def test_graph(self):
"""
1 5
| \ / \
| 3 -- 4 6
| / \ /
2 7
:return:
"""
g = nx.Graph()
g.add_nodes_from([1, 2, 3, 4, 5, 6, 7])
g.add_edges_from([(1, 2), (2, 3), (3, 1), (3, 4), (4, 5), (5, 6),
(6, 7), (7, 4)])
g.node[1]['infected'] = True
g.node[2]['infected'] = True
g.node[3]['infected'] = True
g.node[4]['infected'] = True
g.node[5]['infected'] = False
g.node[6]['infected'] = False
g.node[7]['infected'] = False
g_i = nx.Graph()
g_i.add_nodes_from([1, 2, 3, 4])
g_i.add_edges_from([(1, 2), (2, 3), (3, 1), (3, 4)])
sz = AlgorithmSZ()
source_estimation = sz.run(g, g_i)
self.assertEqual(source_estimation, 3)
print("Source of rumor is %s" % source_estimation)
def test_graph(self):
"""
1 5
| \ / \
| 3 -- 4 6
| / \ /
2 7
:return:
"""
g = nx.Graph()
g.add_nodes_from([1, 2, 3, 4, 5, 6, 7])
g.add_edges_from([(1, 2), (2, 3), (3, 1), (3, 4), (4,5), (5, 6), (6, 7), (7, 4)])
g.node[1]['infected'] = True
g.node[2]['infected'] = True
g.node[3]['infected'] = True
g.node[4]['infected'] = True
g.node[5]['infected'] = False
g.node[6]['infected'] = False
g.node[7]['infected'] = False
sz = AlgorithmSZ()
source_estimation = sz.run(g, v=4)
self.assertEqual(source_estimation, 3)
print("Source of rumor is %s" % source_estimation)
def test_less_elementary(self):
"""
1
/ \
2 3
/ \
4 5
:return:
"""
g = nx.Graph()
g.add_nodes_from([1, 2, 3, 4, 5])
g.add_edges_from([(1, 2), (1, 3), (2, 4), (2, 5)])
g.node[1]['infected'] = False
g.node[3]['infected'] = False
g.node[4]['infected'] = True
g.node[5]['infected'] = True
g.node[2]['infected'] = True
g_i = nx.Graph()
g_i.add_nodes_from([2, 4, 5])
g_i.add_edges_from([(2, 4), (2, 5)])
sz = AlgorithmSZ()
source_estimation = sz.run(g, g_i)
self.assertEqual(source_estimation, 2)
print("Source of rumor is %s" % source_estimation)
def test_elementary(self):
"""
1
/ \
2 3
:return:
"""
g = nx.Graph()
g.add_nodes_from([1, 2, 3])
g.add_edges_from([(1, 2), (1, 3)]) # FIXME do you need to add also (2, 1) and (3, 1) ?
g.node[1]['infected'] = True
g.node[2]['infected'] = True
g.node[3]['infected'] = True
sz = AlgorithmSZ()
# Test various v options (root of the spanning tree)
source_estimation = sz.run(g, v=1)
self.assertEqual(source_estimation, 1)
source_estimation2 = sz.run(g, v=2)
self.assertEqual(source_estimation2, 1)
source_estimation3 = sz.run(g, v=3)
self.assertEqual(source_estimation3, 1)
print("Source of rumor is %s" % source_estimation)
def test_less_elementary(self):
"""
1
/ \
2 3
/ \
4 5
:return:
"""
g = nx.Graph()
g.add_nodes_from([1, 2, 3, 4, 5])
g.add_edges_from([(1, 2), (1, 3), (2, 4), (2, 5)])
g.node[1]['infected'] = False
g.node[3]['infected'] = False
g.node[4]['infected'] = True
g.node[5]['infected'] = True
g.node[2]['infected'] = True
sz = AlgorithmSZ()
source_estimation = sz.run(g, v=2)
self.assertEqual(source_estimation, 2)
source_estimation2 = sz.run(g, v=4)
self.assertEqual(source_estimation2, 2)
source_estimation3 = sz.run(g, v=5)
self.assertEqual(source_estimation3, 2)
print("Source of rumor is %s" % source_estimation)
def test_wheel_graph(self):
g = nx.wheel_graph(10)
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
g.node[1]['infected'] = True
g.node[5]['infected'] = True
sz = AlgorithmSZ()
source_estimation = sz.run(g, g)
print("Source of rumor is %s" % source_estimation)
def test_wheel_graph(self):
g = nx.wheel_graph(10)
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
g.node[1]['infected'] = True
g.node[5]['infected'] = True
sz = AlgorithmSZ()
source_estimation = sz.run(g, v=1)
print("Source of rumor is %s" % source_estimation)
def test_other_graph(self):
g = nx.chvatal_graph()
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
g_i = nx.Graph()
g_i.add_nodes_from([0])
sz = AlgorithmSZ()
source_estimation = sz.run(g, g_i)
print("Source of rumor is %s" % source_estimation)
def test_other_graph(self):
g = nx.chvatal_graph()
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
sz = AlgorithmSZ()
source_estimation = sz.run(g, v=0)
print("Source of rumor is %s" % source_estimation)
nx.draw_networkx(g, node_color=['b' if g.node[n]['infected'] else 'r' for n in g])
plt.show()
def test_other_graph(self):
g = nx.chvatal_graph()
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
g_i = nx.Graph()
g_i.add_nodes_from([0])
sz = AlgorithmSZ()
source_estimation = sz.run(g, g_i)
print("Source of rumor is %s" % source_estimation)
def test_random_graph(self):
g = nx.fast_gnp_random_graph(10, 0.5)
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[1]['infected'] = True
g.node[2]['infected'] = True
g.node[3]['infected'] = True
g.node[4]['infected'] = True
# FIXME Cannot assert anything because graph is random !
sz = AlgorithmSZ()
source_estimation = sz.run(g, g)
print("Source of rumor is %s" % source_estimation)
def test_random_graph(self):
g = nx.fast_gnp_random_graph(10, 0.5)
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[1]['infected'] = True
g.node[2]['infected'] = True
g.node[3]['infected'] = True
g.node[4]['infected'] = True
# FIXME Cannot assert anything because graph is random !
sz = AlgorithmSZ()
source_estimation = sz.run(g, v=4)
print("Source of rumor is %s" % source_estimation)
def test_elementary(self):
"""
1
/ \
2 3
:return:
"""
g = nx.Graph()
g.add_nodes_from([1, 2, 3])
g.add_edges_from([(1, 2), (1, 3)]) # FIXME do you need to add also (2, 1) and (3, 1) ?
g.node[1]['infected'] = True
g.node[2]['infected'] = True
g.node[3]['infected'] = True
sz = AlgorithmSZ()
source_estimation = sz.run(g, g)
self.assertEqual(source_estimation, 1)
print("Source of rumor is %s" % source_estimation)
def test_elementary(self):
"""
1
/ \
2 3
:return:
"""
g = nx.Graph()
g.add_nodes_from([1, 2, 3])
g.add_edges_from([
(1, 2), (1, 3)
]) # FIXME do you need to add also (2, 1) and (3, 1) ?
g.node[1]['infected'] = True
g.node[2]['infected'] = True
g.node[3]['infected'] = True
sz = AlgorithmSZ()
source_estimation = sz.run(g, g)
self.assertEqual(source_estimation, 1)
print("Source of rumor is %s" % source_estimation)
