InitializationStrategy

InitializationStrategy(name: str | None = None)

Bases: StrategyBase, ABC

Base class for strategies that produce an initial selection of k items.

Use the factory methods (fast, random_one_shot, random_batched, eager) to create instances.

get_next_samples(
    state: SolverState, k_remaining: int | int32
) -> NDArray[int32]

Return next batch of samples to be added to the initial selection.

This method is called repeatedly by the Solver, until enough samples have been selected to reach the desired selection size.

Parameters:

Name	Type	Description	Default
`state`	`SolverState`	(SolverState) The current solver state, to fetch problem size, constraints, distances, etc..., so initial selection can be made in an informed way.	required
`k_remaining`	`int \| int32`	(int) number of samples that remain to be selected.	required

Returns:

Type	Description
`NDArray[int32]`	np.array of unique np.int32 values, shape=(b,), with indices of samples to be added to the selection. b can be any value in range [1, k_remaining]. Samples should be unique and not yet selected.

fast() -> InitFast

Deterministic greedy initialization.

Selects items one-by-one, always picking the one that maximizes the diversity contribution wrt the current selection. Fast but seed-independent.

random_one_shot(
    uniform: bool = False, ignore_constraints: bool = False
) -> InitRandomOneShot

Random initialization that selects all k items in a single batch.

Probabilities are biased by the global diversity contribution (unless uniform=True).

Parameters:

Name	Type	Description	Default
`uniform`	`bool`	If True, sample uniformly instead of using contribution-based probabilities.	`False`
`ignore_constraints`	`bool`	If True, ignore constraints during sampling.	`False`

random_batched(b: int, ignore_constraints: bool = False) -> InitRandomBatched

Random initialization that selects items in batches of b.

Diversity contributions are re-evaluated between batches.

Parameters:

Name	Type	Description	Default
`b`	`int`	Batch size (number of items to select per batch).	required
`ignore_constraints`	`bool`	If True, ignore constraints during sampling.	`False`

eager(nc: int, ignore_constraints: bool = False) -> InitEager

Greedy initialization that evaluates nc random candidates per step and picks the best one.

Higher nc gives better quality but is slower.

Parameters:

Name	Type	Description	Default
`nc`	`int`	Number of candidates to evaluate at each step.	required
`ignore_constraints`	`bool`	If True, ignore constraints during sampling.	`False`