高斯密度与3西格玛检验

import numpy as np
import matplotlib.pyplot as plt

def normal_distribution1(mu=0,sigma=1,space=1000):
    x =  np.linspace(mu-(3*sigma-(-3*sigma)),mu+(3*sigma-(-3*sigma)),space)
    return 1/(np.sqrt(2*np.pi) * sigma) * np.exp(-1*((x-mu)**2)/(2*(sigma**2)))
    
def three_sigm_criteria(normalx):
    mu = normalx.mean()
    std = normalx.std()
    def check_sigm_n(n):
        tmp1 = mu-std <= normalx
        n1 = tmp1.size
        tmp2 = normalx[tmp1]<=(mu+n*std)
        n2 = tmp2.size
        return tmp2.dot(np.ones(n2))/n2
    return {"{}{}".format("sigm",i):(check_sigm_n(i)>v,check_sigm_n(i)) for i,v in zip(range(1,4),(0.6827,0.9545,0.9973))}


three_sigm_criteria(normal_distribution2(0,1))

本文地址：https://blog.csdn.net/weixin_43069769/article/details/107154789