File size: 2,084 Bytes
5dd640d 818e32c 5dd640d 818e32c 5dd640d 818e32c 5dd640d 818e32c 5dd640d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
"""
Threshold Network for Majority Gate
A formally verified single-neuron threshold network computing 8-bit majority.
Outputs 1 when 5 or more of the 8 inputs are true (strict majority).
"""
import torch
from safetensors.torch import load_file
class ThresholdMajority:
"""
Majority gate implemented as a threshold neuron.
Circuit: output = (sum of inputs + bias >= 0)
With all weights=1, bias=-5: fires when hamming weight >= 5.
"""
def __init__(self, weights_dict):
self.weight = weights_dict['weight']
self.bias = weights_dict['bias']
def __call__(self, *bits):
inputs = torch.tensor([float(b) for b in bits])
weighted_sum = (inputs * self.weight).sum() + self.bias
return (weighted_sum >= 0).float()
@classmethod
def from_safetensors(cls, path="model.safetensors"):
return cls(load_file(path))
def forward(x0, x1, x2, x3, x4, x5, x6, x7, weights):
"""
Forward pass with Heaviside activation.
Args:
x0-x7: Individual input bits
weights: Dict with 'weight' and 'bias' tensors
Returns:
1 if majority (5+ of 8) are true, else 0
"""
x = torch.tensor([float(x0), float(x1), float(x2), float(x3),
float(x4), float(x5), float(x6), float(x7)])
weighted_sum = (x * weights['weight']).sum() + weights['bias']
return (weighted_sum >= 0).float()
if __name__ == "__main__":
weights = load_file("model.safetensors")
model = ThresholdMajority(weights)
print("Majority Gate Tests:")
print("-" * 40)
test_cases = [
[0,0,0,0,0,0,0,0],
[1,0,0,0,0,0,0,0],
[1,1,1,1,0,0,0,0],
[1,1,1,1,1,0,0,0],
[1,1,1,1,1,1,0,0],
[1,1,1,1,1,1,1,1],
]
for bits in test_cases:
hw = sum(bits)
out = int(model(bits).item())
expected = 1 if hw >= 5 else 0
status = "OK" if out == expected else "FAIL"
print(f"HW={hw}: Majority({bits}) = {out} [{status}]")
|