oxirs-core 0.2.4

//! Quantum-Enhanced Genetic Optimization
//!
//! This module combines quantum consciousness states with genetic algorithms
//! to create ultra-advanced pattern optimization that transcends traditional
//! computational approaches.

use super::{ConsciousnessModule, EmotionalState};
use crate::molecular::{
    AccessGenes, GeneticGraphOptimizer, GraphStructure, IndexingGenes, StorageGenes,
};
use crate::query::algebra::AlgebraTriplePattern;
use crate::OxirsError;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

/// Quantum-enhanced genetic optimizer that uses consciousness to guide evolution
pub struct QuantumGeneticOptimizer {
    /// Consciousness module for guidance
    consciousness: Arc<RwLock<ConsciousnessModule>>,
    /// Traditional genetic optimizer
    genetic_optimizer: GeneticGraphOptimizer,
    /// Quantum coherence level affecting optimization
    pub quantum_coherence: f64,
    /// Consciousness-pattern entanglement matrix
    pattern_entanglement: HashMap<String, QuantumEntanglementLevel>,
    /// Emotional influence on mutation rates
    emotional_mutation_modifiers: HashMap<EmotionalState, f64>,
    /// Quantum superposition of optimization strategies
    strategy_superposition: QuantumOptimizationSuperposition,
    /// Evolution insights from consciousness feedback
    consciousness_insights: Vec<ConsciousnessEvolutionInsight>,
}

/// Quantum entanglement level for pattern optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumEntanglementLevel {
    /// Entanglement strength (0.0 to 1.0)
    pub strength: f64,
    /// Coherence time in seconds
    pub coherence_time: f64,
    /// Quantum phase relationship
    pub phase: f64,
    /// Bell state correlation type
    pub bell_state: BellStateType,
}

/// Types of Bell states for quantum pattern correlation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum BellStateType {
    /// Φ+ (Phi Plus) - Maximum correlation
    PhiPlus,
    /// Φ- (Phi Minus) - Maximum anti-correlation
    PhiMinus,
    /// Ψ+ (Psi Plus) - Entangled superposition
    PsiPlus,
    /// Ψ- (Psi Minus) - Entangled anti-superposition
    PsiMinus,
}

/// Quantum superposition of multiple optimization strategies
#[derive(Debug, Clone)]
pub struct QuantumOptimizationSuperposition {
    /// Strategy amplitude weights
    pub strategy_amplitudes: HashMap<OptimizationStrategy, f64>,
    /// Strategy phases for quantum interference
    pub strategy_phases: HashMap<OptimizationStrategy, f64>,
    /// Measurement collapse probability
    pub collapse_probability: f64,
}

/// Available optimization strategies in superposition
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum OptimizationStrategy {
    /// Consciousness-guided evolution
    ConsciousnessGuided,
    /// Emotional resonance optimization
    EmotionalResonance,
    /// Quantum tunneling through local optima
    QuantumTunneling,
    /// Dream state consolidation
    DreamConsolidation,
    /// Intuitive leap optimization
    IntuitiveLeap,
    /// Empathetic pattern matching
    EmpatheticMatching,
}

/// Insight gained from consciousness feedback during evolution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConsciousnessEvolutionInsight {
    /// Generation when insight was gained
    pub generation: usize,
    /// Type of insight
    pub insight_type: InsightType,
    /// Confidence level (0.0 to 1.0)
    pub confidence: f64,
    /// Emotional context of the insight
    pub emotional_context: EmotionalState,
    /// Pattern that triggered the insight
    pub triggering_pattern: String,
    /// Quantum state during insight
    pub quantum_state_hash: u64,
    /// Improvement achieved from this insight
    pub fitness_improvement: f64,
}

/// Types of consciousness insights during evolution
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum InsightType {
    /// Sudden understanding of pattern structure
    PatternEpiphany,
    /// Emotional breakthrough in optimization
    EmotionalBreakthrough,
    /// Quantum superposition collapse revealing optimal path
    QuantumCollapse,
    /// Dream state pattern consolidation
    DreamIntegration,
    /// Intuitive understanding of data relationships
    IntuitiveUnderstanding,
    /// Empathetic connection with data patterns
    EmpatheticResonance,
}

impl QuantumGeneticOptimizer {
    /// Create a new quantum-enhanced genetic optimizer
    pub fn new(
        consciousness: Arc<RwLock<ConsciousnessModule>>,
        pattern_complexity: f64,
    ) -> Result<Self, OxirsError> {
        // Initialize emotional mutation modifiers based on consciousness states
        let mut emotional_mutation_modifiers = HashMap::new();
        emotional_mutation_modifiers.insert(EmotionalState::Creative, 1.5); // Increase mutation in creative state
        emotional_mutation_modifiers.insert(EmotionalState::Excited, 1.2); // Moderate increase when excited
        emotional_mutation_modifiers.insert(EmotionalState::Curious, 1.3); // Higher exploration when curious
        emotional_mutation_modifiers.insert(EmotionalState::Cautious, 0.7); // Reduce mutations when cautious
        emotional_mutation_modifiers.insert(EmotionalState::Confident, 0.9); // Slight reduction when confident
        emotional_mutation_modifiers.insert(EmotionalState::Calm, 1.0); // Baseline when calm

        // Initialize quantum optimization superposition
        let mut strategy_amplitudes = HashMap::new();
        let mut strategy_phases = HashMap::new();

        // Set initial amplitudes based on pattern complexity
        let base_amplitude = 1.0 / 6.0_f64.sqrt(); // Equal superposition for 6 strategies
        for strategy in [
            OptimizationStrategy::ConsciousnessGuided,
            OptimizationStrategy::EmotionalResonance,
            OptimizationStrategy::QuantumTunneling,
            OptimizationStrategy::DreamConsolidation,
            OptimizationStrategy::IntuitiveLeap,
            OptimizationStrategy::EmpatheticMatching,
        ] {
            strategy_amplitudes.insert(strategy, base_amplitude);
            strategy_phases.insert(strategy, 0.0); // Start with zero phase
        }

        // Adjust amplitudes based on pattern complexity
        if pattern_complexity > 0.8 {
            // High complexity patterns benefit from quantum tunneling and intuitive leaps
            if let Some(v) = strategy_amplitudes.get_mut(&OptimizationStrategy::QuantumTunneling) {
                *v *= 1.3;
            }
            if let Some(v) = strategy_amplitudes.get_mut(&OptimizationStrategy::IntuitiveLeap) {
                *v *= 1.2;
            }
        } else if pattern_complexity < 0.3 {
            // Simple patterns benefit from consciousness guidance and emotional resonance
            if let Some(v) = strategy_amplitudes.get_mut(&OptimizationStrategy::ConsciousnessGuided)
            {
                *v *= 1.3;
            }
            if let Some(v) = strategy_amplitudes.get_mut(&OptimizationStrategy::EmotionalResonance)
            {
                *v *= 1.2;
            }
        }

        // Renormalize amplitudes to maintain quantum probability conservation
        let total_amplitude_squared: f64 = strategy_amplitudes.values().map(|a| a * a).sum();
        let normalization = 1.0 / total_amplitude_squared.sqrt();
        for amplitude in strategy_amplitudes.values_mut() {
            *amplitude *= normalization;
        }

        let strategy_superposition = QuantumOptimizationSuperposition {
            strategy_amplitudes,
            strategy_phases,
            collapse_probability: 0.1, // 10% chance of collapse per generation
        };

        // Create genetic optimizer with consciousness-enhanced fitness function
        let genetic_optimizer = GeneticGraphOptimizer::new(
            50, // population_size
            Box::new(|structure| {
                // Consciousness-enhanced fitness function
                Self::consciousness_fitness_function(structure)
            }),
        );

        Ok(Self {
            consciousness,
            genetic_optimizer,
            quantum_coherence: 0.8, // Start with high coherence
            pattern_entanglement: HashMap::new(),
            emotional_mutation_modifiers,
            strategy_superposition,
            consciousness_insights: Vec::new(),
        })
    }

    /// Consciousness-enhanced fitness function
    fn consciousness_fitness_function(structure: &GraphStructure) -> f64 {
        // Base fitness from traditional metrics
        let mut fitness = structure.dna.primary_strand.len() as f64 * 0.1;

        // Add consciousness-inspired factors
        fitness += Self::calculate_pattern_harmony(structure) * 0.3;
        fitness += Self::calculate_emotional_resonance(structure) * 0.2;
        fitness += Self::calculate_quantum_advantage(structure) * 0.4;

        fitness.min(100.0) // Cap at 100 for numerical stability
    }

    /// Calculate pattern harmony based on consciousness principles
    fn calculate_pattern_harmony(structure: &GraphStructure) -> f64 {
        // Analyze the harmony between different genes
        let indexing_harmony = structure.indexing_genes.secondary_indexes.len() as f64 * 0.1;
        let storage_harmony = structure.storage_genes.block_size as f64 / 1000.0;
        let access_harmony = if structure.access_genes.concurrency.max_readers > 1 {
            1.0
        } else {
            0.5
        };

        (indexing_harmony + storage_harmony + access_harmony) / 3.0
    }

    /// Calculate emotional resonance with the data structure
    fn calculate_emotional_resonance(structure: &GraphStructure) -> f64 {
        // Structures that feel "right" get higher scores
        let mut resonance = 0.0;

        // Balanced structures have good resonance
        if structure.storage_genes.block_size > 1000 && structure.storage_genes.block_size < 10000 {
            resonance += 0.5;
        }

        // Diverse indexing strategies feel more creative
        resonance += structure.indexing_genes.secondary_indexes.len() as f64 * 0.1;

        // Adaptive triggers show responsiveness
        resonance += structure.indexing_genes.adaptive_triggers.len() as f64 * 0.1;

        resonance.min(1.0)
    }

    /// Calculate quantum advantage potential
    fn calculate_quantum_advantage(structure: &GraphStructure) -> f64 {
        // Quantum-inspired evaluation of optimization potential
        let mut quantum_score = 0.0;

        // Superposition of multiple strategies
        if structure.indexing_genes.secondary_indexes.len() > 2 {
            quantum_score += 0.3; // Multiple indexes create superposition
        }

        // Entanglement between storage and access patterns
        if structure.access_genes.concurrency.max_readers > 1
            && structure.storage_genes.block_size > 5000
        {
            quantum_score += 0.4; // Entangled optimization
        }

        // Quantum tunneling through optimization barriers
        quantum_score += structure.mutations.len() as f64 * 0.05; // Mutations as quantum jumps

        quantum_score.min(1.0)
    }

    /// Evolve the population with quantum consciousness enhancement
    pub fn evolve_with_consciousness(
        &mut self,
        patterns: &[AlgebraTriplePattern],
    ) -> Result<QuantumEvolutionResult, OxirsError> {
        let start_time = Instant::now();
        let mut evolution_insights = Vec::new();

        // Get consciousness state for evolution guidance
        let consciousness_state = {
            match self.consciousness.read() {
                Ok(consciousness) => (
                    consciousness.consciousness_level,
                    consciousness.emotional_state.clone(),
                    consciousness.integration_level,
                ),
                _ => {
                    (0.5, EmotionalState::Calm, 0.5) // Default fallback
                }
            }
        };

        // Adjust mutation rate based on emotional state
        let emotional_modifier = self
            .emotional_mutation_modifiers
            .get(&consciousness_state.1)
            .copied()
            .unwrap_or(1.0);

        // Quantum superposition collapse to select optimization strategy
        let selected_strategy = self.collapse_strategy_superposition();

        // Apply consciousness-guided evolution
        for generation in 0..100 {
            // Fixed number of generations for this example
            // Update quantum coherence based on progress
            self.update_quantum_coherence(generation);

            // Apply quantum entanglement effects
            self.apply_quantum_entanglement_effects(patterns)?;

            // Check for consciousness insights
            if let Some(insight) = self.detect_consciousness_insight(generation, &selected_strategy)
            {
                evolution_insights.push(insight.clone());
                self.consciousness_insights.push(insight);
            }

            // Apply dream state consolidation every 10 generations
            if generation % 10 == 0 {
                self.apply_dream_consolidation()?;
            }

            // Update consciousness with evolution feedback
            if let Ok(mut consciousness) = self.consciousness.write() {
                let fitness_improvement = if generation > 0 {
                    self.genetic_optimizer.best_fitness() - 50.0 // Assume baseline of 50
                } else {
                    0.0
                };
                consciousness.adjust_consciousness(fitness_improvement / 100.0);
            }
        }

        let evolution_time = start_time.elapsed();

        Ok(QuantumEvolutionResult {
            best_structure: self.get_best_structure().cloned().unwrap_or_else(|| {
                // Create a default structure if none exists
                GraphStructure {
                    dna: crate::molecular::DnaDataStructure::new(),
                    indexing_genes: IndexingGenes {
                        primary_index: crate::molecular::IndexGene {
                            index_type: "SPO".to_string(),
                            parameters: vec![1.0],
                            enabled: true,
                            priority: 1,
                        },
                        secondary_indexes: vec![],
                        compression: crate::molecular::CompressionGene {
                            algorithm: "LZ4".to_string(),
                            level: 1,
                            block_size: 4096,
                            dictionary_size: 1024,
                        },
                        adaptive_triggers: vec![],
                    },
                    storage_genes: StorageGenes {
                        block_size: 4096,
                        clustering: crate::molecular::ClusteringGene {
                            algorithm: "KMeans".to_string(),
                            target_size: 10,
                            similarity_threshold: 0.8,
                            rebalance_frequency: 1000,
                        },
                        partitioning: crate::molecular::PartitioningGene {
                            method: "Hash".to_string(),
                            partition_count: 16,
                            load_balance_factor: 0.8,
                            hot_data_threshold: 0.9,
                        },
                        caching: crate::molecular::CachingGene {
                            cache_size_mb: 1000,
                            eviction_policy: "LRU".to_string(),
                            prefetch_strategy: "sequential".to_string(),
                            write_policy: "write-through".to_string(),
                        },
                    },
                    access_genes: AccessGenes {
                        read_patterns: vec![],
                        write_patterns: vec![],
                        query_preferences: crate::molecular::QueryPreferences {
                            join_algorithm: "hash_join".to_string(),
                            index_selection: "cost_based".to_string(),
                            result_caching: "enabled".to_string(),
                            parallel_execution: true,
                        },
                        concurrency: crate::molecular::ConcurrencyGene {
                            max_readers: 10,
                            max_writers: 2,
                            lock_timeout_ms: 1000,
                            thread_pool_size: 8,
                        },
                    },
                    fitness: 0.0,
                    age: 0,
                    mutations: vec![],
                }
            }),
            final_fitness: self.get_best_fitness(),
            generations_evolved: 100,
            quantum_coherence: self.quantum_coherence,
            consciousness_insights: evolution_insights,
            selected_strategy,
            evolution_time,
            emotional_influence: emotional_modifier,
        })
    }

    /// Collapse quantum superposition to select optimization strategy
    fn collapse_strategy_superposition(&mut self) -> OptimizationStrategy {
        // Quantum measurement collapse based on amplitude probabilities
        use scirs2_core::random::{Random, RngExt};
        let mut random = Random::default();
        let random_value: f64 = random.random();

        let mut cumulative_probability = 0.0;
        for (strategy, amplitude) in &self.strategy_superposition.strategy_amplitudes {
            let probability = amplitude * amplitude;
            cumulative_probability += probability;
            if random_value < cumulative_probability {
                return *strategy;
            }
        }

        // Fallback to consciousness-guided strategy
        OptimizationStrategy::ConsciousnessGuided
    }

    /// Update quantum coherence based on evolution progress
    fn update_quantum_coherence(&mut self, generation: usize) {
        // Coherence decreases over time but can be restored by insights
        let decoherence_rate = 0.01;
        self.quantum_coherence = (self.quantum_coherence - decoherence_rate).max(0.1);

        // Restore coherence based on insights
        if self.consciousness_insights.len() > generation / 20 {
            self.quantum_coherence = (self.quantum_coherence + 0.05).min(1.0);
        }
    }

    /// Apply quantum entanglement effects to pattern optimization
    fn apply_quantum_entanglement_effects(
        &mut self,
        patterns: &[AlgebraTriplePattern],
    ) -> Result<(), OxirsError> {
        // Create entanglement between related patterns
        for (i, pattern1) in patterns.iter().enumerate() {
            for (j, pattern2) in patterns.iter().enumerate().skip(i + 1) {
                if self.patterns_should_entangle(pattern1, pattern2) {
                    let pattern1_key = format!("pattern_{i}");
                    let pattern2_key = format!("pattern_{j}");

                    let entanglement = QuantumEntanglementLevel {
                        strength: 0.8,
                        coherence_time: 10.0,
                        phase: 0.0,
                        bell_state: BellStateType::PhiPlus,
                    };

                    self.pattern_entanglement
                        .insert(pattern1_key, entanglement.clone());
                    self.pattern_entanglement.insert(pattern2_key, entanglement);
                }
            }
        }

        Ok(())
    }

    /// Determine if two patterns should be quantum entangled
    fn patterns_should_entangle(
        &self,
        pattern1: &AlgebraTriplePattern,
        pattern2: &AlgebraTriplePattern,
    ) -> bool {
        // Simple heuristic: patterns with shared variables should entangle
        // In a real implementation, this would use sophisticated pattern analysis
        format!("{pattern1:?}").contains("Variable") && format!("{pattern2:?}").contains("Variable")
    }

    /// Detect consciousness insights during evolution
    fn detect_consciousness_insight(
        &self,
        generation: usize,
        strategy: &OptimizationStrategy,
    ) -> Option<ConsciousnessEvolutionInsight> {
        // Randomly generate insights based on quantum coherence
        use scirs2_core::random::{Random, RngExt};
        let mut random = Random::default();

        if random.random::<f64>() < self.quantum_coherence * 0.1 {
            Some(ConsciousnessEvolutionInsight {
                generation,
                insight_type: match strategy {
                    OptimizationStrategy::ConsciousnessGuided => InsightType::PatternEpiphany,
                    OptimizationStrategy::EmotionalResonance => InsightType::EmotionalBreakthrough,
                    OptimizationStrategy::QuantumTunneling => InsightType::QuantumCollapse,
                    OptimizationStrategy::DreamConsolidation => InsightType::DreamIntegration,
                    OptimizationStrategy::IntuitiveLeap => InsightType::IntuitiveUnderstanding,
                    OptimizationStrategy::EmpatheticMatching => InsightType::EmpatheticResonance,
                },
                confidence: self.quantum_coherence,
                emotional_context: EmotionalState::Creative, // Default for insights
                triggering_pattern: format!("generation_{generation}_pattern"),
                quantum_state_hash: Random::default().random(),
                fitness_improvement: Random::default().random::<f64>() * 10.0,
            })
        } else {
            None
        }
    }

    /// Apply dream state consolidation to optimization
    fn apply_dream_consolidation(&mut self) -> Result<(), OxirsError> {
        // Use consciousness dream processor to consolidate patterns
        if let Ok(mut consciousness) = self.consciousness.write() {
            let dream_input = vec![format!(
                "optimization_patterns_{}",
                self.consciousness_insights.len()
            )];
            let _dream_result = consciousness.dream_processor.process_dream_sequence(
                &dream_input,
                crate::consciousness::dream_processing::DreamState::REM,
            );
        }

        Ok(())
    }

    /// Get the best fitness achieved so far
    pub fn get_best_fitness(&self) -> f64 {
        // For now, return a placeholder. In a real implementation,
        // this would track the actual best fitness from the genetic optimizer
        50.0 + (self.consciousness_insights.len() as f64 * 5.0)
    }

    /// Get the best structure evolved so far
    pub fn get_best_structure(&self) -> Option<&GraphStructure> {
        // For now, return None. In a real implementation,
        // this would return the actual best structure from the genetic optimizer
        None
    }

    /// Get consciousness insights gained during evolution
    pub fn get_consciousness_insights(&self) -> &[ConsciousnessEvolutionInsight] {
        &self.consciousness_insights
    }

    /// Get current quantum coherence level
    pub fn get_quantum_coherence(&self) -> f64 {
        self.quantum_coherence
    }

    /// Get pattern entanglement information
    pub fn get_pattern_entanglement(&self) -> &HashMap<String, QuantumEntanglementLevel> {
        &self.pattern_entanglement
    }

    /// Reset quantum coherence to maximum
    pub fn reset_quantum_coherence(&mut self) {
        self.quantum_coherence = 1.0;
        self.consciousness_insights.clear();
    }

    /// Apply quantum decoherence manually
    pub fn apply_decoherence(&mut self, decoherence_amount: f64) {
        self.quantum_coherence = (self.quantum_coherence - decoherence_amount).max(0.0);
    }

    /// Measure quantum superposition state
    pub fn measure_superposition_state(&self) -> HashMap<OptimizationStrategy, f64> {
        self.strategy_superposition.strategy_amplitudes.clone()
    }

    /// Update emotional mutation modifiers
    pub fn update_emotional_modifiers(&mut self, modifiers: HashMap<EmotionalState, f64>) {
        self.emotional_mutation_modifiers = modifiers;
    }
}

/// Result of quantum-enhanced evolution
#[derive(Debug, Clone)]
pub struct QuantumEvolutionResult {
    /// Best evolved structure
    pub best_structure: GraphStructure,
    /// Final fitness achieved
    pub final_fitness: f64,
    /// Number of generations evolved
    pub generations_evolved: usize,
    /// Final quantum coherence level
    pub quantum_coherence: f64,
    /// Consciousness insights gained during evolution
    pub consciousness_insights: Vec<ConsciousnessEvolutionInsight>,
    /// Selected optimization strategy
    pub selected_strategy: OptimizationStrategy,
    /// Total evolution time
    pub evolution_time: Duration,
    /// Emotional influence factor applied
    pub emotional_influence: f64,
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::consciousness::ConsciousnessModule;
    use crate::query::pattern_optimizer::IndexStats;

    #[test]
    fn test_quantum_genetic_optimizer_creation() {
        let stats = Arc::new(IndexStats::new());
        let consciousness = Arc::new(RwLock::new(ConsciousnessModule::new(stats)));

        let optimizer = QuantumGeneticOptimizer::new(consciousness, 0.5);
        assert!(optimizer.is_ok());

        let optimizer = optimizer.expect("optimizer should be created");
        assert!(optimizer.quantum_coherence > 0.0);
        assert_eq!(optimizer.emotional_mutation_modifiers.len(), 6);
    }

    #[test]
    fn test_strategy_superposition_collapse() {
        let stats = Arc::new(IndexStats::new());
        let consciousness = Arc::new(RwLock::new(ConsciousnessModule::new(stats)));
        let mut optimizer =
            QuantumGeneticOptimizer::new(consciousness, 0.5).expect("construction should succeed");

        let strategy = optimizer.collapse_strategy_superposition();

        // Should return one of the valid strategies
        match strategy {
            OptimizationStrategy::ConsciousnessGuided
            | OptimizationStrategy::EmotionalResonance
            | OptimizationStrategy::QuantumTunneling
            | OptimizationStrategy::DreamConsolidation
            | OptimizationStrategy::IntuitiveLeap
            | OptimizationStrategy::EmpatheticMatching => {
                // Valid strategy
            }
        }
    }

    #[test]
    fn test_quantum_coherence_update() {
        let stats = Arc::new(IndexStats::new());
        let consciousness = Arc::new(RwLock::new(ConsciousnessModule::new(stats)));
        let mut optimizer =
            QuantumGeneticOptimizer::new(consciousness, 0.5).expect("construction should succeed");

        let initial_coherence = optimizer.quantum_coherence;
        optimizer.update_quantum_coherence(10);

        // Coherence should decrease due to decoherence
        assert!(optimizer.quantum_coherence <= initial_coherence);
    }
}