def __init__(self, length = 10, fileList = None):
particle = []
for i in xrange(length):
particle.append(Particle(float(i) / length))
self.particle = particle
self.length = length
self.pFilter = ParticleFilter()
if fileList is None:
self.pFilter.load(config.data['datafile']['test'])
else:
self.pFilter.load(fileList)
self.setFitCalculator(fitCalculus)
class ParticleFactory:
def __init__(self, length = 10, fileList = None):
particle = []
for i in xrange(length):
particle.append(Particle(float(i) / length))
self.particle = particle
self.length = length
self.pFilter = ParticleFilter()
if fileList is None:
self.pFilter.load(config.data['datafile']['test'])
else:
self.pFilter.load(fileList)
self.setFitCalculator(fitCalculus)
def accept(self, data, direction = 1):
self.pFilter.accept(data, direction)
def move(self, step):
ratio = float(step) / self.pFilter.standardLength
totallWeight = 0.0
for p in self.particle:
p.move(ratio)
self._fitCalculus(p, self.pFilter.eval(p.getPos()))
weight = p.getWeight()
totallWeight += weight
# normalize
if totallWeight > 0.0:
for p in self.particle:
p.setWeight(p.getWeight() / totallWeight)
def setFitCalculator(self, func):
self._fitCalculus = func
def getActiveParticle(self):
return [p for p in self.particle if p.getWeight() > 0.0]
def getBest(self):
bestP = self.particle[0]
best = bestP.getWeight()
for p in self.particle:
if p.getWeight() > best:
best = p.getWeight()
bestP = p
return bestP
def getPredict(self):
total = 0.0
for p in self.particle:
total += p.getWeight() * p.getPos()
return total
def resample(self):
accum = 0.0
state = self.getActiveParticle()
distribution = []
for x in state:
accum += x.getWeight()
distribution.append(accum)
newParticle = []
for _ in self.particle:
newParticle.append(Particle(state[bisect.bisect_left(distribution, random.uniform(0, 1))].getPos()))
self.particle = newParticle
