def sample_model(self): """ Returns a list of model numbers, of length self.nbatch, obtained by sampling from a categorical distribution with probabilities self.modelprior, and then perturbing with a uniform model perturbation kernel. """ models = [0] * self.nbatch if self.nmodel > 1: # Sample models from prior distribution for i in range(self.nbatch): models[i] = statistics.w_choice(self.margins_prev) # perturb models if len(self.dead_models) < self.nmodel - 1: 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 = set(self.dead_models[:]) not_available.add(models[i]) available_indexes = np.array( list(set(range(self.nmodel)) - not_available)) rnd.shuffle(available_indexes) perturbed_model = available_indexes[0] models[i] = perturbed_model return models[:]
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[:]
def sample_model_from_prior(self): """ Returns a list of model numbers, of length self.nbatch, drawn from a categorical distribution with probabilities self.modelprior """ models = [0] * self.nbatch if self.nmodel > 1: for i in range(self.nbatch): models[i] = statistics.w_choice(self.modelprior) return models