用于验证百分百胜利的赌博博弈模型 缺点是需要准备大量资金 由于不会化简一个含有迭代的公式 数学模型 还没能成功后建立

赌博博弈模型实验.py

# -*- coding: utf-8 -*-
from pylab import *
'''
用于验证百分百胜利的赌博博弈模型
缺点是需要准备大量资金
数学模型由于不会化简一个基于迭代的公式
还没能成功后建立

'''

totalround = 100  # 赌博次数
alll = 0  # 初始本金（可以欠钱）
batch = 10  # 显示几次中间结果

goalinit = 10  # 每次目标挣多少
winrate = 0.7  # 胜率
lossrate = 0.95  # 费用率
'''
赢的回报率为
（赌注/胜率）*费用率
即 (pay/winrate)*lossrate

蓝线:总资产
红线:那一局的胜负
绿线:连续为输的情况下 对应的赔款(赢一局即清空为0)
'''
#totalround=batch=200

getRate = lossrate/winrate
payWinRate = lambda x:x*getRate
winPayRate = lambda y:y/(getRate-1)


gen = np.random.random(totalround)

shuffle(gen)
wins = gen < winrate
#wins = gen < winrate*0
#wins[-1] = True

goal = goalinit
pay = winPayRate(goalinit)
loss = 0
alls,losss = [],[]
for ind,win in enumerate(wins):
    alll -= pay
    loss += pay
    oldgoal = goal
    if win:
        get = payWinRate(pay)
        alll += get
        goal = goalinit
        pay = winPayRate(goalinit)
        loss = 0
    else:
        goal = loss+goalinit
        pay = winPayRate(goal)
    alls += [alll]
    losss+= [loss]
#    continue
#    print ind,oldgoal,pay,win,loss,goal,alll
    if ind%(totalround//batch)==0:
        print 'ind:%d, oldgoal:%.2f, pay:%.2f, win:%s, loss:%.2f, goal:%.2f, all:%d'%(
               ind,oldgoal,pay,win,loss,goal,alll)
        plot(alls)
        plot(losss)
        plot(goalinit*2/batch*totalround*winrate*wins[:ind+1])
        show()
losss = np.array(losss)
alls = np.array(alls)