def random_almost_equi_partitions_with_walk(graph, num_partitions, num_blocks, delta, step = "Basis", jump_size = 50):
'''This produces a delta almost equi partition... it keeps looping until it finds
the required amounts
'''
# print("am here")
# print(step)
found_partitions = []
counter = 0
tree = random_spanning_tree_wilson(graph)
while len(found_partitions) < num_partitions:
counter += 1
if step == "Basis":
for i in range(jump_size):
tree, edge_to_remove, edge_to_add = propose_step(graph, tree)
if step == "Broder":
for i in range(jump_size):
tree, edge_to_remove, edge_to_add = propose_Broder_step(graph, tree)
edge_list = almost_equi_split(tree, num_blocks, delta)
#If the almost equi split was not a delta split, then it returns none...
if edge_list != None:
blocks = remove_edges_map(graph, tree, edge_list)
found_partitions.append(blocks)
print(len(found_partitions), "waiting time:", counter)
counter = 0
return found_partitions
def make_partition_list(graph, number_samples = 100, tree_algorithm = random_spanning_tree_wilson, equi = True):
#Note -- currently this is configured only for 2 partitions
total_number_trees_edges_pairs = np.exp(log_number_trees(graph))*(len(graph.nodes()) - 1)
uniform_trees = []
for i in range(number_samples):
uniform_trees.append(tree_algorithm(graph))
partitions = []
for tree in uniform_trees:
if equi == -1:
e = random.choice(list(tree.edges()))
blocks = remove_edges_map(graph, tree, [e])
new_partition = partition_class(graph, blocks, tree, e, total_number_trees_edges_pairs)
new_partition.set_likelihood()
partitions.append(new_partition)
else:
out = almost_equi_split(tree, 2,.1)
if out != None:
e = out[0]
blocks = remove_edges_map(graph, tree, [e])
new_partition = partition_class(graph, blocks, tree, e, total_number_trees_edges_pairs)
new_partition.set_likelihood()
partitions.append(new_partition)
return partitions
def random_almost_equi_partitions(graph, num_partitions, num_blocks, delta):
'''This produces a delta almost equi partition... it keeps looping until it finds
the required amounts
'''
found_partitions = []
counter = 0
while len(found_partitions) < num_partitions:
counter += 1
tree = random_spanning_tree_wilson(graph)
edge_list = almost_equi_split(tree, num_blocks, delta)
#If the almost equi split was not a delta split, then it returns none...
if edge_list != None:
blocks = remove_edges_map(graph, tree, edge_list)
found_partitions.append(blocks)
print(len(found_partitions), "waiting time:", counter)
counter = 0
return found_partitions