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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-2to4decoder
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2-to-4 binary decoder. Converts 2-bit input to one-hot 4-bit output.
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## Function
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decode(A1, A0) -> [Y0, Y1, Y2, Y3]
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Yi = 1 iff input = i
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## Truth Table
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| A1 | A0 | Y0 | Y1 | Y2 | Y3 |
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|----|----|----|----|----|-----|
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| 0 | 0 | 1 | 0 | 0 | 0 |
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| 0 | 1 | 0 | 1 | 0 | 0 |
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| 1 | 0 | 0 | 0 | 1 | 0 |
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| 1 | 1 | 0 | 0 | 0 | 1 |
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## Architecture
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Single layer with 4 neurons. Each Yi matches pattern i.
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| Output | Weights [A1, A0] | Bias |
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|--------|------------------|------|
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| Y0 | [-1, -1] | 0 |
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| Y1 | [-1, +1] | -1 |
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| Y2 | [+1, -1] | -1 |
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| Y3 | [+1, +1] | -2 |
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## Parameters
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|---|---|
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| Inputs | 2 |
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| Outputs | 4 |
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| Neurons | 4 |
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| Layers | 1 |
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| Parameters | 12 |
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| Magnitude | 12 |
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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 decode2to4(a1, a0):
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inp = torch.tensor([float(a1), float(a0)])
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return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0) for i in range(4)]
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print(decode2to4(1, 0)) # [0, 0, 1, 0] - input 2
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```
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## License
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MIT
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