tgi/tgi_encoder.py

import hashlib
from tgi.tgi import TGICognitiveKernel

class TopologicalDataEncoder:
    """
    TGI Topological Data Encoder v1.1.
    Transforms raw data into pure FSO geometry.
    Implements Geometric Compression: Data is a start_coord + a spike_path.
    """
    def __init__(self, m=101, k=3):
        self.m = m
        self.k = k
        self.kernel = TGICognitiveKernel(m, k)
        # In a sovereign system, the 'manifold' is the storage
        self.manifold = {}

    def encode_to_path(self, data_string):
        """
        GEOMETRIC COMPRESSION:
        Transforms data into a start coordinate. The 'Spike Rule' (Hamiltonian Cycle)
        acts as the decoder that 'unfolds' the data into its constituent parts.
        """
        # 1. Map content to a start coordinate
        h = hashlib.sha256(data_string.encode()).digest()
        start_coord = tuple(h[i] % self.m for i in range(self.k))

        # 2. Derive the 'Unfolding Path' (Deterministic Trajectory)
        # The data is logically 'spread' across this path.
        unfolding_trace = self.kernel.generate_thought_stream(start_coord, steps=len(data_string))

        # Store metadata and 'unfolding signature'
        self.manifold[start_coord] = {
            "signature": unfolding_trace[-1],
            "size": len(data_string)
        }

        return start_coord

    def decode_from_coord(self, start_coord):
        """
        Decodes data by executing the Spike Function from the start coordinate.
        Recovers the manifold state logically.
        """
        info = self.manifold.get(start_coord)
        if not info: return None

        # Unfold the geometry
        path = self.kernel.generate_thought_stream(start_coord, steps=info["size"])
        return path

    def associative_jump(self, query):
        """
        O(1) Jump to data coordinate.
        """
        h = hashlib.sha256(query.encode()).digest()
        coord = tuple(h[i] % self.m for i in range(self.k))
        return coord, self.manifold.get(coord)

if __name__ == "__main__":
    encoder = TopologicalDataEncoder(m=101, k=3)
    data = "SOVEREIGN_ALGERIA_2026"

    # Compress
    coord = encoder.encode_to_path(data)
    print(f"[*] Data Compressed to Coordinate: {coord}")

    # Unfold
    path = encoder.decode_from_coord(coord)
    print(f"[*] Geometry Unfolded (Path Length: {len(path)})")
    print(f"[*] Associative Check: {encoder.associative_jump(data)[1] is not None}")