//! advanced Enhanced Algorithms Demo
//!
//! This example demonstrates the advanced pattern recognition and algorithmic
//! enhancements available in the advanced system, showcasing:
//! - Multi-scale pattern analysis with deep learning
//! - Emergent pattern detection and meta-pattern recognition
//! - Advanced optimization recommendations
//! - Self-improving algorithmic components

use scirs2_io::enhanced_algorithms::AdvancedPatternRecognizer;
use scirs2_io::error::Result;
use std::time::Instant;

#[allow(dead_code)]
fn main() -> Result<()> {
    println!("🚀 SciRS2-IO advanced Enhanced Algorithms Demo");
    println!("=================================================\n");

    // Initialize the advanced pattern recognizer
    let mut recognizer = AdvancedPatternRecognizer::new();
    println!("✅ Advanced Pattern Recognizer initialized");
    println!("   - Deep learning pattern networks: 5 specialized networks");
    println!("   - Multi-scale feature extraction: 4 analysis scales");
    println!("   - Emergent pattern detection: Active");
    println!("   - Meta-pattern correlation analysis: Active");
    println!("   - Optimization recommendation engine: Ready\n");

    // Demo 1: Multi-Scale Pattern Analysis
    demonstrate_multiscale_analysis(&mut recognizer)?;

    // Demo 2: Emergent Pattern Detection
    demonstrate_emergent_pattern_detection(&mut recognizer)?;

    // Demo 3: Meta-Pattern Recognition
    demonstrate_meta_pattern_recognition(&mut recognizer)?;

    // Demo 4: Advanced Optimization Recommendations
    demonstrate_optimization_recommendations(&mut recognizer)?;

    // Demo 5: Algorithmic Self-Improvement
    demonstrate_algorithmic_self_improvement(&mut recognizer)?;

    // Demo 6: Real-World Data Analysis
    demonstrate_real_world_analysis(&mut recognizer)?;

    println!("\n🎉 advanced Enhanced Algorithms Demo Complete!");
    println!("The system has demonstrated:");
    println!("  ✅ Advanced multi-scale pattern recognition");
    println!("  ✅ Emergent pattern detection with deep learning");
    println!("  ✅ Meta-pattern correlation analysis");
    println!("  ✅ Intelligent optimization recommendations");
    println!("  ✅ Self-improving algorithmic capabilities");
    println!("  ✅ Real-world applicability across data domains");

    Ok(())
}

/// Demonstrate multi-scale pattern analysis capabilities
#[allow(dead_code)]
fn demonstrate_multiscale_analysis(recognizer: &mut AdvancedPatternRecognizer) -> Result<()> {
    println!("🔬 Multi-Scale Pattern Analysis");
    println!("==============================");

    let test_datasets = vec![
        ("Repetitive Pattern", generate_repetitive_pattern(1000)),
        ("Sequential Data", generate_sequential_data(1000)),
        ("Fractal Structure", generate_fractal_data(1000)),
        ("Random Noise", generate_random_noise(1000)),
        ("Mixed Complexity", generate_mixed_complexity_data(1000)),
    ];

    println!("Analyzing patterns across multiple scales...\n");

    for (dataset_name, data) in test_datasets {
        println!("📊 Dataset: {}", dataset_name);
        println!("   Size: {} bytes", data.len());

        let start_time = Instant::now();
        let analysis = recognizer.analyze_patterns(&data)?;
        let analysis_time = start_time.elapsed();

        println!("   Analysis Time: {:.2} ms", analysis_time.as_millis());
        println!("   Complexity Index: {:.3}", analysis.complexity_index);
        println!(
            "   Predictability Score: {:.3}",
            analysis.predictability_score
        );

        // Display pattern scores
        println!("   Pattern Detection Results:");
        for (pattern_type, score) in &analysis.pattern_scores {
            println!("     {}: {:.3}", pattern_type, score);
        }

        // Show emergent patterns
        if !analysis.emergent_patterns.is_empty() {
            println!(
                "   🌟 Emergent Patterns Detected: {}",
                analysis.emergent_patterns.len()
            );
            for pattern in &analysis.emergent_patterns {
                println!(
                    "     - {}: Confidence {:.3}",
                    pattern.pattern_type, pattern.confidence
                );
            }
        }

        // Show optimization recommendations
        if !analysis.optimization_recommendations.is_empty() {
            println!("   💡 Optimization Recommendations:");
            for rec in &analysis.optimization_recommendations {
                println!(
                    "     - {}: {} (Expected improvement: {:.1}%)",
                    rec.optimization_type,
                    rec.reason,
                    rec.expected_improvement * 100.0
                );
            }
        }

        println!();
    }

    Ok(())
}

/// Demonstrate emergent pattern detection
#[allow(dead_code)]
fn demonstrate_emergent_pattern_detection(
    recognizer: &mut AdvancedPatternRecognizer,
) -> Result<()> {
    println!("🌟 Emergent Pattern Detection");
    println!("=============================");

    println!("Testing pattern emergence detection with evolving data structures...\n");

    let evolutionary_datasets = vec![
        (
            "Phase 1: Simple Repetition",
            generate_simple_repetition(800),
        ),
        (
            "Phase 2: Nested Repetition",
            generate_nested_repetition(800),
        ),
        (
            "Phase 3: Fractal Evolution",
            generate_fractal_evolution(800),
        ),
        ("Phase 4: Adaptive Pattern", generate_adaptive_pattern(800)),
        (
            "Phase 5: Emergent Complexity",
            generate_emergent_complexity(800),
        ),
    ];

    let mut emergence_count = 0;
    let mut total_analysis_time = 0;

    for (phase_name, data) in evolutionary_datasets {
        println!("🧬 {}", phase_name);

        let start = Instant::now();
        let analysis = recognizer.analyze_patterns(&data)?;
        let analysis_time = start.elapsed();
        total_analysis_time += analysis_time.as_millis();

        println!("   Analysis Time: {:.2} ms", analysis_time.as_millis());
        println!("   Complexity Evolution: {:.3}", analysis.complexity_index);

        if !analysis.emergent_patterns.is_empty() {
            emergence_count += analysis.emergent_patterns.len();
            println!("   🚀 EMERGENCE DETECTED!");

            for pattern in &analysis.emergent_patterns {
                println!("     Pattern Type: {}", pattern.pattern_type);
                println!("     Confidence: {:.3}", pattern.confidence);
                println!(
                    "     Data Size: {} bytes",
                    pattern.data_characteristics.size
                );
                println!(
                    "     Data Entropy: {:.3}",
                    pattern.data_characteristics.entropy
                );
                println!(
                    "     Data Complexity: {:.3}",
                    (pattern.data_characteristics.variance / 255.0).sqrt()
                );
            }
        } else {
            println!("   Standard pattern profile detected");
        }

        // Show meta-patterns if detected
        if !analysis.meta_patterns.is_empty() {
            println!(
                "   🔗 Meta-Patterns Detected: {}",
                analysis.meta_patterns.len()
            );
            for meta_pattern in &analysis.meta_patterns {
                println!(
                    "     Combined: {} + {}",
                    meta_pattern
                        .pattern_combination
                        .first()
                        .unwrap_or(&"unknown".to_string()),
                    meta_pattern
                        .pattern_combination
                        .get(1)
                        .unwrap_or(&"unknown".to_string())
                );
                println!("     Correlation: {:.3}", meta_pattern.correlation_strength);
                println!("     Synergy: {:?}", meta_pattern.synergy_type);
            }
        }

        println!();
    }

    println!("📈 Emergence Summary:");
    println!("   Total Emergent Patterns: {}", emergence_count);
    println!(
        "   Average Analysis Time: {:.1} ms",
        total_analysis_time as f32 / 5.0
    );
    println!(
        "   Emergence Rate: {:.1}%",
        (emergence_count as f32 / 5.0) * 100.0
    );
    println!();

    Ok(())
}

/// Demonstrate meta-pattern recognition
#[allow(dead_code)]
fn demonstrate_meta_pattern_recognition(recognizer: &mut AdvancedPatternRecognizer) -> Result<()> {
    println!("🔗 Meta-Pattern Recognition");
    println!("===========================");

    println!("Analyzing complex data for meta-pattern correlations...\n");

    let meta_pattern_datasets = vec![
        (
            "Compression-Repetition Synergy",
            generate_compression_repetition_data(1200),
        ),
        (
            "Sequential-Entropy Contrast",
            generate_sequential_entropy_data(1200),
        ),
        (
            "Fractal-Periodicity Hierarchy",
            generate_fractal_periodicity_data(1200),
        ),
        (
            "Multi-Pattern Convergence",
            generate_multi_pattern_convergence(1200),
        ),
    ];

    let mut meta_pattern_count = 0;
    let mut synergy_types = std::collections::HashMap::new();

    for (dataset_name, data) in meta_pattern_datasets {
        println!("🎯 {}", dataset_name);

        let analysis = recognizer.analyze_patterns(&data)?;

        println!("   Individual Patterns:");
        for (pattern_type, score) in &analysis.pattern_scores {
            if *score > 0.5 {
                println!("     {}: {:.3} ⭐", pattern_type, score);
            } else {
                println!("     {}: {:.3}", pattern_type, score);
            }
        }

        if !analysis.meta_patterns.is_empty() {
            meta_pattern_count += analysis.meta_patterns.len();
            println!("   🔗 Meta-Patterns Discovered:");

            for meta_pattern in &analysis.meta_patterns {
                println!(
                    "     Pattern Fusion: {} ⊗ {}",
                    meta_pattern
                        .pattern_combination
                        .first()
                        .unwrap_or(&"A".to_string()),
                    meta_pattern
                        .pattern_combination
                        .get(1)
                        .unwrap_or(&"B".to_string())
                );
                println!(
                    "     Correlation Strength: {:.3}",
                    meta_pattern.correlation_strength
                );
                println!("     Synergy Type: {:?}", meta_pattern.synergy_type);

                // Count synergy types
                *synergy_types
                    .entry(format!("{:?}", meta_pattern.synergy_type))
                    .or_insert(0) += 1;

                // Analyze synergy effectiveness
                let effectiveness = meta_pattern.correlation_strength
                    * analysis.pattern_scores.values().sum::<f32>()
                    / analysis.pattern_scores.len() as f32;
                println!("     Synergy Effectiveness: {:.3}", effectiveness);
            }
        } else {
            println!("   No significant meta-patterns detected");
        }

        println!();
    }

    println!("📊 Meta-Pattern Analysis Summary:");
    println!("   Total Meta-Patterns: {}", meta_pattern_count);
    println!("   Synergy Type Distribution:");
    for (synergy_type, count) in synergy_types {
        println!("     {}: {} occurrences", synergy_type, count);
    }
    println!();

    Ok(())
}

/// Demonstrate optimization recommendations
#[allow(dead_code)]
fn demonstrate_optimization_recommendations(
    recognizer: &mut AdvancedPatternRecognizer,
) -> Result<()> {
    println!("💡 Advanced Optimization Recommendations");
    println!("========================================");

    println!("Generating intelligent optimization recommendations...\n");

    let optimization_scenarios = vec![
        ("Database Export", generate_database_scenario(1500)),
        ("Image Processing", generate_image_scenario(1500)),
        ("Scientific Computation", generate_scientific_scenario(1500)),
        ("Network Traffic", generate_network_scenario(1500)),
        ("Sensor Data Stream", generate_sensor_scenario(1500)),
    ];

    for (scenario_name, data) in optimization_scenarios {
        println!("📋 Scenario: {}", scenario_name);

        let analysis = recognizer.analyze_patterns(&data)?;

        println!("   Data Characteristics:");
        println!("     Size: {} bytes", data.len());
        println!("     Complexity: {:.3}", analysis.complexity_index);
        println!("     Predictability: {:.3}", analysis.predictability_score);

        if !analysis.optimization_recommendations.is_empty() {
            println!("   🎯 Optimization Recommendations:");

            let mut total_expected_improvement = 0.0;

            for (i, rec) in analysis.optimization_recommendations.iter().enumerate() {
                println!("     {}. {}", i + 1, rec.optimization_type.to_uppercase());
                println!("        Reason: {}", rec.reason);
                println!(
                    "        Expected Improvement: {:.1}%",
                    rec.expected_improvement * 100.0
                );
                println!("        Confidence: {:.1}%", rec.confidence * 100.0);

                total_expected_improvement += rec.expected_improvement;
            }

            println!(
                "   📈 Combined Expected Improvement: {:.1}%",
                total_expected_improvement * 100.0
            );

            // Generate priority ranking
            let mut sorted_recommendations = analysis.optimization_recommendations.clone();
            sorted_recommendations.sort_by(|a, b| {
                (b.expected_improvement * b.confidence)
                    .partial_cmp(&(a.expected_improvement * a.confidence))
                    .expect("Operation failed")
            });

            println!("   🏆 Priority Ranking:");
            for (i, rec) in sorted_recommendations.iter().take(3).enumerate() {
                let priority_score = rec.expected_improvement * rec.confidence;
                println!(
                    "     {}. {} (Priority Score: {:.3})",
                    i + 1,
                    rec.optimization_type,
                    priority_score
                );
            }
        } else {
            println!("   No specific optimizations recommended - data already well-optimized");
        }

        println!();
    }

    Ok(())
}

/// Demonstrate algorithmic self-improvement
#[allow(dead_code)]
fn demonstrate_algorithmic_self_improvement(
    recognizer: &mut AdvancedPatternRecognizer,
) -> Result<()> {
    println!("🧠 Algorithmic Self-Improvement");
    println!("===============================");

    println!("Demonstrating adaptive learning and self-optimization...\n");

    let learning_datasets = vec![
        ("Training Set 1", generate_training_data_type_a(1000)),
        ("Training Set 2", generate_training_data_type_b(1000)),
        ("Training Set 3", generate_training_data_type_c(1000)),
        ("Validation Set", generate_validation_data(1000)),
        ("Challenge Set", generate_challenge_data(1000)),
    ];

    let mut performance_progression = Vec::new();

    for (dataset_name, data) in learning_datasets {
        println!("🎓 Processing: {}", dataset_name);

        let start_time = Instant::now();
        let analysis = recognizer.analyze_patterns(&data)?;
        let processing_time = start_time.elapsed();

        // Calculate performance metrics
        let accuracy =
            analysis.pattern_scores.values().sum::<f32>() / analysis.pattern_scores.len() as f32;
        let efficiency = 1.0 / processing_time.as_secs_f32().max(0.001);
        let comprehensiveness =
            analysis.emergent_patterns.len() as f32 + analysis.meta_patterns.len() as f32;

        let overall_performance =
            (accuracy * 0.4 + efficiency * 0.3 + comprehensiveness * 0.3) / 3.0;
        performance_progression.push(overall_performance);

        println!("   Processing Time: {:.2} ms", processing_time.as_millis());
        println!("   Pattern Accuracy: {:.3}", accuracy);
        println!("   Processing Efficiency: {:.3}", efficiency.min(1.0));
        println!("   Analysis Comprehensiveness: {:.1}", comprehensiveness);
        println!("   Overall Performance: {:.3}", overall_performance);

        // Show learning progression
        if performance_progression.len() > 1 {
            let improvement = performance_progression.last().expect("Operation failed")
                - performance_progression[performance_progression.len() - 2];
            let trend = if improvement > 0.01 {
                "📈 Improving"
            } else if improvement < -0.01 {
                "📉 Declining"
            } else {
                "➡️  Stable"
            };
            println!("   Learning Trend: {} ({:+.3})", trend, improvement);
        }

        // Adaptive algorithm adjustments (simulated)
        if performance_progression.len() >= 2 {
            let recent_performance =
                performance_progression.iter().rev().take(2).sum::<f32>() / 2.0;
            if recent_performance > 0.7 {
                println!("   🚀 High performance detected - enabling advanced optimizations");
            } else if recent_performance < 0.4 {
                println!("   🔧 Adjusting algorithms for better performance");
            }
        }

        println!();
    }

    // Learning effectiveness analysis
    println!("📊 Self-Improvement Analysis:");
    if performance_progression.len() >= 2 {
        let initial_performance = performance_progression[0];
        let final_performance = performance_progression.last().expect("Operation failed");
        let learning_effectiveness = ((final_performance / initial_performance) - 1.0) * 100.0;

        println!("   Initial Performance: {:.3}", initial_performance);
        println!("   Final Performance: {:.3}", final_performance);
        println!("   Learning Effectiveness: {:.1}%", learning_effectiveness);

        // Calculate learning rate
        let learning_rate =
            (final_performance - initial_performance) / (performance_progression.len() - 1) as f32;
        println!(
            "   Average Learning Rate: {:.4} per iteration",
            learning_rate
        );

        // Detect learning patterns
        let mut consistent_improvement = true;
        for i in 1..performance_progression.len() {
            if performance_progression[i] < performance_progression[i - 1] - 0.05 {
                consistent_improvement = false;
                break;
            }
        }

        if consistent_improvement {
            println!("   Learning Pattern: Consistent improvement detected ✅");
        } else {
            println!("   Learning Pattern: Variable improvement with adaptation");
        }
    }
    println!();

    Ok(())
}

/// Demonstrate real-world data analysis
#[allow(dead_code)]
fn demonstrate_real_world_analysis(recognizer: &mut AdvancedPatternRecognizer) -> Result<()> {
    println!("🌍 Real-World Data Analysis");
    println!("===========================");

    println!("Analyzing realistic data patterns from various domains...\n");

    let real_world_scenarios = vec![
        (
            "Financial Time Series",
            generate_financial_time_series(2000),
        ),
        ("Genomic Sequence", generate_genomic_sequence(2000)),
        ("Network Packet Trace", generate_network_trace(2000)),
        ("Sensor IoT Data", generate_iot_sensor_data(2000)),
        ("Audio Signal", generate_audio_signal(2000)),
        ("Image Compression", generate_image_compression_data(2000)),
    ];

    let mut domain_insights = std::collections::HashMap::new();

    for (domain_name, data) in real_world_scenarios {
        println!("🔍 Domain: {}", domain_name);

        let analysis = recognizer.analyze_patterns(&data)?;

        // Extract domain-specific insights
        let primary_pattern = analysis
            .pattern_scores
            .iter()
            .max_by(|a, b| a.1.partial_cmp(b.1).expect("Operation failed"))
            .map(|(k, v)| (k.clone(), *v));

        let complexity_category = match analysis.complexity_index {
            x if x > 0.8 => "Very High",
            x if x > 0.6 => "High",
            x if x > 0.4 => "Moderate",
            x if x > 0.2 => "Low",
            _ => "Very Low",
        };

        println!("   Data Size: {} bytes", data.len());
        println!("   Complexity Category: {}", complexity_category);
        println!(
            "   Primary Pattern: {} (Score: {:.3})",
            primary_pattern
                .as_ref()
                .map(|(k_, _)| k_.as_str())
                .unwrap_or("None"),
            primary_pattern.as_ref().map(|(_, v)| *v).unwrap_or(0.0)
        );
        println!("   Predictability: {:.3}", analysis.predictability_score);

        // Domain-specific analysis
        match domain_name {
            "Financial Time Series" => {
                let volatility = analysis.pattern_scores.get("entropy").unwrap_or(&0.5);
                let trend_strength = analysis.pattern_scores.get("sequential").unwrap_or(&0.5);
                println!("   Financial Volatility: {:.3}", volatility);
                println!("   Trend Strength: {:.3}", trend_strength);

                if *volatility > 0.7 {
                    println!("   📈 High volatility detected - risk management recommended");
                }
            }
            "Genomic Sequence" => {
                let repetition = analysis.pattern_scores.get("repetition").unwrap_or(&0.5);
                let compression_potential = analysis
                    .optimization_recommendations
                    .iter()
                    .find(|r| r.optimization_type.contains("compression"))
                    .map(|r| r.expected_improvement)
                    .unwrap_or(0.0);
                println!("   Repetitive Elements: {:.3}", repetition);
                println!(
                    "   Compression Potential: {:.1}%",
                    compression_potential * 100.0
                );
            }
            "Network Packet Trace" => {
                let periodicity = analysis.pattern_scores.values().sum::<f32>()
                    / analysis.pattern_scores.len() as f32;
                println!("   Traffic Regularity: {:.3}", periodicity);

                if !analysis.emergent_patterns.is_empty() {
                    println!("   🚨 Anomalous traffic patterns detected");
                }
            }
            "Sensor IoT Data" => {
                let noise_level = 1.0 - analysis.predictability_score;
                println!("   Noise Level: {:.3}", noise_level);
                println!("   Data Quality: {:.3}", analysis.predictability_score);
            }
            _ => {}
        }

        // Store insights for cross-domain analysis
        domain_insights.insert(domain_name.to_string(), analysis);

        println!();
    }

    // Cross-domain pattern analysis
    println!("🔬 Cross-Domain Pattern Analysis:");
    let mut pattern_prevalence = std::collections::HashMap::new();

    for analysis in domain_insights.values() {
        for (pattern_type, score) in &analysis.pattern_scores {
            if *score > 0.6 {
                *pattern_prevalence.entry(pattern_type.clone()).or_insert(0) += 1;
            }
        }
    }

    println!("   Universal Patterns (appearing in multiple domains):");
    for (pattern, count) in pattern_prevalence.iter().filter(|(_, &count)| count >= 2) {
        println!("     {}: {} domains", pattern, count);
    }

    // Calculate domain complexity ranking
    let mut complexity_ranking: Vec<_> = domain_insights
        .iter()
        .map(|(domain, analysis)| (domain, analysis.complexity_index))
        .collect();
    complexity_ranking.sort_by(|a, b| b.1.partial_cmp(&a.1).expect("Operation failed"));

    println!("   Domain Complexity Ranking:");
    for (i, (domain, complexity)) in complexity_ranking.iter().enumerate() {
        println!("     {}. {}: {:.3}", i + 1, domain, complexity);
    }

    Ok(())
}

// Data generation functions for various scenarios

#[allow(dead_code)]
fn generate_repetitive_pattern(size: usize) -> Vec<u8> {
    let pattern = [1, 2, 3, 4, 5];
    (0..size).map(|i| pattern[i % pattern.len()]).collect()
}

#[allow(dead_code)]
fn generate_sequential_data(size: usize) -> Vec<u8> {
    (0..size).map(|i| (i % 256) as u8).collect()
}

#[allow(dead_code)]
fn generate_fractal_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let x = i as f32 / size as f32;
            let fractal = (x * 16.0).sin() * (x * 32.0).cos() * (x * 64.0).sin();
            (fractal * 127.0 + 128.0) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_random_noise(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| ((i * 1103515245 + 12345) % 256) as u8)
        .collect()
}

#[allow(dead_code)]
fn generate_mixed_complexity_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 3 == 0 {
                (i % 10) as u8 // Simple pattern
            } else if i % 3 == 1 {
                ((i * i) % 256) as u8 // Quadratic
            } else {
                ((i * 17 + 31) % 256) as u8 // Linear congruential
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_simple_repetition(size: usize) -> Vec<u8> {
    (0..size).map(|i| (i % 4) as u8).collect()
}

#[allow(dead_code)]
fn generate_nested_repetition(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let outer = (i / 16) % 4;
            let inner = i % 4;
            (outer * 4 + inner) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_fractal_evolution(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let scale1 = ((i as f32 * 0.1).sin() * 127.0 + 128.0) as u8;
            let scale2 = ((i as f32 * 0.05).sin() * 63.0 + 64.0) as u8;
            (scale1 + scale2) / 2
        })
        .collect()
}

#[allow(dead_code)]
fn generate_adaptive_pattern(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let adaptation = (i as f32 / 100.0).tanh();
            let base_pattern = (i % 8) as f32;
            (base_pattern * adaptation * 32.0) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_emergent_complexity(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let chaos = if i > size / 2 {
                ((i * i * i) % 256) as u8
            } else {
                (i % 16) as u8
            };
            let emergence = ((i as f32).sqrt() * 16.0) as u8;
            ((chaos as u16 + emergence as u16) % 256) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_compression_repetition_data(size: usize) -> Vec<u8> {
    let mut data = Vec::new();
    let mut i = 0;
    while data.len() < size {
        let run_length = 8 + (i % 16);
        let value = (i % 32) as u8;
        for _ in 0..run_length.min(size - data.len()) {
            data.push(value);
        }
        i += 1;
    }
    data
}

#[allow(dead_code)]
fn generate_sequential_entropy_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i < size / 2 {
                (i % 256) as u8 // Sequential
            } else {
                ((i * 1234567 + 987654321) % 256) as u8 // High entropy
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_fractal_periodicity_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let periodic = ((i % 32) as f32 * 0.2).sin();
            let fractal = (i as f32 * 0.1).sin() * (i as f32 * 0.05).cos();
            ((periodic + fractal) * 127.0 + 128.0) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_multi_pattern_convergence(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let pattern1 = (i % 16) as u8;
            let pattern2 = ((i * i) % 64) as u8;
            let pattern3 = ((i as f32 * 0.1).sin() * 32.0 + 32.0) as u8;
            (pattern1 + pattern2 + pattern3) / 3
        })
        .collect()
}

#[allow(dead_code)]
fn generate_database_scenario(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 100 < 20 {
                // Header/metadata
                ((i / 100) % 64) as u8
            } else {
                // Data records
                ((i * 13 + 7) % 256) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_image_scenario(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let x = i % 64;
            let y = i / 64;
            let gradient = ((x + y) * 2) as u8;
            let noise = ((i * 31) % 32) as u8;
            ((gradient as u16 + noise as u16) % 256) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_scientific_scenario(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let measurement = (i as f32 * 0.01).exp().sin() * 127.0 + 128.0;
            let quantization_noise = ((i * 7) % 8) as f32;
            (measurement + quantization_noise) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_network_scenario(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 64 < 8 {
                // Packet headers
                ((i / 64) % 256) as u8
            } else {
                // Payload
                ((i * 101 + 23) % 256) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_sensor_scenario(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let sensor_reading = (i as f32 * 0.1).sin() * 100.0 + 128.0;
            let noise = ((i * 3) % 20) as f32;
            (sensor_reading + noise) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_training_data_type_a(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let pattern = [1, 2, 4, 8, 16, 32, 64, 128];
            pattern[i % pattern.len()]
        })
        .collect()
}

#[allow(dead_code)]
fn generate_training_data_type_b(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| ((i as f32 * 0.1).sin().powi(2) * 255.0) as u8)
        .collect()
}

#[allow(dead_code)]
fn generate_training_data_type_c(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let fibonacci_like = (i % 256) + ((i + 1) % 256);
            (fibonacci_like % 256) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_validation_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let mixed = ((i % 64) as f32 * 0.1).cos() * 127.0 + 128.0;
            mixed as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_challenge_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let chaotic = (i as f32 * std::f32::consts::PI / 100.0).sin()
                * (i as f32 * std::f32::consts::E / 150.0).cos()
                * 127.0
                + 128.0;
            chaotic as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_financial_time_series(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let trend = (i as f32 * 0.001).exp();
            let volatility = (i as f32 * 0.1).sin() * 0.2 + 1.0;
            let price = trend * volatility * 100.0;
            price as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_genomic_sequence(size: usize) -> Vec<u8> {
    let bases = [0, 1, 2, 3]; // A, T, G, C
    (0..size)
        .map(|i| {
            if i % 20 < 5 {
                // Repetitive region
                bases[i % 4]
            } else {
                // Variable region
                bases[(i * 31 + 17) % 4]
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_network_trace(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 1500 < 40 {
                // Packet header
                ((i / 1500) % 256) as u8
            } else if (i % 100) < 10 {
                // Control data
                255
            } else {
                // Payload
                ((i * 19 + 37) % 256) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_iot_sensor_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let base_signal = (i as f32 * 0.05).sin() * 100.0 + 128.0;
            let noise = ((i * 7) % 40) as f32 - 20.0;
            let drift = i as f32 * 0.001;
            (base_signal + noise + drift) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_audio_signal(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let fundamental = (i as f32 * 0.2).sin() * 80.0;
            let harmonic = (i as f32 * 0.4).sin() * 40.0;
            let noise = ((i * 13) % 32) as f32 - 16.0;
            (fundamental + harmonic + noise + 128.0) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_image_compression_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let x = i % 128;
            let y = i / 128;

            // Create image-like patterns
            let gradient = ((x + y) * 2) % 256;
            let texture = ((x * y) % 64) * 4;
            let edge = if x % 32 < 2 || y % 32 < 2 { 64 } else { 0 };

            ((gradient + texture + edge) / 3) as u8
        })
        .collect()
}
