//! Cross-domain knowledge transfer system for FFT operations

use super::types::*;
use crate::error::FFTResult;
use scirs2_core::numeric::Float;
use std::collections::HashMap;
use std::fmt::Debug;

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

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

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

/// 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)]
pub struct PerformanceProfile<F: Float> {
    /// Typical execution times
    pub execution_times: Vec<F>,
    /// Memory usage patterns
    pub memory_patterns: Vec<usize>,
    /// Accuracy expectations
    pub accuracy_profile: AccuracyProfile<F>,
}

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

/// 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,
}

/// 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).expect("Failed to convert constant to float"),
            regularization: F::from(0.1).expect("Failed to convert constant to float"),
            confidence_threshold: F::from(0.8).expect("Failed to convert constant to float"),
        }
    }
}

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

/// 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,
}

impl<F: Float + Debug> CrossDomainKnowledgeSystem<F> {
    pub fn new() -> FFTResult<Self> {
        Ok(Self {
            knowledge_base: KnowledgeBase::new()?,
            transfer_model: TransferLearningModel::new()?,
            domain_adapter: DomainAdapter::new()?,
        })
    }
}

impl<F: Float> KnowledgeBase<F> {
    pub fn new() -> FFTResult<Self> {
        Ok(Self {
            domain_knowledge: HashMap::new(),
            cross_domain_patterns: Vec::new(),
            confidence_scores: HashMap::new(),
        })
    }
}

impl<F: Float> TransferLearningModel<F> {
    pub fn new() -> FFTResult<Self> {
        Ok(Self {
            source_models: HashMap::new(),
            transfer_weights: HashMap::new(),
            adaptation_params: AdaptationParameters::default(),
        })
    }
}

impl<F: Float> DomainAdapter<F> {
    pub fn new() -> FFTResult<Self> {
        Ok(Self {
            domain_mappings: HashMap::new(),
            adaptation_strategies: Vec::new(),
        })
    }
}
