| """
|
| Threshold Network for MOD-6 Circuit
|
|
|
| A formally verified threshold network computing Hamming weight mod 6.
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| Uses the algebraic weight pattern [1, 1, 1, 1, 1, -5, 1, 1].
|
| """
|
|
|
| import torch
|
| from safetensors.torch import load_file
|
|
|
|
|
| class ThresholdMod6:
|
| """
|
| MOD-6 circuit using threshold logic.
|
|
|
| Weight pattern: (1, 1, 1, 1, 1, 1-m) for m=6 at position 6
|
| """
|
|
|
| def __init__(self, weights_dict):
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| self.weight = weights_dict['weight']
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| self.bias = weights_dict['bias']
|
|
|
| def __call__(self, bits):
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| inputs = torch.tensor([float(b) for b in bits])
|
| weighted_sum = (inputs * self.weight).sum() + self.bias
|
| return weighted_sum
|
|
|
| @classmethod
|
| def from_safetensors(cls, path="model.safetensors"):
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| return cls(load_file(path))
|
|
|
|
|
| if __name__ == "__main__":
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| weights = load_file("model.safetensors")
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| model = ThresholdMod6(weights)
|
|
|
| print("MOD-6 Circuit Tests:")
|
| print("-" * 40)
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| for hw in range(9):
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| bits = [1]*hw + [0]*(8-hw)
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| out = model(bits).item()
|
| expected = hw % 6
|
| print(f"HW={hw}: weighted_sum={out:.0f}, HW mod 6 = {expected}")
|
|
|
