SirRoboGarage/common_libs/gun_harness/virtual_bullets.nim

## Virtual bullet tracker.
## Spawns virtual bullets per gun×power bin every tick (no real firing).
## Resolves by travel distance. Rolling window fitness per gun×power.
## Calls onResult() on the owning gun when a bullet resolves.

import std/math
import gun_interface

const
  PowerBins*    = [1.0, 1.5, 2.0, 3.0]  ## 4 bins; ponytail: fixed array, add runtime config if needed
  WindowSize*   = 100                     ## rolling window ticks for fitness
  MaxBullets*   = 2048                    ## hard cap; ponytail: ring buffer, resize if more guns added
  MinHitRate*          = 0.40   ## 40% threshold for acceptable power selection
  MinObsBeforeCompete* = 15    ## min observations before a gun×bin enters competition

type
  GunId*   = int  ## index into the guns seq

  VirtualBullet* = object
    gunId*:      GunId
    powerBin*:   int       ## index into PowerBins
    fireX*, fireY*: float
    aimX*, aimY*:   float  ## predicted target (absolute)
    bulletSpeed*:  float
    travelDist*:   float   ## accumulated px so far
    fireDist*:     float   ## distance to target at fire time
    active*:       bool

  FitnessWindow* = object
    ## Ring buffer of hit booleans.
    hits*:  array[WindowSize, bool]
    count*: int  ## total samples so far (capped at WindowSize for rate)
    head*:  int

  GunFitness* = object
    bins*: array[len(PowerBins), FitnessWindow]

  VirtualTracker* = object
    bullets*:  array[MaxBullets, VirtualBullet]
    head*:     int  ## ring buffer head
    fitness*:  seq[GunFitness]  ## indexed by GunId

proc initTracker*(numGuns: int): VirtualTracker =
  result.fitness = newSeq[GunFitness](numGuns)

proc hitRate*(fw: FitnessWindow): float =
  ## Returns fraction of hits in the rolling window. 0.0 when no data.
  if fw.count == 0: return 0.0
  let n = min(fw.count, WindowSize)
  var h = 0
  for i in 0..<n: h += (if fw.hits[i]: 1 else: 0)
  result = h.float / n.float

proc record(fw: var FitnessWindow, hit: bool) =
  fw.hits[fw.head] = hit
  fw.head = (fw.head + 1) mod WindowSize
  inc fw.count

proc spawnBullets*(t: var VirtualTracker, gunId: GunId,
                   predictions: array[len(PowerBins), GunPrediction],
                   state: WorldState) =
  ## Call once per gun per tick with predictions for all power bins.
  for binIdx in 0..<len(PowerBins):
    let power   = PowerBins[binIdx]
    let speed   = bulletSpeed(power)
    let pred    = predictions[binIdx]
    let fireDist = hypot(pred.x - state.selfX, pred.y - state.selfY)
    let slot    = t.head mod MaxBullets
    t.bullets[slot] = VirtualBullet(
      gunId:      gunId,
      powerBin:   binIdx,
      fireX:      state.selfX,
      fireY:      state.selfY,
      aimX:       pred.x,
      aimY:       pred.y,
      bulletSpeed: speed,
      travelDist: 0.0,
      fireDist:   fireDist,
      active:     true,
    )
    t.head = (t.head + 1) mod MaxBullets

proc tickBullets*(t: var VirtualTracker, state: WorldState,
                  onResolved: proc(gunId: GunId, binIdx: int, e: FeedbackEvent)) =
  ## Advance all active bullets one tick. Resolve when bullet reaches target distance.
  for i in 0..<MaxBullets:
    var b = addr t.bullets[i]
    if not b.active: continue
    b.travelDist += b.bulletSpeed
    if b.travelDist < b.fireDist: continue

    # Resolved: compute miss distance against current enemy position
    # Direction from fire point to aim point
    let dx    = b.aimX - b.fireX
    let dy    = b.aimY - b.fireY
    let dist  = hypot(dx, dy)
    let (bx, by) =
      if dist < 1e-6: (b.aimX, b.aimY)
      else: (b.fireX + dx / dist * b.travelDist,
             b.fireY + dy / dist * b.travelDist)
    let missDist = hypot(bx - state.enemyX, by - state.enemyY)
    let hit = missDist < BotRadius

    t.fitness[b.gunId].bins[b.powerBin].record(hit)

    let fe = FeedbackEvent(
      prediction:  GunPrediction(x: b.aimX, y: b.aimY),
      actualX:     state.enemyX,
      actualY:     state.enemyY,
      bulletPower: PowerBins[b.powerBin],
      missDistance: missDist,
      hit:         hit,
    )
    onResolved(b.gunId, b.powerBin, fe)
    b.active = false

proc bestPower*(t: VirtualTracker, gunId: GunId): (int, float) =
  ## Returns (binIdx, power) with highest power that has >= MinHitRate.
  ## Falls back to lowest power bin if nothing qualifies yet.
  result = (0, PowerBins[0])
  for binIdx in countdown(len(PowerBins) - 1, 0):
    let rate = t.fitness[gunId].bins[binIdx].hitRate()
    if rate >= MinHitRate or t.fitness[gunId].bins[binIdx].count == 0:
      return (binIdx, PowerBins[binIdx])

proc bestGun*(t: VirtualTracker): GunId =
  ## Pick gun with highest hit rate across all power bins.
  ## Guns with fewer than MinObsBeforeCompete observations across all bins are
  ## skipped unless every gun is below threshold (then fall back to best of all).
  ## ponytail: O(n*bins), fine for small gun counts
  proc bestAmong(t: VirtualTracker, requireMin: bool): GunId =
    var bestRate = -1.0
    result = 0
    for gunId in 0..<t.fitness.len:
      var maxCount = 0
      for binIdx in 0..<len(PowerBins):
        maxCount = max(maxCount, t.fitness[gunId].bins[binIdx].count)
      if requireMin and maxCount < MinObsBeforeCompete: continue
      for binIdx in 0..<len(PowerBins):
        let r = t.fitness[gunId].bins[binIdx].hitRate()
        if r > bestRate:
          bestRate = r
          result = gunId

  # Try gated selection first; fall back to ungated if nothing qualifies
  var anyQualifies = false
  for gunId in 0..<t.fitness.len:
    for binIdx in 0..<len(PowerBins):
      if t.fitness[gunId].bins[binIdx].count >= MinObsBeforeCompete:
        anyQualifies = true
        break
    if anyQualifies: break

  result = if anyQualifies: t.bestAmong(true) else: t.bestAmong(false)