File size: 4,638 Bytes
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import numpy as np
# Mock Vocabulary for Demonstration
MOCK_VOCAB = {
0: "gpio_pin",
1: "lora_chirp",
2: "reset_gateway",
3: "svd_matrix",
4: "shannon_entropy",
5: "logits_prior",
6: "zymatica_bot",
7: "rust_compile",
8: "python_script",
9: "fail_error"
}
def classify_token(token_str):
s = token_str.lower()
# Defaults
domain, subdomain, operation, modality, depth, polarity = 0, 0, 0, 0, 0, 0
# Domain 1: Hardware & Networks
if any(k in s for k in ['gpio', 'pin', 'lora', 'chirp', 'reset', 'gateway']):
domain = 1
if 'lora' in s or 'chirp' in s:
subdomain = 1
elif 'gpio' in s or 'pin' in s:
subdomain = 2
elif 'gateway' in s:
subdomain = 3
# Domain 2: Mathematics & Info Theory
elif any(k in s for k in ['svd', 'matrix', 'shannon', 'entropy', 'logits', 'prior']):
domain = 2
if 'svd' in s or 'matrix' in s:
subdomain = 1
elif 'entropy' in s or 'shannon' in s:
subdomain = 2
elif 'logits' in s:
subdomain = 3
# Domain 3: Dialogue & Persona
elif any(k in s for k in ['zymatica', 'bot']):
domain = 3
subdomain = 1
# Domain 4: Software & Runtimes
elif any(k in s for k in ['rust', 'compile', 'python', 'script']):
domain = 4
if 'rust' in s:
subdomain = 1
else:
subdomain = 2
# Operations (Actions)
if 'reset' in s or 'compile' in s:
operation = 1
elif 'script' in s:
operation = 2
# Modalities
if 'matrix' in s or 'pin' in s:
modality = 1
elif 'entropy' in s:
modality = 2
# Depth & Polarity
depth = len(s) % 16
if 'fail' in s or 'error' in s:
polarity = 2
elif 'ok' in s or 'success' in s:
polarity = 1
return domain, subdomain, operation, modality, depth, polarity
def pack_radicals(d, s, o, m, dp, p):
rc = (d << 4) | (s & 0xF)
rf = (o << 4) | (m & 0xF)
ra = (dp << 4) | (p & 0xF)
return rc, rf, ra
def unpack_radicals(rc, rf, ra):
d = rc >> 4
s = rc & 0xF
o = rf >> 4
m = rf & 0xF
dp = ra >> 4
p = ra & 0xF
return d, s, o, m, dp, p
def run_proof():
print("======================================================================")
print("ZYMATICA | Cuneiform-U Semantic Hypercube Coordinate Packaging Proof")
print("======================================================================\n")
print("[1] Classifying Mock Vocabulary into 6D Semantic Space...")
coords_map = {}
for tid, token in MOCK_VOCAB.items():
coords = classify_token(token)
coords_map[token] = coords
print(f" Token {tid:2d}: '{token:15s}' -> 6D Coordinates: {coords}")
print("\n[2] Packaging Coordinates into 3-Byte Radicals...")
packed_map = {}
for token, coords in coords_map.items():
rc, rf, ra = pack_radicals(*coords)
packed_map[token] = (rc, rf, ra)
print(f" Token '{token:15s}' -> packed radicals: RC=0x{rc:02X}, RF=0x{rf:02X}, RA=0x{ra:02X} (Total: 3 Bytes)")
print("\n[3] Verifying Lossless Reconstruction of Coordinates from Radicals...")
for token, packed in packed_map.items():
rc, rf, ra = packed
orig_coords = coords_map[token]
unpacked = unpack_radicals(rc, rf, ra)
assert orig_coords == unpacked, f"Mismatch for token {token}!"
print(" -> Unpacking status: 100% Exact Coordinate Reconstruct Match.")
print("\n[4] Calculating Hypercube Geometric Distances...")
# Calculate Euclidean distance between a hardware token, another hardware token, and a math token
tok1, tok2, tok3 = "gpio_pin", "lora_chirp", "svd_matrix"
c1, c2, c3 = np.array(coords_map[tok1]), np.array(coords_map[tok2]), np.array(coords_map[tok3])
dist_1_2 = np.linalg.norm(c1 - c2)
dist_1_3 = np.linalg.norm(c1 - c3)
print(f" - Coordinate distance between '{tok1}' and '{tok2}' (Same Domain): {dist_1_2:.4f}")
print(f" - Coordinate distance between '{tok1}' and '{tok3}' (Different Domain): {dist_1_3:.4f}")
print(f" -> Neighborhood status: Related domain tokens are geometrically clustered closer.")
print("\n[VERIFICATION] Cuneiform-U hypercube radical structure verified.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Zymatica Cuneiform-U Hypercube Packing Proof")
parser.add_argument("--test", action="store_true", help="Run in test mode")
args = parser.parse_args()
run_proof()
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