def solve(G):
"""
Args:
G: networkx.Graph
Returns:
T: networkx.Graph
"""
g = GraphSolver(G)
start = g.find_leaf_path()
T = g.dijkstra_solve_graph(start, calculate_heuristic, first_heuristic)
optimize_sorted(g, T)
rebuilder = Rebuilder(g)
min_T = T.copy()
min_cost = average_pairwise_distance(T)
# print('*', min_cost)
for _ in range(50):
if rebuilder.rebuild():
g = GraphSolver(G)
for v in min_T.nodes:
g.visit(v)
for e in min_T.edges:
g.add_edge(e)
# print('reset')
g.dijkstra_solve_graph(start, calculate_heuristic, first_heuristic)
optimize_sorted(g, g.T)
cost = average_pairwise_distance(g.T)
# print(cost)
if cost < min_cost:
min_cost = cost
min_T = g.T.copy()
return min_T
def solve(G):
"""
Args:
G: networkx.Graph
Returns:
T: networkx.Graph
"""
g = GraphSolver(G)
start = g.find_leaf_path()
if start is None:
start = first_heuristic(g)
graph_choices = []
for v in g.neighbors(start):
newG = G.copy()
new_g = GraphSolver(newG)
new_g.visit(start)
new_g.visit(v, (start, v))
graph_choices.append(new_g)
minT = G
minCost = float('inf')
for g_option in graph_choices:
g_option.dijkstra_solve_graph(start, calculate_heuristic,
first_heuristic)
optimize_sorted(g_option, g_option.T, cycle_killer_fn=kill_cycle)
cost = average_pairwise_distance(g_option.T)
if cost < minCost:
minCost = cost
minT = g_option.T
return minT
def solve(G):
"""
Args:
G: networkx.Graph
Returns:
T: networkx.Graph
"""
g = GraphSolver(G)
single_node = process(g)
if single_node >= 0:
return g.T
g.visit(first_heuristic(g))
T = g.mst_solve_graph(heuristic)
optimize_sorted(g, T, cycle_killer_fn=kill_cycle)
return T
def solve(G):
"""
Args:
G: networkx.Graph
Returns:
T: networkx.Graph
"""
t = most_between(G)
solver = GraphSolver(G)
for node in t.nodes:
solver.visit(node)
for edge in t.edges:
solver.add_edge(edge)
optimize_sorted(solver, solver.T, kill_cycle_all_paths)
return solver.T
def solve(G):
"""
Args:
G: networkx.Graph
Returns:
T: networkx.Graph
"""
t = find_shortest_paths(G)
solver = GraphSolver(G)
for node in t.nodes:
solver.visit(node)
for edge in t.edges:
solver.add_edge(edge)
optimize_sorted(solver, solver.T, kill_cycle_all_paths)
check = is_valid_network(G, solver.T)
if not check:
raise Exception('invalid')
return solver.T
def solve(G):
global saved_costs, solver_filenames
g = GraphSolver(G)
for v in list(g.G.nodes):
if len(
list(g.neighbors(v))
) == g.n - 1: # Special case when one vertex is connected to all of them
g.visit(v)
return g.T
solvers = [
import_module(solver_filename) for solver_filename in solver_filenames
]
skipped_costs = []
skipped_solvers = []
# Don't calculate for inputs we already know costs for deterministically
skip = []
if cacher is not None:
for i in range(len(solvers)):
# It turns out all of our algorithms are not deterministic so that condition is going to be deleted
if cacher.is_cached(input_filename, solver_filenames[i]):
# if getattr(solvers[i], 'isDeterministic', False) and cacher.is_cached(input_filename, solver_filenames[i]):
skipped_costs.append(
cacher.get_cost(input_filename, solver_filenames[i]))
skip.append(i)
skipped_solvers.append(solver_filenames[i])
solver_filenames = [
f for i, f in enumerate(solver_filenames) if i not in skip
]
solvers = [s for i, s in enumerate(solvers) if i not in skip]
trees = [] # to be populated
############### Parallelizing ########################
if len(solvers) > 0:
pool = Pool(len(solvers))
async_solvers = [
pool.apply_async(solver.solve, [G]) for solver in solvers
]
trees = [
async_solver.get(1000000000) for async_solver in async_solvers
]
pool.close()
pool.join()
################ Non - parallelizing ####################
# trees = [solver.solve(G) for solver in solvers]
#########################################################
# Cache costs
costs = [average_pairwise_distance(t) for t in trees]
if cacher is not None:
for i in range(len(trees)):
cacher.cache_if_better_or_none(input_filename, solver_filenames[i],
costs[i], None, trees[i])
# Create lists with the same length
all_trees = [None] * len(skipped_costs) + trees
all_costs = skipped_costs + costs
solver_filenames = skipped_solvers + solver_filenames
# Sort
all_trees = [
tree for c, tree in sorted(zip(all_costs, all_trees),
key=lambda pair: pair[0])
]
solver_filenames = [
filename for c, filename in sorted(zip(all_costs, solver_filenames),
key=lambda pair: pair[0])
]
all_costs = sorted(all_costs)
# Print saved costs
second_smallest = all_costs[1]
individual_saved_costs = [
second_smallest - cost if second_smallest - cost > 0 else 0
for cost in all_costs
]
saved_costs = [sum(x) for x in zip(saved_costs, individual_saved_costs)]
print(saved_costs)
# Get the tree to return
min_tree = all_trees[0]
if min_tree is None:
out_file = join(OUTPUT_DIRECTORY, input_filename[:-3],
solver_filenames[0] + '.out')
if isfile(out_file):
print('read outfile', out_file)
min_tree = read_output_file(out_file, G)
else:
print(
"WARNING: all_prev_outputs.txt is probably out of sync. {} was not found."
.format(out_file))
print('Recalculating for input {}'.format(input_filename))
# Recalculate for this input
prev_type = cacher.set_cache_type('none')
min_tree = solve(G)
cacher.set_cache_type(prev_type)
return min_tree
def optimize_additions(solver: GraphSolver,
tree: Graph,
orig_cost: float = None):
graph = solver.G
if orig_cost is None:
orig_cost = average_pairwise_distance(tree)
# get all edges to consider
edges = []
for node in tree.nodes:
for neighbor in graph.neighbors(node):
if not edge_exists(tree.edges,
(node, neighbor)) and not edge_exists(
edges, (node, neighbor)):
edges.append((node, neighbor))
# for each edge (consider order randomly)
while edges:
# random.seed(0)
added_edge = random.choice(edges)
edges.remove(added_edge)
weight = graph[added_edge[0]][added_edge[1]]['weight']
# if added edge creates a cycle
if added_edge[1] in tree.nodes:
solver.add_edge(added_edge)
while (True):
try:
cycle: list = find_cycle(tree, added_edge[0])
except: # No cycle
break
try:
cycle.remove(added_edge)
except ValueError:
cycle.remove(added_edge[::-1])
replaced_edge, new_cost = kill_cycle(solver, cycle, orig_cost)
# print("replaced_edge:", replaced_edge)
# print("cost:", new_cost)
if replaced_edge:
orig_cost = new_cost
solver.remove_edge(replaced_edge)
else:
solver.remove_edge(added_edge)
# if other vertex not in tree
else:
v = added_edge[1]
solver.visit(v, added_edge)
# add_edge(tree, added_edge, weight)
new_cost = average_pairwise_distance(tree)
if new_cost < orig_cost:
orig_cost = new_cost
add_neighbors(solver, edges, v)
else:
solver.unvisit(v)
# remove considered edge
return orig_cost