def build_potential_graphs(cycle_mapping):
graphs = {}
base_decider = InequalityDecider()
base_decider.add_cycle_mapping(problem_def, cycle_mapping)
decider = base_decider.freeze()
for result, inputs in problem_def.iteritems():
my_cycles = tuple(map(operator.itemgetter(1), filter(lambda a: a[0]==result, cycle_mapping)))
other_cycles = map(operator.itemgetter(1), filter(lambda a: a[0]!=result, cycle_mapping))
all_states = set(map(lambda s: string.join(s, ""), itertools.permutations(["a","a","b","b","c"])))
other_states = all_states-set(itertools.chain(*my_cycles))
for input in inputs:
graphs[input] = (decider.build_transition_graph(my_cycles, other_states, input), my_cycles[0][0])
return graphs
all_states = set(map(lambda s: string.join(s, ""), itertools.permutations(["a","a","b","b","c"])))
other_states = all_states-set(itertools.chain(*my_cycles))
for input in inputs:
graphs[input] = (decider.build_transition_graph(my_cycles, other_states, input), my_cycles[0][0])
return graphs
result = list()
for i in xrange(len(cliques)):
if i % total_slices == my_slice:
clique = cliques[i]
print "starting %d %s"%(i, str(clique))
cycle_mapping = map(lambda c: (c[0], tuple(cycles[c[1]])), clique)
decider = InequalityDecider()
decider.add_cycle_mapping(problem_def, cycle_mapping)
if decider.satisfiable():
print " satisfiable"
graphs = build_potential_graphs(cycle_mapping)
good_states = list()
for state in set(map(lambda s: string.join(s, ""), itertools.permutations(["a","a","b","b","c"]))):
try:
decider = InequalityDecider()
decider.add_cycle_mapping(problem_def, cycle_mapping)
for input, graph_plus in graphs.iteritems():
path = nx.shortest_path(graph_plus[0], state, graph_plus[1])
for f, t in zip(path[:-1], path[1:]):
decider.add_transition(f, t, input)
if decider.satisfiable():
def build_decider(prefix):
basedecider = InequalityDecider()
basedecider.add_cycle_mapping(problem_def, cycle_mapping)
for transition in prefix:
basedecider.add_transition(*transition)
return basedecider.freeze()
