Python:可以用多维向量做PCA吗？

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

# 创建一个多维向量数据集
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# 初始化PCA对象，指定要降到的维度
pca = PCA(n_components=2)

# 拟合数据并进行PCA转换
transformed_data = pca.fit_transform(data)

# 打印转换后的数据
print("Transformed data:\n", transformed_data)

# 可视化降维后的数据
plt.scatter(transformed_data[:, 0], transformed_data[:, 1])
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('PCA of Multidimensional Data')
plt.show()

explained_variance = pca.explained_variance_ratio_
cumulative_variance = np.cumsum(explained_variance)
print("Explained variance ratio:\n", explained_variance)
print("Cumulative explained variance:\n", cumulative_variance)