def q_without_ros():
skel = GraphSkeleton()
skel.V = ["prize_door", "guest_door", "monty_door"]
skel.E = [["prize_door", "monty_door"],
["guest_door", "monty_door"]]
skel.toporder()
nd = NodeData()
nd.Vdata = {
"prize_door": {
"numoutcomes": 3,
"parents": None,
"children": ["monty_door"],
"vals": ["A", "B", "C"],
"cprob": [1.0/3, 1.0/3, 1.0/3],
},
"guest_door": {
"numoutcomes": 3,
"parents": None,
"children": ["monty_door"],
"vals": ["A", "B", "C"],
"cprob": [1.0/3, 1.0/3, 1.0/3],
},
"monty_door": {
"numoutcomes": 3,
"parents": ["prize_door", "guest_door"],
"children": None,
"vals": ["A", "B", "C"],
"cprob": {
"['A', 'A']": [0., 0.5, 0.5],
"['B', 'B']": [0.5, 0., 0.5],
"['C', 'C']": [0.5, 0.5, 0.],
"['A', 'B']": [0., 0., 1.],
"['A', 'C']": [0., 1., 0.],
"['B', 'A']": [0., 0., 1.],
"['B', 'C']": [1., 0., 0.],
"['C', 'A']": [0., 1., 0.],
"['C', 'B']": [1., 0., 0.],
},
},
}
bn = DiscreteBayesianNetwork(skel, nd)
fn = TableCPDFactorization(bn)
query = {
"prize_door": ["A","B","C"],
}
evidence = {
"guest_door": "A",
"monty_door": "B",
}
res = fn.condprobve(query, evidence)
print res.vals
print res.scope
print res.card
print res.stride
def q_without_ros():
skel = GraphSkeleton()
skel.V = ["prize_door", "guest_door", "monty_door"]
skel.E = [["prize_door", "monty_door"], ["guest_door", "monty_door"]]
skel.toporder()
nd = NodeData()
nd.Vdata = {
"prize_door": {
"numoutcomes": 3,
"parents": None,
"children": ["monty_door"],
"vals": ["A", "B", "C"],
"cprob": [1.0 / 3, 1.0 / 3, 1.0 / 3],
},
"guest_door": {
"numoutcomes": 3,
"parents": None,
"children": ["monty_door"],
"vals": ["A", "B", "C"],
"cprob": [1.0 / 3, 1.0 / 3, 1.0 / 3],
},
"monty_door": {
"numoutcomes": 3,
"parents": ["prize_door", "guest_door"],
"children": None,
"vals": ["A", "B", "C"],
"cprob": {
"['A', 'A']": [0., 0.5, 0.5],
"['B', 'B']": [0.5, 0., 0.5],
"['C', 'C']": [0.5, 0.5, 0.],
"['A', 'B']": [0., 0., 1.],
"['A', 'C']": [0., 1., 0.],
"['B', 'A']": [0., 0., 1.],
"['B', 'C']": [1., 0., 0.],
"['C', 'A']": [0., 1., 0.],
"['C', 'B']": [1., 0., 0.],
},
},
}
bn = DiscreteBayesianNetwork(skel, nd)
fn = TableCPDFactorization(bn)
query = {
"prize_door": ["A", "B", "C"],
}
evidence = {
"guest_door": "A",
"monty_door": "B",
}
res = fn.condprobve(query, evidence)
print res.vals
print res.scope
print res.card
print res.stride
def construct(self):
skel = GraphSkeleton()
skel.V = self.nodes.keys()
skel.E = []
for node, ndata in self.nodes.iteritems():
if ndata['parents']:
for p in ndata['parents']:
skel.E.append([p, node])
self.nodes[p]['children'].append(node)
for node, ndata in self.nodes.iteritems():
if len(ndata['children']) == 0:
ndata['children'] = None
data = NodeData()
data.Vdata = self.nodes
skel.toporder()
bn = DiscreteBayesianNetwork(skel, data)
return bn
def __init__(self, nodes):
self.nodes = {}
self.children = defaultdict(list)
self.parents = defaultdict(list)
self.outputs = {}
for name, node_spec in nodes.iteritems():
node_type = node_spec["type"]
if node_type == "inferred":
parents = node_spec["parents"]
# store the relationship between these elements
for parent in parents:
normalised = normalise_name(parent)
self.parents[name].append(normalised)
self.children[normalised].append(name)
truth_table = parse_truth_table(node_spec["p"], parents)
node = make_node(truth_table, parents, node_type)
self.nodes[name] = node
if node_type == "fsm_input":
node = make_node([1.0, 0.0], None, node_type)
self.nodes[name] = node
if node_type == "sensor_input":
proxy_node = make_node([1.0, 0.0], None, "proxy")
proxy_name = "_proxy_%s" % name
self.nodes[proxy_name] = proxy_node
self.children[proxy_name].append(name)
node = make_node({
"['T']": [1.0, 0.0],
"['F']": [0.0, 1.0]
}, [proxy_name], node_type)
self.nodes[name] = node
if node_type == "output":
self.outputs[name] = node_spec
for node in self.nodes:
if len(self.children[node]) > 0:
self.nodes[node]["children"] = self.children[node]
else:
self.nodes[node]["children"] = None
# certainty scaling
self.event_caution = 0.0
og = OrderedSkeleton()
og.V = self.nodes.keys()
edges = []
for k, children in self.children.iteritems():
for child in children:
edges.append((k, child))
og.E = edges
og.toporder()
nd = NodeData()
nd.Vdata = self.nodes
#logging.debug(pprint.pformat(nd.Vdata))
self.net = DiscreteBayesianNetwork(og, nd)
self.factor_net = TableCPDFactorization(self.net)
def discrete_nodedata_from_ros(nodes):
nd = NodeData()
nd.Vdata = {n.name: dict_from_ros_discrete_node(n) for n in nodes}
return nd
def discrete_nodedata_from_ros(nodes):
nd = NodeData()
nd.Vdata = {n.name: dict_from_ros_discrete_node(n) for n in nodes}
return nd