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README.md

@@ -1,70 +1,70 @@
----
-license: mit
-tags:
-- pytorch
-- safetensors
-- threshold-logic
-- neuromorphic
----
-
-# threshold-4to2encoder
-
-4-to-2 priority encoder. Outputs binary index of highest-priority set input.
-
-## Function
-
-encode(I3, I2, I1, I0) -> (Y1, Y0)
-
-Priority: I3 > I2 > I1 > I0
-
-## Truth Table (selected rows)
-
-| I3 | I2 | I1 | I0 | Y1 | Y0 | Index |
-|----|----|----|----|----|----|-------|
-| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| 0 | 0 | 1 | x | 0 | 1 | 1 |
-| 0 | 1 | x | x | 1 | 0 | 2 |
-| 1 | x | x | x | 1 | 1 | 3 |
-
-## Architecture
-
-Single layer with 2 neurons:
-
-| Output | Weights [I3, I2, I1, I0] | Bias | Function |
-|--------|--------------------------|------|----------|
-| Y1 | [1, 1, 0, 0] | -1 | I3 OR I2 |
-| Y0 | [3, -2, 1, 0] | -1 | I3 OR (NOT I2 AND I1) |
-
-## Parameters
-
-| | |
-|---|---|
-| Inputs | 4 |
-| Outputs | 2 |
-| Neurons | 2 |
-| Layers | 1 |
-| Parameters | 10 |
-| Magnitude | 10 |
-
-## Usage
-
-```python
-from safetensors.torch import load_file
-import torch
-
-w = load_file('model.safetensors')
-
-def encode4to2(i3, i2, i1, i0):
-    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
-    y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
-    y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
-    return y1, y0
-
-print(encode4to2(0, 1, 1, 0))  # (1, 0) -> index 2 (I2 is highest)
-print(encode4to2(1, 0, 0, 1))  # (1, 1) -> index 3 (I3 is highest)
-```
-
-## License
-
-MIT
+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-4to2encoder
+
+4-to-2 priority encoder. Outputs binary index of highest-priority set input.
+
+## Function
+
+encode(I3, I2, I1, I0) -> (Y1, Y0)
+
+Priority: I3 > I2 > I1 > I0
+
+## Truth Table (selected rows)
+
+| I3 | I2 | I1 | I0 | Y1 | Y0 | Index |
+|----|----|----|----|----|----|-------|
+| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+| 0 | 0 | 0 | 1 | 0 | 0 | 0 |
+| 0 | 0 | 1 | x | 0 | 1 | 1 |
+| 0 | 1 | x | x | 1 | 0 | 2 |
+| 1 | x | x | x | 1 | 1 | 3 |
+
+## Architecture
+
+Single layer with 2 neurons:
+
+| Output | Weights [I3, I2, I1, I0] | Bias | Function |
+|--------|--------------------------|------|----------|
+| Y1 | [1, 1, 0, 0] | -1 | I3 OR I2 |
+| Y0 | [3, -2, 1, 0] | -1 | I3 OR (NOT I2 AND I1) |
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 4 |
+| Outputs | 2 |
+| Neurons | 2 |
+| Layers | 1 |
+| Parameters | 10 |
+| Magnitude | 10 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def encode4to2(i3, i2, i1, i0):
+    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
+    y1 = int((inp @ w['y1.weight'].T + w['y1.bias'] >= 0).item())
+    y0 = int((inp @ w['y0.weight'].T + w['y0.bias'] >= 0).item())
+    return y1, y0
+
+print(encode4to2(0, 1, 1, 0))  # (1, 0) -> index 2 (I2 is highest)
+print(encode4to2(1, 0, 0, 1))  # (1, 1) -> index 3 (I3 is highest)
+```
+
+## License
+
+MIT

config.json

@@ -1,9 +1,9 @@
-{
-  "name": "threshold-4to2encoder",
-  "description": "4-to-2 priority encoder",
-  "inputs": 4,
-  "outputs": 2,
-  "neurons": 2,
-  "layers": 1,
-  "parameters": 10
-}
+{
+  "name": "threshold-4to2encoder",
+  "description": "4-to-2 priority encoder",
+  "inputs": 4,
+  "outputs": 2,
+  "neurons": 2,
+  "layers": 1,
+  "parameters": 10
+}

create_safetensors.py

@@ -1,45 +1,45 @@
-import torch
-from safetensors.torch import save_file
-
-# Priority encoder: outputs binary index of highest-set input
-# Inputs: I3, I2, I1, I0 (I3 is highest priority)
-# Outputs: Y1, Y0 (binary encoding)
-
-weights = {}
-
-# Y1: fires when I3 or I2 is set (highest bit is 2 or 3)
-weights['y1.weight'] = torch.tensor([[1.0, 1.0, 0.0, 0.0]], dtype=torch.float32)
-weights['y1.bias'] = torch.tensor([-1.0], dtype=torch.float32)
-
-# Y0: fires when I3 is set, OR when I2 is NOT set but I1 is set
-# This gives output bit 0 for indices 1 and 3
-weights['y0.weight'] = torch.tensor([[3.0, -2.0, 1.0, 0.0]], dtype=torch.float32)
-weights['y0.bias'] = torch.tensor([-1.0], dtype=torch.float32)
-
-save_file(weights, 'model.safetensors')
-
-def encode4to2(i3, i2, i1, i0):
-    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
-    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
-    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
-    return y1, y0
-
-print("Verifying 4to2encoder...")
-errors = 0
-for val in range(16):
-    i3, i2, i1, i0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
-    y1, y0 = encode4to2(i3, i2, i1, i0)
-
-    # Expected: binary of highest set bit position
-    if i3: expected = (1, 1)      # 3
-    elif i2: expected = (1, 0)    # 2
-    elif i1: expected = (0, 1)    # 1
-    else: expected = (0, 0)       # 0 or none
-
-    if (y1, y0) != expected:
-        errors += 1
-        print(f"ERROR: I={i3}{i2}{i1}{i0} -> ({y1},{y0}), expected {expected}")
-
-if errors == 0:
-    print("All 16 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
+
+# Priority encoder: outputs binary index of highest-set input
+# Inputs: I3, I2, I1, I0 (I3 is highest priority)
+# Outputs: Y1, Y0 (binary encoding)
+
+weights = {}
+
+# Y1: fires when I3 or I2 is set (highest bit is 2 or 3)
+weights['y1.weight'] = torch.tensor([[1.0, 1.0, 0.0, 0.0]], dtype=torch.float32)
+weights['y1.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+
+# Y0: fires when I3 is set, OR when I2 is NOT set but I1 is set
+# This gives output bit 0 for indices 1 and 3
+weights['y0.weight'] = torch.tensor([[3.0, -2.0, 1.0, 0.0]], dtype=torch.float32)
+weights['y0.bias'] = torch.tensor([-1.0], dtype=torch.float32)
+
+save_file(weights, 'model.safetensors')
+
+def encode4to2(i3, i2, i1, i0):
+    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
+    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
+    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
+    return y1, y0
+
+print("Verifying 4to2encoder...")
+errors = 0
+for val in range(16):
+    i3, i2, i1, i0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
+    y1, y0 = encode4to2(i3, i2, i1, i0)
+
+    # Expected: binary of highest set bit position
+    if i3: expected = (1, 1)      # 3
+    elif i2: expected = (1, 0)    # 2
+    elif i1: expected = (0, 1)    # 1
+    else: expected = (0, 0)       # 0 or none
+
+    if (y1, y0) != expected:
+        errors += 1
+        print(f"ERROR: I={i3}{i2}{i1}{i0} -> ({y1},{y0}), expected {expected}")
+
+if errors == 0:
+    print("All 16 test cases passed!")
+print(f"Magnitude: {sum(t.abs().sum().item() for t in weights.values()):.0f}")

model.py

@@ -1,20 +1,20 @@
-import torch
-from safetensors.torch import load_file
-
-def load_model(path='model.safetensors'):
-    return load_file(path)
-
-def encode4to2(i3, i2, i1, i0, weights):
-    """Priority encoder: returns binary index of highest-set input."""
-    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
-    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
-    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
-    return y1, y0
-
-if __name__ == '__main__':
-    w = load_model()
-    print('4-to-2 Priority Encoder:')
-    for val in range(16):
-        i3, i2, i1, i0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
-        y1, y0 = encode4to2(i3, i2, i1, i0, w)
-        print(f'  {i3}{i2}{i1}{i0} -> {y1}{y0} (={2*y1+y0})')
+import torch
+from safetensors.torch import load_file
+
+def load_model(path='model.safetensors'):
+    return load_file(path)
+
+def encode4to2(i3, i2, i1, i0, weights):
+    """Priority encoder: returns binary index of highest-set input."""
+    inp = torch.tensor([float(i3), float(i2), float(i1), float(i0)])
+    y1 = int((inp @ weights['y1.weight'].T + weights['y1.bias'] >= 0).item())
+    y0 = int((inp @ weights['y0.weight'].T + weights['y0.bias'] >= 0).item())
+    return y1, y0
+
+if __name__ == '__main__':
+    w = load_model()
+    print('4-to-2 Priority Encoder:')
+    for val in range(16):
+        i3, i2, i1, i0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
+        y1, y0 = encode4to2(i3, i2, i1, i0, w)
+        print(f'  {i3}{i2}{i1}{i0} -> {y1}{y0} (={2*y1+y0})')