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Threshold Network for 4-input XNOR Gate
XNOR4(a,b,c,d) = 1 when even number of inputs are 1 (0, 2, or 4)
Built as: XNOR(XNOR(a,b), XNOR(c,d))
"""
import torch
from safetensors.torch import load_file
def xnor2(x, y, w, prefix):
"""2-input XNOR using NOR + AND -> OR structure."""
inp = torch.tensor([float(x), float(y)])
n1 = int((inp * w[f'{prefix}.layer1.n1.weight']).sum() + w[f'{prefix}.layer1.n1.bias'] >= 0)
n2 = int((inp * w[f'{prefix}.layer1.n2.weight']).sum() + w[f'{prefix}.layer1.n2.bias'] >= 0)
h = torch.tensor([float(n1), float(n2)])
return int((h * w[f'{prefix}.layer2.weight']).sum() + w[f'{prefix}.layer2.bias'] >= 0)
class ThresholdXNOR4:
def __init__(self, weights_dict):
self.w = weights_dict
def __call__(self, a, b, c, d):
# Tree structure: XNOR(XNOR(a,b), XNOR(c,d))
xnor_ab = xnor2(a, b, self.w, 'xnor1')
xnor_cd = xnor2(c, d, self.w, 'xnor2')
result = xnor2(xnor_ab, xnor_cd, self.w, 'xnor3')
return float(result)
@classmethod
def from_safetensors(cls, path="model.safetensors"):
return cls(load_file(path))
if __name__ == "__main__":
weights = load_file("model.safetensors")
model = ThresholdXNOR4(weights)
print("4-input XNOR Gate Truth Table:")
print("-" * 35)
correct = 0
for a in [0, 1]:
for b in [0, 1]:
for c in [0, 1]:
for d in [0, 1]:
out = int(model(a, b, c, d))
# XNOR4 = even parity = NOT XOR4
expected = 1 - (a ^ b ^ c ^ d)
status = "OK" if out == expected else "FAIL"
if out == expected:
correct += 1
print(f"XNOR4({a},{b},{c},{d}) = {out} [{status}]")
print(f"\nTotal: {correct}/16 correct")
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