//! Intelligent error recovery with ML-powered suggestions
//!
//! This module provides advanced error recovery capabilities using pattern recognition
//! and machine learning to suggest optimal recovery strategies based on historical
//! error patterns and data characteristics.

use crate::error::{StatsError, StatsResult};
use crate::error_recovery_system::{
    EnhancedStatsError, RecoveryAction, RecoverySuggestion, SuggestionType,
};
use scirs2_core::random::Rng;
use std::collections::HashMap;

/// Intelligent error recovery analyzer
pub struct IntelligentErrorRecovery {
    /// Historical error patterns
    error_patterns: HashMap<String, ErrorPattern>,
    /// Success rates for different recovery strategies
    recovery_success_rates: HashMap<String, f64>,
    /// Configuration
    config: RecoveryConfig,
}

/// Error pattern for machine learning
#[derive(Debug, Clone)]
pub struct ErrorPattern {
    /// Error type signature
    pub error_signature: String,
    /// Data characteristics when error occurred
    pub data_features: Vec<f64>,
    /// Successful recovery actions
    pub successful_actions: Vec<RecoveryAction>,
    /// Occurrence frequency
    pub frequency: usize,
    /// Average resolution time
    pub avg_resolution_time: f64,
}

/// Recovery configuration
#[derive(Debug, Clone)]
pub struct RecoveryConfig {
    /// Maximum number of suggestions to provide
    pub max_suggestions: usize,
    /// Minimum confidence threshold for suggestions
    pub min_confidence: f64,
    /// Enable ML-powered suggestions
    pub enable_ml_suggestions: bool,
    /// Enable automatic recovery attempts
    pub enable_auto_recovery: bool,
    /// Maximum auto-recovery attempts
    pub max_auto_attempts: usize,
}

impl Default for RecoveryConfig {
    fn default() -> Self {
        Self {
            max_suggestions: 5,
            min_confidence: 0.6,
            enable_ml_suggestions: true,
            enable_auto_recovery: false,
            max_auto_attempts: 3,
        }
    }
}

/// Recovery strategy with estimated success probability
#[derive(Debug, Clone)]
pub struct IntelligentRecoveryStrategy {
    /// Recovery suggestion
    pub suggestion: RecoverySuggestion,
    /// Estimated success probability
    pub success_probability: f64,
    /// Estimated execution time
    pub estimated_time: f64,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
    /// Risk assessment
    pub risk_level: RiskLevel,
}

/// Resource requirements for recovery
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Memory requirement in MB
    pub memory_mb: f64,
    /// CPU cores needed
    pub cpu_cores: usize,
    /// Estimated wall-clock time
    pub wall_time_seconds: f64,
    /// Requires GPU acceleration
    pub requires_gpu: bool,
}

/// Risk level for recovery strategies
#[derive(Debug, Clone, PartialEq)]
pub enum RiskLevel {
    /// Low risk, unlikely to cause issues
    Low,
    /// Medium risk, may affect performance
    Medium,
    /// High risk, may cause data loss or corruption
    High,
    /// Critical risk, may crash the system
    Critical,
}

impl IntelligentErrorRecovery {
    /// Create new intelligent error recovery system
    pub fn new(config: RecoveryConfig) -> Self {
        Self {
            error_patterns: HashMap::new(),
            recovery_success_rates: HashMap::new(),
            config,
        }
    }

    /// Analyze error and provide intelligent recovery suggestions
    pub fn analyze_and_suggest(
        &mut self,
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        // Extract features from error context
        let features = self.extract_error_features(error)?;

        // Find similar error patterns
        let similar_patterns = self.find_similar_patterns(&features);

        // Generate recovery strategies
        let mut strategies = Vec::new();

        // Add pattern-based suggestions
        strategies.extend(self.generate_patternbased_suggestions(&similar_patterns, error)?);

        // Add heuristic-based suggestions
        strategies.extend(self.generate_heuristic_suggestions(error)?);

        // Add ML-powered suggestions if enabled
        if self.config.enable_ml_suggestions {
            strategies.extend(self.generate_ml_suggestions(&features, error)?);
        }

        // Rank and filter strategies
        strategies.sort_by(|a, b| {
            b.success_probability
                .partial_cmp(&a.success_probability)
                .unwrap()
        });
        strategies.truncate(self.config.max_suggestions);

        // Filter by confidence threshold
        strategies.retain(|s| s.success_probability >= self.config.min_confidence);

        Ok(strategies)
    }

    /// Extract numerical features from error for ML analysis
    fn extract_error_features(&self, error: &EnhancedStatsError) -> StatsResult<Vec<f64>> {
        let mut features = Vec::new();

        // Error type features
        features.push(self.encode_error_type(&error.error));

        // Data size features
        if let Some(ref size_info) = error.context.data_characteristics.size_info {
            features.push(size_info.n_elements as f64);
            features.push(size_info.memory_usage_mb);
            features.push(size_info.shape.len() as f64); // Dimensionality
        } else {
            features.extend_from_slice(&[0.0, 0.0, 0.0]);
        }

        // Data range features
        if let Some(ref range_info) = error.context.data_characteristics.range_info {
            features.push(range_info.max - range_info.min); // Range
            features.push(if range_info.has_infinite { 1.0 } else { 0.0 });
            features.push(if range_info.has_nan { 1.0 } else { 0.0 });
            features.push(if range_info.has_zero { 1.0 } else { 0.0 });
        } else {
            features.extend_from_slice(&[0.0, 0.0, 0.0, 0.0]);
        }

        // System features
        features.push(error.context.system_info.cpu_cores.unwrap_or(1) as f64);
        features.push(
            error
                .context
                .system_info
                .available_memory_mb
                .unwrap_or(1000.0),
        );
        features.push(if error.context.system_info.parallel_available {
            1.0
        } else {
            0.0
        });

        // Algorithm features
        features.push(self.encode_algorithm(&error.context.computation_state.algorithm));

        // Convergence features
        if let Some(ref conv_status) = error.context.computation_state.convergence_status {
            features.push(self.encode_convergence_status(conv_status));
        } else {
            features.push(0.0);
        }

        Ok(features)
    }

    /// Encode error type as numerical feature
    fn encode_error_type(&self, error: &StatsError) -> f64 {
        match error {
            StatsError::DimensionMismatch(_) => 1.0,
            StatsError::InvalidArgument(_) => 2.0,
            StatsError::ComputationError(_) => 3.0,
            StatsError::ConvergenceError(_) => 4.0,
            StatsError::InsufficientData(_) => 5.0,
            _ => 0.0,
        }
    }

    /// Encode algorithm as numerical feature
    fn encode_algorithm(&self, algorithm: &str) -> f64 {
        match algorithm.to_lowercase().as_str() {
            algo if algo.contains("linear") => 1.0,
            algo if algo.contains("nonlinear") => 2.0,
            algo if algo.contains("iterative") => 3.0,
            algo if algo.contains("mcmc") => 4.0,
            algo if algo.contains("optimization") => 5.0,
            _ => 0.0,
        }
    }

    /// Encode convergence status as numerical feature
    fn encode_convergence_status(
        &self,
        status: &crate::error_recovery_system::ConvergenceStatus,
    ) -> f64 {
        use crate::error_recovery_system::ConvergenceStatus;
        match status {
            ConvergenceStatus::NotStarted => 0.0,
            ConvergenceStatus::InProgress => 1.0,
            ConvergenceStatus::Converged => 2.0,
            ConvergenceStatus::FailedToConverge => 3.0,
            ConvergenceStatus::Diverged => 4.0,
        }
    }

    /// Find similar error patterns using feature similarity
    fn find_similar_patterns(&self, features: &[f64]) -> Vec<&ErrorPattern> {
        let mut similarities: Vec<(&ErrorPattern, f64)> = self
            .error_patterns
            .values()
            .map(|pattern| {
                let similarity = self.compute_feature_similarity(features, &pattern.data_features);
                (pattern, similarity)
            })
            .collect();

        similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        similarities.into_iter()
            .take(5) // Top 5 similar patterns
            .filter(|(_, sim)| *sim > 0.7) // Similarity threshold
            .map(|(pattern, _)| pattern)
            .collect()
    }

    /// Compute cosine similarity between feature vectors
    fn compute_feature_similarity(&self, features1: &[f64], features2: &[f64]) -> f64 {
        if features1.len() != features2.len() {
            return 0.0;
        }

        let dot_product: f64 = features1
            .iter()
            .zip(features2.iter())
            .map(|(a, b)| a * b)
            .sum();
        let norm1: f64 = features1.iter().map(|x| x * x).sum::<f64>().sqrt();
        let norm2: f64 = features2.iter().map(|x| x * x).sum::<f64>().sqrt();

        if norm1 == 0.0 || norm2 == 0.0 {
            0.0
        } else {
            dot_product / (norm1 * norm2)
        }
    }

    /// Generate suggestions based on similar error patterns
    fn generate_patternbased_suggestions(
        &self,
        similar_patterns: &[&ErrorPattern],
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut strategies = Vec::new();

        for pattern in similar_patterns {
            for action in &pattern.successful_actions {
                let success_rate = self
                    .recovery_success_rates
                    .get(&format!("{:?}", action))
                    .copied()
                    .unwrap_or(0.5);

                let suggestion = RecoverySuggestion {
                    suggestion_type: self.action_to_suggestion_type(action),
                    description: self.generate_action_description(action, error),
                    action: action.clone(),
                    expected_outcome: self.generate_expected_outcome(action),
                    confidence: success_rate,
                    prerequisites: self.generate_prerequisites(action),
                };

                let strategy = IntelligentRecoveryStrategy {
                    suggestion,
                    success_probability: success_rate * (pattern.frequency as f64 / 100.0).min(1.0),
                    estimated_time: pattern.avg_resolution_time,
                    resource_requirements: self.estimate_resource_requirements(action),
                    risk_level: self.assess_risk_level(action),
                };

                strategies.push(strategy);
            }
        }

        Ok(strategies)
    }

    /// Generate heuristic-based suggestions
    fn generate_heuristic_suggestions(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut strategies = Vec::new();

        match &error.error {
            StatsError::DimensionMismatch(_) => {
                strategies.push(self.create_dimension_fix_strategy(error)?);
            }
            StatsError::ComputationError(_) => {
                strategies.extend(self.create_computation_fix_strategies(error)?);
            }
            StatsError::ConvergenceError(_) => {
                strategies.extend(self.create_convergence_fix_strategies(error)?);
            }
            StatsError::InsufficientData(_) => {
                strategies.push(self.createdata_augmentation_strategy(error)?);
            }
            _ => {
                strategies.push(self.create_generic_strategy(error)?);
            }
        }

        Ok(strategies)
    }

    /// Generate ML-powered suggestions
    fn generate_ml_suggestions(
        &self,
        features: &[f64],
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        // Placeholder for actual ML model
        // In a real implementation, this would use a trained model
        let mut strategies = Vec::new();

        // Simple rule-based ML simulation
        if let Some(ref size_info) = error.context.data_characteristics.size_info {
            if size_info.memory_usage_mb > 1000.0 {
                strategies.push(self.create_memory_optimization_strategy(error)?);
            }
        }

        if error.context.system_info.parallel_available {
            strategies.push(self.create_parallelization_strategy(error)?);
        }

        Ok(strategies)
    }

    /// Create dimension mismatch fix strategy
    fn create_dimension_fix_strategy(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        let suggestion = RecoverySuggestion {
            suggestion_type: SuggestionType::InputValidation,
            description: "Check and reshape input arrays to match expected dimensions".to_string(),
            action: RecoveryAction::ValidateInputs {
                validation_checks: vec![],
            },
            expected_outcome: "Arrays will have compatible dimensions for computation".to_string(),
            confidence: 0.9,
            prerequisites: vec!["Access to input data".to_string()],
        };

        Ok(IntelligentRecoveryStrategy {
            suggestion,
            success_probability: 0.9,
            estimated_time: 0.1, // Very fast fix
            resource_requirements: ResourceRequirements {
                memory_mb: 1.0,
                cpu_cores: 1,
                wall_time_seconds: 0.1,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Create computation error fix strategies
    fn create_computation_fix_strategies(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut strategies = Vec::new();

        // Numerical stability strategy
        let numerical_strategy = IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::ParameterAdjustment,
                description: "Increase numerical precision and add regularization".to_string(),
                action: RecoveryAction::AdjustTolerance {
                    new_tolerance: 1e-12,
                },
                expected_outcome: "Improved numerical stability and convergence".to_string(),
                confidence: 0.75,
                prerequisites: vec!["Iterative algorithm".to_string()],
            },
            success_probability: 0.75,
            estimated_time: 1.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 10.0,
                cpu_cores: 1,
                wall_time_seconds: 1.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        };
        strategies.push(numerical_strategy);

        // Algorithm switch strategy
        let algorithm_strategy = IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::AlgorithmChange,
                description: "Switch to a more robust numerical algorithm".to_string(),
                action: RecoveryAction::SwitchAlgorithm {
                    new_algorithm: "robust_svd".to_string(),
                },
                expected_outcome: "More stable computation with better _error handling".to_string(),
                confidence: 0.8,
                prerequisites: vec!["Alternative algorithm available".to_string()],
            },
            success_probability: 0.8,
            estimated_time: 2.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 50.0,
                cpu_cores: 1,
                wall_time_seconds: 2.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Medium,
        };
        strategies.push(algorithm_strategy);

        Ok(strategies)
    }

    /// Create convergence fix strategies
    fn create_convergence_fix_strategies(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut strategies = Vec::new();

        // Increase iterations strategy
        strategies.push(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::ParameterAdjustment,
                description: "Increase maximum iterations and adjust convergence criteria"
                    .to_string(),
                action: RecoveryAction::IncreaseIterations { factor: 2.0 },
                expected_outcome: "Algorithm will have more time to converge".to_string(),
                confidence: 0.7,
                prerequisites: vec!["Iterative algorithm".to_string()],
            },
            success_probability: 0.7,
            estimated_time: 10.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 20.0,
                cpu_cores: 1,
                wall_time_seconds: 10.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        });

        Ok(strategies)
    }

    /// Create data augmentation strategy
    fn createdata_augmentation_strategy(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::DataPreprocessing,
                description: "Apply data augmentation or use regularization techniques".to_string(),
                action: RecoveryAction::SimplePreprocessData,
                expected_outcome: "Sufficient data for reliable statistical analysis".to_string(),
                confidence: 0.6,
                prerequisites: vec![
                    "Access to additional data or regularization methods".to_string()
                ],
            },
            success_probability: 0.6,
            estimated_time: 5.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 100.0,
                cpu_cores: 1,
                wall_time_seconds: 5.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Medium,
        })
    }

    /// Create memory optimization strategy
    fn create_memory_optimization_strategy(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::ResourceIncrease,
                description: "Use memory-efficient algorithms and chunked processing".to_string(),
                action: RecoveryAction::UseChunkedProcessing { chunksize: 1000 },
                expected_outcome: "Reduced memory usage while maintaining accuracy".to_string(),
                confidence: 0.85,
                prerequisites: vec!["Large dataset".to_string()],
            },
            success_probability: 0.85,
            estimated_time: 20.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 500.0, // Reduced from original
                cpu_cores: 1,
                wall_time_seconds: 20.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Create parallelization strategy
    fn create_parallelization_strategy(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        let cores = error.context.system_info.cpu_cores.unwrap_or(1);

        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::ResourceIncrease,
                description: format!("Enable parallel processing using {} CPU cores", cores),
                action: RecoveryAction::EnableParallelProcessing { num_threads: cores },
                expected_outcome: "Faster computation with improved scalability".to_string(),
                confidence: 0.8,
                prerequisites: vec!["Multi-core system".to_string()],
            },
            success_probability: 0.8,
            estimated_time: 2.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 50.0,
                cpu_cores: cores,
                wall_time_seconds: 2.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Create generic recovery strategy
    fn create_generic_strategy(
        &self,
        error: &EnhancedStatsError,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::InputValidation,
                description: "Review input parameters and data quality".to_string(),
                action: RecoveryAction::SimpleValidateInputs,
                expected_outcome: "Identification and correction of input issues".to_string(),
                confidence: 0.5,
                prerequisites: vec!["Access to input validation tools".to_string()],
            },
            success_probability: 0.5,
            estimated_time: 1.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 5.0,
                cpu_cores: 1,
                wall_time_seconds: 1.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Convert recovery action to suggestion type
    fn action_to_suggestion_type(&self, action: &RecoveryAction) -> SuggestionType {
        match action {
            RecoveryAction::AdjustTolerance { .. } => SuggestionType::ParameterAdjustment,
            RecoveryAction::IncreaseIterations { .. } => SuggestionType::ParameterAdjustment,
            RecoveryAction::SwitchAlgorithm { .. } => SuggestionType::AlgorithmChange,
            RecoveryAction::SimplePreprocessData => SuggestionType::DataPreprocessing,
            RecoveryAction::SimpleValidateInputs => SuggestionType::InputValidation,
            RecoveryAction::UseChunkedProcessing { .. } => SuggestionType::ResourceIncrease,
            RecoveryAction::EnableParallelProcessing { .. } => SuggestionType::ResourceIncrease,
            RecoveryAction::ApplyRegularization { .. } => SuggestionType::ParameterAdjustment,
            RecoveryAction::UseApproximation { .. } => SuggestionType::Approximation,
            _ => SuggestionType::InputValidation,
        }
    }

    /// Generate action description
    fn generate_action_description(
        &self,
        action: &RecoveryAction,
        error: &EnhancedStatsError,
    ) -> String {
        match action {
            RecoveryAction::AdjustTolerance { new_tolerance } => {
                format!("Adjust convergence tolerance to {}", new_tolerance)
            }
            RecoveryAction::IncreaseIterations { factor } => {
                format!("Increase maximum iterations by factor of {}", factor)
            }
            RecoveryAction::SwitchAlgorithm { new_algorithm } => {
                format!("Switch to {} algorithm", new_algorithm)
            }
            _ => "Apply recovery action".to_string(),
        }
    }

    /// Generate expected outcome
    fn generate_expected_outcome(&self, action: &RecoveryAction) -> String {
        match action {
            RecoveryAction::AdjustTolerance { .. } => "Improved numerical stability".to_string(),
            RecoveryAction::IncreaseIterations { .. } => "Better convergence".to_string(),
            RecoveryAction::SwitchAlgorithm { .. } => "More robust computation".to_string(),
            _ => "Resolved error condition".to_string(),
        }
    }

    /// Generate prerequisites
    fn generate_prerequisites(&self, action: &RecoveryAction) -> Vec<String> {
        match action {
            RecoveryAction::SwitchAlgorithm { .. } => {
                vec!["Alternative algorithm available".to_string()]
            }
            RecoveryAction::EnableParallelProcessing { .. } => {
                vec!["Multi-core system".to_string()]
            }
            _ => vec![],
        }
    }

    /// Estimate resource requirements
    fn estimate_resource_requirements(&self, action: &RecoveryAction) -> ResourceRequirements {
        match action {
            RecoveryAction::IncreaseIterations { factor } => ResourceRequirements {
                memory_mb: 10.0,
                cpu_cores: 1,
                wall_time_seconds: 5.0 * factor,
                requires_gpu: false,
            },
            RecoveryAction::EnableParallelProcessing { num_threads } => ResourceRequirements {
                memory_mb: 50.0,
                cpu_cores: *num_threads,
                wall_time_seconds: 2.0,
                requires_gpu: false,
            },
            _ => ResourceRequirements {
                memory_mb: 5.0,
                cpu_cores: 1,
                wall_time_seconds: 1.0,
                requires_gpu: false,
            },
        }
    }

    /// Assess risk level
    fn assess_risk_level(&self, action: &RecoveryAction) -> RiskLevel {
        match action {
            RecoveryAction::SwitchAlgorithm { .. } => RiskLevel::Medium,
            RecoveryAction::IncreaseIterations { .. } => RiskLevel::Low,
            _ => RiskLevel::Low,
        }
    }

    /// Record successful recovery for learning
    pub fn record_successful_recovery(&mut self, action: &RecoveryAction) {
        let key = format!("{:?}", action);
        let current_rate = self
            .recovery_success_rates
            .get(&key)
            .copied()
            .unwrap_or(0.5);
        // Simple exponential moving average update
        let new_rate = 0.9 * current_rate + 0.1 * 1.0;
        self.recovery_success_rates.insert(key, new_rate);
    }

    /// Record failed recovery for learning
    pub fn record_failed_recovery(&mut self, action: &RecoveryAction) {
        let key = format!("{:?}", action);
        let current_rate = self
            .recovery_success_rates
            .get(&key)
            .copied()
            .unwrap_or(0.5);
        // Simple exponential moving average update
        let new_rate = 0.9 * current_rate + 0.1 * 0.0;
        self.recovery_success_rates.insert(key, new_rate);
    }
}

/// Convenience function to create enhanced error recovery system
#[allow(dead_code)]
pub fn create_intelligent_recovery() -> IntelligentErrorRecovery {
    IntelligentErrorRecovery::new(RecoveryConfig::default())
}

/// Convenience function to analyze error and get recovery suggestions
#[allow(dead_code)]
pub fn get_intelligent_suggestions(
    error: &EnhancedStatsError,
) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
    let mut recovery = create_intelligent_recovery();
    recovery.analyze_and_suggest(error)
}

/// Advanced neural network-based error pattern recognition
pub struct NeuralErrorClassifier {
    /// Neural network weights (simplified representation)
    weights: Vec<Vec<f64>>,
    /// Bias terms
    biases: Vec<f64>,
    /// Training data for continuous learning
    trainingdata: Vec<(Vec<f64>, usize)>, // (features, class)
    /// Learning rate for online learning
    learning_rate: f64,
}

impl Default for NeuralErrorClassifier {
    fn default() -> Self {
        Self::new()
    }
}

impl NeuralErrorClassifier {
    /// Create new neural classifier
    pub fn new() -> Self {
        // Initialize with small random weights
        let mut rng = scirs2_core::random::thread_rng();
        let mut weights = Vec::new();
        let inputsize = 12; // Number of features
        let hiddensize = 8;
        let outputsize = 5; // Number of error classes

        // Input to hidden layer
        let mut layer1 = Vec::new();
        for _ in 0..hiddensize {
            let mut neuron_weights = Vec::new();
            for _ in 0..inputsize {
                neuron_weights.push((rng.random::<f64>() - 0.5) * 0.1);
            }
            layer1.push(neuron_weights);
        }
        weights.push(layer1.into_iter().flatten().collect());

        // Hidden to output layer
        let mut layer2 = Vec::new();
        for _ in 0..outputsize {
            let mut neuron_weights = Vec::new();
            for _ in 0..hiddensize {
                neuron_weights.push((rng.random::<f64>() - 0.5) * 0.1);
            }
            layer2.push(neuron_weights);
        }
        weights.push(layer2.into_iter().flatten().collect());

        Self {
            weights,
            biases: vec![0.0; hiddensize + outputsize],
            trainingdata: Vec::new(),
            learning_rate: 0.01,
        }
    }

    /// Classify error pattern and predict best recovery strategy
    pub fn classify_error_pattern(&self, features: &[f64]) -> (usize, f64) {
        let output = self.forward_pass(features);
        let (best_class, confidence) = output
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
            .map(|(i, &conf)| (i, conf))
            .unwrap_or((0, 0.0));

        (best_class, confidence)
    }

    /// Forward pass through the neural network
    fn forward_pass(&self, features: &[f64]) -> Vec<f64> {
        let hiddensize = 8;
        let outputsize = 5;

        // Input to hidden layer
        let mut hidden = vec![0.0; hiddensize];
        for i in 0..hiddensize {
            let mut sum = self.biases[i];
            for j in 0..features.len().min(12) {
                sum += self.weights[0][i * 12 + j] * features[j];
            }
            hidden[i] = Self::relu(sum);
        }

        // Hidden to output layer
        let mut output = vec![0.0; outputsize];
        for i in 0..outputsize {
            let mut sum = self.biases[hiddensize + i];
            for j in 0..hiddensize {
                if self.weights.len() > 1 && i * hiddensize + j < self.weights[1].len() {
                    sum += self.weights[1][i * hiddensize + j] * hidden[j];
                }
            }
            output[i] = Self::sigmoid(sum);
        }

        output
    }

    /// ReLU activation function
    fn relu(x: f64) -> f64 {
        x.max(0.0)
    }

    /// Sigmoid activation function
    fn sigmoid(x: f64) -> f64 {
        1.0 / (1.0 + (-x).exp())
    }

    /// Online learning update
    pub fn update_from_feedback(&mut self, features: &[f64], correctclass: usize, success: bool) {
        self.trainingdata.push((features.to_vec(), correctclass));

        if success {
            // Positive reinforcement - strengthen this prediction
            self.reinforce_prediction(features, correctclass, 1.0);
        } else {
            // Negative reinforcement - weaken this prediction
            self.reinforce_prediction(features, correctclass, -0.5);
        }

        // Trim training data to prevent memory overflow
        if self.trainingdata.len() > 1000 {
            self.trainingdata.drain(0..500);
        }
    }

    /// Reinforce or weaken prediction
    fn reinforce_prediction(&mut self, features: &[f64], targetclass: usize, strength: f64) {
        let prediction = self.forward_pass(features);
        let error = strength
            * (if targetclass < prediction.len() {
                1.0 - prediction[targetclass]
            } else {
                1.0
            });

        // Simple gradient-like update (simplified backpropagation)
        let update_magnitude = self.learning_rate * error;

        // Update biases
        if targetclass < self.biases.len() {
            self.biases[targetclass] += update_magnitude;
        }

        // Update some weights (simplified)
        for weight_layer in &mut self.weights {
            for weight in weight_layer.iter_mut().take(10) {
                *weight += update_magnitude * 0.1;
            }
        }
    }
}

/// Enhanced machine learning error recovery system
pub struct MLEnhancedErrorRecovery {
    /// Base intelligent recovery system
    base_recovery: IntelligentErrorRecovery,
    /// Neural classifier for pattern recognition
    neural_classifier: NeuralErrorClassifier,
    /// Ensemble of recovery strategies
    strategy_ensemble: RecoveryStrategyEnsemble,
    /// Adaptive learning configuration
    ml_config: MLRecoveryConfig,
}

/// Configuration for ML-enhanced recovery
#[derive(Debug, Clone)]
pub struct MLRecoveryConfig {
    /// Enable neural network predictions
    pub use_neural_classifier: bool,
    /// Enable ensemble methods
    pub use_strategy_ensemble: bool,
    /// Enable adaptive learning
    pub enable_online_learning: bool,
    /// Minimum confidence for ML suggestions
    pub ml_confidence_threshold: f64,
    /// Weight for ML vs heuristic suggestions
    pub ml_weight: f64,
}

impl Default for MLRecoveryConfig {
    fn default() -> Self {
        Self {
            use_neural_classifier: true,
            use_strategy_ensemble: true,
            enable_online_learning: true,
            ml_confidence_threshold: 0.7,
            ml_weight: 0.6,
        }
    }
}

/// Ensemble of recovery strategies with voting
pub struct RecoveryStrategyEnsemble {
    /// Individual strategy generators
    strategy_generators: Vec<Box<dyn StrategyGenerator>>,
    /// Voting weights for each generator
    generator_weights: Vec<f64>,
    /// Performance history for adaptive weighting
    performance_history: HashMap<String, Vec<bool>>,
}

/// Trait for strategy generation
pub trait StrategyGenerator {
    fn generate_strategies(
        &self,
        error: &EnhancedStatsError,
        features: &[f64],
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>>;
    fn name(&self) -> &str;
}

/// Similarity-based strategy generator
pub struct SimilarityBasedGenerator {
    historical_patterns: Vec<(Vec<f64>, IntelligentRecoveryStrategy)>,
}

impl StrategyGenerator for SimilarityBasedGenerator {
    fn generate_strategies(
        &self,
        error: &EnhancedStatsError,
        features: &[f64],
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut strategies = Vec::new();

        // Find most similar historical patterns
        let mut similarities: Vec<(f64, &IntelligentRecoveryStrategy)> = self
            .historical_patterns
            .iter()
            .map(|(hist_features, strategy)| {
                let similarity = self.compute_similarity(features, hist_features);
                (similarity, strategy)
            })
            .collect();

        similarities.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap());

        // Return top 3 most similar strategies
        for (similarity, strategy) in similarities.into_iter().take(3) {
            if similarity > 0.6 {
                let mut adjusted_strategy = strategy.clone();
                adjusted_strategy.success_probability *= similarity;
                strategies.push(adjusted_strategy);
            }
        }

        Ok(strategies)
    }

    fn name(&self) -> &str {
        "SimilarityBased"
    }
}

impl Default for SimilarityBasedGenerator {
    fn default() -> Self {
        Self::new()
    }
}

impl SimilarityBasedGenerator {
    pub fn new() -> Self {
        Self {
            historical_patterns: Vec::new(),
        }
    }

    pub fn add_pattern(&mut self, features: Vec<f64>, strategy: IntelligentRecoveryStrategy) {
        self.historical_patterns.push((features, strategy));

        // Limit size to prevent memory issues
        if self.historical_patterns.len() > 500 {
            self.historical_patterns.drain(0..100);
        }
    }

    fn compute_similarity(&self, features1: &[f64], features2: &[f64]) -> f64 {
        if features1.len() != features2.len() {
            return 0.0;
        }

        let dot_product: f64 = features1
            .iter()
            .zip(features2.iter())
            .map(|(a, b)| a * b)
            .sum();
        let norm1: f64 = features1.iter().map(|x| x * x).sum::<f64>().sqrt();
        let norm2: f64 = features2.iter().map(|x| x * x).sum::<f64>().sqrt();

        if norm1 == 0.0 || norm2 == 0.0 {
            0.0
        } else {
            dot_product / (norm1 * norm2)
        }
    }
}

/// Clustering-based strategy generator
pub struct ClusteringBasedGenerator {
    error_clusters: Vec<ErrorCluster>,
}

#[derive(Debug, Clone)]
pub struct ErrorCluster {
    center: Vec<f64>,
    strategies: Vec<IntelligentRecoveryStrategy>,
    radius: f64,
}

impl StrategyGenerator for ClusteringBasedGenerator {
    fn generate_strategies(
        &self,
        error: &EnhancedStatsError,
        features: &[f64],
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut strategies = Vec::new();

        // Find closest cluster
        for cluster in &self.error_clusters {
            let distance = self.euclidean_distance(features, &cluster.center);
            if distance <= cluster.radius {
                // Add strategies from this cluster
                for strategy in &cluster.strategies {
                    let mut adjusted_strategy = strategy.clone();
                    // Adjust confidence based on distance to cluster center
                    let proximity_factor = 1.0 - (distance / cluster.radius);
                    adjusted_strategy.success_probability *= proximity_factor;
                    strategies.push(adjusted_strategy);
                }
            }
        }

        Ok(strategies)
    }

    fn name(&self) -> &str {
        "ClusteringBased"
    }
}

impl Default for ClusteringBasedGenerator {
    fn default() -> Self {
        Self::new()
    }
}

impl ClusteringBasedGenerator {
    pub fn new() -> Self {
        Self {
            error_clusters: Vec::new(),
        }
    }

    fn euclidean_distance(&self, features1: &[f64], features2: &[f64]) -> f64 {
        features1
            .iter()
            .zip(features2.iter())
            .map(|(a, b)| (a - b).powi(2))
            .sum::<f64>()
            .sqrt()
    }
}

impl Default for RecoveryStrategyEnsemble {
    fn default() -> Self {
        Self::new()
    }
}

impl RecoveryStrategyEnsemble {
    pub fn new() -> Self {
        let mut ensemble = Self {
            strategy_generators: Vec::new(),
            generator_weights: Vec::new(),
            performance_history: HashMap::new(),
        };

        // Add default generators
        ensemble.add_generator(Box::new(SimilarityBasedGenerator::new()), 1.0);
        ensemble.add_generator(Box::new(ClusteringBasedGenerator::new()), 1.0);

        ensemble
    }

    pub fn add_generator(&mut self, generator: Box<dyn StrategyGenerator>, initialweight: f64) {
        self.strategy_generators.push(generator);
        self.generator_weights.push(initialweight);
    }

    /// Generate ensemble strategies with voting
    pub fn generate_ensemble_strategies(
        &self,
        error: &EnhancedStatsError,
        features: &[f64],
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut all_strategies = Vec::new();

        // Collect strategies from all generators
        for (i, generator) in self.strategy_generators.iter().enumerate() {
            if let Ok(mut strategies) = generator.generate_strategies(error, features) {
                let weight = self.generator_weights[i];

                // Apply generator weight to strategy confidence
                for strategy in &mut strategies {
                    strategy.success_probability *= weight;
                }

                all_strategies.extend(strategies);
            }
        }

        // Merge similar strategies and rank by weighted confidence
        let merged_strategies = self.merge_similar_strategies(all_strategies);

        Ok(merged_strategies)
    }

    /// Merge similar strategies to avoid redundancy
    fn merge_similar_strategies(
        &self,
        strategies: Vec<IntelligentRecoveryStrategy>,
    ) -> Vec<IntelligentRecoveryStrategy> {
        let mut merged = Vec::new();

        for strategy in strategies {
            let mut found_similar = false;

            for existing in &mut merged {
                if self.strategies_similar(&strategy, existing) {
                    // Merge strategies by averaging probabilities
                    existing.success_probability =
                        (existing.success_probability + strategy.success_probability) / 2.0;
                    found_similar = true;
                    break;
                }
            }

            if !found_similar {
                merged.push(strategy);
            }
        }

        // Sort by success probability
        merged.sort_by(|a, b| {
            b.success_probability
                .partial_cmp(&a.success_probability)
                .unwrap()
        });

        merged
    }

    /// Check if two strategies are similar
    fn strategies_similar(
        &self,
        strategy1: &IntelligentRecoveryStrategy,
        strategy2: &IntelligentRecoveryStrategy,
    ) -> bool {
        // Simple similarity check based on suggestion type and action
        strategy1.suggestion.suggestion_type == strategy2.suggestion.suggestion_type
            && std::mem::discriminant(&strategy1.suggestion.action)
                == std::mem::discriminant(&strategy2.suggestion.action)
    }

    /// Update generator weights based on performance feedback
    pub fn update_weights(&mut self, generatorname: &str, success: bool) {
        self.performance_history
            .entry(generatorname.to_string())
            .or_default()
            .push(success);

        // Update weights based on recent performance
        for (i, generator) in self.strategy_generators.iter().enumerate() {
            if generator.name() == generatorname {
                if let Some(history) = self.performance_history.get(generatorname) {
                    let recent_success_rate = history.iter()
                        .rev()
                        .take(20) // Last 20 outcomes
                        .map(|&s| if s { 1.0 } else { 0.0 })
                        .sum::<f64>()
                        / history.len().min(20) as f64;

                    // Adaptive weight update
                    self.generator_weights[i] = 0.5 + recent_success_rate;
                }
            }
        }
    }
}

impl MLEnhancedErrorRecovery {
    /// Create new ML-enhanced error recovery system
    pub fn new(config: MLRecoveryConfig) -> Self {
        Self {
            base_recovery: IntelligentErrorRecovery::new(RecoveryConfig::default()),
            neural_classifier: NeuralErrorClassifier::new(),
            strategy_ensemble: RecoveryStrategyEnsemble::new(),
            ml_config: config,
        }
    }

    /// Analyze error with ML enhancement
    pub fn analyze_with_ml(
        &mut self,
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut all_strategies = Vec::new();

        // Get base strategies
        let base_strategies = self.base_recovery.analyze_and_suggest(error)?;

        // Extract features for ML
        let features = self.extract_enhanced_features(error)?;

        // Neural network predictions
        if self.ml_config.use_neural_classifier {
            let (predicted_class, confidence) =
                self.neural_classifier.classify_error_pattern(&features);

            if confidence >= self.ml_config.ml_confidence_threshold {
                let ml_strategies =
                    self.generate_neural_strategies(predicted_class, confidence, error)?;
                all_strategies.extend(ml_strategies);
            }
        }

        // Ensemble strategies
        if self.ml_config.use_strategy_ensemble {
            let ensemble_strategies = self
                .strategy_ensemble
                .generate_ensemble_strategies(error, &features)?;
            all_strategies.extend(ensemble_strategies);
        }

        // Combine base and ML strategies with weighting
        let combined_strategies = self.combine_strategies(base_strategies, all_strategies);

        Ok(combined_strategies)
    }

    /// Extract enhanced features for ML
    fn extract_enhanced_features(&self, error: &EnhancedStatsError) -> StatsResult<Vec<f64>> {
        let mut features = Vec::new();

        // Add base features
        let base_features = self.base_recovery.extract_error_features(error)?;
        features.extend(base_features);

        // Add time-based features
        features.push(
            std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap()
                .as_secs() as f64
                % 86400.0,
        ); // Time of day

        // Add error frequency features (if available)
        features.push(1.0); // Placeholder for error frequency

        // Add system load features (simplified)
        features.push(0.5); // Placeholder for CPU load
        features.push(0.3); // Placeholder for memory usage

        Ok(features)
    }

    /// Generate strategies based on neural network predictions
    fn generate_neural_strategies(
        &self,
        predicted_class: usize,
        confidence: f64,
        error: &EnhancedStatsError,
    ) -> StatsResult<Vec<IntelligentRecoveryStrategy>> {
        let mut strategies = Vec::new();

        // Map neural network classes to recovery strategies
        let strategy = match predicted_class {
            0 => self.createdata_preprocessing_strategy(error, confidence)?,
            1 => self.create_algorithm_optimization_strategy(error, confidence)?,
            2 => self.create_numerical_stability_strategy(error, confidence)?,
            3 => self.create_resource_scaling_strategy(error, confidence)?,
            4 => self.create_approximation_strategy(error, confidence)?,
            _ => self.create_adaptive_strategy(error, confidence)?,
        };

        strategies.push(strategy);
        Ok(strategies)
    }

    /// Create data preprocessing strategy
    fn createdata_preprocessing_strategy(
        &self,
        error: &EnhancedStatsError,
        confidence: f64,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::DataPreprocessing,
                description: "Apply ML-suggested data preprocessing pipeline".to_string(),
                action: RecoveryAction::SimplePreprocessData,
                expected_outcome: "Improved data quality and computational stability".to_string(),
                confidence,
                prerequisites: vec!["Raw data access".to_string()],
            },
            success_probability: confidence,
            estimated_time: 3.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 100.0,
                cpu_cores: 2,
                wall_time_seconds: 3.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Create algorithm optimization strategy
    fn create_algorithm_optimization_strategy(
        &self,
        error: &EnhancedStatsError,
        confidence: f64,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::AlgorithmChange,
                description: "Switch to ML-optimized algorithm variant".to_string(),
                action: RecoveryAction::SwitchAlgorithm {
                    new_algorithm: "ml_optimized".to_string(),
                },
                expected_outcome: "Better performance and numerical stability".to_string(),
                confidence,
                prerequisites: vec!["Alternative algorithm implementation".to_string()],
            },
            success_probability: confidence,
            estimated_time: 2.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 50.0,
                cpu_cores: 1,
                wall_time_seconds: 2.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Medium,
        })
    }

    /// Create numerical stability strategy
    fn create_numerical_stability_strategy(
        &self,
        error: &EnhancedStatsError,
        confidence: f64,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::ParameterAdjustment,
                description: "Apply ML-tuned numerical stability parameters".to_string(),
                action: RecoveryAction::AdjustTolerance {
                    new_tolerance: 1e-10,
                },
                expected_outcome: "Enhanced numerical precision and stability".to_string(),
                confidence,
                prerequisites: vec!["Iterative computation".to_string()],
            },
            success_probability: confidence,
            estimated_time: 1.5,
            resource_requirements: ResourceRequirements {
                memory_mb: 20.0,
                cpu_cores: 1,
                wall_time_seconds: 1.5,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Create resource scaling strategy
    fn create_resource_scaling_strategy(
        &self,
        error: &EnhancedStatsError,
        confidence: f64,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        let cores = error.context.system_info.cpu_cores.unwrap_or(1);

        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::ResourceIncrease,
                description: "Apply ML-optimized resource scaling".to_string(),
                action: RecoveryAction::EnableParallelProcessing { num_threads: cores },
                expected_outcome: "Optimal resource utilization and performance".to_string(),
                confidence,
                prerequisites: vec!["Multi-core system".to_string()],
            },
            success_probability: confidence,
            estimated_time: 1.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 75.0,
                cpu_cores: cores,
                wall_time_seconds: 1.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Create approximation strategy
    fn create_approximation_strategy(
        &self,
        error: &EnhancedStatsError,
        confidence: f64,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::Approximation,
                description: "Use ML-guided approximation methods".to_string(),
                action: RecoveryAction::UseApproximation {
                    approximation_method: "neural_approximation".to_string(),
                },
                expected_outcome: "Fast approximate solution with controlled _error".to_string(),
                confidence,
                prerequisites: vec!["Approximation tolerance defined".to_string()],
            },
            success_probability: confidence,
            estimated_time: 0.5,
            resource_requirements: ResourceRequirements {
                memory_mb: 30.0,
                cpu_cores: 1,
                wall_time_seconds: 0.5,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Medium,
        })
    }

    /// Create adaptive strategy
    fn create_adaptive_strategy(
        &self,
        error: &EnhancedStatsError,
        confidence: f64,
    ) -> StatsResult<IntelligentRecoveryStrategy> {
        Ok(IntelligentRecoveryStrategy {
            suggestion: RecoverySuggestion {
                suggestion_type: SuggestionType::InputValidation,
                description: "Apply adaptive ML-learned recovery approach".to_string(),
                action: RecoveryAction::SimpleValidateInputs,
                expected_outcome: "Context-aware _error resolution".to_string(),
                confidence,
                prerequisites: vec!["Input data available".to_string()],
            },
            success_probability: confidence,
            estimated_time: 2.0,
            resource_requirements: ResourceRequirements {
                memory_mb: 40.0,
                cpu_cores: 1,
                wall_time_seconds: 2.0,
                requires_gpu: false,
            },
            risk_level: RiskLevel::Low,
        })
    }

    /// Combine base and ML strategies with intelligent weighting
    fn combine_strategies(
        &self,
        base_strategies: Vec<IntelligentRecoveryStrategy>,
        ml_strategies: Vec<IntelligentRecoveryStrategy>,
    ) -> Vec<IntelligentRecoveryStrategy> {
        let mut combined = Vec::new();

        // Weight base _strategies
        for mut strategy in base_strategies {
            strategy.success_probability *= 1.0 - self.ml_config.ml_weight;
            combined.push(strategy);
        }

        // Weight ML _strategies
        for mut strategy in ml_strategies {
            strategy.success_probability *= self.ml_config.ml_weight;
            combined.push(strategy);
        }

        // Sort by success probability
        combined.sort_by(|a, b| {
            b.success_probability
                .partial_cmp(&a.success_probability)
                .unwrap()
        });

        // Remove duplicates and limit results
        combined.truncate(8);
        combined
    }

    /// Provide feedback for online learning
    pub fn provide_feedback(
        &mut self,
        error: &EnhancedStatsError,
        strategy_used: &IntelligentRecoveryStrategy,
        success: bool,
    ) -> StatsResult<()> {
        // Update neural classifier
        if self.ml_config.enable_online_learning {
            let features = self.extract_enhanced_features(error)?;
            let strategy_class = self.strategy_to_class(strategy_used);
            self.neural_classifier
                .update_from_feedback(&features, strategy_class, success);
        }

        // Update ensemble weights
        let generator_name = "ensemble"; // Simplified
        self.strategy_ensemble
            .update_weights(generator_name, success);

        // Update base recovery system
        if success {
            self.base_recovery
                .record_successful_recovery(&strategy_used.suggestion.action);
        } else {
            self.base_recovery
                .record_failed_recovery(&strategy_used.suggestion.action);
        }

        Ok(())
    }

    /// Map strategy to neural network class
    fn strategy_to_class(&self, strategy: &IntelligentRecoveryStrategy) -> usize {
        match strategy.suggestion.suggestion_type {
            SuggestionType::DataPreprocessing => 0,
            SuggestionType::AlgorithmChange => 1,
            SuggestionType::ParameterAdjustment => 2,
            SuggestionType::ResourceIncrease => 3,
            SuggestionType::Approximation => 4,
            _ => 5,
        }
    }
}

/// Advanced error recovery with ML capabilities
#[allow(dead_code)]
pub fn create_ml_enhanced_recovery() -> MLEnhancedErrorRecovery {
    MLEnhancedErrorRecovery::new(MLRecoveryConfig::default())
}
