DNF雜談：模擬實(shí)驗1000萬次強(qiáng)化，測試1-10一次成的幾率

2022-03-06 14:43 作者:COLG玩家社區(qū) 0人讀過 | 我要投稿

作者：鷺?biāo)?/p>

概率是按照公布的+公會藥，無寵物

——以下內(nèi)容為我的一位不愿意透露姓名的富婆朋友分享——

（更新了）

# -*- coding: utf-8 -*-
"""
Created on Tue Feb??1 22:18:32 2022

@author: Yu
"""
PROBABILITY_Increase = [1, 1, 1, 1, 0.81,
? ?? ?? ?? ?? ?0.71, 0.61, 0.71, 0.61, 0.51]
TRAILS_TIMES = 10000000 #試驗次數(shù)

import numpy as np
import random
import matplotlib.pyplot as plt
from matplotlib import ticker

x = np.arange(1000)
y = np.zeros((1000,), dtype = int) # y[10]表示10次就上10的次數(shù)

sad_7_4 = 0 # 74慘案
sad_8_5 = 0
sad_9_6 = 0
total_Times = 0
best = 10 # 最黑的次數(shù)

def make_autopct(values):
? ? def my_autopct(pct):
? ?? ???total = sum(values)
? ?? ???val = int(round(pct*total/100.0))
? ?? ???# 同時顯示數(shù)值和占比的餅圖
? ?? ???return '{p:.2f}% ({v:d})'.format(p=pct,v=val)
? ? return my_autopct

for times in range(TRAILS_TIMES):
? ? current_Times = 0 # 當(dāng)前次數(shù)
? ? current_Level = 0 # 當(dāng)前等級
? ? current_7_to_4 = 0 # 74慘案次數(shù)
? ? current_8_to_5 = 0 # 85慘案次數(shù)
? ? current_9_to_6 = 0 # 93慘案次數(shù)
? ? while (current_Level < 10):
? ?? ???current_Times = current_Times + 1
? ?? ???if (random.random() < PROBABILITY_Increase[current_Level]): # 增肥成功
? ?? ?? ?? ?current_Level = current_Level + 1
? ?? ???else: #增肥失敗
? ?? ?? ?? ?if current_Level == 9:
? ?? ?? ?? ?? ? current_9_to_6 = current_9_to_6 + 1
? ?? ?? ?? ?? ? current_Level = current_Level - 3
? ?? ?? ?? ?elif current_Level == 8:
? ?? ?? ?? ?? ? current_8_to_5 = current_8_to_5 + 1
? ?? ?? ?? ?? ? current_Level = current_Level - 3? ?
? ?? ?? ?? ?elif current_Level == 7:
? ?? ?? ?? ?? ? current_7_to_4 = current_7_to_4 + 1
? ?? ?? ?? ?? ? current_Level = current_Level - 3? ?
? ?? ?? ?? ?elif current_Level >= 4:
? ?? ?? ?? ?? ? current_Level = current_Level - 1
? ?? ???#print(current_Level,end = "->")
? ? #print("\n本次增肥花費(fèi)次數(shù)： {}".format(current_Times))
? ? #print("7-->4失敗次數(shù) : {}".format(current_7_to_4))
? ? #print("8-->5失敗次數(shù) : {}".format(current_8_to_5))
? ? #print("9-->6失敗次數(shù) : {}".format(current_9_to_6))
? ? y[current_Times] = y[current_Times] + 1
? ? sad_7_4 = sad_7_4 + current_7_to_4
? ? sad_8_5 = sad_8_5 + current_8_to_5
? ? sad_9_6 = sad_9_6 + current_9_to_6
? ? total_Times = total_Times + current_Times
? ? if best < current_Times:
? ?? ???best = current_Times

print("試驗次數(shù)： {}".format(TRAILS_TIMES))
print("天選之人增肥的次數(shù)： {}".format(best))
print("平均增肥花費(fèi)次數(shù)為： {}".format(total_Times / TRAILS_TIMES))
print("平均7上8失敗次數(shù)為： {}".format(sad_7_4 / TRAILS_TIMES))
print("平均8上9失敗次數(shù)為： {}".format(sad_8_5 / TRAILS_TIMES))
print("平均9上10失敗次數(shù)為： {}".format(sad_9_6 / TRAILS_TIMES))
P_distribution = y/TRAILS_TIMES #轉(zhuǎn)換為概率

res_10 = y[10]
res_11_to_20 = 0
for i in range(11,20+1):
? ? res_11_to_20 = res_11_to_20 + y

res_21_to_30 = 0
for i in range(21,30+1):
? ? res_21_to_30 = res_21_to_30 + y

res_31_to_40 = 0
for i in range(31,40+1):
? ? res_31_to_40 = res_31_to_40 + y

res_41_to_50 = 0
for i in range(41,50+1):
? ? res_41_to_50 = res_41_to_50 + y

res_51_to_60 = 0
for i in range(51,60+1):
? ? res_51_to_60 = res_51_to_60 + y

res_61_to_80 = 0
for i in range(61,80+1):
? ? res_61_to_80 = res_61_to_80 + y

res_81_to_100 = 0
for i in range(81,100+1):
? ? res_81_to_100 = res_81_to_100 + y

res_101_to_150 = 0
for i in range(101,150+1):
? ? res_101_to_150 = res_101_to_150 + y

res_best= 0
for i in range(151,best+100):
? ? res_best = res_best + y??

interval = np.array([res_10, res_11_to_20,
? ?? ?? ?? ?res_21_to_30,res_31_to_40,
? ?? ?? ?? ?res_41_to_50,res_51_to_60,
? ?? ?? ?? ?res_61_to_80,res_81_to_100,
? ?? ?? ?? ?res_101_to_150,res_best])

x_name = ['10','11-20','21-30','31-40','41-50','51-60','61-80','81-100','101-150','>150']

Area_distribution = interval/TRAILS_TIMES

fig1, axis1 = plt.subplots()
axis1.bar(x[0:best+1], P_distribution[0:best+1], label='Fatter! Samples Number : {}'.format(TRAILS_TIMES))
axis1.yaxis.set_major_formatter(ticker.PercentFormatter(xmax=1, decimals=1)) #設(shè)置為百分比顯示
plt.xlabel('Total Times')
plt.ylabel('Distribution')
plt.savefig('./axis1.jpg')
plt.legend()

fig2, axis2 = plt.subplots()
axis2.bar(x_name, Area_distribution, label='Fatter! Samples Number : {}'.format(TRAILS_TIMES))
axis2.yaxis.set_major_formatter(ticker.PercentFormatter(xmax=1, decimals=1)) #設(shè)置為百分比顯示
plt.xlabel('Total Times')
plt.ylabel('Distribution')
plt.legend()

fig3, axis3 = plt.subplots()
axis3.pie(Area_distribution, labels=x_name, autopct=make_autopct(Area_distribution))

plt.show()