boofun.core.representations.sparse_truth_table

Sparse truth table representation for Boolean functions.

This module implements memory-efficient sparse representations for Boolean functions where most outputs are 0 or 1, significantly reducing memory usage for large functions.

Classes

AdaptiveTruthTableRepresentation([...])	Adaptive truth table that automatically chooses between dense and sparse formats.
SparseTruthTableRepresentation([...])	Sparse truth table representation for memory efficiency.

class boofun.core.representations.sparse_truth_table.SparseTruthTableRepresentation(compression_threshold: float = 0.1)

Sparse truth table representation for memory efficiency.

Stores only non-default values to save memory for large functions. Data format: {

‘default_value’: bool, # Value for unspecified indices ‘exceptions’: Dict[int, bool], # Indices with non-default values ‘n_vars’: int, # Number of variables ‘size’: int # Total size (2^n_vars)

}

__init__(compression_threshold: float = 0.1)

Initialize sparse representation.

Parameters:

compression_threshold – Use sparse format if non-default ratio < threshold

evaluate(inputs: ndarray, data: Dict[str, Any], space: Space, n_vars: int) → bool | ndarray

Evaluate the sparse truth table at given inputs.

Parameters:

• inputs – Input values (integer indices or binary vectors)

• data – Sparse truth table data

• space – Evaluation space

• n_vars – Number of variables

Returns:

Boolean result(s)

dump(data: Dict[str, Any], space=None, **kwargs) → Dict[str, Any]

Export sparse truth table in serializable format.

convert_from(source_repr: BooleanFunctionRepresentation, source_data: Any, space: Space, n_vars: int, **kwargs) → Dict[str, Any]

Convert from any representation to sparse truth table.

convert_to(target_repr: BooleanFunctionRepresentation, source_data: Any, space: Space, n_vars: int, **kwargs) → ndarray

Convert sparse truth table to another representation.

create_empty(n_vars: int, **kwargs) → Dict[str, Any]

Create empty sparse truth table (all False).

is_complete(data: Dict[str, Any]) → bool

Check if representation is complete.

time_complexity_rank(n_vars: int) → Dict[str, int]

Return time complexity for sparse operations.

get_storage_requirements(n_vars: int) → Dict[str, int]

Return storage requirements for sparse representation.

get_compression_stats(data: Dict[str, Any]) → Dict[str, float]

Get compression statistics.

class boofun.core.representations.sparse_truth_table.AdaptiveTruthTableRepresentation(sparse_threshold: float = 0.3)

Adaptive truth table that automatically chooses between dense and sparse formats.

Dynamically selects the most memory-efficient representation based on the data.

__init__(sparse_threshold: float = 0.3)

Initialize adaptive representation.

Parameters:

sparse_threshold – Use sparse format if non-default ratio < threshold

evaluate(inputs: ndarray, data: Dict[str, Any], space: Space, n_vars: int) → bool | ndarray

Evaluate using the appropriate internal representation.

convert_from(source_repr: BooleanFunctionRepresentation, source_data: Any, space: Space, n_vars: int, **kwargs) → Dict[str, Any]

Convert and choose optimal format.

convert_to(target_repr: BooleanFunctionRepresentation, source_data: Any, space: Space, n_vars: int, **kwargs) → ndarray

Convert to another representation.

dump(data: Dict[str, Any], space=None, **kwargs) → Dict[str, Any]

Export adaptive representation.

create_empty(n_vars: int, **kwargs) → Dict[str, Any]

Create empty adaptive representation.

is_complete(data: Dict[str, Any]) → bool

Check if representation is complete.

time_complexity_rank(n_vars: int) → Dict[str, int]

Return time complexity (depends on chosen format).

get_storage_requirements(n_vars: int) → Dict[str, int]

Return adaptive storage requirements.