补全代码，使用 PCA 对MNIST的数据降维到2维 做一下KMeans并显示： import numpy as np from sklearn.datasets import fetch_openml # Load data from https://www.openml.org/d/554 X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) print(X.shape,y.shape) #(70000, 784) (70000,) print(np.unique(y)) ##可只选择前10000条数据做实验 X=X[:10000] y=y[:10000]

import numpy as np 
from sklearn.datasets import fetch_openml 
from sklearn.decomposition import PCA 
from sklearn.cluster import KMeans 
import matplotlib.pyplot as plt 

# Load data from https://www.openml.org/d/554 
X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) 

# Print the shape of X and y
print(X.shape, y.shape) #(70000, 784) (70000,)

# Print the unique values of y
print(np.unique(y))

# Select the first 10000 samples for experiment
X = X[:10000]
y = y[:10000]

# Perform PCA to reduce dimensionality to 2
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

# Perform KMeans clustering on the reduced data
kmeans = KMeans(n_clusters=10)
kmeans.fit(X_pca)

# Plot the clustered data
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=kmeans.labels_, cmap='viridis')
plt.title('KMeans Clustering on MNIST Data')
plt.show()