tools Namespace Reference

Functions
def	initParamsTable (keys, maxs, mins, num=100, threads=4, tableName='smcTable.txt')
def	writeToTable (tableName, table, dim, num, thread, keys)
def	getKeysAndData (fileName)
def	resampledParamsTable (keys, smcSamples, proposal, num=100, thread=4, maxNumComponents=10, priorWeight=1e3, tableName='smcTableNew.txt', verbose=False)
def	getGMMFromPosterior (smcSamples, posterior, priorWeight)
def	get_pool (mpi=False, threads=1)

Function Documentation

get_pool()

def tools.get_pool	(		mpi = False,
			threads = 1
	)

def get_pool(mpi=False,threads=1):
   if mpi: # using MPI
      from mpipool import MPIPool
      pool = MPIPool()
      pool.start()
      if not pool.is_master():
         sys.exit(0)
   elif threads>1: # using multiprocessing
      from multiprocessing import Pool
      pool = Pool(processes=threads,maxtasksperchild=10)
   else:
      raise RuntimeError ("Wrong arguments: either mpi=True or threads>1.")
   return pool

Referenced by smc.smc.initialize().

getGMMFromPosterior()

def tools.getGMMFromPosterior	(		smcSamples,
			posterior,
			priorWeight
	)

def getGMMFromPosterior(smcSamples,posterior,priorWeight):
    # resample parameters from a proposal PDF
    ResampleIndices = residual_resample(posterior)
    smcNewSamples = smcSamples[ResampleIndices]
    n_components = int(self._numSamples/5)
    gmm = mixture.BayesianGaussianMixture(n_components=n_components,weight_concentration_prior=priorWeight,covariance_type='full',tol = 1e-5,max_iter=int(1e5),n_init=100)
    gmm.fit(smcNewSamples)
    return gmm
 

References int(), and resample.residual_resample().

Referenced by smc.smc.trainGMMinTime().

getKeysAndData()

def tools.getKeysAndData

(

fileName

)

def getKeysAndData(fileName):
    data = np.genfromtxt(fileName)
    fopen = open(fileName,'r')
    keys = (fopen.read().splitlines()[0]).split('\t\t')
    if '#' in keys: keys.remove('#')
    keysAndData = {}
    for key in keys: keysAndData[key] = data[:,keys.index(key)]
    return keysAndData
 

References oomph::DoubleVectorHelpers.split().

Referenced by smc.smc.getDPMData(), and smc.smc.getObsDataFromFile().

initParamsTable()

def tools.initParamsTable	(		keys,
			maxs,
			mins,
			num = 100,
			threads = 4,
			tableName = 'smcTable.txt'
	)

:param dim: type integer, number of parameters
:param num: type integer, number of sampling points for Monte Carlo Simulation
:param threads: type integer, number of thread for each parallel simulation
:param maxs: type tuples, maximums ranges of parameters
:param mins: type tuples, minimums ranges of parameters
:param keys: type strings, names of parameters

def initParamsTable(keys,maxs,mins,num=100,threads=4,tableName='smcTable.txt'):
    """
   :param dim: type integer, number of parameters
   :param num: type integer, number of sampling points for Monte Carlo Simulation
   :param threads: type integer, number of thread for each parallel simulation
   :param maxs: type tuples, maximums ranges of parameters
   :param mins: type tuples, minimums ranges of parameters
   :param keys: type strings, names of parameters
    """
    dim = len(keys)
    sequencer = ghalton.Halton(dim)
    table = sequencer.get(num)
    for i in range(dim):
        for j in range(num):
            mean = .5*(maxs[i]+mins[i])
            std  = .5*(maxs[i]-mins[i])
            table[j][i] = mean+(table[j][i]-.5)*2*std
    # write parameters in the format for Yade batch mode
    writeToTable(tableName,table,dim,num,threads,keys)
    return table, tableName
 

References writeToTable().

Referenced by smc.smc.getInitParams().

resampledParamsTable()

def tools.resampledParamsTable	(		keys,
			smcSamples,
			proposal,
			num = 100,
			thread = 4,
			maxNumComponents = 10,
			priorWeight = 1e3,
			tableName = 'smcTableNew.txt',
			verbose = False
	)

def resampledParamsTable(keys,smcSamples,proposal,num=100,thread=4,maxNumComponents=10,priorWeight=1e3,tableName='smcTableNew.txt',verbose=False):
    if (verbose):
        print("Called resampledParamsTable(keys=%s, smcSamples=%s, proposal=%s, num=%s, thread=%s, maxNumComponents=%s, priorWeight=%s, tableName=%s)"
              % (keys,smcSamples,proposal,num,thread,maxNumComponents,priorWeight,tableName))
    dim = len(keys)
    # resample parameters from a proposal PDF
    ResampleIndices = unWeighted_resample(proposal,10*num)
    smcNewSamples = smcSamples[ResampleIndices]
    # normalize parameter samples
    sampleMaxs = np.zeros(smcSamples.shape[1])
    for i in range(sampleMaxs.shape[0]):
        sampleMaxs[i] = max(smcNewSamples[:, i])
        smcNewSamples[:, i] /= sampleMaxs[i]
    # regenerate new SMC samples from Bayesian gaussian mixture model
    # details on http://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html
    gmm = mixture.BayesianGaussianMixture(n_components=maxNumComponents,weight_concentration_prior=priorWeight,covariance_type='full',tol = 1e-5,max_iter=int(1e5),n_init=100)
    gmm.fit(smcNewSamples)
    smcNewSamples, _ = gmm.sample(num)
    # print("BayesianGaussianMixture fit: means %s, weights %s, covs %s"
    #     % (gmm.means_*sampleMaxs, gmm.weights_, gmm.covariances_))
    if verbose:
        print("Fitting smcNewSamples=%s with %s" % (smcNewSamples, gmm))
    # scale resampled parameters back to their right units
    for i in range(sampleMaxs.shape[0]): smcNewSamples[:, i] *= sampleMaxs[i]
    # write parameters in the format for Yade batch mode
    writeToTable(tableName,smcNewSamples,dim,num,thread,keys)
    return smcNewSamples, tableName, gmm, maxNumComponents
 

References int(), max, Eigen::internal.print(), resample.unWeighted_resample(), and writeToTable().

Referenced by smc.smc.resampleParams().

writeToTable()

def tools.writeToTable	(		tableName,
			table,
			dim,
			num,
			thread,
			keys
	)

def writeToTable(tableName,table,dim,num,thread,keys):
    #print("Writing %s" % tableName)
    # output parameter table with thread number for each Yade simulation session
    fout = open(tableName,'w')
    fout.write(' '.join(['!OMP_NUM_THREADS','key']+keys+['\n']))
    for j in range(num):
        fout.write(' '.join(['%2i'%thread,'%9i'%j]+['%15.5e'%table[j][i] for i in range(dim)]+['\n']))
    fout.close()
 

Referenced by initParamsTable(), and resampledParamsTable().