def test_distribution_K_l_1(num_samples=100):
"""Unrooted binary trees (class K) with one black node."""
BoltzmannSamplerBase.oracle = EvaluationOracle(reference_evals)
grammar = binary_tree_grammar()
grammar.init()
symbolic_x = 'x*G_1_dx(x,y)'
symbolic_y = 'D(x*G_1_dx(x,y),y)'
sampled_class = 'K'
grammar._precompute_evals(sampled_class, symbolic_x, symbolic_y)
# There are only 2 possibilities.
graphs_labs = [
(nx.path_graph(2), 1, 4),
(nx.path_graph(3), 2,
5), # TODO here something looks wrong when you run it.
]
test_distribution_for_l_size(
grammar,
sampled_class,
symbolic_x,
symbolic_y,
1, # l-size
graphs_labs,
num_samples)
def test_distribution_K_l_2(num_samples=100):
BoltzmannSamplerBase.oracle = EvaluationOracle(reference_evals)
grammar = binary_tree_grammar()
grammar.init()
symbolic_x = 'x*G_1_dx(x,y)'
symbolic_y = 'D(x*G_1_dx(x,y),y)'
sampled_class = 'K'
grammar._precompute_evals(sampled_class, symbolic_x, symbolic_y)
star_path_1 = nx.star_graph(3)
star_path_1.add_edge(1, 4)
star_path_2 = nx.star_graph(3)
star_path_2.add_edge(1, 4)
star_path_2.add_edge(4, 5)
other = nx.Graph(star_path_1)
other.add_edge(4, 5)
other.add_edge(4, 6)
# The first factor is due to the position of the leaves and the second are the labellings.
graphs_labs_u_size = [(nx.path_graph(3), 1 * 2, 5),
(nx.path_graph(4), 4 * 2, 6),
(nx.path_graph(5), 4 * 2, 7),
(star_path_1, 2 * 2, 7), (star_path_2, 4 * 2, 8),
(other, 1 * 2, 9)]
test_distribution_for_l_size(
grammar,
sampled_class,
symbolic_x,
symbolic_y,
2, # l-size
graphs_labs_u_size,
num_samples)
def test_distribution_R_b_l_1(num_samples=100):
BoltzmannSamplerBase.oracle = EvaluationOracle(planar_graph_evals_n100)
grammar = binary_tree_grammar()
grammar.init()
symbolic_x = 'x*G_1_dx(x,y)'
symbolic_y = 'D(x*G_1_dx(x,y),y)'
sampled_class = 'R_b'
grammar._precompute_evals(sampled_class, symbolic_x, symbolic_y)
# In this case the labs column does not correspond to actual labelings
# but to distinction because of the root.
graphs_labs_u_size = [
(nx.path_graph(1), 1, 2),
(nx.path_graph(2), 2, 3),
(nx.path_graph(3), 1, 4),
]
test_distribution_for_l_size(
grammar,
sampled_class,
symbolic_x,
symbolic_y,
1, # l-size
graphs_labs_u_size,
num_samples)
def binary_tree_oldtest_V2():
binary_tree_test_oracle = EvaluationOracle({
'x': 0.0475080992953792,
'y': 1.0,
# 'R_b_as(x,y)': 1,
# 'R_w_as(x,y)': 1,
'R_w(x,y)': 1,
'R_b(x,y)': 1,
# 'R_b_head(x,y)': 0.000001,
# 'R_w_head(x,y)': 0.9
})
BoltzmannSamplerBase.oracle = binary_tree_test_oracle
# BoltzmannSampler.oracle = EvaluationOracle(planar_graph_evals_n100)
grammar = binary_tree_grammar()
grammar.init()
symbolic_x = 'x'
symbolic_y = 'y'
# [print(query) for query in sorted(binary_tree_grammar.collect_oracle_queries('K_dy', symbolic_x, symbolic_y))]
tree = grammar.sample('K_dy', symbolic_x, symbolic_y)
# print(tree)
print(tree.base_class_object().get_attribute('numblacknodes'), end="\t")
print(tree.base_class_object().get_attribute('numwhitenodes'), end="\t")
print(tree.base_class_object().get_attribute('numtotal'), end="\t")
return tree.base_class_object()
def binary_tree_oldtest():
binary_tree_test_oracle = EvaluationOracle({
'x': 0.0475080992953792,
'y': 1.0,
# 'R_b_as(x,y)': 1,
# 'R_w_as(x,y)': 1,
'R_w(x,y)': 1,
'R_b(x,y)': 1,
# 'R_b_head(x,y)': 0.000001,
# 'R_w_head(x,y)': 0.9
})
# BoltzmannSampler.oracle = binary_tree_test_oracle
BoltzmannSamplerBase.oracle = EvaluationOracle(planar_graph_evals_n100)
grammar = binary_tree_grammar()
grammar.init()
# symbolic_x = 'x'
symbolic_x = 'x*G_1_dx(x,y)'
# symbolic_y = 'y'
symbolic_y = 'D(x*G_1_dx(x,y),y)'
print("Needed oracle entries:")
[
print(query) for query in sorted(
grammar._collect_oracle_queries('K_dy', symbolic_x, symbolic_y))
]
tree = grammar.sample('R_b', symbolic_x, symbolic_y)
print("Black nodes: {}".format(tree.black_nodes_count))
print("White nodes: {}".format(tree.white_nodes_count))
print("Total nodes: {}".format(tree.black_nodes_count +
tree.white_nodes_count))
print("Total leaves: {}".format(tree.leaves_count))
return tree
def irreducible_dissection_grammar():
"""Builds the dissection grammar. Must still be initialized with init().
Returns
-------
DecompositionGrammar
The grammar for sampling from J_a and J_a_dx.
"""
# Some shorthands to keep the grammar readable.
L = pybo.LAtomSampler
Rule = pybo.AliasSampler
K = Rule('K')
K_dx = Rule('K_dx')
K_dx_dx = Rule('K_dx_dx')
I = Rule('I')
I_dx = Rule('I_dx')
I_dx_dx = Rule('I_dx_dx')
J = Rule('J')
J_dx = Rule('J_dx')
J_dx_dx = Rule('J_dx_dx')
Bij = pybo.BijectionSampler
Rej = pybo.RejectionSampler
grammar = pybo.DecompositionGrammar()
# This grammar depends on the binary tree grammar so we add it.
grammar.rules = binary_tree_grammar().rules
EarlyRejectionControl.grammar = grammar
grammar.add_rules({
# Non-derived dissections (standard, rooted, admissible).
'I':
Bij(K, closure),
# We drop the 3*L*U factor here.
# This bijection does not preserve l-size/u-size.
'J':
Bij(I, add_random_root_edge),
'J_a':
Rej(J, is_admissible),
# Derived dissections.
# The result is not a derived class, the bijection does not preserve l-size.
'I_dx':
Bij(K_dx, closure),
# We drop the factor 3*U.
# This bijection does not preserve l-size/u-size.
'J_dx':
Bij(I + L() * I_dx, add_random_root_edge),
'J_a_dx':
Rej(J_dx, is_admissible),
# Bi-derived dissections.
# Does not preserve l-size, result is not a derived class.
'I_dx_dx':
Bij(K_dx_dx, closure),
# We dropped a factor.
'J_dx_dx':
Bij(2 * I_dx + L() * I_dx_dx, add_random_root_edge),
'J_a_dx_dx':
Rej(J_dx_dx, is_admissible)
})
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