pyjuice.structures.PD

pyjuice.structures.PD(data_shape: Tuple, num_latents: int, split_intervals: Optional[Union[int, Tuple[int]]] = None, split_points: Optional[Sequence[Sequence[int]]] = None, max_split_depth: Optional[int] = None, max_prod_block_conns: int = 4, structure_type: str = 'sum_dominated', input_layer_fn: Optional[Callable] = None, input_dist: Optional[Distribution] = None, input_node_type: Type[Distribution] = <class 'pyjuice.nodes.distributions.categorical.Categorical'>, input_node_params: Dict = {'num_cats': 256}, tie_homogeneous_params: bool = False, block_size: Optional[int] = None)

Generate PCs with the PD structure (https://arxiv.org/pdf/1202.3732.pdf).

Parameters:

• data_shape (Tuple) – shape of the data (e.g., [H, W, 3] for images and [S] for sequences)

• num_latents (int) – size of the latent space

• split_intervals (Optional[Union[int, Tuple[int]]]) – a sequence of integers specifying the interval between split points in every dimension; either this or split_points needs to be specified

• split_points (Optional[Sequence[Sequence[int]]]) – a sequence of split points in each dimension; either this or split_intervals needs to be specified

• max_split_depth (Optional[int]) – maximum depth of the splits

• max_prod_block_conns (int) – the maximum number of product nodes connected to every sum node

• structure_type (str) – whether to reuse sum nodes; no reuse: “sum_dominated”, reuse: “prod_dominated”

• input_dist (Distribution) – input distribution

• tie_homogeneous_params (bool) – whether to tie parameters of sum/input nodes with compatible structures

• block_size (int) – block size