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threshold-exactly4outof5
phanerozoic/threshold-exactly4outof5
threshold-exactly4outof5 is a machine learning model from phanerozoic. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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.py1.9 KB · 52%
At a glance
- License
- mit
- Access
- Public
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026
- SHA
- 035d82d3
Model card
From the Hugging Face model README
threshold-exactly4outof5
Exactly 4 of 5 inputs high.
Function
exactly4outof5(a, b, c, d, e) = 1 if (a + b + c + d + e) == 4, else 0
Truth Table (selected)
| sum | out |
|---|---|
| 0 | 0 |
| 1 | 0 |
| 2 | 0 |
| 3 | 0 |
| 4 | 1 |
| 5 | 0 |
Architecture
Two layers required (exactly-k is not linearly separable).
Layer 1:
- N1: sum >= 4 (weights [1,1,1,1,1], bias -4)
- N2: sum <= 4 (weights [-1,-1,-1,-1,-1], bias 4)
Layer 2:
- AND(N1, N2): weights [1,1], bias -2
Parameters
| Inputs | 5 |
| Outputs | 1 |
| Neurons | 3 |
| Layers | 2 |
| Parameters | 15 |
| Magnitude | 22 |
Usage
from safetensors.torch import load_file
import torch
w = load_file('model.safetensors')
def exactly4of5(a, b, c, d, e):
inp = torch.tensor([float(a), float(b), float(c), float(d), float(e)])
l1 = (inp @ w['layer1.weight'].T + w['layer1.bias'] >= 0).float()
out = (l1 @ w['layer2.weight'].T + w['layer2.bias'] >= 0).float()
return int(out.item())
print(exactly4of5(1, 1, 1, 1, 0)) # 1 (sum=4)
print(exactly4of5(1, 1, 1, 1, 1)) # 0 (sum=5)
License
MIT
- License
- mit
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026