def discrete_structure_estimation_cb(self, req):
states = [{ns.node: ns.state
for ns in s.node_states} for s in req.states]
pvalparam = 0.05 # default value
indegree = 1 # default value
if req.pvalparam != 0.0:
pvalparam = req.pvalparam
if req.indegree != 0:
indegree = req.indegree
res = self.learner.discrete_constraint_estimatestruct(
states, pvalparam=pvalparam, indegree=indegree)
return DiscreteStructureEstimationResponse(
U.graph_skeleton_to_ros(res))
def lg_structure_estimation_cb(self, req):
states = [{ns.node: ns.state
for ns in s.node_states} for s in req.states]
rospy.logdebug(states)
pvalparam = 0.05 # default value
bins = 10 # default value
indegree = 1 # default value
if req.pvalparam != 0.0:
pvalparam = req.pvalparam
if req.bins != 0:
bins = req.bins
if req.indegree != 0:
indegree = req.indegree
rospy.logdebug("bins: %d, pvalparam: %f, indegree: %d" %
(bins, pvalparam, indegree))
res = self.learner.lg_constraint_estimatestruct(states,
pvalparam=pvalparam,
bins=bins,
indegree=indegree)
rospy.logdebug("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
rospy.logdebug(res.V)
rospy.logdebug(res.E)
return LinearGaussianStructureEstimationResponse(
U.graph_skeleton_to_ros(res))
