1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
|
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import run_module_suite
from scipy import sparse
from scipy.sparse import csgraph
def _explicit_laplacian(x, normed=False):
if sparse.issparse(x):
x = x.todense()
x = np.asarray(x)
y = -1.0 * x
for j in range(y.shape[0]):
y[j,j] = x[j,j+1:].sum() + x[j,:j].sum()
if normed:
d = np.diag(y).copy()
d[d == 0] = 1.0
y /= d[:,None]**.5
y /= d[None,:]**.5
return y
def _check_graph_laplacian(mat, normed):
if not hasattr(mat, 'shape'):
mat = eval(mat, dict(np=np, sparse=sparse))
if sparse.issparse(mat):
sp_mat = mat
mat = sp_mat.todense()
else:
sp_mat = sparse.csr_matrix(mat)
laplacian = csgraph.laplacian(mat, normed=normed)
n_nodes = mat.shape[0]
if not normed:
np.testing.assert_array_almost_equal(laplacian.sum(axis=0),
np.zeros(n_nodes))
np.testing.assert_array_almost_equal(laplacian.T,
laplacian)
np.testing.assert_array_almost_equal(
laplacian,
csgraph.laplacian(sp_mat, normed=normed).todense())
np.testing.assert_array_almost_equal(
laplacian,
_explicit_laplacian(mat, normed=normed))
def test_graph_laplacian():
mats = ('np.arange(10) * np.arange(10)[:, np.newaxis]',
'np.ones((7, 7))',
'np.eye(19)',
'sparse.diags([1, 1], [-1, 1], shape=(4,4))',
'sparse.diags([1, 1], [-1, 1], shape=(4,4)).todense()',
'np.asarray(sparse.diags([1, 1], [-1, 1], shape=(4,4)).todense())',
'np.vander(np.arange(4)) + np.vander(np.arange(4)).T',
)
for mat_str in mats:
for normed in (True, False):
yield _check_graph_laplacian, mat_str, normed
if __name__ == '__main__':
run_module_suite()
|
