//! advanced Mode I/O Processing Demonstration
//!
//! This example showcases the advanced intelligent I/O processing capabilities
//! of the advanced coordinator and enhanced algorithms.

use scirs2_io::advanced_coordinator::AdvancedCoordinator;
use scirs2_io::enhanced_algorithms::AdvancedPatternRecognizer;
use std::time::Instant;

#[allow(dead_code)]
fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("🚀 advanced Mode I/O Processing Demonstration");
    println!("================================================\n");

    // Create sample data with different patterns
    let samples = create_sample_datasets();

    // Initialize advanced coordinator
    println!("🧠 Initializing advanced Coordinator...");
    let mut coordinator = AdvancedCoordinator::new()?;
    println!("✅ advanced Coordinator initialized successfully\n");

    // Initialize Advanced Pattern Recognizer
    println!("🔍 Initializing Advanced Pattern Recognizer...");
    let mut pattern_recognizer = AdvancedPatternRecognizer::new();
    println!("✅ Advanced Pattern Recognizer initialized successfully\n");

    // Process each sample dataset
    for (name, data) in samples {
        println!("📊 Processing dataset: '{}'", name);
        println!("   Data size: {} bytes", data.len());

        // advanced processing
        let start_time = Instant::now();
        let processing_result = coordinator.process_advanced_intelligent(&data)?;
        let processing_duration = start_time.elapsed();

        println!("   🎯 Strategy used: {:?}", processing_result.strategy_used);
        println!(
            "   ⚡ Processing time: {:.2}ms",
            processing_duration.as_millis()
        );
        println!(
            "   📈 Efficiency score: {:.3}",
            processing_result.efficiency_score
        );
        println!(
            "   🏆 Quality score: {:.3}",
            processing_result.quality_metrics.overall_quality
        );
        println!(
            "   🧠 Intelligence level: {:?}",
            processing_result.intelligence_level
        );

        // Advanced pattern analysis
        let pattern_analysis = pattern_recognizer.analyze_patterns(&data)?;

        println!("   🔍 Pattern Analysis:");
        for (pattern_type, score) in &pattern_analysis.pattern_scores {
            println!("     - {}: {:.3}", pattern_type, score);
        }

        if !pattern_analysis.emergent_patterns.is_empty() {
            println!(
                "   🌟 Emergent patterns detected: {}",
                pattern_analysis.emergent_patterns.len()
            );
        }

        if !pattern_analysis.optimization_recommendations.is_empty() {
            println!("   💡 Optimization recommendations:");
            for rec in &pattern_analysis.optimization_recommendations {
                println!(
                    "     - {}: {} (confidence: {:.2})",
                    rec.optimization_type, rec.reason, rec.confidence
                );
            }
        }

        println!(
            "   ✨ Complexity index: {:.3}",
            pattern_analysis.complexity_index
        );
        println!(
            "   🎲 Predictability score: {:.3}\n",
            pattern_analysis.predictability_score
        );
    }

    // Get comprehensive statistics
    println!("📈 advanced System Statistics:");
    let stats = coordinator.get_comprehensive_statistics()?;
    println!(
        "   🔬 Total operations processed: {}",
        stats.total_operations_processed
    );
    println!(
        "   🧠 Average intelligence level: {:?}",
        stats.average_intelligence_level
    );
    println!(
        "   🌟 Emergent behaviors detected: {}",
        stats.emergent_behaviors_detected
    );
    println!(
        "   🎯 Meta-learning accuracy: {:.3}",
        stats.meta_learning_accuracy
    );
    println!(
        "   ⚡ Overall system efficiency: {:.3}",
        stats.overall_system_efficiency
    );

    println!("\n🎉 advanced Mode demonstration completed successfully!");
    Ok(())
}

/// Create various sample datasets with different characteristics for testing
#[allow(dead_code)]
fn create_sample_datasets() -> Vec<(String, Vec<u8>)> {
    vec![
        // Highly repetitive data (good for compression)
        (
            "Repetitive Pattern".to_string(),
            vec![0xAA; 1000]
                .into_iter()
                .chain(vec![0xBB; 1000])
                .chain(vec![0xCC; 1000])
                .collect(),
        ),
        // Sequential data
        (
            "Sequential Pattern".to_string(),
            (0..=255u8).cycle().take(2000).collect(),
        ),
        // Fractal-like pattern
        (
            "Fractal Pattern".to_string(),
            generate_fractal_pattern(1500),
        ),
        // Random-like data (high entropy)
        (
            "High Entropy Data".to_string(),
            generate_pseudo_random_data(1200),
        ),
        // Mixed pattern data
        (
            "Mixed Patterns".to_string(),
            generate_mixed_pattern_data(1800),
        ),
    ]
}

/// Generate fractal-like pattern data
#[allow(dead_code)]
fn generate_fractal_pattern(size: usize) -> Vec<u8> {
    let mut data = Vec::with_capacity(size);
    for i in 0..size {
        let value = ((i as f32).sin() * 127.0 + 128.0) as u8;
        data.push(value);
    }
    data
}

/// Generate pseudo-random data
#[allow(dead_code)]
fn generate_pseudo_random_data(size: usize) -> Vec<u8> {
    let mut data = Vec::with_capacity(size);
    let mut state = 12345u32;

    for _ in 0..size {
        state = state.wrapping_mul(1103515245).wrapping_add(12345);
        data.push((state >> 16) as u8);
    }
    data
}

/// Generate mixed pattern data combining different characteristics
#[allow(dead_code)]
fn generate_mixed_pattern_data(size: usize) -> Vec<u8> {
    let mut data = Vec::with_capacity(size);

    for i in 0..size {
        let value = match i % 4 {
            0 => (i % 256) as u8, // Sequential
            1 => {
                if i % 8 < 4 {
                    0xFF
                } else {
                    0x00
                }
            } // Repetitive
            2 => ((i as f32 * 0.1).sin() * 127.0 + 128.0) as u8, // Sinusoidal
            _ => (i.wrapping_mul(17).wrapping_add(7) % 256) as u8, // Pseudo-random
        };
        data.push(value);
    }
    data
}
