def main(instance, niter, seed):
root = MipTreeNode.root(instance)
sols = mcts.run(root, iter_limit=niter, rng_seed=seed)
info("solutions found: {}".format(sols))
info("best found objective: {}".format(sols.best.value))
if sols.best.is_feas:
info("best solution (non-zeros):")
for var, val in sols.best.data.items():
if is_nonzero(val):
info("\t{}:\t{}".format(var, val))
return root, sols
def verify_instance(instance, *args, **kwargs):
if isinstance(instance, (tuple, list)):
items, capacity, optimum = instance
elif callable(instance):
items, capacity, optimum = instance()
else:
raise TypeError("unexpected instance value: {}".format(instance))
mcts.config_logging(level="INFO")
root = KnapsackTreeNode.root([items, capacity])
sols = mcts.run(root, *args, **kwargs)
assert set(sols.best.data) == optimum
assert root.is_exhausted
kk_edges, diff = karmarkar_karp(self.labels)
return mcts.Solution(value=objective(diff), data=edges + kk_edges)
def make_partition(edges):
adj = {JOIN: defaultdict(set), SPLIT: defaultdict(set)}
for i, j, edge_type in edges:
adj_edge_type = adj[edge_type]
adj_edge_type[i].add(j)
adj_edge_type[j].add(i)
nverts = len(edges) + 1
subset = [None] * nverts
_assign_subset(adj, subset, 0, 0)
return subset
def _assign_subset(adj, subset, i, s):
subset[i] = s
for edge_type in (JOIN, SPLIT):
adj_edge_type = adj[edge_type]
adj_edge_type_i = adj_edge_type.pop(i, ())
s_j = s if edge_type == JOIN else 1 - s
for j in adj_edge_type_i:
adj_edge_type[j].remove(i)
_assign_subset(adj, subset, j, s_j)
mcts.config_logging()
r = TreeNode.root("instances/npp/hard1000.dat")
s = mcts.run(r, iter_limit=1000)