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
时间: 2023-11-29 18:06:47 浏览: 165
代码功能:使用KMeans算法对鸢尾花数据集进行聚类,并绘制聚类结果的散点图。
1. 导入所需的库和数据集:
```python
from sklearn.datasets import load_iris
data,target=load_iris(return_X_y=True)
```
2. 输出特征值和目标值的形状:
```python
print('feature_value:',data.shape)
print('target:',target)
```
3. 导入KMeans算法,对数据进行聚类:
```python
from sklearn.cluster import KMeans
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)
```
4. 输出聚类的预测结果,并计算准确率:
```python
predict=kmeans.predict(data)
print('predict:',predict)
import numpy as np
accurancy=np.mean(predict==target)*100
print('i=',i)
print('accurancy',accurancy)
```
5. 绘制散点图:
```python
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,第一个子图是原始数据的散点图,第二个子图是聚类结果的散点图,红色、黄色和紫色分别代表三个不同的簇。
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