def propose(self,trace,scaffold):
from particle import Particle
self.trace = trace
self.scaffold = scaffold
assertTrace(self.trace,self.scaffold)
#print map(len, scaffold.border)
self.T = len(self.scaffold.border)
T = self.T
P = self.P
# assert T == 1 # TODO temporary
rhoDBs = [None for t in range(T)]
rhoWeights = [None for t in range(T)]
for t in reversed(range(T)):
rhoWeights[t],rhoDBs[t] = detachAndExtract(trace,scaffold.border[t],scaffold)
assertTorus(scaffold)
particles = [Particle(trace) for p in range(P+1)]
self.particles = particles
particleWeights = [None for p in range(P+1)]
# Simulate and calculate initial xiWeights
for p in range(P):
particleWeights[p] = regenAndAttach(particles[p],scaffold.border[0],scaffold,False,OmegaDB(),{})
particleWeights[P] = regenAndAttach(particles[P],scaffold.border[0],scaffold,True,rhoDBs[0],{})
assert_almost_equal(particleWeights[P],rhoWeights[0])
# for every time step,
for t in range(1,T):
newParticles = [None for p in range(P+1)]
newParticleWeights = [None for p in range(P+1)]
# Sample new particle and propagate
for p in range(P):
parent = sampleLogCategorical(particleWeights)
newParticles[p] = Particle(particles[parent])
newParticleWeights[p] = regenAndAttach(newParticles[p],self.scaffold.border[t],self.scaffold,False,OmegaDB(),{})
newParticles[P] = Particle(particles[P])
newParticleWeights[P] = regenAndAttach(newParticles[P],self.scaffold.border[t],self.scaffold,True,rhoDBs[t],{})
assert_almost_equal(newParticleWeights[P],rhoWeights[t])
particles = newParticles
particleWeights = newParticleWeights
# Now sample a NEW particle in proportion to its weight
finalIndex = sampleLogCategorical(particleWeights[0:-1])
assert finalIndex < P
self.finalIndex = finalIndex
self.particles = particles
return particles[finalIndex],self._compute_alpha(particleWeights, finalIndex)
def propose(self,trace,scaffold):
self.trace = trace
self.scaffold = scaffold
assertTrace(self.trace,self.scaffold)
self.T = len(self.scaffold.border)
T = self.T
P = self.P
rhoWeights = [None for t in range(T)]
omegaDBs = [[None for p in range(P+1)] for t in range(T)]
ancestorIndices = [[None for p in range(P)] + [P] for t in range(T)]
self.omegaDBs = omegaDBs
self.ancestorIndices = ancestorIndices
for t in reversed(range(T)):
(rhoWeights[t],omegaDBs[t][P]) = detachAndExtract(trace,scaffold.border[t],scaffold)
assertTorus(scaffold)
xiWeights = [None for p in range(P)]
# Simulate and calculate initial xiWeights
for p in range(P):
regenAndAttach(trace,scaffold.border[0],scaffold,False,OmegaDB(),{})
(xiWeights[p],omegaDBs[0][p]) = detachAndExtract(trace,scaffold.border[0],scaffold)
# for every time step,
for t in range(1,T):
newWeights = [None for p in range(P)]
# Sample new particle and propagate
for p in range(P):
extendedWeights = xiWeights + [rhoWeights[t-1]]
ancestorIndices[t][p] = sampleLogCategorical(extendedWeights)
path = constructAncestorPath(ancestorIndices,t,p)
restoreAncestorPath(trace,self.scaffold.border,self.scaffold,omegaDBs,t,path)
regenAndAttach(trace,self.scaffold.border[t],self.scaffold,False,OmegaDB(),{})
(newWeights[p],omegaDBs[t][p]) = detachAndExtract(trace,self.scaffold.border[t],self.scaffold)
detachRest(trace,self.scaffold.border,self.scaffold,t)
xiWeights = newWeights
# Now sample a NEW particle in proportion to its weight
finalIndex = sampleLogCategorical(xiWeights)
path = constructAncestorPath(ancestorIndices,T-1,finalIndex) + [finalIndex]
assert len(path) == T
restoreAncestorPath(trace,self.scaffold.border,self.scaffold,omegaDBs,T,path)
assertTrace(self.trace,self.scaffold)
return trace,self._compute_alpha(rhoWeights[T-1], xiWeights, finalIndex)
def propose(self,trace,scaffold):
from particle import Particle
assertTrace(trace,scaffold)
pnodes = scaffold.getPrincipalNodes()
currentValues = getCurrentValues(trace,pnodes)
allSetsOfValues = getCartesianProductOfEnumeratedValues(trace,pnodes)
registerDeterministicLKernels(trace,scaffold,pnodes,currentValues)
detachAndExtract(trace,scaffold.border[0],scaffold)
assertTorus(scaffold)
xiWeights = []
xiParticles = []
for p in range(len(allSetsOfValues)):
newValues = allSetsOfValues[p]
xiParticle = Particle(trace)
assertTorus(scaffold)
registerDeterministicLKernels(trace,scaffold,pnodes,newValues)
xiParticles.append(xiParticle)
xiWeights.append(regenAndAttach(xiParticle,scaffold.border[0],scaffold,False,OmegaDB(),{}))
# Now sample a NEW particle in proportion to its weight
finalIndex = sampleLogCategorical(xiWeights)
self.finalParticle = xiParticles[finalIndex]
return self.finalParticle,0