oxirs-chat 0.2.4

//! Temporal Consciousness
//!
//! Temporal pattern recognition and future projection.

use anyhow::Result;
use std::collections::{HashMap, VecDeque};

use super::super::*;
use super::state_machine::ConsciousnessState;

#[derive(Debug, Clone)]
pub struct TemporalPatternRecognition {
    patterns: Vec<String>,
    confidence: f64,
}

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

impl TemporalPatternRecognition {
    pub fn new() -> Self {
        Self {
            patterns: Vec::new(),
            confidence: 0.0,
        }
    }

    /// Find patterns relevant to the given query
    pub fn find_relevant_patterns(&self, query: &str) -> Result<Vec<String>> {
        // Simple pattern matching based on keyword overlap
        let query_words: Vec<&str> = query.split_whitespace().collect();
        let relevant_patterns = self
            .patterns
            .iter()
            .filter(|pattern| {
                let pattern_words: Vec<&str> = pattern.split_whitespace().collect();
                query_words.iter().any(|word| pattern_words.contains(word))
            })
            .cloned()
            .collect();
        Ok(relevant_patterns)
    }

    /// Update patterns with new information
    pub fn update_patterns(&mut self, new_patterns: Vec<String>) {
        for pattern in new_patterns {
            if !self.patterns.contains(&pattern) {
                self.patterns.push(pattern);
            }
        }
        // Update confidence based on pattern count
        self.confidence = (self.patterns.len() as f64).min(100.0) / 100.0;
    }
}

/// Future projection engine for predictions
#[derive(Debug, Clone)]
pub struct FutureProjectionEngine {
    predictions: Vec<String>,
    horizon: Duration,
}

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

impl FutureProjectionEngine {
    pub fn new() -> Self {
        Self {
            predictions: Vec::new(),
            horizon: Duration::from_secs(3600),
        }
    }

    /// Project future implications based on current events
    pub fn project_implications(
        &self,
        query: &str,
        events: &[TemporalEvent],
    ) -> Result<Vec<String>> {
        let mut implications = Vec::new();

        // Analyze events for potential future implications
        for event in events {
            if event.content.contains(&query.to_lowercase())
                || query.to_lowercase().contains(&event.content.to_lowercase())
            {
                let implication = format!(
                    "Based on recent event '{}', potential future development could involve {}",
                    event.content, query
                );
                implications.push(implication);
            }
        }

        // Add general implications based on existing predictions
        if implications.is_empty() {
            implications.push(format!(
                "Future implications for '{query}' will depend on emerging patterns"
            ));
        }

        Ok(implications)
    }
}

/// Temporal processing metrics
#[derive(Debug, Clone)]
pub struct TemporalMetrics {
    pub pattern_detection_rate: f64,
    pub prediction_accuracy: f64,
    pub temporal_coherence: f64,
}

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

impl TemporalMetrics {
    pub fn new() -> Self {
        Self {
            pattern_detection_rate: 0.0,
            prediction_accuracy: 0.0,
            temporal_coherence: 0.0,
        }
    }
}

/// Temporal pattern structure
#[derive(Debug, Clone)]
pub struct TemporalPattern {
    pub pattern_type: String,
    pub frequency: Duration,
    pub confidence: f64,
    pub occurrences: Vec<std::time::Instant>,
}

/// Long-term temporal trend
#[derive(Debug, Clone)]
pub struct TemporalTrend {
    pub trend_name: String,
    pub direction: f64, // positive for increasing, negative for decreasing
    pub strength: f64,
    pub timespan: Duration,
}

/// Cyclic event in temporal patterns
#[derive(Debug, Clone)]
pub struct CyclicEvent {
    pub event_type: String,
    pub cycle_duration: Duration,
    pub last_occurrence: std::time::Instant,
    pub intensity: f64,
}

/// Maintains awareness of historical context and temporal patterns
#[derive(Debug, Clone)]
pub struct TemporalConsciousness {
    temporal_memory: TemporalMemoryBank,
    pattern_recognition: TemporalPatternRecognition,
    future_projection: FutureProjectionEngine,
    temporal_metrics: TemporalMetrics,
}

#[derive(Debug, Clone)]
pub struct TemporalMemoryBank {
    short_term: VecDeque<TemporalEvent>,
    medium_term: Vec<TemporalPattern>,
    long_term: Vec<TemporalTrend>,
    cyclic_patterns: HashMap<Duration, Vec<CyclicEvent>>,
}

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

impl TemporalMemoryBank {
    pub fn new() -> Self {
        Self {
            short_term: VecDeque::new(),
            medium_term: Vec::new(),
            long_term: Vec::new(),
            cyclic_patterns: HashMap::new(),
        }
    }

    /// Get recent events within the specified duration
    pub fn get_recent_events(&self, duration: Duration) -> Vec<TemporalEvent> {
        let now = std::time::Instant::now();
        self.short_term
            .iter()
            .filter(|event| now.duration_since(event.timestamp) <= duration)
            .cloned()
            .collect()
    }
}

#[derive(Debug, Clone)]
pub struct TemporalEvent {
    content: String,
    timestamp: std::time::Instant,
    significance: f64,
    context_tags: Vec<String>,
    emotional_valence: f64,
}

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

impl TemporalConsciousness {
    pub fn new() -> Self {
        Self {
            temporal_memory: TemporalMemoryBank::new(),
            pattern_recognition: TemporalPatternRecognition::new(),
            future_projection: FutureProjectionEngine::new(),
            temporal_metrics: TemporalMetrics::new(),
        }
    }

    /// Analyze temporal context for consciousness processing
    pub fn analyze_temporal_context(
        &self,
        query: &str,
        current_time: std::time::Instant,
    ) -> Result<TemporalContext> {
        let recent_events = self
            .temporal_memory
            .get_recent_events(Duration::from_secs(3600));
        let relevant_patterns = self.pattern_recognition.find_relevant_patterns(query)?;
        let future_implications = self
            .future_projection
            .project_implications(query, &recent_events)?;

        Ok(TemporalContext {
            recent_events,
            relevant_patterns,
            future_implications,
            temporal_coherence: self.calculate_temporal_coherence(),
            time_awareness: self.calculate_time_awareness(current_time),
        })
    }

    /// Record new temporal event
    pub fn record_event(
        &mut self,
        content: String,
        significance: f64,
        tags: Vec<String>,
    ) -> Result<()> {
        let event = TemporalEvent {
            content,
            timestamp: std::time::Instant::now(),
            significance,
            context_tags: tags,
            emotional_valence: 0.0, // Could be calculated from content
        };

        self.temporal_memory.short_term.push_back(event);

        // Keep short-term memory bounded
        if self.temporal_memory.short_term.len() > 100 {
            self.temporal_memory.short_term.pop_front();
        }

        // Update patterns
        let event_contents: Vec<String> = self
            .temporal_memory
            .short_term
            .iter()
            .map(|e| e.content.clone())
            .collect();
        self.pattern_recognition.update_patterns(event_contents);

        Ok(())
    }

    /// Calculate temporal coherence based on memory patterns
    fn calculate_temporal_coherence(&self) -> f64 {
        if self.temporal_memory.short_term.is_empty() {
            return 0.5; // Neutral coherence when no events
        }

        // Calculate coherence based on event timestamps and significance
        let total_events = self.temporal_memory.short_term.len() as f64;
        let avg_significance: f64 = self
            .temporal_memory
            .short_term
            .iter()
            .map(|e| e.significance)
            .sum::<f64>()
            / total_events;

        // Normalize to 0-1 range
        avg_significance.clamp(0.0, 1.0)
    }

    /// Calculate time awareness based on current time and recent events
    fn calculate_time_awareness(&self, current_time: std::time::Instant) -> f64 {
        if self.temporal_memory.short_term.is_empty() {
            return 0.5; // Neutral awareness when no events
        }

        // Calculate awareness based on how recent the events are
        let recent_threshold = Duration::from_secs(300); // 5 minutes
        let recent_events = self
            .temporal_memory
            .short_term
            .iter()
            .filter(|e| current_time.duration_since(e.timestamp) <= recent_threshold)
            .count();

        let total_events = self.temporal_memory.short_term.len();
        let recent_ratio = recent_events as f64 / total_events as f64;

        // High ratio of recent events = high time awareness
        recent_ratio.clamp(0.0, 1.0)
    }
}

/// Supporting structures for consciousness enhancements
#[derive(Debug, Clone)]
pub struct TransitionRule {
    target_state: ConsciousnessState,
    condition: TransitionCondition,
    probability: f64,
}

impl TransitionRule {
    pub fn new(
        target_state: ConsciousnessState,
        condition: TransitionCondition,
        probability: f64,
    ) -> Self {
        Self {
            target_state,
            condition,
            probability,
        }
    }
}

#[derive(Debug, Clone)]
pub enum TransitionCondition {
    QueryComplexity(f64),
    EmotionalContent(f64),
    KeywordPresence(Vec<&'static str>),
    LogicalPattern,
    PhilosophicalContent,
    TimeElapsed(Duration),
}

#[derive(Debug, Clone)]
pub struct StateProcessingParameters {
    pub attention_focus: f64,
    pub emotional_sensitivity: f64,
    pub creativity_boost: f64,
    pub analytical_depth: f64,
    pub memory_consolidation: f64,
}

impl Default for StateProcessingParameters {
    fn default() -> Self {
        Self {
            attention_focus: 0.5,
            emotional_sensitivity: 0.5,
            creativity_boost: 0.5,
            analytical_depth: 0.5,
            memory_consolidation: 0.5,
        }
    }
}

#[derive(Debug, Clone)]
pub struct StateMetrics {
    state_transitions: usize,
    average_state_duration: Duration,
    most_frequent_state: ConsciousnessState,
    state_effectiveness: HashMap<ConsciousnessState, f64>,
}

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

impl StateMetrics {
    pub fn new() -> Self {
        Self {
            state_transitions: 0,
            average_state_duration: Duration::from_secs(0),
            most_frequent_state: ConsciousnessState::Baseline,
            state_effectiveness: HashMap::new(),
        }
    }
}

// Additional supporting structures would continue here...

// Use NeuralActivation from consciousness_types instead
// This provides an extensive foundation for advanced consciousness processing