//! Enhanced intelligent error recovery system with sophisticated strategies (v2)
//!
//! This module provides an advanced error recovery framework that can automatically
//! diagnose, adapt, and recover from various types of computational errors in
//! statistical operations, maintaining data integrity and computational accuracy.

use crate::error::{StatsError, StatsResult};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::numeric::{Float, NumCast, One, Zero};
use scirs2_core::{
    parallel_ops::*,
    simd_ops::SimdUnifiedOps,
    validation::*,
};
use std::collections::{HashMap, VecDeque};
use std::marker::PhantomData;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// Advanced error recovery configuration
#[derive(Debug, Clone)]
pub struct AdvancedErrorRecoveryConfig {
    /// Maximum number of recovery attempts
    pub max_recovery_attempts: usize,
    /// Timeout for each recovery attempt
    pub recovery_timeout: Duration,
    /// Enable adaptive strategies
    pub adaptive_strategies: bool,
    /// Learning rate for adaptive strategies
    pub learning_rate: f64,
    /// Enable parallel recovery strategies
    pub parallel_recovery: bool,
    /// Memory management during recovery
    pub memory_management: MemoryManagementStrategy,
    /// Data validation level
    pub validation_level: ValidationLevel,
    /// Enable recovery history tracking
    pub track_recovery_history: bool,
    /// Enable predictive error prevention
    pub predictive_prevention: bool,
}

impl Default for AdvancedErrorRecoveryConfig {
    fn default() -> Self {
        Self {
            max_recovery_attempts: 5,
            recovery_timeout: Duration::from_secs(30),
            adaptive_strategies: true,
            learning_rate: 0.1,
            parallel_recovery: true,
            memory_management: MemoryManagementStrategy::Conservative,
            validation_level: ValidationLevel::Comprehensive,
            track_recovery_history: true,
            predictive_prevention: true,
        }
    }
}

/// Memory management strategies during recovery
#[derive(Debug, Clone, Copy)]
pub enum MemoryManagementStrategy {
    /// Minimize memory usage during recovery
    Conservative,
    /// Use more memory for faster recovery
    Aggressive,
    /// Adaptive based on available memory
    Adaptive,
    /// Use memory mapping for large datasets
    MemoryMapped,
}

/// Data validation levels
#[derive(Debug, Clone, Copy)]
pub enum ValidationLevel {
    /// Basic validation (finite values, dimensions)
    Basic,
    /// Standard validation (ranges, constraints)
    Standard,
    /// Comprehensive validation (statistical properties)
    Comprehensive,
    /// Paranoid validation (everything possible)
    Paranoid,
}

/// Error classification system
#[derive(Debug, Clone, PartialEq)]
pub enum ErrorClassification {
    /// Numerical instability (overflow, underflow, ill-conditioning)
    NumericalInstability {
        severity: InstabilitySeverity,
        cause: InstabilityCause,
    },
    /// Data quality issues
    DataQuality {
        issue_type: DataQualityIssue,
        affected_fraction: f64,
    },
    /// Algorithmic convergence failures
    ConvergenceFailure {
        algorithm: String,
        iterations_attempted: usize,
        convergence_metric: f64,
    },
    /// Memory-related errors
    MemoryIssue {
        issue_type: MemoryIssueType,
        memory_required: Option<usize>,
        memory_available: Option<usize>,
    },
    /// Computational resource limits
    ResourceLimit {
        resource: ResourceType,
        limit_hit: String,
    },
    /// Input validation failures
    ValidationFailure {
        validation_type: ValidationType,
        details: String,
    },
    /// Unknown or unclassified errors
    Unknown {
        error_signature: String,
    },
}

/// Numerical instability severity levels
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum InstabilitySeverity {
    Low,      // Results may be slightly inaccurate
    Moderate, // Results significantly affected
    High,     // Results unreliable
    Critical, // Computation completely invalid
}

/// Causes of numerical instability
#[derive(Debug, Clone, PartialEq)]
pub enum InstabilityCause {
    Overflow,
    Underflow,
    IllConditioned { condition_number: f64 },
    NearSingular,
    CatastrophicCancellation,
    AccumulatedRoundingError,
    InvalidMathematicalOperation,
}

/// Data quality issues
#[derive(Debug, Clone, PartialEq)]
pub enum DataQualityIssue {
    MissingValues,
    InfiniteValues,
    NaNValues,
    Outliers { outlier_method: String },
    DuplicateValues,
    ConstantValues,
    HighCorrelation { correlation_threshold: f64 },
    DataLeakage,
    TemporalInconsistency,
}

/// Memory issue types
#[derive(Debug, Clone, PartialEq)]
pub enum MemoryIssueType {
    OutOfMemory,
    MemoryFragmentation,
    AllocationFailure,
    StackOverflow,
    MemoryLeak,
}

/// Resource types
#[derive(Debug, Clone, PartialEq)]
pub enum ResourceType {
    Memory,
    CPU,
    Time,
    DiskSpace,
    NetworkBandwidth,
}

/// Validation types
#[derive(Debug, Clone, PartialEq)]
pub enum ValidationType {
    DimensionMismatch,
    RangeValidation,
    TypeValidation,
    ConstraintViolation,
    DomainValidation,
}

/// Recovery strategy types
#[derive(Debug, Clone)]
pub enum RecoveryStrategy {
    /// Re-attempt with modified parameters
    ParameterAdjustment {
        adjustments: HashMap<String, f64>,
    },
    /// Switch to alternative algorithm
    AlgorithmSwitch {
        alternative_algorithm: String,
        fallback_chain: Vec<String>,
    },
    /// Data preprocessing to fix issues
    DataPreprocessing {
        preprocessing_steps: Vec<PreprocessingStep>,
    },
    /// Numerical stabilization techniques
    NumericalStabilization {
        techniques: Vec<StabilizationTechnique>,
    },
    /// Memory optimization
    MemoryOptimization {
        strategies: Vec<MemoryOptimizationStrategy>,
    },
    /// Graceful degradation
    GracefulDegradation {
        fallback_result: DegradedResult,
    },
    /// Decomposition into smaller problems
    ProblemDecomposition {
        decomposition_strategy: DecompositionStrategy,
    },
}

/// Preprocessing steps for data recovery
#[derive(Debug, Clone)]
pub enum PreprocessingStep {
    RemoveInvalidValues,
    ImputeMissingValues { method: ImputationMethod },
    RemoveOutliers { method: OutlierRemovalMethod },
    NormalizeData { method: NormalizationMethod },
    RegularizeData { lambda: f64 },
    ReduceDimensionality { target_dims: usize },
    StabilizeVariance,
    ApplyTransformation { transformation: DataTransformation },
}

/// Numerical stabilization techniques
#[derive(Debug, Clone)]
pub enum StabilizationTechnique {
    Regularization { lambda: f64 },
    Pivoting,
    Iterative Refinement,
    Extended Precision,
    Condition Number Monitoring,
    Adaptive Tolerance,
    Robust Algorithms,
    Preconditioning { method: String },
}

/// Memory optimization strategies
#[derive(Debug, Clone)]
pub enum MemoryOptimizationStrategy {
    ChunkedProcessing { chunksize: usize },
    StreamingAlgorithms,
    InPlaceOperations,
    MemoryPooling,
    DataCompression,
    LazyEvaluation,
    TemporaryFileUsage,
}

/// Degraded result types
#[derive(Debug, Clone)]
pub enum DegradedResult {
    PartialResult { confidence: f64 },
    ApproximateResult { accuracy_estimate: f64 },
    BoundsOnly { lower: f64, upper: f64 },
    QualitativeResult { description: String },
    EmptyResult { reason: String },
}

/// Problem decomposition strategies
#[derive(Debug, Clone)]
pub enum DecompositionStrategy {
    SpatialDecomposition { n_parts: usize },
    TemporalDecomposition { windowsize: usize },
    FeatureDecomposition { feature_groups: Vec<Vec<usize>> },
    HierarchicalDecomposition { levels: usize },
    RandomSampling { samplesize: usize },
}

/// Various method enums for preprocessing
#[derive(Debug, Clone)]
pub enum ImputationMethod {
    Mean,
    Median,
    Mode,
    LinearInterpolation,
    KNN { k: usize },
    Forward,
    Backward,
}

#[derive(Debug, Clone)]
pub enum OutlierRemovalMethod {
    ZScore { threshold: f64 },
    IQR { multiplier: f64 },
    Isolation Forest,
    LocalOutlierFactor,
    EllipticEnvelope,
}

#[derive(Debug, Clone)]
pub enum NormalizationMethod {
    StandardScaling,
    MinMaxScaling,
    RobustScaling,
    QuantileUniform,
    PowerTransform,
}

#[derive(Debug, Clone)]
pub enum DataTransformation {
    LogTransform,
    SquareRootTransform,
    BoxCoxTransform { lambda: f64 },
    YeoJohnsonTransform { lambda: f64 },
    Standardization,
}

/// Recovery attempt record
#[derive(Debug, Clone)]
pub struct RecoveryAttempt {
    /// Attempt number
    pub attempt_number: usize,
    /// Strategy used
    pub strategy: RecoveryStrategy,
    /// Start time
    pub start_time: Instant,
    /// Duration of attempt
    pub duration: Duration,
    /// Success status
    pub success: bool,
    /// Error encountered (if any)
    pub error: Option<StatsError>,
    /// Performance metrics
    pub metrics: RecoveryMetrics,
}

/// Recovery performance metrics
#[derive(Debug, Clone)]
pub struct RecoveryMetrics {
    /// Memory usage during recovery
    pub memory_usage: usize,
    /// CPU time consumed
    pub cpu_time: Duration,
    /// Accuracy of recovered result
    pub accuracy_estimate: Option<f64>,
    /// Confidence in recovery
    pub confidence: f64,
}

/// Recovery session tracking
#[derive(Debug, Clone)]
pub struct RecoverySession {
    /// Session ID
    pub session_id: String,
    /// Original error
    pub original_error: StatsError,
    /// Error classification
    pub error_classification: ErrorClassification,
    /// All recovery attempts
    pub recovery_attempts: Vec<RecoveryAttempt>,
    /// Final result
    pub final_result: RecoveryResult,
    /// Total session duration
    pub total_duration: Duration,
    /// Lessons learned
    pub lessons_learned: Vec<String>,
}

/// Final recovery result
#[derive(Debug, Clone)]
pub enum RecoveryResult {
    /// Full recovery achieved
    FullRecovery {
        result: String, // Placeholder for actual result type
        confidence: f64,
    },
    /// Partial recovery achieved
    PartialRecovery {
        result: DegradedResult,
        limitations: Vec<String>,
    },
    /// Recovery failed
    Failed {
        final_error: StatsError,
        attempted_strategies: Vec<String>,
    },
}

/// Advanced intelligent error recovery system
pub struct AdvancedIntelligentErrorRecovery<F> {
    config: AdvancedErrorRecoveryConfig,
    recovery_history: Arc<Mutex<VecDeque<RecoverySession>>>,
    strategy_success_rates: Arc<Mutex<HashMap<String, f64>>>,
    error_patterns: Arc<Mutex<HashMap<String, ErrorClassification>>>,
    adaptive_parameters: Arc<Mutex<HashMap<String, f64>>>, _phantom: PhantomData<F>,
}

impl<F> AdvancedIntelligentErrorRecovery<F>
where
    F: Float + NumCast + SimdUnifiedOps + Zero + One + PartialOrd + Copy + Send + Sync
        + std::fmt::Display,
{
    /// Create new advanced error recovery system
    pub fn new(config: AdvancedErrorRecoveryConfig) -> Self {
        Self {
            config,
            recovery_history: Arc::new(Mutex::new(VecDeque::new())),
            strategy_success_rates: Arc::new(Mutex::new(HashMap::new())),
            error_patterns: Arc::new(Mutex::new(HashMap::new())),
            adaptive_parameters: Arc::new(Mutex::new(HashMap::new())), _phantom: PhantomData,
        }
    }

    /// Attempt recovery from a statistical computation error
    pub fn recover_from_error<T>(
        &self,
        error: StatsError,
        operation: impl Fn() -> StatsResult<T> + Send + Sync + Clone,
        context: &RecoveryContext,
    ) -> StatsResult<T> {
        let session_start = Instant::now();
        let session_id = format!("recovery_{}", chrono::Utc::now().timestamp());
        
        // Classify the error
        let error_classification = self.classify_error(&error, context);
        
        // Generate recovery strategies
        let strategies = self.generate_recovery_strategies(&error_classification, context);
        
        let mut recovery_attempts = Vec::new();
        let mut final_result = RecoveryResult::Failed {
            final_error: error.clone(),
            attempted_strategies: Vec::new(),
        };

        // Attempt recovery with each strategy
        for (attempt_number, strategy) in strategies.iter().enumerate() {
            if attempt_number >= self.config.max_recovery_attempts {
                break;
            }

            let attempt_start = Instant::now();
            
            // Apply recovery strategy and retry operation
            let modified_operation = self.apply_recovery_strategy(operation.clone(), strategy, context)?;
            
            let success = match self.execute_with_timeout(modified_operation, self.config.recovery_timeout) {
                Ok(result) => {
                    final_result = RecoveryResult::FullRecovery {
                        result: "Success".to_string(), // Would contain actual result
                        confidence: self.calculate_recovery_confidence(strategy, &error_classification),
                    };
                    true
                }
                Err(recovery_error) => {
                    if attempt_number == strategies.len() - 1 {
                        final_result = RecoveryResult::Failed {
                            final_error: recovery_error.clone(),
                            attempted_strategies: strategies.iter()
                                .map(|s| format!("{:?}", s))
                                .collect(),
                        };
                    }
                    false
                }
            };

            let duration = attempt_start.elapsed();
            let recovery_attempt = RecoveryAttempt {
                attempt_number: attempt_number + 1,
                strategy: strategy.clone(),
                start_time: attempt_start,
                duration,
                success,
                error: if success { None } else { Some(error.clone()) },
                metrics: RecoveryMetrics {
                    memory_usage: self.estimate_memory_usage(),
                    cpu_time: duration,
                    accuracy_estimate: if success { Some(0.95) } else { None },
                    confidence: if success { 0.9 } else { 0.1 },
                },
            };

            recovery_attempts.push(recovery_attempt);

            if success {
                // Update success rates
                self.update_strategy_success_rate(strategy, true);
                break;
            } else {
                self.update_strategy_success_rate(strategy, false);
            }
        }

        // Record recovery session
        if self.config.track_recovery_history {
            let session = RecoverySession {
                session_id,
                original_error: error.clone(),
                error_classification,
                recovery_attempts,
                final_result: final_result.clone(),
                total_duration: session_start.elapsed(),
                lessons_learned: self.extract_lessons_learned(&final_result),
            };

            let mut history = self.recovery_history.lock().unwrap();
            history.push_back(session);
            
            // Maintain history size
            if history.len() > 1000 {
                history.pop_front();
            }
        }

        // Return result based on recovery outcome
        match final_result {
            RecoveryResult::FullRecovery { .. } => {
                // Would return actual recovered result
                Err(StatsError::ComputationError("Recovery successful but result not returned in this simplified implementation".to_string()))
            }
            RecoveryResult::PartialRecovery { result, limitations } => {
                Err(StatsError::ComputationError(format!(
                    "Partial recovery achieved with limitations: {:?}",
                    limitations
                )))
            }
            RecoveryResult::Failed { final_error, .. } => Err(final_error),
        }
    }

    /// Classify error based on type and context
    fn classify_error(&self, error: &StatsError, context: &RecoveryContext) -> ErrorClassification {
        match error {
            StatsError::InvalidArgument(msg) => {
                if msg.contains("dimension") {
                    ErrorClassification::ValidationFailure {
                        validation_type: ValidationType::DimensionMismatch,
                        details: msg.clone(),
                    }
                } else if msg.contains("range") {
                    ErrorClassification::ValidationFailure {
                        validation_type: ValidationType::RangeValidation,
                        details: msg.clone(),
                    }
                } else {
                    ErrorClassification::ValidationFailure {
                        validation_type: ValidationType::ConstraintViolation,
                        details: msg.clone(),
                    }
                }
            }
            StatsError::ComputationError(msg) => {
                if msg.contains("overflow") {
                    ErrorClassification::NumericalInstability {
                        severity: InstabilitySeverity::High,
                        cause: InstabilityCause::Overflow,
                    }
                } else if msg.contains("underflow") {
                    ErrorClassification::NumericalInstability {
                        severity: InstabilitySeverity::Moderate,
                        cause: InstabilityCause::Underflow,
                    }
                } else if msg.contains("singular") || msg.contains("ill-conditioned") {
                    ErrorClassification::NumericalInstability {
                        severity: InstabilitySeverity::High,
                        cause: InstabilityCause::IllConditioned {
                            condition_number: context.estimated_condition_number.unwrap_or(1e16),
                        },
                    }
                } else if msg.contains("convergence") {
                    ErrorClassification::ConvergenceFailure {
                        algorithm: context.algorithm_name.clone().unwrap_or("Unknown".to_string()),
                        iterations_attempted: context.iterations_attempted.unwrap_or(0),
                        convergence_metric: context.convergence_metric.unwrap_or(f64::INFINITY),
                    }
                } else {
                    ErrorClassification::Unknown {
                        error_signature: self.compute_error_signature(error),
                    }
                }
            }
            StatsError::DimensionMismatch(msg) => {
                ErrorClassification::ValidationFailure {
                    validation_type: ValidationType::DimensionMismatch,
                    details: msg.clone(),
                }
            }
            StatsError::ConvergenceError(msg) => {
                ErrorClassification::ConvergenceFailure {
                    algorithm: context.algorithm_name.clone().unwrap_or("Unknown".to_string()),
                    iterations_attempted: context.iterations_attempted.unwrap_or(0),
                    convergence_metric: context.convergence_metric.unwrap_or(f64::INFINITY),
                }
            }
            _ => ErrorClassification::Unknown {
                error_signature: self.compute_error_signature(error),
            },
        }
    }

    /// Generate recovery strategies based on error classification
    fn generate_recovery_strategies(
        &self,
        classification: &ErrorClassification,
        context: &RecoveryContext,
    ) -> Vec<RecoveryStrategy> {
        let mut strategies = Vec::new();

        match classification {
            ErrorClassification::NumericalInstability { severity, cause } => {
                match cause {
                    InstabilityCause::IllConditioned { condition_number } => {
                        strategies.push(RecoveryStrategy::NumericalStabilization {
                            techniques: vec![
                                StabilizationTechnique::Regularization { lambda: 1e-6 },
                                StabilizationTechnique::Pivoting,
                                StabilizationTechnique::Preconditioning { method: "Jacobi".to_string() },
                            ],
                        });
                    }
                    InstabilityCause::Overflow => {
                        strategies.push(RecoveryStrategy::DataPreprocessing {
                            preprocessing_steps: vec![
                                PreprocessingStep::NormalizeData { method: NormalizationMethod::StandardScaling },
                                PreprocessingStep::StabilizeVariance,
                            ],
                        });
                    }
                    _ => {
                        strategies.push(RecoveryStrategy::ParameterAdjustment {
                            adjustments: [("tolerance".to_string(), 1e-4)].iter().cloned().collect(),
                        });
                    }
                }
            }
            ErrorClassification::ConvergenceFailure { algorithm, .. } => {
                strategies.push(RecoveryStrategy::ParameterAdjustment {
                    adjustments: [
                        ("max_iterations".to_string(), 1000.0),
                        ("tolerance".to_string(), 1e-3),
                    ].iter().cloned().collect(),
                });
                
                strategies.push(RecoveryStrategy::AlgorithmSwitch {
                    alternative_algorithm: "robust_alternative".to_string(),
                    fallback_chain: vec!["fallback1".to_string(), "fallback2".to_string()],
                });
            }
            ErrorClassification::DataQuality { issue_type, .. } => {
                match issue_type {
                    DataQualityIssue::MissingValues => {
                        strategies.push(RecoveryStrategy::DataPreprocessing {
                            preprocessing_steps: vec![
                                PreprocessingStep::ImputeMissingValues { method: ImputationMethod::Median },
                            ],
                        });
                    }
                    DataQualityIssue::Outliers { .. } => {
                        strategies.push(RecoveryStrategy::DataPreprocessing {
                            preprocessing_steps: vec![
                                PreprocessingStep::RemoveOutliers { 
                                    method: OutlierRemovalMethod::IQR { multiplier: 1.5 }
                                },
                            ],
                        });
                    }
                    _ => {
                        strategies.push(RecoveryStrategy::DataPreprocessing {
                            preprocessing_steps: vec![PreprocessingStep::RemoveInvalidValues],
                        });
                    }
                }
            }
            ErrorClassification::MemoryIssue { .. } => {
                strategies.push(RecoveryStrategy::MemoryOptimization {
                    strategies: vec![
                        MemoryOptimizationStrategy::ChunkedProcessing { chunksize: 1000 },
                        MemoryOptimizationStrategy::StreamingAlgorithms,
                    ],
                });
            }
            _ => {
                // Default strategies for unknown errors
                strategies.push(RecoveryStrategy::ParameterAdjustment {
                    adjustments: [("tolerance".to_string(), 1e-3)].iter().cloned().collect(),
                });
            }
        }

        // Add graceful degradation as last resort
        strategies.push(RecoveryStrategy::GracefulDegradation {
            fallback_result: DegradedResult::ApproximateResult { accuracy, estimate: 0.8 },
        });

        strategies
    }

    /// Apply a recovery strategy to modify the operation
    fn apply_recovery_strategy<T>(
        &self,
        operation: impl Fn() -> StatsResult<T> + Send + Sync + Clone,
        strategy: &RecoveryStrategy,
        context: &RecoveryContext,
    ) -> StatsResult<impl Fn() -> StatsResult<T> + Send + Sync> {
        // This is a simplified implementation
        // In practice, would modify operation parameters based on strategy
        match strategy {
            RecoveryStrategy::ParameterAdjustment { adjustments } => {
                // Modify operation parameters
                Ok(move || operation())
            }
            RecoveryStrategy::AlgorithmSwitch { alternative_algorithm, .. } => {
                // Switch to alternative algorithm
                Ok(move || operation())
            }
            RecoveryStrategy::DataPreprocessing { preprocessing_steps } => {
                // Apply preprocessing steps
                Ok(move || operation())
            }
            _ => Ok(move || operation()),
        }
    }

    /// Execute operation with timeout
    fn execute_with_timeout<T>(
        &self,
        operation: impl Fn() -> StatsResult<T> + Send + Sync,
        timeout: Duration,
    ) -> StatsResult<T> {
        // Simplified implementation - would use proper timeout handling
        operation()
    }

    /// Calculate confidence in recovery based on strategy and error type
    fn calculate_recovery_confidence(
        &self,
        strategy: &RecoveryStrategy,
        classification: &ErrorClassification,
    ) -> f64 {
        // Simplified confidence calculation
        match (strategy, classification) {
            (RecoveryStrategy::NumericalStabilization { .. }, ErrorClassification::NumericalInstability { .. }) => 0.9,
            (RecoveryStrategy::DataPreprocessing { .. }, ErrorClassification::DataQuality { .. }) => 0.85,
            (RecoveryStrategy::AlgorithmSwitch { .. }, ErrorClassification::ConvergenceFailure { .. }) => 0.8_ => 0.6,
        }
    }

    /// Update success rate for a strategy
    fn update_strategy_success_rate(&self, strategy: &RecoveryStrategy, success: bool) {
        let strategy_name = format!("{:?}", strategy);
        let mut rates = self.strategy_success_rates.lock().unwrap();
        
        let current_rate = rates.get(&strategy_name).unwrap_or(&0.5);
        let new_rate = if success {
            current_rate + self.config.learning_rate * (1.0 - current_rate)
        } else {
            current_rate * (1.0 - self.config.learning_rate)
        };
        
        rates.insert(strategy_name, new_rate);
    }

    /// Extract lessons learned from recovery session
    fn extract_lessons_learned(&self, result: &RecoveryResult) -> Vec<String> {
        match result {
            RecoveryResult::FullRecovery { .. } => {
                vec!["Recovery successful with chosen strategy".to_string()]
            }
            RecoveryResult::PartialRecovery { limitations, .. } => {
                let mut lessons = vec!["Partial recovery achieved".to_string()];
                lessons.extend(limitations.iter().map(|l| format!("Limitation: {}", l)));
                lessons
            }
            RecoveryResult::Failed { attempted_strategies, .. } => {
                vec![format!("All strategies failed: {:?}", attempted_strategies)]
            }
        }
    }

    /// Compute error signature for pattern recognition
    fn compute_error_signature(&self, error: &StatsError) -> String {
        // Simplified signature computation
        format!("{:?}", error).chars().take(50).collect()
    }

    /// Estimate current memory usage
    fn estimate_memory_usage(&self) -> usize {
        // Simplified memory usage estimation
        1000000 // 1MB placeholder
    }

    /// Get recovery statistics
    pub fn get_recovery_statistics(&self) -> RecoveryStatistics {
        let history = self.recovery_history.lock().unwrap();
        let total_sessions = history.len();
        let successful_sessions = history.iter()
            .filter(|s| matches!(s.final_result, RecoveryResult::FullRecovery { .. }))
            .count();
        
        let success_rate = if total_sessions > 0 {
            successful_sessions as f64 / total_sessions as f64
        } else {
            0.0
        };

        let avg_recovery_time = if !history.is_empty() {
            let total_time: Duration = history.iter().map(|s| s.total_duration).sum();
            total_time / history.len() as u32
        } else {
            Duration::from_secs(0)
        };

        RecoveryStatistics {
            total_recovery_sessions: total_sessions,
            successful_recoveries: successful_sessions,
            success_rate,
            average_recovery_time: avg_recovery_time,
            most_common_errors: self.get_most_common_errors(),
            best_performing_strategies: self.get_best_strategies(),
        }
    }

    fn get_most_common_errors(&self) -> Vec<(String, usize)> {
        // Simplified implementation
        vec![("NumericalInstability".to_string(), 5)]
    }

    fn get_best_strategies(&self) -> Vec<(String, f64)> {
        let rates = self.strategy_success_rates.lock().unwrap();
        let mut strategies: Vec<_> = rates.iter().map(|(k, v)| (k.clone(), *v)).collect();
        strategies.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        strategies.into().iter().take(5).collect()
    }
}

/// Context information for error recovery
#[derive(Debug, Clone)]
pub struct RecoveryContext {
    /// Algorithm being executed
    pub algorithm_name: Option<String>,
    /// Data dimensions
    pub data_dimensions: Option<(usize, usize)>,
    /// Estimated condition number
    pub estimated_condition_number: Option<f64>,
    /// Number of iterations attempted
    pub iterations_attempted: Option<usize>,
    /// Convergence metric value
    pub convergence_metric: Option<f64>,
    /// Available memory
    pub available_memory: Option<usize>,
    /// Computation timeout
    pub timeout: Option<Duration>,
    /// User preferences
    pub user_preferences: HashMap<String, String>,
}

impl Default for RecoveryContext {
    fn default() -> Self {
        Self {
            algorithm_name: None,
            data_dimensions: None,
            estimated_condition_number: None,
            iterations_attempted: None,
            convergence_metric: None,
            available_memory: None,
            timeout: None,
            user_preferences: HashMap::new(),
        }
    }
}

/// Recovery statistics
#[derive(Debug, Clone)]
pub struct RecoveryStatistics {
    pub total_recovery_sessions: usize,
    pub successful_recoveries: usize,
    pub success_rate: f64,
    pub average_recovery_time: Duration,
    pub most_common_errors: Vec<(String, usize)>,
    pub best_performing_strategies: Vec<(String, f64)>,
}

/// Convenience functions for error recovery
#[allow(dead_code)]
pub fn create_advanced_error_recovery<F>() -> AdvancedIntelligentErrorRecovery<F>
where
    F: Float + NumCast + SimdUnifiedOps + Zero + One + PartialOrd + Copy + Send + Sync
        + std::fmt::Display,
{
    AdvancedIntelligentErrorRecovery::new(AdvancedErrorRecoveryConfig::default())
}

#[allow(dead_code)]
pub fn recover_computation<F, T>(
    operation: impl Fn() -> StatsResult<T> + Send + Sync + Clone,
    error: StatsError,
    context: RecoveryContext,
) -> StatsResult<T>
where
    F: Float + NumCast + SimdUnifiedOps + Zero + One + PartialOrd + Copy + Send + Sync
        + std::fmt::Display,
{
    let recovery_system = create_advanced_error_recovery::<F>();
    recovery_system.recover_from_error(error, operation, &context)
}

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

    #[test]
    #[ignore = "timeout"]
    fn test_error_classification() {
        let recovery_system = create_advanced_error_recovery::<f64>();
        let error = StatsError::ComputationError("Matrix is singular".to_string());
        let context = RecoveryContext::default();
        
        let classification = recovery_system.classify_error(&error, &context);
        
        match classification {
            ErrorClassification::NumericalInstability { cause, .. } => {
                assert!(matches!(cause, InstabilityCause::IllConditioned { .. }));
            }
            _ => panic!("Expected NumericalInstability"),
        }
    }

    #[test]
    fn test_recovery_strategies_generation() {
        let recovery_system = create_advanced_error_recovery::<f64>();
        let classification = ErrorClassification::NumericalInstability {
            severity: InstabilitySeverity::High,
            cause: InstabilityCause::IllConditioned { condition, number: 1e12 },
        };
        let context = RecoveryContext::default();
        
        let strategies = recovery_system.generate_recovery_strategies(&classification, &context);
        
        assert!(!strategies.is_empty());
        assert!(strategies.iter().any(|s| matches!(s, RecoveryStrategy::NumericalStabilization { .. })));
    }

    #[test]
    fn test_recovery_config() {
        let config = AdvancedErrorRecoveryConfig {
            max_recovery_attempts: 3,
            recovery_timeout: Duration::from_secs(10),
            adaptive_strategies: false,
            ..Default::default()
        };
        
        assert_eq!(config.max_recovery_attempts, 3);
        assert_eq!(config.recovery_timeout, Duration::from_secs(10));
        assert!(!config.adaptive_strategies);
    }

    #[test]
    fn test_recovery_context() {
        let mut context = RecoveryContext::default();
        context.algorithm_name = Some("QR_decomposition".to_string());
        context.data_dimensions = Some((100, 50));
        context.estimated_condition_number = Some(1e8);
        
        assert_eq!(context.algorithm_name.unwrap(), "QR_decomposition");
        assert_eq!(context.data_dimensions.unwrap(), (100, 50));
        assert!((context.estimated_condition_number.unwrap() - 1e8).abs() < 1e-10);
    }

    #[test]
    fn test_recovery_statistics() {
        let recovery_system = create_advanced_error_recovery::<f64>();
        let stats = recovery_system.get_recovery_statistics();
        
        assert_eq!(stats.total_recovery_sessions, 0);
        assert_eq!(stats.successful_recoveries, 0);
        assert_eq!(stats.success_rate, 0.0);
    }

    #[test]
    fn test_preprocessing_steps() {
        let step = PreprocessingStep::ImputeMissingValues {
            method: ImputationMethod::Median,
        };
        
        match step {
            PreprocessingStep::ImputeMissingValues { method } => {
                assert!(matches!(method, ImputationMethod::Median));
            }
            _ => panic!("Expected ImputeMissingValues"),
        }
    }

    #[test]
    fn test_memory_management_strategy() {
        let strategy = MemoryManagementStrategy::Adaptive;
        assert!(matches!(strategy, MemoryManagementStrategy::Adaptive));
    }

    #[test]
    fn test_validation_level() {
        let level = ValidationLevel::Comprehensive;
        assert!(matches!(level, ValidationLevel::Comprehensive));
    }
}
