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---
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license: mit
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tags:
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- pytorch
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- safetensors
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- threshold-logic
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- neuromorphic
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---
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# threshold-4to2encoder
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4-to-2 priority encoder. Outputs binary index of highest-priority set input.
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## Function
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encode(I3, I2, I1, I0) -> (Y1, Y0)
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Priority: I3 > I2 > I1 > I0
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## Truth Table (selected rows)
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| I3 | I2 | I1 | I0 | Y1 | Y0 | Index |
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|----|----|----|----|----|----|-------|
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| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 0 | 0 | 0 | 1 | 0 | 0 | 0 |
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| 0 | 0 | 1 | x | 0 | 1 | 1 |
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| 0 | 1 | x | x | 1 | 0 | 2 |
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| 1 | x | x | x | 1 | 1 | 3 |
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## Architecture
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Single layer with 2 neurons:
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| Output | Weights [I3, I2, I1, I0] | Bias | Function |
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|--------|--------------------------|------|----------|
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| Y1 | [1, 1, 0, 0] | -1 | I3 OR I2 |
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| Y0 | [3, -2, 1, 0] | -1 | I3 OR (NOT I2 AND I1) |
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## Parameters
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|---|---|
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| Inputs | 4 |
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| Outputs | 2 |
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| Neurons | 2 |
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| Layers | 1 |
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| Parameters | 10 |
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| Magnitude | 10 |
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## Usage
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```python
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from safetensors.torch import load_file
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import torch
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w = load_file('model.safetensors')
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def encode4to2(i3, i2, i1, i0):
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inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
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y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
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y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
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return y1, y0
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print(encode4to2(0, 1, 1, 0)) # (1, 0) -> index 2 (I2 is highest)
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print(encode4to2(1, 0, 0, 1)) # (1, 1) -> index 3 (I3 is highest)
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```
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## License
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MIT
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