def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
bd = self.getBeliefDistribution()
allPossible = util.Counter()
for p in self.legalPositions:
npd = self.getPositionDistribution(
self.setGhostPosition(gameState, p))
for newPos, prob in npd.items():
allPossible[newPos] += prob * bd[p]
maxV = max([v for k, v in allPossible.items()])
if maxV == 0:
self.initializeUniformly(gameState)
else:
items = sorted(allPossible.items())
self.ppList = util.nSample([v for k, v in items],
[k for k, v in items],
self.numParticles)
def sample_data(self, trainingData, trainingLabels, sample_weights):
# "*** YOUR CODE HERE ***"
sampleSize = int(len(trainingData) * 0.5)
sampleData = util.Counter()
sampleLabels = util.Counter()
# cumulative_weights = sample_weights[:]
# for i in range(1,len(cumulative_weights)):
# cumulative_weights[i] += cumulative_weights[i-1]
# for i in range(sampleSize):
# idx = random.uniform(0.0, 1.0)
# for j in range(len(trainingData)):
# if idx < cumulative_weights[j]:
# sampleData[i] = trainingData[j]
# sampleLabels[i] = trainingLabels[j]
# break
samplePairs = util.nSample(sample_weights,
zip(trainingData, trainingLabels),
sampleSize)
for i in range(len(samplePairs)):
sampleData[i], sampleLabels[i] = samplePairs[i]
return sampleData, sampleLabels
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
"*** YOUR CODE HERE ***"
noisyDistance = observation
emissionModel = busters.getObservationDistribution(
noisyDistance) # P(e|X)
pacmanPosition = gameState.getPacmanPosition()
if noisyDistance is None:
# update all particles so that ghost appears in prison cell
jailed_position = self.getJailPosition()
self.particles = [
jailed_position for _ in range(self.numParticles)
]
else:
# update particle weights
particleWeights = util.Counter()
for p in self.particles:
trueDistance = util.manhattanDistance(p, pacmanPosition)
particleWeights[p] += emissionModel[
trueDistance] #update weight with true distance
# resample
if particleWeights.totalCount() == 0:
self.initializeUniformly(gameState)
else:
items = sorted(particleWeights.items())
values, distribution = map(list, zip(*items))
samples = util.nSample(distribution, values, self.numParticles)
# samples = list()
# for _ in range(self.numParticles):
# sample = util.sample(particleWeights)
# samples.append(sample)
# samples = util.nSample(particleWeights, self.particles, self.numParticles)
self.particles = samples
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
allPossible = util.Counter()
for oldPos in self.list_particles:
newPosDist = self.getPositionDistribution(
self.setGhostPosition(gameState, oldPos))
for newPos, prob in newPosDist.items():
allPossible[newPos] = prob * self.getBeliefDistribution(
)[oldPos] + allPossible[newPos]
items = sorted(allPossible.items())
distribution = [i[1] for i in items]
values = [i[0] for i in items]
self.list_particles = util.nSample(distribution, values,
self.numParticles)
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
new_beliefs = util.Counter()
for p in self.particles:
newPosDist = self.getPositionDistribution(
self.setGhostPosition(gameState, p))
for newPos, prob in newPosDist.items():
new_beliefs[newPos] += prob
self.particles = util.nSample(new_beliefs.values(), new_beliefs.keys(),
self.numParticles)
def train(self, trainingData, trainingLabels):
"""
The training loop samples from the data "num_classifiers" time. Size of each sample is
specified by "ratio". So len(sample)/len(trainingData) should equal ratio.
"""
self.features = trainingData[0].keys()
# "*** YOUR CODE HERE ***"
for i in range(self.num_classifiers):
sampleData = util.Counter()
sampleLabels = util.Counter()
samplePairs = util.nSample([1.0 / len(trainingData)] *
len(trainingData),
zip(trainingData, trainingLabels),
int(self.ratio * len(trainingData)))
for j in range(len(samplePairs)):
sampleData[j], sampleLabels[j] = samplePairs[j]
# for j in range(int(self.ratio * len(trainingData))):
# idx = random.randint(0, len(trainingData)-1)
# sampleData[j], sampleLabels[j] = trainingData[idx], trainingLabels[idx]
self.classifiers[i].train(sampleData, sampleLabels)
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
# util.raiseNotDefined()
beliefs = self.getBeliefDistribution()
allPossible = util.Counter()
# for all possible legal positions
for oldPos in self.legalPositions:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
for newPos, prob in newPosDist.items():
allPossible[newPos] += prob * beliefs[oldPos]
if(allPossible.totalCount() == 0):
self.initializeUniformly(gameState)
else:
allPossible.normalize()
items = sorted(allPossible.items())
values = [i[0] for i in items]
distribution = [i[1] for i in items]
self.particles = util.nSample(distribution, values, self.numParticles)
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts:
return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
beliefs = self.getBeliefDistribution()
legalPositionTuples = beliefs.sortedKeys()
# "*** YOUR CODE HERE ***"
new_beliefs = util.Counter()
for p in legalPositionTuples:
new_beliefs[p] = beliefs[p]
for i in xrange(self.numGhosts):
if noisyDistances[i] != None:
trueDistance = util.manhattanDistance(p[i], pacmanPosition)
new_beliefs[p] *= emissionModels[i][trueDistance]
if(new_beliefs.totalCount() == 0):
self.initializeParticles()
else:
new_beliefs.normalize()
items = sorted(new_beliefs.items())
values = [i[0] for i in items]
distribution = [i[1] for i in items]
self.particles = util.nSample(distribution, values, self.numParticles)
for i in xrange(self.numGhosts):
if noisyDistances[i] == None:
self.particles = [self.getParticleWithGhostInJail(p, i) for p in self.particles]
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts:
return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
updated = []
for i in range(self.numGhosts):
# print(noisyDistances)
if noisyDistances[i] == None:
for index, particle in enumerate(self.particleList):
self.particleList[index] = self.getParticleWithGhostInJail(particle, i)
weights = util.Counter()
for particle in self.particleList:
totalprob = 1
for i in range(self.numGhosts):
if noisyDistances[i] != None:
dist = util.manhattanDistance(particle[i], pacmanPosition)
prob = emissionModels[i][dist]
totalprob = totalprob * prob
weights[particle] += totalprob
# print(weights)
weights.normalize()
keys = weights.keys()
distribution = weights.values()
if all(val == 0 for val in distribution):
self.initializeParticles()
else:
self.particleList = util.nSample(distribution, keys, len(self.particleList))
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
allPossible = util.Counter()
beliefDistribution = self.getBeliefDistribution()
for p in self.legalPositions:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, p))
for newPos, prob in newPosDist.items():
allPossible[newPos] += prob * beliefDistribution[p]
allPossible.normalize()
if not allPossible.totalCount():
self.initializeUniformly(gameState)
allPossible = self.getBeliefDistribution()
self.particles = util.nSample(allPossible.values(), allPossible.keys(), self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
beliefs = self.getBeliefDistribution()
# Replace this code with a correct observation update
# Be sure to handle the "jail" edge case where the ghost is eaten
# and noisyDistance is None
allPossible = util.Counter()
# First, consider case where ghost is captured.
if noisyDistance is None:
jailPos = self.getJailPosition()
allPossible[jailPos] = 1.0
else:
#P(x_t|e_1:t) = P(e_t|x_t) * sum_x_t-1 (P(x_t|x_t-1) * P(x_t-1,e_t:t-1))
for p in self.particles:
trueDistance = util.manhattanDistance(p, pacmanPosition)
cond_prob_ev_t = emissionModel[trueDistance] #p(e_t|x_t)
# assume ghost is standing still?
allPossible[p] = cond_prob_ev_t * beliefs[p]
allPossible.normalize()
# First handle the case where all particles get zero weight
nonZero = [p for p in allPossible if allPossible[p] > 0]
if len(nonZero) == 0:
self.initializeUniformly(gameState)
else:
weights = [allPossible[p] for p in allPossible]
positions = [p for p in allPossible]
self.particles = util.nSample(weights, positions,
self.numParticles)
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
# COMPLETE / FIX THIS
res = []
for part in self.particles:
newPosDist = self.getPositionDistribution(
self.setGhostPosition(gameState, part))
items = newPosDist.items()
distribution = [i[1] for i in items]
values = [i[0] for i in items]
res.append(util.nSample(distribution, values, 1)[0])
self.particles = res
"""res = []
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
# handle the edge case where we eat the
# ghost and definitely send it to jail
# by putting all of the particles in jail
if noisyDistance is None:
self.particles = [self.getJailPosition()]*self.numParticles
return
# otherwise, weight each particle
new_particles = util.Counter()
for particle in self.particles:
trueDistance = util.manhattanDistance(particle, pacmanPosition)
# p(e_t | x_t)
emissionProb = emissionModel[trueDistance]
# we can't just assign the key here, because
# we have the sample particle repeated multiple times,
# so we have to increment
new_particles.incrementAll([particle], emissionProb)
if not new_particles.totalCount():
self.initializeUniformly(gameState)
else:
# this way is slower
#resampled_particles = []
#for _ in range(self.numParticles):
# resampled_particles.append(util.sample(new_particles))
#self.particles = resampled_particles
# this way is faster
self.particles = util.nSample(new_particles.values(), new_particles.keys(), self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution. // can be used for resampling
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
if noisyDistance == None: # the ghost has been captured reset all the particles to be at jail positon
self.particles = [self.getJailPosition()]
return
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
weights = util.Counter()
for pos in self.legalPositions:
trueDistance = util.manhattanDistance(pacmanPosition, pos)
posWeight = emissionModel[trueDistance]
weights[pos] = posWeight
newBeliefs = util.Counter()
for particle in self.particles:
newBeliefs[particle] += weights[particle]
totalParticlesWeight = newBeliefs.totalCount()
if totalParticlesWeight == 0.0:
self.initializeUniformly(gameState)
else:
# resample from the new belief distribution
newBeliefs.divideAll(totalParticlesWeight)
self.particles = []
# for particle in self.numParticles:
# particleSample = util.sample(newBeliefs)
# self.particles.append(particleSample)
items = sorted(newBeliefs.items())
distribution = [i[1] for i in items]
values = [i[0] for i in items]
self.particles = util.nSample(distribution, values,
self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
pos = []
if noisyDistance is None:
# if the ghost is captured
for p in range(self.numParticles):
pos.append(self.getJailPosition())
self.particles = pos
else:
weights = []
values = []
for i in range(len(self.particles)):
trueDistance = util.manhattanDistance(self.particles[i],
pacmanPosition)
if emissionModel[trueDistance] >= 0:
weights.append(
emissionModel[trueDistance]) # update beliefs
values.append(self.particles[i])
sum_weights = sum(weights)
if sum_weights == 0:
self.particles = self.initializeUniformly(gameState)
else:
samples = util.nSample(weights, values, self.numParticles)
self.particles = samples
allPossible = self.getBeliefDistribution()
allPossible.normalize()
return allPossible
"*** END YOUR CODE HERE ***"
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
# References: Textbook pp. 598-599
# jail edge case
if noisyDistance == None:
pos = self.getJailPosition()
self.particles = [pos]*self.numParticles
return
# Step 1: calculate weightss
weights = util.Counter()
for pos,prob in self.getBeliefDistribution().items():
trueDistance = util.manhattanDistance(pos, pacmanPosition)
weights[pos] = emissionModel[trueDistance]*prob
weights.normalize()
# Step 2: resample based on the weights
if weights.totalCount() == 0:
self.initializeUniformly(gameState) # edge case
else:
distribution = []
values = []
for pos, prob in weights.items():
values.append(pos)
distribution.append(prob)
self.particles = util.nSample(distribution, values, self.numParticles)
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts:
return
emissionModels = [
busters.getObservationDistribution(dist) for dist in noisyDistances
]
distribution = [1.0] * len(self.particles)
for ghostIndex in range(self.numGhosts):
if noisyDistances[ghostIndex] != None:
for i in xrange(len(self.particles)):
weight = emissionModels[ghostIndex][util.manhattanDistance(
self.particles[i][ghostIndex], pacmanPosition)]
distribution[i] *= weight
if sum(distribution) == 0.0:
self.initializeParticles()
else:
self.particles = util.nSample(distribution, self.particles,
self.numParticles)
for ghostIndex in range(self.numGhosts):
if noisyDistances[ghostIndex] == None:
for i in xrange(len(self.particles)):
self.particles[i] = self.getParticleWithGhostInJail(
self.particles[i], ghostIndex)
"""
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated so
that the ghost appears in its prison cell, position self.getJailPosition(i)
where "i" is the index of the ghost.
You can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None (a noisy distance
of None will be returned if, and only if, the ghost is
captured).
2) When all particles receive 0 weight, they should be recreated from the
prior distribution by calling initializeParticles. After all particles
are generated randomly, any ghosts that are eaten (have noisyDistance of 0)
must be changed to the jail Position. This will involve changing each
particle if a ghost has been eaten.
** Remember ** We store particles as tuples, but to edit a specific particle,
it must be converted to a list, edited, and then converted back to a tuple. Since
this is a common operation when placing a ghost in the jail for a particle, we have
provided a helper method named self.getParticleWithGhostInJail(particle, ghostIndex)
that performs these three operations for you.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts: return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
"*** YOUR CODE HERE ***"
weights = util.Counter()
newParticleList = []
for x in range(len(noisyDistances)):
if noisyDistances[x] == None:
for y in range(len(self.particleList)):
self.particleList[y] = self.getParticleWithGhostInJail(self.particleList[y], x)
for i in range(len(self.particleList)):
likelihood = 1
trueDistances = [util.manhattanDistance(pacmanPosition, self.particleList[i][j]) for j in range(len(self.particleList[i]))]
for j in range(len(trueDistances)):
if tuple(list(self.particleList[i])[j]) != self.getJailPosition(j):
likelihood = likelihood * emissionModels[j][trueDistances[j]]
weights[i]= likelihood
if all(x == 0 for x in weights.values()):
self.initializeParticles()
else:
newParticleList = util.nSample(weights,self.particleList, len(self.particleList))
self.particleList = newParticleList
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
#get/set the belief distribution
self.beliefs = self.getBeliefDistribution()
#going to use a counter to keep track of the updated particles
updatedParticles = util.Counter()
#moving particles based on probability of ghost movement
for p in self.parts:
position, numP = p
ghostPositionDistribution = self.getPositionDistribution(
self.setGhostPosition(gameState, position))
for newPosition, probability in ghostPositionDistribution.items():
updatedParticles[newPosition] += numP * probability
self.beliefs = updatedParticles
self.beliefs.normalize()
#***********Resampling*********************
#Get the possible particle indicies
x = 0
particleIndicies = []
while x < self.numParticles:
particleIndicies.append(x)
x += 1
#resample each particle and drop them into the location, 'bucket', that
#corresponds to the new location after sampling
buckets = util.Counter()
for b in util.nSample(self.beliefs.values(), particleIndicies,
self.numParticles):
buckets[b] += 1
#after we have the buckets (resampled locations), convert back to a
#particle list and reset the list
count = 0
sampledList = []
for l, nump in self.parts:
sampledList.append((l, buckets[count]))
count += 1
self.parts = sampledList
return self.beliefs
def resample(self, beliefs):
"""
Helper function, try to resample particles from beliefs(util.Counter type)
using util.nSample()
"""
# Resample particles from a belief distribution
distribution = [i[1] for i in beliefs.items()]
values = [i[0] for i in beliefs.items()]
self.particles = util.nSample(distribution, values, self.numParticles)
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = self.index
noisyDistances = gameState.getAgentDistances(
) # gives noisy distances of ALL agents
# emissionModels = [gameState.getDistanceProb(dist) for dist in noisyDistances]
for enemy_num in self.getOpponents(gameState):
beliefDist = self.getBeliefDistribution()
W = util.Counter()
# JAIL? unhandled so far
for p in self.particles:
trueDistance = self.getMazeDistance(p[enemy_num],
pacmanPosition)
W[p] = (beliefDist[p] * gameState.getDistanceProb(
trueDistance, noisyDistances[enemy_num]))
# we resample after we get weights for each ghost
if W.totalCount() == 0:
self.particles = self.initializeParticles()
else:
values = []
keys = []
for key, value in W.items():
keys.append(key)
values.append(value)
self.particles = util.nSample(values, keys, self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(
noisyDistance
) # dictionary that maps true position to P(Noisy Distance | True distance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
weights = util.Counter()
if noisyDistance == None:
self.particleList = []
num = 0
while num < self.numParticles:
self.particleList.append(self.getJailPosition())
num += 1
# print self.particleList
else:
for particle in self.particleList:
dist = util.manhattanDistance(particle, pacmanPosition)
prob = emissionModel[dist]
weights[particle] += prob
weights.normalize()
keys = weights.keys()
distribution = weights.values()
if all(val == 0 for val in distribution):
self.initializeUniformly(gameState)
# print(weights)
else:
self.particleList = util.nSample(distribution, keys, len(self.particleList))
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts:
return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
"*** YOUR CODE HERE ***"
beliefDistribution = util.Counter()
# For all
for particle in self.particles:
partialProbability = 1
for idx in range(self.numGhosts):
if noisyDistances[idx] is None:
particle = self.getParticleWithGhostInJail(particle, idx)
else:
trueDistance = util.manhattanDistance(particle[idx], pacmanPosition)
partialProbability *= emissionModels[idx][trueDistance]
beliefDistribution[particle] += partialProbability
if beliefDistribution.totalCount():
self.particles = util.nSample(beliefDistribution.values(), beliefDistribution.keys(), self.numParticles)
else:
self.initializeParticles()
for idx in range(self.numGhosts):
if noisyDistances[idx] is None:
self.particles = [self.getParticleWithGhostInJail(particle, idx) for particle in self.particles]
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts:
return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
"*** YOUR CODE HERE ***"
currentDistribution = self.getBeliefDistribution()
for particle in currentDistribution:
for i in range(self.numGhosts):
if noisyDistances[i] != None:
trueDistance = util.manhattanDistance(pacmanPosition, particle[i])
currentDistribution[particle] *= emissionModels[i][trueDistance]
if currentDistribution.totalCount() == 0:
self.initializeParticles()
else:
items = sorted(currentDistribution.items())
distribution = [v for k, v in items]
value = [k for k, v in items]
self.particles = util.nSample(distribution, value, self.numParticles)
# put all the particles with ghost in jail to jail
for i in range(self.numGhosts):
if noisyDistances[i] == None:
for j in range(self.numParticles):
self.particles[j] = self.getParticleWithGhostInJail(self.particles[j], i)
def sample_data(self, trainingData, trainingLabels, sample_weights):
"*** YOUR CODE HERE ***"
#Convert weighted dataset to unweighted dataset
#total = sum(sample_weights)
#distribution = [sample_weights[i] *1.0/total for i in range(len(sample_weights))]
new_pairs = util.nSample(sample_weights,
zip(trainingData, trainingLabels),
int(0.5 * len(trainingData)))
new_data, new_labels = zip(*new_pairs)
return new_data, new_labels
def nSampleCounterWR(counts, n, aslist=False):
if counts.totalCount() != 1:
counts = util.normalize(counts)
pairs = counts.items()
keys = [k for k, v in pairs]
values = [v for k, v in pairs]
sampled = util.nSample(values, keys, n)
if aslist:
return sampled
return CounterFromIterable(sampled)
def nSampleCounterWR(counts, n, aslist = False):
if counts.totalCount() != 1:
counts = util.normalize(counts)
pairs = counts.items()
keys = [ k for k,v in pairs ]
values = [ v for k,v in pairs ]
sampled = util.nSample(values, keys, n)
if aslist:
return sampled
return CounterFromIterable(sampled)
def sample_data(self, trainingData, trainingLabels, sample_weights):
# "*** YOUR CODE HERE ***"
# util.raiseNotDefined()
L = [i for i in range(len(trainingData))]
# indices = [np.random.choice(L , p=sample_weights) for _ in range(int(len(trainingData) / 2))]
indices = util.nSample(sample_weights, L, int(
len(trainingData) * 0.55)) # sample is set to 0.55
data = [trainingData[i] for i in indices]
labels = [trainingLabels[i] for i in indices]
return data, labels
def sample_data(self, trainingData, trainingLabels, sample_weights):
"*** YOUR CODE HERE ***"
N = len(trainingData)
indices = util.nSample(sample_weights, list(range(N)), int(0.7 * N))
sampleData = []
sampleLabels = []
for i in indices:
sampleData.append(trainingData[i])
sampleLabels.append(trainingLabels[i])
return sampleData, sampleLabels
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
if noisyDistance is None:
temp = []
for p in range(0, self.numParticles):
temp.append(self.getJailPosition())
self.listOfParticles = temp
return
beliefs = self.getBeliefDistribution()
iterate = beliefs.keys()
for p in iterate:
trueDistance = util.manhattanDistance(p, pacmanPosition)
beliefs[p] = beliefs[p] * emissionModel[trueDistance]
beliefs.normalize()
if beliefs.totalCount() == 0:
self.initializeUniformly(gameState)
return
items = sorted(beliefs.items())
distribution = [i[1] for i in items]
values = [i[0] for i in items]
self.listOfParticles = util.nSample(distribution, values,
self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(
noisyDistance) # prob noisy distance given true distance
pacmanPosition = gameState.getPacmanPosition()
# create new local variable W, a vector of weighted counts for each value of X, initially zero
W = util.Counter()
if noisyDistance == None:
jailPos = self.getJailPosition()
self.particles = [jailPos] * self.numParticles
else:
beliefDist = self.getBeliefDistribution()
for p in self.particles:
trueDistance = util.manhattanDistance(p, pacmanPosition)
W[p] = beliefDist[p] * emissionModel[trueDistance]
if W.totalCount() == 0:
self.particles = self.initializeUniformly(gameState)
else:
values = []
keys = []
for key, value in W.items():
keys.append(key)
values.append(value)
self.particles = util.nSample(values, keys, self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
# check for case when noisyDistance = None
if (noisyDistance == None):
newParticleList = []
for dummy in range(self.numParticles):
newParticleList.append(self.getJailPosition())
self.particleList = newParticleList
return
# observation use the particle list
allWeightsZero = True
weights = []
for p in self.particleList:
distance = util.manhattanDistance(pacmanPosition, p)
currentWeight = emissionModel[distance]
weights.append(currentWeight)
if (currentWeight != 0.0):
allWeightsZero = False
# resample the particles
if (allWeightsZero == True):
self.initializeUniformly(self)
else:
resampled = util.nSample(weights, self.particleList, self.numParticles)
#print resampled
self.particleList = resampled
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
belief = self.getBeliefDistribution()
newBelief = util.Counter()
if noisyDistance == None:
self.particles = [
self.getJailPosition() for particle in self.particles
]
return
for particle in belief:
dist = util.manhattanDistance(particle, pacmanPosition)
newBelief[particle] = emissionModel[dist] * belief[particle]
if newBelief.totalCount() == 0:
self.initializeUniformly(gameState)
newBelief = self.getBeliefDistribution()
newBelief = util.normalize(newBelief)
keys = [key for key in newBelief]
values = [newBelief[key] for key in keys]
self.particles = util.nSample(values, keys, self.numParticles)
def observe(self, observation, gameState):
#print "observe"
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
weights = []
priors = self.getBeliefDistribution()
if(noisyDistance != None):
for x in range (0, len(self.legalPositions)):
p = self.legalPositions[x]
trueDistance = util.manhattanDistance(p, pacmanPosition)
priorBelief = priors[p]
weights.append(emissionModel[trueDistance] * priorBelief)
if sum(weights) == 0:
self.initializeUniformly(gameState)
else:
self.particles = util.nSample(weights, self.legalPositions, self.numParticles)
else:
self.particles = [self.getJailPosition()] * self.numParticles
"*** END YOUR CODE HERE ***"
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
#calculates new weights depending on particles
weights = util.Counter()
for i in range(0, self.numParticles):
p = self.particles[i]
trueDistance = util.manhattanDistance(p, pacmanPosition)
weights[p] += emissionModel[trueDistance]
# tha jail case and putting ghost there
if noisyDistance == None:
pj = self.getJailPosition()
for p in self.legalPositions:
weights[p] = 0
weights[pj] = 1
weights.normalize()
# the case where beliefs are rewighted to zero
if weights.totalCount() == 0:
self.initializeUniformly(gameState)
else:
# sampling according to new weights
dist = weights.copy()
items = sorted(dist.items())
dist = [i[1] for i in items]
values = [i[0] for i in items]
self.particles = util.nSample(dist, values, self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
"*** YOUR CODE HERE ***"
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
samples = []
if noisyDistance != None:
weight = util.Counter()
i = 0
for particle in self.parDist:
dist = util.manhattanDistance(pacmanPosition, particle)
weight[i] = (emissionModel[dist])
i += 1
if weight.totalCount() == 0:
samples = self.initializeUniformly(gameState)
else:
values = [i[0] for i in weight.items()]
sampleList = util.nSample(weight, values, self.numParticles)
for i in sampleList:
samples.append(self.parDist[i])
else:
for i in range(self.numParticles):
samples.append(self.getJailPosition())
self.parDist = samples
return self.getBeliefDistribution()
util.raiseNotDefined()
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make
sure to handle the special case where all particles have weight
0 after reweighting based on observation. If this happens,
resample particles uniformly at random from the set of legal
positions (self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, **all** particles should be updated so
that the ghost appears in its prison cell, self.getJailPosition()
You can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None (a noisy distance
of None will be returned if, and only if, the ghost is
captured).
2) When all particles receive 0 weight, they should be recreated from the
prior distribution by calling initializeUniformly. The total weight
for a belief distribution can be found by calling totalCount on
a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution
You may also want to use util.manhattanDistance to calculate the distance
between a particle and pacman's position.
"""
#print "IS THERE ANYBODY OUT TEHRE"
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
junk = []
if noisyDistance == None:
for k in range(0, self.numParticles-1):
junk.append(self.getJailPosition())
self.parcles = junk
return
counter = util.Counter()
for p in self.parcles:
t = util.manhattanDistance(p, pacmanPosition)
counter[p] += emissionModel[t]
counter.normalize()
if counter.totalCount() == 0:
self.initializeUniformly(gameState)
else:
items = counter.items()
distribution = [i[1] for i in items]
values = [i[0] for i in items]
trash = util.nSample(distribution, values, self.numParticles)
self.parcles = trash
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make
sure to handle the special case where all particles have weight
0 after reweighting based on observation. If this happens,
resample particles uniformly at random from the set of legal
positions (self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, **all** particles should be updated so
that the ghost appears in its prison cell, self.getJailPosition()
You can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None (a noisy distance
of None will be returned if, and only if, the ghost is
captured).
2) When all particles receive 0 weight, they should be recreated from the
prior distribution by calling initializeUniformly. The total weight
for a belief distribution can be found by calling totalCount on
a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution
You may also want to use util.manhattanDistance to calculate the distance
between a particle and pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
weights = util.Counter()
"*** YOUR CODE HERE ***"
newParticleList = []
for i in range(len(self.particleList)):
trueDist = util.manhattanDistance(pacmanPosition, self.particleList[i])
weights[i] = emissionModel[trueDist]
if noisyDistance == None:
for i in range(len(self.particleList)):
self.particleList[i] = self.getJailPosition()
elif all(x == 0 for x in weights.values()):
self.initializeUniformly(gameState)
else:
newParticleList = util.nSample(weights,self.particleList, len(self.particleList))
self.particleList = newParticleList
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
# util.raiseNotDefined()
if noisyDistance == None:
self.particles = [self.getJailPosition()] * self.numParticles
else:
currentDistribution = self.getBeliefDistribution()
for p in currentDistribution:
trueDistance = util.manhattanDistance(p, pacmanPosition)
currentDistribution[p] *= emissionModel[trueDistance]
if currentDistribution.totalCount() == 0:
self.initializeUniformly(gameState)
else:
items = sorted(currentDistribution.items())
distribution = [v for k, v in items]
value = [k for k, v in items]
self.particles = util.nSample(distribution, value, self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make sure to
handle the special case where all particles have weight 0 after
reweighting based on observation. If this happens, resample particles
uniformly at random from the set of legal positions
(self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell,
self.getJailPosition()
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeUniformly. The total
weight for a belief distribution can be found by calling totalCount
on a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
You may also want to use util.manhattanDistance to calculate the
distance between a particle and Pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
# "*** YOUR CODE HERE ***"
# util.raiseNotDefined()
if noisyDistance is None:
ghostPos = self.getJailPosition()
self.particles = [ghostPos for _ in xrange(self.numParticles)]
return
# Time update
weights = util.Counter()
for particle in self.particles:
weights[particle] += emissionModel[util.manhattanDistance(pacmanPosition, particle)]
if sum(weights.values()) == 0:
self.initializeUniformly(gameState)
return
# resample
# self.particles = [util.sampleFromCounter(weights) for _ in xrange(self.numParticles)]
values = weights.keys()
distribution = [weights[key] for key in values]
self.particles = util.nSample(distribution, values, self.numParticles)
def distributeParticles(self, gameState, particles):
newEnemyParticles = {}
for enemyIndex in particles:
probParticles = util.Counter()
for (x, y) in particles[enemyIndex]:
for (a, b) in self.getPotentialPositions(gameState, x, y):
probParticles[(a, b)] += particles[enemyIndex][(x, y)]
samples = util.nSample(probParticles.values(), probParticles.keys(), self.numParticles)
newEnemyParticles[enemyIndex] = util.Counter()
for sample in samples:
if sample not in newEnemyParticles[enemyIndex]:
newEnemyParticles[enemyIndex][sample] = 0
newEnemyParticles[enemyIndex][sample] += 1
return newEnemyParticles
def observe(self, noisyDistance, gameState,agentID):
"""
Update beliefs based on the given distance observation.
What if a ghost was eaten by agent?
The former assumption will be reinitialized, which is apparently unnecssary.
We need to find the method which can determine whether a certain agent is eaten, then like "go to jail", we just put them in the inital pos.gameState.getInitialAgentPosition(agentID)
"""
AgentPosition = gameState.getAgentPosition(agentID)
weights=[1 for i in range(self.numParticles)]
for index in range(self.numParticles):
for i in range(2):
trueDistance=util.manhattanDistance(self.Particles[index][i],AgentPosition)
weights[index]*=gameState.getDistanceProb(trueDistance,noisyDistance[self.enemies[i]])
if sum(weights)==0:
self.initializeUniformly(gameState)
return
else:
newParticals=util.nSample(weights,self.Particles,self.numParticles)
self.Particles=newParticals
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
pacmanPosition = gameState.getPacmanPosition()
weights = util.Counter()
total = 0;
# Use a counts array to avoid extra calls to getPositionDistribution()
counts = util.Counter()
for oldPos in self.particles:
counts[oldPos] += 1
for oldPos, count in counts.items():
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
for pos, prob in newPosDist.items():
weights[pos] += count*prob
total += count*prob
# if all weights are 0
if total == 0:
self.initializeUniformly(gameState)
return
items = sorted(weights.items())
distribution = [i[1] for i in items]
values = [i[0] for i in items]
self.particles = util.nSample(distribution,values,self.numParticles)
def elapseTime(self, gameState):
"""
Update beliefs for a time step elapsing.
As in the elapseTime method of ExactInference, you should use:
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos))
to obtain the distribution over new positions for the ghost, given its
previous position (oldPos) as well as Pacman's current position.
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution.
"""
"*** YOUR CODE HERE ***"
#print "elapseTime"
weights = util.Counter()
priors = self.getBeliefDistribution()
for p in self.legalPositions:
newPosDist = util.Counter()
newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, p))
#print newPosDist
"newPostDist[p] = Pr( ghost is at position p at time t + 1 | ghost is at position oldPos at time t )"
for newPos, prob in newPosDist.items():
weights[newPos] += prob * priors[p]
if sum(weights.values()) == 0:
self.initializeUniformly(gameState)
else:
dist = []
for x in range(0, len(self.legalPositions)):
pos = self.legalPositions[x]
dist.append(weights[pos])
self.particles = util.nSample(dist, self.legalPositions, self.numParticles)
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated so
that the ghost appears in its prison cell, position self.getJailPosition(i)
where "i" is the index of the ghost.
You can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None (a noisy distance
of None will be returned if, and only if, the ghost is
captured).
2) When all particles receive 0 weight, they should be recreated from the
prior distribution by calling initializeParticles. After all particles
are generated randomly, any ghosts that are eaten (have noisyDistance of 0)
must be changed to the jail Position. This will involve changing each
particle if a ghost has been eaten.
** Remember ** We store particles as tuples, but to edit a specific particle,
it must be converted to a list, edited, and then converted back to a tuple. Since
this is a common operation when placing a ghost in the jail for a particle, we have
provided a helper method named self.getParticleWithGhostInJail(particle, ghostIndex)
that performs these three operations for you.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts: return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
"*** YOUR CODE HERE ***"
for ghostIndex in range(self.numGhosts):
# similar to ParticleFilter
newWeightsCounter = util.Counter()
if noisyDistances[ghostIndex] is None:
self.particlesList = [self.getParticleWithGhostInJail(particle, ghostIndex)
for particle in self.particlesList]
else:
for p in self.legalPositions:
trueDistance = util.manhattanDistance(pacmanPosition, p)
if emissionModels[ghostIndex][trueDistance] > 0.0:
newWeightsCounter[p] = emissionModels[ghostIndex][trueDistance]
particlesDistribution = []
particlesForTheGhost = []
# update particlesList by resampling
for particle in self.particlesList:
particlesDistribution.append(newWeightsCounter[particle[ghostIndex]])
particlesForTheGhost.append(particle[ghostIndex])
particlesDistribution = util.normalize(particlesDistribution)
# check for all zero distribution
if sum(particlesDistribution) == 0.0:
self.initializeParticles()
else:
particlesForTheGhost = util.nSample(particlesDistribution, particlesForTheGhost, self.numParticles)
# replace the position for the current ghost
self.particlesList = [self.replaceOnePositionInParticle(self.particlesList[i], particlesForTheGhost[i], ghostIndex)
for i in range(self.numParticles)]
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated so
that the ghost appears in its prison cell, position self.getJailPosition(i)
where "i" is the index of the ghost.
You can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None (a noisy distance
of None will be returned if, and only if, the ghost is
captured).
2) When all particles receive 0 weight, they should be recreated from the
prior distribution by calling initializeParticles. After all particles
are generated randomly, any ghosts that are eaten (have noisyDistance of 0)
must be changed to the jail Position. This will involve changing each
particle if a ghost has been eaten.
** Remember ** We store particles as tuples, but to edit a specific particle,
it must be converted to a list, edited, and then converted back to a tuple. Since
this is a common operation when placing a ghost in the jail for a particle, we have
provided a helper method named self.getParticleWithGhostInJail(particle, ghostIndex)
that performs these three operations for you.
"""
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts: return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
pacmanPosition = gameState.getPacmanPosition()
myWeight = util.Counter()
for part in self.particles:
p = list(part)
pweight =1
for i in range(self.numGhosts):
if noisyDistances[i] != None:
dist = util.manhattanDistance(pacmanPosition,p[i])
pweight = pweight * emissionModels[i][dist]
myWeight[part] += pweight
#Make sure that all weights aren't 0
if myWeight.totalCount()==0:
self.initializeParticles()
for p in range(self.numParticles):
for i in range(self.numGhosts):
if noisyDistances[i] == None:
self.particles[p] = self.getParticleWithGhostInJail(self.particles[p],i)
return
#Sample from weights
newParticles = []
sample = util.nSample(myWeight.values(), myWeight.keys(), self.numParticles)
for p in range(self.numParticles):
for i in range(self.numGhosts):
if noisyDistances[i] == None:
sample[p] = self.getParticleWithGhostInJail(sample[p],i)
self.particles = sample
"""
particleMap = {}
for i in range(self.numGhosts):
# for part in self.particles:
# if part[i] in gameState.data.layout.walls.asList():
# print "before observe"
#generate weights
myModel = emissionModels[i]
myWeight = util.Counter()
if noisyDistances[i] == None:
particleMap[i] = [self.getJailPosition(i) for p in range(self.numParticles)]
continue
else:
for p in self.particles:
dist = util.manhattanDistance(p[i],pacmanPosition)
myWeight[p[i]] += myModel[dist]
#Make sure that all weights aren't 0
if myWeight.totalCount()==0:
myWeight = util.Counter()
for position in self.legalPositions:
myWeight[position] += 1
#Sample from weights
particleMap[i] = util.nSample(myWeight.values(), myWeight.keys(), self.numParticles)
if noisyDistances[i] == None:
particleMap[i] = [self.getJailPosition(i) for p in range(self.numParticles)]
# for part in particleMap[i]:
# if part in gameState.data.layout.walls.asList():
# print "after observe"
self.particles = zip(*particleMap.values())
"""
'''
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated so
that the ghost appears in its prison cell, position self.getJailPosition(i)
where "i" is the index of the ghost.
You can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None (a noisy distance
of None will be returned if, and only if, the ghost is
captured).
2) When all particles receive 0 weight, they should be recreated from the
prior distribution by calling initializeParticles. After all particles
are generated randomly, any ghosts that are eaten (have noisyDistance of 0)
must be changed to the jail Position. This will involve changing each
particle if a ghost has been eaten.
** Remember ** We store particles as tuples, but to edit a specific particle,
it must be converted to a list, edited, and then converted back to a tuple. Since
this is a common operation when placing a ghost in the jail for a particle, we have
provided a helper method named self.getParticleWithGhostInJail(particle, ghostIndex)
that performs these three operations for you.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
#print noisyDistances
#print self.particles
for g in range(0, self.numGhosts):
if noisyDistances[g] == None:
junk = []
for xx in self.particles:
junk.append(self.getParticleWithGhostInJail(xx, g))
self.particles = junk
#print self.particles
#util.raiseNotDefined()
if len(noisyDistances) < self.numGhosts: return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
#print emissionModels
h = util.Counter()
for x in self.particles:
w = 1.0
#print x
for k in range(0, self.numGhosts):
if noisyDistances[k] != None:
#print k
z = util.manhattanDistance(pacmanPosition, x[k])
#print emissionModels
#print z
#print emissionModels[k][z].__str__() + " HELLO "
#util.raiseNotDefined()
w *= emissionModels[k][z]
#print w
h[x] += w
#print emissionModels
#h.normalize()
#print h
if h.totalCount() == 0:
self.initializeParticles()
garbage = []
for a in range(0, self.numGhosts):
for x in self.particles:
if noisyDistances[a] == None:
garbage.append(self.getParticleWithGhostInJail(x, a))
else:
garbage.append(x)
self.particles = garbage
return
items = h.items()
distribution = [i[1] for i in items]
values = [i[0] for i in items]
trash = util.nSample(distribution, values, self.numParticles)
#print trash
self.particles = trash
def nSampleFromCounter(ctr, n):
items = sorted(ctr.items())
return util.nSample([v for k,v in items], [k for k,v in items], n)
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts:
return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
"*** YOUR CODE HERE ***"
# jail edge case
for i in range(self.numGhosts):
if noisyDistances[i] == None:
pos = self.getJailPosition(i)
for j,particle in enumerate(self.particles):
self.particles[j] = self.getParticleWithGhostInJail(particle,i)
# Step 1: calculate weights
weights = util.Counter()
for particle,N in self.getBeliefDistribution().items():
trueDistance = util.manhattanDistance(particle[i], pacmanPosition)
prob = 1.0
for i in range(self.numGhosts):
if noisyDistances[i] != None:
distance = util.manhattanDistance(particle[i], pacmanPosition)
prob *= emissionModels[i][distance]
weights[particle] += prob*N
weights.normalize()
# Step 2: resample based on the weights
if weights.totalCount() == 0:
self.initializeParticles() # edge case
else:
distribution = []
values = []
for pos, prob in weights.items():
values.append(pos)
distribution.append(prob)
self.particles = util.nSample(distribution, values, self.numParticles)
def observeState(self, gameState):
"""
Resamples the set of particles using the likelihood of the noisy
observations.
To loop over the ghosts, use:
for i in range(self.numGhosts):
...
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, all particles should be updated
so that the ghost appears in its prison cell, position
self.getJailPosition(i) where `i` is the index of the ghost.
As before, you can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None.
2) When all particles receive 0 weight, they should be recreated from
the prior distribution by calling initializeParticles. After all
particles are generated randomly, any ghosts that are eaten (have
noisyDistance of None) must be changed to the jail Position. This
will involve changing each particle if a ghost has been eaten.
self.getParticleWithGhostInJail is a helper method to edit a specific
particle. Since we store particles as tuples, they must be converted to
a list, edited, and then converted back to a tuple. This is a common
operation when placing a ghost in jail.
"""
pacmanPosition = gameState.getPacmanPosition()
noisyDistances = gameState.getNoisyGhostDistances()
if len(noisyDistances) < self.numGhosts:
return
emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances]
#Update jailed ghosts
#print emissionModels
"*** YOUR CODE HERE ***"
for g in range(self.numGhosts):
if(noisyDistances[g] == None):
for x in range(0, self.numParticles):
self.particles[x] = self.getParticleWithGhostInJail(self.particles[x], g)
for y in range(0, len(self.permutations)):
self.permutations[y] = self.getParticleWithGhostInJail(self.permutations[y], g)
weights = [1.0] * len(self.permutations)
priors = self.getBeliefDistribution()
for x in range (0, len(self.permutations)):
p = self.permutations[x]
weights[x] *= priors[p]
for i in range(self.numGhosts):
if noisyDistances[i] != None:
trueDistance = util.manhattanDistance(p[i], pacmanPosition)
weights[x] *= emissionModels[i][trueDistance]
if sum(weights) == 0:
self.initializeParticles()
for g in range(self.numGhosts):
if(noisyDistances[g] == None):
for x in range(0, self.numParticles):
self.particles[x] = self.getParticleWithGhostInJail(self.particles[x], g)
else:
self.particles = util.nSample(weights, self.permutations, self.numParticles)
def observe(self, observation, gameState):
"""
Update beliefs based on the given distance observation. Make
sure to handle the special case where all particles have weight
0 after reweighting based on observation. If this happens,
resample particles uniformly at random from the set of legal
positions (self.legalPositions).
A correct implementation will handle two special cases:
1) When a ghost is captured by Pacman, **all** particles should be updated so
that the ghost appears in its prison cell, self.getJailPosition()
You can check if a ghost has been captured by Pacman by
checking if it has a noisyDistance of None (a noisy distance
of None will be returned if, and only if, the ghost is
captured).
2) When all particles receive 0 weight, they should be recreated from the
prior distribution by calling initializeUniformly. The total weight
for a belief distribution can be found by calling totalCount on
a Counter object
util.sample(Counter object) is a helper method to generate a sample from
a belief distribution
You may also want to use util.manhattanDistance to calculate the distance
between a particle and pacman's position.
"""
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
"*** YOUR CODE HERE ***"
newWeights = util.Counter()
#print "\n********************\n"
#print "noisyDistance: "
#print noisyDistance
#print "emissionModel: "
#print emissionModel
#print "\n********************\n"
if noisyDistance is None:
self.particlesList = [self.getJailPosition()] * self.numParticles
else:
# calculate weights
for p in self.legalPositions:
trueDistance = util.manhattanDistance(pacmanPosition, p)
if emissionModel[trueDistance] > 0: newWeights[p] = emissionModel[trueDistance]
# resample all the particles
# distribution for each particle
particlesDist = []
for i in range(self.numParticles):
particlesDist.append(newWeights[self.particlesList[i]])
# use util.nSample for speed up reason
if sum(particlesDist)==0:
self.initializeUniformly(gameState)
else:
particlesDist = util.normalize(particlesDist)
self.particlesList = util.nSample(particlesDist, self.particlesList, self.numParticles)
