def sampling_iteration(self):
# states
f_state = self.state.copy().F()
l_state = self.state.copy().L()
# aka L^-1 state
flf_state = self.state.copy().FLF()
r_state = self.state.copy().R()
# rates
l_rates = self.transition_rates(self.state, l_state)
flf_rates = self.transition_rates(self.state, flf_state)
f_rates = flf_rates - np.min((flf_rates, l_rates), axis=0)
r_rates = self.p_r * np.ones((1, self.nbatch))
# draws from exponential distributions
l_draws = draw_from(l_rates[0])
f_draws = draw_from(f_rates[0])
r_draws = draw_from(r_rates[0])
# choose min for each particle
l_idx, f_idx, r_idx = min_idx([l_draws, f_draws, r_draws])
# record dwelling times
self.dwelling_times = np.amin(
np.concatenate((l_draws, f_draws, r_draws)), axis=0)
# cache current state as FLF state for next L transition
self.state.cache_flf_state(l_idx, self.state)
# cache FL as FLF state for for particles that made transition to F
# self.state.cache_flf_state(f_idx, l_state.F())
# update accepted proposed states
self.state.update(l_idx, l_state)
self.state.update(f_idx, f_state)
self.state.update(r_idx, r_state)
# clear flf cache for particles that transition to R, F
self.state.clear_flf_cache(r_idx)
self.state.clear_flf_cache(f_idx)
self.l_count += len(l_idx)
self.f_count += len(f_idx)
self.r_count += len(r_idx)
def sampling_iteration(self):
"""Perform a single sampling step
"""
# F operator
f_state = self.state.copy().F()
# FL operator
fl_state = self.state.copy().L().F()
# rates
fl_rates = self.transition_rates(self.state, fl_state)
f_rates = np.ones((1, self.nbatch))
r_rates = self.p_r * np.ones((1, self.nbatch))
# draws from exponential distributions
fl_draws = draw_from(fl_rates[0])
f_draws = draw_from(f_rates[0])
r_draws = draw_from(r_rates[0])
# choose min for each particle
f_idx, fl_idx, r_idx = min_idx([f_draws, fl_draws, r_draws])
# record dwelling times
self.dwelling_times = np.amin(
np.concatenate((fl_draws, f_draws, r_draws)), axis=0)
# update accepted FL transitions
self.state.update(fl_idx, fl_state)
# update accepted F transitions
self.state.update(f_idx, f_state)
# corrupt the momentum and update accepted R transition
# inefficiently corrupts momentum for all state then selects a subset
R_state = self.state.copy().R()
self.state.update(r_idx, R_state)
self.fl_count += len(fl_idx)
self.f_count += len(f_idx)
self.r_count += len(r_idx)
def sampling_iteration(self):
"""Perform a single sampling step
"""
# F operator
f_state = self.state.copy().F()
# FL operator
fl_state = self.state.copy().L().F()
# rates
fl_rates = self.transition_rates(self.state, fl_state)
f_rates = np.ones((1, self.nbatch))
r_rates = self.p_r * np.ones((1, self.nbatch))
# draws from exponential distributions
fl_draws = draw_from(fl_rates[0])
f_draws = draw_from(f_rates[0])
r_draws = draw_from(r_rates[0])
# choose min for each particle
f_idx, fl_idx, r_idx = min_idx([f_draws, fl_draws, r_draws])
# record dwelling times
self.dwelling_times = np.amin(
np.concatenate((fl_draws, f_draws, r_draws)), axis=0)
# update accepted FL transitions
self.state.update(fl_idx, fl_state)
# update accepted F transitions
self.state.update(f_idx, f_state)
# corrupt the momentum and update accepted R transition
# inefficiently corrupts momentum for all state then selects a subset
R_state = self.state.copy().R()
self.state.update(r_idx, R_state)
self.fl_count += len(fl_idx)
self.f_count += len(f_idx)
self.r_count += len(r_idx)
def sampling_iteration(self):
# states
f_state = self.state.copy().F()
l_state = self.state.copy().L()
# aka L^-1 state
flf_state = self.state.copy().FLF()
r_state = self.state.copy().R()
try:
# rates
l_rates = self.transition_rates(self.state, l_state)
flf_rates = self.transition_rates(self.state, flf_state)
f_rates = flf_rates - np.min((flf_rates, l_rates), axis=0)
r_rates = self.p_r * np.ones((1, self.nbatch))
# draws from exponential distributions
l_draws = draw_from(l_rates[0])
f_draws = draw_from(f_rates[0])
r_draws = draw_from(r_rates[0])
# infinite rate due to taking too large of a step
except ValueError:
# take smaller steps, but go the same overall distance
self.epsilon *= 0.5
self.num_leapfrog_steps *= 2
depth = np.log(self.original_epsilon / self.epsilon) / np.log(2)
print("Ecountered infinite rate, doubling back. Depth: {}".format(depth))
# try again
self.state.reset_flf_cache()
self.sampling_iteration()
# restore the old guys
self.epsilon *= 2
self.num_leapfrog_steps = int(self.num_leapfrog_steps / 2)
return
# choose min for each particle
l_idx, f_idx, r_idx = min_idx([l_draws, f_draws, r_draws])
# record dwelling times
self.dwelling_times = np.amin(
np.concatenate((l_draws, f_draws, r_draws)), axis=0)
# cache current state as FLF state for next L transition
self.state.cache_flf_state(l_idx, self.state)
# cache FL as FLF state for for particles that made transition to F
# self.state.cache_flf_state(f_idx, l_state.F())
# update accepted proposed states
self.state.update(l_idx, l_state)
self.state.update(f_idx, f_state)
self.state.update(r_idx, r_state)
# clear flf cache for particles that transition to R, F
self.state.clear_flf_cache(r_idx)
self.state.clear_flf_cache(f_idx)
self.l_count += len(l_idx)
self.f_count += len(f_idx)
self.r_count += len(r_idx)
def sampling_iteration(self):
# states
f_state = self.state.copy().F()
l_state = self.state.copy().L()
# aka L^-1 state
flf_state = self.state.copy().FLF()
r_state = self.state.copy().R()
try:
# rates
l_rates = self.transition_rates(self.state, l_state)
flf_rates = self.transition_rates(self.state, flf_state)
f_rates = flf_rates - np.min((flf_rates, l_rates), axis=0)
r_rates = self.p_r * np.ones((1, self.nbatch))
# draws from exponential distributions
l_draws = draw_from(l_rates[0])
f_draws = draw_from(f_rates[0])
r_draws = draw_from(r_rates[0])
# infinite rate due to taking too large of a step
except ValueError:
# take smaller steps, but go the same overall distance
self.epsilon *= 0.5
self.num_leapfrog_steps *= 2
depth = np.log(self.original_epsilon / self.epsilon) / np.log(2)
print("Ecountered infinite rate, doubling back. Depth: {}".format(depth))
# try again
self.state.reset_flf_cache()
self.sampling_iteration()
# restore the old guys
self.epsilon *= 2
self.num_leapfrog_steps = int(self.num_leapfrog_steps / 2)
return
# choose min for each particle
l_idx, f_idx, r_idx = min_idx([l_draws, f_draws, r_draws])
# record dwelling times
self.dwelling_times = np.amin(
np.concatenate((l_draws, f_draws, r_draws)), axis=0)
# cache current state as FLF state for next L transition
self.state.cache_flf_state(l_idx, self.state)
# cache FL as FLF state for for particles that made transition to F
# self.state.cache_flf_state(f_idx, l_state.F())
# update accepted proposed states
self.state.update(l_idx, l_state)
self.state.update(f_idx, f_state)
self.state.update(r_idx, r_state)
# clear flf cache for particles that transition to R, F
self.state.clear_flf_cache(r_idx)
self.state.clear_flf_cache(f_idx)
self.l_count += len(l_idx)
self.f_count += len(f_idx)
self.r_count += len(r_idx)
