from sklearn import decomposition

import numpy as np

A1_mean = [1, 1]

A1_cov = [[2, .99], [1, 1]]

A1 = np.random.multivariate_normal(A1_mean, A1_cov, 50)

A2_mean = [5, 5]

A2_cov = [[2, .99], [1, 1]]

A2 = np.random.multivariate_normal(A2_mean, A2_cov, 50)

A = np.vstack((A1, A2)) #A1:50*2；A2：50*2,水平连接

B_mean = [5, 0]

B_cov = [[.5, -1], [-0.9, .5]]

B = np.random.multivariate_normal(B_mean, B_cov, 100)

import matplotlib.pyplot as plt

plt.scatter(A[:,0],A[:,1],c='r',marker='o')

plt.scatter(B[:,0],B[:,1],c='g',marker='*')

plt.show()

#很蠢的想法，把A和B合并，然后进行一维可分

kpca = decomposition.KernelPCA(kernel='cosine', n_components=1)

AB = np.vstack((A, B))

AB_transformed = kpca.fit_transform(AB)

plt.scatter(AB_transformed,AB_transformed,c='b',marker='*')

plt.show()

kpca = decomposition.KernelPCA(n_components=1)

AB = np.vstack((A, B))

AB_transformed = kpca.fit_transform(AB)

plt.scatter(AB_transformed,AB_transformed,c='b',marker='*')

plt.show()