ml_models
            .insert("performance_optimizer".to_string(), performance_model);
    }

    /// Train a machine learning model
    pub fn train_model(
        &mut self,
        model_id: &str,
        training_data: &[TrainingExample],
    ) -> Result<(), String> {
        if let Some(model) = self.ml_models.get_mut(model_id) {
            model.state = ModelState::Training;

            // Simplified training process
            let improvement = Self::simulate_training(model, training_data);

            model.accuracy = improvement;
            model.training_samples = training_data.len();
            model.last_training = Instant::now();
            model.state = ModelState::Trained;

            Ok(())
        } else {
            Err(format!("Model '{}' not found", model_id))
        }
    }

    /// Get model prediction
    pub fn predict(&self, model_id: &str, features: &[f64]) -> Option<ModelPrediction> {
        if let Some(model) = self.ml_models.get(model_id) {
            if model.state != ModelState::Trained {
                return None;
            }

            // Simplified prediction
            let prediction_value = self.compute_prediction(model, features);
            let confidence = model.accuracy;

            Some(ModelPrediction {
                value: prediction_value,
                confidence,
                model_id: model_id.to_string(),
                timestamp: Instant::now(),
            })
        } else {
            None
        }
    }

    // Private helper methods

    fn simulate_training(model: &mut MLModel, training_data: &[TrainingExample]) -> f64 {
        // Simplified training simulation
        let data_quality = if training_data.len() > 100 { 0.8 } else { 0.5 };
        let model_complexity = match model.model_type.as_str() {
            "linear_regression" => 0.6,
            "neural_network" => 0.8,
            "decision_tree" => 0.7,
            _ => 0.5,
        };

        (data_quality + model_complexity) / 2.0
    }

    fn compute_prediction(&self, model: &MLModel, features: &[f64]) -> f64 {
        // Simplified prediction computation (dot product + bias)
        let prediction = features
            .iter()
            .zip(model.parameters.weights.iter())
            .map(|(f, w)| f * w)
            .sum::<f64>()
            + model.parameters.bias;

        prediction
    }
}

impl PredictiveAllocationEngine {
    /// Create new predictive allocation engine
    pub fn new() -> Self {
        Self {
            enabled: false,
            prediction_horizon: Duration::from_secs(60),
            model_accuracy: 0.0,
            prediction_history: Vec::new(),
            feature_extractors: Vec::new(),
            prediction_cache: HashMap::new(),
        }
    }

    /// Enable prediction engine
    pub fn enable(&mut self) {
        self.enabled = true;
        self.initialize_feature_extractors();
    }

    /// Make allocation prediction
    pub fn predict_allocation(
        &mut self,
        context: &AllocationContext,
    ) -> Option<AllocationPrediction> {
        if !self.enabled {
            return None;
        }

        // Extract features
        let features = self.extract_features(context);

        // Make prediction
        let prediction = AllocationPrediction {
            timestamp: Instant::now(),
            size: self.predict_size(&features),
            confidence: self.model_accuracy,
            allocator: self.predict_allocator(&features),
            features,
            actual_outcome: None,
        };

        // Cache prediction
        let cache_key = format!("{}_{}", prediction.allocator, prediction.size);
        self.prediction_cache.insert(
            cache_key,
            CachedPrediction {
                prediction: prediction.clone(),
                cached_at: Instant::now(),
                expires_at: Instant::now() + self.prediction_horizon,
                hit_count: 0,
            },
        );

        // Add to history
        self.prediction_history.push(prediction.clone());

        // Limit history size
        if self.prediction_history.len() > 1000 {
            self.prediction_history.remove(0);
        }

        Some(prediction)
    }

    /// Update prediction accuracy based on actual outcomes
    pub fn update_accuracy(&mut self, prediction_id: usize, outcome: AllocationOutcome) {
        if let Some(prediction) = self.prediction_history.get_mut(prediction_id) {
            prediction.actual_outcome = Some(outcome.clone());

            // Update model accuracy
            let prediction_error = (prediction.size as f64 - outcome.actual_size as f64).abs();
            let relative_error = prediction_error / outcome.actual_size.max(1) as f64;
            let accuracy = 1.0 - relative_error.min(1.0);

            // Update running average
            self.model_accuracy = (self.model_accuracy + accuracy) / 2.0;
        }
    }

    /// Get prediction statistics
    pub fn get_prediction_stats(&self) -> PredictionStats {
        let total_predictions = self.prediction_history.len();
        let predictions_with_outcomes = self
            .prediction_history
            .iter()
            .filter(|p| p.actual_outcome.is_some())
            .count();

        let accuracy_sum = self
            .prediction_history
            .iter()
            .filter_map(|p| p.actual_outcome.as_ref())
            .zip(self.prediction_history.iter())
            .map(|(outcome, prediction)| {
                let error = (prediction.size as f64 - outcome.actual_size as f64).abs();
                1.0 - (error / outcome.actual_size.max(1) as f64).min(1.0)
            })
            .sum::<f64>();

        let average_accuracy = if predictions_with_outcomes > 0 {
            accuracy_sum / predictions_with_outcomes as f64
        } else {
            0.0
        };

        PredictionStats {
            total_predictions,
            validated_predictions: predictions_with_outcomes,
            average_accuracy,
            cache_hit_rate: self.calculate_cache_hit_rate(),
        }
    }

    // Private helper methods

    fn initialize_feature_extractors(&mut self) {
        self.feature_extractors = vec![
            FeatureExtractor {
                name: "temporal".to_string(),
                dimensions: 3,
                extractor_type: FeatureType::Temporal,
                last_extraction: Instant::now(),
            },
            FeatureExtractor {
                name: "statistical".to_string(),
                dimensions: 4,
                extractor_type: FeatureType::Statistical,
                last_extraction: Instant::now(),
            },
            FeatureExtractor {
                name: "pattern".to_string(),
                dimensions: 2,
                extractor_type: FeatureType::Pattern,
                last_extraction: Instant::now(),
            },
        ];
    }

    fn extract_features(&self, context: &AllocationContext) -> Vec<f64> {
        let mut features = Vec::new();

        for extractor in &self.feature_extractors {
            match extractor.extractor_type {
                FeatureType::Temporal => {
                    let now = Instant::now();
                    features.push(context.timestamp.elapsed().as_secs_f64());
                    features.push(
                        (now.duration_since(context.timestamp).as_secs_f64() % 86400.0) / 86400.0,
                    ); // Time of day
                    features.push(
                        (now.duration_since(context.timestamp).as_secs_f64() % (86400.0 * 7.0))
                            / (86400.0 * 7.0),
                    ); // Day of week
                }
                FeatureType::Statistical => {
                    features.push(
                        context.recent_allocation_sizes.iter().sum::<f64>()
                            / context.recent_allocation_sizes.len().max(1) as f64,
                    );
                    features.push(context.current_memory_pressure);
                    features.push(context.thread_count as f64);
                    features.push(context.available_memory as f64);
                }
                FeatureType::Pattern => {
                    features.push(context.allocation_frequency);
                    features.push(context.fragmentation_level);
                }
                FeatureType::SystemState => {
                    // Would add system-specific features
                }
            }
        }

        features
    }

    fn predict_size(&self, features: &[f64]) -> usize {
        // Simplified size prediction
        let base_size = features.get(0).unwrap_or(&1024.0);
        (base_size * 1.2) as usize
    }

    fn predict_allocator(&self, features: &[f64]) -> String {
        // Simplified allocator prediction
        let memory_pressure = features.get(1).unwrap_or(&0.5);
        if *memory_pressure > 0.7 {
            "high_pressure_allocator".to_string()
        } else {
            "default_allocator".to_string()
        }
    }

    fn calculate_cache_hit_rate(&self) -> f64 {
        let total_hits: u32 = self.prediction_cache.values().map(|c| c.hit_count).sum();
        let total_predictions = self.prediction_cache.len() as u32;

        if total_predictions > 0 {
            total_hits as f64 / total_predictions as f64
        } else {
            0.0
        }
    }
}

impl AutoOptimizationSystem {
    /// Create new auto-optimization system
    pub fn new() -> Self {
        Self {
            enabled: false,
            optimization_queue: Vec::new(),
            last_optimization: None,
            strategies: Vec::new(),
            history: Vec::new(),
        }
    }

    /// Enable auto-optimization
    pub fn enable(&mut self) {
        self.enabled = true;
        self.initialize_strategies();
    }

    /// Add optimization task
    pub fn add_task(&mut self, task: OptimizationTask) {
        self.optimization_queue.push(task);
        self.optimization_queue
            .sort_by_key(|t| std::cmp::Reverse(t.priority));
    }

    /// Process optimization queue
    pub fn process_queue(&mut self) -> Vec<OptimizationResult> {
        if !self.enabled || self.optimization_queue.is_empty() {
            return Vec::new();
        }

        let mut results = Vec::new();
        let max_concurrent = 3; // Process up to 3 tasks concurrently

        for _ in 0..max_concurrent {
            if let Some(task) = self.optimization_queue.pop() {
                let result = self.execute_optimization_task(&task);
                results.push(result);
            } else {
                break;
            }
        }

        self.last_optimization = Some(Instant::now());
        results
    }

    // Private helper methods

    fn initialize_strategies(&mut self) {
        self.strategies = vec![
            OptimizationStrategy {
                name: "Cache Optimization".to_string(),
                strategy_type: StrategyType::CacheOptimization,
                conditions: vec![OptimizationCondition {
                    description: "Low cache hit rate".to_string(),
                    metric: "cache_hit_rate".to_string(),
                    threshold: 0.8,
                    operator: ComparisonOperator::LessThan,
                }],
                expected_impact: 0.3,
                complexity: OptimizationComplexity::Medium,
            },
            OptimizationStrategy {
                name: "Fragmentation Reduction".to_string(),
                strategy_type: StrategyType::FragmentationReduction,
                conditions: vec![OptimizationCondition {
                    description: "High fragmentation level".to_string(),
                    metric: "fragmentation_level".to_string(),
                    threshold: 0.3,
                    operator: ComparisonOperator::GreaterThan,
                }],
                expected_impact: 0.4,
                complexity: OptimizationComplexity::High,
            },
        ];
    }

    fn execute_optimization_task(&mut self, task: &OptimizationTask) -> OptimizationResult {
        let start_time = Instant::now();

        // Simulate optimization execution
        let success = task.estimated_benefit > 0.3; // Simple success criterion
        let improvement = if success { task.estimated_benefit } else { 0.0 };

        let result = OptimizationResult {
            timestamp: Instant::now(),
            task_id: task.task_id.clone(),
            optimization_type: task.task_type.clone(),
            success,
            performance_improvement: improvement,
            duration: start_time.elapsed(),
            resource_cost: task.estimated_benefit * 100.0, // Simplified cost calculation
            error_message: if success {
                None
            } else {
                Some("Optimization failed to meet threshold".to_string())
            },
        };

        self.history.push(result.clone());
        result
    }
}

impl AnomalyDetector {
    /// Create new anomaly detector
    pub fn new() -> Self {
        Self {
            enabled: false,
            sensitivity: 0.5,
            detected_anomalies: 0,
            detection_models: Vec::new(),
            anomaly_history: Vec::new(),
            baseline_metrics: HashMap::new(),
        }
    }

    /// Enable anomaly detection
    pub fn enable(&mut self, sensitivity: f64) {
        self.enabled = true;
        self.sensitivity = sensitivity.clamp(0.0, 1.0);
        self.initialize_detection_models();
    }

    /// Detect anomalies in metrics
    pub fn detect_anomalies(&mut self, metrics: &HashMap<String, f64>) -> Vec<DetectedAnomaly> {
        if !self.enabled {
            return Vec::new();
        }

        let mut anomalies = Vec::new();

        for (metric_name, &value) in metrics {
            if let Some(baseline) = self.baseline_metrics.get(metric_name) {
                if let Some(anomaly) = self.check_statistical_anomaly(metric_name, value, baseline)
                {
                    anomalies.push(anomaly);
                    self.detected_anomalies += 1;
                }
            } else {
                // Create new baseline
                self.baseline_metrics.insert(
                    metric_name.clone(),
                    BaselineMetric {
                        name: metric_name.clone(),
                        mean: value,
                        std_dev: 0.0,
                        min_value: value,
                        max_value: value,
                        sample_count: 1,
                        last_update: Instant::now(),
                    },
                );
            }
        }

        // Update anomaly history
        self.anomaly_history.extend(anomalies.clone());
        if self.anomaly_history.len() > 1000 {
            self.anomaly_history.truncate(1000);
        }

        anomalies
    }

    /// Update baseline metrics
    pub fn update_baselines(&mut self, metrics: &HashMap<String, f64>) {
        for (metric_name, &value) in metrics {
            if let Some(baseline) = self.baseline_metrics.get_mut(metric_name) {
                // Update running statistics
                let new_count = baseline.sample_count + 1;
                let delta = value - baseline.mean;
                let new_mean = baseline.mean + delta / new_count as f64;

                let delta2 = value - new_mean;
                let new_variance = (baseline.std_dev.powi(2) * (baseline.sample_count - 1) as f64
                    + delta * delta2)
                    / new_count as f64;

                baseline.mean = new_mean;
                baseline.std_dev = new_variance.sqrt();
                baseline.min_value = baseline.min_value.min(value);
                baseline.max_value = baseline.max_value.max(value);
                baseline.sample_count = new_count;
                baseline.last_update = Instant::now();
            }
        }
    }

    // Private helper methods

    fn initialize_detection_models(&mut self) {
        self.detection_models = vec![AnomalyModel {
            name: "Statistical Outlier".to_string(),
            model_type: AnomalyModelType::StatisticalOutlier,
            threshold: 3.0, // 3-sigma threshold
            parameters: AnomalyModelParameters {
                window_size: 100,
                custom_params: HashMap::new(),
            },
            last_update: Instant::now(),
        }];
    }

    fn check_statistical_anomaly(
        &self,
        metric_name: &str,
        value: f64,
        baseline: &BaselineMetric,
    ) -> Option<DetectedAnomaly> {
        if baseline.sample_count < 10 || baseline.std_dev == 0.0 {
            return None; // Not enough data or no variance
        }

        let z_score = (value - baseline.mean).abs() / baseline.std_dev;
        let threshold = 3.0 * self.sensitivity; // Adjustable threshold

        if z_score > threshold {
            let anomaly_type = if value > baseline.mean {
                AnomalyType::HighOutlier
            } else {
                AnomalyType::LowOutlier
            };

            Some(DetectedAnomaly {
                timestamp: Instant::now(),
                anomaly_type,
                metric_name: metric_name.to_string(),
                score: z_score,
                expected_value: baseline.mean,
                actual_value: value,
                confidence: (z_score / (threshold + 1.0)).min(1.0),
                suggested_action: Some(format!("Investigate {} metric", metric_name)),
            })
        } else {
            None
        }
    }
}

// Supporting types

/// Training example for ML models
#[derive(Debug, Clone)]
pub struct TrainingExample {
    pub features: Vec<f64>,
    pub target: f64,
    pub weight: f64,
}

/// Model prediction result
#[derive(Debug, Clone)]
pub struct ModelPrediction {
    pub value: f64,
    pub confidence: f64,
    pub model_id: String,
    pub timestamp: Instant,
}

/// Allocation context for predictions
#[derive(Debug, Clone)]
pub struct AllocationContext {
    pub timestamp: Instant,
    pub thread_count: usize,
    pub available_memory: usize,
    pub current_memory_pressure: f64,
    pub recent_allocation_sizes: Vec<f64>,
    pub allocation_frequency: f64,
    pub fragmentation_level: f64,
}

/// Prediction statistics
#[derive(Debug, Clone)]
pub struct PredictionStats {
    pub total_predictions: usize,
    pub validated_predictions: usize,
    pub average_accuracy: f64,
    pub cache_hit_rate: f64,
}

impl Default for OptimizationConfig {
    fn default() -> Self {
        Self {
            auto_optimize: false,
            aggressiveness: 0.5,
            max_concurrent: 2,
            timeout: Duration::from_secs(300),
            min_improvement: 0.1,
        }
    }
}
