scirs2-transform 0.4.0

//! Advanced Mode Showcase - Demonstrates advanced optimization features
//!
//! This example showcases the Advanced MODE implementations in scirs2-transform,
//! including neuromorphic adaptation and quantum-inspired optimization.

use scirs2_core::ndarray::Array1;
use scirs2_transform::{
    auto_feature_engineering::DatasetMetaFeatures, error::Result, AdvancedNeuromorphicProcessor,
    AdvancedQuantumOptimizer,
};

#[allow(dead_code)]
fn main() -> Result<()> {
    println!("🚀 Advanced MODE Showcase - Advanced Data Transformation Optimization");
    println!("========================================================================");

    // Example 1: Neuromorphic Adaptation
    println!("\n✅ Advanced MODE: Neuromorphic Adaptation System");
    println!("--------------------------------------------------");

    demonstrate_neuromorphic_adaptation()?;

    // Example 2: Quantum-Inspired Optimization
    println!("\n✅ Advanced MODE: Quantum-Inspired Optimization");
    println!("------------------------------------------------");

    demonstrate_quantum_optimization()?;

    println!("\n🎯 Advanced MODE Showcase completed successfully!");
    println!("Advanced optimization features are ready for production use.");

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_neuromorphic_adaptation() -> Result<()> {
    // Create optimized neuromorphic processor
    let mut processor = AdvancedNeuromorphicProcessor::new(10, 20, 10);

    // Configure for high-throughput workload
    processor.tune_for_workload(1000.0, 0.05); // 1000 samples/sec, 50ms latency

    // Create sample meta-features for different datasets
    let meta_features_batch = vec![
        DatasetMetaFeatures {
            n_samples: 1000,
            n_features: 50,
            sparsity: 0.1,
            mean_correlation: 0.2,
            std_correlation: 0.15,
            mean_skewness: 0.8,
            mean_kurtosis: 2.5,
            missing_ratio: 0.05,
            variance_ratio: 0.85,
            outlier_ratio: 0.03,
            has_missing: true,
        },
        DatasetMetaFeatures {
            n_samples: 5000,
            n_features: 100,
            sparsity: 0.3,
            mean_correlation: 0.5,
            std_correlation: 0.25,
            mean_skewness: 1.2,
            mean_kurtosis: 3.8,
            missing_ratio: 0.15,
            variance_ratio: 0.65,
            outlier_ratio: 0.08,
            has_missing: true,
        },
        DatasetMetaFeatures {
            n_samples: 10000,
            n_features: 200,
            sparsity: 0.05,
            mean_correlation: 0.1,
            std_correlation: 0.08,
            mean_skewness: 0.3,
            mean_kurtosis: 1.8,
            missing_ratio: 0.02,
            variance_ratio: 0.95,
            outlier_ratio: 0.01,
            has_missing: true,
        },
    ];

    // Process batch with optimized parallel processing
    println!(
        "Processing {} datasets with neuromorphic adaptation...",
        meta_features_batch.len()
    );
    let start_time = std::time::Instant::now();

    let transformation_recommendations = processor.process_batch(&meta_features_batch)?;

    let processing_time = start_time.elapsed();
    println!(
        "✅ Processed {} datasets in {:.3}ms",
        meta_features_batch.len(),
        processing_time.as_millis()
    );

    // Display recommendations
    for (i, recommendations) in transformation_recommendations.iter().enumerate() {
        println!(
            "Dataset {}: {} transformations recommended",
            i + 1,
            recommendations.len()
        );
        for (j, config) in recommendations.iter().take(3).enumerate() {
            println!(
                "  {}. {:?} (performance: {:.3})",
                j + 1,
                config.transformation_type,
                config.expected_performance
            );
        }
    }

    // Get performance diagnostics
    let diagnostics = processor.get_advanced_diagnostics();
    println!("\n📊 Advanced Performance Metrics:");
    println!("   Throughput: {:.1} samples/sec", diagnostics.throughput);
    println!("   Memory Efficiency: {:.3}", diagnostics.memory_efficiency);
    println!(
        "   Network Utilization: {:.3}",
        diagnostics.network_utilization
    );
    println!("   Energy Efficiency: {:.3}", diagnostics.energy_efficiency);

    // Demonstrate learning from feedback
    println!("\n🧠 Learning from feedback...");
    for (i, meta_features) in meta_features_batch.iter().enumerate() {
        let applied_configs = &transformation_recommendations[i];
        let simulated_performance = 0.75 + (i as f64 * 0.1); // Simulate varying performance

        processor.learn_from_feedback(meta_features, applied_configs, simulated_performance)?;
        println!(
            "   Learned from dataset {} (performance: {:.3})",
            i + 1,
            simulated_performance
        );
    }

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_quantum_optimization() -> Result<()> {
    // Create quantum-inspired optimizer for transformation parameter tuning
    let dimension = 5;
    let population_size = 30;
    let bounds = vec![
        (0.0, 1.0),  // Normalization parameter
        (0.1, 10.0), // Scale factor
        (1.0, 5.0),  // Polynomial degree
        (0.0, 1.0),  // Threshold parameter
        (0.0, 1.0),  // Regularization parameter
    ];

    let mut optimizer = AdvancedQuantumOptimizer::new(dimension, population_size, bounds, 100)?;

    // Define a complex multi-modal optimization objective
    let objective_function = |params: &Array1<f64>| -> f64 {
        // Simulate complex transformation pipeline performance
        let x = params[0];
        let y = params[1];
        let z = params[2];
        let w = params[3];
        let v = params[4];

        // Multi-modal function with quantum-inspired landscape
        let term1 = -(x - 0.3).powi(2) - (y - 5.0).powi(2);
        let term2 = -(z - 2.5).powi(2) - (w - 0.7).powi(2);
        let term3 = -(v - 0.5).powi(2);
        let quantum_interference = (x * y * z * 10.0).sin() * 0.1;

        (term1 + term2 + term3 + quantum_interference).exp()
    };

    println!("Optimizing transformation parameters with quantum-inspired algorithm...");
    let start_time = std::time::Instant::now();

    let (optimal_params, best_fitness) = optimizer.optimize_advanced(objective_function, 100)?;

    let optimization_time = start_time.elapsed();
    println!(
        "✅ Optimization completed in {:.3}ms",
        optimization_time.as_millis()
    );

    println!("🎯 Optimal Parameters Found:");
    for (i, &param) in optimal_params.iter().enumerate() {
        let param_names = [
            "Normalization",
            "Scale",
            "Degree",
            "Threshold",
            "Regularization",
        ];
        println!("   {}: {:.6}", param_names[i], param);
    }
    println!("   Best Fitness: {:.6}", best_fitness);

    // Get quantum optimization diagnostics
    let diagnostics = optimizer.get_advanced_diagnostics();
    let adaptive_params = optimizer.get_adaptive_params();

    println!("\n⚡ Advanced Quantum Metrics:");
    println!(
        "   Convergence Rate: {:.1} iter/sec",
        diagnostics.convergence_rate
    );
    println!(
        "   Quantum Efficiency: {:.3}",
        diagnostics.quantum_efficiency
    );
    println!("   Exploration Ratio: {:.3}", diagnostics.exploration_ratio);
    println!("   Parallel Speedup: {:.2}x", diagnostics.parallel_speedup);
    println!(
        "   Energy Consumption: {:.3}",
        diagnostics.energy_consumption
    );

    println!("\n🔬 Adaptive Quantum Parameters:");
    println!(
        "   Collapse Probability: {:.3}",
        adaptive_params.collapse_probability
    );
    println!(
        "   Entanglement Strength: {:.3}",
        adaptive_params.entanglement_strength
    );
    println!("   Phase Speed: {:.3}", adaptive_params.phase_speed);
    println!(
        "   Tunneling Probability: {:.3}",
        adaptive_params.tunneling_probability
    );

    Ok(())
}