python 计算泊松概率百分比
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Calculate poisson probability percentage
提问by Alex Coventry
When you use the POISSON function in Excel (or in OpenOffice Calc), it takes two arguments:
当您在 Excel（或 OpenOffice Calc）中使用 POISSON 函数时，它需要两个参数：
- an integer
- an 'average' number
- 一个整数
- 一个“平均”数字
and returns a float.
并返回一个浮点数。
In Python (I tried RandomArray and NumPy) it returns an array of random poisson numbers. What I really want is the percentage that this event will occur (it is a constant number and the array has every time different numbers - so is it an average?).
在 Python 中（我尝试过 RandomArray 和 NumPy），它返回一个随机泊松数数组。我真正想要的是这个事件发生的百分比（它是一个常数，数组每次都有不同的数字 - 那么它是平均值吗？）。
for example:
例如：
print poisson(2.6,6)
returns [1 3 3 0 1 3](and every time I run it, it's different).
返回[1 3 3 0 1 3]（每次我运行它时，它都是不同的）。
The number I get from calc/excel is 3.19 (POISSON(6,2.16,0)*100).
我从 calc/excel 得到的数字是 3.19 ( POISSON(6,2.16,0)*100)。
Am I using the python's poisson wrong (no pun!) or am I missing something?
我是否错误地使用了蟒蛇的泊松（不是双关语！）还是我遗漏了什么？
采纳答案by Ned Batchelder
It is easy to do by hand, but you can overflow doing it that way. You can do the exponent and factorial in a loop to avoid the overflow:
手工制作很容易，但这样做可能会溢出。您可以在循环中执行指数和阶乘以避免溢出：
def poisson_probability(actual, mean):
# naive: math.exp(-mean) * mean**actual / factorial(actual)
# iterative, to keep the components from getting too large or small:
p = math.exp(-mean)
for i in xrange(actual):
p *= mean
p /= i+1
return p
回答by Alex Coventry
scipyhas what you want
scipy有你想要的
>>> scipy.stats.distributions
<module 'scipy.stats.distributions' from '/home/coventry/lib/python2.5/site-packages/scipy/stats/distributions.pyc'>
>>> scipy.stats.distributions.poisson.pmf(6, 2.6)
array(0.031867055625524499)
It's worth noting that it's pretty easy to calculate by hand, too.
值得一提的是，它很容易通过手来计算，太。
