Random generation of Bayesian network

pyAgrum.randomBN(*, n=5, names=None, ratio_arc=1.2, domain_size=2)

Creates a random BN using the (forced) keyword parameters. This function use pyAgrum.BNGenerator but the random variables will be named w.r.t. a topological order.

Warning

Number of nodes given with arg n`or `names must be bigger than 4, in order to be consistant

Examples

>>> bn=gum.randomBN()
>>> bn=gum.randomBN(n=10)
>>> bn=gum.randomBN(names="ABCDEF")
>>> bn=gum.randomBN(names=["Asia","Tuberculosis","Smoking"],ratio_arc=1.5,domain_size=3)

Warning

This function has only keyword parameters (no positional).

Parameters:

  • n (int) – number of nodes

  • names (List[str]) – list of names

  • ratio_arc (float) – number of arcs = n * ratio_arc

  • domain_size (int) – the domain size for the variables.

Return type:

BayesNet

Returns:

pyAgrum.BayesNet

class pyAgrum.BNGenerator

BNGenerator is used to easily generate Bayesian networks.

BNGenerator() -> BNGenerator

default constructor

generate(n_nodes=10, n_arcs=15, n_modmax=4)

Generate a new Bayesian network

Parameters:

  • n_nodes (int) – the number of nodes (default=10)

  • n_arcs (int) – the number of arcs (default=15)

  • n_nodmax (int) – the max number of modalities for a node (default=4)

  • n_modmax (int) –

Returns:

the generated Bayesian network

Return type:

pyAgrum.BayesNet

Raises: