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mightyscape-1.1-deprecated/extensions/networkx/linalg/tests/test_spectrum.py

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2020-07-30 01:16:18 +02:00
import pytest
numpy = pytest.importorskip('numpy')
npt = pytest.importorskip('numpy.testing')
scipy = pytest.importorskip('scipy')
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestSpectrum(object):
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.P = nx.path_graph(3)
cls.WG = nx.Graph((u, v, {'weight': 0.5, 'other': 0.3})
for (u, v) in cls.G.edges())
cls.WG.add_node(4)
cls.DG = nx.DiGraph()
nx.add_path(cls.DG, [0, 1, 2])
def test_laplacian_spectrum(self):
"Laplacian eigenvalues"
evals = numpy.array([0, 0, 1, 3, 4])
e = sorted(nx.laplacian_spectrum(self.G))
npt.assert_almost_equal(e, evals)
e = sorted(nx.laplacian_spectrum(self.WG, weight=None))
npt.assert_almost_equal(e, evals)
e = sorted(nx.laplacian_spectrum(self.WG))
npt.assert_almost_equal(e, 0.5 * evals)
e = sorted(nx.laplacian_spectrum(self.WG, weight='other'))
npt.assert_almost_equal(e, 0.3 * evals)
def test_normalized_laplacian_spectrum(self):
"Normalized Laplacian eigenvalues"
evals = numpy.array([0, 0, 0.7712864461218, 1.5, 1.7287135538781])
e = sorted(nx.normalized_laplacian_spectrum(self.G))
npt.assert_almost_equal(e, evals)
e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight=None))
npt.assert_almost_equal(e, evals)
e = sorted(nx.normalized_laplacian_spectrum(self.WG))
npt.assert_almost_equal(e, evals)
e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight='other'))
npt.assert_almost_equal(e, evals)
def test_adjacency_spectrum(self):
"Adjacency eigenvalues"
evals = numpy.array([-numpy.sqrt(2), 0, numpy.sqrt(2)])
e = sorted(nx.adjacency_spectrum(self.P))
npt.assert_almost_equal(e, evals)
def test_modularity_spectrum(self):
"Modularity eigenvalues"
evals = numpy.array([-1.5, 0., 0.])
e = sorted(nx.modularity_spectrum(self.P))
npt.assert_almost_equal(e, evals)
# Directed modularity eigenvalues
evals = numpy.array([-0.5, 0., 0.])
e = sorted(nx.modularity_spectrum(self.DG))
npt.assert_almost_equal(e, evals)
def test_bethe_hessian_spectrum(self):
"Bethe Hessian eigenvalues"
evals = numpy.array([0.5 * (9 - numpy.sqrt(33)), 4,
0.5 * (9 + numpy.sqrt(33))])
e = sorted(nx.bethe_hessian_spectrum(self.P, r=2))
npt.assert_almost_equal(e, evals)
# Collapses back to Laplacian:
e1 = sorted(nx.bethe_hessian_spectrum(self.P, r=1))
e2 = sorted(nx.laplacian_spectrum(self.P))
npt.assert_almost_equal(e1, e2)