def proposal(self, current, current_log_pdf, **kwargs):
if self.surrogate.n >= self.num_minimum_samples_to_use_drift:
return AdaptiveLangevin.proposal(self, current, current_log_pdf, **kwargs)
else:
# random walk until using gradients
logger.debug("Waiting to use kernel gradient. Seen %d/%d data." % \
(self.surrogate.n, self.num_minimum_samples_to_use_drift))
return AdaptiveMetropolis.proposal(self, current, current_log_pdf, **kwargs)
return AdaptiveMetropolis.proposal(self, current, current_log_pdf, **kwargs)
def proposal(self, current, current_log_pdf, **kwargs):
if self.surrogate.n >= self.num_minimum_samples_to_use_drift:
return AdaptiveLangevin.proposal(self, current, current_log_pdf,
**kwargs)
else:
# random walk until using gradients
logger.debug("Waiting to use kernel gradient. Seen %d/%d data." % \
(self.surrogate.n, self.num_minimum_samples_to_use_drift))
return AdaptiveMetropolis.proposal(self, current, current_log_pdf,
**kwargs)
return AdaptiveMetropolis.proposal(self, current, current_log_pdf,
**kwargs)
def __init__(self,
D,
target_log_pdf,
grad,
step_size,
schedule=None,
acc_star=None):
StaticLangevin.__init__(self, D, target_log_pdf, grad, step_size,
schedule, acc_star)
# gamma2 in AM is not used
gamma2_dummy = 0.1
AdaptiveMetropolis.__init__(self, D, target_log_pdf, step_size,
gamma2_dummy, schedule, acc_star)
def get_AM_5(D, target_log_pdf):
step_size = 5.
acc_star = 0.234
gamma2 = 0.1
instance = AdaptiveMetropolis(D, target_log_pdf, step_size, gamma2, one_over_4th_root_t_schedule, acc_star)
return instance
def get_AdaptiveMetropolis_instance(D, target_log_pdf):
step_size = 8.
schedule = one_over_sqrt_t_schedule
acc_star = 0.234
gamma2 = 0.1
instance = AdaptiveMetropolis(D, target_log_pdf, step_size, gamma2, schedule, acc_star)
return instance
def set_batch(self, Z):
return AdaptiveMetropolis.set_batch(self, Z)
def update(self, Z, num_new=1, log_weights=None):
return AdaptiveMetropolis.update(self, Z, num_new, log_weights)
def get_AdaptiveMetropolis_instance(D, target_log_pdf):
step_size = 1.
gamma2 = 0.1
instance = AdaptiveMetropolis(D, target_log_pdf, step_size, gamma2)
return instance
def set_batch(self, Z):
return AdaptiveMetropolis.set_batch(self, Z)
def update(self, Z, num_new=1, log_weights=None):
return AdaptiveMetropolis.update(self, Z, num_new, log_weights)
def __init__(self, D, target_log_pdf, grad, step_size, schedule=None, acc_star=None):
StaticLangevin.__init__(self, D, target_log_pdf, grad, step_size, schedule, acc_star)
# gamma2 in AM is not used
gamma2_dummy = 0.1
AdaptiveMetropolis.__init__(self, D, target_log_pdf, step_size, gamma2_dummy, schedule, acc_star)
