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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+- error-correction
+- bch
+- hamming
+---
+
+# threshold-bch-encoder
+
+BCH(7,4) encoder (Hamming code). Encodes 4 data bits to 7-bit codeword. Can correct 1 error.
+
+## Encoding
+
+```
+D3 D2 D1 D0 → C6 C5 C4 C3 C2 C1 C0
+              ─────────── ────────
+              data bits   parity
+```
+
+## Parameters
+
+| | |
+|---|---|
+| Data bits | 4 |
+| Codeword | 7 |
+| Correction | 1 bit |
+| Parameters | 65 |
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-bch-encoder",
+  "description": "BCH(7,4) / Hamming encoder",
+  "inputs": 4,
+  "outputs": 7,
+  "neurons": 13,
+  "layers": 2,
+  "parameters": 65
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+weights = {}
+
+# BCH(7,4) Encoder - encodes 4 data bits to 7-bit codeword
+# Hamming(7,4) equivalent - can correct 1 error
+
+def add_neuron(name, w_list, bias):
+    weights[f'{name}.weight'] = torch.tensor([w_list], dtype=torch.float32)
+    weights[f'{name}.bias'] = torch.tensor([bias], dtype=torch.float32)
+
+# Input: D3=0, D2=1, D1=2, D0=3
+# Output: C6-C0 where C6-C3 = D3-D0, C2-C0 = parity
+
+# Data bits pass through (threshold >= 1)
+add_neuron('c6', [1.0, 0.0, 0.0, 0.0], -1.0)  # D3
+add_neuron('c5', [0.0, 1.0, 0.0, 0.0], -1.0)  # D2
+add_neuron('c4', [0.0, 0.0, 1.0, 0.0], -1.0)  # D1
+add_neuron('c3', [0.0, 0.0, 0.0, 1.0], -1.0)  # D0
+
+# Parity bits using odd parity (sum mod 2)
+# For 3-input XOR: output 1 if 1 or 3 inputs are 1
+# C2 = D3 XOR D2 XOR D0: fires on 1 or 3 of {D3,D2,D0}
+add_neuron('c2_atleast1', [1.0, 1.0, 0.0, 1.0], -1.0)  # >= 1
+add_neuron('c2_atleast2', [1.0, 1.0, 0.0, 1.0], -2.0)  # >= 2
+add_neuron('c2_atleast3', [1.0, 1.0, 0.0, 1.0], -3.0)  # >= 3
+
+# C1 = D3 XOR D1 XOR D0
+add_neuron('c1_atleast1', [1.0, 0.0, 1.0, 1.0], -1.0)
+add_neuron('c1_atleast2', [1.0, 0.0, 1.0, 1.0], -2.0)
+add_neuron('c1_atleast3', [1.0, 0.0, 1.0, 1.0], -3.0)
+
+# C0 = D2 XOR D1 XOR D0
+add_neuron('c0_atleast1', [0.0, 1.0, 1.0, 1.0], -1.0)
+add_neuron('c0_atleast2', [0.0, 1.0, 1.0, 1.0], -2.0)
+add_neuron('c0_atleast3', [0.0, 1.0, 1.0, 1.0], -3.0)
+
+save_file(weights, 'model.safetensors')
+
+def xor3(a, b, c):
+    return a ^ b ^ c
+
+def bch_encode(d3, d2, d1, d0):
+    c6, c5, c4, c3 = d3, d2, d1, d0
+    c2 = xor3(d3, d2, d0)
+    c1 = xor3(d3, d1, d0)
+    c0 = xor3(d2, d1, d0)
+    return c6, c5, c4, c3, c2, c1, c0
+
+print("Verifying BCH(7,4) encoder...")
+errors = 0
+for d in range(16):
+    d3, d2, d1, d0 = (d>>3)&1, (d>>2)&1, (d>>1)&1, d&1
+    c = bch_encode(d3, d2, d1, d0)
+
+    # Verify data preservation
+    if (c[0], c[1], c[2], c[3]) != (d3, d2, d1, d0):
+        errors += 1
+        print(f"Data error: d={d:04b}")
+
+    # Verify parity
+    if c[4] != xor3(d3, d2, d0):
+        errors += 1
+    if c[5] != xor3(d3, d1, d0):
+        errors += 1
+    if c[6] != xor3(d2, d1, d0):
+        errors += 1
+
+if errors == 0:
+    print("All 16 test cases passed!")
+else:
+    print(f"FAILED: {errors} errors")
+
+mag = sum(t.abs().sum().item() for t in weights.values())
+print(f"Magnitude: {mag:.0f}")
+print(f"Parameters: {sum(t.numel() for t in weights.values())}")

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2a4ff3dc79dcbd9dc96dc2e635a0bb62d44711392719a9305d0e616d43ff895
+size 2100