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8b1e807 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import hashlib
import time
from tgi.tgi import TGICognitiveKernel
class TopologicalCompressionEngine:
"""
TGI Advanced Compression v1.0.
Transforms data into a (Start Coordinate + Spike Sequence).
Data is logically sharded along the manifold's Hamiltonian path.
"""
def __init__(self, m=256, k=3):
self.m = m
self.k = k
self.kernel = TGICognitiveKernel(m, k)
self.storage_manifold = {} # {coord: shard}
def _generate_stateless_gates(self):
# Local logic gates for O(1) routing
level = [[(0,1,2) for _ in range(self.m)] for _ in range(self.m)]
identity, swap12, swap01 = (0, 1, 2), (0, 2, 1), (1, 0, 2)
swap02 = lambda p: (p[2], p[1], p[0])
P = [identity] * self.m
if self.m >= 2: P[self.m-2], P[self.m-1] = swap12, swap01
for s in range(self.m):
for j in range(self.m):
level[s][j] = swap02(P[s]) if j == 0 and s != (self.m - 2) else P[s]
return level
def compress_data(self, label, data_string, shard_size=32):
"""
GEOMETRIC COMPRESSION:
Saves only the start coordinate. The 'Spike' (gates) reconstructs the path.
"""
# 1. Start Coordinate derived from label
h = hashlib.sha256(label.encode()).digest()
start_coord = tuple(h[i] % self.m for i in range(self.k))
shards = [data_string[i:i+shard_size] for i in range(0, len(data_string), shard_size)]
gates = self._generate_stateless_gates()
curr = start_coord
print(f"[*] Compressing '{label}' -> Starting at {start_coord} across {len(shards)} shards.")
for i, shard in enumerate(shards):
self.storage_manifold[curr] = shard
# Step via O(1) Gates
s = sum(curr) % self.m
direction = gates[s][curr[0]][0]
next_coord = list(curr)
next_coord[direction] = (next_coord[direction] + 1) % self.m
curr = tuple(next_coord)
return start_coord, len(shards)
def reconstruct_data(self, start_coord, num_shards):
"""
O(1) Reconstruction Logic: Follows the breadcrumbs.
"""
start_time = time.perf_counter()
gates = self._generate_stateless_gates()
reconstructed = ""
curr = start_coord
for _ in range(num_shards):
shard = self.storage_manifold.get(curr)
if not shard: break
reconstructed += shard
s = sum(curr) % self.m
direction = gates[s][curr[0]][0]
next_coord = list(curr)
next_coord[direction] = (next_coord[direction] + 1) % self.m
curr = tuple(next_coord)
latency = (time.perf_counter() - start_time) * 1000
return reconstructed, latency
if __name__ == "__main__":
tce = TopologicalCompressionEngine(m=256, k=3)
data = "ALGERIA_SOVEREIGN_INFRASTRUCTURE_v2.0_CODE_BLOCK_AGI"
coord, count = tce.compress_data("agi_core", data)
out, lat = tce.reconstruct_data(coord, count)
print(f"[*] Reconstructed in {lat:.4f} ms: {out}")
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