randomDnf: sample normal form

In MartinFXP/B-NEM: Training of logical models from indirect measurements of perturbation experiments

sample normal form

Description

creates a random normal form or hyper-graph

Usage

randomDnf(
  vertices = 10,
  negation = TRUE,
  max.edge.size = NULL,
  max.edges = NULL,
  dag = FALSE
)

Arguments

vertices	number of vertices
negation	if TRUE, negations (NOT gates) are allowed
max.edge.size	maximal number of inputs per edge
max.edges	maximal number of hyper-edges
dag	if TRUE, graph will be acyclic

Value

random hyper-graph in normal form

Author(s)

Martin Pirkl

Examples

g <- randomDnf(10)

MartinFXP/B-NEM documentation built on Oct. 27, 2023, 8:24 p.m.