def main():
#model = Model('toygauss.cfg')
model = Model('example.cfg')
#model = Model('oblateTransit/sorted_example.cfg')
#model = Model('oblateTransit/testmarching_example.cfg')
minvals,maxvals,e,Np = model.Getinitial()
#print minvals,maxvals,e
sampler = MNest(minvals,maxvals,e,Np, "uniform")
#what I intended to use, for sampler does not need to know the names
#sampler = MNest(priortypes,minvals,maxvals,guessvals)
print "running MultiNest algorithm... this may take a few minutes\n"
nest = 0
Flag = True
NumRecluster = 0
NumLeval = Np
Alltheta = np.zeros([model.D,int(Np)])
theta = np.zeros(model.D)
logL = np.zeros(Np)*1.0
logLmax = 0.
logLmin = 0.
THRESH = 1.0e-7
FullReclusterPeriod = 15000
sampler.getAlltheta(Alltheta)
model.Get_L(Alltheta.ravel(),logL,Np)
sampler.SetAllPoint(logL)
#print Alltheta[0,0],Alltheta[1,0]
#Flag = False
Debug = False
count=0
logzinfo = np.zeros(3)
count = 0
while Flag:
X_i = np.exp(-1.0*nest/(1.0*Np))
#discard and resample, get logLmax for convergence check as byproduct
if(Debug):
print '----------------------'
ntotal = sampler.countTotal()
posterior = np.zeros([model.D,ntotal])
prob = np.zeros(ntotal)
sampler.getPosterior(posterior,prob)
print posterior
print prob
sampler.DisgardWorstPoint(int(nest))
if(Debug):
print '----------------------'
ntotal = sampler.countTotal()
posterior = np.zeros([model.D,ntotal])
prob = np.zeros(ntotal)
sampler.getPosterior(posterior,prob)
print posterior
print prob
logLmax = sampler.getlogLmax()
logLmin = sampler.getlogLmin()
#print logLmax,logLmin
FlagSample = True
templogL = np.array([0.])
#print 'before reset'
while FlagSample:
sampler.ResetWorstPoint(theta)
model.Get_L(theta,templogL,1)
NumLeval += 1
#print theta, templogL,logLmin
FlagSample=(logLmin > templogL[0])
#FlagSample=False
temp = templogL[0]
#print templogL[0]
sampler.ResetWorstPointLogL(temp)
#print 'after reset'
if (Debug):
print '----------------------'
ntotal = sampler.countTotal()
posterior = np.zeros([model.D,ntotal])
prob = np.zeros(ntotal)
sampler.getPosterior(posterior,prob)
print posterior
print prob
if nest % FullReclusterPeriod == 0:
NumRecluster += 1
sampler.FullRecluster(X_i)
else:
NumRecluster += sampler.Recluster(X_i,model.repartition)
#ellipsoidal partitioning
#if(nest==0 or sampler.ClusteringQuality(X_i) > model.repartition):
# count+=1
# # recluster?
# if count==5:
# print 'before recluster'
# sampler.Recluster(X_i)
# print 'after recluster'
# NumRecluster+=1
# count=0
# #sampler.EllipsoidalRescaling(X_i);
# Flag1 = True
#else:
# print sampler.ClusteringQuality(X_i)
# sampler.EllipsoidalRescaling(X_i);
# print sampler.ClusteringQuality(X_i)
# Flag1 = False
nest+=1
zold = logzinfo[1]
sampler.getlogZ(logzinfo)
if(nest%1000==0):
print "logZ after ",nest+Np," iterations: %f" % logzinfo[1]
#print nest,X_i
#Flag = False
Flag = THRESH < abs(zold-logzinfo[1])
count +=1
if(count>2000):
for i in xrange(1000):
sampler.ResetWorstPoint(theta)
model.Get_L(theta,templogL,1)
print theta[0],theta[1],templogL
break
#Flag = THRESH < abs(X_i*logLmax)
#print 'before output'
#output
return
def main():
model = Model('example.cfg')
minvals,maxvals,e,Np = model.Getinitial()
sampler = MNest(minvals,maxvals,e,Np, "uniform")
sampler.OutputClusters()
print str(0)+"\t"+str(1)+"\t"+str(sampler.getVtot())+"\n"
#what I intended to use, for sampler does not need to know the names
#sampler = MNest(priortypes,minvals,maxvals,guessvals)
#print "# running MultiNest algorithm... this may take a few minutes\n"
nest = 0
Flag = True
NumRecluster = 0
NumLeval = Np
Alltheta = np.zeros([model.D,int(Np)])
theta = np.zeros(model.D)
logL = np.zeros(Np)*1.0
logLmax = 0.
logLmin = 0.
THRESH = model.thresh #1.0*10**-2.5
FullReclusterPeriod = int(np.sqrt(Np))
SinceLastReclustering = 0
sampler.getAlltheta(Alltheta)
model.Get_L(Alltheta.ravel(),logL,Np)
sampler.SetAllPoint(logL)
#print Alltheta[0,0],Alltheta[1,0]
#Flag = False
Debug = False
count=0
logzinfo = np.zeros(3)
while Flag:
X_i = np.exp(-1.0*nest/(1.0*Np))
#discard and resample, get logLmax for convergence check as byproduct
if(Debug):
print '# ----------------------'
ntotal = sampler.countTotal()
posterior = np.zeros([model.D,ntotal])
prob = np.zeros(ntotal)
logWts = np.zeros(ntotal)
sampler.getPosterior(posterior,prob,logWts)
print "#",posterior
print "#",prob
sampler.DisgardWorstPoint(int(nest))
if(Debug):
print '# ----------------------'
ntotal = sampler.countTotal()
posterior = np.zeros([model.D,ntotal])
prob = np.zeros(ntotal)
logWts = np.zeros(ntotal)
sampler.getPosterior(posterior,prob,logWts)
print "#",posterior
print "#",prob
logLmax = sampler.getlogLmax()
logLmin = sampler.getlogLmin()
#print logLmax,logLmin
FlagSample = True
templogL = np.array([0.])
#print 'before reset'
while FlagSample:
#print "ResetWorstPoint called with",theta
sampler.ResetWorstPoint(theta)
model.Get_L(theta,templogL,1)
NumLeval += 1
#print theta, templogL,logLmin
FlagSample=(logLmin > templogL[0])
#FlagSample=False
temp = templogL[0]
#print templogL[0]
sampler.ResetWorstPointLogL(temp)
#print 'after reset'
if (Debug):
print '# ----------------------'
ntotal = sampler.countTotal()
posterior = np.zeros([model.D,ntotal])
prob = np.zeros(ntotal)
logWts = np.zeros(ntotal)
sampler.getPosterior(posterior,prob,logWts)
print "#",posterior
print "#",prob
sampler.CalcVtot()
#if nest % 1000 == 0:
# sampler.OutputClusters()
# print str(nest)+"\t"+str(X_i)+"\t"+str(sampler.getVtot())+"\n"
if sampler.ClusteringQuality(X_i) > model.repartition and SinceLastReclustering > FullReclusterPeriod:
SinceLastReclustering = 0
NumRecluster += 1
#print "reclustering",nest,"..."
sampler.FullRecluster(X_i)
#sampler.MergeEllipsoids(X_i)
#print "done."
sampler.CalcVtot()
sampler.OutputClusters()
print str(nest)+"\t"+str(X_i)+"\t"+str(sampler.getVtot())+"\n"
else:
SinceLastReclustering += 1
nest+=1
zold = logzinfo[1]
sampler.getlogZ(logzinfo)
#Flag = THRESH < abs(zold-logzinfo[1])
#if(NumRecluster> 20):
# break
#Flag = THRESH < abs(X_i*logLmax)
Flag = THRESH < abs(X_i*np.exp(logLmax-logzinfo[1]))
#print 'before output'
#output
ntotal = sampler.countTotal()
print "# number of iterations = ",ntotal
print "# sampling efficiency = ",1.0*ntotal/NumLeval
posterior = np.zeros([model.D,ntotal])
prob = np.zeros(ntotal)
logWts = np.ones(ntotal)*-999.9
sampler.getPosterior(posterior,prob,logWts)
print "# dimensions of posterior sampling = ",posterior.shape
print "# number of likelihood evaluations = ",NumLeval
#print "number of modellikelihood evaluations = ",model.NL_
print "# number of reclusterings = ",NumRecluster
logzinfo = np.zeros(3)
sampler.getlogZ(logzinfo)
model.Output(posterior.ravel(),prob,np.exp(logWts-logzinfo[1]))
print "# information: H=%f bits" % logzinfo[0]
print "# global evidence: logZ = %f +/- %f" % (logzinfo[1],logzinfo[2])
#os.system("tail -n %d multivar.tab >> temp9.txt" % model.Np_)
return
