def __init__(self, approx=None, estimator=KSD, kernel=test_functions.rbf, **kwargs):
warnings.warn('You are using experimental inference Operator. '
'It requires careful choice of temperature, default is 1. '
'Default temperature works well for low dimensional problems and '
'for significant `n_obj_mc`. Temperature > 1 gives more exploration '
'power to algorithm, < 1 leads to undesirable results. Please take '
'it in account when looking at inference result. Posterior variance '
'is often **underestimated** when using temperature = 1.')
if approx is None:
approx = FullRank(
model=kwargs.pop('model', None),
local_rv=kwargs.pop('local_rv', None)
)
super().__init__(estimator=estimator, approx=approx, kernel=kernel, **kwargs)
def from_mean_field(cls, mean_field, gpu_compat=False):
"""
Construct FullRankADVI from MeanField approximation
Parameters
----------
mean_field : :class:`MeanField`
approximation to start with
Other Parameters
----------------
gpu_compat : `bool`
use GPU compatible version or not
Returns
-------
:class:`FullRankADVI`
"""
full_rank = FullRank.from_mean_field(mean_field, gpu_compat)
inference = object.__new__(cls)
Inference.__init__(inference, KL, full_rank, None)
return inference
def from_mean_field(cls, mean_field, gpu_compat=False):
"""
Construct FullRankADVI from MeanField approximation
Parameters
----------
mean_field : :class:`MeanField`
approximation to start with
Other Parameters
----------------
gpu_compat : `bool`
use GPU compatible version or not
Returns
-------
:class:`FullRankADVI`
"""
full_rank = FullRank.from_mean_field(mean_field, gpu_compat)
inference = object.__new__(cls)
Inference.__init__(inference, KL, full_rank, None)
return inference
def from_mean_field(cls, mean_field, gpu_compat=False):
"""
Construct FullRankADVI from MeanField approximation
Parameters
----------
mean_field : MeanField
approximation to start with
Flags
-----
gpu_compat : bool
use GPU compatible version or not
Returns
-------
FullRankADVI
"""
full_rank = FullRank.from_mean_field(mean_field, gpu_compat)
inference = object.__new__(cls)
objective = KL(full_rank)(None)
inference.objective = objective
inference.hist = np.asarray(())
return inference
def from_mean_field(cls, mean_field, gpu_compat=False):
"""
Construct FullRankADVI from MeanField approximation
Parameters
----------
mean_field : MeanField
approximation to start with
Flags
-----
gpu_compat : bool
use GPU compatible version or not
Returns
-------
FullRankADVI
"""
full_rank = FullRank.from_mean_field(mean_field, gpu_compat)
inference = object.__new__(cls)
objective = KL(full_rank)(None)
inference.objective = objective
inference.hist = np.asarray(())
return inference
def __init__(self, *args, **kwargs):
super().__init__(FullRank(*args, **kwargs))