oxirs-core 0.2.4

//! Emotional Learning Network for Consciousness-Inspired Computing
//!
//! This module implements advanced emotional intelligence for graph processing,
//! enabling the system to learn emotional associations and improve decision-making
//! through emotional context and memory.

use super::{EmotionalState, QueryContext};
use crate::query::algebra::AlgebraTriplePattern;
use crate::OxirsError;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, SystemTime};

/// Emotional learning network for enhanced decision making
#[derive(Debug)]
pub struct EmotionalLearningNetwork {
    /// Emotional memory system
    pub emotional_memory: Arc<RwLock<EmotionalMemory>>,
    /// Emotion prediction network
    pub emotion_predictor: EmotionPredictor,
    /// Empathy engine for understanding user emotions
    pub empathy_engine: EmpathyEngine,
    /// Emotional regulation strategies
    pub emotion_regulation: EmotionRegulation,
    /// Emotional contagion simulator
    pub emotional_contagion: EmotionalContagion,
    /// Mood tracking system
    pub mood_tracker: MoodTracker,
}

/// Comprehensive emotional memory system
#[derive(Debug, Clone)]
pub struct EmotionalMemory {
    /// Long-term emotional associations
    pub long_term_associations: HashMap<String, EmotionalAssociation>,
    /// Short-term emotional context
    pub short_term_context: VecDeque<EmotionalExperience>,
    /// Emotional episode memory
    pub episode_memory: Vec<EmotionalEpisode>,
    /// Emotional schema patterns
    pub emotional_schemas: HashMap<String, EmotionalSchema>,
    /// Emotional significance map
    pub significance_map: HashMap<String, f64>,
}

/// Emotional association between patterns and feelings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmotionalAssociation {
    /// Pattern signature
    pub pattern_signature: String,
    /// Associated emotional state
    pub emotion: EmotionalState,
    /// Emotional intensity (0.0 to 1.0)
    pub intensity: f64,
    /// Confidence in association
    pub confidence: f64,
    /// Frequency of association
    pub frequency: usize,
    /// Last reinforcement time
    pub last_reinforcement: SystemTime,
    /// Decay rate
    pub decay_rate: f64,
}

/// Emotional experience record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmotionalExperience {
    /// Timestamp of experience
    pub timestamp: SystemTime,
    /// Emotional state during experience
    pub emotion: EmotionalState,
    /// Context that triggered emotion
    pub context: String,
    /// Outcome quality
    pub outcome_quality: f64,
    /// Arousal level
    pub arousal: f64,
    /// Valence (positive/negative)
    pub valence: f64,
    /// Dominance/control feeling
    pub dominance: f64,
}

/// Emotional episode with narrative structure
#[derive(Debug, Clone)]
pub struct EmotionalEpisode {
    /// Episode identifier
    pub episode_id: String,
    /// Start time
    pub start_time: SystemTime,
    /// End time
    pub end_time: Option<SystemTime>,
    /// Sequence of experiences
    pub experiences: Vec<EmotionalExperience>,
    /// Episode theme/category
    pub theme: String,
    /// Overall emotional trajectory
    pub emotional_arc: EmotionalArc,
    /// Resolution outcome
    pub resolution: Option<EpisodeResolution>,
}

/// Emotional arc representing the progression of emotions
#[derive(Debug, Clone)]
pub struct EmotionalArc {
    /// Peak emotional intensity
    pub peak_intensity: f64,
    /// Emotional complexity (variety of emotions)
    pub complexity: f64,
    /// Arc shape (rising, falling, stable, etc.)
    pub arc_shape: ArcShape,
    /// Emotional coherence
    pub coherence: f64,
}

/// Different shapes of emotional arcs
#[derive(Debug, Clone)]
pub enum ArcShape {
    Rising,        // Emotions intensify over time
    Falling,       // Emotions diminish over time
    Stable,        // Consistent emotional level
    Rollercoaster, // High variability
    UCurve,        // Low, then high
    InvertedU,     // High, then low
}

/// Episode resolution outcomes
#[derive(Debug, Clone)]
pub struct EpisodeResolution {
    /// Success/failure indicator
    pub success: bool,
    /// Lessons learned
    pub lessons: Vec<String>,
    /// Emotional growth achieved
    pub growth_achieved: f64,
    /// Resilience building
    pub resilience_gained: f64,
}

/// Emotional schema for pattern recognition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmotionalSchema {
    /// Schema name
    pub name: String,
    /// Typical emotional sequence
    pub emotional_sequence: Vec<EmotionalState>,
    /// Trigger patterns
    pub triggers: Vec<String>,
    /// Expected outcomes
    pub expected_outcomes: HashMap<String, f64>,
    /// Adaptation strategies
    pub adaptation_strategies: Vec<String>,
}

/// Emotion prediction system
#[derive(Debug)]
pub struct EmotionPredictor {
    /// Neural network for emotion prediction
    pub prediction_network: EmotionPredictionNetwork,
    /// Historical prediction accuracy
    pub accuracy_history: VecDeque<f64>,
    /// Prediction confidence thresholds
    pub confidence_thresholds: HashMap<String, f64>,
}

/// Simplified neural network for emotion prediction
#[derive(Debug)]
pub struct EmotionPredictionNetwork {
    /// Input layer weights
    pub input_weights: Vec<Vec<f64>>,
    /// Hidden layer weights
    pub hidden_weights: Vec<Vec<f64>>,
    /// Output layer weights
    pub output_weights: Vec<Vec<f64>>,
    /// Learning rate
    pub learning_rate: f64,
    /// Activation function type
    pub activation_function: ActivationFunction,
}

/// Activation functions for neural network
#[derive(Debug)]
pub enum ActivationFunction {
    Sigmoid,
    Tanh,
    ReLU,
    Softmax,
}

/// Empathy engine for understanding and responding to emotions
#[derive(Debug)]
pub struct EmpathyEngine {
    /// Empathy level (0.0 to 1.0)
    pub empathy_level: f64,
    /// Emotional mirroring capability
    pub mirroring_strength: f64,
    /// Perspective-taking ability
    pub perspective_taking: f64,
    /// Emotional contagion resistance
    pub contagion_resistance: f64,
    /// Compassion response patterns
    pub compassion_patterns: HashMap<EmotionalState, CompassionResponse>,
}

/// Compassion response for different emotional states
#[derive(Debug, Clone)]
pub struct CompassionResponse {
    /// Response type
    pub response_type: CompassionType,
    /// Intensity of response
    pub intensity: f64,
    /// Response message/action
    pub response_action: String,
    /// Effectiveness rating
    pub effectiveness: f64,
}

/// Types of compassionate responses
#[derive(Debug, Clone)]
pub enum CompassionType {
    Active,    // Direct help/intervention
    Passive,   // Supportive presence
    Cognitive, // Analytical support
    Emotional, // Emotional validation
    Practical, // Practical assistance
}

/// Emotion regulation strategies
#[derive(Debug)]
pub struct EmotionRegulation {
    /// Available regulation strategies
    pub strategies: HashMap<String, RegulationStrategy>,
    /// Strategy effectiveness tracking
    pub strategy_effectiveness: HashMap<String, f64>,
    /// Current regulation goals
    pub regulation_goals: Vec<RegulationGoal>,
    /// Regulation skills proficiency
    pub skills_proficiency: HashMap<String, f64>,
}

/// Individual emotion regulation strategy
#[derive(Debug, Clone)]
pub struct RegulationStrategy {
    /// Strategy name
    pub name: String,
    /// Strategy type
    pub strategy_type: RegulationType,
    /// Effectiveness for different emotions
    pub emotion_effectiveness: HashMap<EmotionalState, f64>,
    /// Implementation steps
    pub implementation_steps: Vec<String>,
    /// Energy cost
    pub energy_cost: f64,
    /// Time required
    pub time_required: Duration,
}

/// Types of emotion regulation
#[derive(Debug, Clone)]
pub enum RegulationType {
    Reappraisal, // Cognitive reframing
    Suppression, // Emotion hiding
    Distraction, // Attention redirection
    Acceptance,  // Emotional acceptance
    Expression,  // Emotional expression
    Mindfulness, // Present-moment awareness
}

/// Regulation goal
#[derive(Debug, Clone)]
pub struct RegulationGoal {
    /// Target emotional state
    pub target_emotion: EmotionalState,
    /// Target intensity
    pub target_intensity: f64,
    /// Time frame
    pub time_frame: Duration,
    /// Priority level
    pub priority: f64,
}

/// Emotional contagion simulation
#[derive(Debug)]
pub struct EmotionalContagion {
    /// Contagion susceptibility
    pub susceptibility: f64,
    /// Transmission strength
    pub transmission_strength: f64,
    /// Immunity factors
    pub immunity_factors: HashMap<EmotionalState, f64>,
    /// Contagion network
    pub contagion_network: HashMap<String, Vec<String>>,
}

/// Mood tracking system
#[derive(Debug)]
pub struct MoodTracker {
    /// Current mood state
    pub current_mood: MoodState,
    /// Mood history
    pub mood_history: VecDeque<MoodEntry>,
    /// Mood patterns
    pub mood_patterns: HashMap<String, MoodPattern>,
    /// Mood prediction model
    pub mood_predictor: MoodPredictor,
}

/// Comprehensive mood state
#[derive(Debug, Clone)]
pub struct MoodState {
    /// Primary mood
    pub primary_mood: EmotionalState,
    /// Mood intensity
    pub intensity: f64,
    /// Mood stability
    pub stability: f64,
    /// Mood complexity
    pub complexity: f64,
    /// Associated emotions
    pub associated_emotions: Vec<(EmotionalState, f64)>,
}

/// Mood history entry
#[derive(Debug, Clone)]
pub struct MoodEntry {
    /// Timestamp
    pub timestamp: SystemTime,
    /// Mood state
    pub mood: MoodState,
    /// Contributing factors
    pub factors: Vec<String>,
    /// Context information
    pub context: String,
}

/// Mood pattern recognition
#[derive(Debug, Clone)]
pub struct MoodPattern {
    /// Pattern name
    pub name: String,
    /// Pattern sequence
    pub sequence: Vec<EmotionalState>,
    /// Pattern frequency
    pub frequency: f64,
    /// Pattern duration
    pub duration: Duration,
    /// Trigger conditions
    pub triggers: Vec<String>,
}

/// Mood prediction model
#[derive(Debug)]
pub struct MoodPredictor {
    /// Prediction accuracy
    pub accuracy: f64,
    /// Prediction horizon
    pub horizon: Duration,
    /// Confidence intervals
    pub confidence_intervals: HashMap<String, (f64, f64)>,
}

impl EmotionalLearningNetwork {
    /// Create a new emotional learning network
    pub fn new() -> Self {
        Self {
            emotional_memory: Arc::new(RwLock::new(EmotionalMemory {
                long_term_associations: HashMap::new(),
                short_term_context: VecDeque::with_capacity(100),
                episode_memory: Vec::new(),
                emotional_schemas: Self::initialize_emotional_schemas(),
                significance_map: HashMap::new(),
            })),
            emotion_predictor: EmotionPredictor::new(),
            empathy_engine: EmpathyEngine::new(),
            emotion_regulation: EmotionRegulation::new(),
            emotional_contagion: EmotionalContagion::new(),
            mood_tracker: MoodTracker::new(),
        }
    }

    /// Initialize basic emotional schemas
    fn initialize_emotional_schemas() -> HashMap<String, EmotionalSchema> {
        let mut schemas = HashMap::new();

        schemas.insert(
            "problem_solving".to_string(),
            EmotionalSchema {
                name: "Problem Solving".to_string(),
                emotional_sequence: vec![
                    EmotionalState::Curious,
                    EmotionalState::Cautious,
                    EmotionalState::Creative,
                    EmotionalState::Confident,
                ],
                triggers: vec![
                    "complex_query".to_string(),
                    "optimization_needed".to_string(),
                ],
                expected_outcomes: HashMap::from([
                    ("success".to_string(), 0.8),
                    ("learning".to_string(), 0.9),
                ]),
                adaptation_strategies: vec![
                    "break_down_problem".to_string(),
                    "seek_patterns".to_string(),
                    "creative_exploration".to_string(),
                ],
            },
        );

        schemas.insert(
            "performance_optimization".to_string(),
            EmotionalSchema {
                name: "Performance Optimization".to_string(),
                emotional_sequence: vec![
                    EmotionalState::Excited,
                    EmotionalState::Creative,
                    EmotionalState::Confident,
                ],
                triggers: vec![
                    "slow_query".to_string(),
                    "efficiency_improvement".to_string(),
                ],
                expected_outcomes: HashMap::from([
                    ("performance_gain".to_string(), 0.85),
                    ("satisfaction".to_string(), 0.75),
                ]),
                adaptation_strategies: vec![
                    "analyze_bottlenecks".to_string(),
                    "explore_alternatives".to_string(),
                ],
            },
        );

        schemas
    }

    /// Learn emotional association from experience
    pub fn learn_emotional_association(
        &self,
        pattern: &str,
        emotion: EmotionalState,
        outcome_quality: f64,
    ) -> Result<(), OxirsError> {
        if let Ok(mut memory) = self.emotional_memory.write() {
            let intensity = (outcome_quality + 1.0) / 2.0; // Convert to 0-1 range

            let association = memory
                .long_term_associations
                .entry(pattern.to_string())
                .or_insert(EmotionalAssociation {
                    pattern_signature: pattern.to_string(),
                    emotion: emotion.clone(),
                    intensity: 0.0,
                    confidence: 0.0,
                    frequency: 0,
                    last_reinforcement: SystemTime::now(),
                    decay_rate: 0.01,
                });

            // Update association with learning
            association.emotion = emotion.clone();
            association.intensity = association.intensity * 0.9 + intensity * 0.1;
            association.frequency += 1;
            association.last_reinforcement = SystemTime::now();
            association.confidence =
                (association.frequency as f64 / (association.frequency as f64 + 10.0)).min(0.95);

            // Add to short-term context
            let experience = EmotionalExperience {
                timestamp: SystemTime::now(),
                emotion,
                context: pattern.to_string(),
                outcome_quality,
                arousal: intensity,
                valence: if outcome_quality > 0.0 {
                    outcome_quality
                } else {
                    -outcome_quality.abs()
                },
                dominance: intensity,
            };

            memory.short_term_context.push_back(experience);
            if memory.short_term_context.len() > 100 {
                memory.short_term_context.pop_front();
            }
        }

        Ok(())
    }

    /// Predict emotional response to a pattern
    pub fn predict_emotional_response(
        &self,
        pattern: &str,
    ) -> Result<EmotionalPrediction, OxirsError> {
        let memory_prediction = match self.emotional_memory.read() {
            Ok(memory) => {
                memory
                    .long_term_associations
                    .get(pattern)
                    .map(|assoc| EmotionalPrediction {
                        predicted_emotion: assoc.emotion.clone(),
                        confidence: assoc.confidence,
                        intensity: assoc.intensity,
                        reasoning: "Historical association".to_string(),
                    })
            }
            _ => None,
        };

        if let Some(prediction) = memory_prediction {
            Ok(prediction)
        } else {
            // Use neural network prediction
            let network_prediction = self.emotion_predictor.predict_emotion(pattern)?;
            Ok(network_prediction)
        }
    }

    /// Generate empathetic response
    pub fn generate_empathetic_response(
        &self,
        user_emotion: EmotionalState,
    ) -> Result<CompassionResponse, OxirsError> {
        let compassion_response = self
            .empathy_engine
            .compassion_patterns
            .get(&user_emotion)
            .cloned()
            .unwrap_or(CompassionResponse {
                response_type: CompassionType::Passive,
                intensity: 0.5,
                response_action: "I understand this might be challenging.".to_string(),
                effectiveness: 0.6,
            });

        Ok(compassion_response)
    }

    /// Apply emotion regulation strategy
    pub fn regulate_emotion(
        &mut self,
        current_emotion: EmotionalState,
        target_emotion: EmotionalState,
    ) -> Result<RegulationOutcome, OxirsError> {
        // Find best strategy for this emotion transition
        let best_strategy = self.emotion_regulation.strategies.values().max_by(|a, b| {
            let a_effectiveness = a
                .emotion_effectiveness
                .get(&current_emotion)
                .unwrap_or(&0.0);
            let b_effectiveness = b
                .emotion_effectiveness
                .get(&current_emotion)
                .unwrap_or(&0.0);
            a_effectiveness
                .partial_cmp(b_effectiveness)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        if let Some(strategy) = best_strategy {
            // Simulate strategy application
            let effectiveness = strategy
                .emotion_effectiveness
                .get(&current_emotion)
                .unwrap_or(&0.5);
            let success_probability = effectiveness * (1.0 + fastrand::f64() * 0.2 - 0.1);

            Ok(RegulationOutcome {
                strategy_used: strategy.name.clone(),
                success: success_probability > 0.7,
                effectiveness: *effectiveness,
                emotional_change: if success_probability > 0.7 {
                    target_emotion
                } else {
                    current_emotion
                },
                side_effects: if success_probability < 0.3 {
                    vec!["Temporary emotional suppression".to_string()]
                } else {
                    Vec::new()
                },
            })
        } else {
            Ok(RegulationOutcome {
                strategy_used: "Default acceptance".to_string(),
                success: false,
                effectiveness: 0.3,
                emotional_change: current_emotion,
                side_effects: Vec::new(),
            })
        }
    }

    /// Update mood tracking
    pub fn update_mood(
        &mut self,
        new_emotion: EmotionalState,
        context: &str,
    ) -> Result<(), OxirsError> {
        // Update current mood
        let mood_entry = MoodEntry {
            timestamp: SystemTime::now(),
            mood: MoodState {
                primary_mood: new_emotion.clone(),
                intensity: 0.5 + fastrand::f64() * 0.5,
                stability: self.mood_tracker.current_mood.stability * 0.9 + 0.1,
                complexity: self.calculate_mood_complexity(&new_emotion),
                associated_emotions: vec![(new_emotion.clone(), 1.0)],
            },
            factors: vec![context.to_string()],
            context: context.to_string(),
        };

        self.mood_tracker.mood_history.push_back(mood_entry.clone());
        if self.mood_tracker.mood_history.len() > 1000 {
            self.mood_tracker.mood_history.pop_front();
        }

        self.mood_tracker.current_mood = mood_entry.mood;

        Ok(())
    }

    /// Calculate mood complexity
    fn calculate_mood_complexity(&self, _emotion: &EmotionalState) -> f64 {
        // Simple complexity based on emotional state variety in recent history
        let recent_emotions: std::collections::HashSet<_> = self
            .mood_tracker
            .mood_history
            .iter()
            .rev()
            .take(10)
            .map(|entry| &entry.mood.primary_mood)
            .collect();

        (recent_emotions.len() as f64 / 6.0).min(1.0) // 6 is max number of emotional states
    }

    /// Get emotional insights for decision making
    pub fn get_emotional_insights(
        &self,
        patterns: &[AlgebraTriplePattern],
        context: &QueryContext,
    ) -> Result<EmotionalInsights, OxirsError> {
        let mut pattern_emotions = HashMap::new();
        let mut confidence_scores = HashMap::new();

        for (i, pattern) in patterns.iter().enumerate() {
            let pattern_signature = format!("{pattern:?}");
            if let Ok(prediction) = self.predict_emotional_response(&pattern_signature) {
                pattern_emotions.insert(i, prediction.predicted_emotion);
                confidence_scores.insert(i, prediction.confidence);
            }
        }

        Ok(EmotionalInsights {
            pattern_emotions,
            confidence_scores,
            overall_mood: self.mood_tracker.current_mood.clone(),
            recommended_approach: self.recommend_emotional_approach(context)?,
            empathy_level: self.empathy_engine.empathy_level,
            regulation_suggestions: self.get_regulation_suggestions()?,
        })
    }

    /// Recommend emotional approach for query processing
    fn recommend_emotional_approach(
        &self,
        context: &QueryContext,
    ) -> Result<EmotionalApproach, OxirsError> {
        let approach = match (&context.complexity, &context.performance_req) {
            (crate::consciousness::ComplexityLevel::Simple, _) => EmotionalApproach {
                primary_emotion: EmotionalState::Calm,
                secondary_emotions: vec![EmotionalState::Confident],
                processing_style: "Direct and efficient".to_string(),
                risk_tolerance: 0.3,
            },
            (crate::consciousness::ComplexityLevel::VeryComplex, _) => EmotionalApproach {
                primary_emotion: EmotionalState::Creative,
                secondary_emotions: vec![EmotionalState::Curious, EmotionalState::Cautious],
                processing_style: "Exploratory and careful".to_string(),
                risk_tolerance: 0.7,
            },
            _ => EmotionalApproach {
                primary_emotion: EmotionalState::Confident,
                secondary_emotions: vec![EmotionalState::Curious],
                processing_style: "Balanced and adaptive".to_string(),
                risk_tolerance: 0.5,
            },
        };

        Ok(approach)
    }

    /// Get emotion regulation suggestions
    fn get_regulation_suggestions(&self) -> Result<Vec<String>, OxirsError> {
        let current_mood = &self.mood_tracker.current_mood;

        let suggestions = match current_mood.primary_mood {
            EmotionalState::Excited => vec![
                "Channel excitement into creative optimization".to_string(),
                "Use energy for parallel processing exploration".to_string(),
            ],
            EmotionalState::Cautious => vec![
                "Thoroughly validate query plans before execution".to_string(),
                "Implement additional error checking".to_string(),
            ],
            EmotionalState::Creative => vec![
                "Explore novel optimization strategies".to_string(),
                "Consider unconventional query paths".to_string(),
            ],
            _ => vec![
                "Maintain current emotional balance".to_string(),
                "Continue with standard processing approach".to_string(),
            ],
        };

        Ok(suggestions)
    }
}

/// Emotional prediction result
#[derive(Debug, Clone)]
pub struct EmotionalPrediction {
    pub predicted_emotion: EmotionalState,
    pub confidence: f64,
    pub intensity: f64,
    pub reasoning: String,
}

/// Emotion regulation outcome
#[derive(Debug, Clone)]
pub struct RegulationOutcome {
    pub strategy_used: String,
    pub success: bool,
    pub effectiveness: f64,
    pub emotional_change: EmotionalState,
    pub side_effects: Vec<String>,
}

/// Emotional insights for decision making
#[derive(Debug, Clone)]
pub struct EmotionalInsights {
    pub pattern_emotions: HashMap<usize, EmotionalState>,
    pub confidence_scores: HashMap<usize, f64>,
    pub overall_mood: MoodState,
    pub recommended_approach: EmotionalApproach,
    pub empathy_level: f64,
    pub regulation_suggestions: Vec<String>,
}

/// Recommended emotional approach
#[derive(Debug, Clone)]
pub struct EmotionalApproach {
    pub primary_emotion: EmotionalState,
    pub secondary_emotions: Vec<EmotionalState>,
    pub processing_style: String,
    pub risk_tolerance: f64,
}

impl EmotionPredictor {
    fn new() -> Self {
        Self {
            prediction_network: EmotionPredictionNetwork::new(),
            accuracy_history: VecDeque::with_capacity(1000),
            confidence_thresholds: HashMap::from([
                ("high".to_string(), 0.8),
                ("medium".to_string(), 0.6),
                ("low".to_string(), 0.4),
            ]),
        }
    }

    fn predict_emotion(&self, pattern: &str) -> Result<EmotionalPrediction, OxirsError> {
        // Simplified prediction based on pattern characteristics
        let emotion = if pattern.contains("error") || pattern.contains("fail") {
            EmotionalState::Cautious
        } else if pattern.contains("creative") || pattern.contains("novel") {
            EmotionalState::Creative
        } else if pattern.contains("simple") || pattern.contains("basic") {
            EmotionalState::Calm
        } else if pattern.contains("complex") || pattern.contains("challenging") {
            EmotionalState::Curious
        } else if pattern.contains("success") || pattern.contains("optimal") {
            EmotionalState::Confident
        } else {
            EmotionalState::Excited
        };

        Ok(EmotionalPrediction {
            predicted_emotion: emotion,
            confidence: 0.6 + fastrand::f64() * 0.3,
            intensity: 0.3 + fastrand::f64() * 0.7,
            reasoning: "Pattern-based heuristic prediction".to_string(),
        })
    }
}

impl EmotionPredictionNetwork {
    fn new() -> Self {
        Self {
            input_weights: vec![vec![fastrand::f64() * 2.0 - 1.0; 10]; 8],
            hidden_weights: vec![vec![fastrand::f64() * 2.0 - 1.0; 8]; 6],
            output_weights: vec![vec![fastrand::f64() * 2.0 - 1.0; 6]; 6],
            learning_rate: 0.01,
            activation_function: ActivationFunction::Sigmoid,
        }
    }
}

impl EmpathyEngine {
    fn new() -> Self {
        let mut compassion_patterns = HashMap::new();

        compassion_patterns.insert(
            EmotionalState::Cautious,
            CompassionResponse {
                response_type: CompassionType::Cognitive,
                intensity: 0.7,
                response_action: "Let me help you analyze this carefully and find a safe approach."
                    .to_string(),
                effectiveness: 0.8,
            },
        );

        compassion_patterns.insert(
            EmotionalState::Curious,
            CompassionResponse {
                response_type: CompassionType::Active,
                intensity: 0.8,
                response_action: "That's an interesting question! Let's explore this together."
                    .to_string(),
                effectiveness: 0.9,
            },
        );

        Self {
            empathy_level: 0.75,
            mirroring_strength: 0.6,
            perspective_taking: 0.8,
            contagion_resistance: 0.4,
            compassion_patterns,
        }
    }
}

impl EmotionRegulation {
    fn new() -> Self {
        let mut strategies = HashMap::new();

        strategies.insert(
            "reappraisal".to_string(),
            RegulationStrategy {
                name: "Cognitive Reappraisal".to_string(),
                strategy_type: RegulationType::Reappraisal,
                emotion_effectiveness: HashMap::from([
                    (EmotionalState::Cautious, 0.8),
                    (EmotionalState::Excited, 0.6),
                ]),
                implementation_steps: vec![
                    "Identify triggering thoughts".to_string(),
                    "Challenge negative assumptions".to_string(),
                    "Reframe situation positively".to_string(),
                ],
                energy_cost: 0.6,
                time_required: Duration::from_millis(100),
            },
        );

        Self {
            strategies,
            strategy_effectiveness: HashMap::new(),
            regulation_goals: Vec::new(),
            skills_proficiency: HashMap::new(),
        }
    }
}

impl EmotionalContagion {
    fn new() -> Self {
        Self {
            susceptibility: 0.3,
            transmission_strength: 0.4,
            immunity_factors: HashMap::new(),
            contagion_network: HashMap::new(),
        }
    }
}

impl MoodTracker {
    fn new() -> Self {
        Self {
            current_mood: MoodState {
                primary_mood: EmotionalState::Calm,
                intensity: 0.5,
                stability: 0.7,
                complexity: 0.3,
                associated_emotions: vec![(EmotionalState::Calm, 1.0)],
            },
            mood_history: VecDeque::with_capacity(1000),
            mood_patterns: HashMap::new(),
            mood_predictor: MoodPredictor {
                accuracy: 0.65,
                horizon: Duration::from_secs(3600), // 1 hour
                confidence_intervals: HashMap::new(),
            },
        }
    }
}

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

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

    #[test]
    fn test_emotional_learning_network_creation() {
        let network = EmotionalLearningNetwork::new();
        assert!(network.empathy_engine.empathy_level > 0.0);
        assert!(!network.emotion_regulation.strategies.is_empty());
    }

    #[test]
    fn test_emotional_association_learning() {
        let network = EmotionalLearningNetwork::new();
        let result =
            network.learn_emotional_association("test_pattern", EmotionalState::Confident, 0.8);
        assert!(result.is_ok());
    }

    #[test]
    fn test_emotion_prediction() {
        let network = EmotionalLearningNetwork::new();
        let prediction = network.predict_emotional_response("error_pattern");
        assert!(prediction.is_ok());

        let prediction = prediction.expect("prediction should succeed");
        assert!(prediction.confidence >= 0.0 && prediction.confidence <= 1.0);
    }

    #[test]
    fn test_empathetic_response() {
        let network = EmotionalLearningNetwork::new();
        let response = network.generate_empathetic_response(EmotionalState::Cautious);
        assert!(response.is_ok());

        let response = response.expect("response should be available");
        assert!(response.intensity > 0.0);
        assert!(!response.response_action.is_empty());
    }

    #[test]
    fn test_emotion_regulation() {
        let mut network = EmotionalLearningNetwork::new();
        let outcome = network.regulate_emotion(EmotionalState::Cautious, EmotionalState::Confident);
        assert!(outcome.is_ok());

        let outcome = outcome.expect("outcome should be available");
        assert!(!outcome.strategy_used.is_empty());
        assert!(outcome.effectiveness >= 0.0 && outcome.effectiveness <= 1.0);
    }

    #[test]
    fn test_mood_update() {
        let mut network = EmotionalLearningNetwork::new();
        let result = network.update_mood(EmotionalState::Creative, "test_context");
        assert!(result.is_ok());

        assert_eq!(
            network.mood_tracker.current_mood.primary_mood,
            EmotionalState::Creative
        );
    }
}