def test_edmonds3_minbranch2():
G = G1()
G.add_edge(8, 9, weight=-10)
x = branchings.minimum_branching(G)
edges = [(8, 9, -10)]
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
def test_edmonds3_minbranch2():
G = G1()
G.add_edge(8, 9, weight=-10)
x = branchings.minimum_branching(G)
edges = [(8, 9, -10)]
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
def test_edmonds1_minbranch():
# Using -G_array and min should give the same as optimal_arborescence_1,
# but with all edges negative.
edges = [(u, v, -w) for (u, v, w) in optimal_arborescence_1]
G = nx.from_numpy_array(-G_array, create_using=nx.DiGraph)
# Quickly make sure max branching is empty.
x = branchings.maximum_branching(G)
x_ = build_branching([])
assert_equal_branchings(x, x_)
# Now test the min branching.
x = branchings.minimum_branching(G)
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
def test_edmonds1_minbranch():
# Using -G_array and min should give the same as optimal_arborescence_1,
# but with all edges negative.
edges = [ (u, v, -w) for (u, v, w) in optimal_arborescence_1 ]
G = nx.DiGraph()
G = nx.from_numpy_matrix(-G_array, create_using=G)
# Quickly make sure max branching is empty.
x = branchings.maximum_branching(G)
x_ = build_branching([])
assert_equal_branchings(x, x_)
# Now test the min branching.
x = branchings.minimum_branching(G)
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
def test_edmonds3_minbranch1():
G = G1()
x = branchings.minimum_branching(G)
edges = []
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
def test_edmonds3_minbranch1():
G = G1()
x = branchings.minimum_branching(G)
edges = []
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
