def sample(self, burninPos, burninProb, burninRstate, datas):
"""
Starts the sampling process. The master node (mpi rank = 0) persists the result to the disk
"""
burninPos = mpiBCast(burninPos)
burninProb = mpiBCast(burninProb)
burninRstate = mpiBCast(burninRstate)
self.log("MPI Process rank "+ str(self.rank)+" starts sampling")
super(MpiCosmoHammerSampler, self).sample(burninPos, burninProb, burninRstate, datas);
def sample(self, burninPos, burninProb, burninRstate, datas):
"""
Starts the sampling process. The master node (mpi rank = 0) persists the result to the disk
"""
burninPos = mpiBCast(burninPos)
burninProb = mpiBCast(burninProb)
burninRstate = mpiBCast(burninRstate)
self.log("MPI Process rank " + str(self.rank) + " starts sampling")
super(MpiCosmoHammerSampler, self).sample(burninPos, burninProb,
burninRstate, datas)
def loadBurnin(self):
"""
loads the burn in form the file system
"""
if(self.isMaster()):
pos, prob, rstate = super(MpiCosmoHammerSampler, self).loadBurnin()
else:
pos, prob, rstate = []
pos = mpiBCast(pos)
prob = mpiBCast(prob)
rstate = mpiBCast(rstate)
self.log("loading done")
return pos, prob, rstate
def loadBurnin(self):
"""
loads the burn in form the file system
"""
if (self.isMaster()):
pos, prob, rstate = super(MpiCosmoHammerSampler, self).loadBurnin()
else:
pos, prob, rstate = []
pos = mpiBCast(pos)
prob = mpiBCast(prob)
rstate = mpiBCast(rstate)
self.log("loading done")
return pos, prob, rstate
def sampleBurnin(self, p0):
"""
Starts the sampling process. The master node (mpi rank = 0) persists the result to the disk
"""
p0 = mpiBCast(p0)
self.log("MPI Process rank "+ str(self.rank)+" starts sampling")
return super(MpiCosmoHammerSampler, self).sampleBurnin(p0);
def sampleBurnin(self, p0):
"""
Starts the sampling process. The master node (mpi rank = 0) persists the result to the disk
"""
p0 = mpiBCast(p0)
self.log("MPI Process rank " + str(self.rank) + " starts sampling")
return super(MpiCosmoHammerSampler, self).sampleBurnin(p0)
def _converged(self, it, p, m, n):
if(self.isMaster()):
converged = super(MpiParticleSwarmOptimizer, self)._converged(it, p, m, n)
else:
converged = False
converged = mpiBCast(converged)
return converged
def _converged(self, it, p, m, n):
if (self.isMaster()):
converged = super(MpiParticleSwarmOptimizer,
self)._converged(it, p, m, n)
else:
converged = False
converged = mpiBCast(converged)
return converged
def _get_fitness(self, swarm):
mapFunction = self.pool.map
mpiSwarm = mpiBCast(swarm)
pos = numpy.array([part.position for part in mpiSwarm])
results = mapFunction(self.func, pos)
lnprob = numpy.array([l[0] for l in results])
for i, particle in enumerate(swarm):
particle.fitness = lnprob[i]
particle.position = pos[i]
def _get_fitness(self,swarm):
mapFunction = self.pool.map
mpiSwarm = mpiBCast(swarm)
pos = numpy.array([part.position for part in mpiSwarm])
results = mapFunction(self.func, pos)
lnprob = numpy.array([l[0] for l in results])
for i, particle in enumerate(swarm):
particle.fitness = lnprob[i]
particle.position = pos[i]
for i, cswarm in enumerate(
pso.sample(maxIter=maxIter, p=fracPart, m=tol, n=meanNorm)):
if (pso.isMaster()):
for particle in cswarm:
f.write("%s\t%f\t" % (i, particle.fitness))
f.write("\t".join(
['{:025.20f}'.format(p)
for p in particle.position]) + "\n")
f.flush()
#--------- compute differences ------------
if (pso.isMaster()):
log(logger, "Iteration: %s .Time: %s seconds" % (count, time() - t1))
log(logger,
"Best found: %f \n %s" % (pso.gbest.fitness, pso.gbest.position))
p0 = np.array([part.position for part in pso.swarm])
p0 = mpiBCast(p0)
smp.reset()
print "emcee"
for pos, prob, rstate, datas in smp.sample(p0,
iterations=mcmc_it,
storechain=False):
if (pso.isMaster()):
for i in range(partCount):
f.write("%s\t%f\t" % (-count, prob[i]))
f.write("\t".join(['{:025.20f}'.format(q)
for q in pos[i]]) + "\n")
f.flush()
if (pso.isMaster()):
delta = np.abs(1.0 - np.abs(pso.gbest.fitness / ofly_lnp))
ofly_lnp = pso.gbest.fitness
def createInitPos(self):
"""
Factory method to create initial positions
"""
#bcast the positions to ensure that all mpi nodes start at the same position
return mpiBCast(super(MpiCosmoHammerSampler, self).createInitPos())
def createInitPos(self):
"""
Factory method to create initial positions
"""
#bcast the positions to ensure that all mpi nodes start at the same position
return mpiBCast(super(MpiCosmoHammerSampler, self).createInitPos())
