Delete hybrid_training_demo.py

hybrid_training_demo.py

@@ -1,168 +0,0 @@
-#!/usr/bin/env python3
-"""
-Hybrid Julia-Rust Training Demo for Spikenaut v2
-Shows the new architecture capabilities and performance improvements
-"""
-
-import json
-import time
-from datetime import datetime
-
-def demonstrate_hybrid_architecture():
-    """Demonstrate the Julia-Rust hybrid training architecture"""
-    
-    print("🦁 Spikenaut v2 - Hybrid Julia-Rust Architecture Demo")
-    print("=" * 60)
-    
-    # Architecture overview
-    print("\n🚀 Hybrid Training System:")
-    print("┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐")
-    print("│   Rust Layer    │    │   jlrs Bridge    │    │   Julia Layer   │")
-    print("│                 │    │                  │    │                 │")
-    print("│ • Telemetry    │───▶│ • Zero-copy IPC  │───▶│ • E-prop Core   │")
-    print("│ • Spike Encode  │    │ • <1µs overhead  │    │ • OTTT Traces   │")
-    print("│ • Reward Calc   │    │ • Direct calls   │    │ • Fast Math     │")
-    print("│ • Inference     │    │ • 50 Hz @ 50µs   │    │ • Export .mem   │")
-    print("└─────────────────┘    └──────────────────┘    └─────────────────┘")
-    
-    # Performance metrics
-    print("\n📊 Performance Breakthrough:")
-    metrics = {
-        "Training Speed": "35µs per tick (target: <50µs) ✅",
-        "IPC Overhead": "0.8µs (near-zero) ✅",
-        "Memory Usage": "1.6KB (ultra-efficient) ✅",
-        "Accuracy": "95%+ on sync completion prediction ✅",
-        "Development Speed": "3-5× faster iteration ✅"
-    }
-    
-    for metric, result in metrics.items():
-        print(f"  • {metric}: {result}")
-    
-    # Training data
-    print("\n📈 Real Blockchain Training Data:")
-    print("  • Kaspa Sync: March 21, 2026 - 60,937 lines of block acceptance")
-    print("  • Monero Sync: March 22, 2026 - 71,333 lines of completion data")
-    print("  • Combined: 132,270 neuromorphic events")
-    print("  • Reward Signals: 0.95-1.0 (near-perfect for E-prop)")
-    
-    # Learning algorithm
-    print("\n🧠 E-prop + OTTT Learning Algorithm:")
-    print("  1. OTTT Presynaptic Traces: â_j[t+1] = λ · â_j[t] + s_j[t+1]")
-    print("  2. Forward Pass: LIF neuron dynamics")
-    print("  3. E-prop Eligibility: e_{ij}[t+1] = λ · e_{ij}[t] + â_j[t] · pseudo_dz")
-    print("  4. Weight Update: Δw_{ij} = R[t] · e_{ij}[t+1] · η_eprop")
-    print("  5. L1 Normalization: Synaptic budget management")
-    
-    # Julia optimization
-    print("\n⚡ Julia Optimization:")
-    print("  @inline function eprop_update!(network, spikes, reward)")
-    print("      @simd for j in 1:N_CHANNELS")
-    print("          @inbounds network.pre_traces[j] = λ * network.pre_traces[j] + spikes[j]")
-    print("      end")
-    print("      # Fast-sigmoid surrogate gradients")
-    print("      # Reward-modulated weight updates")
-    print("  end")
-    
-    # jlrs integration
-    print("\n🔗 jlrs Zero-Copy Bridge:")
-    print("  let response = self.julia.scope(|mut global, frame| {")
-    print("      let spikes_array = Array::from_slice(frame, &packet.spikes)?;")
-    print("      let response_data = frame.call(")
-    print("          self.training_module,")
-    print("          \"eprop_update!\",")
-    print("          &[spikes_array.into(), reward.into()]")
-    print("      )?;")
-    print("      Ok(response_data)")
-    print("  })?;")
-    
-    print("\n🎯 Usage:")
-    print("  # Build with Julia support")
-    print("  cargo build --release --features julia")
-    print("  ")
-    print("  # Run hybrid training")
-    print("  ./training/run_hybrid_training.sh research/complete_sync_harvest.jsonl 20 research")
-    print("  ")
-    print("  # Export FPGA parameters")
-    print("  julia training/julia_eprop.jl data.jsonl 20 research")
-
-def create_training_sample():
-    """Create a sample of the hybrid training results"""
-    
-    sample_data = {
-        "architecture": "Julia-Rust Hybrid",
-        "training_session": {
-            "timestamp": datetime.now().isoformat(),
-            "data_source": "Kaspa + Monero sync completion",
-            "epochs": 20,
-            "samples": 132270
-        },
-        "performance": {
-            "training_speed_us_per_tick": 35.0,
-            "ipc_overhead_us": 0.8,
-            "memory_usage_kb": 1.6,
-            "accuracy_percent": 95.2
-        },
-        "learning_results": [
-            {
-                "epoch": 1,
-                "reward": 0.9800,
-                "spike_rate": 0.180,
-                "weight_mean": 0.9000,
-                "weight_std": 0.1200,
-                "processing_time_ms": 1.8
-            },
-            {
-                "epoch": 5,
-                "reward": 0.9960,
-                "spike_rate": 0.204,
-                "weight_mean": 0.9640,
-                "weight_std": 0.0880,
-                "processing_time_ms": 1.5
-            },
-            {
-                "epoch": 10,
-                "reward": 0.9990,
-                "spike_rate": 0.220,
-                "weight_mean": 0.9820,
-                "weight_std": 0.0400,
-                "processing_time_ms": 1.2
-            },
-            {
-                "epoch": 20,
-                "reward": 1.0000,
-                "spike_rate": 0.235,
-                "weight_mean": 0.9950,
-                "weight_std": 0.0050,
-                "processing_time_ms": 0.9
-            }
-        ],
-        "fpga_parameters": {
-            "thresholds": "16 values in Q8.8 format",
-            "weights": "256 values in Q8.8 format (16x16 matrix)",
-            "decay_rates": "16 values in Q8.8 format"
-        }
-    }
-    
-    return sample_data
-
-def main():
-    """Main demonstration function"""
-    
-    # Show architecture demo
-    demonstrate_hybrid_architecture()
-    
-    # Create and save sample data
-    sample = create_training_sample()
-    
-    print("\n📁 Sample Training Data Generated:")
-    print(json.dumps(sample, indent=2))
-    
-    # Save to file for HuggingFace dataset
-    with open('/home/raulmc/Eagle-Lander/huggingface-spikenaut-v2/hybrid_training_sample.json', 'w') as f:
-        json.dump(sample, f, indent=2)
-    
-    print(f"\n✅ Sample saved to: hybrid_training_sample.json")
-    print("\n🚀 Ready for HuggingFace repository update!")
-
-if __name__ == "__main__":
-    main()