def find_best(old_path, new_path, graph_num, print_output=False):
"""
This method finds the best of the two input paths for the graph with number graph_num,
and returns it.
:param: old_path - our current answer for this graph
:param: new_path - the new path to check
:param: graph_num - the graph file number. For example, for file 121.in, pass in 121
:param: print_output - boolean that reflects whether you want this function to print information
:return: the best path of the input paths
"""
graph = Graph(open('instances/{0}.in'.format(graph_num)).read())
new_path_cost = graph.path_cost(new_path)
old_path_cost = graph.path_cost(old_path)
if print_output:
print "New Path: ", new_path, " with cost: ", new_path_cost
print "Old Path: ", old_path, " with cost: ", old_path_cost
if not graph.is_valid_hamiltonian(new_path):
if print_output:
print "New path is not a valid hamiltonian"
return old_path
elif new_path_cost < old_path_cost:
if print_output:
print "New path selected"
return new_path
else:
print "Old path selected"
return old_path
def test_ten_nodes2_valid_hamiltonian(self):
graph = Graph(open(TEST_PATH + 'ten_nodes2.in').read())
actual_path, actual_dist = kruskalsSolver(graph)
self._check(graph.is_valid_hamiltonian(actual_path), True)
elif heuristic == Graph.BINOCULARS:
mapping[g.path_cost(tuple(path))] += ["Binoculars" + t]
print "path with cost {0} found by {1}".format(g.path_cost(tuple(path)), "Binoculars" + t)
else:
print "{0} found no valid path".format(heuristic)
except ValueError as e:
print "\t\tERROR in {0}".format(heuristic)
continue
#
# Trying other algorithms:
#
# Kruskals
kruskals_path, kruskals_cost = kruskalsSolver(g)
if kruskals_path == None or not g.is_valid_hamiltonian(kruskals_path):
print "\t\tERROR in KRUSKALS"
else:
results.append(kruskals_path)
# instantiate a list for an empty path
try:
a = mapping[g.path_cost(tuple(kruskals_path))]
except KeyError:
mapping[g.path_cost(tuple(kruskals_path))] = []
mapping[g.path_cost(tuple(kruskals_path))] += ["Kruskals"]
# Direct Dynamic Solver (if applicable)
if g.num_nodes <= DYNAMIC_THRESHOLD:
print "USING DYNAMIC SOLVER for " + str(i)
dynamic_path, dynamic_cost = dynamicSolver(g)
