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)