josiann.moves.ensemble.EnsembleMove

class josiann.moves.ensemble.EnsembleMove(*, bounds=None, repr_attributes=(), **kwargs)[source]

Base class for building moves that require an ensemble of walkers to evolve in parallel.

Instantiate a Move.

Parameters:

bounds (Optional[ndarray[tuple[int, ...], dtype[float64 | int64]]] (default: None)) – optional sequence of (min, max) bounds for values to propose in each dimension.
repr_attributes (tuple[str, ...] (default: ())) – list of attribute names to include in the string representation of this Move.
kwargs (Any)

Methods

`EnsembleMove.__init__`	Instantiate a Move.
`EnsembleMove.get_proposal`	Generate a new proposed vector x.
`EnsembleMove.set_bounds`	Set bounds for the move.

Methods

EnsembleMove.__init__(*, bounds=None, repr_attributes=(), **kwargs)

Instantiate a Move.

Parameters:

bounds (Optional[ndarray[tuple[int, ...], dtype[float64 | int64]]] (default: None)) – optional sequence of (min, max) bounds for values to propose in each dimension.

repr_attributes (tuple[str, ...] (default: ())) – list of attribute names to include in the string representation of this Move.

kwargs (Any)

EnsembleMove.get_proposal(x, state)

Generate a new proposed vector x.

Parameters:

x (ndarray[tuple[int, ...], dtype[TypeVar(DT_ARR, bound= float64 | int64)]]) – current vector x of shape (ndim,).

state (State) – current state of the SA algorithm.

Return type:

ndarray[tuple[int, ...], dtype[TypeVar(DT_ARR, bound= float64 | int64)]]

Returns:

New proposed vector x of shape (ndim,).

EnsembleMove.set_bounds(bounds)

Set bounds for the move.

Parameters:

bounds (tuple[float, float] | Sequence[tuple[float, float]] | None) – sequence of (min, max) bounds for values to propose in each dimension.

Return type:

None