Concatenate

class bayesflow.adapters.transforms.Concatenate(keys: Sequence[str], *, into: str, axis: int = -1, _indices: list | None = None)

Bases: Transform

Concatenate multiple arrays into a new key. Used to specify how data variables should be treated by the network.

Parameters:

keyssequence of str,

Input a list of strings, where the strings are the names of data variables.

intostr

A string telling the network how to use the variables named in keys.

axisint, optional

Along which axis to concatenate the keys. The last axis is used by default.

Examples

Suppose you have a simulator that generates variables “beta” and “sigma” from priors and then observation variables “x” and “y”. We can then use concatonate in the following way

>>> adapter = (
    bf.Adapter()
        .concatenate(["beta", "sigma"], into="inference_variables")
        .concatenate(["x", "y"], into="summary_variables")
)

classmethod from_config(config: dict, custom_objects=None) → Concatenate

get_config() → dict

forward(data: dict[str, any], *, strict: bool = True, **kwargs) → dict[str, any]

inverse(data: dict[str, any], *, strict: bool = False, **kwargs) → dict[str, any]

extra_repr() → str

__call__(data: dict[str, ndarray], *, inverse: bool = False, **kwargs) → dict[str, ndarray]

Call self as a function.