PCA

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

# X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])

X = np.array([[1, 0.44,0.29,.33],
              [.44, 1,.35,.32],
              [.29, .35,1,.60],
              [.33, .32,.60,1]])

print('打印x：\n{}'.format(X))

print("\n\n\n")

a, b = np.linalg.eig(X)
print('打印特征值a：\n{}'.format(a))
print('打印特征向量b：\n{}'.format(b))

print("\n\n\n")

# pca = PCA(n_components=2)
# newX = pca.fit_transform(X)
print(X)
# print(newX)
# print(pca.explained_variance_ratio_)

# pca = PCA(n_components=2)
pca = PCA()
newX = pca.fit_transform(X)

print('打印newX：\n{}'.format(newX))

# print(newX)
print(pca.explained_variance_ratio_)
plt.scatter(X[:, 0], X[:, 1], marker='o')
plt.scatter(newX[:, 0], newX[:, 0] * 0, color='r')
plt.show()