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README.md

@@ -1,68 +1,68 @@
----
-license: mit
-tags:
-- pytorch
-- safetensors
-- threshold-logic
-- neuromorphic
----
-
-# threshold-1outof3
-
-At least 1 of 3 inputs high. Equivalent to 3-input OR gate.
-
-## Function
-
-1outof3(a, b, c) = 1 if (a + b + c) >= 1, else 0
-
-Equivalently: OR(a, b, c)
-
-## 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 | 1 |
-| 1 | 0 | 0 | 1 | 1 |
-| 1 | 0 | 1 | 2 | 1 |
-| 1 | 1 | 0 | 2 | 1 |
-| 1 | 1 | 1 | 3 | 1 |
-
-## Architecture
-
-Single neuron: weights [1, 1, 1], bias -1
-
-Fires when: a + b + c - 1 >= 0, i.e., sum >= 1
-
-## Parameters
-
-| | |
-|---|---|
-| Inputs | 3 |
-| Outputs | 1 |
-| Neurons | 1 |
-| Layers | 1 |
-| Parameters | 4 |
-| Magnitude | 4 |
-
-## Usage
-
-```python
-from safetensors.torch import load_file
-import torch
-
-w = load_file('model.safetensors')
-
-def atleast1of3(a, b, c):
-    inp = torch.tensor([float(a), float(b), float(c)])
-    return int((inp @ w['neuron.weight'].T + w['neuron.bias'] >= 0).item())
-
-print(atleast1of3(0, 0, 0))  # 0
-print(atleast1of3(0, 0, 1))  # 1
-```
-
-## License
-
-MIT
+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-1outof3
+
+At least 1 of 3 inputs high. Equivalent to 3-input OR gate.
+
+## Function
+
+1outof3(a, b, c) = 1 if (a + b + c) >= 1, else 0
+
+Equivalently: OR(a, b, c)
+
+## 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 | 1 |
+| 1 | 0 | 0 | 1 | 1 |
+| 1 | 0 | 1 | 2 | 1 |
+| 1 | 1 | 0 | 2 | 1 |
+| 1 | 1 | 1 | 3 | 1 |
+
+## Architecture
+
+Single neuron: weights [1, 1, 1], bias -1
+
+Fires when: a + b + c - 1 >= 0, i.e., sum >= 1
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 3 |
+| Outputs | 1 |
+| Neurons | 1 |
+| Layers | 1 |
+| Parameters | 4 |
+| Magnitude | 4 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def atleast1of3(a, b, c):
+    inp = torch.tensor([float(a), float(b), float(c)])
+    return int((inp @ w['neuron.weight'].T + w['neuron.bias'] >= 0).item())
+
+print(atleast1of3(0, 0, 0))  # 0
+print(atleast1of3(0, 0, 1))  # 1
+```
+
+## License
+
+MIT

config.json

@@ -1,9 +1,9 @@
-{
-  "name": "threshold-1outof3",
-  "description": "At least 1 of 3 inputs high (3-input OR)",
-  "inputs": 3,
-  "outputs": 1,
-  "neurons": 1,
-  "layers": 1,
-  "parameters": 4
-}
+{
+  "name": "threshold-1outof3",
+  "description": "At least 1 of 3 inputs high (3-input OR)",
+  "inputs": 3,
+  "outputs": 1,
+  "neurons": 1,
+  "layers": 1,
+  "parameters": 4
+}

create_safetensors.py

@@ -1,25 +1,25 @@
-import torch
-from safetensors.torch import save_file
-
-weights = {
-    'neuron.weight': torch.tensor([[1.0, 1.0, 1.0]], dtype=torch.float32),
-    'neuron.bias': torch.tensor([-1.0], dtype=torch.float32)
-}
-save_file(weights, 'model.safetensors')
-
-def test(a, b, c):
-    inp = torch.tensor([float(a), float(b), float(c)])
-    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
-
-print("Verifying 1outof3...")
-errors = 0
-for i in range(8):
-    a, b, c = (i >> 2) & 1, (i >> 1) & 1, i & 1
-    result = test(a, b, c)
-    expected = 1 if (a + b + c) >= 1 else 0
-    if result != expected:
-        errors += 1
-        print(f"ERROR: {a}{b}{c} -> {result}, expected {expected}")
-if errors == 0:
-    print("All 8 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 = {
+    'neuron.weight': torch.tensor([[1.0, 1.0, 1.0]], dtype=torch.float32),
+    'neuron.bias': torch.tensor([-1.0], dtype=torch.float32)
+}
+save_file(weights, 'model.safetensors')
+
+def test(a, b, c):
+    inp = torch.tensor([float(a), float(b), float(c)])
+    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
+
+print("Verifying 1outof3...")
+errors = 0
+for i in range(8):
+    a, b, c = (i >> 2) & 1, (i >> 1) & 1, i & 1
+    result = test(a, b, c)
+    expected = 1 if (a + b + c) >= 1 else 0
+    if result != expected:
+        errors += 1
+        print(f"ERROR: {a}{b}{c} -> {result}, expected {expected}")
+if errors == 0:
+    print("All 8 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 atleast1of3(a, b, c, weights):
-    """Returns 1 if at least 1 of 3 inputs is high (OR gate)"""
-    inp = torch.tensor([float(a), float(b), float(c)])
-    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
-
-if __name__ == '__main__':
-    w = load_model()
-    print('1outof3 (OR3) truth table:')
-    for i in range(8):
-        a, b, c = (i >> 2) & 1, (i >> 1) & 1, i & 1
-        print(f'  {a}{b}{c} -> {atleast1of3(a, b, c, w)}')
+import torch
+from safetensors.torch import load_file
+
+def load_model(path='model.safetensors'):
+    return load_file(path)
+
+def atleast1of3(a, b, c, weights):
+    """Returns 1 if at least 1 of 3 inputs is high (OR gate)"""
+    inp = torch.tensor([float(a), float(b), float(c)])
+    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
+
+if __name__ == '__main__':
+    w = load_model()
+    print('1outof3 (OR3) truth table:')
+    for i in range(8):
+        a, b, c = (i >> 2) & 1, (i >> 1) & 1, i & 1
+        print(f'  {a}{b}{c} -> {atleast1of3(a, b, c, w)}')