# -*- coding: utf-8 -*-
#+TITLE: Explicit vs. on-the-fly: two interfaces for exploring automata
#+DESCRIPTION: Explanation of the explicit and on-the-fly automata interfaces in Spot
#+SETUPFILE: setup.org
#+HTML_LINK_UP: tut.html


When exploring automata (i.e., following its transition structure),
there are two different interfaces that can be used:
  1. the *on-the-fly* =twa= interface, and
  2. the *explicit* =twa_graph= interface.

To demonstrate the difference between the two interfaces, we will
write an small depth-first search that prints all states accessible
from the initial state of an automaton.

* The explicit interface
   :PROPERTIES:
   :CUSTOM_ID: explicit-interface
   :END:

The explicit interface can only be used on =twa_graph= objects.  In
this interface, states and edges are referred to by numbers that are
indices into state and edge vectors.  This interface is lightweight,
and is the preferred interface for writing most automata algorithms in
Spot.

** How this interface works

The picture below gives a partial view of the classes involved:

#+BEGIN_SRC plantuml :file uml-explicit-classes.png
  package stl {
    class "Forward Iterator" <<Concept>>
    hide "Forward Iterator" members
    hide "Forward Iterator" circle
  }
  package spot {
    package internal {
      class "state_out<digraph<twa_graph_state, twa_graph_edge_data>>" {
         unsigned t_
         __
         edge_iterator<digraph<twa_graph_state, twa_graph_edge_data>> begin()
         edge_iterator<digraph<twa_graph_state, twa_graph_edge_data>> end()
      }

      class "edge_iterator<digraph<twa_graph_state, twa_graph_edge_data>>" {
        unsigned t_
        __
        bool operator==(edge_iterator)
        bool operator!=(edge_iterator)
        edge_storage<twa_graph_edge_data>& operator*(edge_iterator)
        edge_storage<twa_graph_edge_data>* operator->(edge_iterator)
        edge_iterator operator++()
        .. other methods hidden ..
      }

      "Forward Iterator" <|.. "edge_iterator<digraph<twa_graph_state, twa_graph_edge_data>>": model of

      class "edge_storage<twa_graph_edge_data>" {
        +unsigned src
        +unsigned dst
        +unsigned next_succ
      }

      class "distate_storage<twa_graph_state>" {
        +unsigned succ
        +unsigned succ_tail
      }

    }

    class twa_graph {
      +typedef digraph<twa_graph_state, twa_graph_edge_data> graph_t
      __
      +twa_graph(const bdd_dict_ptr&)
      +graph_t& get_graph()
      .. exploration ..
      +unsigned get_init_state_number()
      +internal::state_out<graph_t> out(unsigned src)
      .. other methods hidden ..
    }

    twa <|-- twa_graph
    abstract class twa
    hide twa members

    class "digraph<twa_graph_state, twa_graph_edge_data>" {
      +internal::edge_storage<twa_graph_edge_data> edge_storage(unsigned s)
      +internal::distate_storage<twa_graph_state> state_storage(unsigned s)
      +internal::state_out<graph_t> out(unsigned src)
      .. other details omitted ..
    }

    twa_graph *--> "1" "digraph<twa_graph_state, twa_graph_edge_data>"

    class twa_graph_edge_data {
     +bdd cond
     +acc_cond::mark_t acc
    }

    "digraph<twa_graph_state, twa_graph_edge_data>" *--> "*" "edge_storage<twa_graph_edge_data>"
    "digraph<twa_graph_state, twa_graph_edge_data>" *--> "*" "distate_storage<twa_graph_state>"

    twa_graph_edge_data <|-- "edge_storage<twa_graph_edge_data>"
    twa_graph_state <|-- "distate_storage<twa_graph_state>"
    hide twa_graph_state members

    class acc_cond
    hide acc_cond members
    twa *--> acc_cond

    "state_out<digraph<twa_graph_state, twa_graph_edge_data>>" o--> "digraph<twa_graph_state, twa_graph_edge_data>"
    "digraph<twa_graph_state, twa_graph_edge_data>" ...> "state_out<digraph<twa_graph_state, twa_graph_edge_data>>" : "create"
    "state_out<digraph<twa_graph_state, twa_graph_edge_data>>" ...> "edge_iterator<digraph<twa_graph_state, twa_graph_edge_data>>" : "create"
    "edge_iterator<digraph<twa_graph_state, twa_graph_edge_data>>" o--> "digraph<twa_graph_state, twa_graph_edge_data>"
  }

#+END_SRC

#+RESULTS:
[[file:uml-explicit-classes.png]]


An ω-automaton can be defined as a labeled directed graph, plus an
initial state and an acceptance condition.  The =twa_graph= of Spot
stores exactly these three components: the transition structure is
stored as an instance of =digraph= (directed graph), the initial state
is just a number, and the acceptance condition is an instance of
=acc_cond= which is actually inherited from the =twa= parent.  You can
ignore the =twa= inheritance for now, we will discuss it when we talk
about [[#on-the-fly-interface][the on-the-fly interface in the next section]].

In this section we are discussing the "explicit interface", which is a
way of exploring the stored graph directly.

The =digraph= template class in Spot is parameterized by classes
representing additional data to store on state, and on edges.  In the
case of a =twa_graph=, these extra data are implemented as
=twa_graph_state= (but we won't be concerned about this type) and
=twa_graph_edge_data=.  The class =twa_graph_edge_data= has two
fields: =cond= is a BDD representing the label of the edge, and
=acc= represents the acceptance sets to which the edge belong.

The =digraph= stores a vector of states, and a vector of edges, but
both states and edges need to be equipped with field necessary to
represent the graph structure.  In particular, a state holds two edges
numbers representing the first (=succ=) and last (=succ_tail=) edges
exiting the state (that "last edge" is only useful to append new
transitions, it is not used for iteration), and each edge has three
additional fields: =src= (the source state), =dst= (the destination
state), and =next_succ= (the index of the next edge leaving =src=, in
the edge vector).  By way of template inheritance, these
=digraph=-fields are combined with the =twa_graph= specific fields, so
that all edges can be represented by an instance of
=std::vector<internal::edge_storage<twa_graph_edges_data>>=: each
element of this vector acts as a structure with 5 fields; likewise for
the state vector.

Calling =get_init_state_number()= will return a state number which is
just an index into the state vector of the underlying graph.

From a state number =s=, it is possible to iterate over all successors
by doing a =for= loop on =out(s)=, as in:

#+BEGIN_SRC C++ :results verbatim :exports both
  #include <iostream>
  #include <spot/twa/twagraph.hh>
  #include <spot/tl/parse.hh>
  #include <spot/twaalgos/translate.hh>

  void example(spot::const_twa_graph_ptr aut)
  {
    unsigned s = aut->get_init_state_number();
    for (auto& e: aut->out(s))
      std::cout << e.src << "->" << e.dst << '\n';
  }

  int main()
  {
    // Create a small example automaton
    spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
    if (pf.format_errors(std::cerr))
      return 1;
    example(spot::translator().run(pf.f));
  }
#+END_SRC

#+RESULTS:
: 2->0
: 2->1
: 2->2

In the above lines, =aut->out(s)= delegates to
=aut->get_graphs().out(s)= and returns a =state_out<graph_t>=
instance, which is a small temporary object masquerading as an STL
container with =begin()= and =end()= methods.  The ranged-for loop
syntax of C++ works exactly as if we had typed

#+BEGIN_SRC C++
  // You could write this, but why not let the compiler do it for you?
  // In any case, do not spell out the types of tmp and i, as those
  // should be considered internal details.
  void example(spot::const_twa_graph_ptr aut)
  {
    unsigned s = aut->get_init_state_number();
    auto tmp = aut->get_graph().out(s);
    for (auto i = tmp.begin(), end = tmp.end(); i != end; ++i)
      std::cout << i->src << "->" << i->dst << '\n';
  }
#+END_SRC

In the above =example()= function, the iterators =i= and =end= are
instances of the =internal::edge_iterator<spot::twa_graph::graph_t>=
class, which redefines enough operators to act like an STL Foward
Iterator over all outgoing edges of =s=.  Note that the =tmp= and =i=
objects hold a pointer to the graph, but it does not really matter
because the compiler will optimize this away.

In fact after operators are inlined and useless temporary variables
removed, the above loop compiles to something equivalent to this:

#+BEGIN_SRC C++
  // You could also write this lower-level version, and that sometimes
  // helps (e.g., if you want to pause the loop and then resume it, as
  // we will do later).
  void example(spot::const_twa_graph_ptr aut)
  {
    unsigned s = aut->get_init_state_number();
    auto& g = aut->get_graph();
    unsigned b = g.state_storage(s).succ; // first edge of state s
    while (b)
      {
        auto& e = g.edge_storage(b);
        std::cout << e.src << "->" << e.dst << '\n';
        b = e.next_succ;
      }
  }
#+END_SRC

Note that ~next_succ==0~ marks the last edge in a successor group;
this is why edge numbers start at 1.

Despite the various levels of abstractions, these three loops compile
to exactly the same machine code.

** Recursive DFS

Let us write a DFS using this interface.  A recursive version is easy:
we call =dfs_rec()= from the initial state, that function updates a
vector of visited states in order to not visit them twice, and recurse
on all successors of the given state.

#+BEGIN_SRC C++ :results verbatim :exports both
  #include <iostream>
  #include <spot/twa/twagraph.hh>
  #include <spot/tl/parse.hh>
  #include <spot/twaalgos/translate.hh>

  void dfs_rec(spot::const_twa_graph_ptr aut, unsigned state, std::vector<bool>& seen)
  {
    seen[state] = true;
    for (auto& e: aut->out(state))
      {
         std::cout << e.src << "->" << e.dst << '\n';
         if (!seen[e.dst])
           dfs_rec(aut, e.dst, seen);
      }
  }

  void dfs(spot::const_twa_graph_ptr aut)
  {
    std::vector<bool> seen(aut->num_states());
    dfs_rec(aut, aut->get_init_state_number(), seen);
  }

  int main()
  {
    // Create a small example automaton
    spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
    if (pf.format_errors(std::cerr))
      return 1;
    dfs(spot::translator().run(pf.f));
  }
#+END_SRC

#+RESULTS:
: 2->0
: 0->0
: 2->1
: 1->1
: 2->2

** Iterative DFS (two versions)

 Recursive graph algorithms are usually avoided, especially if large
 graphs should be processed.

 By maintaining a stack of states to process, we can visit all
 accessible transitions in a "DFS-ish" way, but without producing
 exactly the same output as above.

 #+BEGIN_SRC C++ :results verbatim :exports both
   #include <iostream>
   #include <stack>
   #include <spot/twa/twagraph.hh>
   #include <spot/tl/parse.hh>
   #include <spot/twaalgos/translate.hh>

   void almost_dfs(spot::const_twa_graph_ptr aut)
   {
     std::vector<bool> seen(aut->num_states());
     std::stack<unsigned> todo;
     auto push_state = [&](unsigned state)
       {
         todo.push(state);
         seen[state] = true;
       };
     push_state(aut->get_init_state_number());
     while (!todo.empty())
       {
         unsigned s = todo.top();
         todo.pop();
         for (auto& e: aut->out(s))
           {
              std::cout << e.src << "->" << e.dst << '\n';
              if (!seen[e.dst])
                push_state(e.dst);
           }
       }
   }

   int main()
   {
     // Create a small example automaton
     spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
     if (pf.format_errors(std::cerr))
       return 1;
     almost_dfs(spot::translator().run(pf.f));
   }
 #+END_SRC

 #+RESULTS:
 : 2->0
 : 2->1
 : 2->2
 : 1->1
 : 0->0

 So this still prints all accessible edges, but not in the same order
 as our recursive DFS.  This is because this version prints all the
 outgoing edges of one state before processing the successors.

 For many algorithms, this different ordering makes no difference, and
 this order should even be preferred: groups of transitions leaving
 the same state are usually stored consecutively in memory, so they
 are better processed in chain, rather than trying to follow exactly
 the order we would get from a recursive DFS, which will jump at
 random places in the edge vector.

 In fact writing the iterative equivalent of the recursive =dfs()= is
 a bit challenging if we do not want to be wasteful.  Clearly, we
 can no longer use the ranged-for loop, because we need to process
 one edge, save the current iterator on a stack to process the
 successor, and finally advance the iterator once we pop back to it.

Given the above data structure, it is tempting to use a
=std::stack<spot::internal::edge_iterator<spot::twa_graph::graph_t>>=,
but that is a bad idea.  Remember that those =internal::edge_iterator=
are meant to be short-lived temporary objects, and they all store a
pointer to graph.  We do not want to store multiple copies of this
pointer on our stack.  Besides, *you* do not want to ever write
=spot::internal= in your code.

So a better implementation (better than the
=std::stack<spot::internal::edge_iterator<...>>= suggestion) would be
to maintain a stack of edge numbers.  Indeed, each edge
stores the number of the next edge leaving the same source
state, so this is enough to remember where we are.

 #+BEGIN_SRC C++ :results verbatim :exports both
   #include <iostream>
   #include <stack>
   #include <spot/twa/twagraph.hh>
   #include <spot/tl/parse.hh>
   #include <spot/twaalgos/translate.hh>

   void dfs(spot::const_twa_graph_ptr aut)
   {
     std::vector<bool> seen(aut->num_states());
     std::stack<unsigned> todo;    // Now storing edges numbers
     auto& gr = aut->get_graph();
     auto push_state = [&](unsigned state)
       {
         todo.push(gr.state_storage(state).succ);
         seen[state] = true;
       };
     push_state(aut->get_init_state_number());
     while (!todo.empty())
       {
         unsigned edge = todo.top();
         todo.pop();
         if (edge == 0U)           // No more outgoing edge
           continue;
         auto& e = gr.edge_storage(edge);
         todo.push(e.next_succ); // Prepare next sibling edge.
         if (!seen[e.dst])
            push_state(e.dst);
         std::cout << e.src << "->" << e.dst << '\n';
       }
   }

   int main()
   {
     // Create a small example automaton
     spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
     if (pf.format_errors(std::cerr))
       return 1;
     dfs(spot::translator().run(pf.f));
   }
 #+END_SRC

 #+RESULTS:
 : 2->0
 : 0->0
 : 2->1
 : 1->1
 : 2->2

This version is functionally equivalent to the recursive one, but its
implementation requires more knowledge of the graph data structure
than both the recursive and the =almost_dfs()= version.

* The on-the-fly =twa= interface
   :PROPERTIES:
   :CUSTOM_ID: on-the-fly-interface
   :END:

The =twa= class defines an abstract interface suitable for on-the-fly
exploration of an automaton.  Subclasses of =twa= need not represent
the entire automaton in memory; if they prefer, they can compute it as
it is explored.

Naturally =twa_graph=, even if it stores the automaton graph
explicitly, is a subclasse of =twa=, so it also implements the
on-the-fly interface but without computing anything.

** How this interface works

The following class diagram has two classes in common with the
previous one: =twa= and =twa_graph=, but this time the focus is on the
abstract interface defined in =twa=, not in the explicit interface
defined in =twa_graph=.

#+BEGIN_SRC plantuml :file uml-otf-classes.png
package spot {
  package internal {
    class succ_iterable {
      +internal::succ_iterator begin()
      +internal::succ_iterator end()
    }

    class succ_iterator {
      succ_iterator(twa_succ_iterator*)
      bool operator==(succ_iterator)
      bool operator!=(succ_iterator)
      const twa_succ_iterator* operator*()
      void operator++()
    }
  }

  class acc_cond
  hide acc_cond members

  together {
  abstract class twa {
    #twa_succ_iterator* iter_cache_
    #bdd_dict_ptr dict_
    __
    #twa(const bdd_dict_ptr&)
    .. exploration ..
    +{abstract}state* get_init_state()
    +{abstract}twa_succ_iterator* succ_iter(state*)
    +internal::succ_iterable succ(const state*)
    +void release_iter(twa_succ_iterator*)
    .. state manipulation ..
    +{abstract} std::string format_state(const state*)
    +state* project_state(const state*, const const_twa_ptr&)
    .. other methods not shown..
  }
  abstract class twa_succ_iterator {
    .. iteration ..
    {abstract}+bool first()
    {abstract}+bool next()
    {abstract}+bool done()
    .. inspection ..
    {abstract}+const state* dst()
    {abstract}+bdd cond()
    {abstract}+acc_cond::mark_t acc()
  }

  abstract class state {
    +{abstract}int compare(const state*)
    +{abstract}size_t hash()
    +{abstract}state* clone()
    +void destroy()
    #~state()
  }
  }



  class twa_graph

  twa <|-- twa_graph
  twa *--> acc_cond

  class twa_graph_state

  hide twa_graph members
  hide twa_graph_state members
  hide twa_graph_succ_iterator members

  twa_succ_iterator <|-- twa_graph_succ_iterator
  succ_iterable o--> twa
  twa_succ_iterator <--* succ_iterable
  twa_succ_iterator <--o succ_iterator
  twa ...> succ_iterable : "create"
  succ_iterable ...> succ_iterator : "create"
  state <|-- twa_graph_state
}
#+END_SRC

#+RESULTS:
[[file:uml-otf-classes.png]]


To explore a =twa=, one would first call =twa::get_init_state()=,
which returns a pointer to a =state=.  Then, calling
=twa::succ_iter()= on this =state= will return a =twa_succ_iterator=
that allows iterating over all successors.

Different subclasses of =twa= will instantiate different subclasses of
=state= and =twa_succ_iterator= .  In the case of =twa_graph=, the
subclasses used are =twa_graph_succ_iterator= and =twa_graph_state=,
but you can ignore that until you have to write your own =twa=
subclass.

The interface puts few requirement on memory management: we want to be
able to write automata that can forget about their states (and
recompute them), so there is no guarantee that reaching the same state
twice will return the same pointer twice.  Even calling
=get_init_state()= twice could return two different pointers.  The
only way to decide whether two =state*= =s1= and =s2= represent the
same state is to check that ~s1->compare(s2) == 0~.

As far as memory management goes, there are roughly two types of =twa=
subclasses: those that always create new =state= instances, and those
that reuse =state= instances (either because they have a cache, or
because, as in the case of =twa_graph=, they know the entire graph).

From the user's perspective, =state= should never be passed to
=delete= (their protected destructor will prevent that).  Instead, we
should call =state::destroy()=.  Doing so allows each subclass to
override the default behavior of =destroy()= (which is to call
=delete=).  States can be cloned using the =state::clone()= method, in
which case each copy has to be destroyed.

=twa_succ_iterator= instances are allocated and should be deleted once
done, but to save some work, they can also be returned to the
automaton with =twa::release_iter=.  By default, this method stores the
last iterator received to recycle it in the next call to =succ_iter()=,
saving a =delete= and =new= pair.

To summarize, here is a crude loop over the successors of the initial
state:

#+BEGIN_SRC C++ :results verbatim :exports both
  #include <iostream>
  #include <spot/twa/twa.hh>
  #include <spot/tl/parse.hh>
  #include <spot/twaalgos/translate.hh>

  void example(spot::const_twa_ptr aut)
  {
    const spot::state* s = aut->get_init_state();
    spot::twa_succ_iterator* i = aut->succ_iter(s);
    for (i->first(); !i->done(); i->next())
      {
         const spot::state* dst = i->dst();
         std::cout << aut->format_state(s) << "->"
                   << aut->format_state(dst) << '\n';
         dst->destroy();
      }
    aut->release_iter(i); // "delete i;" is OK too, but inferior
    s->destroy();
  }

  int main()
  {
    // Create a small example automaton
    spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
    if (pf.format_errors(std::cerr))
      return 1;
    example(spot::translator().run(pf.f));
  }
#+END_SRC

#+RESULTS:
: 2->0
: 2->1
: 2->2

Notice that a =twa_succ_iterator= allows iterating over outgoing
edges, but only offers access to =dst()=, =acc()=, and =cond()= for
this edge.  The source state is not available from the iterator.  This
is usually not a problem: since the iterator was created from this
state, it is /usually/ known.

Let us improve the above loop.  In the previous example, each of
=first()=, =done()=, =next()= is a virtual method call.  So if there
are $n$ successors, there will be $1$ call to =first()=, $n$ calls to
=next()=, and $n+1$ calls to =done()=, so a total of $2n+2$ virtual
method calls.

However =first()= and =next()= also return a Boolean stating whether
the loop could continue.  This allows rewriting the above code as
follows:

#+BEGIN_SRC C++
  void example(spot::const_twa_ptr aut)
  {
    const spot::state* s = aut->get_init_state();
    spot::twa_succ_iterator* i = aut->succ_iter(s);
    if (i->first())
      do
        {
           const spot::state* dst = i->dst();
           std::cout << aut->format_state(s) << "->"
                     << aut->format_state(dst) << '\n';
           dst->destroy();
        }
      while(i->next());
    aut->release_iter(i);
    s->destroy();
  }
#+END_SRC

Now we have only $1$ call to =first()= and $n$ calls to =next()=,
so we halved to number of virtual calls.

Using C++11's ranged =for= loop, this example can be reduced to the
following equivalent code:

#+BEGIN_SRC C++ :results verbatim :exports both
  #include <iostream>
  #include <spot/twa/twa.hh>
  #include <spot/tl/parse.hh>
  #include <spot/twaalgos/translate.hh>

  void example(spot::const_twa_ptr aut)
  {
    const spot::state* s = aut->get_init_state();
    for (auto i: aut->succ(s))
      {
         const spot::state* dst = i->dst();
         std::cout << aut->format_state(s) << "->"
                   << aut->format_state(dst) << '\n';
         dst->destroy();
      }
    s->destroy();
  }

  int main()
  {
    // Create a small example automaton
    spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
    if (pf.format_errors(std::cerr))
      return 1;
    example(spot::translator().run(pf.f));
  }
#+END_SRC

#+RESULTS:
: 2->0
: 2->1
: 2->2

This works in a similar way as =out(s)= in the explicit interface.
Calling =aut->succ(s)= creates a fake container
(=internal::succ_iterable=) with =begin()= and =end()= methods that
return STL-like iterators (=internal::succ_iterator=).  Incrementing
the =internal::succ_iterator= will actually increment the
=twa_succ_iterator= they hold.  Upon completion of the loop, the
temporary =internal::succ_iterable= is destroyed and its destructor
passes the iterator back to =aut->release_iter()= for recycling.

** Recursive DFS (v1)

We can now write a recursive DFS easily.  The only pain is to keep
track of the states to =destroy()= them only after we do not need them
anymore.  This tracking can be done using the data structure we use to
remember what states we have already seen.

#+BEGIN_SRC C++ :results verbatim :exports both
  #include <iostream>
  #include <unordered_set>
  #include <spot/twa/twa.hh>
  #include <spot/tl/parse.hh>
  #include <spot/twa/twaproduct.hh>
  #include <spot/twaalgos/translate.hh>

  typedef std::unordered_set<const spot::state*,
                             spot::state_ptr_hash,
                             spot::state_ptr_equal> seen_t;

  void dfs_rec(spot::const_twa_ptr aut, const spot::state* s, seen_t& seen)
  {
    if (seen.insert(s).second == false)
      {
        s->destroy();
        return;
      }
    for (auto i: aut->succ(s))
      {
         const spot::state* dst = i->dst();
         std::cout << aut->format_state(s) << "->"
                   << aut->format_state(dst) << '\n';
         dfs_rec(aut, dst, seen);
         // Do not destroy dst, as it is either destroyed by dfs_rec()
         // or stored in seen.
      }
  }

  void dfs(spot::const_twa_ptr aut)
  {
    seen_t seen;
    dfs_rec(aut, aut->get_init_state(), seen);

    // Do not forget to destroy all states in seen.
    for (auto s: seen)
      s->destroy();
  }

  int main()
  {
    // Create a small example automaton
    spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
    if (pf.format_errors(std::cerr))
      return 1;
    dfs(spot::translator().run(pf.f));
  }
#+END_SRC

#+RESULTS:
: 2->0
: 0->0
: 2->1
: 1->1
: 2->2

** Recursive DFS (v2)

Using a hash map to keep a unique pointer to each state is quite
common.  The class =spot::state_unicity_table= can be used for this
purpose.  =spot::state_unicity_table::operator()= inputs a =state*=,
and returns either the same state, or the first equal state seen
previously (in that case the passed state is destroyed).  The
=spot::state_unicity_table::is_new()= behaves similarly, but returns
=nullptr= for states that already exist.

With this class, the recursive code can be simplified down to this:

#+BEGIN_SRC C++ :results verbatim :exports both
  #include <iostream>
  #include <spot/twa/twa.hh>
  #include <spot/tl/parse.hh>
  #include <spot/twa/twaproduct.hh>
  #include <spot/twaalgos/translate.hh>

  void dfs_rec(spot::const_twa_ptr aut, const spot::state* s,
               spot::state_unicity_table& seen)
  {
    if (seen.is_new(s))
      for (auto i: aut->succ(s))
        {
          const spot::state* dst = i->dst();
          std::cout << aut->format_state(s) << "->"
                    << aut->format_state(dst) << '\n';
          dfs_rec(aut, dst, seen);
        }
  }

  void dfs(spot::const_twa_ptr aut)
  {
    spot::state_unicity_table seen;
    dfs_rec(aut, aut->get_init_state(), seen);
  }

  int main()
  {
    // Create a small example automaton
    spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
    if (pf.format_errors(std::cerr))
      return 1;
    dfs(spot::translator().run(pf.f));
  }
#+END_SRC

#+RESULTS:
: 2->0
: 0->0
: 2->1
: 1->1
: 2->2

Note how this completely hides all the calls to =state::destroy()=.
They are performed in =state_unicity_table::is_new()= and in
=state_unicity_table::~state_unicity_table()=.

** Iterative DFS

For a non-recursive version, let us use a stack of
=twa_succ_iterator=.  However these iterators do not know their
source, so we better store that in the stack as well if we want to
print it.

#+BEGIN_SRC C++ :results verbatim :exports both
  #include <iostream>
  #include <stack>
  #include <spot/twa/twa.hh>
  #include <spot/tl/parse.hh>
  #include <spot/twa/twaproduct.hh>
  #include <spot/twaalgos/translate.hh>

  void dfs(spot::const_twa_ptr aut)
  {
    spot::state_unicity_table seen;
    std::stack<std::pair<const spot::state*,
                         spot::twa_succ_iterator*>> todo;

    // push receives a newly-allocated state and immediately store it in
    // seen.  Therefore any state on todo is already in seen and does
    // not need to be destroyed.
    auto push = [&](const spot::state* s)
      {
         if (seen.is_new(s))
           {
             spot::twa_succ_iterator* it = aut->succ_iter(s);
             if (it->first())
               todo.emplace(s, it);
             else                 // No successor for s
               aut->release_iter(it);
           }
      };
    push(aut->get_init_state());
    while (!todo.empty())
      {
         const spot::state* src = todo.top().first;
         spot::twa_succ_iterator* srcit = todo.top().second;
         const spot::state* dst = srcit->dst();
         std::cout << aut->format_state(src) << "->"
                   << aut->format_state(dst) << '\n';
         // Advance the iterator, and maybe release it.
         if (!srcit->next())
           {
              aut->release_iter(srcit);
              todo.pop();
           }
         push(dst);
      }
  }

  int main()
  {
    // Create a small example automaton
    spot::parsed_formula pf = spot::parse_infix_psl("FGa | FGb");
    if (pf.format_errors(std::cerr))
      return 1;
    dfs(spot::translator().run(pf.f));
  }
#+END_SRC

#+RESULTS:
: 2->0
: 0->0
: 2->1
: 1->1
: 2->2