first commit
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"""
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Olympus 回合生成器核心 — 正向生成
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流程: 生成 cascade → 自然累计 → 总分落桶
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核心规则（与 olympus/engine.py 对齐）:
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1. 一旋转内多次消除，中间步只记基础分，末帧统一引爆炸弹
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2. 炸弹倍数跨旋转累计（accumulator 贯穿整个回合：基础旋转 + 免费旋转）
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3. 每步 accumulated_symbols 记录本旋转累计消除符号
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4. 落桶用理论总分（最高 1370），发放封顶 1000（末步截断 + win_capped）
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"""
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import json
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import random
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from collections import Counter
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from olympus.config import (
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SYMBOL_RULES, SHORT_TO_SYMBOL, SCATTER_SHORT, BOMB_SHORT,
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SYMBOL_WEIGHTS, BOMB_CELL_PROBABILITY, BOMB_VALUES, BOMB_WEIGHT_POWER,
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GRID_ROWS, GRID_COLS, GRID_SIZE, MIN_WIN_COUNT,
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SCATTER_PAYOUTS, SCATTER_FREE_SPIN_TRIGGER, SCATTER_RETRIGGER_TRIGGER,
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FREE_SPINS_AWARDED, FREE_SPIN_RETRIGGER_COUNT, MAX_FREE_SPINS,
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BASE_FS_TRIGGER_PROB, BASE_NEARMISS_SCATTER, FS_TRIGGER_SCATTER, FREESPIN_SCATTER,
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FS_SPIN_WIN_PROB, FS_CASCADE_CONTINUE_PROB, FS_CASCADE_MAX, FS_GUARANTEE_COMBOS,
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FS_GUARANTEE_COST, FS_DRY_FORCE_PROB, FS_DRY_FORCE_HARD,
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FS_SMALL_BOMB_MAX, FS_BIG_BOMB_TARGET, FS_SCREEN_BOMB_PROB, FS_MAX_BOMBS_PER_SPIN,
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FS_ONLY_TARGET_MUL,
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ORNAMENTS, MAX_WIN_MULTIPLIER, bomb_quality, bigwin_level,
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)
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REGULAR_SHORTS = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
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MAX_WIN = float(MAX_WIN_MULTIPLIER) # 发放封顶 1000
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BUCKET_CEIL = max(o['range_high'] for o in ORNAMENTS) # 落桶理论上限 1370
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# 保底消除预算（1 倍底注口径）：最小档 0.25，连消后期望 1/(1-p) 波
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FS_FLOOR_UNIT = min(c[2] for c in FS_GUARANTEE_COMBOS)
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FS_FLOOR_SPEND = FS_FLOOR_UNIT / (1.0 - FS_CASCADE_CONTINUE_PROB) # ≈0.45/旋
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FS_FLOOR_BUDGET_CAP = FREE_SPINS_AWARDED * FS_FLOOR_SPEND # ≈6.8/局
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# ====================================================================
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# 工具
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# ====================================================================
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def _payout(short, cnt):
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return float(SYMBOL_RULES[SHORT_TO_SYMBOL[short]].payout_for(cnt))
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def _weighted_sym(exclude=()):
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syms = [s for s in REGULAR_SHORTS if s not in exclude]
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wts = [SYMBOL_WEIGHTS[s] for s in syms]
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r = random.uniform(0, sum(wts))
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a = 0.0
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for s, w in zip(syms, wts):
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a += w
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if r <= a:
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return s
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return syms[-1]
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def _weighted_choice(dist):
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r = random.random()
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cum, last = 0.0, None
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for k, p in dist.items():
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last = k
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cum += p
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if r <= cum:
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return k
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return last
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def _bucket(score):
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"""理论总分落桶（score 可到 1370+，超出归顶桶）"""
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if score <= 0:
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return 1
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if score > BUCKET_CEIL:
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return ORNAMENTS[-1]['id']
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for o in ORNAMENTS:
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if o['range_low'] <= score <= o['range_high']:
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return o['id']
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for o in ORNAMENTS[1:]:
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if score <= o['range_high']:
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return o['id']
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return ORNAMENTS[-1]['id']
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def _gen_bomb_value(max_val=None):
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opts = [v for v in BOMB_VALUES if max_val is None or v <= max_val]
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wts = [1.0 / (v ** BOMB_WEIGHT_POWER) for v in opts]
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r = random.uniform(0, sum(wts))
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a = 0.0
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for v, w in zip(opts, wts):
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a += w
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if r <= a:
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return v
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return opts[-1]
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def _bomb_combos(target):
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"""列出 ≤3 颗炸弹、和最接近 target 的所有组合：(误差, 组合)，按误差升序"""
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out = []
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vals = BOMB_VALUES
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for a in vals:
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out.append((abs(a - target), [a]))
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if a > target * 1.5:
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break
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for a in vals:
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if a > target:
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break
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for b in vals:
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s = a + b
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if s > target * 1.5:
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break
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out.append((abs(s - target), [a, b]))
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for a in vals:
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if a > target:
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break
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for b in vals:
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if a + b > target:
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break
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for c in vals:
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s = a + b + c
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if s > target * 1.5:
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break
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out.append((abs(s - target), [a, b, c]))
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out.sort(key=lambda x: (x[0], len(x[1])))
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return out
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def _best_bombs(target):
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"""确定性最优（只用于评估方案误差）"""
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combos = _bomb_combos(target)
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return combos[0][1] if combos else [2]
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def _decompose_best(target):
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"""在"接近最优"的组合里随机挑一个，并强烈偏向单颗 —— 一屏一颗，
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玩家才看得出"消一次爆一颗、倍数一颗一颗涨"；改前每次都返回同一套
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（比如某些目标永远是 [3] 或 [3,6]），看起来"消除必定带 3 和 6 倍炸弹"。"""
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combos = _bomb_combos(target)
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if not combos:
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return [2]
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best_err = combos[0][0]
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pool = [c for e, c in combos if e <= best_err + max(0.5, best_err * 0.15)]
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weights = [1.0 / (len(c) ** 3) for c in pool] # 1 颗 : 2 颗 : 3 颗 = 1 : 1/8 : 1/27
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return random.choices(pool, weights=weights)[0]
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# ====================================================================
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# 网格
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# ====================================================================
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def _empty_grid():
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return [[''] * GRID_COLS for _ in range(GRID_ROWS)]
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def _count(grid):
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c = Counter()
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for row in grid:
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for cell in row:
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if cell:
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c[cell] += 1
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return c
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def _winners(grid):
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w = {}
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for sym, cnt in _count(grid).items():
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if sym in (BOMB_SHORT, SCATTER_SHORT, ''):
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continue
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if cnt >= MIN_WIN_COUNT:
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pay = _payout(sym, cnt)
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if pay > 0:
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w[sym] = (cnt, pay)
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return w
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def _no_extra(grid, allowed):
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"""打散计划外的 ≥8 同符号组"""
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backup = [s for s in REGULAR_SHORTS if s not in allowed] or ['I']
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for _ in range(50):
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dirty = False
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for sym, cnt in _count(grid).items():
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if sym in (BOMB_SHORT, SCATTER_SHORT, '') or sym in allowed:
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continue
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if cnt >= MIN_WIN_COUNT:
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excess = cnt - (MIN_WIN_COUNT - 1)
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for r in range(GRID_ROWS):
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for c in range(GRID_COLS):
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if grid[r][c] == sym and excess > 0:
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grid[r][c] = random.choice(backup)
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excess -= 1
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dirty = True
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if not dirty:
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break
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def _fill(grid, mul_map, allow_bomb, track=False, exclude_syms=(),
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bomb_prob=BOMB_CELL_PROBABILITY, bomb_max=None, max_bombs=None):
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"""填充空格。allow_bomb 时按 bomb_prob 概率放炸弹（bomb_max 限制炸弹值、max_bombs 限制每屏颗数）。
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track 返回填充数据（仅普通符号）"""
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filled = [] if track else None
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placed = len(mul_map)
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for r in range(GRID_ROWS):
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for c in range(GRID_COLS):
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if grid[r][c] == '':
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pos = r * GRID_COLS + c
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if allow_bomb and random.random() < bomb_prob \
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and (max_bombs is None or placed < max_bombs):
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grid[r][c] = BOMB_SHORT
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mul_map[pos] = _gen_bomb_value(bomb_max)
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placed += 1
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else:
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sym = _weighted_sym(exclude_syms)
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grid[r][c] = sym
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if track:
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filled.append({"pos": pos + 1, "symbol": sym})
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return filled
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def _collapse(grid, remove, mul_map):
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"""消除指定位置(0-based)，重力下落。炸弹/散布随位置移动，mul_map 同步重映射"""
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rs = set(remove)
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drops = []
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ng = _empty_grid()
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pos_remap = {}
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for c in range(GRID_COLS):
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surviving = []
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for r in range(GRID_ROWS - 1, -1, -1):
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p = r * GRID_COLS + c
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if p not in rs:
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surviving.append((p, grid[r][c]))
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for i, (old_pos, sym) in enumerate(surviving):
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new_r = GRID_ROWS - 1 - i
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new_pos = new_r * GRID_COLS + c
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ng[new_r][c] = sym
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pos_remap[old_pos] = new_pos
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if new_pos != old_pos:
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drops.append({"from": old_pos + 1, "to": new_pos + 1, "symbol": sym})
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new_mul = {pos_remap[p]: v for p, v in mul_map.items() if p in pos_remap}
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return ng, drops, new_mul
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def _place_scatter(grid, count, protect=()):
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cand = [(r, c) for r in range(GRID_ROWS) for c in range(GRID_COLS)
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if grid[r][c] not in ({SCATTER_SHORT, BOMB_SHORT} | set(protect))]
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for r, c in random.sample(cand, min(count, len(cand))):
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grid[r][c] = SCATTER_SHORT
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def _count_scatter(grid):
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return sum(1 for row in grid for cell in row if cell == SCATTER_SHORT)
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def _force_small_win(grid, prefer=()):
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"""就地补一组最小档消除（默认 8 连低价值符号），不覆盖已有 S/M 格。
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prefer = 这一波"刚落下 / 刚补位"的格子（0 基）。保底消除优先种在这些格子上：
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它们本来就有 drop/fill 记录，前端按动画数据落地就能与 grid_state 完全一致；
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种在**没移动过**的格子上则没有任何动画记录，前端只能靠 grid_state 兜底。"""
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sym, cnt, _ = random.choice(FS_GUARANTEE_COMBOS)
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free = [p for p in range(GRID_SIZE)
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if grid[p // GRID_COLS][p % GRID_COLS] not in (SCATTER_SHORT, BOMB_SHORT)]
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if len(free) < cnt:
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return
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fresh = [p for p in prefer if p in set(free)]
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if len(fresh) >= cnt:
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picks = random.sample(fresh, cnt)
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else:
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rest = [p for p in free if p not in set(fresh)]
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picks = fresh + random.sample(rest, cnt - len(fresh))
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for p in picks:
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grid[p // GRID_COLS][p % GRID_COLS] = sym
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_no_extra(grid, {sym})
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def _scatter_payout(count):
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if count >= SCATTER_FREE_SPIN_TRIGGER:
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return float(SCATTER_PAYOUTS.get(min(count, 6), 0))
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return 0.0
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# ====================================================================
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# 强制中奖（bias 补桶用）
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# ====================================================================
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# (基础分, 组合) 按基础分降序
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_BASE_OPTIONS = [
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(60.0, [('A', 12), ('B', 10)]),
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(50.0, [('A', 12)]),
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(35.0, [('A', 10), ('B', 10)]),
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(25.0, [('A', 10)]),
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(12.5, [('A', 8), ('B', 8)]),
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(10.0, [('A', 8)]),
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(5.0, [('C', 10)]),
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(3.5, [('C', 8), ('D', 8)]),
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(2.5, [('B', 8)]),
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(2.0, [('C', 8)]),
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(1.5, [('D', 8)]),
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(1.0, [('E', 8)]),
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(0.8, [('F', 8)]),
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(0.5, [('G', 8)]),
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(0.4, [('H', 8)]),
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(0.25, [('I', 8)]),
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]
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def _plan_for(target):
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"""全局搜索 (基础组合, 炸弹列表)，使 base × max(1, Σ炸弹) 最接近 target。
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在"误差在最优 +5 个百分点以内"的若干方案里随机挑一个 —— 改前永远返回同一套最优解，
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所以同一档位的回合盘面/炸弹老是那几个（"某个符号消除必定带着 3 和 6 倍炸弹"）。"""
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if target <= 0:
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return [], []
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plans = []
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for base, combo in _BASE_OPTIONS:
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plans.append((abs(base - target) / target, combo, None)) # 方案A：无炸弹
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m = target / base
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if m >= 2: # 方案B：配炸弹
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achieved = base * sum(_best_bombs(m))
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plans.append((abs(achieved - target) / target, combo, m))
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plans.sort(key=lambda p: p[0])
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best_err = plans[0][0]
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pool = [p for p in plans if p[0] <= best_err + 0.05]
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_, combo, m = random.choice(pool)
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return combo, ([] if m is None else _decompose_best(m))
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def _plan_base_only(target):
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"""只挑基础组合（不植炸弹）：在接近最优的组合里随机。acc 已经提供倍数时用这个。"""
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if target <= 0:
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return [('I', 8)]
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cands = sorted((abs(base - target) / target, combo) for base, combo in _BASE_OPTIONS)
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best = cands[0][0]
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pool = [c for e, c in cands if e <= best + 0.05]
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return random.choice(pool)
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def _force_win(grid, target, mul_map, acc=0.0, allow_bomb=True):
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"""把 target 当"本旋最终赢额"来造奖：先按当前累计倍数 acc 折算需要的基础分。
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买单旋赢额 = 基础分 × max(1, acc + 本旋炸弹)，所以：
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· acc 已经 >1 时只挑基础分、**不再植炸弹**（否则 acc 和炸弹的倍数会被算两次，
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实测把 10 倍目标打成 40~50 倍）；
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· 该模式不允许炸弹（普通旋转/底注）时同理只挑基础分；
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· 其余情况（buy_fs 且 acc=0）按原逻辑植炸弹，大奖局（100 倍以上）靠这一步才能到。"""
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need_base = target / max(1.0, acc) if acc > 0 else target
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if need_base >= 0.25:
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if acc > 1 or not allow_bomb:
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combo, bombs = _plan_base_only(need_base), []
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else:
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combo, bombs = _plan_for(need_base)
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else:
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combo, bombs = [('I', 8)], [] # acc 本身已超过目标：只给最小档，不再加炸弹
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used = set()
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win_syms = set()
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for sym, cnt in combo:
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win_syms.add(sym)
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free = [p for p in range(GRID_SIZE) if p not in used]
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for p in random.sample(free, min(cnt, len(free))):
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r, c = divmod(p, GRID_COLS)
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grid[r][c] = sym
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used.add(p)
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free = [p for p in range(GRID_SIZE) if p not in used]
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for v in bombs:
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if not free:
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break
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p = free.pop(random.randrange(len(free)))
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r, c = divmod(p, GRID_COLS)
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grid[r][c] = BOMB_SHORT
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mul_map[p] = v
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used.add(p)
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for p in range(GRID_SIZE):
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r, c = divmod(p, GRID_COLS)
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if grid[r][c] == '':
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grid[r][c] = _weighted_sym(exclude=win_syms)
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_no_extra(grid, win_syms)
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return win_syms
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# ====================================================================
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||||
# 核心：一次旋转的 cascade 链
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# ====================================================================
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def _spin(mode_code, bias=None, acc=0, is_free_spin=False, target_scatter=0,
|
||||
allow_bomb=True, win_prob=0.0, continue_prob=0.0, cascade_max=0,
|
||||
bomb_prob=None, bomb_max=None, max_bombs=None):
|
||||
"""
|
||||
一次旋转（含多次 cascade）。
|
||||
炸弹跨 cascade 保留不消除；末帧统一结算: total = base_sum * max(1, acc+bomb_sum)
|
||||
acc 只在本旋转有消除时累加炸弹（与 engine.end_spin 一致）。
|
||||
allow_bomb=False 时初始盘也不出炸弹；级联补位一律不出。
|
||||
**炸弹只存在于免费旋转内**：普通旋转/底注的基础旋（含进 FS 的那张触发盘）一律不出炸弹；
|
||||
三个模式进 FS 后规则完全一致（炸弹 + 累计器 + 每旋保底消除）。
|
||||
win_prob/continue_prob：本盘凑不出消除时按概率补最小档消除（首盘/级联各自取一个），
|
||||
cascade_max 限制单旋最大波数 —— 让 FS 里"每旋都在消"。
|
||||
返回 (steps, spin_total, acc_out, scatter_count)
|
||||
"""
|
||||
if not is_free_spin:
|
||||
allow_bomb = False
|
||||
steps = []
|
||||
seq = 0
|
||||
mul_map = {} # {pos0: bomb_val} 跨 cascade 累积
|
||||
if bomb_prob is None:
|
||||
bomb_prob = BOMB_CELL_PROBABILITY
|
||||
# 本旋是不是"大奖旋"（补桶植入目标额的那一旋）：只有它按累计倍数结算
|
||||
plant_spin = isinstance(bias, (int, float)) and bias > 0
|
||||
|
||||
grid = None
|
||||
prev_win_pos = []
|
||||
sym_acc = Counter() # 本旋转累计消除符号
|
||||
spin_winners = [] # 落库留档用（接口已不下发，面板由 symbol_links 汇总）
|
||||
spin_links = [] # 本旋累积连线上报：[{symbol,count,loc,base_score,tumble}]，新旋清空
|
||||
total_base = 0.0
|
||||
recorded = 0.0 # 已记入前置步骤的赢额
|
||||
removed, drops, fills = [], [], []
|
||||
acc_before_spin = acc
|
||||
|
||||
while True:
|
||||
seq += 1
|
||||
if seq == 1:
|
||||
grid = _empty_grid()
|
||||
if isinstance(bias, (int, float)) and bias > 0:
|
||||
win_syms = _force_win(grid, float(bias), mul_map, acc, allow_bomb)
|
||||
else:
|
||||
_fill(grid, mul_map, allow_bomb, bomb_prob=bomb_prob, bomb_max=bomb_max,
|
||||
max_bombs=max_bombs)
|
||||
if bias == 'zero':
|
||||
_no_extra(grid, set())
|
||||
if target_scatter > 0:
|
||||
_place_scatter(grid, target_scatter, protect=set(_winners(grid)))
|
||||
removed, drops, fills = [], [], []
|
||||
else:
|
||||
removed = [p + 1 for p in prev_win_pos]
|
||||
grid, drops, mul_map = _collapse(grid, prev_win_pos, mul_map)
|
||||
fills = _fill(grid, mul_map, allow_bomb=False, track=True)
|
||||
|
||||
# 保底消除：本盘自然凑不出 8 连时，按概率补一组最小档（首盘用 win_prob，级联用 continue_prob）
|
||||
prob = win_prob if seq == 1 else continue_prob
|
||||
if prob > 0 and (not cascade_max or seq < cascade_max) \
|
||||
and random.random() < prob and not _winners(grid):
|
||||
_force_small_win(grid, prefer=[f['pos'] - 1 for f in fills])
|
||||
|
||||
# 保底消除 / 打散会覆盖"刚落下"或"刚补位"的格子 —— 这两份动画数据里的 symbol 按**最终盘面**
|
||||
# 回填，前端按"消→落→补"重建出来的盘面才与 grid_state 完全一致（否则会画出不对的符号）。
|
||||
if drops:
|
||||
drops = [dict(d, symbol=grid[(d['to'] - 1) // GRID_COLS][(d['to'] - 1) % GRID_COLS])
|
||||
for d in drops]
|
||||
if fills:
|
||||
fills = [dict(f, symbol=grid[(f['pos'] - 1) // GRID_COLS][(f['pos'] - 1) % GRID_COLS])
|
||||
for f in fills]
|
||||
|
||||
w = _winners(grid)
|
||||
is_final = (len(w) == 0)
|
||||
|
||||
base = 0.0
|
||||
win_pos = []
|
||||
mul_det = [{"pos": p + 1, "val": v, "quality": bomb_quality(v)}
|
||||
for p, v in sorted(mul_map.items())]
|
||||
bomb_sum = sum(mul_map.values())
|
||||
|
||||
# symbol_links = **本旋累积**的连线上报（每波消除往上追加，tumble 标明是旋内第几波）——
|
||||
# 它同时充当"获胜者面板"（前端按 symbol 汇总即可），所以 spin_winners 字段已删。
|
||||
for sym, (cnt, pay) in sorted(w.items()):
|
||||
pos_list = [r * GRID_COLS + c for r in range(GRID_ROWS)
|
||||
for c in range(GRID_COLS) if grid[r][c] == sym][:cnt]
|
||||
base += pay
|
||||
win_pos.extend(pos_list)
|
||||
sym_acc[sym] += cnt
|
||||
spin_links.append({
|
||||
'symbol': sym, 'count': cnt,
|
||||
'loc': [p + 1 for p in pos_list],
|
||||
'base_score': round(pay, 4),
|
||||
'tumble': seq, # 本旋第几波消除（= 本步 tumbleNum）
|
||||
})
|
||||
total_base += base
|
||||
|
||||
# 末帧：统一引爆（一旋转内不管几次消除，消完才爆）
|
||||
# 累计器仅免费旋转有效；基础旋转炸弹只乘本旋赢额、不留存
|
||||
scatter_win = 0.0
|
||||
scatter_count = 0
|
||||
acc_after = acc
|
||||
if is_final:
|
||||
scatter_count = _count_scatter(grid)
|
||||
scatter_win = _scatter_payout(scatter_count)
|
||||
if total_base > 0:
|
||||
if is_free_spin:
|
||||
acc_after = acc + bomb_sum # FS：炸弹并入累计器，跨旋传递
|
||||
# 本旋赢额 = 基础分 × max(1, 累计 + 本旋炸弹) —— 累计倍数作用到每一次消除
|
||||
# （玩家要的就是这个滚雪球：0.4×2 + 0.5×4 = 2.8）
|
||||
effective = max(1, acc_after)
|
||||
else:
|
||||
acc_after = acc # 基础旋：无累计器
|
||||
effective = max(1, bomb_sum)
|
||||
win_amt = total_base * effective - recorded + scatter_win
|
||||
else:
|
||||
win_amt = scatter_win # 无消除→炸弹作废、累计不变
|
||||
else:
|
||||
win_amt = base # 中间步只记基础分
|
||||
recorded += win_amt if not is_final else 0.0
|
||||
|
||||
# scatter 触发/重触发（仅末帧）
|
||||
scatter_trigger, retrigger_info = None, None
|
||||
if is_final:
|
||||
if is_free_spin and scatter_count >= SCATTER_RETRIGGER_TRIGGER:
|
||||
retrigger_info = {'scatterCount': scatter_count,
|
||||
'extraSpins': FREE_SPIN_RETRIGGER_COUNT,
|
||||
'scatterWin': round(scatter_win, 2)}
|
||||
elif not is_free_spin and scatter_count >= SCATTER_FREE_SPIN_TRIGGER:
|
||||
scatter_trigger = {'scatterCount': scatter_count,
|
||||
'freeSpins': FREE_SPINS_AWARDED,
|
||||
'scatterWin': round(scatter_win, 2)}
|
||||
|
||||
steps.append({
|
||||
'cascade_seq': seq, 'free_spin_id': 0,
|
||||
'grid_state': [row[:] for row in grid],
|
||||
'multiplier_detail': json.dumps(mul_det),
|
||||
'symbol_links': json.dumps(spin_links, ensure_ascii=False), # 本旋累积（含 tumble）
|
||||
'accumulated_symbols': json.dumps(dict(sym_acc)), # 仅落库留档，不下发
|
||||
'spin_winners': json.dumps(spin_winners, ensure_ascii=False), # 仅落库留档，不下发
|
||||
'removed_positions': json.dumps(removed), # 仅落库留档，不下发
|
||||
'drop_data': json.dumps(drops),
|
||||
'fill_data': json.dumps(fills if fills else []),
|
||||
'scatter_trigger': json.dumps(scatter_trigger) if scatter_trigger else None,
|
||||
'retrigger_info': json.dumps(retrigger_info) if retrigger_info else None,
|
||||
'base_score': round(base, 2), # 仅落库留档，不下发
|
||||
'win_amount': round(win_amt, 2),
|
||||
'accumulator_before': round(acc_before_spin, 2),
|
||||
'accumulator_after': round(acc_after if is_final else acc, 2),
|
||||
'spin_win': 0, 'win_capped': 0, 'bigwin_level': None,
|
||||
'is_final': 1 if is_final else 0,
|
||||
})
|
||||
|
||||
if is_final:
|
||||
total = round(sum(s['win_amount'] for s in steps), 2)
|
||||
return steps, total, acc_after, scatter_count
|
||||
prev_win_pos = win_pos
|
||||
|
||||
|
||||
# ====================================================================
|
||||
# 免费旋转会话（15 次起，≥3S 重触发 +5，上限 30）
|
||||
# ====================================================================
|
||||
|
||||
|
||||
def _fs_next_target(spin_no, wins, total_spins, quota):
|
||||
"""下一旋"该保底消除"的位置：间隔随机，但均值 = 剩余旋数 / 剩余配额。
|
||||
|
||||
为什么要随机：等距铺（每 quota/总局数 旋必消一次）会让玩家几局就看穿"到第 4 旋肯定消"——
|
||||
实测等距版本有 87% 的局"最长连空正好 4 旋"。这里改成每次消除后重抽下一次的位置：
|
||||
间隔 = round(均值 + δ)，δ ∈ {-2..2} 权重 {1,2,3,2,1}（均值 0，所以整局的消除次数不变），
|
||||
玩家看到的空转长度在 1~5 旋之间跳，而不是恒定 4。剩余配额为 0 时返回 None（后面只靠自然消）。
|
||||
"""
|
||||
left_spins = total_spins - spin_no
|
||||
left_quota = quota - wins
|
||||
if left_spins <= 0 or left_quota <= 0:
|
||||
return None
|
||||
delta = random.choices((-2, -1, 0, 1, 2), weights=(1, 2, 3, 2, 1))[0]
|
||||
return spin_no + max(1, int(round(left_spins / left_quota + delta)))
|
||||
|
||||
|
||||
def _free_spin_session(mode_code, slot_target=None, acc=0, fs_budget=None):
|
||||
"""免费旋转会话（15 次起，≥3S 重触发 +5，上限 30）。
|
||||
|
||||
**三个模式同一套规则**（炸弹 + 累计器 + 每旋保底消除），区别只在"这一局值多少钱"：
|
||||
· buy_fs：价格换来桶目标，拆成「保底消除预算 fs_budget + 一旋大奖 slot_target」；
|
||||
· normal/ante 自然触发：没有价格可拆 → 只给一份同档保底预算、不植大奖旋，
|
||||
整局赔付落在哪由自然结果决定。
|
||||
fs_budget 按"配额 + 随机间隔"花：预算能买 k 次消除（k ≈ √(预算/FS_GUARANTEE_COST)），
|
||||
每次消除后重抽下一次的位置（间隔随机、均值 = 剩余旋数/剩余配额），自然消除也算一次进度；
|
||||
空转太久再按 FS_DRY_FORCE_PROB 软兜底 —— 旧写法按"剩余预算÷剩余旋数"算每旋概率，累计倍数
|
||||
一涨就把预算烧穿，后半局十几旋连空；等距铺法又会让玩家看穿"到第 4 旋肯定消"（见 _fs_next_target）。
|
||||
slot_target 只作用于其中随机一旋（它按累计倍数结算，不占保底配额）。
|
||||
"""
|
||||
total_spins = FREE_SPINS_AWARDED
|
||||
fs_index = 0
|
||||
all_steps = []
|
||||
fs_total = 0.0
|
||||
sc_dist = FREESPIN_SCATTER.get(mode_code, FREESPIN_SCATTER['default'])
|
||||
budget = fs_budget
|
||||
# 炸弹大小按档位配：低倍局用小球（预算买得起多次"消除+爆"），高倍局用全量（2~1000 的大球）
|
||||
target_mul = float(slot_target) if isinstance(slot_target, (int, float)) else 0.0
|
||||
spin_bomb_max = None if target_mul >= FS_BIG_BOMB_TARGET else FS_SMALL_BOMB_MAX
|
||||
bias_slot = random.randrange(FREE_SPINS_AWARDED) if slot_target else -1
|
||||
# 保底配额：预算 ÷ 单次消除边际赔付再开方 = 这一局"买得起"几次消除
|
||||
quota = None if budget is None else max(1, int((max(0.0, budget) / FS_GUARANTEE_COST) ** 0.5))
|
||||
wins = 0 # 已消除的旋数（自然 + 保底）
|
||||
dry = 0 # 已经连续空转了几旋（兜底用）
|
||||
target = None if quota is None else _fs_next_target(0, 0, total_spins, quota)
|
||||
while fs_index < total_spins:
|
||||
fs_index += 1
|
||||
is_target_slot = (fs_index - 1) == bias_slot
|
||||
spin_bias = slot_target if is_target_slot else None
|
||||
forced = False
|
||||
if spin_bias is None and quota is not None:
|
||||
# 到点了 / 空转太久被软兜底抽中 → 本旋必补一次保底消除
|
||||
forced = target is not None and fs_index >= target
|
||||
if not forced and dry >= FS_DRY_FORCE_HARD:
|
||||
forced = True
|
||||
elif not forced and dry in FS_DRY_FORCE_PROB:
|
||||
forced = random.random() < FS_DRY_FORCE_PROB[dry]
|
||||
p = 1.0 if forced else 0.0
|
||||
else:
|
||||
p = FS_SPIN_WIN_PROB
|
||||
sc = _weighted_choice(sc_dist)
|
||||
# 已到 30 旋上限：这一旋不再落 3+ 个 S —— 否则会写出"extraSpins=5 但没有旋可加"的
|
||||
# retrigger_info（玩家白看一次 +5，前端还得自己夹 30）。上限之前的那一旋照常触发第 3 次 +5。
|
||||
if total_spins >= MAX_FREE_SPINS:
|
||||
sc = min(sc, SCATTER_RETRIGGER_TRIGGER - 1)
|
||||
spin_steps, spin_score, acc, sc_count = _spin(
|
||||
mode_code, spin_bias, acc, is_free_spin=True, target_scatter=sc,
|
||||
win_prob=p, continue_prob=FS_CASCADE_CONTINUE_PROB,
|
||||
cascade_max=FS_CASCADE_MAX,
|
||||
bomb_prob=FS_SCREEN_BOMB_PROB, bomb_max=spin_bomb_max,
|
||||
max_bombs=FS_MAX_BOMBS_PER_SPIN)
|
||||
if sc_count >= SCATTER_RETRIGGER_TRIGGER and total_spins < MAX_FREE_SPINS:
|
||||
total_spins += FREE_SPIN_RETRIGGER_COUNT
|
||||
# 目标大奖旋的花费由目标额单独控制，不占用保底预算
|
||||
if budget is not None and not is_target_slot:
|
||||
budget -= spin_score
|
||||
for s in spin_steps:
|
||||
s['free_spin_id'] = fs_index
|
||||
fs_total += spin_score
|
||||
all_steps.extend(spin_steps)
|
||||
# 进度：消了（不管自然还是保底）就重新抽下一次的位置 —— 整局消除次数仍由配额决定
|
||||
if spin_bias is None and quota is not None:
|
||||
if any(float(s['base_score']) > 0 for s in spin_steps):
|
||||
wins += 1
|
||||
dry = 0
|
||||
target = _fs_next_target(fs_index, wins, total_spins, quota)
|
||||
else:
|
||||
dry += 1
|
||||
return all_steps, fs_total
|
||||
|
||||
|
||||
# ====================================================================
|
||||
# 主入口
|
||||
# ====================================================================
|
||||
|
||||
def generate_round(box, bias=None):
|
||||
"""
|
||||
生成一个完整回合。box: {'box_id','box_type',...} bias: None/'zero'/目标倍率(float)
|
||||
返回 (round_data, steps, orn_id)
|
||||
"""
|
||||
mode_code = box['box_type']
|
||||
buy_fs = mode_code == 'buy_fs'
|
||||
# 目标高于"基础旋（无炸弹）能赔到的上限"时，只能生成自然触发 FS 的回合来装这个桶：
|
||||
# normal/ante 的 100x/150x/200x 桶没有别的来源（基础旋不出炸弹，最多 ~60x）。
|
||||
must_free_spins = isinstance(bias, (int, float)) and not buy_fs \
|
||||
and float(bias) >= FS_ONLY_TARGET_MUL
|
||||
is_fs = False
|
||||
if buy_fs:
|
||||
is_fs = True # 购买模式保证进 FS（互斥：无底注效果）
|
||||
elif must_free_spins:
|
||||
is_fs = True
|
||||
elif mode_code in BASE_FS_TRIGGER_PROB and bias is None:
|
||||
is_fs = random.random() < BASE_FS_TRIGGER_PROB[mode_code]
|
||||
|
||||
if is_fs:
|
||||
# 触发盘：三模式都固定/摇出 SCATTER，且**不出炸弹**（炸弹只存在于免费旋转内）
|
||||
trigger_sc = 4 if buy_fs else _weighted_choice(FS_TRIGGER_SCATTER)
|
||||
base_steps, base_total, _, _ = _spin(
|
||||
mode_code, None, 0, is_free_spin=False, target_scatter=trigger_sc,
|
||||
allow_bomb=False)
|
||||
# FS 预算：买 FS / 高倍桶（bias=桶目标）拆成「保底消除预算 + 一旋大奖」；
|
||||
# normal/ante 自然摇出来的 FS（bias=None）没有价格可拆 → 只给同档保底预算、不植大奖旋，
|
||||
# 整局赔付由自然消除 + 炸弹累计决定（实测约 80x、5 旋有消除，落在模式自己的桶里）。
|
||||
fs_budget, slot_target = None, None
|
||||
if isinstance(bias, (int, float)) and bias > 0:
|
||||
budget = max(0.0, float(bias) - float(base_total))
|
||||
if budget <= FS_FLOOR_BUDGET_CAP:
|
||||
fs_budget = budget # 低倍局：全靠保底小消
|
||||
else:
|
||||
fs_budget = FS_FLOOR_BUDGET_CAP # 保底小消是平价结算（×1），预算可以给足
|
||||
slot_target = budget - fs_budget # 其余给"大奖旋"（它按累计倍数结算）
|
||||
elif not buy_fs:
|
||||
fs_budget = FS_FLOOR_BUDGET_CAP
|
||||
# 炸弹累计器仅免费旋转内生效：FS 会话从 0 起累，触发盘炸弹不带入
|
||||
fs_steps, fs_total = _free_spin_session(
|
||||
mode_code, slot_target=slot_target, acc=0, fs_budget=fs_budget)
|
||||
steps = base_steps + fs_steps
|
||||
else:
|
||||
nearmiss = _weighted_choice(BASE_NEARMISS_SCATTER.get(mode_code, {0: 1.0}))
|
||||
steps, _, _, _ = _spin(mode_code, bias, 0,
|
||||
is_free_spin=False, target_scatter=nearmiss)
|
||||
|
||||
# 理论总分（落桶用，可到 1370+）
|
||||
raw_total = round(sum(s['win_amount'] for s in steps), 2)
|
||||
orn_id = _bucket(raw_total)
|
||||
|
||||
# 发放截断 + 口径重写：累计到 1000 即终止，动画=到账零偏差。
|
||||
# win_amount = **本旋累计到本步的赢额**（该旋末步就是这一旋的总额）；
|
||||
# spin_win = **本局累计到本步的赢额**（最后一步 = 整局实付 = totalWin）
|
||||
# 前端因此不需要自己相加：本步增量 = 与上一步相减，本旋总额 = 本旋末步的 win_amount。
|
||||
raw_increments = [s['win_amount'] for s in steps]
|
||||
cumul, spin_cumul, capped_steps, hit = 0.0, {}, [], False
|
||||
for s, inc in zip(steps, raw_increments):
|
||||
if cumul + inc >= MAX_WIN: # 封顶：本步补足到 1000 并终止
|
||||
inc = round(MAX_WIN - cumul, 2)
|
||||
s['win_capped'] = 1
|
||||
s['is_final'] = 1
|
||||
hit = True
|
||||
key = s['free_spin_id']
|
||||
spin_cumul[key] = round(spin_cumul.get(key, 0.0) + inc, 2)
|
||||
cumul = round(cumul + inc, 2)
|
||||
s['win_amount'] = spin_cumul[key] # 本旋累计（末步即该旋总额）
|
||||
s['spin_win'] = cumul # 本局累计
|
||||
capped_steps.append(s)
|
||||
if hit:
|
||||
break
|
||||
if hit:
|
||||
steps = capped_steps
|
||||
paid_total = round(cumul, 2)
|
||||
|
||||
# 爆奖动画（小/中/大）：**只有免费旋转里才判档** —— normal/ante 靠自然触发 FS、
|
||||
# buy_fs 本身即 FS；基础旋（free_spin_id=0）一律 null，不播大奖动画。
|
||||
# 单旋赢额 = 该旋末帧的 win_amount；旋与旋互不影响，同档一局可多次播放。
|
||||
for s in steps:
|
||||
if s['is_final'] and s['free_spin_id'] > 0:
|
||||
s['bigwin_level'] = bigwin_level(mode_code, float(s['win_amount']))
|
||||
|
||||
fs_cnt = len({s['free_spin_id'] for s in steps if s['free_spin_id'] > 0})
|
||||
rd = {
|
||||
'box_id': box['box_id'], 'ornament_id': orn_id,
|
||||
'actual_mul': round(paid_total, 4), # 实付倍率 ≤1000
|
||||
'free_spin_count': fs_cnt,
|
||||
'total_cascades': len(steps),
|
||||
}
|
||||
return rd, steps, orn_id
|
||||
|
||||
|
||||
# ====================================================================
|
||||
# 分布补偿：两阶段填桶
|
||||
# ====================================================================
|
||||
|
||||
def _bias_for(orn_id):
|
||||
if orn_id == 1:
|
||||
return 'zero'
|
||||
o = next(x for x in ORNAMENTS if x['id'] == orn_id)
|
||||
return float(o['expect_mul'])
|
||||
|
||||
|
||||
def _calibrate_bias(box, orn_id, samples=8):
|
||||
"""给某个桶在线找一个"更容易撞上"的 bias（只改传给生成器的目标值，不动任何规则）。
|
||||
|
||||
为什么需要：`_bias_for` 用的是桶的名义 expect_mul，但 buy_fs 的整局赔付被 FS 会话抬高了 ——
|
||||
实测 bias=10（桶 7 名义 10x）时实际赔付 min 14 / 中位 58，**永远撞不进 8~12**，命中率仅 3%，
|
||||
stage2 只能白跑几万次（单箱 5~10 分钟）。这里拿几个候选值各试几局，挑命中率最高的那个。"""
|
||||
target = _bias_for(orn_id)
|
||||
best, best_hit = target, -1
|
||||
for mult in (1.0, 0.5, 0.3, 0.15, 0.08):
|
||||
b = target * mult
|
||||
hit = 0
|
||||
for _ in range(samples):
|
||||
_, _, oid = generate_round(box, b)
|
||||
hit += (oid == orn_id)
|
||||
if hit > best_hit:
|
||||
best, best_hit = b, hit
|
||||
if hit == samples: # 已经全中，不用再试更小/更大的
|
||||
break
|
||||
return best
|
||||
|
||||
|
||||
def fill_distribution(box, target_counts, max_rounds=None):
|
||||
"""阶段1 自由生成积累常见桶；阶段2 按缺额 bias 补桶。返回 (rounds, 实际分布)
|
||||
|
||||
阶段2 的尝试上限按 total×60：保底消除改成"随机间隔"之后，低倍桶（10x/16x）的命中率大约减半
|
||||
（同样的钱摊到整局、摊到累计倍数高的旋上更贵），尝试数不够会填不满池子（实测 ×30 时
|
||||
10x 桶只填到 178/297，×60 满池）。
|
||||
某个桶饿死时（试了 4 倍于它条数的次数还没填满）会重新校准它的 bias —— 只对慢桶生效，
|
||||
命中率本来就高的桶走原来的名义值，不额外花钱。"""
|
||||
gen = Counter()
|
||||
rounds = []
|
||||
total_target = sum(target_counts.values())
|
||||
bias_use = {} # orn_id -> 校准后的 bias
|
||||
tries = Counter() # orn_id -> 已尝试次数
|
||||
|
||||
def _accept(oid):
|
||||
if oid in target_counts and gen[oid] < target_counts[oid]:
|
||||
gen[oid] += 1
|
||||
return True
|
||||
return False
|
||||
|
||||
free_budget = min(total_target * 3, 12000)
|
||||
for _ in range(free_budget):
|
||||
if sum(gen.values()) >= total_target:
|
||||
break
|
||||
rd, steps, oid = generate_round(box, None)
|
||||
if _accept(oid):
|
||||
rounds.append((rd, steps))
|
||||
|
||||
max_att = (max_rounds or total_target * 60)
|
||||
for _ in range(max_att):
|
||||
deficit = [(oid, target_counts[oid] - gen[oid])
|
||||
for oid in target_counts if gen[oid] < target_counts[oid]]
|
||||
if not deficit:
|
||||
break
|
||||
# 按缺口加权随机选桶，避免“最难桶”饿死其他桶
|
||||
need = random.choices([d[0] for d in deficit],
|
||||
weights=[d[1] for d in deficit])[0]
|
||||
tries[need] += 1
|
||||
if need not in bias_use and tries[need] > target_counts[need] * 4:
|
||||
bias_use[need] = _calibrate_bias(box, need)
|
||||
rd, steps, oid = generate_round(box, bias_use.get(need, _bias_for(need)))
|
||||
if _accept(oid):
|
||||
rounds.append((rd, steps))
|
||||
|
||||
return rounds, dict(gen)
|
||||
Reference in New Issue
Block a user