boofun.core.representations.dnf_form

Disjunctive Normal Form (DNF) representation for Boolean functions.

DNF represents a Boolean function as a disjunction (OR) of conjunctions (AND) of literals (variables or their negations).

Example: f(x₁,x₂,x₃) = (x₁ ∧ ¬x₂) ∨ (¬x₁ ∧ x₂ ∧ x₃)

Functions

create_dnf_from_minterms(minterms, n_vars)	Create DNF from list of minterm indices.
dnf_to_cnf(dnf)	Convert DNF to CNF using distribution laws.
minimize_dnf(dnf)	Minimize DNF using Quine-McCluskey algorithm (simplified).

Classes

DNFFormula(terms, n_vars)	DNF formula as a list of terms.
DNFRepresentation()	DNF representation for Boolean functions.
DNFTerm(positive_vars, negative_vars)	A single term (minterm) in DNF form.

class boofun.core.representations.dnf_form.DNFTerm(positive_vars: Set[int], negative_vars: Set[int])

A single term (minterm) in DNF form.

positive_vars

Set of variables that appear positively

Type:

Set[int]

negative_vars

Set of variables that appear negatively

Type:

Set[int]

positive_vars: Set[int]

negative_vars: Set[int]

evaluate(x: List[int] | ndarray) → bool

Evaluate this DNF term.

Parameters:

x – Binary input vector

Returns:

Boolean result of this term

get_variables() → Set[int]

Get all variables in this term.

to_string(var_names: List[str] | None = None) → str

Convert term to string representation.

Parameters:

var_names – Optional variable names, defaults to x₀, x₁, …

Returns:

String representation of the term

to_dict() → Dict[str, Any]

Export term to dictionary.

classmethod from_dict(data: Dict[str, Any]) → DNFTerm

Create term from dictionary.

__init__(positive_vars: Set[int], negative_vars: Set[int]) → None

class boofun.core.representations.dnf_form.DNFFormula(terms: List[DNFTerm], n_vars: int)

DNF formula as a list of terms.

terms

List of DNF terms

Type:

List[boofun.core.representations.dnf_form.DNFTerm]

n_vars

Number of variables

Type:

int

terms: List[DNFTerm]

n_vars: int

evaluate(x: List[int] | ndarray) → bool

Evaluate DNF formula.

Parameters:

x – Binary input vector

Returns:

Boolean result (OR of all terms)

add_term(term: DNFTerm) → None

Add a term to the DNF formula.

simplify() → DNFFormula

Simplify DNF by removing redundant terms.

Returns:

Simplified DNF formula

to_string(var_names: List[str] | None = None) → str

Convert DNF to string representation.

Parameters:

var_names – Optional variable names

Returns:

String representation of DNF

to_dict() → Dict[str, Any]

Export DNF to dictionary.

classmethod from_dict(data: Dict[str, Any]) → DNFFormula

Create DNF from dictionary.

__init__(terms: List[DNFTerm], n_vars: int) → None

class boofun.core.representations.dnf_form.DNFRepresentation

DNF representation for Boolean functions.

evaluate(inputs: ndarray, data: DNFFormula, space: Space, n_vars: int) → bool | ndarray

Evaluate DNF representation.

Parameters:

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

• data – DNF formula

• space – Evaluation space

• n_vars – Number of variables

Returns:

Boolean result(s)

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

Export DNF representation.

convert_from(source_repr: BooleanFunctionRepresentation, source_data: Any, space: Space, n_vars: int, **kwargs) → DNFFormula

Convert from another representation to DNF.

Uses truth table method to generate minterms.

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

Convert DNF to another representation.

create_empty(n_vars: int, **kwargs) → DNFFormula

Create empty DNF (constant False).

is_complete(data: DNFFormula) → bool

Check if DNF is complete.

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

Return time complexity for DNF operations.

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

Return storage requirements for DNF representation.

boofun.core.representations.dnf_form.create_dnf_from_minterms(minterms: List[int], n_vars: int) → DNFFormula

Create DNF from list of minterm indices.

Parameters:

• minterms – List of minterm indices where function is True

• n_vars – Number of variables

Returns:

DNF formula

boofun.core.representations.dnf_form.minimize_dnf(dnf: DNFFormula) → DNFFormula

Minimize DNF using Quine-McCluskey algorithm (simplified).

Parameters:

dnf – DNF formula to minimize

Returns:

Minimized DNF formula