File: //lib/python3/dist-packages/networkx/linalg/tests/test_graphmatrix.py
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 TestGraphMatrix(object):
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.OI = numpy.array([[-1, -1, -1, 0],
[1, 0, 0, -1],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 0]])
cls.A = numpy.array([[0, 1, 1, 1, 0],
[1, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
cls.WG = havel_hakimi_graph(deg)
cls.WG.add_edges_from((u, v, {'weight': 0.5, 'other': 0.3})
for (u, v) in cls.G.edges())
cls.WA = numpy.array([[0, 0.5, 0.5, 0.5, 0],
[0.5, 0, 0.5, 0, 0],
[0.5, 0.5, 0, 0, 0],
[0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
cls.MG = nx.MultiGraph(cls.G)
cls.MG2 = cls.MG.copy()
cls.MG2.add_edge(0, 1)
cls.MG2A = numpy.array([[0, 2, 1, 1, 0],
[2, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
cls.MGOI = numpy.array([[-1, -1, -1, -1, 0],
[1, 1, 0, 0, -1],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 0]])
cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {'weight': 8})])
cls.no_edges_A = numpy.array([[0, 0], [0, 0]])
def test_incidence_matrix(self):
"Conversion to incidence matrix"
I = nx.incidence_matrix(self.G,
nodelist=sorted(self.G),
edgelist=sorted(self.G.edges()),
oriented=True).todense().astype(int)
npt.assert_equal(I, self.OI)
I = nx.incidence_matrix(self.G,
nodelist=sorted(self.G),
edgelist=sorted(self.G.edges()),
oriented=False).todense().astype(int)
npt.assert_equal(I, numpy.abs(self.OI))
I = nx.incidence_matrix(self.MG,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG.edges()),
oriented=True).todense().astype(int)
npt.assert_equal(I, self.OI)
I = nx.incidence_matrix(self.MG,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG.edges()),
oriented=False).todense().astype(int)
npt.assert_equal(I, numpy.abs(self.OI))
I = nx.incidence_matrix(self.MG2,
nodelist=sorted(self.MG2),
edgelist=sorted(self.MG2.edges()),
oriented=True).todense().astype(int)
npt.assert_equal(I, self.MGOI)
I = nx.incidence_matrix(self.MG2,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG2.edges()),
oriented=False).todense().astype(int)
npt.assert_equal(I, numpy.abs(self.MGOI))
def test_weighted_incidence_matrix(self):
I = nx.incidence_matrix(self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True).todense().astype(int)
npt.assert_equal(I, self.OI)
I = nx.incidence_matrix(self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=False).todense().astype(int)
npt.assert_equal(I, numpy.abs(self.OI))
# npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True,
# weight='weight').todense(),0.5*self.OI)
# npt.assert_equal(nx.incidence_matrix(self.WG,weight='weight').todense(),
# numpy.abs(0.5*self.OI))
# npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True,weight='other').todense(),
# 0.3*self.OI)
I = nx.incidence_matrix(self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
weight='weight').todense()
npt.assert_equal(I, 0.5 * self.OI)
I = nx.incidence_matrix(self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=False,
weight='weight').todense()
npt.assert_equal(I, numpy.abs(0.5 * self.OI))
I = nx.incidence_matrix(self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
weight='other').todense()
npt.assert_equal(I, 0.3 * self.OI)
# WMG=nx.MultiGraph(self.WG)
# WMG.add_edge(0,1,weight=0.5,other=0.3)
# npt.assert_equal(nx.incidence_matrix(WMG,weight='weight').todense(),
# numpy.abs(0.5*self.MGOI))
# npt.assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True).todense(),
# 0.5*self.MGOI)
# npt.assert_equal(nx.incidence_matrix(WMG,weight='other',oriented=True).todense(),
# 0.3*self.MGOI)
WMG = nx.MultiGraph(self.WG)
WMG.add_edge(0, 1, weight=0.5, other=0.3)
I = nx.incidence_matrix(WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=True,
weight='weight').todense()
npt.assert_equal(I, 0.5 * self.MGOI)
I = nx.incidence_matrix(WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=False,
weight='weight').todense()
npt.assert_equal(I, numpy.abs(0.5 * self.MGOI))
I = nx.incidence_matrix(WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=True,
weight='other').todense()
npt.assert_equal(I, 0.3 * self.MGOI)
def test_adjacency_matrix(self):
"Conversion to adjacency matrix"
npt.assert_equal(nx.adj_matrix(self.G).todense(), self.A)
npt.assert_equal(nx.adj_matrix(self.MG).todense(), self.A)
npt.assert_equal(nx.adj_matrix(self.MG2).todense(), self.MG2A)
npt.assert_equal(nx.adj_matrix(self.G, nodelist=[0, 1]).todense(), self.A[:2, :2])
npt.assert_equal(nx.adj_matrix(self.WG).todense(), self.WA)
npt.assert_equal(nx.adj_matrix(self.WG, weight=None).todense(), self.A)
npt.assert_equal(nx.adj_matrix(self.MG2, weight=None).todense(), self.MG2A)
npt.assert_equal(nx.adj_matrix(self.WG, weight='other').todense(), 0.6 * self.WA)
npt.assert_equal(nx.adj_matrix(self.no_edges_G, nodelist=[1, 3]).todense(), self.no_edges_A)