Auto-Tuner 해부 ③: Grid Search 엔진 — Impact, Dampening, Silence 최적화

for 후보값 in 탐색범위:
    분리도 = 시뮬레이션(현재파라미터를 후보값으로 교체)
    if 분리도 > 최고기록:
        최고기록 = 분리도
        최적값 = 후보값
추천 = 최적값

GRID_RANGE_RATIO_BY_PHASE = {
  1 => BASE_GRID_RANGE_RATIO,      # 0.20
  2 => BASE_GRID_RANGE_RATIO,      # 0.20
  3 => 0.30,
  4 => 0.40,
  5 => 0.50
}.freeze

현재값 = 5.0, 범위 = ±30% → [3.5, 3.8, 4.1, 4.4, 4.7, 5.0, 5.3, 5.6, 5.9, 6.2, 6.5]

def optimize_impact(impact_type, current_value)
  range = current_value * grid_range_ratio  # Phase별
  candidates = (0..GRID_POINTS).map { |i|
    low + (high - low) * i / GRID_POINTS
  }

  best_val = current_value
  best_sep = current_separation[:separation]

  candidates.each do |candidate|
    override = { impact_type.id => candidate }
    sep = simulate_separation(impact_override: override)
    if sep > best_sep
      best_sep = sep
      best_val = candidate
    end
  end

  { current: current_value, recommended: best_val,
    improvement: best_sep - current_separation[:separation] }
end

action: improvement >= MIN_SEPARATION_IMPROVEMENT ? 'adjust' : 'keep'

def compound_with_dampening(scores, dampening)
  return 0.0 if scores.empty?
  sorted = scores.sort.reverse
  # 가장 강한 시그널은 100% 반영
  # 나머지는 dampening 비율만큼만 반영
  sorted[0] + sorted[1..].sum * dampening
end

Compound = 5.0 + (1.0 + 0.7) × 0.25
         = 5.0 + 0.425
         = 5.425

def optimize_dampening
  candidates = (0..GRID_POINTS).map { |i| i.to_f / GRID_POINTS }  # [0.0, 0.09, ..., 1.0]

  best_dampening = BayesianUpdate::SIGNAL_DAMPENING
  best_sep = current_separation[:separation]

  candidates.each do |d|
    sep = simulate_separation(dampening_override: d)
    if sep > best_sep
      best_sep = sep
      best_dampening = d
    end
  end

  { current: BayesianUpdate::SIGNAL_DAMPENING,
    recommended: best_dampening,
    improvement: best_sep - current_separation[:separation] }
end

unit_penalty = 0.4 × 0.3 = 0.12

β += 0.12 × 3 = 0.36

[0.0, 0.09, 0.18, ..., 0.91, 1.0]

simulate_separation(impact_override, dampening_override, silence_ratio_override)
  ├─ for 각 Won 프로젝트:
  │    └─ p_win = simulate_project(project, overrides)
  ├─ for 각 Lost 프로젝트:
  │    └─ p_win = simulate_project(project, overrides)
  │
  ├─ won_avg = mean(Won p_win들)
  ├─ lost_avg = mean(Lost p_win들)
  └─ return won_avg - lost_avg

def simulate_project(project, impact_override, dampening, silence_ratio)
  alpha = project.prior_alpha.to_f    # ── ① Prior 초기값
  beta  = project.prior_beta.to_f

  activities.each do |activity|        # ── ② 시간순 활동 순회
    swv = stage.swv.to_f              # ── ③ 스테이지 가중치

    # ── ④ Compound Score 계산
    positive_compound, negative_compound =
      simulate_compound_scores_fast(signal_ids, impacts, impact_override, dampening)

    # ── ⑤ 침묵 패널티
    if elapsed >= gap
      count = ((elapsed - gap) / interval) + 1
      beta += unit_penalty * count
    end

    # ── ⑥ α, β 업데이트
    alpha += swv * positive_compound
    beta  += swv * negative_compound
  end

  alpha / (alpha + beta)              # ── ⑦ P(Win) 반환
end

ImpactType 수 × GRID_POINTS × 프로젝트 수 × 활동 수
= 9 × 11 × 100 × 20 = 198,000 연산