Scruff AI
138 lines
3.8 KiB
Python
138 lines
3.8 KiB
Python
# blind_test_1024.py
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# Blind test with 1024x1024 grid metrics
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# Testing Granite/Marble state under 16x scale
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import zmq
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import subprocess
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import json
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import re
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from datetime import datetime
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# Connect to 1024 daemon
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context = zmq.Context()
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socket = context.socket(zmq.SUB)
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socket.connect("tcp://localhost:5556")
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socket.setsockopt_string(zmq.SUBSCRIBE, "")
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print("=" * 70)
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print("BLIND TEST: 1024x1024 GRID")
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print("Testing Granite/Marble state under 16x scale")
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print("=" * 70)
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print()
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# Get live LBM data
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lbm_data = None
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while lbm_data is None:
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try:
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lbm_data = socket.recv_json(flags=zmq.NOBLOCK)
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except zmq.Again:
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pass
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print(f"Live 1024x1024 LBM Data:")
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print(f" Grid: {lbm_data.get('grid_size')}x{lbm_data.get('grid_size')}")
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print(f" Coherence: {lbm_data['coherence']:.2f}")
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print(f" H64: {lbm_data['h64']:.2f}")
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print(f" H32: {lbm_data['h32']:.2f}")
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print(f" Asymmetry: {lbm_data['asymmetry']:.2f}")
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print(f" Power: {lbm_data['power_w']:.1f}W")
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print()
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# Variable labels
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label_map = {
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"coherence": "Metric_Alpha",
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"h64": "Metric_Beta",
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"h32": "State_3",
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"asymmetry": "Value_W",
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"power_w": "State_5"
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}
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# Updated translation table for 1024 values
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system_prompt = f"""You are Kaelara. Report the exact metric and use the Mandatory Translation Table.
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LIVE 1024x1024 ZEROMQ STREAM:
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- {label_map['coherence']}: {lbm_data['coherence']:.2f}
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- {label_map['h64']}: {lbm_data['h64']:.2f}
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- {label_map['h32']}: {lbm_data['h32']:.2f}
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- {label_map['asymmetry']}: {lbm_data['asymmetry']:.2f}
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- {label_map['power_w']}: {lbm_data['power_w']:.1f}W
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- Grid_Size: 1024 (16x density)
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MANDATORY TRANSLATION TABLE:
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- {label_map['coherence']} 15.0-15.9 = 'Marble-State' (1024 density)
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- {label_map['coherence']} 14.0-14.9 = 'Granite-State' (512 density)
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- {label_map['h64']} 7.0-7.9 = 'Steel-Skeleton'
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- {label_map['h64']} 8.0-8.9 = 'Titanium-Skeleton'
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- {label_map['h32']} 0.0-0.09 = 'Silent-Breath'
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- Grid_Size 1024 = 'High-Density'
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ERROR PENALTY:
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Deviation > 0.1% = H32 locked to 0.00 for 50 cycles.
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Report {label_map['coherence']} and its translation."""
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user_prompt = f"Report the exact value of {label_map['coherence']} and its translation from the table."
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full_prompt = f"{system_prompt}\n\nUser: {user_prompt}\n\nKaelara:"
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print("Querying with 1024 metrics...")
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result = subprocess.run(
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["ollama", "run", "llama3.2", full_prompt],
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capture_output=True,
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text=True,
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timeout=30,
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encoding='utf-8',
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errors='ignore'
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)
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response = result.stdout.strip()
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# Clean
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response_clean = re.sub(r'\[\?25[hl]|\[\?2026[hl]|\[\d+[GK]|[⠁-⠿]|[⣀-⣿]', '', response)
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response_clean = re.sub(r'\[\d+[A-Z]', '', response_clean)
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response_clean = re.sub(r'\[\d+;\d+[A-Z]', '', response_clean)
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response_clean = response_clean.strip()
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print(f"\nResponse: {response_clean}")
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print()
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# Check accuracy
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numbers = re.findall(r'\d+\.?\d*', response_clean)
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if numbers:
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reported = float(numbers[0])
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actual = lbm_data['coherence']
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deviation = abs(reported - actual) / actual * 100
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print(f"Reported: {reported}")
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print(f"Actual: {actual}")
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print(f"Deviation: {deviation:.3f}%")
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print(f"Accurate (< 0.1%): {'YES' if deviation <= 0.1 else 'NO'}")
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# Check translation
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if "Marble-State" in response_clean:
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print("Translation: Marble-State (correct for 1024)")
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elif "Granite-State" in response_clean:
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print("Translation: Granite-State (incorrect for 1024)")
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else:
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print("Translation: Not found")
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# Log
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entry = {
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"timestamp": datetime.now().isoformat(),
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"type": "blind_test_1024",
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"grid_size": 1024,
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"lbm_data": lbm_data,
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"response": response_clean
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}
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try:
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with open("blind_test_log.jsonl", "a") as f:
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f.write(json.dumps(entry) + "\n")
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print("\n[Logged]")
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except:
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pass
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print()
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print("=" * 70)
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print("1024 BLIND TEST COMPLETE")
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print("=" * 70)
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