def reject(self): detachRest(self.trace,self.scaffold.border,self.scaffold,self.T) assertTorus(self.scaffold) path = constructAncestorPath(self.ancestorIndices,self.T-1,self.P) + [self.P] assert len(path) == self.T restoreAncestorPath(self.trace,self.scaffold.border,self.scaffold,self.omegaDBs,self.T,path) assertTrace(self.trace,self.scaffold)
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
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 prepare(self, trace, scaffold, compute_gradient = False):
"""Record the trace and scaffold for accepting or rejecting later;
detach along the scaffold and return the weight thereof."""
self.trace = trace
self.scaffold = scaffold
rhoWeight,self.rhoDB = detachAndExtract(trace, scaffold.border[0], scaffold, compute_gradient)
assertTorus(scaffold)
return rhoWeight
def reject(self):
# TODO This is the same as MHOperator reject except for the
# delegation thing -- abstract
if self.delegate is None:
detachAndExtract(self.trace,self.scaffold.border[0],self.scaffold)
assertTorus(self.scaffold)
regenAndAttach(self.trace,self.scaffold.border[0],self.scaffold,True,self.rhoDB,{})
else:
self.delegate.reject()
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):
self.trace = trace
self.scaffold = scaffold
if not registerVariationalLKernels(trace,scaffold):
self.delegate = MHOperator()
return self.delegate.propose(trace,scaffold)
_,self.rhoDB = detachAndExtract(trace,scaffold.border[0],scaffold)
assertTorus(scaffold)
for _ in range(self.numIters):
gradients = {}
gain = regenAndAttach(trace,scaffold.border[0],scaffold,False,OmegaDB(),gradients)
detachAndExtract(trace,scaffold.border[0],scaffold)
assertTorus(scaffold)
for node,lkernel in scaffold.lkernels.iteritems():
if isinstance(lkernel,VariationalLKernel):
assert node in gradients
lkernel.updateParameters(gradients[node],gain,self.stepSize)
rhoWeight = regenAndAttach(trace,scaffold.border[0],scaffold,True,self.rhoDB,{})
detachAndExtract(trace,scaffold.border[0],scaffold)
assertTorus(scaffold)
xiWeight = regenAndAttach(trace,scaffold.border[0],scaffold,False,OmegaDB(),{})
return trace,xiWeight - rhoWeight
def reject(self):
detachAndExtract(self.trace,self.scaffold.border[0],self.scaffold)
assertTorus(self.scaffold)
regenAndAttach(self.trace,self.scaffold.border[0],self.scaffold,True,self.rhoDB,{})
def reject(self):
# Only restore the global section.
detachAndExtract(self.trace,self.global_scaffold.border[0],self.global_scaffold)
assertTorus(self.global_scaffold)
regenAndAttach(self.trace,self.global_scaffold.border[0],self.global_scaffold,True,self.global_rhoDB,{})