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README.md

@@ -1,69 +1,69 @@
----
-license: mit
-tags:
-- pytorch
-- safetensors
-- threshold-logic
-- neuromorphic
-- decoder
----
-
-# threshold-4to16decoder
-
-4-to-16 binary decoder. Converts 4-bit binary input to one-hot 16-bit output.
-
-## Function
-
-decode(a3, a2, a1, a0) -> [y0..y15] where yi=1 iff input=i
-
-## One-Hot Encoding
-
-| Input | a3a2a1a0 | Output |
-|------:|:--------:|--------|
-| 0 | 0000 | 1000000000000000 |
-| 1 | 0001 | 0100000000000000 |
-| 5 | 0101 | 0000010000000000 |
-| 10 | 1010 | 0000000000100000 |
-| 15 | 1111 | 0000000000000001 |
-
-## Architecture
-
-Single layer with 16 neurons. Each neuron yi is a pattern matcher for i:
-- Weight +1 for bit positions that should be 1
-- Weight -1 for bit positions that should be 0
-- Bias = -(number of 1 bits in i)
-
-All neurons run in parallel - no dependencies.
-
-## Parameters
-
-| | |
-|---|---|
-| Inputs | 4 |
-| Outputs | 16 |
-| Neurons | 16 |
-| Layers | 1 |
-| Parameters | 80 |
-| Magnitude | 96 |
-
-## Usage
-
-```python
-from safetensors.torch import load_file
-import torch
-
-w = load_file('model.safetensors')
-
-def decode_4to16(a3, a2, a1, a0):
-    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
-    return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0)
-            for i in range(16)]
-
-# Input 10 -> output 10 is hot
-outputs = decode_4to16(1, 0, 1, 0)
-print(outputs)  # [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]
-```
-
-## License
-
-MIT
+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- decoder
+---
+
+# threshold-4to16decoder
+
+4-to-16 binary decoder. Converts 4-bit binary input to one-hot 16-bit output.
+
+## Function
+
+decode(a3, a2, a1, a0) -> [y0..y15] where yi=1 iff input=i
+
+## One-Hot Encoding
+
+| Input | a3a2a1a0 | Output |
+|------:|:--------:|--------|
+| 0 | 0000 | 1000000000000000 |
+| 1 | 0001 | 0100000000000000 |
+| 5 | 0101 | 0000010000000000 |
+| 10 | 1010 | 0000000000100000 |
+| 15 | 1111 | 0000000000000001 |
+
+## Architecture
+
+Single layer with 16 neurons. Each neuron yi is a pattern matcher for i:
+- Weight +1 for bit positions that should be 1
+- Weight -1 for bit positions that should be 0
+- Bias = -(number of 1 bits in i)
+
+All neurons run in parallel - no dependencies.
+
+## Parameters
+
+| | |
+|---|---|
+| Inputs | 4 |
+| Outputs | 16 |
+| Neurons | 16 |
+| Layers | 1 |
+| Parameters | 80 |
+| Magnitude | 96 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+import torch
+
+w = load_file('model.safetensors')
+
+def decode_4to16(a3, a2, a1, a0):
+    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
+    return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0)
+            for i in range(16)]
+
+# Input 10 -> output 10 is hot
+outputs = decode_4to16(1, 0, 1, 0)
+print(outputs)  # [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]
+```
+
+## License
+
+MIT

config.json

@@ -1,9 +1,9 @@
-{
-  "name": "threshold-4to16decoder",
-  "description": "4-to-16 binary decoder as threshold circuit",
-  "inputs": 4,
-  "outputs": 16,
-  "neurons": 16,
-  "layers": 1,
-  "parameters": 80
-}
+{
+  "name": "threshold-4to16decoder",
+  "description": "4-to-16 binary decoder as threshold circuit",
+  "inputs": 4,
+  "outputs": 16,
+  "neurons": 16,
+  "layers": 1,
+  "parameters": 80
+}

create_safetensors.py

@@ -1,46 +1,46 @@
-import torch
-from safetensors.torch import save_file
-
-weights = {}
-
-# Input order: a3, a2, a1, a0 (MSB to LSB)
-# Output: one-hot y0..y15 where yi=1 iff input=i
-
-for i in range(16):
-    # Pattern matcher: +1 for bits that should be 1, -1 for bits that should be 0
-    w = []
-    for bit_pos in range(3, -1, -1):  # a3, a2, a1, a0
-        bit_val = (i >> bit_pos) & 1
-        w.append(1.0 if bit_val else -1.0)
-    # Bias: -(number of 1 bits in i)
-    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')
-
-# Verify
-def decode(a3, a2, a1, a0):
-    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
-    outputs = []
-    for i in range(16):
-        y = int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0)
-        outputs.append(y)
-    return outputs
-
-print("Verifying 4to16decoder...")
-errors = 0
-for val in range(16):
-    a3, a2, a1, a0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
-    result = decode(a3, a2, a1, a0)
-    expected = [1 if i == val else 0 for i in range(16)]
-    if result != expected:
-        errors += 1
-        print(f"ERROR: {val} ({a3}{a2}{a1}{a0}) -> {result}")
-
-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 = {}
+
+# Input order: a3, a2, a1, a0 (MSB to LSB)
+# Output: one-hot y0..y15 where yi=1 iff input=i
+
+for i in range(16):
+    # Pattern matcher: +1 for bits that should be 1, -1 for bits that should be 0
+    w = []
+    for bit_pos in range(3, -1, -1):  # a3, a2, a1, a0
+        bit_val = (i >> bit_pos) & 1
+        w.append(1.0 if bit_val else -1.0)
+    # Bias: -(number of 1 bits in i)
+    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')
+
+# Verify
+def decode(a3, a2, a1, a0):
+    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
+    outputs = []
+    for i in range(16):
+        y = int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0)
+        outputs.append(y)
+    return outputs
+
+print("Verifying 4to16decoder...")
+errors = 0
+for val in range(16):
+    a3, a2, a1, a0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
+    result = decode(a3, a2, a1, a0)
+    expected = [1 if i == val else 0 for i in range(16)]
+    if result != expected:
+        errors += 1
+        print(f"ERROR: {val} ({a3}{a2}{a1}{a0}) -> {result}")
+
+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

@@ -1,22 +1,22 @@
-import torch
-from safetensors.torch import load_file
-
-def load_model(path='model.safetensors'):
-    return load_file(path)
-
-def decode_4to16(a3, a2, a1, a0, weights):
-    """4-to-16 decoder: converts 4-bit binary to one-hot 16-bit output."""
-    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
-    outputs = []
-    for i in range(16):
-        y = int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0)
-        outputs.append(y)
-    return outputs
-
-if __name__ == '__main__':
-    w = load_model()
-    print('4-to-16 Decoder')
-    for val in range(16):
-        a3, a2, a1, a0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
-        outputs = decode_4to16(a3, a2, a1, a0, w)
-        print(f"  {val:2d} ({a3}{a2}{a1}{a0}) -> {''.join(map(str, outputs))}")
+import torch
+from safetensors.torch import load_file
+
+def load_model(path='model.safetensors'):
+    return load_file(path)
+
+def decode_4to16(a3, a2, a1, a0, weights):
+    """4-to-16 decoder: converts 4-bit binary to one-hot 16-bit output."""
+    inp = torch.tensor([float(a3), float(a2), float(a1), float(a0)])
+    outputs = []
+    for i in range(16):
+        y = int((inp * weights[f'y{i}.weight']).sum() + weights[f'y{i}.bias'] >= 0)
+        outputs.append(y)
+    return outputs
+
+if __name__ == '__main__':
+    w = load_model()
+    print('4-to-16 Decoder')
+    for val in range(16):
+        a3, a2, a1, a0 = (val >> 3) & 1, (val >> 2) & 1, (val >> 1) & 1, val & 1
+        outputs = decode_4to16(a3, a2, a1, a0, w)
+        print(f"  {val:2d} ({a3}{a2}{a1}{a0}) -> {''.join(map(str, outputs))}")