| """
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| Threshold Network for 2-out-of-8 Gate
|
|
|
| A formally verified single-neuron threshold network.
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| Outputs 1 when at least 2 of the 8 inputs are true.
|
| """
|
|
|
| import torch
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| from safetensors.torch import load_file
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|
|
|
|
| class Threshold2OutOf8:
|
| """
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| 2-out-of-8 threshold gate.
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|
|
| Circuit: output = (sum of inputs - 2 >= 0)
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| Fires when hamming weight >= 2.
|
| """
|
|
|
| def __init__(self, weights_dict):
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| self.weight = weights_dict['weight']
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| self.bias = weights_dict['bias']
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|
|
| def __call__(self, bits):
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| inputs = torch.tensor([float(b) for b in bits])
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| weighted_sum = (inputs * self.weight).sum() + self.bias
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| return (weighted_sum >= 0).float()
|
|
|
| @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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|
|
|
|
| def forward(x, weights):
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| x = torch.as_tensor(x, dtype=torch.float32)
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| weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
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| return (weighted_sum >= 0).float()
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|
|
|
|
| if __name__ == "__main__":
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| weights = load_file("model.safetensors")
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| model = Threshold2OutOf8(weights)
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|
|
| print("2-out-of-8 Gate Tests:")
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| print("-" * 35)
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| for hw in range(9):
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| bits = [1]*hw + [0]*(8-hw)
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| out = int(model(bits).item())
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| expected = 1 if hw >= 2 else 0
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| status = "OK" if out == expected else "FAIL"
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| print(f"HW={hw}: {out} [{status}]")
|
|
|
