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"""
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Threshold Network for Minority Gate
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A formally verified single-neuron threshold network computing 8-bit minority.
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Outputs 1 when 3 or fewer of the 8 inputs are true (strict minority).
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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 ThresholdMinority:
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"""
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Minority gate implemented as a threshold neuron.
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Circuit: output = (sum of weighted inputs + bias >= 0)
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With all weights=-1, bias=3: fires when hamming weight <= 3.
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"""
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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()
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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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def forward(x, weights):
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"""
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Forward pass with Heaviside activation.
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Args:
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x: Input tensor of shape [..., 8]
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weights: Dict with 'weight' and 'bias' tensors
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Returns:
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1 if minority (3 or fewer of 8) are true, else 0
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"""
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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 = ThresholdMinority(weights)
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print("Minority Gate Tests:")
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print("-" * 40)
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test_cases = [
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[0,0,0,0,0,0,0,0],
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[1,0,0,0,0,0,0,0],
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[1,1,1,0,0,0,0,0],
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[1,1,1,1,0,0,0,0],
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[1,1,1,1,1,0,0,0],
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[1,1,1,1,1,1,1,1],
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]
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for bits in test_cases:
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hw = sum(bits)
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out = int(model(bits).item())
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expected = 1 if hw <= 3 else 0
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status = "OK" if out == expected else "FAIL"
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print(f"HW={hw}: Minority({bits}) = {out} [{status}]")
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