71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
|
import numpy as np
|
||
|
from numpy.testing import assert_equal
|
||
|
from scipy.sparse.csgraph import reverse_cuthill_mckee, structural_rank
|
||
|
from scipy.sparse import csc_matrix, csr_matrix, coo_matrix
|
||
|
|
||
|
|
||
|
def test_graph_reverse_cuthill_mckee():
|
||
|
A = np.array([[1, 0, 0, 0, 1, 0, 0, 0],
|
||
|
[0, 1, 1, 0, 0, 1, 0, 1],
|
||
|
[0, 1, 1, 0, 1, 0, 0, 0],
|
||
|
[0, 0, 0, 1, 0, 0, 1, 0],
|
||
|
[1, 0, 1, 0, 1, 0, 0, 0],
|
||
|
[0, 1, 0, 0, 0, 1, 0, 1],
|
||
|
[0, 0, 0, 1, 0, 0, 1, 0],
|
||
|
[0, 1, 0, 0, 0, 1, 0, 1]], dtype=int)
|
||
|
|
||
|
graph = csr_matrix(A)
|
||
|
perm = reverse_cuthill_mckee(graph)
|
||
|
correct_perm = np.array([6, 3, 7, 5, 1, 2, 4, 0])
|
||
|
assert_equal(perm, correct_perm)
|
||
|
|
||
|
# Test int64 indices input
|
||
|
graph.indices = graph.indices.astype('int64')
|
||
|
graph.indptr = graph.indptr.astype('int64')
|
||
|
perm = reverse_cuthill_mckee(graph, True)
|
||
|
assert_equal(perm, correct_perm)
|
||
|
|
||
|
|
||
|
def test_graph_reverse_cuthill_mckee_ordering():
|
||
|
data = np.ones(63,dtype=int)
|
||
|
rows = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2,
|
||
|
2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5,
|
||
|
6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9,
|
||
|
9, 10, 10, 10, 10, 10, 11, 11, 11, 11,
|
||
|
12, 12, 12, 13, 13, 13, 13, 14, 14, 14,
|
||
|
14, 15, 15, 15, 15, 15])
|
||
|
cols = np.array([0, 2, 5, 8, 10, 1, 3, 9, 11, 0, 2,
|
||
|
7, 10, 1, 3, 11, 4, 6, 12, 14, 0, 7, 13,
|
||
|
15, 4, 6, 14, 2, 5, 7, 15, 0, 8, 10, 13,
|
||
|
1, 9, 11, 0, 2, 8, 10, 15, 1, 3, 9, 11,
|
||
|
4, 12, 14, 5, 8, 13, 15, 4, 6, 12, 14,
|
||
|
5, 7, 10, 13, 15])
|
||
|
graph = coo_matrix((data, (rows,cols))).tocsr()
|
||
|
perm = reverse_cuthill_mckee(graph)
|
||
|
correct_perm = np.array([12, 14, 4, 6, 10, 8, 2, 15,
|
||
|
0, 13, 7, 5, 9, 11, 1, 3])
|
||
|
assert_equal(perm, correct_perm)
|
||
|
|
||
|
|
||
|
def test_graph_structural_rank():
|
||
|
# Test square matrix #1
|
||
|
A = csc_matrix([[1, 1, 0],
|
||
|
[1, 0, 1],
|
||
|
[0, 1, 0]])
|
||
|
assert_equal(structural_rank(A), 3)
|
||
|
|
||
|
# Test square matrix #2
|
||
|
rows = np.array([0,0,0,0,0,1,1,2,2,3,3,3,3,3,3,4,4,5,5,6,6,7,7])
|
||
|
cols = np.array([0,1,2,3,4,2,5,2,6,0,1,3,5,6,7,4,5,5,6,2,6,2,4])
|
||
|
data = np.ones_like(rows)
|
||
|
B = coo_matrix((data,(rows,cols)), shape=(8,8))
|
||
|
assert_equal(structural_rank(B), 6)
|
||
|
|
||
|
#Test non-square matrix
|
||
|
C = csc_matrix([[1, 0, 2, 0],
|
||
|
[2, 0, 4, 0]])
|
||
|
assert_equal(structural_rank(C), 2)
|
||
|
|
||
|
#Test tall matrix
|
||
|
assert_equal(structural_rank(C.T), 2)
|