给个更完整更详细科学的代码
时间: 2024-02-16 21:03:23 浏览: 24
好的,以下是一个更完整、更详细、更科学的代码,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
print('Loading data...')
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
print('Splitting data...')
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# PCA降维
print('Performing PCA...')
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
print('Performing Kmeans++ clustering...')
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
print('Evaluating clustering...')
score = silhouette_score(X_train_pca, labels)
print('聚类的sh系数为:', score)
# 测试聚类模型
print('Testing clustering model...')
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)
```
以上代码与之前的代码相比,增加了一些输出信息,更详细地展示了代码的运行过程。此外,代码中还添加了一些注释,以帮助您更好地理解代码的每个部分。希望这个代码对您有帮助。