feat(BNNBot): Hebbian weight matrix with virtual bullet learning — 870→7 bit forward pass

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-09-17 23:49:59 +02:00
parent 50e3c6fae9
commit 22aefbddb6
3 changed files with 159 additions and 18 deletions
@@ -0,0 +1,51 @@
+import binary_encoding
+import std/math
+
+const
+  N_IN*  = TOTAL_BITS   # 870
+  N_OUT* = OUTPUT_BITS  # 7
+
+  TRACE_MAX_AGE* = 40
+  LEARNING_RATE* = 0.1
+  MISS_PENALTY*  = -0.03
+  HIT_REWARD*    = 0.1
+  W_CLAMP*       = 5.0
+
+type
+  HebbianNet* = object
+    W*: array[N_IN * N_OUT, float]
+
+  EligibilityTrace* = object
+    input*:  BinaryVector
+    output*: OutputVector
+    age*:    int
+    alive*:  bool
+
+  VirtualBullet* = object
+    trace*:       EligibilityTrace
+    fireX*:       float
+    fireY*:       float
+    aimAngleDeg*: float
+    fireDist*:    float
+    bulletSpeed*: float
+    active*:      bool
+
+proc initHebbianNet*(): HebbianNet =
+  discard  # zero-init by default
+
+proc forward*(net: HebbianNet, input: BinaryVector): OutputVector =
+  for j in 0..<N_OUT:
+    var score = 0.0
+    for i in 0..<N_IN:
+      if input[i] == 1:
+        score += net.W[i * N_OUT + j]
+    result[j] = if score > 0.0: 1'u8 else: 0'u8
+
+proc learn*(net: var HebbianNet, trace: EligibilityTrace, reward: float) =
+  let decayedReward = reward * pow(0.95, float(trace.age))
+  for i in 0..<N_IN:
+    if trace.input[i] == 1:
+      for j in 0..<N_OUT:
+        if trace.output[j] == 1:
+          net.W[i * N_OUT + j] += LEARNING_RATE * decayedReward
+          net.W[i * N_OUT + j]  = clamp(net.W[i * N_OUT + j], -W_CLAMP, W_CLAMP)