def sampleTheModel(self):
ret = [0 for it in range(self.nbatch)]
if self.nmodel > 1:
for i in range(self.nbatch):
ret[i] = statistics.w_choice( range(self.nmodel), self.margins_prev )
if( len(self.dead_models) < self.nmodel-1 ):
#if(0):
# perturb models
for i in range(self.nbatch):
u = rnd.uniform(low=0, high=1)
if u > self.modelKernel:
# sample randomly from other (non dead) models
not_available = self.dead_models[:]
not_available.append(ret[i])
ss = set( not_available )
s = set( range(0,self.nmodel) )
# print "perturbing model ", set( range(self.nmodel) ), ss, s-ss, list(s-ss), numpy.array( list(s-ss) )
ar = numpy.array( list(s-ss) )
rnd.shuffle( ar )
perturbed_model = ar[0]
# print "perturbation ", ret[i], "->", perturbed_model
ret[i] = perturbed_model
return ret[:]
def sampleTheModel(self):
ret = [0 for it in range(self.nbatch)]
if self.nmodel > 1:
for i in range(self.nbatch):
ret[i] = statistics.w_choice(range(self.nmodel),
self.margins_prev)
if (len(self.dead_models) < self.nmodel - 1):
#if(0):
# perturb models
for i in range(self.nbatch):
u = rnd.uniform(low=0, high=1)
if u > self.modelKernel:
# sample randomly from other (non dead) models
not_available = self.dead_models[:]
not_available.append(ret[i])
ss = set(not_available)
s = set(range(0, self.nmodel))
# print "perturbing model ", set( range(self.nmodel) ), ss, s-ss, list(s-ss), numpy.array( list(s-ss) )
ar = numpy.array(list(s - ss))
rnd.shuffle(ar)
perturbed_model = ar[0]
# print "perturbation ", ret[i], "->", perturbed_model
ret[i] = perturbed_model
return ret[:]
def sampleTheModelFromPrior(self):
ret = [0 for it in range(self.nbatch)]
if self.nmodel > 1:
for i in range(self.nbatch):
ret[i] = statistics.w_choice( range(self.nmodel), self.modelprior )
return ret[:]
def sampleTheModelFromPrior(self):
ret = [0 for it in range(self.nbatch)]
if self.nmodel > 1:
for i in range(self.nbatch):
ret[i] = statistics.w_choice(range(self.nmodel),
self.modelprior)
return ret[:]
