//! Meta-Learning Module for Advanced Fusion Algorithms
//!
//! This module provides sophisticated meta-learning capabilities that enable the system
//! to learn how to learn, adapting processing strategies based on input patterns,
//! temporal contexts, and performance feedback. It implements:
//!
//! # Key Features
//! * **Temporal Memory Fusion**: Integrates short-term and long-term memory patterns
//! * **Hierarchical Learning**: Multi-level learning abstraction and strategy development
//! * **Strategy Evolution**: Evolutionary optimization of learning strategies
//! * **Adaptive Memory Consolidation**: Intelligent memory management and consolidation
//! * **Performance Tracking**: Comprehensive learning curve analysis and strategy effectiveness
//!
//! # Meta-Learning Components
//! * Pattern analysis for optimal strategy adaptation
//! * Memory attention mechanisms for relevant experience retrieval
//! * Hierarchical learning structures with cross-level communication
//! * Evolutionary strategy optimization with multiple selection mechanisms
//! * Adaptive parameter updates based on performance feedback
//!
//! # Usage
//! The module provides both basic meta-learning adaptation (`meta_learning_adaptation`)
//! and advanced temporal fusion capabilities (`enhanced_meta_learning_with_temporal_fusion`)
//! for different complexity requirements.

use scirs2_core::ndarray::ArrayStatCompat;
use scirs2_core::ndarray::{s, Array1, Array2, Array3, Axis};
use scirs2_core::random::{Rng, RngExt};
use std::collections::{HashMap, VecDeque};

use super::config::*;
use crate::NdimageResult;

/// Basic Meta-Learning Adaptation
///
/// Implements meta-learning algorithms that learn how to learn, adapting
/// the processing strategies based on the type of input and desired outcomes.
/// This function analyzes input patterns, updates meta-learning parameters,
/// and applies adaptive processing strategies.
#[allow(dead_code)]
pub fn meta_learning_adaptation(
    consciousness_response: &Array2<f64>,
    neural_response: &Array2<f64>,
    causal_response: &Array2<f64>,
    advanced_state: &mut AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = consciousness_response.dim();
    let mut adapted_output = Array2::zeros((height, width));

    // Analyze input patterns to determine optimal adaptation strategy
    let pattern_analysis = analyze_input_patterns(
        consciousness_response,
        neural_response,
        causal_response,
        config,
    )?;

    // Update meta-learning parameters based on pattern analysis
    update_meta_learning_parameters(
        &mut advanced_state.meta_parameters,
        &pattern_analysis,
        config,
    )?;

    // Apply adaptive processing strategies
    for y in 0..height {
        for x in 0..width {
            let consciousness_val = consciousness_response[(y, x)];
            let neural_val = neural_response[(y, x)];
            let causal_val = causal_response[(y, x)];

            // Determine optimal combination weights using meta-learning
            let combination_weights = determine_optimal_weights(
                (consciousness_val, neural_val, causal_val),
                &advanced_state.meta_parameters,
                (y, x),
                config,
            )?;

            // Apply adaptive combination
            let adapted_value = consciousness_val * combination_weights.0
                + neural_val * combination_weights.1
                + causal_val * combination_weights.2;

            adapted_output[(y, x)] = adapted_value;
        }
    }

    // Apply meta-learning update to improve future adaptations
    apply_meta_learning_update(advanced_state, &adapted_output, config)?;

    Ok(adapted_output)
}

/// Enhanced Meta-Learning with Temporal Memory Fusion
///
/// This advanced function implements sophisticated meta-learning that incorporates
/// temporal memory patterns, hierarchical learning, and evolutionary strategy optimization
/// to achieve superior learning performance and generalization.
#[allow(dead_code)]
pub fn enhanced_meta_learning_with_temporal_fusion(
    consciousness_response: &Array2<f64>,
    neural_response: &Array2<f64>,
    causal_response: &Array2<f64>,
    advanced_state: &mut AdvancedState,
    config: &AdvancedConfig,
    meta_learning_system: &mut EnhancedMetaLearningSystem,
    task_context: &str,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = consciousness_response.dim();
    let mut enhanced_output = Array2::zeros((height, width));

    // Step 1: Temporal Memory Fusion
    let temporal_memory_output = apply_temporal_memory_fusion(
        consciousness_response,
        neural_response,
        causal_response,
        &mut meta_learning_system.temporal_memory_fusion,
        task_context,
    )?;

    // Step 2: Hierarchical Learning Processing
    let hierarchical_output = apply_hierarchical_learning(
        &temporal_memory_output,
        &mut meta_learning_system.hierarchical_learner,
        advanced_state,
        config,
    )?;

    // Step 3: Strategy Evolution and Selection
    let evolved_strategies = evolve_learning_strategies(
        &mut meta_learning_system.strategy_evolution,
        &temporal_memory_output,
        &hierarchical_output,
        task_context,
    )?;

    // Step 4: Apply Best Evolved Strategies
    let strategy_enhanced_output = apply_evolved_strategies(
        &hierarchical_output,
        &evolved_strategies,
        advanced_state,
        config,
    )?;

    // Step 5: Memory Consolidation
    perform_adaptive_memory_consolidation(
        &mut meta_learning_system.memory_consolidation,
        &strategy_enhanced_output,
        task_context,
    )?;

    // Step 6: Update Meta-Learning Performance Tracking
    update_meta_learning_performance(
        &mut meta_learning_system.performance_tracker,
        &strategy_enhanced_output,
        task_context,
    )?;

    // Step 7: Final Integration and Output
    for y in 0..height {
        for x in 0..width {
            let temporal_val = temporal_memory_output[(y, x)];
            let hierarchical_val = hierarchical_output[(y, x)];
            let strategy_val = strategy_enhanced_output[(y, x)];

            // Intelligent weighted combination based on meta-learning insights
            let fusion_weights = calculate_adaptive_fusion_weights(
                (temporal_val, hierarchical_val, strategy_val),
                meta_learning_system,
                (y, x),
            )?;

            enhanced_output[(y, x)] = temporal_val * fusion_weights.0
                + hierarchical_val * fusion_weights.1
                + strategy_val * fusion_weights.2;
        }
    }

    // Step 8: Update meta-parameters for future learning
    update_meta_learning_parameters_enhanced(
        &mut advanced_state.meta_parameters,
        &enhanced_output,
        config,
    )?;

    Ok(enhanced_output)
}

/// Apply Temporal Memory Fusion
///
/// Integrates past learning experiences through temporal memory fusion,
/// combining short-term and long-term memory with attention mechanisms.
#[allow(dead_code)]
pub fn apply_temporal_memory_fusion(
    consciousness_response: &Array2<f64>,
    neural_response: &Array2<f64>,
    causal_response: &Array2<f64>,
    temporal_fusion: &mut TemporalMemoryFusion,
    task_context: &str,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = consciousness_response.dim();
    let mut fused_output = Array2::zeros((height, width));

    // Create current memory trace
    let current_trace = create_memory_trace(
        consciousness_response,
        neural_response,
        causal_response,
        task_context,
    )?;

    // Add to short-term memory
    temporal_fusion.short_term_memory.push_back(current_trace);

    // Maintain short-term memory window
    if temporal_fusion.short_term_memory.len() > 20 {
        // Move oldest memory to long-term consolidation
        if let Some(old_trace) = temporal_fusion.short_term_memory.pop_front() {
            consolidate_to_long_term_memory(&old_trace, &mut temporal_fusion.long_term_memory)?;
        }
    }

    // Apply memory attention and fusion
    for y in 0..height {
        for x in 0..width {
            let current_val = consciousness_response[(y, x)];

            // Retrieve relevant memories
            let relevant_memories = retrieve_relevant_memories(
                &temporal_fusion.short_term_memory,
                &temporal_fusion.long_term_memory,
                (y, x),
                task_context,
            )?;

            // Apply temporal fusion
            let fused_val = apply_memory_fusion(
                current_val,
                &relevant_memories,
                &temporal_fusion.fusion_weights,
                &temporal_fusion.decay_factors,
            )?;

            fused_output[(y, x)] = fused_val;
        }
    }

    // Update attention mechanism
    update_memory_attention(&mut temporal_fusion.attention_mechanism, &fused_output)?;

    Ok(fused_output)
}

// ================================
// Helper Functions Implementation
// ================================

/// Analyze Input Patterns for Meta-Learning Strategy Selection
///
/// Performs comprehensive pattern analysis to determine optimal meta-learning strategies.
/// Analyzes statistical properties, frequency characteristics, and spatial patterns.
#[allow(dead_code)]
pub fn analyze_input_patterns(
    consciousness: &Array2<f64>,
    neural: &Array2<f64>,
    causal: &Array2<f64>,
    config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    let (height, width) = consciousness.dim();
    let mut pattern_analysis = Array2::zeros((4, 3)); // 4 metrics, 3 inputs

    // Statistical Analysis
    let consciousness_stats = calculate_statistical_metrics(consciousness);
    let neural_stats = calculate_statistical_metrics(neural);
    let causal_stats = calculate_statistical_metrics(causal);

    pattern_analysis[(0, 0)] = consciousness_stats.0; // Mean
    pattern_analysis[(0, 1)] = neural_stats.0;
    pattern_analysis[(0, 2)] = causal_stats.0;

    pattern_analysis[(1, 0)] = consciousness_stats.1; // Variance
    pattern_analysis[(1, 1)] = neural_stats.1;
    pattern_analysis[(1, 2)] = causal_stats.1;

    // Frequency Domain Analysis
    let consciousness_freq = analyze_frequency_characteristics(consciousness);
    let neural_freq = analyze_frequency_characteristics(neural);
    let causal_freq = analyze_frequency_characteristics(causal);

    pattern_analysis[(2, 0)] = consciousness_freq;
    pattern_analysis[(2, 1)] = neural_freq;
    pattern_analysis[(2, 2)] = causal_freq;

    // Spatial Correlation Analysis
    let consciousness_corr = calculate_spatial_correlation(consciousness);
    let neural_corr = calculate_spatial_correlation(neural);
    let causal_corr = calculate_spatial_correlation(causal);

    pattern_analysis[(3, 0)] = consciousness_corr;
    pattern_analysis[(3, 1)] = neural_corr;
    pattern_analysis[(3, 2)] = causal_corr;

    Ok(pattern_analysis)
}

/// Update Meta-Learning Parameters
///
/// Updates meta-learning parameters based on pattern analysis results.
/// Implements adaptive learning rate and strategy selection.
#[allow(dead_code)]
pub fn update_meta_learning_parameters(
    meta_params: &mut Array2<f64>,
    pattern_analysis: &Array2<f64>,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    let (rows, cols) = meta_params.dim();

    for i in 0..rows.min(pattern_analysis.nrows()) {
        for j in 0..cols.min(pattern_analysis.ncols()) {
            let analysis_value = pattern_analysis[(i, j)];
            let current_param = meta_params[(i, j)];

            // Adaptive parameter update based on pattern characteristics
            let adaptation_factor = calculate_adaptation_factor(analysis_value, config);
            meta_params[(i, j)] = current_param + config.meta_learning_rate * adaptation_factor;

            // Ensure parameters stay within reasonable bounds
            meta_params[(i, j)] = meta_params[(i, j)].max(-10.0).min(10.0);
        }
    }

    Ok(())
}

/// Enhanced Meta-Learning Parameter Update
///
/// Advanced version with temporal consideration and performance feedback.
#[allow(dead_code)]
pub fn update_meta_learning_parameters_enhanced(
    meta_params: &mut Array2<f64>,
    output: &Array2<f64>,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    let (rows, cols) = meta_params.dim();
    let output_mean = output.mean_or(0.0);
    let output_std = output.std(0.0);

    // Calculate performance-based adaptation
    let performance_factor = (output_mean.abs() + output_std).tanh();

    for i in 0..rows {
        for j in 0..cols {
            let gradient = calculate_meta_gradient(i, j, output, config);
            let adaptive_rate = config.meta_learning_rate * performance_factor;

            meta_params[(i, j)] += adaptive_rate * gradient;
            meta_params[(i, j)] = meta_params[(i, j)].max(-5.0).min(5.0);
        }
    }

    Ok(())
}

/// Determine Optimal Combination Weights
///
/// Uses meta-learning insights to determine optimal combination weights
/// for different processing components based on current context.
#[allow(dead_code)]
pub fn determine_optimal_weights(
    inputs: (f64, f64, f64),
    meta_params: &Array2<f64>,
    position: (usize, usize),
    config: &AdvancedConfig,
) -> NdimageResult<(f64, f64, f64)> {
    let (consciousness_val, neural_val, causal_val) = inputs;
    let (y, x) = position;

    // Calculate contextual factors
    let spatial_factor = ((y as f64 / 100.0).sin() + (x as f64 / 100.0).cos()) * 0.1;
    let magnitude_factor = (consciousness_val.abs() + neural_val.abs() + causal_val.abs()) / 3.0;

    // Extract relevant meta-parameters
    let base_weights = if meta_params.nrows() >= 3 && meta_params.ncols() >= 1 {
        (
            meta_params[(0, 0)].tanh() * 0.5 + 0.5,
            meta_params[(1, 0)].tanh() * 0.5 + 0.5,
            meta_params[(2, 0)].tanh() * 0.5 + 0.5,
        )
    } else {
        (0.33, 0.33, 0.34)
    };

    // Apply contextual adjustments
    let consciousness_weight = base_weights.0 + spatial_factor * config.meta_learning_rate;
    let neural_weight = base_weights.1 + magnitude_factor * config.meta_learning_rate;
    let causal_weight =
        base_weights.2 - (spatial_factor + magnitude_factor) * config.meta_learning_rate * 0.5;

    // Normalize weights
    let total_weight = consciousness_weight + neural_weight + causal_weight;
    if total_weight > 0.0 {
        Ok((
            consciousness_weight / total_weight,
            neural_weight / total_weight,
            causal_weight / total_weight,
        ))
    } else {
        Ok((0.33, 0.33, 0.34))
    }
}

/// Apply Meta-Learning Update
///
/// Applies meta-learning updates to improve future adaptations based on current performance.
#[allow(dead_code)]
pub fn apply_meta_learning_update(
    advanced_state: &mut AdvancedState,
    output: &Array2<f64>,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    // Calculate performance metrics
    let performance_score = calculate_performance_score(output);

    // Update processing cycles
    advanced_state.processing_cycles += 1;

    // Update efficiency metrics
    advanced_state.efficiencymetrics.ops_per_second =
        performance_score * config.meta_learning_rate * 1000.0;

    // Store temporal memory if performance is significant
    if performance_score > 0.5 {
        let memory_snapshot = output
            .slice(s![0..output.nrows().min(32), 0..output.ncols().min(32)])
            .to_owned();
        advanced_state
            .temporal_memory
            .push_back(memory_snapshot.insert_axis(Axis(2)));

        // Maintain memory window
        if advanced_state.temporal_memory.len() > config.temporal_window {
            advanced_state.temporal_memory.pop_front();
        }
    }

    Ok(())
}

/// Apply Hierarchical Learning
///
/// Implements hierarchical learning processing across multiple abstraction levels.
#[allow(dead_code)]
pub fn apply_hierarchical_learning(
    input: &Array2<f64>,
    hierarchical_learner: &mut HierarchicalLearner,
    state: &AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    let mut processed_output = input.clone();

    // Process through each hierarchy level
    for level in hierarchical_learner.hierarchy_levels.iter_mut() {
        processed_output = apply_level_processing(&processed_output, level, config)?;

        // Update level performance metrics
        update_level_performance_metrics(level, &processed_output)?;
    }

    // Apply hierarchical attention
    let attention_weights = &hierarchical_learner.hierarchical_attention;
    let final_output = apply_hierarchical_attention(&processed_output, attention_weights)?;

    Ok(final_output)
}

/// Evolve Learning Strategies
///
/// Implements evolutionary optimization of learning strategies using genetic algorithms.
#[allow(dead_code)]
pub fn evolve_learning_strategies(
    strategy_evolution: &mut StrategyEvolution,
    temporal_output: &Array2<f64>,
    hierarchical_output: &Array2<f64>,
    task_context: &str,
) -> NdimageResult<Vec<EvolutionaryStrategy>> {
    let mut rng = scirs2_core::random::rng();

    // Evaluate current population fitness
    for strategy in strategy_evolution.strategy_population.iter_mut() {
        strategy.fitness =
            evaluate_strategy_fitness(strategy, temporal_output, hierarchical_output)?;
    }

    // Selection phase
    let selected_strategies = apply_selection(
        &strategy_evolution.strategy_population,
        &strategy_evolution.selection_mechanisms,
    )?;

    // Mutation and crossover
    let mut new_population = Vec::new();
    for _ in 0..selected_strategies.len() {
        let parent1 = &selected_strategies[rng.random_range(0..selected_strategies.len())];
        let parent2 = &selected_strategies[rng.random_range(0..selected_strategies.len())];

        let mut offspring = crossover_strategies(parent1, parent2)?;
        mutate_strategy(&mut offspring, &strategy_evolution.mutation_params)?;

        new_population.push(offspring);
    }

    // Update population
    strategy_evolution.strategy_population = new_population;

    // Record evolution generation
    record_evolution_generation(strategy_evolution, task_context)?;

    Ok(strategy_evolution.strategy_population.clone())
}

/// Apply Evolved Strategies
///
/// Applies the best evolved strategies to the input data.
#[allow(dead_code)]
pub fn apply_evolved_strategies(
    input: &Array2<f64>,
    strategies: &[EvolutionaryStrategy],
    advanced_state: &AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    if strategies.is_empty() {
        return Ok(input.clone());
    }

    // Find best strategy
    let best_strategy = strategies
        .iter()
        .max_by(|a, b| {
            a.fitness
                .partial_cmp(&b.fitness)
                .unwrap_or(std::cmp::Ordering::Equal)
        })
        .expect("Operation failed");

    // Apply best strategy transformation
    let mut enhanced_output = input.clone();

    // Apply genome-based transformations
    for (i, gene) in best_strategy.genome.iter().enumerate() {
        let transformation_factor = gene.tanh() * config.meta_learning_rate;

        // Apply spatial transformations based on genome
        for ((y, x), value) in enhanced_output.indexed_iter_mut() {
            let spatial_influence = calculate_spatial_influence(y, x, i, &best_strategy.genome);
            *value = *value * (1.0 + transformation_factor * spatial_influence);
        }
    }

    Ok(enhanced_output)
}

/// Perform Adaptive Memory Consolidation
///
/// Implements sophisticated memory consolidation strategies to optimize learning retention.
#[allow(dead_code)]
pub fn perform_adaptive_memory_consolidation(
    consolidation: &mut AdaptiveMemoryConsolidation,
    output: &Array2<f64>,
    task_context: &str,
) -> NdimageResult<()> {
    let performance_score = calculate_performance_score(output);

    // Apply consolidation strategies based on performance
    for strategy in consolidation.consolidation_strategies.iter() {
        match strategy {
            ConsolidationStrategy::ReplayBased { replay_frequency }
                if performance_score > *replay_frequency =>
            {
                apply_replay_consolidation(output, *replay_frequency)?;
            }
            ConsolidationStrategy::ImportanceWeighted {
                importance_threshold,
            } => {
                apply_importance_weighted_consolidation(output, *importance_threshold)?;
            }
            _ => {} // Handle other strategies as needed
        }
    }

    // Update consolidation metrics
    consolidation
        .effectiveness_metrics
        .consolidation_success_rate = (consolidation
        .effectiveness_metrics
        .consolidation_success_rate
        * 0.9)
        + (performance_score * 0.1);

    Ok(())
}

/// Update Meta-Learning Performance Tracking
///
/// Updates performance tracking metrics for strategy effectiveness analysis.
#[allow(dead_code)]
pub fn update_meta_learning_performance(
    tracker: &mut MetaLearningTracker,
    output: &Array2<f64>,
    task_context: &str,
) -> NdimageResult<()> {
    let performance_score = calculate_performance_score(output);
    let learning_time = 1.0; // Simplified timing
    let generalization_score = calculate_generalization_score(output);
    let resource_usage = calculate_resource_usage(output);

    // Create performance record
    let performance = MetaLearningPerformance {
        task_id: task_context.to_string(),
        performance_score,
        learning_time,
        generalization_score,
        resource_usage,
    };

    tracker.performancehistory.push_back(performance);

    // Maintain performance history window
    if tracker.performancehistory.len() > 100 {
        tracker.performancehistory.pop_front();
    }

    // Update learning curves
    update_learning_curves(tracker, task_context, performance_score)?;

    Ok(())
}

/// Calculate Adaptive Fusion Weights
///
/// Calculates intelligent fusion weights based on meta-learning insights.
#[allow(dead_code)]
pub fn calculate_adaptive_fusion_weights(
    values: (f64, f64, f64),
    meta_system: &EnhancedMetaLearningSystem,
    position: (usize, usize),
) -> NdimageResult<(f64, f64, f64)> {
    let (temporal_val, hierarchical_val, strategy_val) = values;
    let (y, x) = position;

    // Calculate attention-based weights
    let attention_weights = &meta_system
        .temporal_memory_fusion
        .attention_mechanism
        .attention_weights;

    let temporal_weight = attention_weights.get("temporal").unwrap_or(&0.33);
    let hierarchical_weight = attention_weights.get("hierarchical").unwrap_or(&0.33);
    let strategy_weight = attention_weights.get("strategy").unwrap_or(&0.34);

    // Apply positional influence
    let position_factor = (y as f64 * x as f64).sqrt() / 1000.0;
    let value_factor = (temporal_val.abs() + hierarchical_val.abs() + strategy_val.abs()) / 3.0;

    // Calculate adaptive weights
    let mut w1 = temporal_weight + position_factor * 0.1;
    let mut w2 = hierarchical_weight + value_factor * 0.1;
    let mut w3 = strategy_weight + (1.0 - position_factor - value_factor) * 0.1;

    // Normalize weights
    let total = w1 + w2 + w3;
    if total > 0.0 {
        w1 /= total;
        w2 /= total;
        w3 /= total;
    } else {
        w1 = 0.33;
        w2 = 0.33;
        w3 = 0.34;
    }

    Ok((w1, w2, w3))
}

// ================================
// Memory Management Functions
// ================================

/// Create Memory Trace from Processing Results
#[allow(dead_code)]
pub fn create_memory_trace(
    consciousness_response: &Array2<f64>,
    neural_response: &Array2<f64>,
    causal_response: &Array2<f64>,
    task_context: &str,
) -> NdimageResult<MemoryTrace> {
    let (height, width) = consciousness_response.dim();
    let mut content = Array2::zeros((height, width));

    // Combine responses into memory content
    for y in 0..height {
        for x in 0..width {
            content[(y, x)] = (consciousness_response[(y, x)]
                + neural_response[(y, x)]
                + causal_response[(y, x)])
                / 3.0;
        }
    }

    // Calculate importance score
    let importance = content.mean_or(0.0).abs() + content.std(0.0);

    // Create memory context
    let context = MemoryContext {
        operation_type: task_context.to_string(),
        data_characteristics: vec![
            consciousness_response.mean_or(0.0),
            neural_response.mean_or(0.0),
            causal_response.mean_or(0.0),
        ],
        performance_outcome: importance,
        environment: HashMap::new(),
    };

    Ok(MemoryTrace {
        content,
        context,
        importance,
        timestamp: 0, // Would use actual timestamp in real implementation
        access_count: 0,
    })
}

/// Consolidate Memory Trace to Long-Term Storage
#[allow(dead_code)]
pub fn consolidate_to_long_term_memory(
    trace: &MemoryTrace,
    long_term_memory: &mut HashMap<String, ConsolidatedMemory>,
) -> NdimageResult<()> {
    let key = trace.context.operation_type.clone();

    let consolidated = ConsolidatedMemory {
        representation: trace.content.clone(),
        strength: trace.importance,
        generalization_scope: trace.importance * 0.8,
        usage_stats: MemoryUsageStats {
            total_accesses: trace.access_count,
            success_rate: 0.5,
            avg_improvement: trace.importance * 0.1,
            last_access: trace.timestamp,
        },
    };

    long_term_memory.insert(key, consolidated);
    Ok(())
}

/// Retrieve Relevant Memories for Current Context
#[allow(dead_code)]
pub fn retrieve_relevant_memories(
    short_term: &VecDeque<MemoryTrace>,
    long_term: &HashMap<String, ConsolidatedMemory>,
    position: (usize, usize),
    context: &str,
) -> NdimageResult<Vec<f64>> {
    let mut relevant_memories = Vec::new();

    // Retrieve from short-term memory
    for trace in short_term.iter().rev().take(5) {
        if trace.context.operation_type.contains(context) {
            let (y, x) = position;
            if y < trace.content.nrows() && x < trace.content.ncols() {
                relevant_memories.push(trace.content[(y, x)]);
            }
        }
    }

    // Retrieve from long-term memory
    if let Some(consolidated) = long_term.get(context) {
        let (y, x) = position;
        if y < consolidated.representation.nrows() && x < consolidated.representation.ncols() {
            relevant_memories.push(consolidated.representation[(y, x)] * consolidated.strength);
        }
    }

    // Ensure we return something even if no relevant memories found
    if relevant_memories.is_empty() {
        relevant_memories.push(0.0);
    }

    Ok(relevant_memories)
}

/// Apply Memory Fusion with Temporal Decay
#[allow(dead_code)]
pub fn apply_memory_fusion(
    current_val: f64,
    memories: &[f64],
    fusion_weights: &Array1<f64>,
    decay_factors: &Array1<f64>,
) -> NdimageResult<f64> {
    if memories.is_empty() {
        return Ok(current_val);
    }

    let mut fused_value = current_val;

    for (i, &memory_val) in memories.iter().enumerate() {
        let weight_idx = i.min(fusion_weights.len() - 1);
        let decay_idx = i.min(decay_factors.len() - 1);

        let weight = fusion_weights[weight_idx];
        let decay = decay_factors[decay_idx];

        fused_value += memory_val * weight * decay;
    }

    Ok(fused_value)
}

/// Update Memory Attention Mechanism
#[allow(dead_code)]
pub fn update_memory_attention(
    attention: &mut MemoryAttention,
    output: &Array2<f64>,
) -> NdimageResult<()> {
    let performance_score = calculate_performance_score(output);

    // Update attention weights based on performance
    for (key, weight) in attention.attention_weights.iter_mut() {
        *weight = (*weight * (1.0 - attention.adaptation_rate))
            + (performance_score * attention.adaptation_rate);
        *weight = weight.max(0.0).min(1.0);
    }

    Ok(())
}

// ================================
// Utility Functions
// ================================

/// Calculate Statistical Metrics (mean, variance)
#[allow(dead_code)]
fn calculate_statistical_metrics(data: &Array2<f64>) -> (f64, f64) {
    let mean = data.mean_or(0.0);
    let variance = data.var(0.0);
    (mean, variance)
}

/// Analyze Frequency Characteristics
#[allow(dead_code)]
fn analyze_frequency_characteristics(data: &Array2<f64>) -> f64 {
    // Simplified frequency analysis - calculate high-frequency content
    let mut high_freq_content = 0.0;
    let (rows, cols) = data.dim();

    for i in 1..rows {
        for j in 1..cols {
            let dx = data[(i, j)] - data[(i - 1, j)];
            let dy = data[(i, j)] - data[(i, j - 1)];
            high_freq_content += (dx * dx + dy * dy).sqrt();
        }
    }

    high_freq_content / ((rows * cols) as f64)
}

/// Calculate Spatial Correlation
#[allow(dead_code)]
fn calculate_spatial_correlation(data: &Array2<f64>) -> f64 {
    let (rows, cols) = data.dim();
    if rows < 2 || cols < 2 {
        return 0.0;
    }

    let mut correlation = 0.0;
    let mut count = 0;

    for i in 0..rows - 1 {
        for j in 0..cols - 1 {
            let current = data[(i, j)];
            let right = data[(i, j + 1)];
            let down = data[(i + 1, j)];

            correlation += current * right + current * down;
            count += 2;
        }
    }

    if count > 0 {
        correlation / count as f64
    } else {
        0.0
    }
}

/// Calculate Adaptation Factor
#[allow(dead_code)]
fn calculate_adaptation_factor(analysis_value: f64, config: &AdvancedConfig) -> f64 {
    let base_factor = analysis_value.tanh();
    let intensity_factor = config.advanced_processing_intensity;
    base_factor * intensity_factor
}

/// Calculate Meta-Gradient for Parameter Updates
#[allow(dead_code)]
fn calculate_meta_gradient(
    i: usize,
    j: usize,
    output: &Array2<f64>,
    config: &AdvancedConfig,
) -> f64 {
    let performance_gradient = if i < output.nrows() && j < output.ncols() {
        output[(i, j)]
    } else {
        output.mean_or(0.0)
    };

    performance_gradient * config.meta_learning_rate
}

/// Calculate Performance Score
#[allow(dead_code)]
fn calculate_performance_score(output: &Array2<f64>) -> f64 {
    let mean = output.mean_or(0.0);
    let std = output.std(0.0);
    (mean.abs() + std).tanh()
}

/// Calculate Generalization Score
#[allow(dead_code)]
fn calculate_generalization_score(output: &Array2<f64>) -> f64 {
    let variance = output.var(0.0);
    let entropy = -variance * variance.ln().max(-10.0);
    entropy.tanh()
}

/// Calculate Resource Usage
#[allow(dead_code)]
fn calculate_resource_usage(output: &Array2<f64>) -> f64 {
    let complexity = output.len() as f64;
    let processing_intensity = output.map(|x| x.abs()).sum();
    (processing_intensity / complexity).tanh()
}

/// Apply Level Processing for Hierarchical Learning
#[allow(dead_code)]
fn apply_level_processing(
    input: &Array2<f64>,
    level: &mut LearningLevel,
    config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    let mut processed = input.clone();
    let abstraction = level.abstraction_degree;

    // Apply abstraction-level specific processing
    for value in processed.iter_mut() {
        *value = *value * abstraction + (1.0 - abstraction) * value.tanh();
    }

    Ok(processed)
}

/// Update Level Performance Metrics
#[allow(dead_code)]
fn update_level_performance_metrics(
    level: &mut LearningLevel,
    output: &Array2<f64>,
) -> NdimageResult<()> {
    let performance = calculate_performance_score(output);
    let old_rate = level.performancemetrics.learning_rate;

    level.performancemetrics.learning_rate = old_rate * 0.9 + performance * 0.1;
    level.performancemetrics.generalization_ability = calculate_generalization_score(output);

    Ok(())
}

/// Apply Hierarchical Attention
#[allow(dead_code)]
fn apply_hierarchical_attention(
    input: &Array2<f64>,
    attention_weights: &Array1<f64>,
) -> NdimageResult<Array2<f64>> {
    let mut output = input.clone();
    let attention_factor = attention_weights.mean_or(1.0);

    for value in output.iter_mut() {
        *value *= attention_factor;
    }

    Ok(output)
}

/// Evaluate Strategy Fitness
#[allow(dead_code)]
fn evaluate_strategy_fitness(
    strategy: &EvolutionaryStrategy,
    temporal_output: &Array2<f64>,
    hierarchical_output: &Array2<f64>,
) -> NdimageResult<f64> {
    let temporal_score = calculate_performance_score(temporal_output);
    let hierarchical_score = calculate_performance_score(hierarchical_output);
    let genome_quality = strategy.genome.mean_or(0.0).abs();

    Ok((temporal_score + hierarchical_score + genome_quality) / 3.0)
}

/// Apply Selection for Evolutionary Strategies
#[allow(dead_code)]
fn apply_selection(
    population: &[EvolutionaryStrategy],
    mechanisms: &[SelectionMechanism],
) -> NdimageResult<Vec<EvolutionaryStrategy>> {
    if population.is_empty() {
        return Ok(Vec::new());
    }

    let mut selected = Vec::new();
    let target_size = population.len();

    for mechanism in mechanisms {
        match mechanism {
            SelectionMechanism::Elite { elite_fraction } => {
                let elite_count = (population.len() as f64 * elite_fraction) as usize;
                let mut sorted_pop = population.to_vec();
                sorted_pop.sort_by(|a, b| {
                    b.fitness
                        .partial_cmp(&a.fitness)
                        .unwrap_or(std::cmp::Ordering::Equal)
                });
                selected.extend(sorted_pop.into_iter().take(elite_count));
            }
            SelectionMechanism::Tournament { tournament_size } => {
                let mut rng = scirs2_core::random::rng();
                for _ in 0..(target_size - selected.len()) {
                    let mut tournament = Vec::new();
                    for _ in 0..*tournament_size {
                        let idx = rng.random_range(0..population.len());
                        tournament.push(&population[idx]);
                    }

                    let winner = tournament
                        .into_iter()
                        .max_by(|a, b| {
                            a.fitness
                                .partial_cmp(&b.fitness)
                                .unwrap_or(std::cmp::Ordering::Equal)
                        })
                        .expect("Operation failed");
                    selected.push(winner.clone());
                }
                break;
            }
            _ => {} // Handle other selection mechanisms as needed
        }
    }

    // Ensure we have enough individuals
    while selected.len() < target_size && !population.is_empty() {
        selected.push(population[0].clone());
    }

    Ok(selected)
}

/// Crossover Between Two Strategies
#[allow(dead_code)]
fn crossover_strategies(
    parent1: &EvolutionaryStrategy,
    parent2: &EvolutionaryStrategy,
) -> NdimageResult<EvolutionaryStrategy> {
    let mut rng = scirs2_core::random::rng();
    let genome_size = parent1.genome.len().min(parent2.genome.len());
    let crossover_point = rng.random_range(1..genome_size);

    let mut new_genome = Array1::zeros(genome_size);

    for i in 0..genome_size {
        if i < crossover_point {
            new_genome[i] = parent1.genome[i];
        } else {
            new_genome[i] = parent2.genome[i];
        }
    }

    Ok(EvolutionaryStrategy {
        genome: new_genome,
        fitness: 0.0,
        age: 0,
        lineage: vec![parent1.lineage.len(), parent2.lineage.len()],
    })
}

/// Mutate Strategy Genome
#[allow(dead_code)]
fn mutate_strategy(
    strategy: &mut EvolutionaryStrategy,
    mutation_params: &MutationParameters,
) -> NdimageResult<()> {
    let mut rng = scirs2_core::random::rng();

    for gene in strategy.genome.iter_mut() {
        if rng.random::<f64>() < mutation_params.mutation_rate {
            let mutation = match mutation_params.mutation_distribution {
                MutationDistribution::Gaussian { sigma } => {
                    rng.random::<f64>() * sigma - sigma / 2.0
                }
                MutationDistribution::Uniform { range } => (rng.random::<f64>() - 0.5) * range,
                MutationDistribution::Adaptive => {
                    let adaptive_strength = mutation_params.mutation_strength * gene.abs();
                    (rng.random::<f64>() - 0.5) * adaptive_strength
                }
                _ => (rng.random::<f64>() - 0.5) * mutation_params.mutation_strength,
            };

            *gene += mutation;
            *gene = gene.max(-10.0).min(10.0); // Clamp values
        }
    }

    Ok(())
}

/// Record Evolution Generation
#[allow(dead_code)]
fn record_evolution_generation(
    strategy_evolution: &mut StrategyEvolution,
    task_context: &str,
) -> NdimageResult<()> {
    let best_fitness = strategy_evolution
        .strategy_population
        .iter()
        .map(|s| s.fitness)
        .fold(0.0, f64::max);

    let avg_fitness = strategy_evolution
        .strategy_population
        .iter()
        .map(|s| s.fitness)
        .sum::<f64>()
        / strategy_evolution.strategy_population.len() as f64;

    let generation = EvolutionGeneration {
        generation: strategy_evolution.evolutionhistory.len(),
        best_fitness,
        average_fitness: avg_fitness,
        diversity: calculate_population_diversity(&strategy_evolution.strategy_population),
        mutations: vec![format!("Context: {}", task_context)],
    };

    strategy_evolution.evolutionhistory.push_back(generation);

    // Maintain history window
    if strategy_evolution.evolutionhistory.len() > 50 {
        strategy_evolution.evolutionhistory.pop_front();
    }

    Ok(())
}

/// Calculate Population Diversity
#[allow(dead_code)]
fn calculate_population_diversity(population: &[EvolutionaryStrategy]) -> f64 {
    if population.len() < 2 {
        return 0.0;
    }

    let mut total_distance = 0.0;
    let mut comparisons = 0;

    for i in 0..population.len() {
        for j in i + 1..population.len() {
            let distance = calculate_genome_distance(&population[i].genome, &population[j].genome);
            total_distance += distance;
            comparisons += 1;
        }
    }

    if comparisons > 0 {
        total_distance / comparisons as f64
    } else {
        0.0
    }
}

/// Calculate Distance Between Genomes
#[allow(dead_code)]
fn calculate_genome_distance(genome1: &Array1<f64>, genome2: &Array1<f64>) -> f64 {
    let min_len = genome1.len().min(genome2.len());
    let mut distance = 0.0;

    for i in 0..min_len {
        distance += (genome1[i] - genome2[i]).powi(2);
    }

    distance.sqrt()
}

/// Calculate Spatial Influence for Strategy Application
#[allow(dead_code)]
fn calculate_spatial_influence(y: usize, x: usize, gene_index: usize, genome: &Array1<f64>) -> f64 {
    let spatial_factor = ((y as f64).sin() + (x as f64).cos()) / 2.0;
    let gene_factor = if gene_index < genome.len() {
        genome[gene_index].tanh()
    } else {
        0.0
    };

    spatial_factor * gene_factor
}

/// Apply Replay Consolidation
#[allow(dead_code)]
fn apply_replay_consolidation(output: &Array2<f64>, strength: f64) -> NdimageResult<()> {
    // Simulate replay consolidation by reinforcing strong patterns
    let _replay_factor = strength * calculate_performance_score(output);
    // Implementation would reinforce memory patterns here
    Ok(())
}

/// Apply Importance-Weighted Consolidation
#[allow(dead_code)]
fn apply_importance_weighted_consolidation(output: &Array2<f64>, decay: f64) -> NdimageResult<()> {
    // Simulate importance-weighted consolidation
    let _importance_weight = (1.0 - decay) * calculate_performance_score(output);
    // Implementation would weight memories by importance here
    Ok(())
}

/// Update Learning Curves
#[allow(dead_code)]
fn update_learning_curves(
    tracker: &mut MetaLearningTracker,
    task_context: &str,
    performance_score: f64,
) -> NdimageResult<()> {
    let learning_curve = tracker
        .learning_curves
        .entry(task_context.to_string())
        .or_insert_with(|| LearningCurve {
            performance_timeline: Vec::new(),
            learning_rate_timeline: Vec::new(),
            convergence_point: None,
        });

    learning_curve.performance_timeline.push(performance_score);
    learning_curve
        .learning_rate_timeline
        .push(performance_score * 0.1);

    // Detect convergence
    if learning_curve.performance_timeline.len() > 10 {
        let recent_variance = calculate_recent_variance(&learning_curve.performance_timeline, 10);
        if recent_variance < 0.01 && learning_curve.convergence_point.is_none() {
            learning_curve.convergence_point = Some(learning_curve.performance_timeline.len());
        }
    }

    Ok(())
}

/// Calculate Recent Variance for Convergence Detection
#[allow(dead_code)]
fn calculate_recent_variance(timeline: &[f64], window_size: usize) -> f64 {
    if timeline.len() < window_size {
        return f64::INFINITY;
    }

    let start = timeline.len() - window_size;
    let recent = &timeline[start..];
    let mean = recent.iter().sum::<f64>() / recent.len() as f64;
    let variance = recent.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / recent.len() as f64;

    variance
}
