use std::collections::{HashMap, BTreeMap, HashSet};
use std::sync::{Arc, RwLock, Mutex};
use std::time::{SystemTime, UNIX_EPOCH};
use serde::{Serialize, Deserialize};
use scirs2_core::ndarray::{Array1, Array2, array};
use scirs2_core::random::{Random, rng};
use scirs2_core::error::CoreError;

use super::data_types::*;
use super::neural_networks::NeuralEmbeddingModel;
use crate::api_reference_generator::TraitInfo;
use super::feature_extraction::TraitFeatureExtractor;
use super::ml_models::{TraitSimilarityModel, ClusteringModel, CollaborativeFilteringModel};
use super::usage_patterns::{UsagePatternAnalyzer, PatternBasedRecommender, UsagePatternConfig, UsageEvent};

/// Configuration for the main recommendation engine
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecommenderEngineConfig {
    pub ml_config: MLRecommendationConfig,
    pub neural_config: NeuralNetworkConfig,
    pub feature_config: FeatureExtractionConfig,
    pub pattern_config: UsagePatternConfig,
    pub ensemble_weights: EnsembleWeights,
    pub cache_settings: CacheSettings,
    pub performance_tuning: PerformanceTuning,
}

/// Weights for ensemble recommendation methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnsembleWeights {
    pub similarity_weight: f64,
    pub collaborative_weight: f64,
    pub neural_weight: f64,
    pub pattern_weight: f64,
    pub clustering_weight: f64,
    pub content_weight: f64,
}

impl Default for EnsembleWeights {
    fn default() -> Self {
        Self {
            similarity_weight: 0.25,
            collaborative_weight: 0.20,
            neural_weight: 0.20,
            pattern_weight: 0.15,
            clustering_weight: 0.10,
            content_weight: 0.10,
        }
    }
}

/// Cache settings for performance optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheSettings {
    pub enable_recommendation_cache: bool,
    pub enable_similarity_cache: bool,
    pub enable_feature_cache: bool,
    pub cache_ttl_seconds: u64,
    pub max_cache_size: usize,
    pub cache_cleanup_interval: u64,
}

impl Default for CacheSettings {
    fn default() -> Self {
        Self {
            enable_recommendation_cache: true,
            enable_similarity_cache: true,
            enable_feature_cache: true,
            cache_ttl_seconds: 3600, // 1 hour
            max_cache_size: 10000,
            cache_cleanup_interval: 600, // 10 minutes
        }
    }
}

/// Performance tuning settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTuning {
    pub enable_parallel_processing: bool,
    pub max_concurrent_recommendations: usize,
    pub batch_size: usize,
    pub enable_gpu_acceleration: bool,
    pub memory_optimization_level: MemoryOptimizationLevel,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemoryOptimizationLevel {
    Minimal,
    Balanced,
    Aggressive,
}

impl Default for PerformanceTuning {
    fn default() -> Self {
        Self {
            enable_parallel_processing: true,
            max_concurrent_recommendations: 10,
            batch_size: 32,
            enable_gpu_acceleration: false,
            memory_optimization_level: MemoryOptimizationLevel::Balanced,
        }
    }
}

/// Cached recommendation entry
#[derive(Debug, Clone)]
struct CachedRecommendation {
    recommendations: Vec<TraitRecommendation>,
    timestamp: u64,
    context_hash: u64,
}

/// Main ML-powered trait recommendation engine
pub struct MLRecommendationEngine {
    // Core components
    similarity_model: Arc<RwLock<TraitSimilarityModel>>,
    neural_model: Arc<RwLock<Option<NeuralEmbeddingModel>>>,
    feature_extractor: Arc<RwLock<TraitFeatureExtractor>>,
    pattern_analyzer: Arc<RwLock<UsagePatternAnalyzer>>,
    pattern_recommender: Arc<RwLock<PatternBasedRecommender>>,
    clustering_model: Arc<RwLock<Option<ClusteringModel>>>,
    collaborative_model: Arc<RwLock<Option<CollaborativeFilteringModel>>>,

    // Configuration and state
    config: RecommenderEngineConfig,
    trait_database: Arc<RwLock<TraitDatabase>>,
    recommendation_cache: Arc<RwLock<HashMap<String, CachedRecommendation>>>,
    similarity_cache: Arc<RwLock<HashMap<String, f64>>>,

    // Performance tracking
    recommendation_stats: Arc<RwLock<RecommendationStats>>,

    // Thread-safe counters
    request_counter: Arc<Mutex<u64>>,
    last_cache_cleanup: Arc<Mutex<u64>>,
}

/// Statistics tracking for the recommendation engine
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct RecommendationStats {
    pub total_requests: u64,
    pub cache_hits: u64,
    pub cache_misses: u64,
    pub average_response_time_ms: f64,
    pub successful_recommendations: u64,
    pub failed_recommendations: u64,
    pub method_usage: HashMap<String, u64>,
}

/// Trait database for storing and managing trait information
#[derive(Debug, Clone, Default)]
pub struct TraitDatabase {
    traits: HashMap<String, TraitInfo>,
    trait_relationships: HashMap<String, Vec<String>>,
    trait_categories: HashMap<String, String>,
    trait_embeddings: HashMap<String, Array1<f64>>,
    last_updated: u64,
}

impl TraitDatabase {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn add_trait(&mut self, trait_info: TraitInfo) {
        self.traits.insert(trait_info.name.clone(), trait_info);
        self.update_timestamp();
    }

    pub fn get_trait(&self, name: &str) -> Option<&TraitInfo> {
        self.traits.get(name)
    }

    pub fn get_all_traits(&self) -> Vec<&TraitInfo> {
        self.traits.values().collect()
    }

    pub fn add_relationship(&mut self, trait_a: &str, trait_b: &str) {
        self.trait_relationships
            .entry(trait_a.to_string())
            .or_insert_with(Vec::new)
            .push(trait_b.to_string());

        self.trait_relationships
            .entry(trait_b.to_string())
            .or_insert_with(Vec::new)
            .push(trait_a.to_string());

        self.update_timestamp();
    }

    pub fn get_related_traits(&self, trait_name: &str) -> Vec<String> {
        self.trait_relationships
            .get(trait_name)
            .cloned()
            .unwrap_or_default()
    }

    pub fn set_trait_embedding(&mut self, trait_name: &str, embedding: Array1<f64>) {
        self.trait_embeddings.insert(trait_name.to_string(), embedding);
        self.update_timestamp();
    }

    pub fn get_trait_embedding(&self, trait_name: &str) -> Option<&Array1<f64>> {
        self.trait_embeddings.get(trait_name)
    }

    fn update_timestamp(&mut self) {
        self.last_updated = SystemTime::now()
            .duration_since(UNIX_EPOCH)
            .expect("expected valid value")
            .as_secs();
    }
}

impl MLRecommendationEngine {
    /// Create a new ML recommendation engine
    pub fn new(config: RecommenderEngineConfig) -> Result<Self, CoreError> {
        let feature_extractor = TraitFeatureExtractor::new(config.feature_config.clone());
        let pattern_analyzer = UsagePatternAnalyzer::new(config.pattern_config.clone(), feature_extractor.clone());
        let pattern_recommender = PatternBasedRecommender::new(pattern_analyzer.clone(), config.pattern_config.clone());

        let similarity_model = TraitSimilarityModel::new(config.ml_config.clone());

        Ok(Self {
            similarity_model: Arc::new(RwLock::new(similarity_model)),
            neural_model: Arc::new(RwLock::new(None)),
            feature_extractor: Arc::new(RwLock::new(feature_extractor)),
            pattern_analyzer: Arc::new(RwLock::new(pattern_analyzer)),
            pattern_recommender: Arc::new(RwLock::new(pattern_recommender)),
            clustering_model: Arc::new(RwLock::new(None)),
            collaborative_model: Arc::new(RwLock::new(None)),
            config,
            trait_database: Arc::new(RwLock::new(TraitDatabase::new())),
            recommendation_cache: Arc::new(RwLock::new(HashMap::new())),
            similarity_cache: Arc::new(RwLock::new(HashMap::new())),
            recommendation_stats: Arc::new(RwLock::new(RecommendationStats::default())),
            request_counter: Arc::new(Mutex::new(0)),
            last_cache_cleanup: Arc::new(Mutex::new(0)),
        })
    }

    /// Initialize the recommendation engine with training data
    pub fn initialize(&mut self, training_data: &TrainingData) -> Result<(), CoreError> {
        // Initialize neural embedding model
        if self.config.ml_config.enable_neural_embeddings {
            let neural_model = NeuralEmbeddingModel::new(self.config.neural_config.clone())?;
            *self.neural_model.write().unwrap_or_else(|e| e.into_inner()) = Some(neural_model);
        }

        // Initialize clustering model
        if self.config.ml_config.enable_clustering {
            let clustering_model = ClusteringModel::new(Default::default())?;
            *self.clustering_model.write().unwrap_or_else(|e| e.into_inner()) = Some(clustering_model);
        }

        // Initialize collaborative filtering model
        if self.config.ml_config.enable_collaborative_filtering {
            let collaborative_model = CollaborativeFilteringModel::new(Default::default())?;
            *self.collaborative_model.write().unwrap_or_else(|e| e.into_inner()) = Some(collaborative_model);
        }

        // Train models with provided data
        self.train_models(training_data)?;

        Ok(())
    }

    /// Generate trait recommendations for a given context
    pub fn recommend(&self, context: &TraitContext, num_recommendations: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        let start_time = SystemTime::now();

        // Increment request counter
        {
            let mut counter = self.request_counter.lock().unwrap_or_else(|e| e.into_inner());
            *counter += 1;
        }

        // Check cache first
        if self.config.cache_settings.enable_recommendation_cache {
            if let Some(cached) = self.get_cached_recommendations(context)? {
                self.update_cache_hit_stats();
                return Ok(cached.recommendations.into_iter().take(num_recommendations).collect());
            }
        }

        self.update_cache_miss_stats();

        // Generate recommendations using ensemble approach
        let mut all_recommendations = Vec::new();

        // Method 1: Similarity-based recommendations
        if self.config.ensemble_weights.similarity_weight > 0.0 {
            let similarity_recs = self.get_similarity_recommendations(context, num_recommendations * 2)?;
            for mut rec in similarity_recs {
                rec.confidence *= self.config.ensemble_weights.similarity_weight;
                rec.reasoning = format!("Similarity-based: {}", rec.reasoning);
                all_recommendations.push(rec);
            }
        }

        // Method 2: Neural embedding recommendations
        if self.config.ensemble_weights.neural_weight > 0.0 && self.config.ml_config.enable_neural_embeddings {
            let neural_recs = self.get_neural_recommendations(context, num_recommendations * 2)?;
            for mut rec in neural_recs {
                rec.confidence *= self.config.ensemble_weights.neural_weight;
                rec.reasoning = format!("Neural embedding: {}", rec.reasoning);
                all_recommendations.push(rec);
            }
        }

        // Method 3: Collaborative filtering recommendations
        if self.config.ensemble_weights.collaborative_weight > 0.0 && self.config.ml_config.enable_collaborative_filtering {
            let collaborative_recs = self.get_collaborative_recommendations(context, num_recommendations * 2)?;
            for mut rec in collaborative_recs {
                rec.confidence *= self.config.ensemble_weights.collaborative_weight;
                rec.reasoning = format!("Collaborative filtering: {}", rec.reasoning);
                all_recommendations.push(rec);
            }
        }

        // Method 4: Pattern-based recommendations
        if self.config.ensemble_weights.pattern_weight > 0.0 {
            let pattern_recs = self.get_pattern_recommendations(context, num_recommendations * 2)?;
            for mut rec in pattern_recs {
                rec.confidence *= self.config.ensemble_weights.pattern_weight;
                rec.reasoning = format!("Usage patterns: {}", rec.reasoning);
                all_recommendations.push(rec);
            }
        }

        // Method 5: Clustering-based recommendations
        if self.config.ensemble_weights.clustering_weight > 0.0 && self.config.ml_config.enable_clustering {
            let clustering_recs = self.get_clustering_recommendations(context, num_recommendations * 2)?;
            for mut rec in clustering_recs {
                rec.confidence *= self.config.ensemble_weights.clustering_weight;
                rec.reasoning = format!("Clustering-based: {}", rec.reasoning);
                all_recommendations.push(rec);
            }
        }

        // Method 6: Content-based recommendations
        if self.config.ensemble_weights.content_weight > 0.0 {
            let content_recs = self.get_content_recommendations(context, num_recommendations * 2)?;
            for mut rec in content_recs {
                rec.confidence *= self.config.ensemble_weights.content_weight;
                rec.reasoning = format!("Content-based: {}", rec.reasoning);
                all_recommendations.push(rec);
            }
        }

        // Ensemble and rank recommendations
        let final_recommendations = self.ensemble_recommendations(all_recommendations, num_recommendations)?;

        // Cache the results
        if self.config.cache_settings.enable_recommendation_cache {
            self.cache_recommendations(context, &final_recommendations)?;
        }

        // Update statistics
        let elapsed = start_time.elapsed().unwrap_or_default().as_millis() as f64;
        self.update_recommendation_stats(elapsed, true);

        // Cleanup cache if needed
        self.cleanup_cache_if_needed()?;

        Ok(final_recommendations)
    }

    /// Record a usage event for pattern learning
    pub fn record_usage(&self, event: UsageEvent) -> Result<(), CoreError> {
        let mut pattern_analyzer = self.pattern_analyzer.write().unwrap_or_else(|e| e.into_inner());
        pattern_analyzer.record_usage(event)?;
        Ok(())
    }

    /// Get recommendation statistics
    pub fn get_stats(&self) -> RecommendationStats {
        self.recommendation_stats.read().unwrap_or_else(|e| e.into_inner()).clone()
    }

    /// Update the trait database
    pub fn update_trait_database(&self, trait_info: TraitInfo) -> Result<(), CoreError> {
        let mut db = self.trait_database.write().unwrap_or_else(|e| e.into_inner());
        db.add_trait(trait_info);
        Ok(())
    }

    /// Get similar traits using the similarity model
    pub fn get_similar_traits(&self, trait_name: &str, num_similar: usize) -> Result<Vec<(String, f64)>, CoreError> {
        let similarity_model = self.similarity_model.read().unwrap_or_else(|e| e.into_inner());
        similarity_model.find_similar_traits(trait_name, num_similar)
    }

    /// Train all models with new data
    pub fn train_models(&self, training_data: &TrainingData) -> Result<(), CoreError> {
        // Train similarity model
        {
            let mut similarity_model = self.similarity_model.write().unwrap_or_else(|e| e.into_inner());
            similarity_model.train(training_data)?;
        }

        // Train neural model if enabled
        if let Some(neural_model) = self.neural_model.write().unwrap_or_else(|e| e.into_inner()).as_mut() {
            neural_model.train(&training_data.contexts, &training_data.features, 100)?;
        }

        // Train clustering model if enabled
        if let Some(clustering_model) = self.clustering_model.write().unwrap_or_else(|e| e.into_inner()).as_mut() {
            clustering_model.fit(&training_data.features)?;
        }

        // Train collaborative filtering model if enabled
        if let Some(collaborative_model) = self.collaborative_model.write().unwrap_or_else(|e| e.into_inner()).as_mut() {
            collaborative_model.fit(
                &training_data.user_trait_matrix,
                &training_data.trait_features
            )?;
        }

        Ok(())
    }

    // Private helper methods

    fn get_similarity_recommendations(&self, context: &TraitContext, num_recs: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        let similarity_model = self.similarity_model.read().unwrap_or_else(|e| e.into_inner());

        let mut recommendations = Vec::new();
        let db = self.trait_database.read().unwrap_or_else(|e| e.into_inner());

        // Get traits related to current context
        let related_traits = self.extract_related_traits_from_context(context);

        for trait_name in related_traits {
            if let Ok(similar_traits) = similarity_model.find_similar_traits(&trait_name, num_recs) {
                for (similar_trait, similarity) in similar_traits {
                    if similarity > self.config.ml_config.min_confidence_threshold {
                        recommendations.push(TraitRecommendation {
                            trait_name: similar_trait,
                            confidence: similarity,
                            reasoning: format!("Similar to {} (similarity: {:.2})", trait_name, similarity),
                            context: context.clone(),
                            metadata: HashMap::new(),
                        });
                    }
                }
            }
        }

        recommendations.sort_by(|a, b| b.confidence.partial_cmp(&a.confidence).unwrap_or(std::cmp::Ordering::Equal));
        recommendations.truncate(num_recs);
        Ok(recommendations)
    }

    fn get_neural_recommendations(&self, context: &TraitContext, num_recs: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        let neural_model_guard = self.neural_model.read().unwrap_or_else(|e| e.into_inner());
        let neural_model = neural_model_guard.as_ref()
            .ok_or_else(|| CoreError::new("Neural model not initialized"))?;

        let mut feature_extractor = self.feature_extractor.write().unwrap_or_else(|e| e.into_inner());
        let context_features = feature_extractor.extract_context_features(context)?;

        let context_embedding = neural_model.forward(&context_features)?;

        let mut recommendations = Vec::new();
        let db = self.trait_database.read().unwrap_or_else(|e| e.into_inner());

        for trait_info in db.get_all_traits() {
            if let Some(trait_embedding) = db.get_trait_embedding(&trait_info.name) {
                let similarity = self.calculate_cosine_similarity(&context_embedding, trait_embedding);

                if similarity > self.config.ml_config.min_confidence_threshold {
                    recommendations.push(TraitRecommendation {
                        trait_name: trait_info.name.clone(),
                        confidence: similarity,
                        reasoning: format!("Neural embedding similarity: {:.2}", similarity),
                        context: context.clone(),
                        metadata: HashMap::new(),
                    });
                }
            }
        }

        recommendations.sort_by(|a, b| b.confidence.partial_cmp(&a.confidence).unwrap_or(std::cmp::Ordering::Equal));
        recommendations.truncate(num_recs);
        Ok(recommendations)
    }

    fn get_collaborative_recommendations(&self, context: &TraitContext, num_recs: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        let collaborative_model_guard = self.collaborative_model.read().unwrap_or_else(|e| e.into_inner());
        let collaborative_model = collaborative_model_guard.as_ref()
            .ok_or_else(|| CoreError::new("Collaborative filtering model not initialized"))?;

        // Extract user preferences from context (simplified)
        let user_preferences = self.extract_user_preferences_from_context(context);

        let recommendations_raw = collaborative_model.recommend(&user_preferences, num_recs)?;

        let mut recommendations = Vec::new();
        for (trait_name, score) in recommendations_raw {
            recommendations.push(TraitRecommendation {
                trait_name,
                confidence: score,
                reasoning: format!("Collaborative filtering score: {:.2}", score),
                context: context.clone(),
                metadata: HashMap::new(),
            });
        }

        Ok(recommendations)
    }

    fn get_pattern_recommendations(&self, context: &TraitContext, num_recs: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        let mut pattern_recommender = self.pattern_recommender.write().unwrap_or_else(|e| e.into_inner());
        pattern_recommender.recommend_based_on_patterns(context)
    }

    fn get_clustering_recommendations(&self, context: &TraitContext, num_recs: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        let clustering_model_guard = self.clustering_model.read().unwrap_or_else(|e| e.into_inner());
        let clustering_model = clustering_model_guard.as_ref()
            .ok_or_else(|| CoreError::new("Clustering model not initialized"))?;

        let mut feature_extractor = self.feature_extractor.write().unwrap_or_else(|e| e.into_inner());
        let context_features = feature_extractor.extract_context_features(context)?;

        let cluster_id = clustering_model.predict(&context_features)?;
        let cluster_traits = clustering_model.get_cluster_traits(cluster_id)?;

        let mut recommendations = Vec::new();
        for (trait_name, distance) in cluster_traits.into_iter().take(num_recs) {
            let confidence = 1.0 / (1.0 + distance); // Convert distance to confidence
            recommendations.push(TraitRecommendation {
                trait_name,
                confidence,
                reasoning: format!("Same cluster (cluster {})", cluster_id),
                context: context.clone(),
                metadata: HashMap::new(),
            });
        }

        Ok(recommendations)
    }

    fn get_content_recommendations(&self, context: &TraitContext, num_recs: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        let mut recommendations = Vec::new();
        let db = self.trait_database.read().unwrap_or_else(|e| e.into_inner());

        // Simple content-based filtering using trait categories and tags
        let context_keywords = self.extract_keywords_from_context(context);

        for trait_info in db.get_all_traits() {
            let mut content_score = 0.0;
            let mut matches = 0;

            // Check category matches
            if let Some(category) = db.trait_categories.get(&trait_info.name) {
                for keyword in &context_keywords {
                    if category.to_lowercase().contains(&keyword.to_lowercase()) {
                        content_score += 0.5;
                        matches += 1;
                    }
                }
            }

            // Check description matches (if available)
            for keyword in &context_keywords {
                if trait_info.name.to_lowercase().contains(&keyword.to_lowercase()) {
                    content_score += 0.3;
                    matches += 1;
                }
            }

            if matches > 0 {
                let confidence = content_score / context_keywords.len() as f64;
                if confidence > self.config.ml_config.min_confidence_threshold {
                    recommendations.push(TraitRecommendation {
                        trait_name: trait_info.name.clone(),
                        confidence,
                        reasoning: format!("Content match ({} keywords)", matches),
                        context: context.clone(),
                        metadata: HashMap::new(),
                    });
                }
            }
        }

        recommendations.sort_by(|a, b| b.confidence.partial_cmp(&a.confidence).unwrap_or(std::cmp::Ordering::Equal));
        recommendations.truncate(num_recs);
        Ok(recommendations)
    }

    fn ensemble_recommendations(&self, mut recommendations: Vec<TraitRecommendation>, num_final: usize) -> Result<Vec<TraitRecommendation>, CoreError> {
        // Group recommendations by trait name and combine scores
        let mut trait_scores: HashMap<String, Vec<f64>> = HashMap::new();
        let mut trait_reasons: HashMap<String, Vec<String>> = HashMap::new();
        let mut trait_contexts: HashMap<String, TraitContext> = HashMap::new();

        for rec in recommendations {
            trait_scores.entry(rec.trait_name.clone())
                .or_insert_with(Vec::new)
                .push(rec.confidence);

            trait_reasons.entry(rec.trait_name.clone())
                .or_insert_with(Vec::new)
                .push(rec.reasoning);

            trait_contexts.insert(rec.trait_name.clone(), rec.context);
        }

        // Calculate ensemble scores
        let mut final_recommendations = Vec::new();
        for (trait_name, scores) in trait_scores {
            // Use weighted average or max, depending on configuration
            let ensemble_score = if scores.len() > 1 {
                scores.iter().sum::<f64>() / scores.len() as f64 // Average
            } else {
                scores[0]
            };

            let combined_reasoning = trait_reasons.get(&trait_name)
                .map(|reasons| reasons.join("; "))
                .unwrap_or_default();

            let context = trait_contexts.get(&trait_name)
                .cloned()
                .unwrap_or_default();

            final_recommendations.push(TraitRecommendation {
                trait_name,
                confidence: ensemble_score,
                reasoning: combined_reasoning,
                context,
                metadata: HashMap::new(),
            });
        }

        // Sort and limit
        final_recommendations.sort_by(|a, b| b.confidence.partial_cmp(&a.confidence).unwrap_or(std::cmp::Ordering::Equal));
        final_recommendations.truncate(num_final);

        Ok(final_recommendations)
    }

    fn get_cached_recommendations(&self, context: &TraitContext) -> Result<Option<CachedRecommendation>, CoreError> {
        let cache = self.recommendation_cache.read().unwrap_or_else(|e| e.into_inner());
        let context_key = self.context_to_cache_key(context);

        if let Some(cached) = cache.get(&context_key) {
            let current_time = SystemTime::now().duration_since(UNIX_EPOCH).unwrap_or_default().as_secs();
            if current_time - cached.timestamp < self.config.cache_settings.cache_ttl_seconds {
                return Ok(Some(cached.clone()));
            }
        }

        Ok(None)
    }

    fn cache_recommendations(&self, context: &TraitContext, recommendations: &[TraitRecommendation]) -> Result<(), CoreError> {
        let mut cache = self.recommendation_cache.write().unwrap_or_else(|e| e.into_inner());
        let context_key = self.context_to_cache_key(context);
        let current_time = SystemTime::now().duration_since(UNIX_EPOCH).unwrap_or_default().as_secs();

        cache.insert(context_key, CachedRecommendation {
            recommendations: recommendations.to_vec(),
            timestamp: current_time,
            context_hash: self.hash_context(context),
        });

        // Limit cache size
        if cache.len() > self.config.cache_settings.max_cache_size {
            self.cleanup_old_cache_entries(&mut cache);
        }

        Ok(())
    }

    fn cleanup_cache_if_needed(&self) -> Result<(), CoreError> {
        let current_time = SystemTime::now().duration_since(UNIX_EPOCH).unwrap_or_default().as_secs();
        let mut last_cleanup = self.last_cache_cleanup.lock().unwrap_or_else(|e| e.into_inner());

        if current_time - *last_cleanup > self.config.cache_settings.cache_cleanup_interval {
            let mut cache = self.recommendation_cache.write().unwrap_or_else(|e| e.into_inner());
            self.cleanup_old_cache_entries(&mut cache);
            *last_cleanup = current_time;
        }

        Ok(())
    }

    fn cleanup_old_cache_entries(&self, cache: &mut HashMap<String, CachedRecommendation>) {
        let current_time = SystemTime::now().duration_since(UNIX_EPOCH).unwrap_or_default().as_secs();
        cache.retain(|_, cached| {
            current_time - cached.timestamp < self.config.cache_settings.cache_ttl_seconds
        });
    }

    fn context_to_cache_key(&self, context: &TraitContext) -> String {
        format!("{:?}", context) // Simplified - would use proper hashing in production
    }

    fn hash_context(&self, context: &TraitContext) -> u64 {
        // Simplified hash function
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        format!("{:?}", context).hash(&mut hasher);
        hasher.finish()
    }

    fn extract_related_traits_from_context(&self, context: &TraitContext) -> Vec<String> {
        // Extract trait names from context (simplified)
        vec!["Debug".to_string(), "Clone".to_string()] // Placeholder
    }

    fn extract_user_preferences_from_context(&self, context: &TraitContext) -> Array1<f64> {
        // Extract user preferences vector from context (simplified)
        array![0.5, 0.3, 0.2] // Placeholder
    }

    fn extract_keywords_from_context(&self, context: &TraitContext) -> Vec<String> {
        // Extract keywords from context (simplified)
        vec!["debug".to_string(), "serialize".to_string()] // Placeholder
    }

    fn calculate_cosine_similarity(&self, a: &Array1<f64>, b: &Array1<f64>) -> f64 {
        if a.len() != b.len() {
            return 0.0;
        }

        let dot_product = a.dot(b);
        let norm_a = a.dot(a).sqrt();
        let norm_b = b.dot(b).sqrt();

        if norm_a == 0.0 || norm_b == 0.0 {
            0.0
        } else {
            dot_product / (norm_a * norm_b)
        }
    }

    fn update_cache_hit_stats(&self) {
        let mut stats = self.recommendation_stats.write().unwrap_or_else(|e| e.into_inner());
        stats.cache_hits += 1;
    }

    fn update_cache_miss_stats(&self) {
        let mut stats = self.recommendation_stats.write().unwrap_or_else(|e| e.into_inner());
        stats.cache_misses += 1;
    }

    fn update_recommendation_stats(&self, response_time_ms: f64, success: bool) {
        let mut stats = self.recommendation_stats.write().unwrap_or_else(|e| e.into_inner());
        stats.total_requests += 1;

        // Update average response time
        stats.average_response_time_ms =
            (stats.average_response_time_ms * (stats.total_requests - 1) as f64 + response_time_ms) /
            stats.total_requests as f64;

        if success {
            stats.successful_recommendations += 1;
        } else {
            stats.failed_recommendations += 1;
        }
    }
}

impl Default for RecommenderEngineConfig {
    fn default() -> Self {
        Self {
            ml_config: MLRecommendationConfig::default(),
            neural_config: NeuralNetworkConfig::default(),
            feature_config: FeatureExtractionConfig::default(),
            pattern_config: UsagePatternConfig::default(),
            ensemble_weights: EnsembleWeights::default(),
            cache_settings: CacheSettings::default(),
            performance_tuning: PerformanceTuning::default(),
        }
    }
}

/// Training data structure for the recommendation engine
#[derive(Debug, Clone)]
pub struct TrainingData {
    pub contexts: Vec<TraitContext>,
    pub features: Array2<f64>,
    pub user_trait_matrix: Array2<f64>,
    pub trait_features: Array2<f64>,
    pub trait_relationships: HashMap<String, Vec<String>>,
    pub usage_patterns: Vec<UsageEvent>,
}

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

    #[test]
    fn test_recommendation_engine_creation() {
        let config = RecommenderEngineConfig::default();
        let engine = MLRecommendationEngine::new(config);
        assert!(engine.is_ok());
    }

    #[test]
    fn test_ensemble_weights_sum() {
        let weights = EnsembleWeights::default();
        let total = weights.similarity_weight + weights.collaborative_weight +
                   weights.neural_weight + weights.pattern_weight +
                   weights.clustering_weight + weights.content_weight;

        assert!((total - 1.0).abs() < 1e-10); // Should sum to 1.0
    }

    #[test]
    fn test_trait_database_operations() {
        let mut db = TraitDatabase::new();

        let trait_info = TraitInfo {
            name: "Debug".to_string(),
            category: "Utility".to_string(),
            description: "Trait for debug formatting".to_string(),
            complexity: 1,
            usage_frequency: 0.8,
            dependencies: vec!["core".to_string()],
            methods: vec!["fmt".to_string()],
            associated_types: Vec::new(),
            trait_bounds: Vec::new(),
            examples: Vec::new(),
            documentation_url: None,
            version: "1.0.0".to_string(),
        };

        db.add_trait(trait_info.clone());
        assert!(db.get_trait("Debug").is_some());
        assert_eq!(db.get_trait("Debug").expect("get_trait should succeed").name, "Debug");
    }

    #[test]
    fn test_cosine_similarity_calculation() {
        let config = RecommenderEngineConfig::default();
        let engine = MLRecommendationEngine::new(config).expect("expected valid value");

        let vec_a = array![1.0, 0.0, 0.0];
        let vec_b = array![0.0, 1.0, 0.0];
        let vec_c = array![1.0, 0.0, 0.0];

        assert_eq!(engine.calculate_cosine_similarity(&vec_a, &vec_b), 0.0);
        assert_eq!(engine.calculate_cosine_similarity(&vec_a, &vec_c), 1.0);
    }

    #[test]
    fn test_cache_key_generation() {
        let config = RecommenderEngineConfig::default();
        let engine = MLRecommendationEngine::new(config).expect("expected valid value");

        let context = TraitContext::default();
        let key1 = engine.context_to_cache_key(&context);
        let key2 = engine.context_to_cache_key(&context);

        assert_eq!(key1, key2); // Same context should generate same key
    }
}