def generate(self, chain):
"""
generates the positions by running the PSO and using the chain's min and max and then calling
the paraboloid fitter in order to estimate the covariance matrix. The position will then
be generated by drawing position from a multivariant gaussian distribution defined by
the best fit and the estimated covariance matrix.
The progress of the PSO is successively stored to a the disk.
"""
if (self.particleCount is None):
self.particleCount = MIN_PARTICLE_COUNT
if (self.mpi):
#only import when needed in order to avoid an error in case mpi4py is not installed
from cosmoHammer.pso.MpiParticleSwarmOptimizer import MpiParticleSwarmOptimizer
pso = MpiParticleSwarmOptimizer(chain,
self.low,
self.high,
self.particleCount,
threads=self.threads)
else:
pso = ParticleSwarmOptimizer(chain,
self.low,
self.high,
self.particleCount,
threads=self.threads)
if (pso.isMaster()):
print("I am the master")
swarm = []
with open(self.filePrefix + self.BEST_FILE_NAME, "w") as f:
for i, cswarm in enumerate(
pso.sample(maxIter=self.maxIter,
p=self.p,
m=self.m,
n=self.n)):
self._save(f, i, pso)
if (i >= 0):
swarm.append(cswarm)
self._save(f, i + 1, pso)
if (pso.isMaster()):
print("Best fit found after %s iteration: %f %s" %
(i + 1, pso.gbest.fitness, pso.gbest.position))
if self.maxIter > 5:
fswarm = []
for i in range(1, 5):
fswarm += swarm[-i]
self._storeSwarm(fswarm)
print("Start process of fitting.It uses all gbest found")
fitter = CurvatureFitter(swarm, pso.gbest)
mean, _cov = fitter.fit()
self._storeFit(pso.gbest, _cov)
pass
def generate(self,chain):
"""
generates the positions by running the PSO and using the chain's min and max and then calling
the paraboloid fitter in order to estimate the covariance matrix. The position will then
be generated by drawing position from a multivariant gaussian distribution defined by
the best fit and the estimated covariance matrix.
The progress of the PSO is successively stored to a the disk.
"""
if(self.particleCount is None):
self.particleCount = MIN_PARTICLE_COUNT
if(self.mpi):
#only import when needed in order to avoid an error in case mpi4py is not installed
from cosmoHammer.pso.MpiParticleSwarmOptimizer import MpiParticleSwarmOptimizer
pso = MpiParticleSwarmOptimizer(chain, self.low, self.high, self.particleCount, threads=self.threads)
else:
pso = ParticleSwarmOptimizer(chain, self.low, self.high, self.particleCount, threads=self.threads)
if(pso.isMaster()):
print("I am the master")
swarm = []
with open(self.filePrefix+self.BEST_FILE_NAME, "w") as f:
for i, cswarm in enumerate(pso.sample(maxIter=self.maxIter,p=self.p,m=self.m,n=self.n)):
self._save(f, i, pso)
if(i>=0):
swarm.append(cswarm)
self._save(f, i+1, pso)
if(pso.isMaster()):
print("Best fit found after %s iteration: %f %s"%(i+1, pso.gbest.fitness, pso.gbest.position))
if self.maxIter > 5 :
fswarm = []
for i in range(1,5):
fswarm += swarm[-i]
self._storeSwarm(fswarm)
print("Start process of fitting.It uses all gbest found")
fitter = CurvatureFitter(swarm, pso.gbest)
mean, _cov = fitter.fit()
self._storeFit(pso.gbest, _cov)
pass
def generate(self):
"""
generates the positions by running the PSO and using the chain's min and max and then calling
the paraboloid fitter in order to estimate the covariance matrix. The position will then
be generated by drawing position from a multivariant gaussian distribution defined by
the best fit and the estimated covariance matrix.
The progress of the PSO is successively stored to a the disk.
"""
chain = self.sampler.likelihoodComputationChain
if(self.particleCount is None):
self.particleCount = self.get_particle_count()
if(self.mpi):
#only import when needed in order to avoid an error in case mpi4py is not installed
from cosmoHammer.sampler.util.pso.MpiParticleSwarmOptimizer import MpiParticleSwarmOptimizer
pso = MpiParticleSwarmOptimizer(chain, chain.min, chain.max, self.particleCount, threads=self.threads)
else:
pso = ParticleSwarmOptimizer(chain, chain.min, chain.max, self.particleCount, threads=self.threads)
swarm = []
with open(self.sampler.filePrefix+self.BEST_FILE_NAME, "w") as f:
for i, cswarm in enumerate(pso.sample(self.maxIter)):
self._save(f, i, pso)
if(i>=0):
swarm.append(cswarm)
self._save(f, i+1, pso)
self.sampler.log("Best fit found after %s iteration: %f %s"%(i+1, pso.gbest.fitness, pso.gbest.position))
fswarm = []
for i in range(1,5):
fswarm += swarm[-i]
self._storeSwarm(fswarm)
fitter = CurvatureFitter(fswarm, pso.gbest)
mean, _cov = fitter.fit()
self._storeFit(pso.gbest, _cov)
# dim = len(mean)-1
# sigma = 0.4
# factor = _cov[dim,dim] / numpy.sqrt(sigma)
# _cov[:-1,dim] = _cov[:-1,dim]/factor
# _cov[dim,:-1] = _cov[dim,:-1]/factor
# _cov[dim,dim] = sigma
# print ""
# fitter = ParaboloidFitter(fswarm, pso.gbest, True)
# mean, _cov = fitter.fit()
sigma = numpy.sqrt(numpy.diag(_cov))
print("=> found sigma:", sigma)
# fitter = ParaboloidFitter(pso.swarm, pso.gbest)
# mean, _cov = fitter.fit()
# sigma = numpy.sqrt(numpy.diag(_cov))
# print "=> found sigma:", sigma
samples = numpy.random.multivariate_normal(mean, _cov, self.sampler.nwalkers)
# print numpy.std(samples, axis=0)
return samples
def generate(self):
"""
generates the positions by running the PSO and using the chain's min and max and then calling
the paraboloid fitter in order to estimate the covariance matrix. The position will then
be generated by drawing position from a multivariant gaussian distribution defined by
the best fit and the estimated covariance matrix.
The progress of the PSO is successively stored to a the disk.
"""
chain = self.sampler.likelihoodComputationChain
if (self.particleCount is None):
self.particleCount = self.get_particle_count()
if (self.mpi):
#only import when needed in order to avoid an error in case mpi4py is not installed
from cosmoHammer.sampler.util.pso.MpiParticleSwarmOptimizer import MpiParticleSwarmOptimizer
pso = MpiParticleSwarmOptimizer(chain,
chain.min,
chain.max,
self.particleCount,
threads=self.threads)
else:
pso = ParticleSwarmOptimizer(chain,
chain.min,
chain.max,
self.particleCount,
threads=self.threads)
swarm = []
with open(self.sampler.filePrefix + self.BEST_FILE_NAME, "w") as f:
for i, cswarm in enumerate(pso.sample(self.maxIter)):
self._save(f, i, pso)
if (i >= 0):
swarm.append(cswarm)
self._save(f, i + 1, pso)
self.sampler.log("Best fit found after %s iteration: %f %s" %
(i + 1, pso.gbest.fitness, pso.gbest.position))
fswarm = []
for i in range(1, 5):
fswarm += swarm[-i]
self._storeSwarm(fswarm)
fitter = CurvatureFitter(fswarm, pso.gbest)
mean, _cov = fitter.fit()
self._storeFit(pso.gbest, _cov)
# dim = len(mean)-1
# sigma = 0.4
# factor = _cov[dim,dim] / numpy.sqrt(sigma)
# _cov[:-1,dim] = _cov[:-1,dim]/factor
# _cov[dim,:-1] = _cov[dim,:-1]/factor
# _cov[dim,dim] = sigma
# print ""
# fitter = ParaboloidFitter(fswarm, pso.gbest, True)
# mean, _cov = fitter.fit()
sigma = numpy.sqrt(numpy.diag(_cov))
print("=> found sigma:", sigma)
# fitter = ParaboloidFitter(pso.swarm, pso.gbest)
# mean, _cov = fitter.fit()
# sigma = numpy.sqrt(numpy.diag(_cov))
# print "=> found sigma:", sigma
samples = numpy.random.multivariate_normal(mean, _cov,
self.sampler.nwalkers)
# print numpy.std(samples, axis=0)
return samples
