main.py

import numpy as np
from contrastive import CPCA

N = 400; D = 30; gap=3
# In B, all the data pts are from the same distribution, which has different variances in three subspaces.
B = np.zeros((N, D))
B[:,0:10] = np.random.normal(0,10,(N,10))
B[:,10:20] = np.random.normal(0,3,(N,10))
B[:,20:30] = np.random.normal(0,1,(N,10))


# In A there are four clusters.
A = np.zeros((N, D))
A[:,0:10] = np.random.normal(0,10,(N,10))
# group 1
A[0:100, 10:20] = np.random.normal(0,1,(100,10))
A[0:100, 20:30] = np.random.normal(0,1,(100,10))
# group 2
A[100:200, 10:20] = np.random.normal(0,1,(100,10))
A[100:200, 20:30] = np.random.normal(gap,1,(100,10))
# group 3
A[200:300, 10:20] = np.random.normal(2*gap,1,(100,10))
A[200:300, 20:30] = np.random.normal(0,1,(100,10))
# group 4
A[300:400, 10:20] = np.random.normal(2*gap,1,(100,10))
A[300:400, 20:30] = np.random.normal(gap,1,(100,10))
A_labels = [0]*100+[1]*100+[2]*100+[3]*100

cpca = CPCA(standardize=False)
cpca.fit_transform(A, B, plot=True, active_labels=A_labels)