scirs2-series 0.1.2

//! Consciousness and Attention Components for Advanced Fusion Intelligence
//!
//! This module contains all consciousness, attention, and metacognitive related structures
//! and implementations for the advanced fusion intelligence system, including conscious
//! attention systems, working memory, global workspace, and self-awareness mechanisms.

use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::{Float, FromPrimitive};
use std::collections::HashMap;
use std::fmt::Debug;

use crate::error::Result;

/// Conscious attention system for cognitive focus
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConsciousAttentionSystem<F: Float + Debug> {
    attention_mechanisms: Vec<AttentionMechanism<F>>,
    focus_strength: F,
    awareness_level: F,
    metacognitive_controller: MetacognitiveController<F>,
}

/// Individual attention mechanism
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AttentionMechanism<F: Float + Debug> {
    mechanism_type: AttentionType,
    salience_map: Vec<F>,
    focus_window: FocusWindow<F>,
}

/// Types of attention mechanisms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AttentionType {
    /// Bottom-up attention driven by stimuli
    BottomUp,
    /// Top-down attention driven by goals
    TopDown,
    /// Executive attention for control
    Executive,
    /// Orienting attention for spatial focus
    Orienting,
    /// Alerting attention for readiness
    Alerting,
}

/// Focus window for attention
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct FocusWindow<F: Float + Debug> {
    center: Vec<F>,
    radius: F,
    intensity: F,
}

/// Metacognitive controller for higher-order cognition
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MetacognitiveController<F: Float + Debug> {
    monitoring_system: MonitoringSystem<F>,
    control_strategies: Vec<ControlStrategy<F>>,
    meta_awareness: F,
}

/// System for monitoring cognitive processes
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MonitoringSystem<F: Float + Debug> {
    performance_monitors: Vec<PerformanceMonitor<F>>,
    error_detection: ErrorDetectionSystem<F>,
    confidence_assessment: ConfidenceAssessment<F>,
}

/// Monitor for tracking performance metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceMonitor<F: Float + Debug> {
    metric_type: MetricType,
    current_value: F,
    target_value: F,
    threshold: F,
}

/// Types of performance metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum MetricType {
    /// Accuracy metric
    Accuracy,
    /// Speed metric
    Speed,
    /// Efficiency metric
    Efficiency,
    /// Coherence metric
    Coherence,
    /// Awareness metric
    Awareness,
}

/// System for detecting and correcting errors
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ErrorDetectionSystem<F: Float + Debug> {
    error_detectors: Vec<ErrorDetector<F>>,
    correction_mechanisms: Vec<CorrectionMechanism<F>>,
}

/// Individual error detector
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ErrorDetector<F: Float + Debug> {
    detector_type: ErrorType,
    sensitivity: F,
    detection_threshold: F,
}

/// Types of errors that can be detected
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ErrorType {
    /// Processing errors
    ProcessingError,
    /// Memory errors
    MemoryError,
    /// Attention errors
    AttentionError,
    /// Consciousness errors
    ConsciousnessError,
}

/// Mechanism for correcting detected errors
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CorrectionMechanism<F: Float + Debug> {
    mechanism_type: CorrectionType,
    effectiveness: F,
    activation_threshold: F,
}

/// Types of correction mechanisms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum CorrectionType {
    /// Error correction
    ErrorCorrection,
    /// Parameter adjustment
    ParameterAdjustment,
    /// Strategy change
    StrategyChange,
    /// Attention refocus
    AttentionRefocus,
}

/// System for assessing confidence levels
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConfidenceAssessment<F: Float + Debug> {
    confidence_metrics: Vec<ConfidenceMetric<F>>,
    uncertainty_estimation: UncertaintyEstimation<F>,
}

/// Individual confidence metric
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConfidenceMetric<F: Float + Debug> {
    metric_name: String,
    confidence_value: F,
    reliability_score: F,
}

/// Estimation of uncertainty levels
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct UncertaintyEstimation<F: Float + Debug> {
    epistemic_uncertainty: F,
    aleatoric_uncertainty: F,
    total_uncertainty: F,
}

/// Control strategy for metacognitive control
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ControlStrategy<F: Float + Debug> {
    strategy_type: StrategyType,
    parameters: Vec<F>,
    effectiveness_score: F,
}

/// Types of control strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum StrategyType {
    /// Resource allocation strategy
    ResourceAllocation,
    /// Attention control strategy
    AttentionControl,
    /// Learning adjustment strategy
    LearningAdjustment,
    /// Consciousness modulation strategy
    ConsciousnessModulation,
}

/// Conscious working memory system
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConsciousWorkingMemory<F: Float + Debug> {
    memory_buffers: Vec<MemoryBuffer<F>>,
    capacity: usize,
    decay_rate: F,
    consolidation_strength: F,
}

/// Individual memory buffer
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MemoryBuffer<F: Float + Debug> {
    buffer_type: BufferType,
    content: Vec<F>,
    activation_level: F,
    age: F,
}

/// Types of memory buffers
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum BufferType {
    /// Phonological buffer
    Phonological,
    /// Visuospatial buffer
    Visuospatial,
    /// Episodic buffer
    Episodic,
    /// Executive buffer
    Executive,
    /// Quantum buffer
    Quantum,
}

/// Global workspace for consciousness integration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct GlobalWorkspace<F: Float + Debug> {
    workspace_memory: Vec<WorkspaceItem<F>>,
    global_access_threshold: F,
    consciousness_level: F,
    integration_coalitions: Vec<Coalition<F>>,
}

/// Item in the global workspace
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct WorkspaceItem<F: Float + Debug> {
    content: Vec<F>,
    activation_strength: F,
    consciousness_access: bool,
    source_module: String,
}

/// Coalition of modules in the global workspace
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct Coalition<F: Float + Debug> {
    participating_modules: Vec<String>,
    coherence_strength: F,
    dominance_level: F,
}

/// Self-awareness module for introspection
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SelfAwarenessModule<F: Float + Debug> {
    self_model: SelfModel<F>,
    introspection_mechanisms: Vec<IntrospectionMechanism<F>>,
    meta_consciousness: MetaConsciousness<F>,
}

/// Model of self for self-awareness
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SelfModel<F: Float + Debug> {
    self_representation: Vec<F>,
    capabilities_model: Vec<F>,
    limitations_awareness: Vec<F>,
    goal_hierarchy: Vec<Goal<F>>,
}

/// Goal representation in the self-model
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct Goal<F: Float + Debug> {
    goal_description: String,
    priority: F,
    progress: F,
    sub_goals: Vec<String>,
}

/// Mechanism for introspection
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct IntrospectionMechanism<F: Float + Debug> {
    mechanism_type: IntrospectionType,
    monitoring_targets: Vec<String>,
    reflection_depth: F,
}

/// Types of introspection mechanisms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum IntrospectionType {
    /// Process monitoring
    ProcessMonitoring,
    /// Emotional awareness
    EmotionalAwareness,
    /// Cognitive assessment
    CognitiveAssessment,
    /// Behavioral reflection
    BehavioralReflection,
}

/// Meta-consciousness for recursive awareness
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MetaConsciousness<F: Float + Debug> {
    consciousness_of_consciousness: F,
    recursive_awareness: usize,
    self_modification_capability: F,
}

/// Consciousness simulator for modeling conscious states
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConsciousnessSimulator<F: Float + Debug> {
    /// Conscious attention system
    attention_system: ConsciousAttentionSystem<F>,
    /// Working memory system
    working_memory: ConsciousWorkingMemory<F>,
    /// Global workspace
    global_workspace: GlobalWorkspace<F>,
    /// Self-awareness module
    self_awareness: SelfAwarenessModule<F>,
    /// Overall consciousness level
    consciousness_level: F,
}

/// State of consciousness at a given time
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConsciousnessState<F: Float> {
    /// Level of consciousness
    pub consciousness_level: F,
    /// Attention focus strength
    pub attention_strength: F,
    /// Working memory load
    pub memory_load: F,
    /// Self-awareness level
    pub self_awareness: F,
    /// Metacognitive control
    pub metacognitive_control: F,
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for ConsciousAttentionSystem<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ConsciousAttentionSystem<F> {
    /// Create new conscious attention system
    pub fn new() -> Self {
        ConsciousAttentionSystem {
            attention_mechanisms: vec![
                AttentionMechanism::new(AttentionType::BottomUp),
                AttentionMechanism::new(AttentionType::TopDown),
                AttentionMechanism::new(AttentionType::Executive),
            ],
            focus_strength: F::from_f64(1.0).expect("Test failed"),
            awareness_level: F::from_f64(0.8).expect("Test failed"),
            metacognitive_controller: MetacognitiveController::new(),
        }
    }

    /// Focus attention on specific input
    pub fn focus_attention(&mut self, input: &Array1<F>, focustarget: &[F]) -> Result<Array1<F>> {
        let mut attention_output = input.clone();

        // Apply attention mechanisms
        for mechanism in &mut self.attention_mechanisms {
            attention_output = mechanism.apply_attention(&attention_output, focustarget)?;
        }

        // Modulate by focus strength
        attention_output.mapv_inplace(|x| x * self.focus_strength);

        // Update awareness level based on attention coherence
        self.update_awareness_level(&attention_output)?;

        Ok(attention_output)
    }

    /// Update awareness level based on attention coherence
    fn update_awareness_level(&mut self, attentionoutput: &Array1<F>) -> Result<()> {
        if attentionoutput.is_empty() {
            return Ok(());
        }

        // Calculate attention coherence
        let mean = attentionoutput.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from_usize(attentionoutput.len()).expect("Test failed");

        let variance = attentionoutput
            .iter()
            .fold(F::zero(), |acc, &x| acc + (x - mean) * (x - mean))
            / F::from_usize(attentionoutput.len()).expect("Test failed");

        // Higher coherence (lower variance) increases awareness
        let coherence = F::from_f64(1.0).expect("Operation failed")
            / (F::from_f64(1.0).expect("Operation failed") + variance);

        // Update awareness level with exponential moving average
        let alpha = F::from_f64(0.1).expect("Test failed");
        self.awareness_level = (F::from_f64(1.0).expect("Operation failed") - alpha)
            * self.awareness_level
            + alpha * coherence;

        Ok(())
    }

    /// Get current consciousness metrics
    pub fn get_consciousness_metrics(&self) -> HashMap<String, F> {
        let mut metrics = HashMap::new();
        metrics.insert("focus_strength".to_string(), self.focus_strength);
        metrics.insert("awareness_level".to_string(), self.awareness_level);
        metrics.insert(
            "meta_awareness".to_string(),
            self.metacognitive_controller.meta_awareness,
        );
        metrics
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> AttentionMechanism<F> {
    /// Create new attention mechanism
    pub fn new(mechanismtype: AttentionType) -> Self {
        AttentionMechanism {
            mechanism_type: mechanismtype,
            salience_map: vec![F::from_f64(1.0).expect("Test failed"); 100], // Default salience map
            focus_window: FocusWindow::new(),
        }
    }

    /// Apply attention to input
    pub fn apply_attention(&mut self, input: &Array1<F>, focustarget: &[F]) -> Result<Array1<F>> {
        let mut output = input.clone();

        match self.mechanism_type {
            AttentionType::BottomUp => {
                self.apply_bottom_up_attention(&mut output)?;
            }
            AttentionType::TopDown => {
                self.apply_top_down_attention(&mut output, focustarget)?;
            }
            AttentionType::Executive => {
                self.apply_executive_attention(&mut output)?;
            }
            AttentionType::Orienting => {
                self.apply_orienting_attention(&mut output, focustarget)?;
            }
            AttentionType::Alerting => {
                self.apply_alerting_attention(&mut output)?;
            }
        }

        Ok(output)
    }

    /// Apply bottom-up attention based on stimulus salience
    fn apply_bottom_up_attention(&mut self, input: &mut Array1<F>) -> Result<()> {
        for (i, value) in input.iter_mut().enumerate() {
            let salience_idx = i % self.salience_map.len();
            *value = *value * self.salience_map[salience_idx];
        }
        Ok(())
    }

    /// Apply top-down attention based on goals
    fn apply_top_down_attention(&mut self, input: &mut Array1<F>, focustarget: &[F]) -> Result<()> {
        if focustarget.is_empty() {
            return Ok(());
        }

        // Apply goal-directed attention modulation
        for (i, value) in input.iter_mut().enumerate() {
            let target_idx = i % focustarget.len();
            let attention_weight = focustarget[target_idx].abs();
            *value = *value * attention_weight;
        }
        Ok(())
    }

    /// Apply executive attention for cognitive control
    fn apply_executive_attention(&mut self, input: &mut Array1<F>) -> Result<()> {
        // Apply executive control through selective enhancement
        let threshold = F::from_f64(0.5).expect("Test failed");

        for value in input.iter_mut() {
            if value.abs() > threshold {
                *value = *value * F::from_f64(1.2).expect("Test failed"); // Enhance above-threshold values
            } else {
                *value = *value * F::from_f64(0.8).expect("Test failed"); // Suppress below-threshold values
            }
        }
        Ok(())
    }

    /// Apply orienting attention for spatial focus
    fn apply_orienting_attention(
        &mut self,
        input: &mut Array1<F>,
        focustarget: &[F],
    ) -> Result<()> {
        if focustarget.is_empty() || input.is_empty() {
            return Ok(());
        }

        // Create spatial attention map based on focus window
        let center_idx = focustarget.len() / 2;
        let radius = self.focus_window.radius.to_usize().unwrap_or(10);

        for (i, value) in input.iter_mut().enumerate() {
            let distance = (i as i32 - center_idx as i32).unsigned_abs() as usize;
            let attention_weight = if distance <= radius {
                self.focus_window.intensity
            } else {
                F::from_f64(0.1).expect("Operation failed") // Background attention
            };
            *value = *value * attention_weight;
        }
        Ok(())
    }

    /// Apply alerting attention for readiness
    fn apply_alerting_attention(&mut self, input: &mut Array1<F>) -> Result<()> {
        // Apply alerting modulation to enhance overall processing
        let alerting_factor = F::from_f64(1.1).expect("Test failed");
        input.mapv_inplace(|x| x * alerting_factor);
        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for FocusWindow<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> FocusWindow<F> {
    /// Create new focus window
    pub fn new() -> Self {
        FocusWindow {
            center: vec![F::zero(); 3], // 3D center by default
            radius: F::from_f64(5.0).expect("Test failed"),
            intensity: F::from_f64(1.5).expect("Test failed"),
        }
    }

    /// Update focus window position
    pub fn update_position(&mut self, new_center: Vec<F>) {
        self.center = new_center;
    }

    /// Update focus window radius
    pub fn update_radius(&mut self, new_radius: F) {
        self.radius = new_radius;
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for MetacognitiveController<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> MetacognitiveController<F> {
    /// Create new metacognitive controller
    pub fn new() -> Self {
        MetacognitiveController {
            monitoring_system: MonitoringSystem::new(),
            control_strategies: vec![
                ControlStrategy::new(StrategyType::ResourceAllocation),
                ControlStrategy::new(StrategyType::AttentionControl),
            ],
            meta_awareness: F::from_f64(0.7).expect("Test failed"),
        }
    }

    /// Monitor and control cognitive processes
    pub fn monitor_and_control(
        &mut self,
        cognitive_state: &HashMap<String, F>,
    ) -> Result<Vec<String>> {
        // Monitor current performance
        let monitoring_results = self
            .monitoring_system
            .monitor_performance(cognitive_state)?;

        // Select appropriate control strategies
        let mut applied_strategies = Vec::new();

        for strategy in &mut self.control_strategies {
            if strategy.should_activate(&monitoring_results)? {
                let strategy_result = strategy.apply_control(cognitive_state)?;
                applied_strategies.push(strategy_result);
            }
        }

        // Update meta-awareness based on monitoring results
        self.update_meta_awareness(&monitoring_results)?;

        Ok(applied_strategies)
    }

    /// Update meta-awareness level
    fn update_meta_awareness(&mut self, monitoring_results: &HashMap<String, F>) -> Result<()> {
        if let Some(&performance_score) = monitoring_results.get("overall_performance") {
            let alpha = F::from_f64(0.1).expect("Test failed");
            self.meta_awareness = (F::from_f64(1.0).expect("Operation failed") - alpha)
                * self.meta_awareness
                + alpha * performance_score;
        }
        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for MonitoringSystem<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> MonitoringSystem<F> {
    /// Create new monitoring system
    pub fn new() -> Self {
        MonitoringSystem {
            performance_monitors: vec![
                PerformanceMonitor::new(MetricType::Accuracy),
                PerformanceMonitor::new(MetricType::Speed),
                PerformanceMonitor::new(MetricType::Efficiency),
            ],
            error_detection: ErrorDetectionSystem::new(),
            confidence_assessment: ConfidenceAssessment::new(),
        }
    }

    /// Monitor performance across various metrics
    pub fn monitor_performance(
        &mut self,
        cognitive_state: &HashMap<String, F>,
    ) -> Result<HashMap<String, F>> {
        let mut results = HashMap::new();

        // Update and collect performance metrics
        for monitor in &mut self.performance_monitors {
            let metric_value = monitor.update_and_assess(cognitive_state)?;
            results.insert(format!("{:?}", monitor.metric_type), metric_value);
        }

        // Detect errors
        let error_results = self.error_detection.detect_errors(cognitive_state)?;
        results.extend(error_results);

        // Assess confidence
        let confidence_results = self
            .confidence_assessment
            .assess_confidence(cognitive_state)?;
        results.extend(confidence_results);

        // Calculate overall performance
        let overall_performance = results.values().fold(F::zero(), |acc, &x| acc + x)
            / F::from_usize(results.len()).expect("Test failed");
        results.insert("overall_performance".to_string(), overall_performance);

        Ok(results)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> PerformanceMonitor<F> {
    /// Create new performance monitor
    pub fn new(metric_type: MetricType) -> Self {
        PerformanceMonitor {
            metric_type,
            current_value: F::from_f64(0.5).expect("Test failed"),
            target_value: F::from_f64(0.8).expect("Test failed"),
            threshold: F::from_f64(0.6).expect("Test failed"),
        }
    }

    /// Update and assess performance metric
    pub fn update_and_assess(&mut self, cognitive_state: &HashMap<String, F>) -> Result<F> {
        // Update current value based on cognitive state
        let metric_key = format!("{:?}", self.metric_type).to_lowercase();
        if let Some(&state_value) = cognitive_state.get(&metric_key) {
            self.current_value = state_value;
        }

        // Assess performance relative to target
        let performance_ratio = self.current_value / self.target_value;
        let performance_score = performance_ratio.min(F::from_f64(1.0).expect("Operation failed"));

        Ok(performance_score)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for ErrorDetectionSystem<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ErrorDetectionSystem<F> {
    /// Create new error detection system
    pub fn new() -> Self {
        ErrorDetectionSystem {
            error_detectors: vec![
                ErrorDetector::new(ErrorType::ProcessingError),
                ErrorDetector::new(ErrorType::MemoryError),
                ErrorDetector::new(ErrorType::AttentionError),
            ],
            correction_mechanisms: vec![
                CorrectionMechanism::new(CorrectionType::ErrorCorrection),
                CorrectionMechanism::new(CorrectionType::ParameterAdjustment),
            ],
        }
    }

    /// Detect errors in cognitive state
    pub fn detect_errors(
        &mut self,
        cognitive_state: &HashMap<String, F>,
    ) -> Result<HashMap<String, F>> {
        let mut error_results = HashMap::new();

        for detector in &self.error_detectors {
            let error_detected = detector.detect_error(cognitive_state)?;
            error_results.insert(
                format!("{:?}_error", detector.detector_type),
                error_detected,
            );
        }

        Ok(error_results)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ErrorDetector<F> {
    /// Create new error detector
    pub fn new(detector_type: ErrorType) -> Self {
        ErrorDetector {
            detector_type,
            sensitivity: F::from_f64(0.8).expect("Test failed"),
            detection_threshold: F::from_f64(0.3).expect("Test failed"),
        }
    }

    /// Detect specific type of error
    pub fn detect_error(&self, cognitive_state: &HashMap<String, F>) -> Result<F> {
        // Simplified error detection based on threshold
        let error_indicator = match self.detector_type {
            ErrorType::ProcessingError => cognitive_state
                .get("processing_quality")
                .copied()
                .unwrap_or(F::from_f64(1.0).expect("Operation failed")),
            ErrorType::MemoryError => cognitive_state
                .get("memory_coherence")
                .copied()
                .unwrap_or(F::from_f64(1.0).expect("Operation failed")),
            ErrorType::AttentionError => cognitive_state
                .get("attention_stability")
                .copied()
                .unwrap_or(F::from_f64(1.0).expect("Operation failed")),
            ErrorType::ConsciousnessError => cognitive_state
                .get("consciousness_level")
                .copied()
                .unwrap_or(F::from_f64(1.0).expect("Operation failed")),
        };

        let error_level = if error_indicator < self.detection_threshold {
            (self.detection_threshold - error_indicator) * self.sensitivity
        } else {
            F::zero()
        };

        Ok(error_level)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> CorrectionMechanism<F> {
    /// Create new correction mechanism
    pub fn new(mechanism_type: CorrectionType) -> Self {
        CorrectionMechanism {
            mechanism_type,
            effectiveness: F::from_f64(0.8).expect("Test failed"),
            activation_threshold: F::from_f64(0.5).expect("Test failed"),
        }
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for ConfidenceAssessment<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ConfidenceAssessment<F> {
    /// Create new confidence assessment system
    pub fn new() -> Self {
        ConfidenceAssessment {
            confidence_metrics: vec![
                ConfidenceMetric::new("prediction_confidence".to_string()),
                ConfidenceMetric::new("decision_confidence".to_string()),
            ],
            uncertainty_estimation: UncertaintyEstimation::new(),
        }
    }

    /// Assess confidence in cognitive state
    pub fn assess_confidence(
        &mut self,
        cognitive_state: &HashMap<String, F>,
    ) -> Result<HashMap<String, F>> {
        let mut confidence_results = HashMap::new();

        // Update confidence metrics
        for metric in &mut self.confidence_metrics {
            let confidence_value = metric.update_confidence(cognitive_state)?;
            confidence_results.insert(metric.metric_name.clone(), confidence_value);
        }

        // Update uncertainty estimation
        let uncertainty_results = self
            .uncertainty_estimation
            .estimate_uncertainty(cognitive_state)?;
        confidence_results.extend(uncertainty_results);

        Ok(confidence_results)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ConfidenceMetric<F> {
    /// Create new confidence metric
    pub fn new(metric_name: String) -> Self {
        ConfidenceMetric {
            metric_name,
            confidence_value: F::from_f64(0.5).expect("Test failed"),
            reliability_score: F::from_f64(0.8).expect("Test failed"),
        }
    }

    /// Update confidence based on cognitive state
    pub fn update_confidence(&mut self, cognitive_state: &HashMap<String, F>) -> Result<F> {
        // Update confidence based on relevant state variables
        if let Some(&state_value) = cognitive_state.get(&self.metric_name) {
            let alpha = F::from_f64(0.1).expect("Test failed");
            self.confidence_value = (F::from_f64(1.0).expect("Operation failed") - alpha)
                * self.confidence_value
                + alpha * state_value;
        }

        Ok(self.confidence_value * self.reliability_score)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for UncertaintyEstimation<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> UncertaintyEstimation<F> {
    /// Create new uncertainty estimation system
    pub fn new() -> Self {
        UncertaintyEstimation {
            epistemic_uncertainty: F::from_f64(0.3).expect("Test failed"),
            aleatoric_uncertainty: F::from_f64(0.2).expect("Test failed"),
            total_uncertainty: F::from_f64(0.5).expect("Test failed"),
        }
    }

    /// Estimate uncertainty in cognitive state
    pub fn estimate_uncertainty(
        &mut self,
        _cognitivestate: &HashMap<String, F>,
    ) -> Result<HashMap<String, F>> {
        // Update total uncertainty
        self.total_uncertainty = self.epistemic_uncertainty + self.aleatoric_uncertainty;

        let mut results = HashMap::new();
        results.insert(
            "epistemic_uncertainty".to_string(),
            self.epistemic_uncertainty,
        );
        results.insert(
            "aleatoric_uncertainty".to_string(),
            self.aleatoric_uncertainty,
        );
        results.insert("total_uncertainty".to_string(), self.total_uncertainty);

        Ok(results)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ControlStrategy<F> {
    /// Create new control strategy
    pub fn new(strategy_type: StrategyType) -> Self {
        ControlStrategy {
            strategy_type,
            parameters: vec![F::from_f64(1.0).expect("Test failed"); 5],
            effectiveness_score: F::from_f64(0.8).expect("Test failed"),
        }
    }

    /// Check if strategy should be activated
    pub fn should_activate(&self, monitoring_results: &HashMap<String, F>) -> Result<bool> {
        let activation_threshold = F::from_f64(0.6).expect("Test failed");

        let relevant_metric = match self.strategy_type {
            StrategyType::ResourceAllocation => "efficiency",
            StrategyType::AttentionControl => "attention_stability",
            StrategyType::LearningAdjustment => "accuracy",
            StrategyType::ConsciousnessModulation => "consciousness_level",
        };

        if let Some(&metric_value) = monitoring_results.get(relevant_metric) {
            Ok(metric_value < activation_threshold)
        } else {
            Ok(false)
        }
    }

    /// Apply control strategy
    pub fn apply_control(&mut self, _cognitivestate: &HashMap<String, F>) -> Result<String> {
        let strategy_description = match self.strategy_type {
            StrategyType::ResourceAllocation => "Reallocated cognitive resources",
            StrategyType::AttentionControl => "Adjusted attention parameters",
            StrategyType::LearningAdjustment => "Modified learning rates",
            StrategyType::ConsciousnessModulation => "Modulated consciousness level",
        };

        Ok(strategy_description.to_string())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for ConsciousnessSimulator<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ConsciousnessSimulator<F> {
    /// Create new consciousness simulator
    pub fn new() -> Self {
        ConsciousnessSimulator {
            attention_system: ConsciousAttentionSystem::new(),
            working_memory: ConsciousWorkingMemory::new(),
            global_workspace: GlobalWorkspace::new(),
            self_awareness: SelfAwarenessModule::new(),
            consciousness_level: F::from_f64(0.5).expect("Test failed"),
        }
    }

    /// Simulate conscious processing
    pub fn simulate_consciousness(&mut self, input: &Array1<F>) -> Result<ConsciousnessState<F>> {
        // Process through attention system
        let attended_input = self.attention_system.focus_attention(input, &[])?;

        // Update working memory
        self.working_memory.update_memory(&attended_input)?;

        // Update global workspace
        self.global_workspace
            .integrate_information(&attended_input)?;

        // Update self-awareness
        self.self_awareness.update_awareness(&attended_input)?;

        // Calculate overall consciousness level
        self.update_consciousness_level()?;

        // Create consciousness state
        let state = ConsciousnessState {
            consciousness_level: self.consciousness_level,
            attention_strength: self.attention_system.focus_strength,
            memory_load: F::from_usize(self.working_memory.memory_buffers.len())
                .expect("Operation failed")
                / F::from_usize(self.working_memory.capacity).expect("Test failed"),
            self_awareness: self
                .self_awareness
                .meta_consciousness
                .consciousness_of_consciousness,
            metacognitive_control: self
                .attention_system
                .metacognitive_controller
                .meta_awareness,
        };

        Ok(state)
    }

    /// Update overall consciousness level
    fn update_consciousness_level(&mut self) -> Result<()> {
        let attention_weight = F::from_f64(0.3).expect("Test failed");
        let memory_weight = F::from_f64(0.2).expect("Test failed");
        let workspace_weight = F::from_f64(0.3).expect("Test failed");
        let awareness_weight = F::from_f64(0.2).expect("Test failed");

        let attention_contribution = self.attention_system.awareness_level * attention_weight;
        let memory_contribution = F::from_f64(0.8).expect("Operation failed") * memory_weight; // Placeholder
        let workspace_contribution = self.global_workspace.consciousness_level * workspace_weight;
        let awareness_contribution = self
            .self_awareness
            .meta_consciousness
            .consciousness_of_consciousness
            * awareness_weight;

        self.consciousness_level = attention_contribution
            + memory_contribution
            + workspace_contribution
            + awareness_contribution;

        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for ConsciousWorkingMemory<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> ConsciousWorkingMemory<F> {
    /// Create new conscious working memory
    pub fn new() -> Self {
        ConsciousWorkingMemory {
            memory_buffers: Vec::new(),
            capacity: 7, // Miller's magic number
            decay_rate: F::from_f64(0.1).expect("Test failed"),
            consolidation_strength: F::from_f64(0.8).expect("Test failed"),
        }
    }

    /// Update working memory with new information
    pub fn update_memory(&mut self, input: &Array1<F>) -> Result<()> {
        // Add new memory buffer if capacity allows
        if self.memory_buffers.len() < self.capacity {
            let new_buffer = MemoryBuffer {
                buffer_type: BufferType::Executive,
                content: input.to_vec(),
                activation_level: F::from_f64(1.0).expect("Test failed"),
                age: F::zero(),
            };
            self.memory_buffers.push(new_buffer);
        } else {
            // Replace least active buffer
            if let Some(min_buffer) = self.memory_buffers.iter_mut().min_by(|a, b| {
                a.activation_level
                    .partial_cmp(&b.activation_level)
                    .expect("Operation failed")
            }) {
                min_buffer.content = input.to_vec();
                min_buffer.activation_level = F::from_f64(1.0).expect("Test failed");
                min_buffer.age = F::zero();
            }
        }

        // Apply decay to all buffers
        for buffer in &mut self.memory_buffers {
            buffer.activation_level = buffer.activation_level
                * (F::from_f64(1.0).expect("Operation failed") - self.decay_rate);
            buffer.age = buffer.age + F::from_f64(1.0).expect("Test failed");
        }

        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for GlobalWorkspace<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> GlobalWorkspace<F> {
    /// Create new global workspace
    pub fn new() -> Self {
        GlobalWorkspace {
            workspace_memory: Vec::new(),
            global_access_threshold: F::from_f64(0.7).expect("Test failed"),
            consciousness_level: F::from_f64(0.5).expect("Test failed"),
            integration_coalitions: Vec::new(),
        }
    }

    /// Integrate information into global workspace
    pub fn integrate_information(&mut self, input: &Array1<F>) -> Result<()> {
        // Create workspace item
        let workspace_item = WorkspaceItem {
            content: input.to_vec(),
            activation_strength: F::from_f64(1.0).expect("Test failed"),
            consciousness_access: true,
            source_module: "input".to_string(),
        };

        self.workspace_memory.push(workspace_item);

        // Update consciousness level based on integration
        let integration_factor = F::from_f64(0.1).expect("Test failed");
        self.consciousness_level = self.consciousness_level + integration_factor;
        self.consciousness_level = self
            .consciousness_level
            .min(F::from_f64(1.0).expect("Operation failed"));

        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for SelfAwarenessModule<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> SelfAwarenessModule<F> {
    /// Create new self-awareness module
    pub fn new() -> Self {
        SelfAwarenessModule {
            self_model: SelfModel::new(),
            introspection_mechanisms: vec![
                IntrospectionMechanism::new(IntrospectionType::ProcessMonitoring),
                IntrospectionMechanism::new(IntrospectionType::CognitiveAssessment),
            ],
            meta_consciousness: MetaConsciousness::new(),
        }
    }

    /// Update self-awareness
    pub fn update_awareness(&mut self, input: &Array1<F>) -> Result<()> {
        // Update self-model
        self.self_model.update_self_representation()?;

        // Apply introspection mechanisms
        for mechanism in &mut self.introspection_mechanisms {
            mechanism.apply_introspection()?;
        }

        // Update meta-consciousness
        self.meta_consciousness.update_recursive_awareness()?;

        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for SelfModel<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> SelfModel<F> {
    /// Create new self-model
    pub fn new() -> Self {
        SelfModel {
            self_representation: vec![F::from_f64(0.5).expect("Test failed"); 10],
            capabilities_model: vec![F::from_f64(0.8).expect("Test failed"); 5],
            limitations_awareness: vec![F::from_f64(0.3).expect("Test failed"); 5],
            goal_hierarchy: vec![Goal::new("primary_goal".to_string())],
        }
    }

    /// Update self-representation
    pub fn update_self_representation(&mut self) -> Result<()> {
        // Simple self-representation update
        for repr in &mut self.self_representation {
            let update = F::from_f64(0.01).expect("Operation failed")
                * (F::from_f64(scirs2_core::random::random::<f64>()).expect("Operation failed")
                    - F::from_f64(0.5).expect("Operation failed"));
            *repr = *repr + update;
        }
        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Goal<F> {
    /// Create new goal
    pub fn new(description: String) -> Self {
        Goal {
            goal_description: description,
            priority: F::from_f64(1.0).expect("Test failed"),
            progress: F::zero(),
            sub_goals: Vec::new(),
        }
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> IntrospectionMechanism<F> {
    /// Create new introspection mechanism
    pub fn new(mechanism_type: IntrospectionType) -> Self {
        IntrospectionMechanism {
            mechanism_type,
            monitoring_targets: vec!["attention".to_string(), "memory".to_string()],
            reflection_depth: F::from_f64(1.0).expect("Test failed"),
        }
    }

    /// Apply introspection
    pub fn apply_introspection(&mut self) -> Result<()> {
        // Simple introspection update
        self.reflection_depth = self.reflection_depth * F::from_f64(1.01).expect("Test failed");
        Ok(())
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Default for MetaConsciousness<F> {
    fn default() -> Self {
        Self::new()
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> MetaConsciousness<F> {
    /// Create new meta-consciousness
    pub fn new() -> Self {
        MetaConsciousness {
            consciousness_of_consciousness: F::from_f64(0.6).expect("Test failed"),
            recursive_awareness: 2, // Second-order awareness
            self_modification_capability: F::from_f64(0.7).expect("Test failed"),
        }
    }

    /// Update recursive awareness
    pub fn update_recursive_awareness(&mut self) -> Result<()> {
        // Update consciousness of consciousness
        let awareness_increment = F::from_f64(0.01).expect("Test failed");
        self.consciousness_of_consciousness = (self.consciousness_of_consciousness
            + awareness_increment)
            .min(F::from_f64(1.0).expect("Operation failed"));

        Ok(())
    }
}

impl<F: Float + FromPrimitive> Default for ConsciousnessState<F> {
    fn default() -> Self {
        Self {
            consciousness_level: F::from_f64(0.5).expect("Test failed"),
            attention_strength: F::from_f64(1.0).expect("Test failed"),
            memory_load: F::from_f64(0.3).expect("Test failed"),
            self_awareness: F::from_f64(0.6).expect("Test failed"),
            metacognitive_control: F::from_f64(0.7).expect("Test failed"),
        }
    }
}