spot  0.9.2
Classes | Functions
Miscellaneous algorithms on TGBA
TGBA algorithms

Classes

class  spot::bfs_steps
 Make a BFS in a spot::tgba to compute a tgba_run::steps.This class should be used to compute the shortest path between a state of a spot::tgba and the first transition or state that matches some conditions. More...
struct  spot::tgba_statistics
struct  spot::tgba_sub_statistics

Functions

sba * spot::degeneralize (const tgba *a)
 Degeneralize a spot::tgba into an equivalent sba with only one acceptance condition.
tgba_explicit_number * 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_number * 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 * 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.
tgba_sub_statistics spot::sub_stats_reachable (const tgba *g)
 Compute subended statistics for an automaton.
tgba_explicit_number * spot::tgba_powerset (const tgba *aut, power_map &pm)
 Build a deterministic automaton, ignoring acceptance conditions.This create a deterministic automaton that recognizes the same language as aut would if its acceptance conditions were ignored. This is the classical powerset algorithm.
tgba_explicit_number * spot::tgba_powerset (const tgba *aut)

Detailed Description


Function Documentation

sba* spot::degeneralize ( const tgba *  a)

Degeneralize a spot::tgba into an equivalent sba with only one acceptance condition.

This algorithms will build a new explicit automaton that has at most (N+1) times the number of states of the original automaton.

If you want to build a degeneralized automaton on-the-fly, see spot::tgba_sba_proxy or spot::tgba_tba_proxy.

See also:
tgba_sba_proxy, tgba_tba_proxy
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.

 \param n The number of states wanted in the automata (>0).  All states
          will be connected, and there will be no dead state.
 \param 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 \a d is considered only when adding the remaining transition.
          A density of 1 means all states will be connected to each other.
 \param ap The list of atomic property that should label the transition.
 \param dict The bdd_dict to used for this automata.
 \param n_acc The number of acceptance sets to use.
          If this number is non null, then there is no guarantee
          that the generated graph contains an accepting cycle (raise
          the value of \a a to improve the chances).
 \param a The probability (between 0.0 and 1.0) that a transition belongs
          to an acceptance set.
 \param t The probability (between 0.0 and 1.0) that an atomic proposition
          is true.
 \param env The environment in which to declare the acceptance conditions.

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

/// { 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 \form#0 following a normal distribution with mean

$1+(n-1)d$ and variance $(n-1)d(1-d)$. (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_sub_statistics spot::sub_stats_reachable ( const tgba *  g)

Compute subended statistics for an automaton.

tgba_explicit_number* 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_number* 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_number* spot::tgba_powerset ( const tgba *  aut,
power_map &  pm 
)

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

If pm is supplied it will be filled with the set of original states associated to each state of the deterministic automaton.

tgba_explicit_number* spot::tgba_powerset ( const tgba *  aut)

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