| """
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| Threshold Network for 4-input XOR Gate
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|
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| Cascade of three standard XORs (OR + NAND + AND structure).
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| """
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|
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| import torch
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| from safetensors.torch import load_file
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|
|
|
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| def xor2(x1, x2, w, prefix):
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| inp = torch.tensor([float(x1), float(x2)])
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| or_out = int((inp * w[f'{prefix}.layer1.or.weight']).sum() + w[f'{prefix}.layer1.or.bias'] >= 0)
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| nand_out = int((inp * w[f'{prefix}.layer1.nand.weight']).sum() + w[f'{prefix}.layer1.nand.bias'] >= 0)
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| h = torch.tensor([float(or_out), float(nand_out)])
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| return int((h * w[f'{prefix}.layer2.and.weight']).sum() + w[f'{prefix}.layer2.and.bias'] >= 0)
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|
|
|
|
| class ThresholdXOR4:
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| def __init__(self, weights_dict):
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| self.w = weights_dict
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|
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| def __call__(self, a, b, c, d):
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| xor_ab = xor2(a, b, self.w, 'xor1')
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| xor_abc = xor2(xor_ab, c, self.w, 'xor2')
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| xor_abcd = xor2(xor_abc, d, self.w, 'xor3')
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| return float(xor_abcd)
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|
|
| @classmethod
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| def from_safetensors(cls, path="model.safetensors"):
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| return cls(load_file(path))
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|
|
|
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| if __name__ == "__main__":
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| weights = load_file("model.safetensors")
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| model = ThresholdXOR4(weights)
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|
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| print("4-input XOR Gate:")
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| correct = 0
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| for a in [0, 1]:
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| for b in [0, 1]:
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| for c in [0, 1]:
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| for d in [0, 1]:
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| out = int(model(a, b, c, d))
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| expected = a ^ b ^ c ^ d
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| if out == expected:
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| correct += 1
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| status = "OK" if out == expected else "FAIL"
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| print(f" XOR4({a},{b},{c},{d}) = {out} [{status}]")
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| print(f"Total: {correct}/16")
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|
|
