def __init__(self, params: ExpertParams, creator: TensorCreator):
super().__init__(creator.device)
float_dtype = get_float(self._device)
self.params = params
flock_size = params.flock_size
self.n_cluster_centers = params.n_cluster_centers
self.seq_lookbehind = params.temporal.seq_lookbehind
# self.context_size = self.n_cluster_centers * 2
self.n_providers = self.params.temporal.n_providers
# Context is: <SP_output>
# <Rewards>
# <Punishments>
#
# <Pred_clusters for next step>
# <NaNs>
# <NaNs>
# With optional NaN Padding depending on the context size in the params
self.output_context = creator.full(
(flock_size, 2, NUMBER_OF_CONTEXT_TYPES, self.n_cluster_centers),
fill_value=float("nan"),
device=self._device,
dtype=float_dtype)
self.index_tensor = creator.arange(
start=0, end=flock_size,
device=self._device).view(-1, 1).expand(flock_size,
self.n_cluster_centers)
self.create_flocks(params, creator)
def __init__(self, creator: TensorCreator, dim, input_shape, split_sizes,
dtype):
super().__init__(creator.device)
self._input_shape = input_shape
self._split_sizes = split_sizes
self._dim = dim
self.output_tensors = []
self._indices = []
if -1 in split_sizes:
raise InvalidParameterException(
"Automatic split size not supported")
input_dim_size = input_shape[dim]
outputs_size = sum(split_sizes)
if outputs_size > input_dim_size:
message = f"The combined output size ({outputs_size} is larger than dimension {dim}: {input_dim_size})"
raise InvalidParameterException(message)
split_start = 0
for split in split_sizes:
output_dims = list(input_shape)
output_dims[dim] = split
output_tensor = creator.zeros(output_dims,
dtype=dtype,
device=self._device)
self.output_tensors.append(output_tensor)
# Create the index which will be used for the data splitting.
index = creator.arange(split_start,
split_start + split,
dtype=torch.long,
device=self._device)
self._indices.append(index)
split_start += split
def _arange_non_divisible(creator: TensorCreator):
return creator.arange(0, end=4, step=2)