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README.md

@@ -1,68 +1,68 @@
----
-license: mit
-tags:
-- pytorch
-- safetensors
-- threshold-logic
-- neuromorphic
----
-
-# threshold-2to4decoder
-
-2-to-4 binary decoder. Converts 2-bit input to one-hot 4-bit output.
-
-## Function
-
-decode(A1, A0) -> [Y0, Y1, Y2, Y3]
-
-Yi = 1 iff input = i
-
-## Truth Table
-
-| A1 | A0 | Y0 | Y1 | Y2 | Y3 |
-|----|----|----|----|----|-----|
-| 0 | 0 | 1 | 0 | 0 | 0 |
-| 0 | 1 | 0 | 1 | 0 | 0 |
-| 1 | 0 | 0 | 0 | 1 | 0 |
-| 1 | 1 | 0 | 0 | 0 | 1 |
-
-## Architecture
-
-Single layer with 4 neurons. Each Yi matches pattern i.
-
-| Output | Weights [A1, A0] | Bias |
-|--------|------------------|------|
-| Y0 | [-1, -1] | 0 |
-| Y1 | [-1, +1] | -1 |
-| Y2 | [+1, -1] | -1 |
-| Y3 | [+1, +1] | -2 |
-
-## Parameters
-
-| | |
-|---|---|
-| Inputs | 2 |
-| Outputs | 4 |
-| Neurons | 4 |
-| Layers | 1 |
-| Parameters | 12 |
-| Magnitude | 12 |
-
-## Usage
-
-```python
-from safetensors.torch import load_file
-import torch
-
-w = load_file('model.safetensors')
-
-def decode2to4(a1, a0):
-    inp = torch.tensor([float(a1), float(a0)])
-    return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0) for i in range(4)]
-
-print(decode2to4(1, 0))  # [0, 0, 1, 0] - input 2
-```
-
-## License
-
-MIT
+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-2to4decoder
+
+2-to-4 binary decoder. Converts 2-bit input to one-hot 4-bit output.
+
+## Function
+
+decode(A1, A0) -> [Y0, Y1, Y2, Y3]
+
+Yi = 1 iff input = i
+
+## Truth Table
+
+| A1 | A0 | Y0 | Y1 | Y2 | Y3 |
+|----|----|----|----|----|-----|
+| 0 | 0 | 1 | 0 | 0 | 0 |
+| 0 | 1 | 0 | 1 | 0 | 0 |
+| 1 | 0 | 0 | 0 | 1 | 0 |
+| 1 | 1 | 0 | 0 | 0 | 1 |
+
+## Architecture
+
+Single layer with 4 neurons. Each Yi matches pattern i.
+
+| Output | Weights [A1, A0] | Bias |
+|--------|------------------|------|
+| Y0 | [-1, -1] | 0 |
+| Y1 | [-1, +1] | -1 |
+| Y2 | [+1, -1] | -1 |
+| Y3 | [+1, +1] | -2 |
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 2 |
+| Outputs | 4 |
+| Neurons | 4 |
+| Layers | 1 |
+| Parameters | 12 |
+| Magnitude | 12 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def decode2to4(a1, a0):
+    inp = torch.tensor([float(a1), float(a0)])
+    return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0) for i in range(4)]
+
+print(decode2to4(1, 0))  # [0, 0, 1, 0] - input 2
+```
+
+## License
+
+MIT

config.json

@@ -1,9 +1,9 @@
-{
-  "name": "threshold-2to4decoder",
-  "description": "2-to-4 binary decoder",
-  "inputs": 2,
-  "outputs": 4,
-  "neurons": 4,
-  "layers": 1,
-  "parameters": 12
-}
+{
+  "name": "threshold-2to4decoder",
+  "description": "2-to-4 binary decoder",
+  "inputs": 2,
+  "outputs": 4,
+  "neurons": 4,
+  "layers": 1,
+  "parameters": 12
+}

create_safetensors.py

@@ -1,36 +1,36 @@
-import torch
-from safetensors.torch import save_file
-
-weights = {}
-
-# Input: A1, A0 (2 bits)
-# Output: Y0-Y3 (one-hot)
-# Yi fires when input = i
-
-for i in range(4):
-    a1_bit = (i >> 1) & 1
-    a0_bit = i & 1
-    w = [1.0 if a1_bit else -1.0, 1.0 if a0_bit else -1.0]
-    bias = -bin(i).count('1')
-    weights[f'y{i}.weight'] = torch.tensor([w], dtype=torch.float32)
-    weights[f'y{i}.bias'] = torch.tensor([float(bias)], dtype=torch.float32)
-
-save_file(weights, 'model.safetensors')
-
-def decode2to4(a1, a0):
-    inp = torch.tensor([float(a1), float(a0)])
-    return [int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0) for i in range(4)]
-
-print("Verifying 2to4decoder...")
-errors = 0
-for val in range(4):
-    a1, a0 = (val >> 1) & 1, val & 1
-    result = decode2to4(a1, a0)
-    expected = [1 if i == val else 0 for i in range(4)]
-    if result != expected:
-        errors += 1
-        print(f"ERROR: {val} -> {result}, expected {expected}")
-
-if errors == 0:
-    print("All 4 test cases passed!")
-print(f"Magnitude: {sum(t.abs().sum().item() for t in weights.values()):.0f}")
+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# Input: A1, A0 (2 bits)
+# Output: Y0-Y3 (one-hot)
+# Yi fires when input = i
+
+for i in range(4):
+    a1_bit = (i >> 1) & 1
+    a0_bit = i & 1
+    w = [1.0 if a1_bit else -1.0, 1.0 if a0_bit else -1.0]
+    bias = -bin(i).count('1')
+    weights[f'y{i}.weight'] = torch.tensor([w], dtype=torch.float32)
+    weights[f'y{i}.bias'] = torch.tensor([float(bias)], dtype=torch.float32)
+
+save_file(weights, 'model.safetensors')
+
+def decode2to4(a1, a0):
+    inp = torch.tensor([float(a1), float(a0)])
+    return [int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0) for i in range(4)]
+
+print("Verifying 2to4decoder...")
+errors = 0
+for val in range(4):
+    a1, a0 = (val >> 1) & 1, val & 1
+    result = decode2to4(a1, a0)
+    expected = [1 if i == val else 0 for i in range(4)]
+    if result != expected:
+        errors += 1
+        print(f"ERROR: {val} -> {result}, expected {expected}")
+
+if errors == 0:
+    print("All 4 test cases passed!")
+print(f"Magnitude: {sum(t.abs().sum().item() for t in weights.values()):.0f}")

model.py

@@ -1,17 +1,17 @@
-import torch
-from safetensors.torch import load_file
-
-def load_model(path='model.safetensors'):
-    return load_file(path)
-
-def decode2to4(a1, a0, weights):
-    inp = torch.tensor([float(a1), float(a0)])
-    return [int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0) for i in range(4)]
-
-if __name__ == '__main__':
-    w = load_model()
-    print('2-to-4 Decoder:')
-    for val in range(4):
-        a1, a0 = (val >> 1) & 1, val & 1
-        result = decode2to4(a1, a0, w)
-        print(f'  {val} ({a1}{a0}) -> {"".join(map(str, result))}')
+import torch
+from safetensors.torch import load_file
+
+def load_model(path='model.safetensors'):
+    return load_file(path)
+
+def decode2to4(a1, a0, weights):
+    inp = torch.tensor([float(a1), float(a0)])
+    return [int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0) for i in range(4)]
+
+if __name__ == '__main__':
+    w = load_model()
+    print('2-to-4 Decoder:')
+    for val in range(4):
+        a1, a0 = (val >> 1) & 1, val & 1
+        result = decode2to4(a1, a0, w)
+        print(f'  {val} ({a1}{a0}) -> {"".join(map(str, result))}')