给我个完整可用的代码
时间: 2024-02-16 14:03:23 浏览: 29
好的,以下是一个简单的可用代码,供您参考:
```python
import numpy as np
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.neighbors import KNeighborsClassifier
# 载入数据
mnist = fetch_openml('mnist_784')
X = mnist.data.astype('float32')
y = mnist.target.astype('int32')
# 划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# PCA降维
pca = PCA(n_components=50) # 将数据降为50维
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
# Kmeans++聚类
kmeans = KMeans(n_clusters=10, init='k-means++', n_init=10, max_iter=300, random_state=42)
kmeans.fit(X_train_pca)
labels = kmeans.labels_
# 聚类评价
score = silhouette_score(X_train_pca, labels)
print('聚类的sh系数为:', score)
# 测试聚类模型
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train_pca, labels)
accuracy = knn.score(X_test_pca, y_test)
print('测试集准确率为:', accuracy)
```
以上代码包括了载入数据、划分为训练集和测试集、PCA降维、Kmeans++聚类、聚类评价、测试聚类模型等步骤。请注意,由于MNIST数据集较大,代码的运行可能需要一定的时间。如果您需要更详细的解释,请随时提出。
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