model.py

import torch
from safetensors.torch import load_file

def load_model(path='model.safetensors'):
    return load_file(path)

def parity7(bits):
    """7-bit XOR (parity)."""
    result = 0
    for b in bits:
        result ^= b
    return result

def hamming1511_encode_ref(d1, d2, d3, d4, d5, d6, d7, d8, d9, d10, d11):
    """Reference encoder (not using threshold weights)."""
    p1 = parity7([d1, d2, d4, d5, d7, d9, d11])
    p2 = parity7([d1, d3, d4, d6, d7, d10, d11])
    p4 = parity7([d2, d3, d4, d8, d9, d10, d11])
    p8 = parity7([d5, d6, d7, d8, d9, d10, d11])
    return [p1, p2, d1, p4, d2, d3, d4, p8, d5, d6, d7, d8, d9, d10, d11]

if __name__ == '__main__':
    print('Hamming(15,11) Encoder')
    print('11 data bits -> 15 coded bits (4 parity + 11 data)')
    print()
    print('Bit positions: p1,p2,d1,p4,d2,d3,d4,p8,d5,d6,d7,d8,d9,d10,d11')
    print()
    print('Examples:')
    for i in range(8):
        bits = [(i >> j) & 1 for j in range(11)]
        encoded = hamming1511_encode_ref(*bits)
        data_str = ''.join(map(str, bits))
        enc_str = ''.join(map(str, encoded))
        print(f'  {data_str} -> {enc_str}')