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"""
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Threshold Network for 4-input XNOR Gate
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XNOR4(a,b,c,d) = 1 when even number of inputs are 1 (0, 2, or 4)
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Built as: XNOR(XNOR(a,b), XNOR(c,d))
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"""
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import torch
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from safetensors.torch import load_file
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def xnor2(x, y, w, prefix):
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"""2-input XNOR using NOR + AND -> OR structure."""
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inp = torch.tensor([float(x), float(y)])
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n1 = int((inp * w[f'{prefix}.layer1.n1.weight']).sum() + w[f'{prefix}.layer1.n1.bias'] >= 0)
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n2 = int((inp * w[f'{prefix}.layer1.n2.weight']).sum() + w[f'{prefix}.layer1.n2.bias'] >= 0)
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h = torch.tensor([float(n1), float(n2)])
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return int((h * w[f'{prefix}.layer2.weight']).sum() + w[f'{prefix}.layer2.bias'] >= 0)
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class ThresholdXNOR4:
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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, d):
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xnor_ab = xnor2(a, b, self.w, 'xnor1')
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xnor_cd = xnor2(c, d, self.w, 'xnor2')
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result = xnor2(xnor_ab, xnor_cd, self.w, 'xnor3')
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return float(result)
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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 = ThresholdXNOR4(weights)
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print("4-input XNOR Gate Truth Table:")
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print("-" * 35)
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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 = 1 - (a ^ b ^ c ^ d)
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status = "OK" if out == expected else "FAIL"
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if out == expected:
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correct += 1
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print(f"XNOR4({a},{b},{c},{d}) = {out} [{status}]")
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print(f"\nTotal: {correct}/16 correct")
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