import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn import datasets
from sklearn import manifold
from sklearn.metrics import accuracy_score# 获取数据
data = datasets.fetch_openml('mnist_784', version=1, return_X_y=True)
pixel_values, targets = data
targets = targets.astype(int)
pixel_array = pixel_values.to_numpy()
single_image = pixel_array[1, :].reshape(28, 28)# 展示图像
plt.imshow(single_image, cmap='gray')
plt.show()
def visualize_tsne(X, y):plt.figure(figsize=(10, 8))sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, palette=sns.color_palette("hls", 10), legend="full", alpha=0.8)plt.title('t-SNE visualization of MNIST data')plt.show()
# 使用 t-SNE 进行降维
tsne = manifold.TSNE(n_components=2, random_state=42, perplexity=25)
transformed_data = tsne.fit_transform(pixel_array)
