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threshold-exactly1outof3
phanerozoic/threshold-exactly1outof3
threshold-exactly1outof3 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 · 51%
At a glance
- License
- mit
- Access
- Public
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026
- SHA
- ca9ffedb
Model card
From the Hugging Face model README
threshold-exactly1outof3
Exactly 1 of 3 inputs high.
Function
exactly1outof3(a, b, c) = 1 if (a + b + c) == 1, else 0
Truth Table
| a | b | c | sum | out |
|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 1 | 1 | 1 |
| 0 | 1 | 0 | 1 | 1 |
| 0 | 1 | 1 | 2 | 0 |
| 1 | 0 | 0 | 1 | 1 |
| 1 | 0 | 1 | 2 | 0 |
| 1 | 1 | 0 | 2 | 0 |
| 1 | 1 | 1 | 3 | 0 |
Architecture
Two layers (not linearly separable):
Layer 1:
- N1: weights [1, 1, 1], bias -1 (fires when sum >= 1)
- N2: weights [-1, -1, -1], bias 1 (fires when sum <= 1)
Layer 2:
- AND: weights [1, 1], bias -2 (fires when both N1 and N2 fire)
Parameters
| Inputs | 3 |
| Outputs | 1 |
| Neurons | 3 |
| Layers | 2 |
| Parameters | 11 |
| Magnitude | 12 |
Usage
from safetensors.torch import load_file
import torch
w = load_file('model.safetensors')
def exactly1of3(a, b, c):
inp = torch.tensor([float(a), float(b), float(c)])
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(exactly1of3(0, 0, 1)) # 1 (sum=1)
print(exactly1of3(0, 1, 1)) # 0 (sum=2)
License
MIT
- License
- mit
- Created
- Jan 23, 2026
- Updated
- Jan 23, 2026