def __init__(self,
dataset,
name,
is_observed=True,
batch_size=(),
indices=()):
self._type = "Empirical"
ranges = {
"dataset": geometric_ranges.UnboundedRange(),
"batch_size": geometric_ranges.UnboundedRange(),
"indices": geometric_ranges.UnboundedRange()
}
super().__init__(name,
dataset=dataset,
indices=indices,
learnable=False,
ranges=ranges,
is_observed=is_observed)
self.distribution = distributions.EmpiricalDistribution()
if batch_size:
self.distribution.batch_size = batch_size
self.batch_size = batch_size
elif indices:
self.distribution.batch_size = len(indices)
self.batch_size = batch_size #TODO: Clean up here
else:
raise ValueError(
"Either the indices or the batch size has to be given as input"
)
def __init__(self,
dataset,
name,
learnable=False,
has_bias=False,
is_observed=False,
batch_size=None,
indices=None,
weights=None): #TODO: Ugly logic
self._type = "Empirical"
input_parameters = {
"dataset": dataset,
"batch_size": batch_size,
"indices": indices,
"weights": weights
}
ranges = {
par_name: geometric_ranges.UnboundedRange()
for par_name, par_value in input_parameters.items()
if par_value is not None
}
kwargs = {
par_name: par_value
for par_name, par_value in input_parameters.items()
if par_value is not None
}
super().__init__(name,
**kwargs,
learnable=learnable,
has_bias=has_bias,
ranges=ranges,
is_observed=is_observed)
if not batch_size:
if indices:
batch_size = len(indices)
else:
raise ValueError(
"Either the indices or the batch size has to be given as input"
)
self.batch_size = batch_size
self.distribution = distributions.EmpiricalDistribution(
batch_size=batch_size, is_observed=is_observed)