Python 有没有办法在PCA/LDA数据上添加圆/椭圆
在PCA/LDA数据上放置圆/椭圆的最佳方法是什么 就像这个例子 在那个密码上Python 有没有办法在PCA/LDA数据上添加圆/椭圆,python,matplotlib,Python,Matplotlib,在PCA/LDA数据上放置圆/椭圆的最佳方法是什么 就像这个例子 在那个密码上 import matplotlib.pyplot as plt from sklearn import datasets from sklearn.decomposition import PCA from sklearn.discriminant_analysis import LinearDiscriminantAnalysis iris = datasets.load_iris() X = iris.
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
iris = datasets.load_iris()
X = iris.data
y = iris.target
target_names = iris.target_names
pca = PCA(n_components=2)
X_r = pca.fit(X).transform(X)
lda = LinearDiscriminantAnalysis(n_components=2)
X_r2 = lda.fit(X, y).transform(X)
# Percentage of variance explained for each components
print('explained variance ratio (first two components): %s'
% str(pca.explained_variance_ratio_))
plt.figure()
colors = ['navy', 'turquoise', 'darkorange']
lw = 2
for color, i, target_name in zip(colors, [0, 1, 2], target_names):
plt.scatter(X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=.8, lw=lw,
label=target_name)
plt.legend(loc='best', shadow=False, scatterpoints=1)
plt.title('PCA of IRIS dataset')
plt.figure()
for color, i, target_name in zip(colors, [0, 1, 2], target_names):
plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], alpha=.8, color=color,
label=target_name)
plt.legend(loc='best', shadow=False, scatterpoints=1)
plt.title('LDA of IRIS dataset')
plt.show()