def _update_kernel_sigma(self): # avoid linalg errors from proposal covariance being rank defficient if len(self.Z) >= self.n: # re-compute median heuristic for kernel self.kernel_sigma = 1. / gamma_median_heuristic(self.Z)
def update(self, z_new, previous_accpept_prob): """ Updates the proposal covariance and potentially scaling parameter, according to schedule. Note that every call increases a counter that is used for the schedule (if set) If not schedule is set, this method does not have any effect unless counting. Parameters: z_new - A 1-dimensional array of size (D) of. previous_accpept_prob - Acceptance probability of previous iteration """ self.next_iteration() if self.schedule is not None: # generate updating weight lmbda = self.schedule(self.t) # project current point phi = feature_map_single(z_new, self.omega, self.u) # update centred = self.mu - phi self.mu = self.mu * (1 - lmbda) + lmbda * phi self.C = self.C * (1 - lmbda) + lmbda * np.outer(centred, centred) # update scalling parameter if wanted if self.acc_star is not None: self.update_step_size(previous_accpept_prob) self.nu2s.append(self.step_size) if self.update_kernel_gamma is not None: # update sliding window self.past_samples.append(z_new) if len(self.past_samples) > self.update_kernel_gamma: self.past_samples.popleft() num_samples_window = len(self.past_samples) # probability of updating if self.update_kernel_gamma_schedule is not None: update_prob = self.update_kernel_gamma_schedule(self.t) else: update_prob = 1. / (self.t + 1) # update kernel bandwidth (if window full yet) if np.random.rand( ) < update_prob and num_samples_window >= self.update_kernel_gamma: # transform past samples into array Z = np.array(self.past_samples) # compute new kernel gamma print("Checking whether to update kernel_gamma") new_kernel_gamma = gamma_median_heuristic( Z, num_samples_window) diff = np.abs(new_kernel_gamma - self.kernel_gamma) # only update if change above tolerance if np.abs(diff > self.update_kernel_gamma_tol): self.kernel_gamma = new_kernel_gamma # re-sample basis self.omega, self.u = sample_basis( self.D, self.m, self.kernel_gamma) # populate feature covariance from past samples self.set_batch_covariance(Z) print( "Updated kernel gamma to %.3f (from %d samples)" % (self.kernel_gamma, num_samples_window))
def update(self, z_new, previous_accpept_prob): """ Updates the proposal covariance and potentially scaling parameter, according to schedule. Note that every call increases a counter that is used for the schedule (if set) If not schedule is set, this method does not have any effect unless counting. Parameters: z_new - A 1-dimensional array of size (D) of. previous_accpept_prob - Acceptance probability of previous iteration """ self.next_iteration() if self.schedule is not None: # generate updating weight lmbda = self.schedule(self.t) # project current point phi = feature_map_single(z_new, self.omega, self.u) # update centred = self.mu - phi self.mu = self.mu * (1 - lmbda) + lmbda * phi self.C = self.C * (1 - lmbda) + lmbda * np.outer(centred, centred) # update scalling parameter if wanted if self.acc_star is not None: self.update_step_size(previous_accpept_prob) self.nu2s.append(self.step_size) if self.update_kernel_gamma is not None: # update sliding window self.past_samples.append(z_new) if len(self.past_samples) > self.update_kernel_gamma: self.past_samples.popleft() num_samples_window = len(self.past_samples) # probability of updating if self.update_kernel_gamma_schedule is not None: update_prob = self.update_kernel_gamma_schedule(self.t) else: update_prob = 1. / (self.t + 1) # update kernel bandwidth (if window full yet) if np.random.rand() < update_prob and num_samples_window >= self.update_kernel_gamma: # transform past samples into array Z = np.array(self.past_samples) # compute new kernel gamma print("Checking whether to update kernel_gamma") new_kernel_gamma = gamma_median_heuristic(Z, num_samples_window) diff = np.abs(new_kernel_gamma - self.kernel_gamma) # only update if change above tolerance if np.abs(diff > self.update_kernel_gamma_tol): self.kernel_gamma = new_kernel_gamma # re-sample basis self.omega, self.u = sample_basis(self.D, self.m, self.kernel_gamma) # populate feature covariance from past samples self.set_batch_covariance(Z) print("Updated kernel gamma to %.3f (from %d samples)" % (self.kernel_gamma, num_samples_window))
def _update_kernel_sigma(self): # avoid linalg errors from proposal covariance being rank defficient if len(self.Z) >= self.n: # re-compute median heuristic for kernel self.kernel_sigma = 1. / gamma_median_heuristic(self.Z)