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feat(SNNBot): 1° bin resolution (360 bins) + neighbor reinforcement

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5° bins caused visible jumping. 360 bins at 1° costs 5.7KB.
Reinforce ±2 neighbors per learn call for faster coverage.
Punish only bins >2° away from correct target.
This commit is contained in:
Davide Cappellini
2026-09-14 23:19:25 +02:00
parent e9ddc7673d
commit fc00e448a1
1 changed files with 12 additions and 10 deletions
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SNNBot_garage/src/reservoir.nim
+12 -10
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@@ -4,15 +4,15 @@ import std/[bitops, math]
const const
INPUT_BITS* = 80 INPUT_BITS* = 80
N_BINS* = 72 N_BINS* = 360
BIN_WIDTH* = 5.0 BIN_WIDTH* = 1.0
WORDS_IN* = 2 # 80 bits → 2 × uint64 (128 bits, only 80 used) WORDS_IN* = 2 # 80 bits → 2 × uint64 (128 bits, only 80 used)
type type
BitVec80* = array[WORDS_IN, uint64] BitVec80* = array[WORDS_IN, uint64]
BinaryAimer* = object BinaryAimer* = object
readout*: array[N_BINS, BitVec80] # 72 bins × 80-bit weight vectors readout*: array[N_BINS, BitVec80] # 360 bins × 80-bit weight vectors
scores*: array[N_BINS, int] scores*: array[N_BINS, int]
proc initAimer*(): BinaryAimer = proc initAimer*(): BinaryAimer =
@@ -59,22 +59,24 @@ proc interpolatedAngle*(a: BinaryAimer, winnerBin: int): float =
result = radToDeg(arctan2(sinAvg, cosAvg)) result = radToDeg(arctan2(sinAvg, cosAvg))
proc learn*(a: var BinaryAimer, input: BitVec80, correctAngle: float) = proc learn*(a: var BinaryAimer, input: BitVec80, correctAngle: float) =
## WTA Hebbian: reinforce correct bin, punish worst wrong bin ## WTA Hebbian: reinforce correct bin + ±2 neighbors, punish worst wrong bin (>2° away)
let correctBin = angleToBin(correctAngle) let correctBin = angleToBin(correctAngle)
# Reinforce: OR input into correct bin # Reinforce correct bin + 2 neighbors on each side
for w in 0 ..< WORDS_IN: for offset in -2 .. 2:
a.readout[correctBin][w] = a.readout[correctBin][w] or input[w] let bin = (correctBin + offset + N_BINS) mod N_BINS
for w in 0 ..< WORDS_IN:
a.readout[bin][w] = a.readout[bin][w] or input[w]
# Find highest-scoring WRONG bin # Punish highest-scoring wrong bin (must be >2 bins away from correct)
var worstBin = -1 var worstBin = -1
var worstScore = -1 var worstScore = -1
for k in 0 ..< N_BINS: for k in 0 ..< N_BINS:
if k != correctBin and a.scores[k] > worstScore: let dist = min(abs(k - correctBin), N_BINS - abs(k - correctBin)) # circular distance
if dist > 2 and a.scores[k] > worstScore:
worstScore = a.scores[k] worstScore = a.scores[k]
worstBin = k worstBin = k
# Punish: AND NOT input from worst wrong bin
if worstBin >= 0: if worstBin >= 0:
for w in 0 ..< WORDS_IN: for w in 0 ..< WORDS_IN:
a.readout[worstBin][w] = a.readout[worstBin][w] and (not input[w]) a.readout[worstBin][w] = a.readout[worstBin][w] and (not input[w])