python中的硬币翻转模拟

Question

我想模拟抛一枚硬币500次。然后，我必须创建一个图表来显示在x轴上有倒装号码的硬币和在y轴上有倒装号码的硬币时头部的运行比例。我编写了Python代码，得到了以下错误：

Traceback (most recent call last):
File "E:\ProgramsPython\My\bayes\Coin Flip.py", line 22, in <module>
ylist = [coinFlip(x) for x in xlist]
File "E:\ProgramsPython\My\bayes\Coin Flip.py", line 16, in coinFlip
return heads / x
ZeroDivisionError: integer division or modulo by zero

我做错了什么？

# -*- coding: cp1251 -*-
import random
import pylab
from matplotlib import mlab
def coinFlip(size):
    heads = 0
    tails = 0

    for x in xrange(size):
        flip = random.randint(0,1)
        if flip == 1: heads += 1
        else: tails += 1



    return heads / x

xmin = 1
xmax = 500
dx = 1
xlist = mlab.frange (xmin, xmax, dx)
ylist = [coinFlip(x) for x in xlist]
pylab.plot(xlist, ylist)
pylab.show()

Answer 1

import numpy as np
from matplotlib import pyplot as plt

flips = np.random.binomial(1, 0.5, 500) # flip 1 coin with 0.5 prob of heads 500 times
heads_so_far = flips.cumsum() * 1.0 #lets use float to avoid truncations later
heads_to_count = [heads_so_far[i-1]/i for i in range(1,len(flips)+1)]
x = range(1,len(flips)+1)
plt.plot(x,heads_to_count)
plt.show()

Answer 2

In [53]: [x for x in xrange(1)]
Out[53]: [0]

x可以等于零。当这种情况发生时（特别是当coinFlip(1)被调用时）

heads / x

引发零分区错误。

---

顺便说一下，因为您使用的是matplotlib，所以必须安装NumPy。因此，您可以像这样使用expresscoinFlip：

import matplotlib.pyplot as plt
import numpy as np

def coinFlip(size):
    flips = np.random.randint(0, 2, size=size)
    return flips.mean()
coinFlip = np.frompyfunc(coinFlip, 1, 1)

xmin, xmax, dx = 1, 500, 1
x = np.arange(xmin, xmax, dx)
y = coinFlip(x)
plt.plot(x, y)
plt.show()

---

或者（使用@pjs的注释），查看在一次500个硬币的投掷中头部的比例是如何变化的：

def coinFlip(size):
    xmin, xmax, dx = 1, size, 1
    x = np.arange(xmin, xmax, dx)
    flips = np.random.randint(0, 2, size=size)
    return x, [flips[:i].mean() for i in x]

x, y = coinFlip(500)
plt.plot(x, y)

---

要在对数刻度上绘制x轴：

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xscale('log')

Answer 3

嗯，错误是说你除以零。所以有一条线，可能在那里。

试着把你的回报改成这个（在我看来还是更有意义的）：

return heads / size