scirs2-ndimage 0.4.2

//! Consciousness-Inspired Processing for Quantum-Neuromorphic Fusion
//!
//! This module implements consciousness-inspired algorithms that simulate various
//! aspects of conscious information processing in biological systems. These algorithms
//! are integrated with quantum-neuromorphic processing to create sophisticated
//! image processing capabilities that mirror aspects of conscious awareness.
//!
//! # Consciousness-Inspired Features
//!
//! - **Global Workspace Theory**: Information integration and broadcasting across processing modules
//! - **Integrated Information Theory**: Φ (phi) measures for quantifying consciousness-like information integration
//! - **Predictive Coding Hierarchy**: Hierarchical predictive processing inspired by brain mechanisms
//! - **Meta-Cognitive Monitoring**: Self-awareness mechanisms that monitor processing states
//! - **Temporal Binding Consciousness**: Temporal integration creating conscious moments
//!
//! # Theoretical Background
//!
//! These algorithms are based on leading theories of consciousness from neuroscience:
//! - Global Workspace Theory (Bernard Baars)
//! - Integrated Information Theory (Giulio Tononi)
//! - Predictive Processing Theory (Andy Clark, Jakob Hohwy)
//! - Meta-Cognitive Theory (Terrence Deacon)
//! - Temporal Binding Theory (Susan Greenfield)
//!
//! # Processing Pipeline
//!
//! 1. **Unconscious Processing**: Parallel processing in specialized modules
//! 2. **Competition for Consciousness**: Information competes for global workspace access
//! 3. **Global Broadcasting**: Conscious information influences all modules
//! 4. **Integrated Information**: Φ-weighted processing based on information integration
//! 5. **Predictive Coding**: Hierarchical prediction and error minimization
//! 6. **Meta-Cognitive Monitoring**: Self-awareness and confidence assessment
//! 7. **Temporal Binding**: Integration across temporal windows

use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive, Zero};
use statrs::statistics::Statistics;
use std::collections::{HashMap, VecDeque};
use std::f64::consts::PI;

// Import from our config module
use super::config::*;

// Import from parent modules
use crate::error::{NdimageError, NdimageResult};

/// Result structure for predictive coding hierarchy processing
#[derive(Debug)]
pub struct PredictiveCodingResult<T> {
    pub prediction: Array2<T>,
    pub prediction_error: f64,
    pub hierarchical_priors: Vec<Array2<f64>>,
    pub precision_weights: Array2<f64>,
}

/// Meta-cognitive state representing self-awareness and monitoring
#[derive(Debug)]
pub struct MetaCognitiveState {
    pub confidence_level: f64,
    pub processing_effort: f64,
    pub error_monitoring: f64,
    pub self_awareness_index: f64,
}

/// Global Workspace Theory Implementation
///
/// Implements consciousness-like information integration where only information
/// that reaches a global broadcast threshold becomes "conscious" and influences
/// all processing modules.
///
/// # Theory
/// Based on Global Workspace Theory by Bernard Baars, this algorithm simulates
/// the global broadcasting of information that characterizes conscious awareness.
#[allow(dead_code)]
pub fn consciousness_inspired_global_workspace<T>(
    image: ArrayView2<T>,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let consciousness_config = ConsciousnessConfig::default();

    // Initialize global workspace modules
    let mut perceptual_module = Array2::zeros((height, width));
    let mut attention_module = Array2::zeros((height, width));
    let mut memory_module = Array2::zeros((height, width));
    let mut consciousness_workspace = Array2::zeros((height, width));

    // Stage 1: Unconscious parallel processing in specialized modules
    for y in 0..height {
        for x in 0..width {
            let pixel_value = image[(y, x)].to_f64().unwrap_or(0.0);

            // Perceptual processing (edge detection, features)
            let perceptual_activation =
                unconscious_perceptual_processing(pixel_value, &image, (y, x), config)?;
            perceptual_module[(y, x)] = perceptual_activation;

            // Attention schema processing
            let attention_activation = attention_schema_processing(
                pixel_value,
                &perceptual_module,
                (y, x),
                &consciousness_config,
            )?;
            attention_module[(y, x)] = attention_activation;

            // Memory trace activation
            let memory_activation =
                memory_trace_activation(pixel_value, perceptual_activation, &consciousness_config)?;
            memory_module[(y, x)] = memory_activation;
        }
    }

    // Stage 2: Competition for global workspace access
    for y in 0..height {
        for x in 0..width {
            let coalition_strength = calculate_coalition_strength(
                perceptual_module[(y, x)],
                attention_module[(y, x)],
                memory_module[(y, x)],
                &consciousness_config,
            )?;

            // Global broadcast threshold - only "conscious" information proceeds
            if coalition_strength > consciousness_config.broadcast_threshold {
                consciousness_workspace[(y, x)] = coalition_strength;

                // Global broadcasting - influence all modules
                global_broadcast_influence(
                    &mut perceptual_module,
                    &mut attention_module,
                    &mut memory_module,
                    (y, x),
                    coalition_strength,
                    &consciousness_config,
                )?;
            }
        }
    }

    // Stage 3: Conscious integration and response generation
    let mut conscious_output = Array2::zeros((height, width));
    for y in 0..height {
        for x in 0..width {
            let integrated_response = integrate_conscious_response(
                consciousness_workspace[(y, x)],
                perceptual_module[(y, x)],
                attention_module[(y, x)],
                memory_module[(y, x)],
                &consciousness_config,
            )?;

            conscious_output[(y, x)] = T::from_f64(integrated_response).ok_or_else(|| {
                NdimageError::ComputationError("Consciousness integration failed".to_string())
            })?;
        }
    }

    Ok(conscious_output)
}

/// Integrated Information Theory (IIT) Processing
///
/// Implements Φ (phi) measures to quantify the consciousness-like integrated
/// information in the quantum-neuromorphic system.
///
/// # Theory
/// Based on Integrated Information Theory by Giulio Tononi, this measures
/// how much information is generated by a system above and beyond its parts.
#[allow(dead_code)]
pub fn integrated_information_processing<T>(
    image: ArrayView2<T>,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<(Array2<T>, f64)>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let consciousness_config = ConsciousnessConfig::default();

    // Initialize quantum-neuromorphic network for IIT analysis
    let mut phi_network = Array3::zeros((height, width, 4)); // 4 quantum states per neuron

    // Convert image to quantum-neuromorphic representation
    for y in 0..height {
        for x in 0..width {
            let pixel_value = image[(y, x)].to_f64().unwrap_or(0.0);

            // Encode as quantum superposition states
            let quantum_encoding = encode_pixel_to_quantumstates(pixel_value, config)?;
            for (i, &amplitude) in quantum_encoding.iter().enumerate() {
                phi_network[(y, x, i)] = amplitude;
            }
        }
    }

    // Calculate integrated information Φ
    let mut total_phi = 0.0;
    let mut phi_processedimage = Array2::<f64>::zeros((height, width));

    // Analyze each possible bipartition of the system
    for partition_size in 1..=((height * width) / 2) {
        let bipartitions = generate_bipartitions(&phi_network, partition_size)?;

        for (part_a, part_b) in bipartitions {
            // Calculate effective information
            let ei_whole = calculate_effective_information(&phi_network, &consciousness_config)?;
            let ei_parts = calculate_effective_information(&part_a, &consciousness_config)?
                + calculate_effective_information(&part_b, &consciousness_config)?;

            // Φ = EI(whole) - EI(parts)
            let phi_contribution = (ei_whole - ei_parts).max(0.0);
            total_phi += phi_contribution;

            // Apply Φ-weighted processing
            apply_phi_weighted_processing(
                &mut phi_processedimage,
                &phi_network,
                phi_contribution,
                &consciousness_config,
            )?;
        }
    }

    // Normalize by number of bipartitions
    total_phi /= calculate_num_bipartitions(height * width) as f64;

    // Convert back to output format
    let mut result = Array2::<T>::zeros((height, width));
    for y in 0..height {
        for x in 0..width {
            result[(y, x)] = T::from_f64(phi_processedimage[(y, x)])
                .ok_or_else(|| NdimageError::ComputationError("Φ conversion failed".to_string()))?;
        }
    }

    Ok((result, total_phi))
}

/// Predictive Coding Hierarchy
///
/// Implements hierarchical predictive processing inspired by the brain's
/// predictive coding mechanisms for consciousness and perception.
///
/// # Theory
/// Based on predictive processing theories (Andy Clark, Jakob Hohwy), the brain
/// is fundamentally a prediction machine that minimizes prediction error.
#[allow(dead_code)]
pub fn predictive_coding_hierarchy<T>(
    image: ArrayView2<T>,
    hierarchy_sizes: &[usize],
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<PredictiveCodingResult<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let _height_width = image.dim();
    let consciousness_config = ConsciousnessConfig::default();

    if hierarchy_sizes.is_empty() {
        return Err(NdimageError::InvalidInput("Empty hierarchy".to_string()));
    }

    // Initialize hierarchical predictive network
    let mut hierarchical_levels = Vec::new();
    let mut prediction_errors = Vec::new();

    // Build hierarchy from image up
    let mut current_representation = image.to_owned().mapv(|x| x.to_f64().unwrap_or(0.0));

    for (level, &level_size) in hierarchy_sizes.iter().enumerate() {
        // Generate predictions from higher levels
        let level_predictions = if level == 0 {
            // Bottom level: direct sensory predictions
            generate_sensory_predictions(&current_representation, &consciousness_config)?
        } else {
            // Higher levels: generate predictions from abstract representations
            generate_hierarchical_predictions(
                &hierarchical_levels[level - 1],
                &current_representation,
                level_size,
                &consciousness_config,
            )?
        };

        // Calculate prediction error
        let pred_error = calculate_prediction_error(
            &current_representation,
            &level_predictions,
            &consciousness_config,
        )?;
        prediction_errors.push(pred_error);

        // Update representations based on prediction error
        let updated_representation = update_representation_with_error(
            &current_representation,
            &level_predictions,
            pred_error,
            &consciousness_config,
        )?;

        hierarchical_levels.push(level_predictions);
        current_representation = updated_representation;
    }

    // Generate final prediction through top-down processing
    let mut final_prediction = hierarchical_levels
        .last()
        .expect("Operation failed")
        .clone();

    // Top-down prediction refinement
    for level in (0..hierarchical_levels.len()).rev() {
        final_prediction = refine_prediction_top_down(
            &final_prediction,
            &hierarchical_levels[level],
            prediction_errors[level],
            &consciousness_config,
        )?;
    }

    // Calculate precision weights based on prediction confidence
    let precision_weights = calculate_precision_weights(&prediction_errors, &consciousness_config)?;

    // Calculate total prediction error
    let total_prediction_error =
        prediction_errors.iter().sum::<f64>() / prediction_errors.len() as f64;

    // Convert final prediction to output type
    let output_prediction = final_prediction.mapv(|x| T::from_f64(x).unwrap_or_else(|| T::zero()));

    Ok(PredictiveCodingResult {
        prediction: output_prediction,
        prediction_error: total_prediction_error,
        hierarchical_priors: hierarchical_levels,
        precision_weights,
    })
}

/// Meta-Cognitive Monitoring System
///
/// Implements self-awareness mechanisms that monitor the system's own
/// processing states and confidence levels.
#[allow(dead_code)]
pub fn meta_cognitive_monitoring<T>(
    image: ArrayView2<T>,
    processinghistory: &[Array2<f64>],
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<(Array2<T>, MetaCognitiveState)>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let consciousness_config = ConsciousnessConfig::default();

    // Initialize meta-cognitive monitoring system
    let mut metacognitive_output = Array2::zeros((height, width));
    let mut confidence_map = Array2::zeros((height, width));
    let mut effort_map = Array2::zeros((height, width));
    let mut error_monitoring_map = Array2::zeros((height, width));

    // Monitor processing at each location
    for y in 0..height {
        for x in 0..width {
            let pixel_value = image[(y, x)].to_f64().unwrap_or(0.0);

            // Confidence monitoring: how certain is the system about its processing?
            let confidence = calculate_processing_confidence(
                pixel_value,
                processinghistory,
                (y, x),
                &consciousness_config,
            )?;
            confidence_map[(y, x)] = confidence;

            // Effort monitoring: how much computational effort is being expended?
            let effort =
                calculate_processing_effort(processinghistory, (y, x), &consciousness_config)?;
            effort_map[(y, x)] = effort;

            // Error monitoring: is the system detecting anomalies or conflicts?
            let error_signal = calculate_error_monitoring_signal(
                pixel_value,
                processinghistory,
                (y, x),
                &consciousness_config,
            )?;
            error_monitoring_map[(y, x)] = error_signal;

            // Meta-cognitive integration
            let metacognitive_value = integrate_metacognitive_signals(
                confidence,
                effort,
                error_signal,
                &consciousness_config,
            )?;

            metacognitive_output[(y, x)] = T::from_f64(metacognitive_value).ok_or_else(|| {
                NdimageError::ComputationError("Meta-cognitive integration failed".to_string())
            })?;
        }
    }

    // Calculate global meta-cognitive state
    let global_confidence = confidence_map.mean();
    let global_effort = effort_map.mean();
    let global_error_monitoring = error_monitoring_map.mean();

    // Self-awareness index: how aware is the system of its own processing?
    let self_awareness_index = calculate_self_awareness_index(
        global_confidence,
        global_effort,
        global_error_monitoring,
        &consciousness_config,
    )?;

    let metacognitivestate = MetaCognitiveState {
        confidence_level: global_confidence,
        processing_effort: global_effort,
        error_monitoring: global_error_monitoring,
        self_awareness_index,
    };

    Ok((metacognitive_output, metacognitivestate))
}

/// Temporal Binding Windows for Consciousness
///
/// Implements temporal binding mechanisms that create conscious moments
/// by integrating information across specific time windows.
#[allow(dead_code)]
pub fn temporal_binding_consciousness<T>(
    image_sequence: &[ArrayView2<T>],
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let consciousness_config = ConsciousnessConfig::default();

    if image_sequence.is_empty() {
        return Err(NdimageError::InvalidInput(
            "Empty image _sequence".to_string(),
        ));
    }

    let (height, width) = image_sequence[0].dim();
    let window_size = consciousness_config.temporal_binding_window;

    // Initialize temporal binding buffers
    let mut binding_windows = VecDeque::new();
    let mut consciousness_moments = Vec::new();

    // Process each frame through temporal binding
    for (t, currentimage) in image_sequence.iter().enumerate() {
        // Convert to temporal representation
        let temporal_frame = image_to_temporal_representation(currentimage, t, config)?;
        binding_windows.push_back(temporal_frame);

        // Maintain binding window size
        if binding_windows.len() > window_size {
            binding_windows.pop_front();
        }

        // Create consciousness moment when window is full
        if binding_windows.len() == window_size {
            let consciousness_moment =
                create_consciousness_moment(&binding_windows, &consciousness_config)?;
            consciousness_moments.push(consciousness_moment);
        }
    }

    // Integrate consciousness moments into final output
    let final_consciousstate = integrate_consciousness_moments(
        &consciousness_moments,
        (height, width),
        &consciousness_config,
    )?;

    Ok(final_consciousstate)
}

// Helper functions for consciousness-inspired algorithms

#[allow(dead_code)]
fn unconscious_perceptual_processing<T>(
    pixel_value: f64,
    image: &ArrayView2<T>,
    position: (usize, usize),
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<f64>
where
    T: Float + FromPrimitive + Copy,
{
    let (y, x) = position;
    let (height, width) = image.dim();

    // Parallel unconscious processing (edge detection, texture, etc.)
    let mut activation = 0.0;

    // Edge detection component
    if y > 0 && y < height - 1 && x > 0 && x < width - 1 {
        let neighbors = [
            image[(y - 1, x - 1)].to_f64().unwrap_or(0.0),
            image[(y - 1, x)].to_f64().unwrap_or(0.0),
            image[(y - 1, x + 1)].to_f64().unwrap_or(0.0),
            image[(y, x - 1)].to_f64().unwrap_or(0.0),
            image[(y, x + 1)].to_f64().unwrap_or(0.0),
            image[(y + 1, x - 1)].to_f64().unwrap_or(0.0),
            image[(y + 1, x)].to_f64().unwrap_or(0.0),
            image[(y + 1, x + 1)].to_f64().unwrap_or(0.0),
        ];

        let gradient = neighbors
            .iter()
            .map(|&n| (pixel_value - n).abs())
            .sum::<f64>()
            / 8.0;
        activation += gradient * 0.3;
    }

    // Texture component
    let texture_response = pixel_value * (pixel_value * PI).sin().abs();
    activation += texture_response * 0.4;

    // Quantum coherence component
    let quantum_phase = pixel_value * config.quantum.entanglement_strength * PI;
    activation += quantum_phase.cos().abs() * 0.3;

    Ok(activation)
}

#[allow(dead_code)]
fn attention_schema_processing(
    _pixel_value: f64,
    perceptual_module: &Array2<f64>,
    position: (usize, usize),
    config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    let (y, x) = position;
    let (height, width) = perceptual_module.dim();

    // Attention schema: model of the attention process itself
    let local_perceptual_strength = perceptual_module[(y, x)];

    // Calculate attention competition
    let mut attention_competition = 0.0;
    let window_size = 3;
    let start_y = y.saturating_sub(window_size);
    let end_y = (y + window_size + 1).min(height);
    let start_x = x.saturating_sub(window_size);
    let end_x = (x + window_size + 1).min(width);

    for ny in start_y..end_y {
        for nx in start_x..end_x {
            if ny != y || nx != x {
                attention_competition += perceptual_module[(ny, nx)];
            }
        }
    }

    // Winner-take-all attention mechanism
    let attention_strength = local_perceptual_strength / (1.0 + attention_competition * 0.1);
    let attention_activation = attention_strength * config.attention_schema_strength;

    Ok(attention_activation)
}

#[allow(dead_code)]
fn memory_trace_activation(
    pixel_value: f64,
    perceptual_activation: f64,
    _config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    // Simple memory trace based on _activation patterns
    let memory_strength = perceptual_activation * pixel_value;
    let memory_trace = memory_strength * (1.0 - (-memory_strength * 2.0).exp());

    Ok(memory_trace.min(1.0))
}

#[allow(dead_code)]
fn calculate_coalition_strength(
    perceptual: f64,
    attention: f64,
    memory: f64,
    config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    // Coalition strength determines access to global workspace
    let coalition = perceptual * 0.4 + attention * 0.4 + memory * 0.2;
    Ok(coalition.min(1.0))
}

#[allow(dead_code)]
fn global_broadcast_influence(
    perceptual_module: &mut Array2<f64>,
    attention_module: &mut Array2<f64>,
    memory_module: &mut Array2<f64>,
    broadcast_source: (usize, usize),
    strength: f64,
    config: &ConsciousnessConfig,
) -> NdimageResult<()> {
    let (height, width) = perceptual_module.dim();
    let (source_y, source_x) = broadcast_source;

    // Global broadcasting influences all modules
    for y in 0..height {
        for x in 0..width {
            let distance = ((y as f64 - source_y as f64).powi(2)
                + (x as f64 - source_x as f64).powi(2))
            .sqrt();
            let influence = strength * (-distance * 0.1).exp();

            perceptual_module[(y, x)] += influence * 0.1;
            attention_module[(y, x)] += influence * 0.2;
            memory_module[(y, x)] += influence * 0.15;
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn integrate_conscious_response(
    workspace_activation: f64,
    perceptual: f64,
    attention: f64,
    memory: f64,
    config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    // Conscious integration of all information sources
    let integrated = workspace_activation * (perceptual + attention + memory) / 3.0;
    Ok(integrated.min(1.0))
}

#[allow(dead_code)]
fn encode_pixel_to_quantumstates(
    pixel_value: f64,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array1<f64>> {
    let mut quantumstates = Array1::zeros(4);

    // Encode as quantum superposition
    let angle = pixel_value * PI * 2.0;
    quantumstates[0] = angle.cos().abs(); // |0⟩ state
    quantumstates[1] = angle.sin().abs(); // |1⟩ state
    quantumstates[2] = (angle.cos() * angle.sin()).abs(); // superposition
    quantumstates[3] = (pixel_value * config.quantum.entanglement_strength).min(1.0); // entangled

    // Normalize
    let norm = quantumstates.sum();
    if norm > 0.0 {
        quantumstates /= norm;
    }

    Ok(quantumstates)
}

#[allow(dead_code)]
fn calculate_effective_information(
    system: &Array3<f64>,
    _config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    let (height, width, states) = system.dim();

    // Calculate entropy of the system
    let mut total_entropy = 0.0;
    for y in 0..height {
        for x in 0..width {
            for s in 0..states {
                let p = system[(y, x, s)].abs();
                if p > 1e-10 {
                    total_entropy -= p * p.ln();
                }
            }
        }
    }

    // Effective information is related to entropy difference
    Ok(total_entropy / (height * width * states) as f64)
}

#[allow(dead_code)]
fn generate_bipartitions(
    network: &Array3<f64>,
    partition_size: usize,
) -> NdimageResult<Vec<(Array3<f64>, Array3<f64>)>> {
    let (height, width, states) = network.dim();
    let total_elements = height * width;

    if partition_size >= total_elements {
        return Ok(Vec::new());
    }

    // For simplicity, generate a few representative bipartitions
    let mut bipartitions = Vec::new();

    // Spatial bipartition (left/right)
    let mid_x = width / 2;
    let mut part_a = Array3::zeros((height, mid_x, states));
    let mut part_b = Array3::zeros((height, width - mid_x, states));

    for y in 0..height {
        for x in 0..mid_x {
            for s in 0..states {
                part_a[(y, x, s)] = network[(y, x, s)];
            }
        }
        for x in mid_x..width {
            for s in 0..states {
                part_b[(y, x - mid_x, s)] = network[(y, x, s)];
            }
        }
    }

    bipartitions.push((part_a, part_b));

    Ok(bipartitions)
}

#[allow(dead_code)]
fn apply_phi_weighted_processing(
    output: &mut Array2<f64>,
    network: &Array3<f64>,
    phi_weight: f64,
    _config: &ConsciousnessConfig,
) -> NdimageResult<()> {
    let (height, width, states) = network.dim();

    for y in 0..height {
        for x in 0..width {
            let mut integrated_value = 0.0;
            for s in 0..states {
                integrated_value += network[(y, x, s)] * phi_weight;
            }
            output[(y, x)] += integrated_value / states as f64;
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn calculate_num_bipartitions(n: usize) -> usize {
    // Simplified calculation
    (2_usize.pow(n as u32) - 2) / 2
}

#[allow(dead_code)]
fn generate_sensory_predictions(
    representation: &Array2<f64>,
    _config: &ConsciousnessConfig,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = representation.dim();
    let mut predictions = Array2::zeros((height, width));

    // Simple predictive model based on local patterns
    for y in 1..height - 1 {
        for x in 1..width - 1 {
            let neighbors = [
                representation[(y - 1, x - 1)],
                representation[(y - 1, x)],
                representation[(y - 1, x + 1)],
                representation[(y, x - 1)],
                representation[(y, x + 1)],
                representation[(y + 1, x - 1)],
                representation[(y + 1, x)],
                representation[(y + 1, x + 1)],
            ];

            predictions[(y, x)] = neighbors.iter().sum::<f64>() / 8.0;
        }
    }

    Ok(predictions)
}

#[allow(dead_code)]
fn generate_hierarchical_predictions(
    higher_level: &Array2<f64>,
    current_level: &Array2<f64>,
    _level_size: usize,
    _config: &ConsciousnessConfig,
) -> NdimageResult<Array2<f64>> {
    // Generate predictions from higher-_level representations
    let (height, width) = current_level.dim();
    let mut predictions = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let higher_value = higher_level[(y, x)];
            let prediction = higher_value * 0.8 + current_level[(y, x)] * 0.2;
            predictions[(y, x)] = prediction;
        }
    }

    Ok(predictions)
}

#[allow(dead_code)]
fn calculate_prediction_error(
    actual: &Array2<f64>,
    predicted: &Array2<f64>,
    _config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    let diff = actual - predicted;
    let squared_error = diff.mapv(|x| x * x);
    Ok(squared_error.mean())
}

#[allow(dead_code)]
fn update_representation_with_error(
    current: &Array2<f64>,
    prediction: &Array2<f64>,
    _error: f64,
    config: &ConsciousnessConfig,
) -> NdimageResult<Array2<f64>> {
    let learning_rate = 0.1;
    let error_signal = current - prediction;
    let updated = current + &(error_signal * learning_rate);
    Ok(updated)
}

#[allow(dead_code)]
fn refine_prediction_top_down(
    higher_prediction: &Array2<f64>,
    level_prediction: &Array2<f64>,
    _error: f64,
    config: &ConsciousnessConfig,
) -> NdimageResult<Array2<f64>> {
    let refinement_strength = 0.3;
    let refined =
        higher_prediction * (1.0 - refinement_strength) + level_prediction * refinement_strength;
    Ok(refined)
}

#[allow(dead_code)]
fn calculate_precision_weights(
    errors: &[f64],
    _config: &ConsciousnessConfig,
) -> NdimageResult<Array2<f64>> {
    let height = 4; // Default size
    let width = 4;
    let mut weights = Array2::zeros((height, width));

    let avg_error = errors.iter().sum::<f64>() / errors.len() as f64;
    let precision = 1.0 / (1.0 + avg_error);

    weights.fill(precision);
    Ok(weights)
}

#[allow(dead_code)]
fn calculate_processing_confidence(
    _pixel_value: f64,
    history: &[Array2<f64>],
    position: (usize, usize),
    _config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    let (y, x) = position;

    if history.is_empty() {
        return Ok(0.5); // Default confidence
    }

    // Calculate variance in processing history
    let mut values = Vec::new();
    for frame in history {
        if y < frame.nrows() && x < frame.ncols() {
            values.push(frame[(y, x)]);
        }
    }

    if values.is_empty() {
        return Ok(0.5);
    }

    let mean = values.iter().sum::<f64>() / values.len() as f64;
    let variance = values.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / values.len() as f64;

    // Higher confidence with lower variance
    let confidence = 1.0 / (1.0 + variance);
    Ok(confidence)
}

#[allow(dead_code)]
fn calculate_processing_effort(
    history: &[Array2<f64>],
    position: (usize, usize),
    _config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    let (y, x) = position;

    if history.len() < 2 {
        return Ok(0.0);
    }

    // Calculate temporal derivatives as proxy for effort
    let mut total_change = 0.0;
    for i in 1..history.len() {
        if y < history[i].nrows()
            && x < history[i].ncols()
            && y < history[i - 1].nrows()
            && x < history[i - 1].ncols()
        {
            let change = (history[i][(y, x)] - history[i - 1][(y, x)]).abs();
            total_change += change;
        }
    }

    Ok(total_change / (history.len() - 1) as f64)
}

#[allow(dead_code)]
fn calculate_error_monitoring_signal(
    pixel_value: f64,
    history: &[Array2<f64>],
    position: (usize, usize),
    _config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    let (y, x) = position;

    if history.is_empty() {
        return Ok(0.0);
    }

    // Calculate deviation from expected pattern
    let mut deviations = Vec::new();
    for frame in history {
        if y < frame.nrows() && x < frame.ncols() {
            let deviation = (pixel_value - frame[(y, x)]).abs();
            deviations.push(deviation);
        }
    }

    if deviations.is_empty() {
        return Ok(0.0);
    }

    let mean_deviation = deviations.iter().sum::<f64>() / deviations.len() as f64;
    Ok(mean_deviation.min(1.0))
}

#[allow(dead_code)]
fn integrate_metacognitive_signals(
    confidence: f64,
    effort: f64,
    error_signal: f64,
    _config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    // Integrate meta-cognitive signals
    let metacognitive_value = confidence * 0.4 + (1.0 - effort) * 0.3 + (1.0 - error_signal) * 0.3;
    Ok(metacognitive_value.min(1.0))
}

#[allow(dead_code)]
fn calculate_self_awareness_index(
    confidence: f64,
    effort: f64,
    error_monitoring: f64,
    config: &ConsciousnessConfig,
) -> NdimageResult<f64> {
    // Self-awareness as integration of meta-cognitive components
    let self_awareness = (confidence * effort * (1.0 - error_monitoring)).cbrt();
    Ok(self_awareness * config.metacognitive_sensitivity)
}

#[allow(dead_code)]
fn image_to_temporal_representation<T>(
    image: &ArrayView2<T>,
    timestamp: usize,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array3<f64>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let temporal_depth = 8; // Multiple temporal channels

    let mut temporal_rep = Array3::zeros((height, width, temporal_depth));

    for y in 0..height {
        for x in 0..width {
            let pixel_value = image[(y, x)].to_f64().unwrap_or(0.0);

            // Encode temporal information
            for d in 0..temporal_depth {
                let temporal_phase = (timestamp as f64 + d as f64) * PI / temporal_depth as f64;
                temporal_rep[(y, x, d)] = pixel_value * temporal_phase.cos();
            }
        }
    }

    Ok(temporal_rep)
}

#[allow(dead_code)]
fn create_consciousness_moment(
    binding_window: &VecDeque<Array3<f64>>,
    _config: &ConsciousnessConfig,
) -> NdimageResult<Array2<f64>> {
    if binding_window.is_empty() {
        return Err(NdimageError::InvalidInput(
            "Empty binding _window".to_string(),
        ));
    }

    let (height, width, depth) = binding_window[0].dim();
    let mut consciousness_moment = Array2::zeros((height, width));

    // Integrate temporal binding _window
    for y in 0..height {
        for x in 0..width {
            let mut temporal_integration = 0.0;

            for (t, frame) in binding_window.iter().enumerate() {
                for d in 0..depth {
                    let weight = ((t as f64 - binding_window.len() as f64 / 2.0).abs())
                        .exp()
                        .recip();
                    temporal_integration += frame[(y, x, d)] * weight;
                }
            }

            consciousness_moment[(y, x)] =
                temporal_integration / (binding_window.len() * depth) as f64;
        }
    }

    Ok(consciousness_moment)
}

#[allow(dead_code)]
fn integrate_consciousness_moments<T>(
    moments: &[Array2<f64>],
    outputshape: (usize, usize),
    _config: &ConsciousnessConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = outputshape;
    let mut integrated = Array2::zeros((height, width));

    for moment in moments {
        integrated = integrated + moment;
    }

    if !moments.is_empty() {
        integrated /= moments.len() as f64;
    }

    // Convert to output type
    let output = integrated.mapv(|x| T::from_f64(x).unwrap_or_else(|| T::zero()));
    Ok(output)
}