def test_distribution_G_1_dx_l_2(num_samples=100):
"""Test if connected planar graphs with exactly 4 nodes have the right distribution."""
BoltzmannSamplerBase.oracle = EvaluationOracle(reference_evals)
grammar = one_connected_graph_grammar()
grammar.init()
graphs_labs = [(nx.path_graph(3), 3, 2), (nx.complete_graph(3), 1, 3)]
test_distribution_for_l_size(grammar,
'G_1_dx',
'x',
'y',
2,
graphs_labs,
num_samples=num_samples)
def test_distribution_G_1_dx_dx_l_2(num_samples=100):
"""Test if connected planar graphs with exactly 4 nodes have the right distribution."""
BoltzmannSamplerBase.oracle = EvaluationOracle(reference_evals)
grammar = one_connected_graph_grammar()
grammar.init()
# All one-connected planar graphs with 4 nodes and the number of their labellings.
# See p.15, Fig. 5.
cycle_with_chord = nx.cycle_graph(4)
cycle_with_chord.add_edge(0, 2)
graphs_labs = [(nx.path_graph(4), 12, 3), (nx.star_graph(3), 4, 3),
(nx.lollipop_graph(3, 1), 12, 4), (nx.cycle_graph(4), 3, 4),
(cycle_with_chord, 6, 5), (nx.complete_graph(4), 1, 6)]
test_distribution_for_l_size(grammar,
'G_1_dx_dx',
'x',
'y',
2,
graphs_labs,
num_samples=num_samples)
def planar_graph_grammar():
"""Constructs the grammar for planar graphs.
Returns
-------
grammar : DecompositionGrammar
The grammar for sampling from G, G_dx and G_dx_dx.
"""
# Some shortcuts to make the grammar more readable.
Rule = pybo.AliasSampler
G_1 = Rule('G_1')
G_1_dx = Rule('G_1_dx')
G_1_dx_dx = Rule('G_1_dx_dx')
G_1_dx_dx_dx = Rule('G_1_dx_dx_dx')
G = Rule('G')
G_dx = Rule('G_dx')
G_dx_dx = Rule('G_dx')
Set = pybo.SetSampler
grammar = pybo.DecompositionGrammar()
grammar.rules = one_connected_graph_grammar().rules
EarlyRejectionControl.grammar = grammar
grammar.add_rules({
'G':
Set(0, G_1),
'G_dx':
G_1_dx * G,
'G_dx_dx':
G_1_dx_dx * G + G_1_dx * G_dx,
'G_dx_dx_dx':
G_1_dx_dx_dx * G + G_1_dx_dx * G_dx + G_1_dx_dx * G_dx +
G_1_dx * G_dx_dx
})
grammar.set_builder(['G', 'G_dx', 'G_dx_dx', 'G_dx_dx_dx'],
PlanarGraphBuilder())
return grammar
def ___sample_combinatorial_class(name,
comb_class,
symbolic_x,
symbolic_y,
size,
exact=True,
derived=0):
start_sampling = timer()
number_trials = 0
BoltzmannSamplerBase.oracle = EvaluationOracle.get_best_oracle_for_size(
size, planar_graph_evals)
grammar = None
if name is "binary_tree":
grammar = binary_tree_grammar()
elif name is "three_connected":
grammar = three_connected_graph_grammar()
elif name is "two_connected":
grammar = two_connected_graph_grammar()
elif name is "one_connected":
grammar = one_connected_graph_grammar()
elif name is "planar_graph":
grammar = planar_graph_grammar()
else:
raise Exception("No such graph class")
assert (grammar is not None)
grammar.init()
node_num = 0
if exact:
while node_num != size:
number_trials += 1
graph = grammar.sample_iterative(comb_class, symbolic_x,
symbolic_y)
if derived == 0 and name is not "planar_graph" and name is not "one_connected":
node_num = graph.number_of_nodes
else:
node_num = graph.l_size + derived
else:
graph = grammar.sample_iterative(comb_class, symbolic_x, symbolic_y)
if derived == 0 and name is not "planar_graph" and name is not "one_connected":
node_num = graph.number_of_nodes
else:
node_num = graph.l_size + derived
if derived == 0 and name is not "planar_graph" and name is not "one_connected":
edge_num = graph.number_of_edges
else:
edge_num = graph.u_size
end_sampling = timer()
time_needed = end_sampling - start_sampling
data = (node_num, edge_num, number_trials, time_needed)
___save_graph_in_file(graph, name)
return data, graph