def __init__(self, target, momentum, num_steps_min=10, num_steps_max=100, step_size_min=0.05,
step_size_max=0.3, adaptation_schedule=standard_sqrt_schedule, acc_star=0.7):
if not isinstance(momentum, GaussianBase):
raise TypeError("Momentum (%s) must be subclass of %s" % \
(str(type(momentum)), str(GaussianBase)))
assert_implements_log_pdf_and_grad(target)
assert_implements_log_pdf_and_grad(momentum)
assert_inout_log_pdf_and_grad(target, momentum.D, assert_grad=False)
assert_positive_int(num_steps_min)
assert_positive_int(num_steps_max)
if not num_steps_min<=num_steps_max:
raise ValueError("Minimum number of leapfrog steps (%d) must be larger than maximum number (%d)." % \
(num_steps_min, num_steps_max))
assert_positive_float(step_size_min)
assert_positive_float(step_size_max)
if not num_steps_min<=num_steps_max:
raise ValueError("Minimum size of leapfrog steps (%d) must be larger than maximum size (%d)." % \
(step_size_min, step_size_max))
step_size = np.array([step_size_min, step_size_max])
ProposalBase.__init__(self, target, momentum.D, step_size, adaptation_schedule, acc_star)
self.momentum = momentum
self.num_steps_min = num_steps_min
self.num_steps_max = num_steps_max
def __init__(self, surrogate, target, momentum, num_steps_min=10, num_steps_max=100, step_size_min=0.05,
step_size_max=0.3, adaptation_schedule=None, acc_star=0.7):
"""
"""
HMCBase.__init__(self, target, momentum, num_steps_min, num_steps_max, step_size_min, step_size_max,
adaptation_schedule, acc_star)
assert_implements_log_pdf_and_grad(surrogate)
self.surrogate = surrogate
self.target = surrogate
self.orig_target = target
def __init__(self, target, D, step_size, adaptation_schedule, acc_star):
self.target = target
self.D = D
self.step_size = step_size
self.adaptation_schedule = adaptation_schedule
self.acc_star = acc_star
self.t = 0
# some sanity checks
assert acc_star is None or acc_star > 0 and acc_star < 1
if adaptation_schedule is not None:
lmbdas = np.array([adaptation_schedule(t) for t in np.arange(100)])
assert np.all(lmbdas >= 0)
assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)
assert_implements_log_pdf_and_grad(target, assert_grad=False)
def __init__(self, target, D, step_size, adaptation_schedule, acc_star):
self.target = target
self.D = D
self.step_size = step_size
self.adaptation_schedule = adaptation_schedule
self.acc_star = acc_star
self.t = 0
# some sanity checks
assert acc_star is None or acc_star > 0 and acc_star < 1
if adaptation_schedule is not None:
lmbdas = np.array([adaptation_schedule(t) for t in np.arange(100)])
assert np.all(lmbdas >= 0)
assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)
assert_implements_log_pdf_and_grad(target, assert_grad=False)
def __init__(self,
surrogate,
target,
momentum,
num_steps_min=10,
num_steps_max=100,
step_size_min=0.05,
step_size_max=0.3,
adaptation_schedule=None,
acc_star=0.7):
"""
"""
HMCBase.__init__(self, target, momentum, num_steps_min, num_steps_max,
step_size_min, step_size_max, adaptation_schedule,
acc_star)
assert_implements_log_pdf_and_grad(surrogate)
self.surrogate = surrogate
self.target = surrogate
self.orig_target = target
def __init__(self,
target,
momentum,
num_steps_min=10,
num_steps_max=100,
step_size_min=0.05,
step_size_max=0.3,
adaptation_schedule=standard_sqrt_schedule,
acc_star=0.7):
if not isinstance(momentum, GaussianBase):
raise TypeError("Momentum (%s) must be subclass of %s" % \
(str(type(momentum)), str(GaussianBase)))
assert_implements_log_pdf_and_grad(target)
assert_implements_log_pdf_and_grad(momentum)
assert_inout_log_pdf_and_grad(target, momentum.D, assert_grad=False)
assert_positive_int(num_steps_min)
assert_positive_int(num_steps_max)
if not num_steps_min <= num_steps_max:
raise ValueError("Minimum number of leapfrog steps (%d) must be larger than maximum number (%d)." % \
(num_steps_min, num_steps_max))
assert_positive_float(step_size_min)
assert_positive_float(step_size_max)
if not num_steps_min <= num_steps_max:
raise ValueError("Minimum size of leapfrog steps (%d) must be larger than maximum size (%d)." % \
(step_size_min, step_size_max))
step_size = np.array([step_size_min, step_size_max])
ProposalBase.__init__(self, target, momentum.D, step_size,
adaptation_schedule, acc_star)
self.momentum = momentum
self.num_steps_min = num_steps_min
self.num_steps_max = num_steps_max
