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目前，我编写了一些代码，可以找到最大化对数似然函数和一些字段数据的参数组合。该模型现在从均匀分布中随机选择参数，然后选择最佳参数集。然而，这种方法不会给出可重复的结果。我想知道是否有人有任何示例代码来说明如何更新此代码以运行小批量梯度下降（或上升）方法以找到最佳参数集？

此外，该模型现在一次使用所有现场数据，而我希望在每次迭代时测试小批量数据。下面是我的代码：

from math import *
import numpy as np
import random
import matplotlib.pyplot as plt

def ff(xs,vx,Dx,XSpill,sigmax0,SP):
    mux = XSpill + vx*SP
    sigmax = sigmax0 + np.sqrt(2.0*Dx*SP)
    return (1.0/(np.sqrt(2.0*np.pi*(sigmax**2.0))))*np.exp((-(x-mux)**2.0)/(2.0*(sigmax**2.0)))

def likelihood(xs,XSpill,ConData,sigmax0,SP):
    vx1 = [random.uniform(-50.0,50.0) for i in range(1000)]
    Dx1 = [random.uniform(0.01,20.1) for i in range(1000)]

    IniIndLikelihood = np.ones([len(xs),len(vx1)])
    Lamda = np.zeros(shape=(len(vx1)))
    Prob = np.zeros(shape=(len(vx1)))
    for ci in range(len(xs)):
        if ConData[ci] > 0.0:
            for i in range(len(vx1)):
            Prob[i] = ff(xs[ci],vx1[i],Dx1[i],XSpill,sigmax0,SP)
            if Prob[i] > 1e-308:
                Lamda[i] = 1/Prob[i]
                IniIndLikelihood[ci,i] = Lamda[i]*np.exp(-Lamda[i]*ConData[ci])
            else:
                Lamda[i] = 0.0
                IniIndLikelihood[ci,i] = 0.0

    CompLikelihood = np.ones([len(vx1)])
    Likelihood = np.zeros([len(vx1)])
    for i in range(len(vx1)):
        for ci in range(len(xs)):
            if ConData[ci] > 0.0:
                if IniIndLikelihood[ci,i] == 0.0:
                    CompLikelihood[i] = 0.0

    MaxLogLike = -22.0
    for i in range(len(vx1)):
        for ci in range(len(xs)):
            if ConData[ci] > 0.0:
                if CompLikelihood[i] == 1.0:
                    Likelihood[i] = Likelihood[i] + np.log(IniIndLikelihood[ci,i])

        if CompLikelihood[i] == 1.0:
            if MaxLogLike == -22.0:
                MaxLogLike = Likelihood[i]
            else:
                MaxLogLike = np.max([MaxLogLike,Likelihood[i]])

    for i in range(len(vx1)):
        if CompLikelihood[i] == 1.0:
            Likelihood[i] = Likelihood[i] - MaxLogLike

    return Likelihood

if __name__ == "__main__":

    sigmax0 = 0.0
    XSpill = 0.0
    SP = 1.0
    xs = [1,3,5,9,20,34,40,60]
    ConData = np.array([5,7,30,5,5,15,30,5])/100

    Result = likelihood(xs,XSpill,ConData,sigmax0,SP)

其中 xs 是位置，ConData 是字段数据的集中度。然后在得到对数似然的概率后，我使用 argmax 来找到最佳的参数组合。

任何建议、链接或示例代码都会有所帮助，因为我无法找到 MLE 方法的 Python 示例！

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