错误代码“name 'dataSet' is not defined”“name 'clusterChanged' is not defined”

在配置“kmeans对自己的图像数据集聚类（及肘部法求最佳K值）”代码时（详细代码可见https://blog.csdn.net/hnu_zzt/article/details/84788131）

import torch
from torch.utils import data
from PIL import Image
import numpy as np
from torchvision import transforms
from numpy import *


transform = transforms.Compose([
    transforms.ToTensor(),  # 将图片转换为Tensor,归一化至[0,1]
    # transforms.Normalize(mean=[.5, .5, .5], std=[.5, .5, .5])  # 标准化至[-1,1]
])


# 定义自己的数据集合
class FlameSet(data.Dataset):
    def __init__(self, root):
        # 所有图片的绝对路径
        imgs = os.listdir(root)
        self.imgs = [os.path.join(root, k) for k in imgs]
        self.transforms = transform

    def __getitem__(self, index):
        img_path = self.imgs[index]
        pil_img = Image.open(img_path)
        if self.transforms:
            data = self.transforms(pil_img)
        else:
            pil_img = np.asarray(pil_img)
            data = torch.from_numpy(pil_img)
        return data

    def __len__(self):
        return len(self.imgs)


# 计算两个矩阵的距离
def euclDistance(vector1, vector2):
    return sqrt(sum(power(vector2 - vector1, 2)))


# 在样本集中随机选取k个样本点作为初始质心
def initCentroids(dataSet, k):
    numSamples, dim = dataSet.shape  # 矩阵的行数、列数
    centroids = zeros((k, dim))  # 感觉要不要你都可以
    for i in range(k):
        index = int(random.uniform(0, numSamples))  # 随机产生一个浮点数，然后将其转化为int型
        centroids[i, :] = dataSet[index, :]
    return centroids


# k-means cluster
# dataSet为一个矩阵
# k为将dataSet矩阵中的样本分成k个类
def kmeans(dataSet, k):
    numSamples = dataSet.shape[0]  # 读取矩阵dataSet的第一维度的长度,即获得有多少个样本数据
    # first column stores which cluster this sample belongs to,
    # second column stores the error between this sample and its centroid
    clusterAssment = mat(zeros((numSamples, 2)))  # 得到一个N*2的零矩阵
    clusterChanged = True

上述代码均未见报错

 ## step 1: init centroids
    centroids = initCentroids(dataSet, k)  # 在样本集中随机选取k个样本点作为初始质心

此行代码报错，内容如下

    while clusterChanged:
        clusterChanged = False
        ## for each sample
        for i in range(numSamples):  # range
            minDist = 100000.0
            minIndex = 0
            ## for each centroid
            ## step 2: find the centroid who is closest
            # 计算每个样本点与质点之间的距离，将其归内到距离最小的那一簇
            for j in range(k):
                distance = euclDistance(centroids[j, :], dataSet[i, :])
                if distance < minDist:
                    minDist = distance
                    minIndex = j

此行代码报错，内容如下

求解决方法~

你后面两端代码的外围代码看不到啊，这没法看出dataSet和clusterChanged变量在哪定义的啊
你得贴出错误代码所在的整个函数，或者整个代码块才能判断啊

你把你代码截图呢，
centroids = initCentroids(dataSet, k)的dataSet应该是def kmeans(dataSet, k)的dataSet啊