Miscellaneous algorithms on TGBA
[TGBA algorithms]

Classes
class	spot::bfs_steps
	Make a BFS in a spot::tgba to compute a tgba_run::steps. More...
struct	spot::tgba_statistics
Functions
tgba_explicit *	spot::tgba_dupexp_bfs (const tgba *aut)
	Build an explicit automata from all states of aut, numbering states in bread first order as they are processed.
tgba_explicit *	spot::tgba_dupexp_dfs (const tgba *aut)
	Build an explicit automata from all states of aut, numbering states in depth first order as they are processed.
tgba_explicit *	spot::tgba_powerset (const tgba *aut)
	Build a deterministic automaton, ignoring acceptance conditions.
tgba *	spot::random_graph (int n, float d, const ltl::atomic_prop_set *ap, bdd_dict *dict, int n_acc=0, float a=0.1, float t=0.5, ltl::environment *env=&ltl::default_environment::instance())
	Construct a tgba randomly.
tgba_statistics	spot::stats_reachable (const tgba *g)
	Compute statistics for an automaton.

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Function Documentation

tgba* spot::random_graph	(	int	n,
		float	d,
		const ltl::atomic_prop_set *	ap,
		bdd_dict *	dict,
		int	n_acc = 0,
		float	a = 0.1,
		float	t = 0.5,
		ltl::environment *	env = &ltl::default_environment::instance()
	)

Construct a tgba randomly.

Parameters:

	n	The number of states wanted in the automata (>0). All states will be connected, and there will be no dead state.
	d	The density of the automata. This is the probability (between 0.0 and 1.0), to add a transition between two states. All states have at least one outgoing transition, so d is considered only when adding the remaining transition. A density of 1 means all states will be connected to each other.
	ap	The list of atomic property that should label the transition.
	dict	The bdd_dict to used for this automata.
	n_acc	The number of acceptance sets to use.
	a	The probability (between 0.0 and 1.0) that a transition belongs to an acceptance set.
	t	The probability (between 0.0 and 1.0) that an atomic proposition is true.
	env	The environment in which to declare the acceptance conditions.

This algorithms is adapted from the one in Fig 6.2 page 48 of

  /// @TechReport{	  tauriainen.00.a66,
  ///   author	= {Heikki Tauriainen},
  ///   title   = {Automated Testing of {B\"u}chi Automata Translators for
  /// 		  {L}inear {T}emporal {L}ogic},
  ///   address	= {Espoo, Finland},
  ///   institution = {Helsinki University of Technology, Laboratory for
  /// 		  Theoretical Computer Science},
  ///   number	= {A66},
  ///   year	= {2000},
  ///   url	= {http://citeseer.nj.nec.com/tauriainen00automated.html},
  ///   type	= {Research Report},
  ///   note	= {Reprint of Master's thesis}
  /// }
  /// 

Although the intent is similar, there are some differences with between the above published algorithm and this implementation . First labels are on transitions, and acceptance conditions are generated too. Second, the number of successors of a node is chosen in following a normal distribution with mean and variance . (This is less accurate, but faster than considering all possible n successors one by one.)

tgba_statistics spot::stats_reachable

(

const tgba *

g

)

Compute statistics for an automaton.

tgba_explicit* spot::tgba_dupexp_bfs

(

const tgba *

aut

)

Build an explicit automata from all states of aut, numbering states in bread first order as they are processed.

tgba_explicit* spot::tgba_dupexp_dfs

(

const tgba *

aut

)

Build an explicit automata from all states of aut, numbering states in depth first order as they are processed.

tgba_explicit* spot::tgba_powerset

(

const tgba *

aut

)

Build a deterministic automaton, ignoring acceptance conditions.

This create a deterministic automaton that recognize the same language as aut would if its acceptance conditions were ignored. This is the classical powerset algorithm.

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