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README.md

@@ -1,63 +1,63 @@
----
-license: mit
-tags:
-- pytorch
-- safetensors
-- threshold-logic
-- neuromorphic
----
-
-# threshold-3outof4
-
-At least 3 of 4 inputs high.
-
-## Function
-
-3outof4(a, b, c, d) = 1 if (a + b + c + d) >= 3, else 0
-
-## Truth Table
-
-| a | b | c | d | sum | out |
-|---|---|---|---|-----|-----|
-| 0 | 0 | 0 | 0 | 0 | 0 |
-| 0 | 0 | 0 | 1 | 1 | 0 |
-| 0 | 0 | 1 | 1 | 2 | 0 |
-| 0 | 1 | 1 | 1 | 3 | 1 |
-| 1 | 1 | 1 | 1 | 4 | 1 |
-
-## Architecture
-
-Single neuron: weights [1, 1, 1, 1], bias -3
-
-Fires when: a + b + c + d - 3 >= 0, i.e., sum >= 3
-
-## Parameters
-
-| | |
-|---|---|
-| Inputs | 4 |
-| Outputs | 1 |
-| Neurons | 1 |
-| Layers | 1 |
-| Parameters | 5 |
-| Magnitude | 7 |
-
-## Usage
-
-```python
-from safetensors.torch import load_file
-import torch
-
-w = load_file('model.safetensors')
-
-def atleast3of4(a, b, c, d):
-    inp = torch.tensor([float(a), float(b), float(c), float(d)])
-    return int((inp @ w['neuron.weight'].T + w['neuron.bias'] >= 0).item())
-
-print(atleast3of4(0, 0, 1, 1))  # 0 (sum=2)
-print(atleast3of4(0, 1, 1, 1))  # 1 (sum=3)
-```
-
-## License
-
-MIT
+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-3outof4
+
+At least 3 of 4 inputs high.
+
+## Function
+
+3outof4(a, b, c, d) = 1 if (a + b + c + d) >= 3, else 0
+
+## Truth Table
+
+| a | b | c | d | sum | out |
+|---|---|---|---|-----|-----|
+| 0 | 0 | 0 | 0 | 0 | 0 |
+| 0 | 0 | 0 | 1 | 1 | 0 |
+| 0 | 0 | 1 | 1 | 2 | 0 |
+| 0 | 1 | 1 | 1 | 3 | 1 |
+| 1 | 1 | 1 | 1 | 4 | 1 |
+
+## Architecture
+
+Single neuron: weights [1, 1, 1, 1], bias -3
+
+Fires when: a + b + c + d - 3 >= 0, i.e., sum >= 3
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 4 |
+| Outputs | 1 |
+| Neurons | 1 |
+| Layers | 1 |
+| Parameters | 5 |
+| Magnitude | 7 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def atleast3of4(a, b, c, d):
+    inp = torch.tensor([float(a), float(b), float(c), float(d)])
+    return int((inp @ w['neuron.weight'].T + w['neuron.bias'] >= 0).item())
+
+print(atleast3of4(0, 0, 1, 1))  # 0 (sum=2)
+print(atleast3of4(0, 1, 1, 1))  # 1 (sum=3)
+```
+
+## License
+
+MIT

config.json

@@ -1,9 +1,9 @@
-{
-  "name": "threshold-3outof4",
-  "description": "At least 3 of 4 inputs high",
-  "inputs": 4,
-  "outputs": 1,
-  "neurons": 1,
-  "layers": 1,
-  "parameters": 5
-}
+{
+  "name": "threshold-3outof4",
+  "description": "At least 3 of 4 inputs high",
+  "inputs": 4,
+  "outputs": 1,
+  "neurons": 1,
+  "layers": 1,
+  "parameters": 5
+}

create_safetensors.py

@@ -1,26 +1,26 @@
-import torch
-from safetensors.torch import save_file
-
-weights = {
-    'neuron.weight': torch.tensor([[1.0, 1.0, 1.0, 1.0]], dtype=torch.float32),
-    'neuron.bias': torch.tensor([-3.0], dtype=torch.float32)
-}
-
-save_file(weights, 'model.safetensors')
-
-def atleast3of4(a, b, c, d):
-    inp = torch.tensor([float(a), float(b), float(c), float(d)])
-    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
-
-print("Verifying 3outof4...")
-errors = 0
-for i in range(16):
-    a, b, c, d = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
-    result = atleast3of4(a, b, c, d)
-    expected = 1 if (a + b + c + d) >= 3 else 0
-    if result != expected:
-        errors += 1
-if errors == 0:
-    print("All 16 test cases passed!")
-mag = sum(t.abs().sum().item() for t in weights.values())
-print(f"Magnitude: {mag:.0f}")
+import torch
+from safetensors.torch import save_file
+
+weights = {
+    'neuron.weight': torch.tensor([[1.0, 1.0, 1.0, 1.0]], dtype=torch.float32),
+    'neuron.bias': torch.tensor([-3.0], dtype=torch.float32)
+}
+
+save_file(weights, 'model.safetensors')
+
+def atleast3of4(a, b, c, d):
+    inp = torch.tensor([float(a), float(b), float(c), float(d)])
+    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
+
+print("Verifying 3outof4...")
+errors = 0
+for i in range(16):
+    a, b, c, d = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
+    result = atleast3of4(a, b, c, d)
+    expected = 1 if (a + b + c + d) >= 3 else 0
+    if result != expected:
+        errors += 1
+if errors == 0:
+    print("All 16 test cases passed!")
+mag = sum(t.abs().sum().item() for t in weights.values())
+print(f"Magnitude: {mag:.0f}")

model.py

@@ -1,16 +1,16 @@
-import torch
-from safetensors.torch import load_file
-
-def load_model(path='model.safetensors'):
-    return load_file(path)
-
-def atleast3of4(a, b, c, d, weights):
-    inp = torch.tensor([float(a), float(b), float(c), float(d)])
-    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
-
-if __name__ == '__main__':
-    w = load_model()
-    print('3outof4 truth table:')
-    for i in range(16):
-        a, b, c, d = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
-        print(f'  {a}{b}{c}{d} -> {atleast3of4(a, b, c, d, w)}')
+import torch
+from safetensors.torch import load_file
+
+def load_model(path='model.safetensors'):
+    return load_file(path)
+
+def atleast3of4(a, b, c, d, weights):
+    inp = torch.tensor([float(a), float(b), float(c), float(d)])
+    return int((inp @ weights['neuron.weight'].T + weights['neuron.bias'] >= 0).item())
+
+if __name__ == '__main__':
+    w = load_model()
+    print('3outof4 truth table:')
+    for i in range(16):
+        a, b, c, d = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1
+        print(f'  {a}{b}{c}{d} -> {atleast3of4(a, b, c, d, w)}')