from sklearn.datasets import load_iris data,target=load_iris(return_X_y=True) print('feature_value:',data.shape) print('target:',target) from sklearn.cluster import KMeans import numpy as np for i in range(0,30): kmeans=KMeans(n_clusters=3,max_iter=30,tol=0.0001, random_state=i).fit(data) label=kmeans.labels_ #print('label:',label) center=kmeans.cluster_centers_ #print('center:',center) predict=kmeans.predict(data) print('i=',i) print('predict:',predict) accuracy=np.mean(predict==target)*100 print('accuracy',accuracy) #模型预测 predict=kmeans.predict(data) print('predict:',predict) import numpy as np accurancy=np.mean(predict==target)*100 print('i=',i) print('accurancy',accurancy) import matplotlib.pyplot as plt import mglearn plt.figure(figsize=(10,8)) plt.subplot(221) mglearn.discrete_scatter(data[:,0],data[:,1],target,markers='^') plt.xlabel('data') plt.ylabel('origin') plt.subplot(222) mglearn.discrete_scatter(data[:,0],data[:,1],y_predict,markers='^') mglearn.discrete_scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],[0,1,2],markers='o',markeredgewidth=2) plt.xlabel('data') plt.ylabel('y_predict') plt.show()写一下注释，要非常详细

注释如下：

# 导入数据集
from sklearn.datasets import load_iris 
data, target = load_iris(return_X_y=True) 
print('feature_value:',data.shape) 
print('target:',target)

# 导入KMeans聚类模型，对数据进行聚类
from sklearn.cluster import KMeans 
import numpy as np 

# 通过循环实现多次聚类，记录每次聚类的预测结果和准确率
for i in range(0,30): 
    kmeans = KMeans(n_clusters=3, max_iter=30, tol=0.0001, random_state=i).fit(data) 
    label = kmeans.labels_ 
    center = kmeans.cluster_centers_ 
    predict = kmeans.predict(data) 
    print('i=',i) 
    print('predict:',predict) 
    accuracy=np.mean(predict==target)*100 
    print('accuracy',accuracy) 

# 对聚类模型进行预测
predict=kmeans.predict(data) 
print('predict:',predict) 
import numpy as np 
accuracy=np.mean(predict==target)*100 
print('i=',i) 
print('accuracy',accuracy)

# 绘制数据散点图和聚类结果散点图
import matplotlib.pyplot as plt 
import mglearn 

plt.figure(figsize=(10,8)) 
plt.subplot(221) 
mglearn.discrete_scatter(data[:,0],data[:,1],target,markers='^') 
plt.xlabel('data') 
plt.ylabel('origin') 

plt.subplot(222) 
mglearn.discrete_scatter(data[:,0],data[:,1],y_predict,markers='^') 
mglearn.discrete_scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],[0,1,2],markers='o',markeredgewidth=2) 
plt.xlabel('data') 
plt.ylabel('y_predict') 

plt.show()

其中，注释解释了代码的主要功能和实现方法，如导入数据集、导入KMeans聚类模型、多次聚类、预测、绘制数据散点图和聚类结果散点图等。