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"""
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Threshold Network for Majority Gate
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A formally verified single-neuron threshold network computing 8-bit majority.
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Outputs 1 when 5 or more of the 8 inputs are true (strict majority).
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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 ThresholdMajority:
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"""
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Majority gate implemented as a threshold neuron.
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Circuit: output = (sum of inputs + bias >= 0)
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With all weights=1, bias=-5: fires when hamming weight >= 5.
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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(x0, x1, x2, x3, x4, x5, x6, x7, weights):
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"""
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Forward pass with Heaviside activation.
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Args:
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x0-x7: Individual input bits
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weights: Dict with 'weight' and 'bias' tensors
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Returns:
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1 if majority (5+ of 8) are true, else 0
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"""
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x = torch.tensor([float(x0), float(x1), float(x2), float(x3),
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float(x4), float(x5), float(x6), float(x7)])
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weighted_sum = (x * weights['weight']).sum() + 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 = ThresholdMajority(weights)
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print("Majority 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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[1,1,1,1,0,0,0,0],
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[1,1,1,1,1,0,0,0],
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[1,1,1,1,1,1,0,0],
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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 >= 5 else 0
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status = "OK" if out == expected else "FAIL"
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print(f"HW={hw}: Majority({bits}) = {out} [{status}]")
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