def test_simple(self):
M = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0.25, 0, 0, 0, 0, 0, 0, 0, 0],
[0.1, 0, 0, 0, 0, 0, 0, 0, 0],
[0.05, 0, 0, 0, 0, 0, 0, 0, 0],
[0.1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0.2, 0, 0.05, 0.1, 0.15],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]
])
expected = np.array([
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 0],
])
G = nx.from_numpy_matrix(M, create_using=nx.DiGraph)
ut.normalizeGraph(G)
expectedG = nx.from_numpy_matrix(expected)
ut.normalizeGraph(G)
resG = tr.treeToBND(M)
N = nx.to_numpy_matrix(resG)
np.testing.assert_array_equal(expected, N)
dr.printInput(G)
dr.printRes(G, resG, expectedG)
def test_simple(self):
M = np.array([
[0, 0, 0, 0, 0, 0, 0, 0],
[0.1, 0, 0, 0, 0, 0, 0, 0],
[0.15, 0, 0, 0, 0, 0, 0, 0],
[0.05, 0, 0, 0, 0, 0, 0, 0],
[0.1, 0, 0, 0, 0, 0.05, 0.35, 0.2],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]
])
expected = np.array([
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 1],
[1, 1, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 1, 0, 1, 0],
])
G = nx.from_numpy_matrix(M, create_using=nx.DiGraph)
ut.normalizeGraph(G)
expectedG = nx.from_numpy_matrix(expected)
ut.normalizeGraph(G)
res = sp.sparseToBND(G)
resG, layers = res[0], res[1]
N = nx.to_numpy_matrix(resG)
dr.printInput(G)
dr.printRes(G, resG, expectedG, layers)
np.testing.assert_array_equal(expected, N)
return G
choice = 1
### a simple random graph
if choice == 0:
n = 20
G = randomGraph(n)
dr.printInput(G)
res = sp.sparseToBND(G)
N, layers = res[0], res[1]
dr.printRes(G, N, None, layers)
### Several test on random graphs
if choice == 1:
N, n = 100000, 20
results = []
inputs = []
for i in tqdm(range(N)):
G = randomGraph(n)
edges = str(G.edges)
avgDegree = ev.getAverageDegree(G)
n = len(G)
res = sp.sparseToBND(G)
print('(1) one single highland graph')
print('(2) all highlands graphs with a given number of nodes')
choice = int(input('Your choice: '))
### Simple Test ###
if choice == 1:
n = int(input('How many nodes ? '))
m = int(input('Degree of the highland nodes ? '))
print(n, m)
G = highlands(n, m)
dr.printInput(G)
res = sp.sparseToBND(G)
N = res[0]
dr.printRes(G, N, None)
### All tests ###
elif choice == 2:
n = int(input('How many nodes ? '))
EPLs = np.zeros(n)
maxs = np.zeros(n)
for m in range(n):
_m = m+1
G = highlands(n, _m)
res = sp.sparseToBND(G)
N = res[0]
EPL = ev.getEPL(G, N)
_max = ev.getMaxDegree(N)
EPLs[m] = EPL
choice = 4
### Simple Test for one wheel ###
if choice == 0:
n = 10
center = 0
nodes = np.array([i for i in range(n)])
G = simpleWheel(nodes, center, direction=ut.Direction.OUTGOING)
dr.printInput(G)
res = sp.sparseToBND(G)
dr.printRes(G, res[0], None, res[1])
### Simple Test for several wheels ###
elif choice == 1:
n = 10
nodes = np.array([i for i in range(n)])
highOutSubsets = [0]
highInSubsets = [5]
G = severalWheels(nodes, highOutSubsets, highInSubsets)
dr.printInput(G)
res = sp.sparseToBND(G)
N, layers = res[0], res[1]
dr.printRes(G, N, None, layers)