def discrete_query_cb(self, req):
nd = U.discrete_nodedata_from_ros(req.nodes)
skel = U.graph_skeleton_from_node_data(nd)
skel.toporder()
bn = DiscreteBayesianNetwork(skel, nd)
fn = TableCPDFactorization(bn)
q = {n: nd.Vdata[n]["vals"] for n in req.query}
ev = {ns.node: ns.state for ns in req.evidence}
rospy.loginfo("resolving query %s with evidence %s" % (q, ev))
ans = fn.condprobve(query=q, evidence=ev)
rospy.loginfo("%s -> %s" % (ans.scope, ans.vals))
res = DiscreteQueryResponse()
node = DiscreteNode()
node.name = ans.scope[0]
node.outcomes = q[node.name]
node.CPT.append(ConditionalProbability(node.outcomes, ans.vals))
res.nodes.append(node)
return res
def discrete_node_from_dict(name, d):
n = DiscreteNode()
n.name = str(name)
n.outcomes = map(str, d["vals"])
if d["parents"]:
n.parents = map(str, d["parents"])
if d["children"]:
n.children = map(str, d["children"])
cprob = d["cprob"]
if isinstance(cprob, dict):
n.CPT = [ConditionalProbability(values=eval(k), probabilities=v) for k,v in (d["cprob"]).items()]
else:
n.CPT = [ConditionalProbability(values=map(str, d["vals"]), probabilities=cprob)]
return n
def discrete_node_from_dict(name, d):
n = DiscreteNode()
n.name = str(name)
n.outcomes = map(str, d["vals"])
if d["parents"]:
n.parents = map(str, d["parents"])
if d["children"]:
n.children = map(str, d["children"])
cprob = d["cprob"]
if isinstance(cprob, dict):
n.CPT = [
ConditionalProbability(values=eval(k), probabilities=v)
for k, v in (d["cprob"]).items()
]
else:
n.CPT = [
ConditionalProbability(values=map(str, d["vals"]),
probabilities=cprob)
]
return n
def q():
rospy.init_node("discrete_bayesian_query_sample")
query_func = rospy.ServiceProxy("pgm_learner/discrete/query",
DiscreteQuery)
prize_door = DiscreteNode()
prize_door.name = "prize_door"
prize_door.children = ["monty_door"]
prize_door.outcomes = ["A", "B", "C"]
prize_door.CPT = [
ConditionalProbability(values=prize_door.outcomes,
probabilities=[1.0 / 3, 1.0 / 3, 1.0 / 3])
]
guest_door = DiscreteNode()
guest_door.name = "guest_door"
guest_door.children = ["monty_door"]
guest_door.outcomes = ["A", "B", "C"]
guest_door.CPT = [
ConditionalProbability(values=guest_door.outcomes,
probabilities=[1.0 / 3, 1.0 / 3, 1.0 / 3])
]
monty_door = DiscreteNode()
monty_door.name = "monty_door"
monty_door.parents = ["prize_door", "guest_door"]
monty_door.outcomes = ["A", "B", "C"]
for prize, guest in it.product(["A", "B", "C"], repeat=2):
print str([prize, guest])
print monty_door_prob(prize, guest)
monty_door.CPT += [
ConditionalProbability(values=[prize, guest],
probabilities=monty_door_prob(prize=prize,
guest=guest))
]
req = DiscreteQueryRequest()
req.nodes = [prize_door, guest_door, monty_door]
req.evidence = [
DiscreteNodeState(node="guest_door", state="A"),
DiscreteNodeState(node="monty_door", state="B"),
]
req.query = ["prize_door"]
print query_func(req)
def q():
rospy.init_node("discrete_bayesian_query_sample")
query_func = rospy.ServiceProxy("pgm_learner/discrete/query", DiscreteQuery)
prize_door = DiscreteNode()
prize_door.name = "prize_door"
prize_door.children = ["monty_door"]
prize_door.outcomes = ["A", "B", "C"]
prize_door.CPT = [ConditionalProbability(values=prize_door.outcomes,
probabilities=[1.0/3,1.0/3,1.0/3])]
guest_door = DiscreteNode()
guest_door.name = "guest_door"
guest_door.children = ["monty_door"]
guest_door.outcomes = ["A", "B", "C"]
guest_door.CPT = [ConditionalProbability(values=guest_door.outcomes,
probabilities=[1.0/3,1.0/3,1.0/3])]
monty_door = DiscreteNode()
monty_door.name = "monty_door"
monty_door.parents = ["prize_door", "guest_door"]
monty_door.outcomes = ["A", "B", "C"]
for prize, guest in it.product(["A","B","C"], repeat=2):
print str([prize, guest])
print monty_door_prob(prize, guest)
monty_door.CPT += [ConditionalProbability(values=[prize, guest],
probabilities=monty_door_prob(prize=prize, guest=guest))]
req = DiscreteQueryRequest()
req.nodes = [prize_door, guest_door, monty_door]
req.evidence = [DiscreteNodeState(node="guest_door", state="A"),
DiscreteNodeState(node="monty_door", state="B"),]
req.query = ["prize_door"]
print query_func(req)
