def PMCMCstep(self, iteration):
#progressbar(iteration / (self.nbiterations - 1))
progressbar(iteration / (self.nbiterations - 1), text = " acceptance rate: %.3f" % \
(sum(self.acceptations[0:iteration]) / iteration))
#print "iteration %i, acceptance rate until now: %.3f" % (iteration, sum(self.acceptations[0:iteration]) / iteration)
if (random.uniform(size = 1) < self.proposalmixtureweights[0]):
transformedthetastar = self.modeltheta.proposal(self.transformedtheta[:, newaxis], self.fixedproposalcovmatrix)[:, 0]
else:
transformedthetastar = self.modeltheta.proposal(self.transformedtheta[:, newaxis], self.adaptiveproposalcovmatrix)[:, 0]
thetastar = self.modeltheta.untransform(transformedthetastar[:,newaxis])[:,0]
proposedSIRs = ParallelSIRs(self.Nx, thetastar[:, newaxis], self.observations, self.modelx)
proposedSIRs.first_step()
proposedSIRs.next_steps()
proposedloglike = proposedSIRs.getTotalLogLike()[0]
proposedlogomega = proposedloglike + self.modeltheta.priorlogdensity(transformedthetastar)
currentlogomega = self.totalLogLike + self.modeltheta.priorlogdensity(self.transformedtheta)
acceptation = (log(random.uniform(size = 1)) < (proposedlogomega - currentlogomega))
# if not(acceptation):
# print "\nproposed loglikelihood:", proposedloglike
# print "\ncurrent loglikelihood:", self.totalLogLike
# print "\naccept:", acceptation
if acceptation:
self.theta = thetastar.copy()
self.transformedtheta = transformedthetastar.copy()
self.totalLogLike = proposedloglike.copy()
self.chain[:, iteration] = self.theta.copy()
self.transformedchain[:, iteration] = self.transformedtheta.copy()
if iteration > 10:
self.adaptiveproposalcovmatrix = (5.6644 / self.thetadim) * cov(self.transformedchain[:, 1:iteration])
#print "\n", self.adaptiveproposalcovmatrix
self.loglikechain[iteration] = self.totalLogLike
self.acceptations[iteration] = acceptation
def next_steps(self):
"""
Perform all the iterations until time T == number of observations.
"""
for t in range(0, self.T):
#print "time %i" % t
progressbar(t / (self.T - 1))
last_tic = time.time()
TandWresults = self.modelx.transitionAndWeight(self.xparticles[newaxis, ...], \
self.observations[t], self.thetaparticles, t + 1)
self.xparticles[...] = TandWresults["states"][0, ...]
if self.AP["prediction"]:
self.prediction(t)
self.logxweights[:] = TandWresults["weights"][0, :]
self.logxweights[isnan(self.logxweights)] = -(10**150)
self.logxweights[isinf(self.logxweights)] = -(10**150)
self.constants[t] = numpymax(self.logxweights)
self.logxweights[:] -= self.constants[t]
self.xweights[:] = exp(self.logxweights)
self.evidences[t] = exp(self.constants[t]) * mean(self.xweights)
covmean = self.computeCovarianceAndMean()
m = (self.shrink) * self.transformedthetaparticles + \
(1 - self.shrink) * transpose(covmean["mean"][newaxis])
noise = transpose(random.multivariate_normal(repeat(0, self.modeltheta.parameterdimension), \
self.hsq * covmean["cov"], size = self.N))
self.transformedthetaparticles[...] = m + noise
self.thetaparticles[...] = self.modeltheta.untransform(self.transformedthetaparticles)
self.resample()
self.resamplingindices.append(t)
new_tic = time.time()
self.computingtimes[t] = new_tic - last_tic
last_tic = new_tic
if t in self.savingtimes or t == self.T - 1:
print "\nsaving particles at time %i" % t
self.thetahistory[self.alreadystored, ...] = self.thetaparticles.copy()
#self.weighthistory[self.alreadystored, ...] = self.xweights.copy()
self.alreadystored += 1
def next_steps(self):
"""
Perform all the iterations until time T == number of observations.
"""
for t in range(self.T):
excluded = t in self.excludedobservations
progressbar(t / (self.T - 1))
if excluded:
print "\nobservations", self.observations[
t, :], "set to be excluded"
last_tic = time.time()
TandWresults = self.modelx.transitionAndWeight(self.xparticles, \
self.observations[t], self.thetaparticles, t + 1)
self.xparticles[...] = TandWresults["states"]
if not (excluded):
self.logxweights[...] = TandWresults["weights"]
# in case the measure function returns nans or infs, set the weigths very low
self.logxweights[isnan(self.logxweights)] = -(10**150)
self.logxweights[isinf(self.logxweights)] = -(10**150)
self.constants[:] = numpymax(self.logxweights, axis=0)
self.logxweights[...] -= self.constants[:]
else:
self.logxweights = zeros((self.Nx, self.Ntheta))
self.constants[:] = numpymax(self.logxweights, axis=0)
self.xweights[...] = exp(self.logxweights)
self.logLike[:] = log(mean(self.xweights,
axis=0)) + self.constants[:]
# prediction: at this point we have the transitioned x-particles and we didn't update
# the weights of the theta-particles, and the x-particles are not weighted
if self.AP["prediction"]:
self.prediction(t)
if t > 0:
self.evidences[t] = self.getEvidence(
self.thetalogweights[t - 1, :], self.logLike)
self.totalLogLike[:] += self.logLike[:]
self.thetalogweights[
t, :] = self.thetalogweights[t - 1, :] + self.logLike[:]
else:
self.evidences[t] = self.getEvidence(
self.thetalogweights[t, :], self.logLike)
self.totalLogLike[:] += self.logLike[:]
self.thetalogweights[
t, :] = self.thetalogweights[t, :] + self.logLike[:]
self.thetalogweights[t, :] -= max(self.thetalogweights[t, :])
self.xresample()
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
if self.AP["dynamicNx"]:
progressbar(t / (self.T - 1),
text=" ESS: %.3f, Nx: %i" % (self.ESS[t], self.Nx))
else:
progressbar(t / (self.T - 1), text=" ESS: %.3f" % self.ESS[t])
while self.ESS[t] < (self.AP["ESSthreshold"] * self.Ntheta):
progressbar(t / (self.T - 1), text =\
" ESS: %.3f - resample move step at iteration = %i" % (self.ESS[t], t))
covdict = self.computeCovarianceAndMean(t)
if self.AP["proposalkernel"] == "randomwalk":
self.proposalcovmatrix = self.AP["rwvariance"] * covdict[
"cov"]
self.proposalmean = None
elif self.AP["proposalkernel"] == "independent":
self.proposalcovmatrix = covdict["cov"]
self.proposalmean = covdict["mean"]
self.thetaresample(t)
self.resamplingindices.append(t)
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
for move in range(self.AP["nbmoves"]):
self.PMCMCstep(t)
acceptrate = self.acceptratios[-1]
progressbar(t / (self.T - 1), text = \
" \nresample move step at iteration = %i - acceptance rate: %.3f\n" % (t, acceptrate))
if self.acceptratios[-1] < self.AP["dynamicNxThreshold"] \
and self.Nx <= (self.AP["NxLimit"] / 2) \
and self.AP["dynamicNx"]:
self.increaseParticlesNb(t)
self.ESS[t] = ESSfunction(
exp(self.thetalogweights[t, :]))
new_tic = time.time()
self.computingtimes[t] = new_tic - last_tic
last_tic = new_tic
""" filtering and smoothing """
if self.AP["filtering"]:
self.filtering(t)
if self.smoothingEnable and t == self.T - 1:
self.smoothing(t)
if t in self.savingtimes or t == self.T - 1:
print "\nsaving particles at time %i" % t
self.thetahistory[self.alreadystored,
...] = self.thetaparticles.copy()
self.weighthistory[self.alreadystored,
...] = exp(self.thetalogweights[t, :])
self.alreadystored += 1
def next_steps(self):
"""
Perform all the iterations until time T == number of observations.
"""
for t in range(self.T):
excluded = t in self.excludedobservations
progressbar(t / (self.T - 1))
if excluded:
print "\nobservations", self.observations[t,:], "set to be excluded"
last_tic = time.time()
TandWresults = self.modelx.transitionAndWeight(self.xparticles, \
self.observations[t], self.thetaparticles, t) # t + 1)
#if states are not time series as in the case of Kangaroo,
#then the 0th initial state scorrespond to no observation, so I dummied an initial time
self.xparticles[...] = TandWresults["states"]
if not(excluded):
self.logxweights[...] = TandWresults["weights"]
# in case the measure function returns nans or infs, set the weigths very low
self.logxweights[isnan(self.logxweights)] = -(10**150)
self.logxweights[isinf(self.logxweights)] = -(10**150)
self.constants[:] = numpymax(self.logxweights, axis = 0)
self.logxweights[...] -= self.constants[:]
else:
self.logxweights = zeros((self.Nx, self.Ntheta))
self.constants[:] = numpymax(self.logxweights, axis = 0)
self.xweights[...] = exp(self.logxweights)
if not(self.modelx.HScoreInference): #if use log score for inference
self.logLike[:] = log(mean(self.xweights, axis = 0)) + self.constants[:]
else:
self.logLike[:] = - TandWresults["simpleHScore"]
# prediction: at this point we have the transitioned x-particles and we didn't update
# the weights of the theta-particles, and the x-particles are not weighted (should be "resampled")
if self.AP["prediction"]:
self.prediction(t)
if t > 0:
self.evidences[t] = self.getEvidence(self.thetalogweights[t-1, :], self.logLike)
self.totalLogLike[:] += self.logLike[:]
self.thetalogweights[t, :] = self.thetalogweights[t-1, :] + self.logLike[:]
else:
self.evidences[t] = self.getEvidence(self.thetalogweights[t, :], self.logLike)
self.totalLogLike[:] += self.logLike[:]
self.thetalogweights[t, :] = self.thetalogweights[t, :] + self.logLike[:]
self.lScore[t] = -log(self.evidences[t])
#store h score
#self.hScore[t] = self.getHScore(t)
if self.modelx.continuousObs: #use latest theta particles
hScoreResults = self.modelx.computeHscore(self.xparticles, self.observations[t], self.thetaparticles, \
exp(self.logxweights), exp(self.thetalogweights[t, :]), t)
else: #use theta particles from the last step
hScoreResults = self.modelx.computeHscore(self.xparticles, self.observations[t], self.thetaparticles_last, \
exp(self.logxweights_last), exp(self.thetalogweights_last), t)
self.hScore[t] = hScoreResults["compositeHScore"]
self.thetalogweights[t, :] -= max(self.thetalogweights[t, :])
self.xresample() #resample is to get ancester index, to prepare for the following state transition
#the above resample step contains the line: self.xparticles[...] = self.xparticles[parentsindices, :]
#therefore, for the filtering distribution at time t, we should use the xparticles before this step
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
if self.AP["dynamicNx"]:
progressbar(t / (self.T - 1), text = " \n effective sample size: %.3f, Nx: %i" % (self.ESS[t], self.Nx))
else:
progressbar(t / (self.T - 1), text = " \n effective sample size: %.3f" % self.ESS[t])
while self.ESS[t] < (self.AP["ESSthreshold"] * self.Ntheta):
progressbar(t / (self.T - 1), text =\
" \n effective sample size: %.3f" % (self.ESS[t])) #- resample move step at iteration = %i" % (self.ESS[t], t))
covdict = self.computeCovarianceAndMean(t)
if self.AP["proposalkernel"] == "randomwalk":
self.proposalcovmatrix = self.AP["rwvariance"] * covdict["cov"]
self.proposalmean = None
elif self.AP["proposalkernel"] == "independent":
self.proposalcovmatrix = covdict["cov"]
self.proposalmean = covdict["mean"]
self.thetaresample(t)
self.resamplingindices.append(t)
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
for move in range(self.AP["nbmoves"]):
self.PMCMCstep(t)
acceptrate = self.acceptratios[-1]
progressbar(t / (self.T - 1), text = \
" \nresample move step at iteration = %i - acceptance rate: %.3f\n" % (t, acceptrate))
if self.acceptratios[-1] < self.AP["dynamicNxThreshold"] \
and self.Nx <= (self.AP["NxLimit"] / 2) \
and self.AP["dynamicNx"]:
self.increaseParticlesNb(t)
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
#update below the quantities for computing H score of discrete-obs.
#put them here after potential PMCMC steps
self.thetaparticles_last = self.thetaparticles
self.logxweights_last = self.logxweights
self.thetalogweights_last = self.thetalogweights[t,:]
new_tic = time.time()
self.computingtimes[t] = new_tic - last_tic
last_tic = new_tic
""" filtering and smoothing """
if self.AP["filtering"]:
self.filtering(t)
if self.smoothingEnable and t == self.T - 1:
self.smoothing(t)
if t in self.savingtimes or t == self.T - 1:
print "\nsaving particles at time %i" % t
self.thetahistory[self.alreadystored, ...] = self.thetaparticles.copy()
self.weighthistory[self.alreadystored, ...] = exp(self.thetalogweights[t, :])
self.alreadystored += 1
def next_steps(self):
"""
Perform all the iterations until time T == number of observations.
"""
for t in range(self.T):
excluded = t in self.excludedobservations
progressbar(t / (self.T - 1))
if excluded:
print "\nobservations", self.observations[t,:], "set to be excluded"
last_tic = time.time()
TandWresults = self.modelx.transitionAndWeight(self.xparticles, \
self.observations[t], self.thetaparticles, t + 1)
self.xparticles[...] = TandWresults["states"]
if not(excluded):
self.logxweights[...] = TandWresults["weights"]
# in case the measure function returns nans or infs, set the weigths very low
self.logxweights[isnan(self.logxweights)] = -(10**150)
self.logxweights[isinf(self.logxweights)] = -(10**150)
self.constants[:] = numpymax(self.logxweights, axis = 0)
self.logxweights[...] -= self.constants[:]
else:
self.logxweights = zeros((self.Nx, self.Ntheta))
self.constants[:] = numpymax(self.logxweights, axis = 0)
self.xweights[...] = exp(self.logxweights)
self.logLike[:] = log(mean(self.xweights, axis = 0)) + self.constants[:]
# prediction: at this point we have the transitioned x-particles and we didn't update
# the weights of the theta-particles, and the x-particles are not weighted
if self.AP["prediction"]:
self.prediction(t)
if t > 0:
self.evidences[t] = self.getEvidence(self.thetalogweights[t-1, :], self.logLike)
self.totalLogLike[:] += self.logLike[:]
self.thetalogweights[t, :] = self.thetalogweights[t-1, :] + self.logLike[:]
else:
self.evidences[t] = self.getEvidence(self.thetalogweights[t, :], self.logLike)
self.totalLogLike[:] += self.logLike[:]
self.thetalogweights[t, :] = self.thetalogweights[t, :] + self.logLike[:]
self.thetalogweights[t, :] -= max(self.thetalogweights[t, :])
self.xresample()
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
if self.AP["dynamicNx"]:
progressbar(t / (self.T - 1), text = " ESS: %.3f, Nx: %i" % (self.ESS[t], self.Nx))
else:
progressbar(t / (self.T - 1), text = " ESS: %.3f" % self.ESS[t])
while self.ESS[t] < (self.AP["ESSthreshold"] * self.Ntheta):
progressbar(t / (self.T - 1), text =\
" ESS: %.3f - resample move step at iteration = %i" % (self.ESS[t], t))
covdict = self.computeCovarianceAndMean(t)
if self.AP["proposalkernel"] == "randomwalk":
self.proposalcovmatrix = self.AP["rwvariance"] * covdict["cov"]
self.proposalmean = None
elif self.AP["proposalkernel"] == "independent":
self.proposalcovmatrix = covdict["cov"]
self.proposalmean = covdict["mean"]
self.thetaresample(t)
self.resamplingindices.append(t)
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
for move in range(self.AP["nbmoves"]):
self.PMCMCstep(t)
acceptrate = self.acceptratios[-1]
progressbar(t / (self.T - 1), text = \
" \nresample move step at iteration = %i - acceptance rate: %.3f\n" % (t, acceptrate))
if self.acceptratios[-1] < self.AP["dynamicNxThreshold"] \
and self.Nx <= (self.AP["NxLimit"] / 2) \
and self.AP["dynamicNx"]:
self.increaseParticlesNb(t)
self.ESS[t] = ESSfunction(exp(self.thetalogweights[t, :]))
new_tic = time.time()
self.computingtimes[t] = new_tic - last_tic
last_tic = new_tic
""" filtering and smoothing """
if self.AP["filtering"]:
self.filtering(t)
if self.smoothingEnable and t == self.T - 1:
self.smoothing(t)
if t in self.savingtimes or t == self.T - 1:
print "\nsaving particles at time %i" % t
self.thetahistory[self.alreadystored, ...] = self.thetaparticles.copy()
self.weighthistory[self.alreadystored, ...] = exp(self.thetalogweights[t, :])
self.alreadystored += 1
