oxirs_core/consciousness/

temporal_consciousness.rs

//! Temporal Consciousness Module
//!
//! This module implements advanced temporal reasoning and consciousness that can
//! understand and process time-based patterns, temporal relationships, and
//! chronological dependencies in RDF data.

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

/// Temporal consciousness for understanding time-based patterns and relationships
pub struct TemporalConsciousness {
    /// Temporal memory for storing time-based experiences
    temporal_memory: TemporalMemory,
    /// Chronological pattern analyzer
    pattern_analyzer: ChronologicalPatternAnalyzer,
    /// Time-aware emotional learning
    temporal_emotions: TemporalEmotionalProcessor,
    /// Future prediction capabilities
    future_predictor: FuturePredictionEngine,
    /// Historical context analyzer
    historical_analyzer: HistoricalContextAnalyzer,
    /// Temporal coherence monitor
    coherence_monitor: TemporalCoherenceMonitor,
}

/// Temporal memory for storing time-based experiences and patterns
#[derive(Debug)]
#[allow(dead_code)]
pub struct TemporalMemory {
    /// Time-indexed experiences
    experiences: BTreeMap<SystemTime, TemporalExperience>,
    /// Pattern occurrence timeline
    pattern_timeline: BTreeMap<SystemTime, Vec<PatternOccurrence>>,
    /// Cyclic pattern detection
    cyclic_patterns: HashMap<String, CyclicPattern>,
    /// Memory retention policy
    retention_policy: MemoryRetentionPolicy,
}

/// A temporal experience stored in consciousness memory
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalExperience {
    /// Unique experience identifier
    pub id: String,
    /// Timestamp of the experience
    pub timestamp: SystemTime,
    /// Patterns involved in the experience
    pub patterns: Vec<String>,
    /// Emotional context at the time
    pub emotional_context: EmotionalState,
    /// Performance outcome
    pub performance_outcome: f64,
    /// Duration of the experience
    pub duration: Duration,
    /// Related experiences (temporal links)
    pub related_experiences: Vec<String>,
}

/// Pattern occurrence in temporal timeline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternOccurrence {
    /// Pattern identifier
    pub pattern_id: String,
    /// Timestamp of occurrence
    pub timestamp: SystemTime,
    /// Frequency at this time
    pub frequency: f64,
    /// Context factors
    pub context_factors: Vec<String>,
    /// Emotional intensity
    pub emotional_intensity: f64,
}

/// Cyclic pattern detected in temporal data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CyclicPattern {
    /// Pattern identifier
    pub pattern_id: String,
    /// Cycle duration
    pub cycle_duration: Duration,
    /// Cycle phase
    pub phase: f64,
    /// Amplitude of the cycle
    pub amplitude: f64,
    /// Confidence in cycle detection
    pub confidence: f64,
    /// Last seen occurrence
    pub last_occurrence: SystemTime,
}

/// Memory retention policy for temporal data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryRetentionPolicy {
    /// Maximum age for short-term memory
    pub short_term_max_age: Duration,
    /// Maximum age for long-term memory
    pub long_term_max_age: Duration,
    /// Compression threshold for old memories
    pub compression_threshold: f64,
    /// Importance threshold for retention
    pub importance_threshold: f64,
}

/// Chronological pattern analyzer for temporal sequences
#[derive(Debug)]
#[allow(dead_code)]
pub struct ChronologicalPatternAnalyzer {
    /// Detected temporal sequences
    temporal_sequences: HashMap<String, TemporalSequence>,
    /// Sequence matching algorithms
    matching_algorithms: Vec<Box<dyn SequenceMatchingAlgorithm>>,
    /// Pattern evolution tracker
    evolution_tracker: PatternEvolutionTracker,
}

/// Temporal sequence of related patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalSequence {
    /// Sequence identifier
    pub id: String,
    /// Ordered pattern steps
    pub steps: Vec<SequenceStep>,
    /// Average duration between steps
    pub step_duration: Duration,
    /// Sequence reliability score
    pub reliability: f64,
    /// Predictive power of sequence
    pub predictive_power: f64,
}

/// A step in a temporal sequence
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SequenceStep {
    /// Step order in sequence
    pub order: usize,
    /// Pattern at this step
    pub pattern: String,
    /// Expected duration from previous step
    pub expected_duration: Duration,
    /// Confidence in this step
    pub confidence: f64,
    /// Alternative patterns at this step
    pub alternatives: Vec<String>,
}

/// Pattern evolution tracker for monitoring changes over time
#[derive(Debug)]
#[allow(dead_code)]
pub struct PatternEvolutionTracker {
    /// Evolution history of patterns
    evolution_history: HashMap<String, Vec<EvolutionSnapshot>>,
    /// Trend analysis results
    trend_analysis: HashMap<String, TrendAnalysis>,
    /// Evolution prediction models
    prediction_models: HashMap<String, EvolutionPredictionModel>,
}

/// Snapshot of pattern state at a specific time
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EvolutionSnapshot {
    /// Timestamp of snapshot
    pub timestamp: SystemTime,
    /// Pattern characteristics at this time
    pub characteristics: PatternCharacteristics,
    /// Performance metrics
    pub performance: f64,
    /// Usage frequency
    pub usage_frequency: f64,
    /// Complexity measure
    pub complexity: f64,
}

/// Characteristics of a pattern at a point in time
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternCharacteristics {
    /// Pattern size/scope
    pub size: f64,
    /// Join complexity
    pub join_complexity: f64,
    /// Variable density
    pub variable_density: f64,
    /// Selectivity score
    pub selectivity: f64,
    /// Emotional association strength
    pub emotional_strength: f64,
}

/// Trend analysis result for pattern evolution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendAnalysis {
    /// Overall trend direction
    pub trend_direction: TrendDirection,
    /// Rate of change
    pub change_rate: f64,
    /// Trend confidence
    pub confidence: f64,
    /// Projected future state
    pub projection: Option<FutureProjection>,
    /// Identified inflection points
    pub inflection_points: Vec<SystemTime>,
}

/// Direction of pattern evolution trend
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum TrendDirection {
    /// Pattern is becoming more complex/frequent
    Increasing,
    /// Pattern is becoming simpler/less frequent
    Decreasing,
    /// Pattern is remaining stable
    Stable,
    /// Pattern shows cyclical behavior
    Cyclical,
    /// Pattern behavior is unpredictable
    Chaotic,
}

/// Future projection of pattern evolution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FutureProjection {
    /// Projected timestamp
    pub timestamp: SystemTime,
    /// Projected characteristics
    pub projected_characteristics: PatternCharacteristics,
    /// Confidence in projection
    pub confidence: f64,
    /// Uncertainty bounds
    pub uncertainty_bounds: (f64, f64),
}

/// Evolution prediction model for patterns
#[derive(Debug)]
#[allow(dead_code)]
pub struct EvolutionPredictionModel {
    /// Model type
    model_type: PredictionModelType,
    /// Model parameters
    parameters: HashMap<String, f64>,
    /// Historical accuracy
    historical_accuracy: f64,
    /// Last training time
    last_training: SystemTime,
}

/// Types of prediction models
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum PredictionModelType {
    /// Linear regression model
    LinearRegression,
    /// Exponential smoothing
    ExponentialSmoothing,
    /// ARIMA time series model
    ARIMA,
    /// Neural network predictor
    NeuralNetwork,
    /// Ensemble model
    Ensemble,
}

/// Temporal emotional processor for time-aware emotional learning
#[derive(Debug)]
#[allow(dead_code)]
pub struct TemporalEmotionalProcessor {
    /// Emotional state history
    emotional_history: BTreeMap<SystemTime, EmotionalState>,
    /// Emotional trend analyzer
    trend_analyzer: EmotionalTrendAnalyzer,
    /// Mood prediction engine
    mood_predictor: MoodPredictionEngine,
    /// Emotional memory consolidation
    memory_consolidator: EmotionalMemoryConsolidator,
}

/// Emotional trend analyzer for understanding emotional patterns over time
#[derive(Debug)]
#[allow(dead_code)]
pub struct EmotionalTrendAnalyzer {
    /// Current emotional trends
    current_trends: HashMap<EmotionalState, EmotionalTrend>,
    /// Transition probabilities between states
    transition_matrix: HashMap<(EmotionalState, EmotionalState), f64>,
    /// Seasonal emotional patterns
    seasonal_patterns: HashMap<String, SeasonalEmotionalPattern>,
}

/// Emotional trend information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmotionalTrend {
    /// Current trend direction
    pub direction: TrendDirection,
    /// Duration of current trend
    pub duration: Duration,
    /// Strength of the trend
    pub strength: f64,
    /// Predicted continuation time
    pub predicted_duration: Duration,
    /// Contributing factors
    pub factors: Vec<String>,
}

/// Seasonal emotional pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeasonalEmotionalPattern {
    /// Season identifier
    pub season: String,
    /// Dominant emotional states
    pub dominant_states: Vec<EmotionalState>,
    /// Pattern strength
    pub strength: f64,
    /// Historical occurrences
    pub occurrences: Vec<SystemTime>,
}

/// Mood prediction engine for forecasting emotional states
#[derive(Debug)]
#[allow(dead_code)]
pub struct MoodPredictionEngine {
    /// Prediction models for each emotional state
    prediction_models: HashMap<EmotionalState, EmotionalPredictionModel>,
    /// External factor correlations
    external_correlations: HashMap<String, f64>,
    /// Prediction horizon
    prediction_horizon: Duration,
}

/// Prediction model for emotional states
#[derive(Debug)]
#[allow(dead_code)]
pub struct EmotionalPredictionModel {
    /// Model parameters
    parameters: HashMap<String, f64>,
    /// Historical accuracy
    accuracy: f64,
    /// Last update time
    last_update: SystemTime,
    /// Confidence intervals
    confidence_intervals: HashMap<String, (f64, f64)>,
}

/// Emotional memory consolidator for long-term emotional learning
#[derive(Debug)]
#[allow(dead_code)]
pub struct EmotionalMemoryConsolidator {
    /// Consolidated emotional experiences
    consolidated_memories: HashMap<String, ConsolidatedEmotionalMemory>,
    /// Consolidation strategies
    consolidation_strategies: Vec<ConsolidationStrategy>,
    /// Memory importance scorer
    importance_scorer: MemoryImportanceScorer,
}

/// Consolidated emotional memory
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConsolidatedEmotionalMemory {
    /// Memory identifier
    pub id: String,
    /// Emotional theme
    pub emotional_theme: EmotionalState,
    /// Consolidated experiences
    pub experiences: Vec<String>,
    /// Emotional intensity
    pub intensity: f64,
    /// Temporal span
    pub temporal_span: Duration,
    /// Learning outcomes
    pub learning_outcomes: Vec<String>,
}

/// Strategy for memory consolidation
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ConsolidationStrategy {
    /// Group by emotional similarity
    EmotionalSimilarity,
    /// Group by temporal proximity
    TemporalProximity,
    /// Group by pattern similarity
    PatternSimilarity,
    /// Group by performance outcomes
    PerformanceOutcomes,
}

/// Memory importance scorer
#[derive(Debug)]
pub struct MemoryImportanceScorer {
    /// Scoring criteria weights
    criteria_weights: HashMap<String, f64>,
    /// Decay function parameters
    decay_parameters: HashMap<String, f64>,
    /// Threshold for importance
    importance_threshold: f64,
}

/// Future prediction engine for anticipating upcoming patterns
#[derive(Debug)]
pub struct FuturePredictionEngine {
    /// Active prediction models
    prediction_models: HashMap<String, TemporalPredictionModel>,
    /// Prediction accuracy tracker
    accuracy_tracker: PredictionAccuracyTracker,
    /// Uncertainty quantification
    uncertainty_quantifier: UncertaintyQuantifier,
}

/// Temporal prediction model
#[derive(Debug)]
pub struct TemporalPredictionModel {
    /// Model identifier
    id: String,
    /// Model type
    model_type: PredictionModelType,
    /// Training data
    training_data: Vec<TemporalDataPoint>,
    /// Model parameters
    parameters: HashMap<String, f64>,
    /// Prediction horizon
    horizon: Duration,
}

/// Temporal data point for training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalDataPoint {
    /// Timestamp
    pub timestamp: SystemTime,
    /// Feature values
    pub features: HashMap<String, f64>,
    /// Target value
    pub target: f64,
    /// Weight/importance
    pub weight: f64,
}

/// Prediction accuracy tracker
#[derive(Debug)]
pub struct PredictionAccuracyTracker {
    /// Accuracy history by model
    accuracy_history: HashMap<String, VecDeque<AccuracyMeasurement>>,
    /// Current accuracy statistics
    current_stats: HashMap<String, AccuracyStatistics>,
}

/// Accuracy measurement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AccuracyMeasurement {
    /// Measurement timestamp
    pub timestamp: SystemTime,
    /// Predicted value
    pub predicted: f64,
    /// Actual value
    pub actual: f64,
    /// Absolute error
    pub error: f64,
    /// Relative error
    pub relative_error: f64,
}

/// Accuracy statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AccuracyStatistics {
    /// Mean absolute error
    pub mae: f64,
    /// Root mean square error
    pub rmse: f64,
    /// Mean absolute percentage error
    pub mape: f64,
    /// R-squared correlation
    pub r_squared: f64,
    /// Number of predictions
    pub prediction_count: usize,
}

/// Uncertainty quantifier for prediction confidence
#[derive(Debug)]
pub struct UncertaintyQuantifier {
    /// Uncertainty estimation method
    estimation_method: UncertaintyMethod,
    /// Confidence intervals
    confidence_intervals: HashMap<String, ConfidenceInterval>,
    /// Uncertainty sources
    uncertainty_sources: HashMap<String, f64>,
}

/// Methods for uncertainty estimation
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum UncertaintyMethod {
    /// Bootstrap sampling
    Bootstrap,
    /// Monte Carlo dropout
    MonteCarloDropout,
    /// Ensemble disagreement
    EnsembleDisagreement,
    /// Quantile regression
    QuantileRegression,
}

/// Confidence interval for predictions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfidenceInterval {
    /// Lower bound
    pub lower: f64,
    /// Upper bound
    pub upper: f64,
    /// Confidence level (e.g., 0.95 for 95%)
    pub confidence_level: f64,
    /// Method used
    pub method: String,
}

/// Historical context analyzer for understanding long-term patterns
#[derive(Debug)]
pub struct HistoricalContextAnalyzer {
    /// Historical pattern database
    pattern_database: HistoricalPatternDatabase,
    /// Context similarity analyzer
    similarity_analyzer: ContextSimilarityAnalyzer,
    /// Historical lesson extractor
    lesson_extractor: HistoricalLessonExtractor,
}

/// Database of historical patterns
#[derive(Debug)]
pub struct HistoricalPatternDatabase {
    /// Stored historical patterns
    patterns: HashMap<String, HistoricalPattern>,
    /// Pattern indexing system
    pattern_index: TemporalIndex,
    /// Search capabilities
    search_engine: PatternSearchEngine,
}

/// Historical pattern record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalPattern {
    /// Pattern identifier
    pub id: String,
    /// Pattern description
    pub description: String,
    /// Occurrence timeline
    pub occurrences: Vec<SystemTime>,
    /// Context factors
    pub context_factors: HashMap<String, f64>,
    /// Outcomes achieved
    pub outcomes: Vec<PatternOutcome>,
    /// Lessons learned
    pub lessons: Vec<String>,
}

/// Outcome of a historical pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternOutcome {
    /// Outcome description
    pub description: String,
    /// Success/failure measure
    pub success_measure: f64,
    /// Contributing factors
    pub factors: Vec<String>,
    /// Emotional impact
    pub emotional_impact: EmotionalState,
}

/// Temporal index for efficient pattern lookup
#[derive(Debug)]
pub struct TemporalIndex {
    /// Time-based index
    time_index: BTreeMap<SystemTime, Vec<String>>,
    /// Context-based index
    context_index: HashMap<String, Vec<String>>,
    /// Outcome-based index
    outcome_index: HashMap<String, Vec<String>>,
}

/// Pattern search engine
#[derive(Debug)]
pub struct PatternSearchEngine {
    /// Search algorithms
    algorithms: Vec<Box<dyn PatternSearchAlgorithm>>,
    /// Search optimization
    optimization_cache: HashMap<String, SearchResult>,
}

/// Context similarity analyzer
#[derive(Debug)]
pub struct ContextSimilarityAnalyzer {
    /// Similarity metrics
    similarity_metrics: Vec<Box<dyn SimilarityMetric>>,
    /// Context weighting
    context_weights: HashMap<String, f64>,
}

/// Historical lesson extractor
#[derive(Debug)]
pub struct HistoricalLessonExtractor {
    /// Lesson extraction rules
    extraction_rules: Vec<LessonExtractionRule>,
    /// Lesson validation criteria
    validation_criteria: Vec<LessonValidationCriterion>,
}

/// Temporal coherence monitor
#[derive(Debug)]
pub struct TemporalCoherenceMonitor {
    /// Coherence measurements
    coherence_measurements: VecDeque<CoherenceMeasurement>,
    /// Coherence thresholds
    coherence_thresholds: HashMap<String, f64>,
    /// Anomaly detector
    anomaly_detector: TemporalAnomalyDetector,
}

/// Coherence measurement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoherenceMeasurement {
    /// Measurement timestamp
    pub timestamp: SystemTime,
    /// Coherence score
    pub coherence_score: f64,
    /// Component coherences
    pub component_coherences: HashMap<String, f64>,
    /// Factors affecting coherence
    pub affecting_factors: Vec<String>,
}

/// Temporal anomaly detector
#[derive(Debug)]
pub struct TemporalAnomalyDetector {
    /// Detection algorithms
    detection_algorithms: Vec<Box<dyn AnomalyDetectionAlgorithm>>,
    /// Anomaly history
    anomaly_history: VecDeque<TemporalAnomaly>,
    /// Detection sensitivity
    sensitivity: f64,
}

/// Temporal anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalAnomaly {
    /// Anomaly identifier
    pub id: String,
    /// Detection timestamp
    pub timestamp: SystemTime,
    /// Anomaly severity
    pub severity: f64,
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Description
    pub description: String,
    /// Affected components
    pub affected_components: Vec<String>,
}

/// Types of temporal anomalies
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum AnomalyType {
    /// Pattern sequence disruption
    SequenceDisruption,
    /// Unexpected pattern emergence
    PatternEmergence,
    /// Pattern disappearance
    PatternDisappearance,
    /// Temporal drift
    TemporalDrift,
    /// Coherence breakdown
    CoherenceBreakdown,
    /// Memory inconsistency
    MemoryInconsistency,
}

// Trait definitions for extensibility

/// Trait for sequence matching algorithms
pub trait SequenceMatchingAlgorithm: std::fmt::Debug + Send + Sync {
    /// Match temporal sequences
    fn match_sequence(&self, sequence: &TemporalSequence, data: &[TemporalDataPoint]) -> f64;

    /// Algorithm name
    fn name(&self) -> &str;
}

/// Trait for pattern search algorithms
pub trait PatternSearchAlgorithm: std::fmt::Debug + Send + Sync {
    /// Search for patterns matching criteria
    fn search(
        &self,
        criteria: &SearchCriteria,
        database: &HistoricalPatternDatabase,
    ) -> SearchResult;

    /// Algorithm name
    fn name(&self) -> &str;
}

/// Search criteria for pattern search
#[derive(Debug, Clone)]
pub struct SearchCriteria {
    /// Temporal constraints
    pub temporal_constraints: Option<(SystemTime, SystemTime)>,
    /// Context constraints
    pub context_constraints: HashMap<String, f64>,
    /// Similarity threshold
    pub similarity_threshold: f64,
    /// Maximum results
    pub max_results: usize,
}

/// Search result
#[derive(Debug, Clone)]
pub struct SearchResult {
    /// Found patterns
    pub patterns: Vec<HistoricalPattern>,
    /// Similarity scores
    pub scores: Vec<f64>,
    /// Search time
    pub search_time: Duration,
}

/// Trait for similarity metrics
pub trait SimilarityMetric: std::fmt::Debug + Send + Sync {
    /// Calculate similarity between contexts
    fn calculate_similarity(
        &self,
        context1: &HashMap<String, f64>,
        context2: &HashMap<String, f64>,
    ) -> f64;

    /// Metric name
    fn name(&self) -> &str;
}

/// Lesson extraction rule
#[derive(Debug, Clone)]
pub struct LessonExtractionRule {
    /// Rule name
    pub name: String,
    /// Condition for rule application
    pub condition: String,
    /// Lesson template
    pub lesson_template: String,
    /// Confidence threshold
    pub confidence_threshold: f64,
}

/// Lesson validation criterion
#[derive(Debug, Clone)]
pub struct LessonValidationCriterion {
    /// Criterion name
    pub name: String,
    /// Validation logic
    pub validation_logic: String,
    /// Importance weight
    pub weight: f64,
}

/// Trait for anomaly detection algorithms
pub trait AnomalyDetectionAlgorithm: std::fmt::Debug + Send + Sync {
    /// Detect anomalies in temporal data
    fn detect_anomalies(&self, data: &[TemporalDataPoint]) -> Vec<TemporalAnomaly>;

    /// Algorithm name
    fn name(&self) -> &str;

    /// Detection sensitivity
    fn sensitivity(&self) -> f64;
}

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

impl TemporalConsciousness {
    /// Create a new temporal consciousness instance
    pub fn new() -> Self {
        Self {
            temporal_memory: TemporalMemory::new(),
            pattern_analyzer: ChronologicalPatternAnalyzer::new(),
            temporal_emotions: TemporalEmotionalProcessor::new(),
            future_predictor: FuturePredictionEngine::new(),
            historical_analyzer: HistoricalContextAnalyzer::new(),
            coherence_monitor: TemporalCoherenceMonitor::new(),
        }
    }

    /// Record a temporal experience
    pub fn record_experience(&mut self, experience: TemporalExperience) -> Result<(), OxirsError> {
        self.temporal_memory.add_experience(experience.clone())?;
        self.temporal_emotions
            .record_emotional_state(experience.timestamp, experience.emotional_context.clone())?;
        self.update_patterns(&experience)?;
        Ok(())
    }

    /// Analyze temporal patterns in current query
    pub fn analyze_temporal_patterns(
        &self,
        patterns: &[AlgebraTriplePattern],
    ) -> Result<TemporalAnalysisResult, OxirsError> {
        let sequence_analysis = self.pattern_analyzer.analyze_patterns(patterns)?;
        let emotional_context = self.temporal_emotions.analyze_current_context()?;
        let predictions = self.future_predictor.predict_outcomes(patterns)?;
        let historical_context = self.historical_analyzer.find_similar_contexts(patterns)?;
        let coherence = self.coherence_monitor.assess_coherence()?;

        let recommendations = self.generate_recommendations(&sequence_analysis, &predictions)?;

        Ok(TemporalAnalysisResult {
            sequence_analysis,
            emotional_context,
            predictions,
            historical_context,
            coherence_score: coherence,
            recommendations,
        })
    }

    /// Update pattern understanding based on experience
    fn update_patterns(&mut self, experience: &TemporalExperience) -> Result<(), OxirsError> {
        self.pattern_analyzer.update_with_experience(experience)?;
        self.temporal_emotions
            .update_emotional_patterns(experience)?;
        self.future_predictor.incorporate_feedback(experience)?;
        Ok(())
    }

    /// Generate recommendations based on temporal analysis
    fn generate_recommendations(
        &self,
        sequence_analysis: &SequenceAnalysisResult,
        predictions: &PredictionResult,
    ) -> Result<Vec<TemporalRecommendation>, OxirsError> {
        let mut recommendations = Vec::new();

        if sequence_analysis.confidence > 0.8 {
            recommendations.push(TemporalRecommendation {
                recommendation_type: RecommendationType::SequenceOptimization,
                description: "High confidence sequence detected - optimize for temporal ordering"
                    .to_string(),
                confidence: sequence_analysis.confidence,
                expected_benefit: 0.15,
            });
        }

        if predictions.uncertainty < 0.3 {
            recommendations.push(TemporalRecommendation {
                recommendation_type: RecommendationType::PredictiveOptimization,
                description: "Low uncertainty prediction - leverage for optimization".to_string(),
                confidence: 1.0 - predictions.uncertainty,
                expected_benefit: 0.12,
            });
        }

        Ok(recommendations)
    }
}

/// Result of temporal analysis
#[derive(Debug, Clone)]
pub struct TemporalAnalysisResult {
    /// Sequence analysis results
    pub sequence_analysis: SequenceAnalysisResult,
    /// Emotional context analysis
    pub emotional_context: EmotionalContextResult,
    /// Future predictions
    pub predictions: PredictionResult,
    /// Historical context
    pub historical_context: HistoricalContextResult,
    /// Overall coherence score
    pub coherence_score: f64,
    /// Temporal recommendations
    pub recommendations: Vec<TemporalRecommendation>,
}

/// Sequence analysis result
#[derive(Debug, Clone)]
pub struct SequenceAnalysisResult {
    /// Detected sequences
    pub sequences: Vec<TemporalSequence>,
    /// Overall confidence
    pub confidence: f64,
    /// Predictive power
    pub predictive_power: f64,
}

/// Emotional context result
#[derive(Debug, Clone)]
pub struct EmotionalContextResult {
    /// Current emotional trend
    pub current_trend: EmotionalTrend,
    /// Predicted emotional states
    pub predicted_states: Vec<(EmotionalState, f64)>,
    /// Emotional stability
    pub stability: f64,
}

/// Prediction result
#[derive(Debug, Clone)]
pub struct PredictionResult {
    /// Predicted outcomes
    pub outcomes: Vec<PredictedOutcome>,
    /// Overall uncertainty
    pub uncertainty: f64,
    /// Confidence intervals
    pub confidence_intervals: Vec<ConfidenceInterval>,
}

/// Predicted outcome
#[derive(Debug, Clone)]
pub struct PredictedOutcome {
    /// Outcome description
    pub description: String,
    /// Probability
    pub probability: f64,
    /// Expected value
    pub expected_value: f64,
    /// Time horizon
    pub time_horizon: Duration,
}

/// Historical context result
#[derive(Debug, Clone)]
pub struct HistoricalContextResult {
    /// Similar historical patterns
    pub similar_patterns: Vec<HistoricalPattern>,
    /// Similarity scores
    pub similarity_scores: Vec<f64>,
    /// Extracted lessons
    pub lessons: Vec<String>,
}

/// Temporal recommendation
#[derive(Debug, Clone)]
pub struct TemporalRecommendation {
    /// Type of recommendation
    pub recommendation_type: RecommendationType,
    /// Description
    pub description: String,
    /// Confidence in recommendation
    pub confidence: f64,
    /// Expected benefit
    pub expected_benefit: f64,
}

/// Types of temporal recommendations
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum RecommendationType {
    /// Optimize for temporal sequences
    SequenceOptimization,
    /// Leverage predictive insights
    PredictiveOptimization,
    /// Apply historical lessons
    HistoricalOptimization,
    /// Improve temporal coherence
    CoherenceOptimization,
    /// Address temporal anomalies
    AnomalyMitigation,
}

// Implementation stubs for the various components

impl TemporalMemory {
    fn new() -> Self {
        Self {
            experiences: BTreeMap::new(),
            pattern_timeline: BTreeMap::new(),
            cyclic_patterns: HashMap::new(),
            retention_policy: MemoryRetentionPolicy::default(),
        }
    }

    fn add_experience(&mut self, experience: TemporalExperience) -> Result<(), OxirsError> {
        self.experiences.insert(experience.timestamp, experience);
        Ok(())
    }
}

impl MemoryRetentionPolicy {
    fn default() -> Self {
        Self {
            short_term_max_age: Duration::from_secs(3600), // 1 hour
            long_term_max_age: Duration::from_secs(86400 * 30), // 30 days
            compression_threshold: 0.5,
            importance_threshold: 0.3,
        }
    }
}

impl ChronologicalPatternAnalyzer {
    fn new() -> Self {
        Self {
            temporal_sequences: HashMap::new(),
            matching_algorithms: vec![],
            evolution_tracker: PatternEvolutionTracker::new(),
        }
    }

    fn analyze_patterns(
        &self,
        _patterns: &[AlgebraTriplePattern],
    ) -> Result<SequenceAnalysisResult, OxirsError> {
        Ok(SequenceAnalysisResult {
            sequences: vec![],
            confidence: 0.5,
            predictive_power: 0.3,
        })
    }

    fn update_with_experience(
        &mut self,
        _experience: &TemporalExperience,
    ) -> Result<(), OxirsError> {
        Ok(())
    }
}

impl PatternEvolutionTracker {
    fn new() -> Self {
        Self {
            evolution_history: HashMap::new(),
            trend_analysis: HashMap::new(),
            prediction_models: HashMap::new(),
        }
    }
}

impl TemporalEmotionalProcessor {
    fn new() -> Self {
        Self {
            emotional_history: BTreeMap::new(),
            trend_analyzer: EmotionalTrendAnalyzer::new(),
            mood_predictor: MoodPredictionEngine::new(),
            memory_consolidator: EmotionalMemoryConsolidator::new(),
        }
    }

    fn record_emotional_state(
        &mut self,
        timestamp: SystemTime,
        state: EmotionalState,
    ) -> Result<(), OxirsError> {
        self.emotional_history.insert(timestamp, state);
        Ok(())
    }

    fn analyze_current_context(&self) -> Result<EmotionalContextResult, OxirsError> {
        Ok(EmotionalContextResult {
            current_trend: EmotionalTrend {
                direction: TrendDirection::Stable,
                duration: Duration::from_secs(300),
                strength: 0.5,
                predicted_duration: Duration::from_secs(600),
                factors: vec!["stability".to_string()],
            },
            predicted_states: vec![(EmotionalState::Calm, 0.7)],
            stability: 0.8,
        })
    }

    fn update_emotional_patterns(
        &mut self,
        _experience: &TemporalExperience,
    ) -> Result<(), OxirsError> {
        Ok(())
    }
}

impl EmotionalTrendAnalyzer {
    fn new() -> Self {
        Self {
            current_trends: HashMap::new(),
            transition_matrix: HashMap::new(),
            seasonal_patterns: HashMap::new(),
        }
    }
}

impl MoodPredictionEngine {
    fn new() -> Self {
        Self {
            prediction_models: HashMap::new(),
            external_correlations: HashMap::new(),
            prediction_horizon: Duration::from_secs(3600),
        }
    }
}

impl EmotionalMemoryConsolidator {
    fn new() -> Self {
        Self {
            consolidated_memories: HashMap::new(),
            consolidation_strategies: vec![ConsolidationStrategy::EmotionalSimilarity],
            importance_scorer: MemoryImportanceScorer::new(),
        }
    }
}

impl MemoryImportanceScorer {
    fn new() -> Self {
        Self {
            criteria_weights: HashMap::new(),
            decay_parameters: HashMap::new(),
            importance_threshold: 0.3,
        }
    }
}

impl FuturePredictionEngine {
    fn new() -> Self {
        Self {
            prediction_models: HashMap::new(),
            accuracy_tracker: PredictionAccuracyTracker::new(),
            uncertainty_quantifier: UncertaintyQuantifier::new(),
        }
    }

    fn predict_outcomes(
        &self,
        _patterns: &[AlgebraTriplePattern],
    ) -> Result<PredictionResult, OxirsError> {
        Ok(PredictionResult {
            outcomes: vec![],
            uncertainty: 0.4,
            confidence_intervals: vec![],
        })
    }

    fn incorporate_feedback(&mut self, _experience: &TemporalExperience) -> Result<(), OxirsError> {
        Ok(())
    }
}

impl PredictionAccuracyTracker {
    fn new() -> Self {
        Self {
            accuracy_history: HashMap::new(),
            current_stats: HashMap::new(),
        }
    }
}

impl UncertaintyQuantifier {
    fn new() -> Self {
        Self {
            estimation_method: UncertaintyMethod::Bootstrap,
            confidence_intervals: HashMap::new(),
            uncertainty_sources: HashMap::new(),
        }
    }
}

impl HistoricalContextAnalyzer {
    fn new() -> Self {
        Self {
            pattern_database: HistoricalPatternDatabase::new(),
            similarity_analyzer: ContextSimilarityAnalyzer::new(),
            lesson_extractor: HistoricalLessonExtractor::new(),
        }
    }

    fn find_similar_contexts(
        &self,
        _patterns: &[AlgebraTriplePattern],
    ) -> Result<HistoricalContextResult, OxirsError> {
        Ok(HistoricalContextResult {
            similar_patterns: vec![],
            similarity_scores: vec![],
            lessons: vec!["Temporal analysis provides context".to_string()],
        })
    }
}

impl HistoricalPatternDatabase {
    fn new() -> Self {
        Self {
            patterns: HashMap::new(),
            pattern_index: TemporalIndex::new(),
            search_engine: PatternSearchEngine::new(),
        }
    }
}

impl TemporalIndex {
    fn new() -> Self {
        Self {
            time_index: BTreeMap::new(),
            context_index: HashMap::new(),
            outcome_index: HashMap::new(),
        }
    }
}

impl PatternSearchEngine {
    fn new() -> Self {
        Self {
            algorithms: vec![],
            optimization_cache: HashMap::new(),
        }
    }
}

impl ContextSimilarityAnalyzer {
    fn new() -> Self {
        Self {
            similarity_metrics: vec![],
            context_weights: HashMap::new(),
        }
    }
}

impl HistoricalLessonExtractor {
    fn new() -> Self {
        Self {
            extraction_rules: vec![],
            validation_criteria: vec![],
        }
    }
}

impl TemporalCoherenceMonitor {
    fn new() -> Self {
        Self {
            coherence_measurements: VecDeque::new(),
            coherence_thresholds: HashMap::new(),
            anomaly_detector: TemporalAnomalyDetector::new(),
        }
    }

    fn assess_coherence(&self) -> Result<f64, OxirsError> {
        Ok(0.75) // Placeholder
    }
}

impl TemporalAnomalyDetector {
    fn new() -> Self {
        Self {
            detection_algorithms: vec![],
            anomaly_history: VecDeque::new(),
            sensitivity: 0.5,
        }
    }
}

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

    #[test]
    fn test_temporal_consciousness_creation() {
        let temporal_consciousness = TemporalConsciousness::new();

        // Basic structure validation
        assert_eq!(temporal_consciousness.temporal_memory.experiences.len(), 0);
        assert_eq!(
            temporal_consciousness
                .pattern_analyzer
                .temporal_sequences
                .len(),
            0
        );
    }

    #[test]
    fn test_temporal_experience_recording() {
        let mut temporal_consciousness = TemporalConsciousness::new();

        let experience = TemporalExperience {
            id: "test_exp_1".to_string(),
            timestamp: SystemTime::now(),
            patterns: vec!["pattern1".to_string()],
            emotional_context: EmotionalState::Curious,
            performance_outcome: 0.8,
            duration: Duration::from_millis(100),
            related_experiences: vec![],
        };

        let result = temporal_consciousness.record_experience(experience);
        assert!(result.is_ok());
        assert_eq!(temporal_consciousness.temporal_memory.experiences.len(), 1);
    }

    #[test]
    fn test_temporal_analysis() {
        let temporal_consciousness = TemporalConsciousness::new();
        let patterns = vec![];

        let result = temporal_consciousness.analyze_temporal_patterns(&patterns);
        assert!(result.is_ok());

        let analysis = result.expect("should have value");
        assert!(analysis.coherence_score >= 0.0 && analysis.coherence_score <= 1.0);
        assert!(analysis.predictions.uncertainty >= 0.0 && analysis.predictions.uncertainty <= 1.0);
    }

    #[test]
    fn test_memory_retention_policy() {
        let policy = MemoryRetentionPolicy::default();
        assert_eq!(policy.short_term_max_age, Duration::from_secs(3600));
        assert_eq!(policy.long_term_max_age, Duration::from_secs(86400 * 30));
        assert_eq!(policy.compression_threshold, 0.5);
        assert_eq!(policy.importance_threshold, 0.3);
    }
}