//! Cross-domain knowledge transfer system for interpolation methods
//!
//! This module provides sophisticated knowledge transfer capabilities that enable
//! interpolation methods to learn from experience across different domains and
//! data types, improving performance through accumulated knowledge.

use scirs2_core::numeric::Float;
use std::collections::HashMap;
use std::fmt::Debug;

use crate::advanced_coordinator_modules::types::{
    DataProfile, InterpolationMethodType, PerformanceMetrics, PerformanceTargets,
};
use crate::error::{InterpolateError, InterpolateResult};

/// Cross-domain knowledge transfer system
#[derive(Debug)]
pub struct CrossDomainInterpolationKnowledge<F: Float + Debug> {
    /// Knowledge base
    knowledge_base: InterpolationKnowledgeBase<F>,
    /// Transfer learning model
    transfer_model: TransferLearningModel<F>,
    /// Domain adaptation system
    domain_adapter: DomainAdapter<F>,
}

impl<F: Float + Debug + std::ops::MulAssign + std::default::Default>
    CrossDomainInterpolationKnowledge<F>
{
    /// Create new cross-domain knowledge system
    pub fn new() -> Self {
        Self {
            knowledge_base: InterpolationKnowledgeBase::new(),
            transfer_model: TransferLearningModel::new(),
            domain_adapter: DomainAdapter::new(),
        }
    }

    /// Learn from interpolation experience
    pub fn learn_from_experience(
        &mut self,
        domain: String,
        data_profile: &DataProfile<F>,
        method: InterpolationMethodType,
        performance: &PerformanceMetrics,
    ) -> InterpolateResult<()> {
        // Add knowledge to domain
        self.knowledge_base.add_domain_experience(
            domain.clone(),
            data_profile,
            method,
            performance,
        )?;

        // Update transfer learning model
        self.transfer_model
            .update_model(&domain, data_profile, performance)?;

        // Update domain adaptation
        self.domain_adapter.update_mappings(&domain, data_profile)?;

        Ok(())
    }

    /// Transfer knowledge to new domain
    pub fn transfer_knowledge(
        &self,
        source_domain: &str,
        target_domain: &str,
        target_profile: &DataProfile<F>,
    ) -> InterpolateResult<TransferKnowledgeResult<F>> {
        // Get source domain knowledge
        let source_knowledge = self
            .knowledge_base
            .get_domain_knowledge(source_domain)
            .ok_or_else(|| InterpolateError::InvalidInput {
                message: format!(
                    "No knowledge available for source domain: {}",
                    source_domain
                ),
            })?;

        // Find applicable patterns
        let applicable_patterns = self.knowledge_base.find_applicable_patterns(
            source_domain,
            target_domain,
            target_profile,
        )?;

        // Perform domain adaptation
        let adapted_knowledge = self.domain_adapter.adapt_knowledge(
            source_knowledge,
            target_profile,
            &applicable_patterns,
        )?;

        // Generate transfer recommendations
        let recommendations = self.transfer_model.generate_recommendations(
            source_domain,
            target_domain,
            &adapted_knowledge,
            target_profile,
        )?;

        Ok(TransferKnowledgeResult {
            adapted_knowledge,
            recommendations,
            confidence_score: self.calculate_transfer_confidence(
                source_domain,
                target_domain,
                &applicable_patterns,
            ),
        })
    }

    /// Get knowledge summary
    pub fn get_knowledge_summary(&self) -> KnowledgeSummary {
        KnowledgeSummary {
            total_domains: self.knowledge_base.domain_knowledge.len(),
            total_patterns: self.knowledge_base.cross_domain_patterns.len(),
            average_confidence: self.knowledge_base.calculate_average_confidence(),
            most_successful_methods: self.knowledge_base.get_most_successful_methods(),
        }
    }

    /// Calculate transfer confidence
    fn calculate_transfer_confidence(
        &self,
        source_domain: &str,
        target_domain: &str,
        patterns: &[CrossDomainPattern<F>],
    ) -> f64 {
        if patterns.is_empty() {
            return 0.1; // Low confidence with no patterns
        }

        let pattern_strengths: Vec<f64> = patterns
            .iter()
            .map(|p| p.transfer_strength.to_f64().unwrap_or(0.0))
            .collect();

        let avg_strength = pattern_strengths.iter().sum::<f64>() / pattern_strengths.len() as f64;

        // Apply domain-specific confidence adjustments
        let domain_confidence = self
            .knowledge_base
            .get_domain_confidence(source_domain)
            .unwrap_or(0.5);

        (avg_strength * 0.7 + domain_confidence * 0.3).min(1.0)
    }
}

impl<F: Float + Debug + std::ops::MulAssign + std::default::Default> Default
    for CrossDomainInterpolationKnowledge<F>
{
    fn default() -> Self {
        Self::new()
    }
}

/// Knowledge base for interpolation
#[derive(Debug)]
pub struct InterpolationKnowledgeBase<F: Float> {
    /// Domain-specific knowledge
    domain_knowledge: HashMap<String, DomainKnowledge<F>>,
    /// Cross-domain patterns
    cross_domain_patterns: Vec<CrossDomainPattern<F>>,
    /// Knowledge confidence scores
    confidence_scores: HashMap<String, f64>,
}

impl<F: Float + std::default::Default> InterpolationKnowledgeBase<F> {
    /// Create new knowledge base
    pub fn new() -> Self {
        Self {
            domain_knowledge: HashMap::new(),
            cross_domain_patterns: Vec::new(),
            confidence_scores: HashMap::new(),
        }
    }

    /// Add domain experience
    pub fn add_domain_experience(
        &mut self,
        domain: String,
        data_profile: &DataProfile<F>,
        method: InterpolationMethodType,
        performance: &PerformanceMetrics,
    ) -> InterpolateResult<()> {
        let domain_knowledge = self
            .domain_knowledge
            .entry(domain.clone())
            .or_insert_with(|| DomainKnowledge::new(domain.clone()));

        domain_knowledge.add_experience(data_profile, method, performance)?;

        // Update confidence score
        let current_confidence = self.confidence_scores.get(&domain).unwrap_or(&0.5);
        let new_confidence = (current_confidence + 0.1).min(1.0);
        self.confidence_scores.insert(domain, new_confidence);

        // Discover new cross-domain patterns
        self.discover_cross_domain_patterns()?;

        Ok(())
    }

    /// Get domain knowledge
    pub fn get_domain_knowledge(&self, domain: &str) -> Option<&DomainKnowledge<F>> {
        self.domain_knowledge.get(domain)
    }

    /// Find applicable patterns
    pub fn find_applicable_patterns(
        &self,
        source_domain: &str,
        target_domain: &str,
        target_profile: &DataProfile<F>,
    ) -> InterpolateResult<Vec<CrossDomainPattern<F>>> {
        let patterns: Vec<CrossDomainPattern<F>> = self
            .cross_domain_patterns
            .iter()
            .filter(|pattern| {
                pattern.source_domains.contains(&source_domain.to_string())
                    && (pattern.target_domains.contains(&target_domain.to_string())
                        || pattern.target_domains.is_empty())
                    && self.pattern_matches_profile(pattern, target_profile)
            })
            .cloned()
            .collect();

        Ok(patterns)
    }

    /// Get domain confidence
    pub fn get_domain_confidence(&self, domain: &str) -> Option<f64> {
        self.confidence_scores.get(domain).copied()
    }

    /// Calculate average confidence
    pub fn calculate_average_confidence(&self) -> f64 {
        if self.confidence_scores.is_empty() {
            return 0.0;
        }

        let sum: f64 = self.confidence_scores.values().sum();
        sum / self.confidence_scores.len() as f64
    }

    /// Get most successful methods
    pub fn get_most_successful_methods(&self) -> Vec<InterpolationMethodType> {
        let mut method_scores: HashMap<InterpolationMethodType, f64> = HashMap::new();

        for domain_knowledge in self.domain_knowledge.values() {
            for method in &domain_knowledge.optimal_methods {
                let score = method_scores.entry(*method).or_insert(0.0);
                *score += domain_knowledge
                    .performance_profile
                    .accuracy_profile
                    .mean_accuracy
                    .to_f64()
                    .unwrap_or(0.0);
            }
        }

        let mut methods: Vec<(InterpolationMethodType, f64)> = method_scores.into_iter().collect();
        methods.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

        methods
            .into_iter()
            .take(5)
            .map(|(method, _)| method)
            .collect()
    }

    /// Discover cross-domain patterns
    fn discover_cross_domain_patterns(&mut self) -> InterpolateResult<()> {
        // Analyze all domain combinations for patterns
        let domains: Vec<String> = self.domain_knowledge.keys().cloned().collect();

        for i in 0..domains.len() {
            for j in (i + 1)..domains.len() {
                let source_domain = &domains[i];
                let target_domain = &domains[j];

                if let Some(pattern) =
                    self.analyze_domain_similarity(source_domain, target_domain)?
                {
                    // Check if pattern already exists
                    if !self
                        .cross_domain_patterns
                        .iter()
                        .any(|p| p.pattern_signature == pattern.pattern_signature)
                    {
                        self.cross_domain_patterns.push(pattern);
                    }
                }
            }
        }

        Ok(())
    }

    /// Analyze similarity between domains
    fn analyze_domain_similarity(
        &self,
        source_domain: &str,
        target_domain: &str,
    ) -> InterpolateResult<Option<CrossDomainPattern<F>>> {
        let source_knowledge = self.domain_knowledge.get(source_domain);
        let target_knowledge = self.domain_knowledge.get(target_domain);

        if let (Some(source), Some(target)) = (source_knowledge, target_knowledge) {
            // Calculate method overlap
            let method_overlap =
                self.calculate_method_overlap(&source.optimal_methods, &target.optimal_methods);

            if method_overlap > 0.5 {
                let pattern_signature =
                    format!("method_overlap_{}_{}", source_domain, target_domain);
                let transfer_strength = F::from(method_overlap).unwrap_or(F::zero());

                return Ok(Some(CrossDomainPattern {
                    source_domains: vec![source_domain.to_string()],
                    target_domains: vec![target_domain.to_string()],
                    pattern_signature,
                    transfer_strength,
                }));
            }
        }

        Ok(None)
    }

    /// Calculate method overlap between domains
    fn calculate_method_overlap(
        &self,
        methods1: &[InterpolationMethodType],
        methods2: &[InterpolationMethodType],
    ) -> f64 {
        if methods1.is_empty() || methods2.is_empty() {
            return 0.0;
        }

        let common_methods = methods1
            .iter()
            .filter(|method| methods2.contains(method))
            .count();

        let total_unique = methods1.len() + methods2.len() - common_methods;

        if total_unique == 0 {
            1.0
        } else {
            common_methods as f64 / total_unique as f64
        }
    }

    /// Check if pattern matches profile
    fn pattern_matches_profile(
        &self,
        pattern: &CrossDomainPattern<F>,
        _profile: &DataProfile<F>,
    ) -> bool {
        // Simplified pattern matching - can be enhanced
        pattern.transfer_strength > F::from(0.3).unwrap_or(F::zero())
    }
}

impl<F: Float + std::default::Default> Default for InterpolationKnowledgeBase<F> {
    fn default() -> Self {
        Self::new()
    }
}

/// Domain-specific knowledge
#[derive(Debug, Clone)]
pub struct DomainKnowledge<F: Float> {
    /// Domain name
    pub domain: String,
    /// Optimal methods for this domain
    pub optimal_methods: Vec<InterpolationMethodType>,
    /// Domain-specific optimizations
    pub optimizations: Vec<DomainOptimization>,
    /// Performance profile
    pub performance_profile: PerformanceProfile<F>,
}

impl<F: Float + std::default::Default> DomainKnowledge<F> {
    /// Create new domain knowledge
    pub fn new(domain: String) -> Self {
        Self {
            domain,
            optimal_methods: Vec::new(),
            optimizations: Vec::new(),
            performance_profile: PerformanceProfile::default(),
        }
    }

    /// Add experience to domain knowledge
    pub fn add_experience(
        &mut self,
        _data_profile: &DataProfile<F>,
        method: InterpolationMethodType,
        performance: &PerformanceMetrics,
    ) -> InterpolateResult<()> {
        // Update optimal methods if this one performed well
        if performance.accuracy > 0.8 && !self.optimal_methods.contains(&method) {
            self.optimal_methods.push(method);
        }

        // Update performance profile
        self.performance_profile
            .execution_times
            .push(F::from(performance.execution_time_ms).unwrap_or(F::zero()));
        self.performance_profile
            .memory_patterns
            .push(performance.memory_usage_bytes);

        // Update accuracy profile
        let accuracy_f = F::from(performance.accuracy).unwrap_or(F::zero());
        self.performance_profile
            .accuracy_profile
            .accuracy_distribution
            .push(accuracy_f);

        // Recalculate mean accuracy
        let accuracies = &self
            .performance_profile
            .accuracy_profile
            .accuracy_distribution;
        if !accuracies.is_empty() {
            let sum = accuracies.iter().fold(F::zero(), |acc, &x| acc + x);
            self.performance_profile.accuracy_profile.mean_accuracy =
                sum / F::from(accuracies.len()).unwrap_or(F::one());
        }

        Ok(())
    }
}

/// Domain optimization
#[derive(Debug, Clone)]
pub struct DomainOptimization {
    /// Optimization name
    pub name: String,
    /// Optimization parameters
    pub parameters: HashMap<String, f64>,
    /// Expected improvement
    pub expected_improvement: f64,
}

/// Performance profile for domains
#[derive(Debug, Clone, Default)]
pub struct PerformanceProfile<F: Float> {
    /// Typical execution times
    pub execution_times: Vec<F>,
    /// Memory usage patterns
    pub memory_patterns: Vec<usize>,
    /// Accuracy profile
    pub accuracy_profile: AccuracyProfile<F>,
}

/// Accuracy profile
#[derive(Debug, Clone, Default)]
pub struct AccuracyProfile<F: Float> {
    /// Mean accuracy
    pub mean_accuracy: F,
    /// Accuracy variance
    pub accuracy_variance: F,
    /// Accuracy distribution
    pub accuracy_distribution: Vec<F>,
}

/// Cross-domain pattern
#[derive(Debug, Clone)]
pub struct CrossDomainPattern<F: Float> {
    /// Source domains
    pub source_domains: Vec<String>,
    /// Target domains
    pub target_domains: Vec<String>,
    /// Pattern signature
    pub pattern_signature: String,
    /// Transfer strength
    pub transfer_strength: F,
}

/// Transfer learning model
#[derive(Debug)]
pub struct TransferLearningModel<F: Float> {
    /// Source domain models
    source_models: HashMap<String, SourceModel<F>>,
    /// Transfer weights
    transfer_weights: HashMap<String, f64>,
    /// Adaptation parameters
    adaptation_params: AdaptationParameters<F>,
}

impl<F: Float> TransferLearningModel<F> {
    /// Create new transfer learning model
    pub fn new() -> Self {
        Self {
            source_models: HashMap::new(),
            transfer_weights: HashMap::new(),
            adaptation_params: AdaptationParameters::default(),
        }
    }

    /// Update model with new data
    pub fn update_model(
        &mut self,
        domain: &str,
        data_profile: &DataProfile<F>,
        performance: &PerformanceMetrics,
    ) -> InterpolateResult<()> {
        // Create or update source model for domain
        let model = self
            .source_models
            .entry(domain.to_string())
            .or_insert_with(|| SourceModel::new());

        model.update_with_performance(data_profile, performance)?;

        // Update transfer weights based on performance
        let weight =
            performance.accuracy * 0.8 + (1.0 - performance.execution_time_ms / 10000.0) * 0.2;
        self.transfer_weights
            .insert(domain.to_string(), weight.clamp(0.0, 1.0));

        Ok(())
    }

    /// Generate transfer recommendations
    pub fn generate_recommendations(
        &self,
        source_domain: &str,
        target_domain: &str,
        adapted_knowledge: &DomainKnowledge<F>,
        _target_profile: &DataProfile<F>,
    ) -> InterpolateResult<Vec<TransferRecommendation>> {
        let mut recommendations = Vec::new();

        // Recommend optimal methods from adapted knowledge
        for method in &adapted_knowledge.optimal_methods {
            let confidence = self.transfer_weights.get(source_domain).unwrap_or(&0.5);

            recommendations.push(TransferRecommendation {
                method: *method,
                confidence: *confidence,
                reasoning: format!(
                    "Method {:?} showed good performance in source domain {}",
                    method, source_domain
                ),
                estimated_improvement: confidence * 0.2,
            });
        }

        // Sort by confidence
        recommendations.sort_by(|a, b| {
            b.confidence
                .partial_cmp(&a.confidence)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(recommendations)
    }
}

impl<F: Float> Default for TransferLearningModel<F> {
    fn default() -> Self {
        Self::new()
    }
}

/// Source model for transfer learning
#[derive(Debug, Clone)]
pub struct SourceModel<F: Float> {
    /// Model parameters
    pub parameters: Vec<F>,
    /// Model accuracy
    pub accuracy: F,
    /// Model complexity
    pub complexity: usize,
}

impl<F: Float> Default for SourceModel<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float> SourceModel<F> {
    /// Create new source model
    pub fn new() -> Self {
        Self {
            parameters: Vec::new(),
            accuracy: F::zero(),
            complexity: 0,
        }
    }

    /// Update model with performance data
    pub fn update_with_performance(
        &mut self,
        _data_profile: &DataProfile<F>,
        performance: &PerformanceMetrics,
    ) -> InterpolateResult<()> {
        self.accuracy = F::from(performance.accuracy).unwrap_or(F::zero());
        self.complexity = performance.memory_usage_bytes / 1024; // Simple complexity measure

        // Update parameters (simplified)
        if self.parameters.is_empty() {
            self.parameters = vec![self.accuracy, F::from(self.complexity).unwrap_or(F::zero())];
        } else {
            // Exponential moving average
            let alpha = F::from(0.1).unwrap_or(F::zero());
            self.parameters[0] = self.parameters[0] * (F::one() - alpha) + self.accuracy * alpha;
        }

        Ok(())
    }
}

/// Adaptation parameters
#[derive(Debug, Clone)]
pub struct AdaptationParameters<F: Float> {
    /// Learning rate for adaptation
    pub learning_rate: F,
    /// Regularization strength
    pub regularization: F,
    /// Transfer confidence threshold
    pub confidence_threshold: F,
}

impl<F: Float> Default for AdaptationParameters<F> {
    fn default() -> Self {
        Self {
            learning_rate: F::from(0.01).unwrap_or(F::zero()),
            regularization: F::from(0.001).unwrap_or(F::zero()),
            confidence_threshold: F::from(0.5).unwrap_or(F::zero()),
        }
    }
}

/// Domain adapter
#[derive(Debug)]
pub struct DomainAdapter<F: Float> {
    /// Domain mappings
    domain_mappings: HashMap<String, DomainMapping<F>>,
    /// Adaptation strategies
    adaptation_strategies: Vec<AdaptationStrategy<F>>,
}

impl<F: Float + std::ops::MulAssign> DomainAdapter<F> {
    /// Create new domain adapter
    pub fn new() -> Self {
        Self {
            domain_mappings: HashMap::new(),
            adaptation_strategies: Vec::new(),
        }
    }

    /// Update domain mappings
    pub fn update_mappings(
        &mut self,
        domain: &str,
        _data_profile: &DataProfile<F>,
    ) -> InterpolateResult<()> {
        // Simplified mapping update
        let mapping_key = format!("general_to_{}", domain);
        let mapping = DomainMapping {
            source_domain: "general".to_string(),
            target_domain: domain.to_string(),
            mapping_params: vec![F::one()],
            mapping_accuracy: F::from(0.8).unwrap_or(F::zero()),
        };

        self.domain_mappings.insert(mapping_key, mapping);
        Ok(())
    }

    /// Adapt knowledge to target domain
    pub fn adapt_knowledge(
        &self,
        source_knowledge: &DomainKnowledge<F>,
        _target_profile: &DataProfile<F>,
        patterns: &[CrossDomainPattern<F>],
    ) -> InterpolateResult<DomainKnowledge<F>> {
        let mut adapted_knowledge = source_knowledge.clone();

        // Apply pattern-based adaptations
        for pattern in patterns {
            let strength = pattern.transfer_strength.to_f64().unwrap_or(0.0);

            if strength > 0.5 {
                // High-confidence pattern - use source methods directly
                // Already copied in clone above
            } else if strength > 0.3 {
                // Medium-confidence pattern - modify methods
                adapted_knowledge.optimal_methods.retain(|method| {
                    // Keep only the most reliable methods
                    matches!(
                        method,
                        InterpolationMethodType::Linear
                            | InterpolationMethodType::CubicSpline
                            | InterpolationMethodType::BSpline
                    )
                });
            }
        }

        // Adjust performance expectations
        let adaptation_factor = F::from(0.9).unwrap_or(F::one());
        adapted_knowledge
            .performance_profile
            .accuracy_profile
            .mean_accuracy *= adaptation_factor;

        Ok(adapted_knowledge)
    }
}

impl<F: Float + std::ops::MulAssign> Default for DomainAdapter<F> {
    fn default() -> Self {
        Self::new()
    }
}

/// Domain mapping
#[derive(Debug, Clone)]
pub struct DomainMapping<F: Float> {
    /// Source domain
    pub source_domain: String,
    /// Target domain
    pub target_domain: String,
    /// Mapping function parameters
    pub mapping_params: Vec<F>,
    /// Mapping accuracy
    pub mapping_accuracy: F,
}

/// Adaptation strategy
#[derive(Debug, Clone)]
pub struct AdaptationStrategy<F: Float> {
    /// Strategy name
    pub name: String,
    /// Strategy parameters
    pub parameters: HashMap<String, F>,
    /// Success rate
    pub success_rate: f64,
}

/// Result of knowledge transfer
#[derive(Debug)]
pub struct TransferKnowledgeResult<F: Float> {
    /// Adapted knowledge for target domain
    pub adapted_knowledge: DomainKnowledge<F>,
    /// Transfer recommendations
    pub recommendations: Vec<TransferRecommendation>,
    /// Confidence in transfer
    pub confidence_score: f64,
}

/// Transfer recommendation
#[derive(Debug, Clone)]
pub struct TransferRecommendation {
    /// Recommended method
    pub method: InterpolationMethodType,
    /// Confidence in recommendation
    pub confidence: f64,
    /// Reasoning for recommendation
    pub reasoning: String,
    /// Estimated performance improvement
    pub estimated_improvement: f64,
}

/// Knowledge summary
#[derive(Debug, Clone)]
pub struct KnowledgeSummary {
    /// Total number of domains
    pub total_domains: usize,
    /// Total number of patterns
    pub total_patterns: usize,
    /// Average confidence across domains
    pub average_confidence: f64,
    /// Most successful methods overall
    pub most_successful_methods: Vec<InterpolationMethodType>,
}
