def test_eigenvectors(self):
np = pytest.importorskip("numpy")
eigenval = np.linalg.eigvals
scipy = pytest.importorskip("scipy")
cs = "ddiiddid"
G = nxt.threshold_graph(cs)
(tgeval, tgevec) = nxt.eigenvectors(cs)
dot = np.dot
assert [abs(dot(lv, lv) - 1.0) < 1e-9 for lv in tgevec] == [True] * 8
lapl = nx.laplacian_matrix(G)
def test_eigenvectors(self):
np = pytest.importorskip("numpy")
eigenval = np.linalg.eigvals
pytest.importorskip("scipy")
cs = "ddiiddid"
G = nxt.threshold_graph(cs)
(tgeval, tgevec) = nxt.eigenvectors(cs)
np.testing.assert_allclose([np.dot(lv, lv) for lv in tgevec],
1.0,
rtol=1e-9)
lapl = nx.laplacian_matrix(G)
def test_eigenvectors(self):
try:
import numpy as N
eigenval=N.linalg.eigvals
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
cs='ddiiddid'
G=nxt.threshold_graph(cs)
(tgeval,tgevec)=nxt.eigenvectors(cs)
dot=N.dot
assert_equal([ abs(dot(lv,lv)-1.0)<1e-9 for lv in tgevec ], [True]*8)
lapl=nx.laplacian_matrix(G)
def test_eigenvectors(self):
try:
import numpy as N
eigenval = N.linalg.eigvals
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
cs = 'ddiiddid'
G = nxt.threshold_graph(cs)
(tgeval, tgevec) = nxt.eigenvectors(cs)
dot = N.dot
assert_equal([abs(dot(lv, lv) - 1.0) < 1e-9 for lv in tgevec], [True] * 8)
lapl = nx.laplacian_matrix(G)
