如何在python中从N个不同的正态分布中采样M次？就处理时间而言，是否有“更快”的方法？

Question

我需要从 N 个不同的正态分布中采样多次 (M) 次。这种重复采样将依次发生数千次。我想以最有效的方式做到这一点，因为我不想在这个过程结束之前就老死。代码看起来像这样：

import numpy as np

# bunch of stuff that is unrelated to the problem

number_of_repeated_processes = 5000
number_of_samples_per_process = 20

# the normal distributions I'm sampling from are described by 2 vectors:
#
# myMEANS <- an numpy array of length 10 containing the means for the distributions
# myVAR <- an numpy array of length 10 containing the variance for the distributions


for i in range(number_of_repeated_processes):
  # myRESULT is a list of arrays containing the results for the sampling
  #
  myRESULT = [np.random.normal(loc=myMEANS[j], scale=myVAR[j], size = number_of_samples_per_process) for j in range(10)]
  #
  # here do something with myRESULT

# end for loop

问题是......有没有更好的方法来获取 myRESULT 矩阵

Answer 1

np.random.normal直接接受means-var作为数组，您可以选择一个大小来覆盖一次运行中的所有采样而无需循环：

myRESULT = np.random.normal(loc=myMEANS, scale=myVAR, size = (number_of_samples_per_process, number_of_repeated_processes,myMEANS.size))

这将为-数组中的每个mean-var对返回一个number_of_samples_per_process按列。例如，要访问-的示例，请使用。这应该会在一定程度上提高你的表现。number_of_repeated_processesmyMEANSmyVARmyMEANS[i]myVAR[i]myRESULT[...,i]