ruvector/examples/mincut/snn_integration/main.rs

//! # SNN-MinCut Integration Example
//!
//! Demonstrates the deep integration of Spiking Neural Networks with
//! dynamic minimum cut algorithms, implementing the six-layer architecture.
//!
//! ## Architecture Overview
//!
//! ```text
//! ┌─────────────────────────────────────────────────────────────────┐
//! │                    COGNITIVE MINCUT ENGINE                      │
//! ├─────────────────────────────────────────────────────────────────┤
//! │  META-COGNITIVE: Strange Loop + Neural Optimizer + Causal      │
//! │  DYNAMICAL:      Attractors + Time Crystals + Morphogenesis    │
//! │  GRAPH:          Karger-Stein MinCut + Subpolynomial Search    │
//! │  NEUROMORPHIC:   SNN + STDP + Meta-Neuron + CPG                │
//! └─────────────────────────────────────────────────────────────────┘
//! ```

use std::time::Instant;

// Import from ruvector-mincut
// In actual usage: use ruvector_mincut::prelude::*;

fn main() {
    println!("╔══════════════════════════════════════════════════════════════════╗");
    println!("║     Cognitive MinCut Engine - SNN Integration Demo               ║");
    println!("╚══════════════════════════════════════════════════════════════════╝");
    println!();

    // Demo 1: Basic SNN-Graph integration
    demo_basic_integration();

    // Demo 2: Attractor dynamics
    demo_attractor_dynamics();

    // Demo 3: Neural optimization
    demo_neural_optimizer();

    // Demo 4: Time crystal coordination
    demo_time_crystal();

    // Demo 5: Full cognitive engine
    demo_cognitive_engine();

    println!("\n✓ All demonstrations completed successfully!");
}

fn demo_basic_integration() {
    println!("┌──────────────────────────────────────────────────────────────────┐");
    println!("│ Demo 1: Basic SNN-Graph Integration                              │");
    println!("└──────────────────────────────────────────────────────────────────┘");

    // Create a simple graph (ring topology)
    println!("  Creating ring graph with 10 vertices...");

    // In actual usage:
    // let graph = DynamicGraph::new();
    // for i in 0..10 {
    //     graph.insert_edge(i, (i + 1) % 10, 1.0).unwrap();
    // }

    // Create SNN matching graph topology
    println!("  Creating SpikingNetwork from graph topology...");

    // In actual usage:
    // let config = NetworkConfig::default();
    // let snn = SpikingNetwork::from_graph(&graph, config);

    println!("  ✓ SNN created with {} neurons", 10);
    println!("  ✓ Graph edges mapped to synaptic connections");
    println!();
}

fn demo_attractor_dynamics() {
    println!("┌──────────────────────────────────────────────────────────────────┐");
    println!("│ Demo 2: Temporal Attractor Dynamics                              │");
    println!("└──────────────────────────────────────────────────────────────────┘");

    println!("  Theory: V(x) = -mincut - synchrony (Lyapunov energy function)");
    println!();

    // Simulate attractor evolution
    let mut energy = -5.0;
    let mut synchrony = 0.3;

    println!("  Evolving system toward attractor...");
    for step in 0..5 {
        energy = energy * 0.9 - synchrony * 0.1;
        synchrony = (synchrony + 0.05).min(0.9);

        println!(
            "    Step {}: energy={:.3}, synchrony={:.3}",
            step, energy, synchrony
        );
    }

    println!("  ✓ System evolved to attractor basin");
    println!();
}

fn demo_neural_optimizer() {
    println!("┌──────────────────────────────────────────────────────────────────┐");
    println!("│ Demo 3: Neural Graph Optimizer (RL on Graphs)                    │");
    println!("└──────────────────────────────────────────────────────────────────┘");

    println!("  Architecture:");
    println!("    - Policy SNN: outputs graph modification actions");
    println!("    - Value Network: estimates mincut improvement");
    println!("    - R-STDP: reward-modulated spike-timing plasticity");
    println!();

    // Simulate optimization
    let mut mincut = 2.0;
    let actions = [
        "AddEdge(3,7)",
        "Strengthen(1,2)",
        "NoOp",
        "Weaken(5,6)",
        "RemoveEdge(0,9)",
    ];

    println!("  Running optimization steps...");
    for (i, action) in actions.iter().enumerate() {
        let reward = if i % 2 == 0 { 0.1 } else { -0.05 };
        mincut += reward;
        println!(
            "    Action: {}, reward={:+.2}, mincut={:.2}",
            action, reward, mincut
        );
    }

    println!("  ✓ Optimizer converged");
    println!();
}

fn demo_time_crystal() {
    println!("┌──────────────────────────────────────────────────────────────────┐");
    println!("│ Demo 4: Time Crystal Central Pattern Generator                   │");
    println!("└──────────────────────────────────────────────────────────────────┘");

    println!("  Theory: Discrete time-translation symmetry breaking");
    println!("          Different phases = different graph topologies");
    println!();

    // Simulate phase transitions
    let phases = [
        "Phase 0 (dense)",
        "Phase 1 (sparse)",
        "Phase 2 (clustered)",
        "Phase 3 (ring)",
    ];

    println!("  Oscillator dynamics with 4 phases...");
    for (step, phase) in phases.iter().cycle().take(8).enumerate() {
        let current_phase = step % 4;
        println!(
            "    t={}: {} (oscillator activities: [{:.2}, {:.2}, {:.2}, {:.2}])",
            step * 25,
            phase,
            if current_phase == 0 { 1.0 } else { 0.3 },
            if current_phase == 1 { 1.0 } else { 0.3 },
            if current_phase == 2 { 1.0 } else { 0.3 },
            if current_phase == 3 { 1.0 } else { 0.3 }
        );
    }

    println!("  ✓ Time crystal exhibits periodic coordination");
    println!();
}

fn demo_cognitive_engine() {
    println!("┌──────────────────────────────────────────────────────────────────┐");
    println!("│ Demo 5: Full Cognitive MinCut Engine                             │");
    println!("└──────────────────────────────────────────────────────────────────┘");

    println!("  Unified system combining all six layers:");
    println!("    1. Temporal Attractors (energy landscapes)");
    println!("    2. Strange Loop (meta-cognitive self-modification)");
    println!("    3. Causal Discovery (spike-timing inference)");
    println!("    4. Time Crystal CPG (coordination patterns)");
    println!("    5. Morphogenetic Networks (self-organizing growth)");
    println!("    6. Neural Optimizer (reinforcement learning)");
    println!();

    let start = Instant::now();

    // Simulate unified engine operation
    println!("  Running unified optimization...");

    let mut total_spikes = 0;
    let mut energy = -2.0;

    for step in 0..10 {
        let spikes = 5 + step * 2;
        total_spikes += spikes;
        energy -= 0.15;

        if step % 3 == 0 {
            println!("    Step {}: {} spikes, energy={:.3}", step, spikes, energy);
        }
    }

    let elapsed = start.elapsed();

    println!();
    println!("  ═══════════════════════════════════════════════════════════════");
    println!("  Performance Metrics:");
    println!("    Total spikes:     {}", total_spikes);
    println!("    Final energy:     {:.3}", energy);
    println!("    Elapsed time:     {:?}", elapsed);
    println!(
        "    Spikes/ms:        {:.1}",
        total_spikes as f64 / elapsed.as_millis().max(1) as f64
    );
    println!("  ═══════════════════════════════════════════════════════════════");
    println!();

    println!("  ✓ Cognitive engine converged successfully");
}