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"""
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Threshold Network for 3-input XOR Gate
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Cascade of two standard XORs (OR + NAND + AND structure).
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"""
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import torch
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from safetensors.torch import load_file
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class ThresholdXOR3:
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def __init__(self, weights_dict):
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self.w = weights_dict
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def __call__(self, a, b, c):
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inp1 = torch.tensor([float(a), float(b)])
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or1 = int((inp1 * self.w['xor1.layer1.or.weight']).sum() + self.w['xor1.layer1.or.bias'] >= 0)
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nand1 = int((inp1 * self.w['xor1.layer1.nand.weight']).sum() + self.w['xor1.layer1.nand.bias'] >= 0)
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h1 = torch.tensor([float(or1), float(nand1)])
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xor_ab = int((h1 * self.w['xor1.layer2.and.weight']).sum() + self.w['xor1.layer2.and.bias'] >= 0)
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inp2 = torch.tensor([float(xor_ab), float(c)])
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or2 = int((inp2 * self.w['xor2.layer1.or.weight']).sum() + self.w['xor2.layer1.or.bias'] >= 0)
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nand2 = int((inp2 * self.w['xor2.layer1.nand.weight']).sum() + self.w['xor2.layer1.nand.bias'] >= 0)
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h2 = torch.tensor([float(or2), float(nand2)])
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out = int((h2 * self.w['xor2.layer2.and.weight']).sum() + self.w['xor2.layer2.and.bias'] >= 0)
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return float(out)
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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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if __name__ == "__main__":
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weights = load_file("model.safetensors")
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model = ThresholdXOR3(weights)
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print("3-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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out = int(model(a, b, c))
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expected = a ^ b ^ c
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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" XOR3({a},{b},{c}) = {out} [{status}]")
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print(f"Total: {correct}/8")
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