def makeStateEstimationSimulation(worldSM, verbose = False):
"""
Make a machine that simulates the state estimation process. It
takes a state machine representing the world, at construction
time. Let i be an input to the world machine. The input is fed
into the world machine, generating (stochastically) an output, o.
The (o, i) pair is fed into a state-estimator using worldSM as its
model. The output of the state estimator is a belief state, b.
The output of this entire composite machine is (b, (o, i)).
@param worldSM: an instance of C{ssm.StochasticSM}
@returns: a state machine that simulates the world and executes
the state estimation process.
"""
return sm.Cascade(sm.Parallel(worldSM, sm.Wire()),
sm.Parallel(StateEstimator(worldSM, verbose = verbose),
sm.Wire()))
def systemSM(circuit, inComponents, motorNodes, initState):
# circuit takes input which is a dictionary of values (or list of them)
# output is a le.Solution instance.
cSM = CircuitSM(circuit.components, inComponents, circuit.groundNode)
# motor input is Solution instance, output is (vel, angle)
motorSM = sm.Cascade(MotorAccel(initState, motorNodes), sm.R(initState))
# world has Solution as input, ((vel, angle), sol) as output
wSM = sm.Parallel(motorSM, sm.Wire())
if inComponents:
# there's another input besides motor feedback
return Feedback2(sm.Cascade(cSM,wSM), MotorFeedback())
else:
# only motor feedback
return Feedback(sm.Cascade(cSM,wSM), MotorFeedback())