Scruff AI
142 lines
5.2 KiB
Python
142 lines
5.2 KiB
Python
#!/usr/bin/env python3
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"""
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Resonance Dynamics Analysis
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Resonance Engine: LTP = Connection Strengthening
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"""
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import csv
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import math
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print("=" * 70)
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print("RESONANCE DYNAMICS ANALYSIS")
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print("Resonance Engine: LTP = Connection Strengthening")
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print("=" * 70 + "\n")
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# Load data
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with open('resonance_telemetry.csv', 'r') as f:
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reader = csv.DictReader(f)
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telemetry = list(reader)
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with open('resonance_metrics.csv', 'r') as f:
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reader = csv.DictReader(f)
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metrics = list(reader)
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print("EXPERIMENT OVERVIEW:")
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print(f" Duration: {len(telemetry)} samples over {telemetry[-1]['step']} steps")
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print(f" Final power: {telemetry[-1]['power_w']} W")
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print(f" Final step rate: {telemetry[-1]['steps_per_sec']} steps/sec\n")
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# Pattern consolidation
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initial = telemetry[0]
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final = telemetry[-1]
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consolidation = int(initial['active_patterns']) / int(final['active_patterns'])
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print("PATTERN CONSOLIDATION:")
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print(f" Initial: {initial['active_patterns']} patterns")
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print(f" Final: {final['active_patterns']} patterns")
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print(f" Consolidation: {consolidation:.1f}:1 ratio\n")
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# Spatial clustering
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clusters = []
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for pattern in metrics:
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x = float(pattern['pos_x'])
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y = float(pattern['pos_y'])
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assigned = False
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for i, cluster in enumerate(clusters):
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dx = x - cluster['center_x']
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dy = y - cluster['center_y']
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dist = math.sqrt(dx*dx + dy*dy)
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if dist < 50:
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clusters[i]['patterns'] += 1
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clusters[i]['total_mass'] += float(pattern['mass'])
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clusters[i]['total_coherence'] += float(pattern['coherence'])
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clusters[i]['center_x'] = (cluster['center_x'] * (cluster['patterns'] - 1) + x) / cluster['patterns']
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clusters[i]['center_y'] = (cluster['center_y'] * (cluster['patterns'] - 1) + y) / cluster['patterns']
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assigned = True
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break
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if not assigned:
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clusters.append({
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'patterns': 1,
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'center_x': x,
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'center_y': y,
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'total_mass': float(pattern['mass']),
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'total_coherence': float(pattern['coherence'])
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})
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print(f"SPATIAL CLUSTERS: {len(clusters)}")
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for i, cluster in enumerate(clusters):
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avg_mass = cluster['total_mass'] / cluster['patterns']
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avg_coherence = cluster['total_coherence'] / cluster['patterns']
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print(f" Cluster {i+1}: ({cluster['center_x']:.0f}, {cluster['center_y']:.0f})")
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print(f" Patterns: {cluster['patterns']}")
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print(f" Avg mass: {avg_mass:.0f}")
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print(f" Avg coherence: {avg_coherence:.3f}\n")
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# Growth analysis
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total_mass = sum(float(p['mass']) for p in metrics)
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avg_mass = total_mass / len(metrics)
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total_growth = sum(float(p['growth_rate']) for p in metrics)
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avg_growth = total_growth / len(metrics)
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print("GROWTH ANALYSIS:")
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print(f" Total mass: {total_mass:.0f}")
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print(f" Average mass: {avg_mass:.0f}")
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print(f" Average growth rate: {avg_growth:.6f} mass/step\n")
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# Coherence vs stability
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total_coherence = sum(float(p['coherence']) for p in metrics)
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total_stability = sum(float(p['stability']) for p in metrics)
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avg_coherence = total_coherence / len(metrics)
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avg_stability = total_stability / len(metrics)
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coherence_stability_ratio = avg_coherence / avg_stability if avg_stability > 0 else 0
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print("COHERENCE vs STABILITY:")
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print(f" Avg coherence: {avg_coherence:.3f} (0-1)")
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print(f" Avg stability: {avg_stability:.3f} (0-1)")
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print(f" Ratio: {coherence_stability_ratio:.2f}")
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if coherence_stability_ratio > 5:
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print(" Interpretation: High coherence, low stability")
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elif coherence_stability_ratio < 2:
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print(" Interpretation: Low coherence, high stability")
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else:
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print(" Interpretation: Balanced\n")
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# LTP analysis
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total_persistence = sum(int(p['persistence']) for p in metrics)
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avg_persistence = total_persistence / len(metrics)
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detection_rate = avg_persistence / 500000 * 100
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print("LTP (LONG-TERM POTENTIATION):")
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print(f" Avg persistence: {avg_persistence:.0f} detections")
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print(f" Detection rate: {detection_rate:.1f}% of steps")
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print(f" Avg lifetime: {float(final['avg_lifetime']):.0f} steps")
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if detection_rate > 50:
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print(" Status: STRONG LTP")
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elif detection_rate > 20:
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print(" Status: MODERATE LTP")
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else:
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print(" Status: WEAK LTP")
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print("\n" + "=" * 70)
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print("KEY FINDINGS:")
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print("=" * 70)
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print("1. Pattern consolidation: Initial 790 → Final 9 patterns")
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print("2. Spatial clustering: 2 distinct clusters formed")
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print("3. Mass accumulation: 156k total mass accumulated")
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print("4. Detection consistency: Patterns detected 0.4% of steps")
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print("5. Coherence/Stability: High coherence (0.20), low stability (0.03)")
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print("6. LTP Status: Weak detection but extreme persistence (491k steps)")
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print("\nInterpretation: Patterns are coherent and persistent,")
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print("but detection is intermittent. This could be:")
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print("- Threshold too sensitive (detecting noise)")
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print("- Patterns moving in/out of detection range")
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print("- Need longer observation for stable LTP")
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print("\nNEXT METRICS (from Cheat Sheet):")
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print("1. Nodal Growth (Plasticity) - Grid adaptation rate")
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print("2. Echo Check (Memory) - Pattern recall accuracy")
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print("3. Laminar vs Turbulent - Homeostasis classification")
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print("4. Ignition Threshold - GPU at 100% measurement") |