I'm looking for rows in B which are close to any of the rows in A
example: eps = 0.1
A = [[1.22, 1.33], [1.45, 1.66]]
B = [[1.25, 1.34], [1.77, 1.66], [1.44, 1.67]]
Result: [[1.22, 1.33], [1.45, 1.66]]
If you are looking to filter to elements in A which are close to any element in B, you can use broadcast and tile to do an exhaustive check:
import numpy as np
eps = .1
A = np.array([[1.22, 1.33], [1.45, 1.66]])
B = np.array([[1.25, 1.34], [1.77, 1.66], [1.44, 1.67]])
# broadcast A based on the shape of B
A_ext = np.broadcast_to(A, (B.shape[0],) + A.shape)
# tile B and reshape, this will allow comparison of all elements in A to all elements in B
B_ext = np.tile(B, A.shape[0]).reshape(A_ext.shape)
# create the boolean array
A_bool = np.abs(A_ext - B_ext) < eps
# reduce array to match number of elements in A
# .all() will result in an array representing which elements in A are close to each element in B
# .any() represents if A is close to any of the elements in B
A_mask = A_bool.all(axis = -1).any(axis = 0)
# final result
A[A_mask]