help="use multi-objective optimization to find optimal set. \
Need cost figures attached to the optional nodes.") #cost= in graphical editor.
parser.add_option_group(group)
(options, args) = parser.parse_args()
if options.verbose:
pass
if options.o_min != None:
options.o_min = int(options.o_min)
if options.o_max != None:
options.o_max = int(options.o_max)
# identify if admissible
#initial run with no options.
parser = ParseQGM()
parser.set_node_ids() #label the nodes with the appropriate ids for SEB
parser.generate_seb() # the Sebastiani goal SAT approach
parser.generate_atms() # the ATMS techne file
parser.print_files() # save the mandatory/non-optional nodes to disk
# admissible = parser.run_seb() # see if the result is admissible.
# if admissible: print "was admissible"
# else: print "not admissible"
# parser.zero_counts()
#load this run's options
#mands, opts, prefs = display_opt.get_options(options.run_id)
#print mands,opts, prefs
#display_opt.clear_model() # set all nodes to plain
#display_opt.set_graph(mands,opts) # set nodes to their appropriate status
# generate option sets
#if options.use_naive:
# results = option_parser.naive_option(parser, opts, options.o_min, options.o_max)
# print results
