在PCA和LDA中绘制凸包- Python

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.datasets import make_classification

# 生成一些示例数据
X, y = make_classification(n_samples=300, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1)

# 应用PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

# 绘制凸包
plt.figure()
for i in range(len(set(y))):
    plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1], label=f'Class {i}')
plt.legend()
plt.title('PCA Convex Hull')
plt.show()

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

# 应用LDA
lda = LDA(n_components=2)
X_lda = lda.fit_transform(X, y)

# 绘制凸包
plt.figure()
for i in range(len(set(y))):
    plt.scatter(X_lda[y == i, 0], X_lda[y == i, 1], label=f'Class {i}')
plt.legend()
plt.title('LDA Convex Hull')
plt.show()