def perfectObsNoiseModel(actualColor):
"""
:param actualColor: actual color in a location
:returns: ``DDist`` over observed colors when in a room that has
``actualColor``. In this case, we observe the actual color with
probability 1.
"""
return dist.DDist({actualColor: 1.0})
def perfectObsNoiseModel(actualColor):
"""
@param actualColor: actual color in a location
@returns: C{DDist} over observed colors when in a room that has
C{actualColor}. In this case, we observe the actual color with
probability 1.
"""
return dist.DDist({actualColor: 1.0})
def perfectTransNoiseModel(nominalLoc, hallwayLength):
"""
@param nominalLoc: location that the robot would have ended up
given perfect dynamics
@param hallwayLength: length of the hallway
@returns: distribution over resulting locations, modeling noisy
execution of commands; in this case, the robot goes to the
nominal location with probability 1.0
"""
return dist.DDist({nominalLoc: 1.0})
def transition_model(self, s, a, p = 0.4):
# Only randomness is in brv and brc after a bounce
# 1- prob of negating nominal velocity
if s == 'over':
return dist.delta_dist('over')
# Current state
((br, bc), (brv, bcv), pp, pv) = s
# Nominal next ball state
new_br = br + self.ball_speed*brv; new_brv = brv
new_bc = bc + self.ball_speed*bcv; new_bcv = bcv
# nominal paddle state, a is action (-1, 0, 1)
new_pp = max(0, min(self.n-1, pp + a))
new_pv = a
new_s = None
hit_r = hit_c = False
# bottom, top contacts
if new_br < 0:
new_br = 0; new_brv = 1; hit_r = True
elif new_br >= self.n:
new_br = self.n - 1; new_brv = -1; hit_r = True
# back, front contacts
if new_bc < 0: # back bounce
new_bc = 0; new_bcv = 1; hit_c = True
elif new_bc >= self.n:
if self.paddle_hit(pp, new_pp, br, bc, new_br, new_bc):
new_bc = self.n-1; new_bcv = -1; hit_c = True
else:
return dist.delta_dist('over')
new_s = ((new_br, new_bc), (new_brv, new_bcv), new_pp, new_pv)
if ((not hit_c) and (not hit_r)):
return dist.delta_dist(new_s)
elif hit_c: # also hit_c and hit_r
if abs(new_brv) > 0:
return dist.DDist({new_s: p,
((new_br, new_bc), (-new_brv, new_bcv), new_pp, new_pv) : 1-p})
else:
return dist.DDist({new_s: p,
((new_br, new_bc), (-1, new_bcv), new_pp, new_pv) : 0.5*(1-p),
((new_br, new_bc), (1, new_bcv), new_pp, new_pv) : 0.5*(1-p)})
elif hit_r:
return dist.DDist({new_s: p,
((new_br, new_bc), (new_brv, -new_bcv), new_pp, new_pv) : 1-p})
def noisyObsNoiseModel(actualColor):
"""
@param actualColor: actual color in a location
@returns: C{DDist} over observed colors when in a room that has
C{actualColor}. In this case, we observe the actual color with
probability 0.8, and the remaining 0.2 probability is divided
uniformly over the other possible colors in this world.
"""
d = {}
for observedColor in possibleColors:
if observedColor == actualColor:
d[observedColor] = 0.8
else:
d[observedColor] = 0.2 / (len(possibleColors) - 1)
return dist.DDist(d)
def leftSlipTransNoiseModel(nominalLoc, hallwayLength):
"""
@param nominalLoc: location that the robot would have ended up
given perfect dynamics
@param hallwayLength: length of the hallway
@returns: distribution over resulting locations, modeling noisy
execution of commands; in this case, the robot goes to the
nominal location with probability 0.9, and goes one step too far
left with probability 0.1.
If any of these locations are out of bounds, then the associated
probability mass stays at C{nominalLoc}.
"""
d = {}
dist.incrDictEntry(d, util.clip(loc - 1, 0, n - 1), 0.1)
dist.incrDictEntry(d, util.clip(loc, 0, n - 1), 0.9)
return dist.DDist(d)
def noisyTransNoiseModel(nominalLoc, hallwayLength):
"""
@param nominalLoc: location that the robot would have ended up
given perfect dynamics
@param hallwayLength: length of the hallway
@returns: distribution over resulting locations, modeling noisy
execution of commands; in this case, the robot goes to the
nominal location with probability 0.8, goes one step too far left with
probability 0.1, and goes one step too far right with probability 0.1.
If any of these locations are out of bounds, then the associated
probability mass goes is assigned to the boundary location (either
0 or C{hallwayLength-1}).
"""
d = {}
dist.incrDictEntry(d, util.clip(nominalLoc - 1, 0, hallwayLength - 1), 0.1)
dist.incrDictEntry(d, util.clip(nominalLoc, 0, hallwayLength - 1), 0.8)
dist.incrDictEntry(d, util.clip(nominalLoc + 1, 0, hallwayLength - 1), 0.1)
return dist.DDist(d)
def getNextValues(self, state, inp):
"""
@param state: Distribution over states of the subject machine,
represented as a C{dist.Dist} object
@param inp: A pair C{(o, a)} of the input and output of the
subject machine on this time step. If this parameter is
C{None}, then no update occurs and the state is returned,
unchanged.
"""
if inp == None:
return (state, state)
(o, i) = inp
total = 0
afterObs = state.d.copy()
for s in state.support():
afterObs[s] *= self.model.observationDistribution(s).prob(o)
total += afterObs[s]
if total == 0:
raise Exception('Observation '+str(o)+\
' has 0 probability in all possible states.')
# Note: afterObs is not normalized at this point; dividing
# through by toal during the transition update
new = {}
tDist = self.model.transitionDistribution(i)
for s in state.support():
tDistS = tDist(s)
oldP = afterObs[s] / total
for sPrime in tDistS.support():
dist.incrDictEntry(new, sPrime, tDistS.prob(sPrime) * oldP)
dSPrime = dist.DDist(new)
return (dSPrime, dSPrime)
def uGivenAS(a):
#! pass
return lambda s: dist.DDist({s: 1.0})
def oGivenS(s):
#! pass
if s == 'empty':
return dist.DDist({'hit': falsePos, 'free': 1 - falsePos})
else: # occ
return dist.DDist({'hit': 1 - falseNeg, 'free': falseNeg})
else: # occ
return dist.DDist({'hit': 1 - falseNeg, 'free': falseNeg})
#!
# Transition model: P(newState | s | a)
def uGivenAS(a):
#! pass
return lambda s: dist.DDist({s: 1.0})
#!
#!cellSSM = None # Your code here
cellSSM = ssm.StochasticSM(
dist.DDist({
'occ': initPOcc,
'empty': 1 - initPOcc
}), uGivenAS, oGivenS)
#!
class BayesGridMap(dynamicGridMap.DynamicGridMap):
def squareColor(self, (xIndex, yIndex)):
p = self.occProb((xIndex, yIndex))
if self.robotCanOccupy((xIndex, yIndex)):
return colors.probToMapColor(p, colors.greenHue)
elif self.occupied((xIndex, yIndex)):
return 'black'
else:
return 'red'
# Observation model: P(obs | state)
def oGivenS(s):
#! pass
if s == 'empty':
return dist.DDist({'hit': falsePos, 'free': 1 - falsePos})
else: # occ
return dist.DDist({'hit': 1 - falseNeg, 'free': falseNeg})
#!
# Transition model: P(newState | s | a)
def uGivenAS(a):
#! pass
return lambda s: dist.DDist({s: 1.0})
#!
#!cellSSM = None # Your code here
cellSSM = ssm.StochasticSM(dist.DDist({'occ': initPOcc, 'empty': 1 - initPOcc}),
uGivenAS, oGivenS)
#!
class BayesGridMap(dynamicGridMap.DynamicGridMap):
def squareColor(self, (xIndex, yIndex)):
p = self.occProb((xIndex, yIndex))
if self.robotCanOccupy((xIndex,yIndex)):
return colors.probToMapColor(p, colors.greenHue)
elif self.occupied((xIndex, yIndex)):
return 'black'
else:
return 'red'
