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, target, D, N, kernel_sigma=1., minimum_size_sigma_learning=100,
step_size=1., gamma2=0.1, adaptation_schedule=standard_sqrt_schedule, acc_star=0.234):
ProposalBase.__init__(self, target, D, step_size, adaptation_schedule, acc_star)
self.kernel_sigma = kernel_sigma
self.minimum_size_sigma_learning = minimum_size_sigma_learning
self.N = N
self.gamma2 = gamma2
self.Z = np.zeros((0, D))
def __init__(self, target, D, step_size=1., gamma2=0.1,
adaptation_schedule=standard_sqrt_schedule, acc_star=0.234):
ProposalBase.__init__(self, target, D, step_size, adaptation_schedule, acc_star)
self.gamma2 = gamma2
# initialise as scaled isotropic, otherwise Cholesky updates fail
self.mu = np.zeros(self.D)
self.L_C = np.eye(self.D) * np.sqrt(self.step_size)
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