主成分分解

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA
iris = datasets.load_iris()
x = iris.data[:,1]
y = iris.data[:,2]
species = iris.target
x_reduced = PCA(n_components=3).fit_transform(iris.data)
fig = plt.figure()
ax = Axes3D(fig)
ax.set_title(‘Iris Dataset by PCA‘,size=14)
ax.scatter(x_reduced[:,0],x_reduced[:,1],x_reduced[:,2],c=species)
ax.set_xlabel(‘First‘)
ax.set_ylabel(‘Second‘)
ax.set_zlabel(‘Third‘)
ax.w_xaxis.set_ticklabels(())
ax.w_yaxis.set_ticklabels(())
ax.w_zaxis.set_ticklabels(())
plt.show()

主成分分解

原文：https://www.cnblogs.com/wei23/p/13127153.html