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"""
Threshold Network for NOT Gate
A formally verified single-neuron threshold network computing logical negation.
Weights are integer-constrained and activation uses the Heaviside step function.
"""
import torch
from safetensors.torch import load_file
class ThresholdNOT:
"""
NOT gate implemented as a threshold neuron.
Circuit: output = (weight * input + bias >= 0)
With weight=-1, bias=0: NOT(0)=1, NOT(1)=0
"""
def __init__(self, weights_dict):
self.weight = weights_dict['weight']
self.bias = weights_dict['bias']
def __call__(self, x):
x = torch.as_tensor(x, dtype=torch.float32)
return (x * self.weight + self.bias >= 0).float()
@classmethod
def from_safetensors(cls, path="model.safetensors"):
return cls(load_file(path))
def forward(x, weights):
"""
Forward pass with Heaviside activation.
Args:
x: Input tensor (0 or 1)
weights: Dict with 'weight' and 'bias' tensors
Returns:
NOT(x)
"""
x = torch.as_tensor(x, dtype=torch.float32)
return (x * weights['weight'] + weights['bias'] >= 0).float()
if __name__ == "__main__":
weights = load_file("model.safetensors")
model = ThresholdNOT(weights)
print("NOT Gate Truth Table:")
print("-" * 20)
for inp in [0, 1]:
out = int(model(inp).item())
expected = 1 - inp
status = "OK" if out == expected else "FAIL"
print(f"NOT({inp}) = {out} [{status}]")
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