network_generators

balanced_tree(height: int, branching_factor: int = 2, edge_sign: int = 1, loop_sign: int = 1) → dict

Creates a balanced tree network of given height and branching_factor. All edges are either positive or negative, depending on edge_sign. The self-loop of the root component is either positive or negative, depending on loop_sign.

arguments:

• height: height of tree

• branching_factor: branching factor of tree

• edge_sign: either 1 or -1 for positive or negative edges

• loop_sign: either 1 or -1 for positive or negative self-loop of first component

returns:

• primes: primes implicants

example:

>>> primes = balanced_tree(height=3)

cycle_graph(n: int, edge_sign: int = 1) → dict

Creates a cycle network with n components. All edges are either positive or negative, depending on edge_sign.

arguments:

• n: number of components

• edge_sign: either 1 or -1 for positive or negative edges

returns:

• primes: primes implicants

example:

>>> primes = cycle_graph(n=3)

path_graph(n: int, edge_sign: int = 1, loop_sign: int = 1) → dict

Creates a path graph network with n nodes. All edges are either positive or negative, depending on edge_sign. The self-loop of the root component is either positive or negative, depending on loop_sign.

arguments:

• n: number of components

• edge_sign: either 1 or -1 for positive or negative edges

• loop_sign: either 1 or -1 for positive or negative self-loop of root component

returns:

• primes: primes implicants

example:

>>> primes = path_graph(n=3, edge_sign=-1, loop_sign=1)

random_boolean_expression(names: List[str], k: int, seed: int = 0) → str

Creates a random Boolean expression by sampling k variables from names and filling a truth table randomly. The expression is guaranteed to be non-constant but it may effectivly be dependent by less than k variables.

arguments:

• names (List[str]): components to sample from

• k: inputs to the expression

• seed: the seed of the randomizer

returns:

• expression: random expression in bnet format

example:

>>> primes = random_boolean_expression(names=list("abcdefgh"), k=4)

random_regular_network(n: int, k: int, connector: str = 'and', edge_sign: int = 1, seed: int = 0) → dict

Creates a random netowrk with n components each with a random truthtable of k inputs. The seed is used for the randomizer. Use 0 to generate a random seed.

arguments:

• n: number of components

• k: inputs of each truthtable

• connector: either “and” or “or”

• edge_sign: either 1 or -1 for positive or negative edges

• seed: the seed of the randomizer

returns:

• primes: primes implicants

example:

>>> primes = random_regular_network(n=3, k=2, connector="or")

random_truth_table_network(n: int, k: int, seed: int = 0) → dict

Creates a random netowrk with n components each with a random truthtable of k inputs. The seed is used for the randomizer. Use 0 to generate a random seed.

arguments:

• n: number of components

• k: inputs of each truthtable

• seed: the seed of the randomizer

returns:

• primes: primes implicants

example:

>>> primes = random_truth_table_network(n=3, k=2)