scipy rv_continuous非常慢

问题描述

我正在使用自定义函数 f(x)通过 copy 的 rv_continuous 类定义自定义分布.我的代码是

  class my_pdf_gen(rv_continuous):def _pdf(自我，x，整数):返回f(x)/积分 

其中 integral 可确保规范化.我可以使用它创建一个实例

  my_pdf = my_pdf_gen(my_int，a = a，b = b，名称='my pdf') 

使用 a，b 值范围的上限和下限，以及 my_int = scipy.integrate.quad(f，a，b)[0] .我还可以使用 my_pdf.rvs(my_int，size = 5)创建随机数据样本，但这非常慢.(当 size = 9 时最长为6秒).

我读到一个人也应该覆盖该类中的其他一些方法(例如 _ppf )，但是从示例中我发现尚不清楚如何在我的情况下实现它./p>

非常感谢！

解决方案

我通过更改方法并使用Monte Carlo的拒绝采样器方法解决了该问题

  def reject_sampler(p，xbounds，pmax):而True:x = np.random.rand(1)*(xbounds [1] -xbounds [0])+ xbounds [0]y = np.random.rand(1)* pmax如果y  

其中 p 是概率密度函数， xbounds 是一个包含pdf上下限的元组，而 pmax 是pdf在域上的最大值.

此处建议使用蒙特卡罗的拒绝抽样器: python:从中随机抽样自定义概率函数

I am using a custom function f(x) to define a custom distribution using copy's rv_continuous class. My code is

class my_pdf_gen(rv_continuous):
    def _pdf(self, x, integral):
        return f(x)/integral

where integral ensure the normalisation. I am able to create an instance of it with

my_pdf = my_pdf_gen(my_int,a = a, b = b, name = 'my pdf')

with a,b the upper and lower limit of the value's range, and my_int= scipy.integrate.quad(f, a, b)[0].I am also able to create a random sample of data using my_pdf.rvs(my_int, size = 5), but this is very slow. (Up to 6 seconds when size=9).

I read that one should also overwrite some other methods in the class (like _ppf), but from the examples I found it isn't clear to me how to achieve it in my case.

Thanks a lot!

解决方案

I solved the problem by changing approach and using Monte Carlo's rejection sampler method

def rejection_sampler(p,xbounds,pmax):
    while True:
        x = np.random.rand(1)*(xbounds[1]-xbounds[0])+xbounds[0]
        y = np.random.rand(1)*pmax
        if y<=p(x):
            return x

where p is the probability density function, xbounds is a tuple containing the upper and lower limits of of the pdf and pmax is the maximum value of the pdf on the domain.

Monte Carlo's rejection sampler was suggested here: python: random sampling from self-defined probability function

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