def _paris_service_cb(self,req):
"""Handler of PARIS service requests."""
resp = PARISSrvResponse()
tcs = ut.ros_pstnu_to_rmpyl_temporal_constraints(req.pstnu)
paris = PARIS(req.gaussian_div,req.gaussian_optimize_partition,
req.gaussian_lr,req.gaussian_tol,req.gaussian_max_iter)
if paris.set_minimum_risk_strong_controllability(tcs):
setup_success = True
if req.makespan:
rospy.loginfo('Setting up makespan optimization.')
paris.set_makespan_optimization()
if req.cc>=0.0:
rospy.loginfo('Setting up chance-constrained optimization.')
paris.set_chance_constraint(req.cc)
rospy.loginfo('Calling PARIS to solve scheduling problem.')
squeeze_dict,risk_bound,sc_schedule = paris.solve()
else:
setup_success = False
squeeze_dict=None
if not setup_success:
rospy.logwarn('Failed to set up strong controllability problem.')
if squeeze_dict != None:
resp.success = True
resp.schedule = ut.schedule_to_schedule_msg(sc_schedule)
resp.squeezes = ut.squeeze_dict_to_squeeze_msgs(squeeze_dict)
resp.risk_upper_bound = risk_bound
rospy.loginfo('Strong schedule found!')
else:
resp.success = False
rospy.logwarn('Failed to solve strong controllability problem.')
resp.header.stamp = rospy.Time.now() #Records time when solution was obtained
return resp
def check_cc_strong_controllability(tcs,example_name='',write_tpns=False,
gaussian_approx='smart_piecewise',gaussian_div=5,
gaussian_optimize_partition=False,gaussian_lr=0.05,
gaussian_tol=1e-4,gaussian_max_iter=1000,
cc=-1.0,makespan=False):
"""
Checks the chance-constrained strong controllability of a pSTN.
"""
pt = PyTemporal()
start_time = time.time()
paris = PARIS(gaussian_div,gaussian_optimize_partition,gaussian_lr,
gaussian_tol,gaussian_max_iter)
paris.init_model()
if paris.set_minimum_risk_strong_controllability(tcs):
if makespan:
paris.set_makespan_optimization()
if cc>=0.0:
paris.set_chance_constraint(cc)
squeeze_dict,risk_bound,sc_schedule = paris.solve()
else:
squeeze_dict=None
if squeeze_dict!=None:
risk_bound = min(risk_bound,1.0) #The bound must saturate at 1 (almost sure to fail)
elapsed = time.time()-start_time
print('\n***** '+example_name)
if squeeze_dict!=None:
print('Found SC reformulation in %.3f ms!'%(elapsed*1000.0))
print('These are the squeezings:')
prob_success=1.0
for tc,tc_dict in squeeze_dict.items():
print('\t%s [%.1f,%.1f]->[%.1f,%.1f]:\tRisk=%.4f%%'%(tc.name,tc.lb,tc.ub,tc_dict['lb'],tc_dict['ub'],tc_dict['risk']*100.0))
prob_success*=(1.0-tc_dict['risk'])
print('\nThis is the schedule:')
for ev_name, ev_time in sc_schedule.items():
print('\t%s: %.2f s'%(ev_name,ev_time))
risk_indep = (1.0-prob_success)
print('\nTotal scheduling risk (assuming independent stochastic durations): %.4f%%'%(risk_indep*100.0))
print('Linear risk bound: %.4f%%'%(risk_bound*100.0))
if risk_bound<risk_indep:
raise ValueError('Boole\'s bound %f less than (independent) risk %f'%(risk_bound,risk_indep))
else:
risk_gap = risk_bound-risk_indep
print('Bound gap: %f%%'%((risk_gap)*100.0))
if write_tpns:
sc_stnu=[]
for tc in tcs:
if tc.type=='controllable':
sc_stnu.append(tc)
else:
if tc in squeeze_dict:
lb = tc_dict['lb']
ub = tc_dict['ub']
else:
lb = tc.lb
ub = tc.ub
sc_stnu.append(TemporalConstraint(start=tc.start,end=tc.end,
ctype='uncontrollable_bounded',
lb=lb, ub=ub))
prog = pt.to_rmpyl(sc_stnu)
prog.to_ptpn(filename='paris_sc_%s.tpn'%(example_name))
else:
prob_success=risk_bound=risk_gap=-1
print('Strongly controllable reformulation failed in %.3f ms...'%(elapsed*1000.0))
if write_tpns:
prog = pt.to_rmpyl(tcs)
prog.to_ptpn(filename='paris_pstn_%s.tpn'%(example_name))
return prob_success,elapsed,risk_bound,risk_gap