把下方Python函数转化成伪代码def AddRandom(noise_std,x_train,min,max): x_train = pd.DataFrame(x_train) m=x_train.shape[0] n=x_train.shape[1] #生成Mask掩码 Mask = matlab.rand(m,n) for i in range(0,m): for j in range(0,n): if Mask[i,j]<=noise_std: loc_ij = x_train.iloc[i,j] if(loc_ij==0): loc_ij=max Mask[i,j]=np.random.uniform(0,max/loc_ij) #Mask[i,j]=0 else: Mask[i,j]=1; x_train_Random = (x_train*Mask).astype(int) return x_train_Random

以下是将Python函数转化成伪代码的结果：

# 伪代码示例: 添加随机噪声

# 定义函数AddRandom
function AddRandom(noise_std, x_train, min, max):
    # 将x_train转化为DataFrame格式
    x_train = DataFrame(x_train)
    
    # 获取x_train的行列数
    m = x_train.shape[0]
    n = x_train.shape[1]
    
    # 生成Mask掩码
    Mask = rand(m, n)
    
    # 遍历Mask，并根据噪声标准差添加随机噪声
    for i in range(0, m):
        for j in range(0, n):
            if Mask[i, j] <= noise_std:
                loc_ij = x_train.iloc[i, j]
                if(loc_ij == 0):
                    loc_ij = max
                Mask[i, j] = np.random.uniform(0, max/loc_ij)
                # Mask[i, j] = 0
            else:
                Mask[i, j] = 1
    
    # 将x_train与Mask相乘，得到带有随机噪声的x_train_Random
    x_train_Random = (x_train * Mask).astype(int)
    
    # 返回x_train_Random
    return x_train_Random

需要注意的是，这只是一种伪代码的写法，与实际的Python代码可能会有所区别。