def smoke_test_random_graph(self):
seed = 42
G=gnp_random_graph(100,0.25,seed)
G=binomial_graph(100,0.25,seed)
G=erdos_renyi_graph(100,0.25,seed)
G=fast_gnp_random_graph(100,0.25,seed)
G=gnm_random_graph(100,20,seed)
G=dense_gnm_random_graph(100,20,seed)
G=watts_strogatz_graph(10,2,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=connected_watts_strogatz_graph(10,2,0.1,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 20)
G=newman_watts_strogatz_graph(10,2,0.0,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=newman_watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_true(G.number_of_edges() >= 20)
G=barabasi_albert_graph(100,1,seed)
G=barabasi_albert_graph(100,3,seed)
assert_equal(G.number_of_edges(),(97*3))
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert_equal(G.number_of_edges(), 97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
assert_greater(G.number_of_edges(), 100 * 3)
assert_less(G.number_of_edges(), 100 * 4)
G=extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
assert_greater(G.number_of_edges(), 100 * 2)
assert_less(G.number_of_edges(), 100 * 4)
G=powerlaw_cluster_graph(100,1,1.0,seed)
G=powerlaw_cluster_graph(100,3,0.0,seed)
assert_equal(G.number_of_edges(),(97*3))
G=random_regular_graph(10,20,seed)
assert_raises(NetworkXError, random_regular_graph, 3, 21)
constructor=[(10,20,0.8),(20,40,0.8)]
G=random_shell_graph(constructor,seed)
G=random_lobster(10,0.1,0.5,seed)
def smoke_test_random_graph(self):
seed = 42
G=gnp_random_graph(100,0.25,seed)
G=binomial_graph(100,0.25,seed)
G=erdos_renyi_graph(100,0.25,seed)
G=fast_gnp_random_graph(100,0.25,seed)
G=gnm_random_graph(100,20,seed)
G=dense_gnm_random_graph(100,20,seed)
G=watts_strogatz_graph(10,2,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=connected_watts_strogatz_graph(10,2,0.1,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 20)
G=newman_watts_strogatz_graph(10,2,0.0,seed)
assert_equal(len(G), 10)
assert_equal(G.number_of_edges(), 10)
G=newman_watts_strogatz_graph(10,4,0.25,seed)
assert_equal(len(G), 10)
assert_true(G.number_of_edges() >= 20)
G=barabasi_albert_graph(100,1,seed)
G=barabasi_albert_graph(100,3,seed)
assert_equal(G.number_of_edges(),(97*3))
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert_equal(G.number_of_edges(), 97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert_equal(G.number_of_edges(), 99)
G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
assert_greater(G.number_of_edges(), 100 * 3)
assert_less(G.number_of_edges(), 100 * 4)
G=extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
assert_greater(G.number_of_edges(), 100 * 2)
assert_less(G.number_of_edges(), 100 * 4)
G=powerlaw_cluster_graph(100,1,1.0,seed)
G=powerlaw_cluster_graph(100,3,0.0,seed)
assert_equal(G.number_of_edges(),(97*3))
G=random_regular_graph(10,20,seed)
assert_raises(NetworkXError, random_regular_graph, 3, 21)
constructor=[(10,20,0.8),(20,40,0.8)]
G=random_shell_graph(constructor,seed)
G=random_lobster(10,0.1,0.5,seed)
def test_random_zero_regular_graph(self):
"""Tests that a 0-regular graph has the correct number of nodes and
edges.
"""
G = random_regular_graph(0, 10)
assert_equal(len(G), 10)
assert_equal(sum(1 for _ in G.edges()), 0)
def test_random_zero_regular_graph(self):
"""Tests that a 0-regular graph has the correct number of nodes and
edges.
"""
G = random_regular_graph(0, 10)
assert_equal(len(G), 10)
assert_equal(sum(1 for _ in G.edges()), 0)
def test_random_zero_regular_graph(self):
"""Tests that a 0-regular graph has the correct number of nodes and
edges.
"""
seed = 42
G = random_regular_graph(0, 10, seed)
assert len(G) == 10
assert sum(1 for _ in G.edges()) == 0
def run_analysis_n_nodes(n, d, cap, n_runs=3):
print(f"Running analysis for {n} nodes and {d} regularity...")
graphs = [lambda: random_regular_graph(d, n)]
results_gr = {}
results_dinitz = {}
for graph, name in zip(graphs, names):
# Initialize both graphs
G_dinitz = graph()
G_gr = G_dinitz.copy()
total_time_gr = 0
total_time_dinitz = 0
for _ in range(n_runs):
# Set random capacities of graph edges
for u, v in G_dinitz.edges:
G_dinitz.edges[u, v]["capacity"] = cap
G_gr.edges[u, v]["capacity"] = cap
# Pick random start and end node
start_node = randint(0, len(G_dinitz.nodes) - 1)
end_node = randint(0, len(G_dinitz.nodes) - 1)
while start_node == end_node:
end_node = randint(0, len(G_dinitz.nodes) - 1)
# Run max-flow
init_time = time.time()
R_dinitz = dinitz(G_dinitz, start_node, end_node)
total_time_dinitz += time.time() - init_time
init_time = time.time()
R_gr = goldberg_rao(G_gr, start_node, end_node)
total_time_gr += time.time() - init_time
# Check correctness
d_mf = R_dinitz.graph["flow_value"]
gr_mf = R_gr.graph["flow_value"]
if d_mf != gr_mf:
vprint(
f"\t\t\tComputed max flow in {name} graph is {d_mf}, but goldberg_rao function computed {gr_mf}"
.upper())
vprint(
f"{d}-regular graph with {len(G_gr.nodes)} nodes, cap {cap} and {len(G_gr.edges)} edges took {total_time_gr / n_runs} seconds with goldberg_rao"
)
vprint(
f"{d}-regular graph with {len(G_dinitz.nodes)} nodes, cap {cap} and {len(G_dinitz.edges)} edges took {total_time_dinitz / n_runs} seconds with dinitz"
)
results_gr[name] = total_time_gr / n_runs
results_dinitz[name] = total_time_dinitz / n_runs
return results_gr, results_dinitz
def run_analysis_n_nodes(n, unit_cap, n_runs=3):
print(f"Running analysis for {n} nodes...")
graphs = [
lambda: balanced_tree(2, int(round(np.log2(n) - 1))),
lambda: binomial_tree(int(round(np.log2(n)))), lambda: cycle_graph(n),
lambda: path_graph(n), lambda: star_graph(n - 1),
lambda: random_regular_graph(3, n), lambda: random_regular_graph(5, n)
]
results_dinitz = {}
for graph, name in zip(graphs, names):
# Initialize both graphs
G_dinitz = graph()
total_time_dinitz = 0
for _ in range(n_runs):
# Set random capacities of graph edges
for u, v in G_dinitz.edges:
cap = randint(1, 100) if not unit_cap else 1
G_dinitz.edges[u, v]["capacity"] = cap
# Pick random start and end node
start_node = randint(0, len(G_dinitz.nodes) - 1)
end_node = randint(0, len(G_dinitz.nodes) - 1)
while start_node == end_node:
end_node = randint(0, len(G_dinitz.nodes) - 1)
# Run max-flow
init_time = time.time()
R_dinitz = dinitz(G_dinitz, start_node, end_node)
total_time_dinitz += time.time() - init_time
vprint(
f"{name} with {len(G_dinitz.nodes)} nodes took {total_time_dinitz / n_runs} seconds with dinitz"
)
results_dinitz[name] = total_time_dinitz / n_runs
return results_dinitz
def run_analysis_n_nodes(n, unit_cap, n_runs=3):
print(f"Running analysis for {n} nodes...")
graphs = [lambda: balanced_tree(2, int(round(np.log2(n)-1))),
lambda: binomial_tree(int(round(np.log2(n)))), lambda: cycle_graph(n),
lambda: path_graph(n), lambda: star_graph(n-1), lambda: random_regular_graph(3, n), lambda: random_regular_graph(5, n)]
results_gr = {}
results_dinitz = {}
for graph, name in zip(graphs, names):
# Initialize both graphs
G_dinitz = graph()
G_gr = G_dinitz.copy()
total_time_gr = 0
total_time_dinitz = 0
for _ in range(n_runs):
# Set random capacities of graph edges
for u, v in G_dinitz.edges:
cap = randint(1, 100) if not unit_cap else 1
G_dinitz.edges[u, v]["capacity"] = cap
G_gr.edges[u, v]["capacity"] = cap
# Pick random start and end node
start_node = randint(0, len(G_dinitz.nodes)-1)
end_node = randint(0, len(G_dinitz.nodes)-1)
while start_node == end_node: end_node = randint(0, len(G_dinitz.nodes)-1)
# Run max-flow
init_time = time.time()
R_dinitz = dinitz(G_dinitz, start_node, end_node)
total_time_dinitz += time.time() - init_time
init_time = time.time()
R_gr = goldberg_rao(G_gr, start_node, end_node)
total_time_gr += time.time() - init_time
# Check correctness
d_mf = R_dinitz.graph["flow_value"]
gr_mf = R_gr.graph["flow_value"]
if d_mf != gr_mf:
vprint(f"\t\t\tComputed max flow in {name} graph is {d_mf}, but goldberg_rao function computed {gr_mf}".upper())
vprint(f"{name} with {len(G_gr.nodes)} nodes took {total_time_gr / n_runs} seconds with goldberg_rao")
vprint(f"{name} with {len(G_dinitz.nodes)} nodes took {total_time_dinitz / n_runs} seconds with dinitz")
results_gr[name] = total_time_gr / n_runs
results_dinitz[name] = total_time_dinitz / n_runs
return results_gr, results_dinitz
def _createGraph(self):
dlg = PropertyViewer(self.name, self.icon,
Nodes=Integer(5, 1, 100, 1),
Degree=Integer(2,1,100,1))
nodes = []
edges = []
if dlg.exec_():
values = dlg.values()
n = values['Nodes']
m = values['Degree']
G = rnd.random_regular_graph(m, n)
nodes = layout.circularNodes(n, 25)
for i in G.edges_iter():
edges.append(i)
return nodes, edges
def smoke_test_random_graph(self):
seed = 42
G = gnp_random_graph(100, 0.25, seed)
G = gnp_random_graph(100, 0.25, seed, directed=True)
G = binomial_graph(100, 0.25, seed)
G = erdos_renyi_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed, directed=True)
G = gnm_random_graph(100, 20, seed)
G = gnm_random_graph(100, 20, seed, directed=True)
G = dense_gnm_random_graph(100, 20, seed)
G = watts_strogatz_graph(10, 2, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed)
assert len(G) == 10
assert G.number_of_edges() == 10
pytest.raises(NetworkXError, connected_watts_strogatz_graph, \
10, 2, 0.1, tries=0)
G = watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 20
G = newman_watts_strogatz_graph(10, 2, 0.0, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = newman_watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() >= 20
G = barabasi_albert_graph(100, 1, seed)
G = barabasi_albert_graph(100, 3, seed)
assert G.number_of_edges() == (97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert G.number_of_edges() == 99
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert G.number_of_edges() == 97 * 3
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert G.number_of_edges() == 99
G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
assert G.number_of_edges() > 100 * 3
assert G.number_of_edges() < 100 * 4
G = extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
assert G.number_of_edges() > 100 * 2
assert G.number_of_edges() < 100 * 4
G = powerlaw_cluster_graph(100, 1, 1.0, seed)
G = powerlaw_cluster_graph(100, 3, 0.0, seed)
assert G.number_of_edges() == (97 * 3)
G = random_regular_graph(10, 20, seed)
pytest.raises(NetworkXError, random_regular_graph, 3, 21)
pytest.raises(NetworkXError, random_regular_graph, 33, 21)
constructor = [(10, 20, 0.8), (20, 40, 0.8)]
G = random_shell_graph(constructor, seed)
G = random_lobster(10, 0.1, 0.5, seed)
# difficult to find seed that requires few tries
seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
G = random_powerlaw_tree(10, 3, seed=14, tries=1)
b_list = np.linspace(3, 5, 10)
rho_list = []
with Pool(num_workers) as pool:
for benefit in tqdm(b_list, leave=False, desc='benefits'):
results = []
for i in tqdm(range(num_refreshes),
leave=False,
desc=f'benefit = {benefit}'):
for j in tqdm(range(network_refresh_interval // num_workers),
desc='Batch',
leave=False):
# Generate a fresh graph with a random cooperator
invader = np.random.randint(graph_size)
graphs = [
random_regular_graph(graph_degree,
graph_size,
seed=time.time())
for _ in range(num_workers)
]
# Initializing graph nodes
for G in graphs:
for node in G:
G.nodes[node][
'name'] = 'C' if node == invader else 'D'
set_result = pool.map(partial(evolve, b=benefit), graphs)
results += set_result
# print('{} --> {}'.format(i*network_refresh_interval+j*num_workers,set_result))
# print("\nFixation probability of C: {}".format(sum(results)/num_trials))
rho_list.append(sum(results) / num_trials)
plt.plot(b_list, rho_list, 'o')
neutral_fix_p = 0.01 * np.ones_like(b_list)
def test_random_graph(self):
seed = 42
G = gnp_random_graph(100, 0.25, seed)
G = gnp_random_graph(100, 0.25, seed, directed=True)
G = binomial_graph(100, 0.25, seed)
G = erdos_renyi_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed)
G = fast_gnp_random_graph(100, 0.25, seed, directed=True)
G = gnm_random_graph(100, 20, seed)
G = gnm_random_graph(100, 20, seed, directed=True)
G = dense_gnm_random_graph(100, 20, seed)
G = watts_strogatz_graph(10, 2, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed)
assert len(G) == 10
assert G.number_of_edges() == 10
pytest.raises(NetworkXError,
connected_watts_strogatz_graph,
10,
2,
0.1,
tries=0)
G = watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 20
G = newman_watts_strogatz_graph(10, 2, 0.0, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = newman_watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() >= 20
G = barabasi_albert_graph(100, 1, seed)
G = barabasi_albert_graph(100, 3, seed)
assert G.number_of_edges() == (97 * 3)
G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert G.number_of_edges() == 99
G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert G.number_of_edges() == 97 * 3
G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert G.number_of_edges() == 99
G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
assert G.number_of_edges() > 100 * 3
assert G.number_of_edges() < 100 * 4
G = extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
assert G.number_of_edges() > 100 * 2
assert G.number_of_edges() < 100 * 4
G = powerlaw_cluster_graph(100, 1, 1.0, seed)
G = powerlaw_cluster_graph(100, 3, 0.0, seed)
assert G.number_of_edges() == (97 * 3)
G = random_regular_graph(10, 20, seed)
pytest.raises(NetworkXError, random_regular_graph, 3, 21)
pytest.raises(NetworkXError, random_regular_graph, 33, 21)
constructor = [(10, 20, 0.8), (20, 40, 0.8)]
G = random_shell_graph(constructor, seed)
def is_caterpillar(g):
"""
A tree is a caterpillar iff all nodes of degree >=3 are surrounded
by at most two nodes of degree two or greater.
ref: http://mathworld.wolfram.com/CaterpillarGraph.html
"""
deg_over_3 = [n for n in g if g.degree(n) >= 3]
for n in deg_over_3:
nbh_deg_over_2 = [
nbh for nbh in g.neighbors(n) if g.degree(nbh) >= 2
]
if not len(nbh_deg_over_2) <= 2:
return False
return True
def is_lobster(g):
"""
A tree is a lobster if it has the property that the removal of leaf
nodes leaves a caterpillar graph (Gallian 2007)
ref: http://mathworld.wolfram.com/LobsterGraph.html
"""
non_leafs = [n for n in g if g.degree(n) > 1]
return is_caterpillar(g.subgraph(non_leafs))
G = random_lobster(10, 0.1, 0.5, seed)
assert max([G.degree(n) for n in G.nodes()]) > 3
assert is_lobster(G)
pytest.raises(NetworkXError, random_lobster, 10, 0.1, 1, seed)
pytest.raises(NetworkXError, random_lobster, 10, 1, 1, seed)
pytest.raises(NetworkXError, random_lobster, 10, 1, 0.5, seed)
# docstring says this should be a caterpillar
G = random_lobster(10, 0.1, 0.0, seed)
assert is_caterpillar(G)
# difficult to find seed that requires few tries
seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
G = random_powerlaw_tree(10, 3, seed=14, tries=1)
