| import torch | |
| from safetensors.torch import load_file | |
| def load_model(path='model.safetensors'): | |
| return load_file(path) | |
| def xor2(a, b, prefix, w): | |
| or_out = int(a * w[f'{prefix}.or.weight'][0] + b * w[f'{prefix}.or.weight'][1] + w[f'{prefix}.or.bias'] >= 0) | |
| nand_out = int(a * w[f'{prefix}.nand.weight'][0] + b * w[f'{prefix}.nand.weight'][1] + w[f'{prefix}.nand.bias'] >= 0) | |
| return int(or_out * w[f'{prefix}.and.weight'][0] + nand_out * w[f'{prefix}.and.weight'][1] + w[f'{prefix}.and.bias'] >= 0) | |
| def parity6(x0, x1, x2, x3, x4, x5, weights): | |
| """6-bit parity: returns 1 if odd number of inputs are high.""" | |
| xor01 = xor2(x0, x1, 'xor_01', weights) | |
| xor23 = xor2(x2, x3, 'xor_23', weights) | |
| xor45 = xor2(x4, x5, 'xor_45', weights) | |
| xor0123 = xor2(xor01, xor23, 'xor_0123', weights) | |
| return xor2(xor0123, xor45, 'xor_final', weights) | |
| if __name__ == '__main__': | |
| w = load_model() | |
| print('parity6 truth table by Hamming weight:') | |
| print('HW | Example | Parity') | |
| print('---+--------------+--------') | |
| for hw in range(7): | |
| bits = [1 if j < hw else 0 for j in range(6)] | |
| result = parity6(*bits, w) | |
| expected = hw % 2 | |
| status = 'OK' if result == expected else 'FAIL' | |
| bits_str = ''.join(str(b) for b in bits) | |
| print(f' {hw} | {bits_str} | {result} {status}') | |