簡單易學的機器學習算法——馬爾可夫鏈蒙特卡羅方法MCMC

import random
from scipy.stats import norm
import matplotlib.pyplot as plt

def cauchy(theta):
    y = 1.0/(1.0 + theta ** 2)
    return y

T = 50 #循環的次數
sigma = 1
thetamin = -30
thetamax = 30
theta = [0.0] * (T + 1)
theta[0] = random.uniform(thetamin,thetamax)

t = 0
while t < T:
    t += 1
    theta_star = norm.rvs(loc=theta[t - 1], scale=sigma, size=1, random_state=None) #生成數據[] 一個

    alpha = min(1,cauchy(theta_star[0])/cauchy(theta[t - 1])) #調用上邊的函數

    u = random.uniform(0,1)
    if u <= alpha: #隨機值小於alpha則接受
        theta[t] = theta_star[0]

    else:
        theta[t] = theta[t-1]
print(theta)