def samples_from_arrays(structures,
rewards=None,
batch_index=None,
metadata=None):
"""Makes a generator of Samples from fields.
Args:
structures: Iterable of structures (1-D np array or list).
rewards: Iterable of float rewards. If None, the corresponding Samples are
given each given a reward of None.
batch_index: Either an int, in which case all Samples created by this
function will be given this batch_index or an iterable of ints for each
corresponding structure.
metadata: Metadata to store in the Sample.
Yields:
A generator of Samples
"""
structures = utils.to_array(structures)
if metadata is None:
metadata = [None] * len(structures)
if rewards is None:
rewards = [None] * len(structures)
else:
rewards = utils.to_array(rewards)
if len(structures) != len(rewards):
raise ValueError(
'Structures and rewards must be same length. Are %s and %s' %
(len(structures), len(rewards)))
if len(metadata) != len(rewards):
raise ValueError(
'Metadata and rewards must be same length. Are %s and %s' %
(len(metadata), len(rewards)))
if batch_index is None:
batch_index = 0
if isinstance(batch_index, int):
batch_index = [batch_index] * len(structures)
for structure, reward, batch_index, meta in zip(structures, rewards,
batch_index, metadata):
yield Sample(structure=structure,
reward=reward,
batch_index=batch_index,
metadata=meta)
def _record_to_dict(record):
mapping = {
key: utils.to_array(value)
for key, value in record.items()
}
if 'batch_index' in mapping:
mapping['batch_index'] = int(mapping['batch_index'])
return mapping
