248 lines
7.7 KiB
Python
248 lines
7.7 KiB
Python
import json
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import random
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import sys
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import numpy as np
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from typing import Dict, List
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from SugarRush1000 import SugarRush1000
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"""
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训练购买免费旋转功能时，一次购买可以获得的平均免费旋转次数
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"""
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def init_symbol_value():
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return {
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"3": 0.1,
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"4": 0.4,
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"5": 1,
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"6": 1.5,
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"7": 2,
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}
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class SugarRushAutoTuner:
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def __init__(
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self,
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target_rtp: float = 96.5,
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batch_spins=20000,
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weights=None,
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feature: str = "normal",
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):
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self.target_rtp = target_rtp
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self.rows = 7
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self.cols = 7
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self.bet = 1.0
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self.batch_spins = batch_spins
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self.feature = feature
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# 符号定义及其价值（价值越高，对 RTP 影响越大）
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self.symbols_config = init_symbol_value()
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# 初始权重
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self.symbol_keys = list(self.symbols_config.keys())
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if weights is not None:
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self.weights = weights
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else:
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self.weights = {k: 20.0 for k in self.symbol_keys}
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# 学习率 (控制调整速度，太小收敛慢，太大容易震荡)
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self.learning_rate = 0.002 # 0.01
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print(f"初始化自动调优器，feature:{feature} 目标 RTP: {self.target_rtp}%")
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def simulate_one_batch(self, spins: int = 20000):
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begin_balance = 1_0000_0000
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game = SugarRush1000(balance=begin_balance, weights=self.weights)
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total_bet = 0.0
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total_win = 0.0
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for _ in range(spins):
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# 执行旋转
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res = game.doSpin()
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if res["error"]:
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break
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# 统计数据
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actual_cost = res["actual_bet"]
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if actual_cost > 0:
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total_bet += actual_cost
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total_win += res["win"]
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# 校验余额是否正确
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assert (
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f"{abs(begin_balance - game.balance):.2f}"
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== f"{abs(total_bet - total_win):.2f}"
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)
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return (total_win / total_bet) * 100
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def simulate_free_batch(self, spins: int = 20000, buy_type: str = "standard"):
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game = SugarRush1000()
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scotter_counts = list(self.weights.keys())
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scotter_counts_weights = list(self.weights.values())
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total_free_spins = 0
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for _ in range(spins):
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scatters_count = random.choices(
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scotter_counts, weights=scotter_counts_weights, k=1
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)[0]
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total_free_spins += game._add_free_spins(int(scatters_count))
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print(f"购买 {spins} 次免费旋转，实际免费旋转次数 {total_free_spins} 次")
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return total_free_spins / spins
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def tune(self, iterations: int = 50):
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"""
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迭代调整权重
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"""
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history = []
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for i in range(iterations):
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# 1. 模拟当前 RTP
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current_rtp = 0
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if self.feature == "normal":
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current_rtp = self.simulate_one_batch(self.batch_spins)
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else:
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current_rtp = self.simulate_free_batch(self.batch_spins, self.feature)
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error = current_rtp - self.target_rtp
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history.append(current_rtp)
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print(
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f"迭代 {i+1}/{iterations} | RTP: {current_rtp:.4f}% | 目标: {self.target_rtp}% | 误差: {error:+.4f}%"
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)
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# 2. 检查是否收敛
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if abs(error) < 0.1:
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print(f"收敛成功！最终 RTP: {current_rtp:.4f}%")
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break
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# 3. 动态调整权重
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# 调整策略：如果 RTP 太高，降低高价值符号的权重，提高低价值符号权重
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# 调整量 = 误差 * 学习率 * 符号价值系数
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# 防止初始误差过大导致权重崩塌
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safe_error = max(min(error, 5.0), -5.0)
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adjustment_factor = safe_error * self.learning_rate
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# 防止权重变为负数
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min_weight = 0
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for sym in self.symbol_keys:
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value = self.symbols_config[sym]
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# 核心算法：
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# 如果 RTP > Target (Error > 0)，我们需要降低 RTP。
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# 对于高价值符号 (value大)，我们需要减小其权重。
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# Adjustment 应该是负的。所以: - adjustment_factor * value
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# 如果 RTP < Target (Error < 0)，我们需要提高 RTP。
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# 对于高价值符号，我们需要增加其权重。
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# Adjustment 应该是正的。所以: - adjustment_factor * value (因为error是负的，负负得正)
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delta = -adjustment_factor * value * self.weights[sym]
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# --- 优化：限制单次最大调整幅度 ---
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# 防止某次调整幅度超过权重的 40%，给算法留点“喘息”空间
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max_change_ratio = 0.4
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if abs(delta) > self.weights[sym] * max_change_ratio:
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delta = np.sign(delta) * self.weights[sym] * max_change_ratio
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new_weight = self.weights[sym] + delta
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# 限制最小权重，防止符号消失
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if new_weight < min_weight:
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new_weight = min_weight
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if new_weight > 500:
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new_weight = 500
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self.weights[sym] = new_weight
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return self.weights, history
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# --- 运行自动调优 ---
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def train_weights():
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print("开始训练权重...")
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# 设置随机种子
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seed = 42
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random.seed(seed)
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np.random.seed(seed)
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print(f"随机种子: {seed}")
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fast = {"iterations": 100, "spins": 50_0000}
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# middle = {"iterations": 15, "spins": 5_0000}
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# slow = {"iterations": 10, "spins": 10_0000}
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current = fast
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configs = [
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("fast", fast),
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# ("middle", middle), ("slow", slow)
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]
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tuner = SugarRushAutoTuner(
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target_rtp=11,
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batch_spins=current["spins"],
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weights={
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"3": 7.300320151890674,
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"4": 4.256044392311739,
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"5": 0.8531843171577663,
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"6": 0.21882648882051972,
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"7": 0.05364159605552199,
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},
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feature="standard",
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)
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for name, config in configs:
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begin_weights = tuner.weights.copy()
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print(f"# {name}调优")
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tuner.batch_spins = config["spins"]
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final_weights, rtp_history = tuner.tune(iterations=config["iterations"])
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print("\n=== 调优前的符号权重 ===")
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for sym, w in begin_weights.items():
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print(f"{sym}: {w:.2f}")
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print("\n 符号出现概率 (%) ===")
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for sym, w in begin_weights.items():
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print(f"{sym}: {(w/sum(begin_weights.values()))*100:.2f}%")
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print("\n=== 最终调整后的符号权重 ===")
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print(json.dumps(final_weights, indent=4, ensure_ascii=False))
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# 将权重转换为概率百分比
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total_w = sum(final_weights.values())
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print("\n=== 符号出现概率 (%) ===")
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for sym, w in final_weights.items():
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print(f"{sym}: {(w/total_w)*100:.2f}%")
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def verify():
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print("开始进行权重校验:")
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for i in range(10):
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tuner = SugarRushAutoTuner(
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target_rtp=56,
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batch_spins=10_0000,
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weights={
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"A": 12.048116942034044,
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"B": 23.797750079057277,
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"C": 38.01412853023891,
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"D": 49.64407598502997,
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"E": 59.15353799355775,
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"F": 70.33559681987649,
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"G": 83.45720386881379,
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"S": 1.0979547602954776,
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},
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)
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print(f"第 {i+1}/10 次校验:")
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tuner.tune(iterations=1)
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if __name__ == "__main__":
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if len(sys.argv) >= 2:
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verify()
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else:
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train_weights()
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