所以我正在模拟一维随机游走，有 1000 次游走，每次走 1000 步。

如何计算步行者返回原点的平均次数，然后计算步行者返回原点次数的标准差？

我觉得我不理解如何操纵它以获得我需要的答案的逻辑，所以如果有人可以提供帮助，那将非常有帮助！我现在的代码是：

def StepsFunc() :
    step = np.random.randint( 2 )

    #step is 0 or 1, want -1 or +1
    if step == 0 :
        step = -1
    return step

#defining another function, which will actually take N number of steps
def Move( N ) : 
    x = 0 # starting from origin position
    i = 0 # count
  
    while i <= N-1 :
        step = StepsFunc()
        x += step
        i += 1
    return x

#number of steps
N = 1000

#number of random walks
walks = 1000

StepsList=[]

num=0
for i in range(walks):
    x = Move( N )
    if x == 0:
        num += 1    
    StepsList.append( x )
    

print("The random walk returns to zero {} times".format(num))
print(np.mean(StepsList)) #average of all walks
print(num/walks) #getting the average number of walks that returned to zero?
#plt.hist(StepsList, bins='auto')

################################################# ###################

更新代码：

################################################# ###################

def Move( N ) : 
    x = 0 # starting from origin position
    i = 0 # count
    steps = [] # history of all positions after each step
    for i in range(N) :
        step = StepsFunc()
        x += step
        steps.append(x)
    return steps

N = 1000 
walks = 1000 

StepsList=[] 

for i in range(walks):
    StepsList.append(Move( N ))
    
ZerosList = []
for x in StepsList:
    temp = 0
    for y in x:
        if y == 0:
            temp += 1
    ZerosList.append(temp) 

我可以使用它来计算平均值和标准差。