代码如下
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

功能: 设置随机种子, 确保结果可复现

def make_seed(SEED=42):
np.random.seed(SEED)

功能: 计算样本与聚类中心的距离, 返回离簇中心最近的类别

params: sample: 单个数据样本, centers: k个簇中心

return: 返回的是当前的样本数据属于那一个簇中心的id或者索引

def distance(sample, centers):
# 这里用差的平方来表示距离
d = np.power(sample - centers, 2).sum(axis=1)
cls = d.argmin()
return cls

功能: 对当前的分类子集进行可视化展示

def clusters_show(clusters, step):
color = ["red", "blue", "pink"]
marker = ["*", "^", "."]
plt.figure(figsize=(8, 8))
plt.title("step: {}".format(step))
plt.xlabel("Density", loc="center")
plt.ylabel("Sugar Content", loc="center")
# 用颜色区分k个簇的数据样本
for i, cluster in enumerate(clusters):
cluster = np.array(cluster)
plt.scatter(cluster[:, 0], cluster[:, 1], c=color[i], marker=marker[i], s=150)
plt.show()

功能: 根据输入的样本集与划分的簇数，分别返回k个簇样本

params： data：样本集, k：聚类簇数

return：返回是每个簇的簇类中心

def k_means(samples, k):

data_number = len(samples)
centers_flag = np.zeros((k,))

# 随机在数据中选择k个聚类中心
centers = samples[np.random.choice(data_number, k, replace=False)]
print(centers)

step = 0
while True:
    # 计算每个样本距离簇中心的距离, 然后分到距离最短的簇中心中
    clusters = [[] for i in range(k)]
    for sample in samples:
        ci = distance(sample, centers)
        clusters[ci].append(sample)

    # 可视化当前的聚类结构
    clusters_show(clusters, step)

    # 分完簇之后更新每个簇的中心点, 得到了簇中心继续进行下一步的聚类
    for i, sub_clusters in enumerate(clusters):
        new_center = np.array(sub_clusters).mean(axis=0)
        # 如果数值有变化则更新, 如果没有变化则设置标志位为1，当所有的标志位为1则退出循环
        if (centers[i] != new_center).all():
            centers[i] = new_center
        else:
            centers_flag[i] = 1

    step += 1
    print("step:{}".format(step), "\n", "centers:{}".format(centers))
    if centers_flag.all():
        break

return centers

功能: 根据簇类中心对簇进行分类，获取最后的分类结果

params: samples是全部的数据样本，centers是聚类好的簇中心

return: 返回的是子数组

def split_data(samples, centers):

# 根据中心样本得知簇数
k = len(centers)
clusters = [[] for i in range(k)]
for sample in samples:
    ci = distance(sample, centers)
    clusters[ci].append(sample)

return clusters

if name == 'main':

make_seed()
# 导入数据
data = pd.read_csv('C:/Users/lenovo/Documents/Tencent Files/2352674321/FileRecv/MobileFile/datasexiguashujvji.csv')
samples = data[["密度", "含糖率"]].values
print(samples)

centers = k_means(samples=samples, k=3)
clusters = split_data(samples=samples, centers=centers)
print(clusters)

应该是你数据的问题，使用这个数据试试，还有这个问题不

from sklearn.datasets import make_blobs

np.random.seed(0)

batch_size = 45
centers = [[1, 1], [-1, -1], [1, -1]]
n_clusters = len(centers)
X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)