org: cleanup tut40
* doc/org/tut40.org: Add more explanations and some cleanup. * python/spot/__init__.py (set_state_players, get_state_winners, get_state_players, set_state_player, get_state_winner, get_state_player, get_strategy): Add these methods to the twa_graph class for convenience. * NEWS, doc/org/tut.org: Mention tut40.org.
This commit is contained in:
Alexandre Duret-Lutz
parent
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commit
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4 changed files with 283 additions and 330 deletions
7
NEWS
7
NEWS
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@ -284,8 +284,11 @@ New in spot 2.9.8.dev (not yet released)
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Python:
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- Bindings for functions related to games and aig-circuits.
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See https://spot.lrde.epita.fr/ipynb/games.html for examples.
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- Bindings for functions related to games.
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See https://spot.lrde.epita.fr/ipynb/games.html and
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https://spot.lrde.epita.fr/tut40.html for examples.
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- Bindings for functions related to syntethis and aig-circuits.
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- spot::twa::prop_set was previously abstent from the Python
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bindings, making it impossible to call make_twa_graph() for copying
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@ -37,6 +37,7 @@ three interfaces supported by Spot: shell commands, Python, or C++.
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- [[file:tut22.org][Creating an automaton by adding states and transitions]]
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- [[file:tut23.org][Creating an alternating automaton by adding states and transitions]]
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- [[file:tut24.org][Iterating over alternating automata]]
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- [[file:tut40.org][Creating a solving a safety game to decide direct simulation]]
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- [[file:tut52.org][Creating an explicit Kripke structure]]
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- [[file:tut90.org][Using the =bdd_dict= to associate atomic proposition to BDD
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variables, or allocate anonymous BDD variables (advanced)]]
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@ -1,25 +1,31 @@
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# -*- coding: utf-8 -*-
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#+TITLE: Using games to compute simulation
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#+DESCRIPTION: Code example for iterating over ω-automata in Spot
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#+TITLE: Using games to check a simulation
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#+DESCRIPTION: Code example for using games in Spot
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#+INCLUDE: setup.org
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#+HTML_LINK_UP: tut.html
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#+PROPERTY: header-args:sh :results verbatim :exports both
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#+PROPERTY: header-args:python :results output :exports both
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#+PROPERTY: header-args:C+++ :results verbatim :exports both
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This example demonstrates how to find the simulations between states using
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games in C++ and python. This algorithm is describe in Fair Simulation
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Relations, Parity Games, and State Space Reduction for Büchi Automata by R.
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Schuller Borbely, Kousha Etessami and Thomas Wilke.
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This example demonstrates how to use Spot's game interface to compute
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a simulation-relation between the states of an automaton. This
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algorithm is inspired from [[https://homepages.inf.ed.ac.uk/kousha/siam_j2005.pdf][Fair Simulation Relations, Parity Games,
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and State Space Reduction for Büchi Automata (Kousha Etessami and
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Thomas Wilke, and Rebecca A. Schuller)]].
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The simulation based reduction (spot.simuation() and spot.reduce_direct_sim())
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does not use this method as is it slower.
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The code below is intended for demonstration of how to construct and
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use games. Spot contains some other (and faster) implementation to
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reduce an automaton using simulation-based reductions (see
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=spot.simulation()= and =spot.reduce_direct_sim()=).
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When all the moves are in the game, we check if the spoiler has a winning
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strategy from a pair of state. If yes, there is simulation otherwise there are
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no.
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Let us start with a definition of simulation for transition-based
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generalized-Büchi automata: A state $s'$ simulates $s$ iff for any
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transition $(s,c,a,d)$ leaving $s$, there exists a transition
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$(s',c',a',d')$ leaving $s'$ with a condition $c'$ that covers $c$,
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some colors $a'\supseteq a$ that covers the colors of $a$ other
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transition, and reaching a destination state $d'$ that simulates $d$.
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We will compute the simulation between 4 and 0 in the following automaton.
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In the following automaton, for instance, XXX
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#+NAME: tut40in
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#+BEGIN_SRC hoa
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@ -27,23 +33,21 @@ HOA: v1
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States: 6
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Start: 0
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AP: 2 "a" "b"
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acc-name: co-Buchi
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Acceptance: 1 Fin(0)
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properties: trans-labels explicit-labels state-acc very-weak
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Acceptance: 1 Inf(0)
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--BODY--
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State: 0
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[1] 1
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[1] 2
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State: 1 {0}
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State: 1
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[0&1] 1
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State: 2
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[0] 3
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State: 3
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[1] 3
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[1] 3 {0}
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State: 4
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[1] 5
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State: 5
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[0] 5
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[0] 5 {0}
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--END--
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#+END_SRC
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@ -52,39 +56,9 @@ State: 5
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cat >tut40.hoa <<EOF
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<<tut40in>>
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EOF
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autfilt --dot tut40.hoa
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autfilt --dot='.#' tut40.hoa
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#+END_SRC
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#+RESULTS: tut40dot
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#+begin_example
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digraph "" {
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rankdir=LR
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label=<<br/>[co-Büchi]>
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labelloc="t"
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node [shape="circle"]
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node [style="filled", fillcolor="#ffffa0"]
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fontname="Lato"
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node [fontname="Lato"]
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edge [fontname="Lato"]
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node[fontsize=12] fontsize=12 stylesheet="spot.css" edge[arrowhead=vee, arrowsize=.7, fontsize=12]
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I [label="", style=invis, width=0]
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I -> 0
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0 [label=<0>]
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0 -> 1 [label=<b>]
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0 -> 2 [label=<b>]
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1 [label=<1>, peripheries=2]
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1 -> 1 [label=<a & b>]
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2 [label=<2>]
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2 -> 3 [label=<a>]
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3 [label=<3>]
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3 -> 3 [label=<b>]
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4 [label=<4>]
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4 -> 5 [label=<b>]
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5 [label=<5>]
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5 -> 5 [label=<a>]
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}
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#+end_example
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#+BEGIN_SRC dot :file tut40in.svg :var txt=tut40dot :exports results
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$txt
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#+END_SRC
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@ -92,122 +66,175 @@ digraph "" {
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#+RESULTS:
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[[file:tut40in.svg]]
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Whether two states are in simulation can be decided as a game between
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two players. If the game is in state $(q,q')$, spoiler (player 0)
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first selects a transition from state $q$, and duplicator (player 1)
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then has to chose a compatible transition from state $q'$. Duplicator
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of course wins if it always manages to select compatibles transitions,
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otherwise spoiler wins.
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The game arena can be encoded by associating each state to a pair of
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integers. States owned by player 0 (rounded rectangles) are pairs
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$(q,q')$ denoting the position of each player. States owned by player
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1 (diamonds) are pairs $(e,q')$ where $e$ is the number of the
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edges that player 0 just took (those numbers appears as =#1=, =#2=,
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etc. in the previous picture).
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Here is how the game arena look like starting from $(q,q')=(4,0)$:
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#+NAME: game40
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#+BEGIN_SRC python :exports results :noweb yes
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<<build_game>>
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aut = spot.automaton("""
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<<tut40in>>
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""")
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g = direct_sim_game(aut, 4, 0)
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#+END_SRC
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#+NAME: game40unsolved
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#+BEGIN_SRC python :exports none :noweb yes
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<<game40>>
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print(g.to_str('dot', '.g'))
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#+END_SRC
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#+BEGIN_SRC dot :file tut40gameunsolved.svg :var txt=game40unsolved :exports results
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$txt
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#+END_SRC
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#+RESULTS:
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[[file:tut40gameunsolved.svg]]
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In this game, player 1, wins if it has a strategy to force the game to
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satisfy the acceptance condition. Here the acceptance condition is
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just true, so any infinite play will satisfy it.
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Clearly, it is enough for player 1 to always go to $(5,1)$ when
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possible. If Spot is used to solve this game, the result can be
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presented as follows, where greens states represents states from which
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player 1 has a winning strategy, and red states are states from which
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player 0 has a winning strategy. The highlighted arrows show those
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strategies.
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#+NAME: game40solved
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#+BEGIN_SRC python :exports none :noweb yes
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<<game40>>
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spot.solve_safety_game(g)
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spot.highlight_strategy(g)
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print(g.to_str('dot', '.g'))
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#+END_SRC
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#+BEGIN_SRC dot :file tut40gamesolved.svg :var txt=game40solved :exports results
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$txt
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#+END_SRC
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#+RESULTS:
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[[file:tut40gamesolved.svg]]
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Since player 1 is winning from state $(4,0)$, we know that state 4
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simulates state 0. Also since player 1 would also win from state
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$(5,1)$, we can tell that state 5 simulates state 1. We also learn
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that state 5 does not simulates states 2 and 3. We could build other
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games, or add more state to this game, to learn about other pairs of
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states.
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* Python
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We now look at how to create such a game in Python.
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First a need to create a game. The game is build by alternating the possible
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moves of the two players: the spoiler (even) and the duplicator (odd). The
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spoiler starts from its state and chose a transition and move to the
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destination. Then, the duplicator search for a transition from its state with a
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compatible condition and acceptance.
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Essentially, a game in Spot is just an automaton equiped with a
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special property to indicate the owner of each states. So it can be
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created using the usual interface:
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#+NAME: build_game
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#+BEGIN_SRC python :exports code
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import spot
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from spot import buddy
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def direct_sim_game(aut, s1, s2):
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if s1 >= aut.num_states() or s2 >= aut.num_states():
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raise ValueError('invalid state number')
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import spot
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from spot import buddy
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game = spot.make_twa_graph(aut.get_dict())
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def direct_sim_game(aut, s1, s2):
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if s1 >= aut.num_states() or s2 >= aut.num_states():
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raise ValueError('invalid state number')
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assert aut.acc().is_generalized_buchi()
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# The names are a pair of int. If even owns the state, the first integer
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# is the number of the edge that spoiler has taken during this turn (so
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# we have the current position of the spoiler (the destination of the
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# edge), the condition and acceptance that duplicator must find). If
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# it is an odd state, the first integer is the state where the spoiler
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# is. In either case, the second is always the duplicator's state.
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names = []
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owners = []
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game = spot.make_twa_graph()
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# The names of the states are pairs of integers
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# (q,q') for states owned by player 0
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# (e,q') for states owned by player 1
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# These arrays are indiced by state numbers.
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names = []
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owners = []
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# The reverse assotiation (x,y) -> state number
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# must be kept for each player, since (x,y) can mean two different thing.
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s_orig_states = {}
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d_orig_states = {}
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# a list of player 0 states to process
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todo = []
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# Keep the game's state from its name. We need two maps as two states can
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# have the same name (but not the same owner). For example (0, 1) can
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# describe a spoiler state (the edge 0, the state 1) and a duplicator
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# state (the states 0 and 1).
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s_orig_states = {}
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d_orig_states = {}
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todo = []
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# Create the state (i, j) for a player if it does not exist yet and
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# returns its state's number in the game.
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def get_and_create_game_state(player, i, j):
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orig_state = s_orig_states if player else d_orig_states
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if not (i, j) in orig_state:
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s = game.new_state()
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names.append((i, j))
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owners.append(player)
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orig_state[(i, j)] = s
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# If it is the duplicator turn and the state does not exist
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# yet, we need to process it.
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if not player:
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todo.append(s)
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return s
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else:
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# Create the state (i, j) for a player if it does not exist yet and
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# returns the state's number in the game.
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def get_game_state(player, i, j):
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orig_state = s_orig_states if player else d_orig_states
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if (i, j) in orig_state:
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return orig_state[(i, j)]
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s = game.new_state()
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names.append((i, j))
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owners.append(player)
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orig_state[(i, j)] = s
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# If it is a new state for Player 0 (spoiler)
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# we need to process it.
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if not player:
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todo.append(s)
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return s
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get_and_create_game_state(False, s1, s2);
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(all_inf, _) = aut.get_acceptance().used_inf_fin_sets()
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game.set_init_state(get_game_state(False, s1, s2))
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while todo:
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cur = todo.pop()
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# todo contains only player 0's states, named with pairs
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# of states.
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(s_src, d_src) = names[cur]
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while todo:
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cur = todo.pop()
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(s_src, d_src) = names[cur]
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# Player 0 is allowed to pick edge from s_src:
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for s_edge in aut.out(s_src):
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edge_idx = aut.edge_number(s_edge)
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st2 = get_game_state(True, edge_idx, d_src)
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# label the edge with true, because it's an automaton,
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# but we do not use this label for the game.
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game.new_edge(cur, st2, buddy.bddtrue)
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# Spoiler starts by picking an edge from s_src. If it does not exist
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# yet, we add a new edge in the game from (s_src,d_src) to
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# (edge_idx,d_src).
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for s_edge in aut.out(s_src):
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edge_idx = aut.edge_number(s_edge)
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cur2 = get_and_create_game_state(True, edge_idx, d_src)
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# Player 1 then try to find an edge with the
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# a compatible same condition and colors, from d_src.
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for d_edge in aut.out(d_src):
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if (buddy.bdd_implies(d_edge.cond, s_edge.cond) \
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and d_edge.acc.subset(s_edge.acc)):
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st3 = get_game_state(False, s_edge.dst, d_edge.dst)
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game.new_edge(st2, st3, buddy.bddtrue)
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game.new_edge(cur, cur2, buddy.bddtrue)
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# Duplicator try to find an edge with the same condition from d_src.
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# If it does not exist yet, it adds a new edge from
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# (s_edge.dst,d_src) to (s_edge.dst, d_edge.dst).
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for d_edge in aut.out(d_src):
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if buddy.bdd_implies(d_edge.cond, s_edge.cond) \
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and (s_edge.acc ^ all_inf).subset(d_edge.acc ^ all_inf):
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cur3 = get_and_create_game_state(False, s_edge.dst, d_edge.dst)
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game.new_edge(cur2, cur3, buddy.bddtrue)
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spot.set_state_players(game, owners)
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names = [s for s in map(str, names)]
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game.set_state_names(names)
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return game
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# Name each state with a string, just so we can read the pairs
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# when the automaton is displayed.
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game.set_state_names(list(map(str, names)))
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# This only line is actually what turns an automaton into a game!
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game.set_state_players(owners)
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return game
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#+END_SRC
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Then we solve it. This game is a safety game, the odd player tries to block
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the even player into a dead end. Even win if it has a winning strategy, aka
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there is no dead end in the game.
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To solve a safety game =g= that has been created by the above method,
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it is enough to just call =solve_safety_game(g)=. This function
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returns the player winning in the initial state. However, as a
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side-effect it defines additional automaton properties that indicate
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the winner of each state, and the associated strategy.
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#+BEGIN_SRC :export code
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spot.solve_safety_game(g)
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#+END_SRC
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Next, we get the winners for each state. We only check the even's state as it
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is the only ones that describe states pairs (the odd's ones describe the
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edge taken and a state). If odd wins from this state, then the first state
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simulates the second.
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Therefore to list all simulation pairs we learned from a game starting
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in state $(i,j)$, we could proceed as follow:
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#+NAME: computesim_tut40
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#+BEGIN_SRC python :exports code
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def compute_simulation(aut, i, j):
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def list_simulations(aut, i, j):
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g = direct_sim_game(aut, i, j)
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spot.solve_safety_game(g)
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winners = spot.get_state_winners(g)
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owners = spot.get_state_players(g)
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winners = g.get_state_winners()
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owners = g.get_state_players()
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names = g.get_state_names()
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simulations = []
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@ -218,220 +245,139 @@ def compute_simulation(aut, i, j):
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return simulations
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#+END_SRC
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#+RESULTS: computesim_tut40
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On our running example, that gives:
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Compute all simulations found when checking if 4 simulates 0.
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#+BEGIN_SRC python :exports code
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print(compute_simulation(aut, 4, 0))
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#+BEGIN_SRC python :results verbatim :exports both :noweb strip-export
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<<game40>>
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<<computesim_tut40>>
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print(list_simulations(aut, 4, 0))
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#+END_SRC
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#+RESULTS:
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#+BEGIN_SRC python :exports results :results verbatim :noweb yes
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import spot
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from spot import buddy
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aut = spot.automaton('''
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<<tut40in>>
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||||
''')
|
||||
|
||||
<<build_game>>
|
||||
|
||||
<<computesim_tut40>>
|
||||
|
||||
print(compute_simulation(aut, 4, 0))
|
||||
|
||||
#+END_SRC
|
||||
|
||||
#+RESULTS:
|
||||
: [(4, 0), (5, 1)]
|
||||
|
||||
* C++
|
||||
|
||||
Here is some almost equivalent code in C++.
|
||||
|
||||
The equivalent code in C++:
|
||||
Here instead of naming states with strings, we use the "product-states"
|
||||
property, which is usually used to display pair of integers that come from a
|
||||
product of automata.
|
||||
|
||||
#+NAME: cppCompute
|
||||
#+BEGIN_SRC C++ :exports code
|
||||
#include <spot/twaalgos/game.hh>
|
||||
#include <spot/twa/twagraph.hh>
|
||||
#include <spot/twaalgos/game.hh>
|
||||
#include <spot/twa/twagraph.hh>
|
||||
|
||||
spot::twa_graph_ptr direct_sim_game(spot::const_twa_graph_ptr aut,
|
||||
unsigned s1, unsigned s2)
|
||||
{
|
||||
if (s1 >= aut->num_states() || s2 >= aut->num_states())
|
||||
throw std::runtime_error("direct_sim_game(): invalid state number");
|
||||
|
||||
auto game = spot::make_twa_graph(aut->get_dict());
|
||||
game->copy_ap_of(aut);
|
||||
|
||||
auto names = new std::vector<std::pair<unsigned, unsigned>>();
|
||||
game->set_named_prop("product-states", names);
|
||||
names->reserve(aut->num_states());
|
||||
|
||||
auto owners = new std::vector<bool>();
|
||||
game->set_named_prop("state-player", owners);
|
||||
owners->reserve(aut->num_states());
|
||||
|
||||
std::map<std::pair<unsigned, unsigned>, unsigned> s_orig_states;
|
||||
std::map<std::pair<unsigned, unsigned>, unsigned> d_orig_states;
|
||||
std::vector<unsigned> todo;
|
||||
todo.reserve(aut->num_states());
|
||||
auto& g = game->get_graph();
|
||||
|
||||
auto all_inf = aut->get_acceptance().used_inf_fin_sets().first;
|
||||
|
||||
auto new_state = [&](bool player, unsigned s1, unsigned s2)
|
||||
spot::twa_graph_ptr direct_sim_game(spot::const_twa_graph_ptr aut,
|
||||
unsigned s1, unsigned s2)
|
||||
{
|
||||
unsigned s;
|
||||
auto& m = player ? s_orig_states : d_orig_states;
|
||||
if (s1 >= aut->num_states() || s2 >= aut->num_states())
|
||||
throw std::runtime_error("direct_sim_game(): invalid state number");
|
||||
|
||||
auto it = std::find_if(m.begin(), m.end(), [s1, s2](auto elm)
|
||||
{ return s1 == elm.first.first && s2 == elm.first.second; });
|
||||
auto game = spot::make_twa_graph(spot::make_bdd_dict());
|
||||
|
||||
if (it == m.end())
|
||||
{
|
||||
s = game->new_state();
|
||||
names->emplace_back(s1, s2);
|
||||
owners->push_back(player);
|
||||
m.insert({{s1, s2}, s});
|
||||
auto names = new std::vector<std::pair<unsigned, unsigned>>();
|
||||
game->set_named_prop("product-states", names);
|
||||
|
||||
if (!player)
|
||||
todo.push_back(s);
|
||||
}
|
||||
else
|
||||
{
|
||||
s = it->second;
|
||||
}
|
||||
auto owners = new std::vector<bool>();
|
||||
game->set_named_prop("state-player", owners);
|
||||
|
||||
return s;
|
||||
};
|
||||
std::map<std::pair<unsigned, unsigned>, unsigned> s_orig_states;
|
||||
std::map<std::pair<unsigned, unsigned>, unsigned> d_orig_states;
|
||||
std::vector<unsigned> todo;
|
||||
|
||||
new_state(false, s1, s2);
|
||||
|
||||
while (!todo.empty())
|
||||
auto new_state = [&](bool player, unsigned s1, unsigned s2)
|
||||
{
|
||||
unsigned cur = todo.back();
|
||||
unsigned s_src = (*names)[cur].first;
|
||||
unsigned d_src = (*names)[cur].second;
|
||||
todo.pop_back();
|
||||
auto& m = player ? s_orig_states : d_orig_states;
|
||||
if (auto it = m.find({s1, s2}); it != m.end())
|
||||
return it->second;
|
||||
unsigned s = game->new_state();
|
||||
names->emplace_back(s1, s2);
|
||||
owners->push_back(player);
|
||||
m.insert({{s1, s2}, s});
|
||||
if (!player)
|
||||
todo.push_back(s);
|
||||
return s;
|
||||
};
|
||||
|
||||
for (const auto& s_edge : aut->out(s_src))
|
||||
{
|
||||
unsigned edge_idx = g.index_of_edge(s_edge);
|
||||
unsigned cur2 = new_state(true, edge_idx, d_src);
|
||||
|
||||
game->new_edge(cur, cur2, bddtrue);
|
||||
|
||||
for (const auto& d_edge : aut->out(d_src))
|
||||
{
|
||||
if (bdd_implies(d_edge.cond, s_edge.cond)
|
||||
&& (s_edge.acc ^ all_inf).subset(d_edge.acc ^ all_inf))
|
||||
{
|
||||
unsigned cur3 = new_state(false, s_edge.dst, d_edge.dst);
|
||||
game->new_edge(cur2, cur3, bddtrue);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return game;
|
||||
}
|
||||
|
||||
bool compute_simulation(spot::const_twa_graph_ptr aut,
|
||||
unsigned i, unsigned j,
|
||||
std::vector<bool>& can_sim)
|
||||
{
|
||||
auto g = direct_sim_game(aut, i, j);
|
||||
bool i_simulates_j = spot::solve_safety_game(g);
|
||||
|
||||
std::vector<bool> winners = spot::get_state_winners(g);
|
||||
std::vector<bool> owners = spot::get_state_players(g);
|
||||
|
||||
typedef std::vector<std::pair<unsigned, unsigned>> names_t;
|
||||
auto names = *g->get_named_prop<names_t>("product-states");
|
||||
|
||||
unsigned n = g->num_states();
|
||||
unsigned n_aut = aut->num_states();
|
||||
|
||||
for (unsigned i = 0; i < n; ++i)
|
||||
if (winners[i] && !owners[i])
|
||||
game->set_init_state(new_state(false, s1, s2));
|
||||
while (!todo.empty())
|
||||
{
|
||||
auto [u, v] = names[i];
|
||||
can_sim[u * n_aut + v] = true;
|
||||
unsigned cur = todo.back();
|
||||
todo.pop_back();
|
||||
auto [s_src, d_src] = (*names)[cur];
|
||||
|
||||
for (const auto& s_edge : aut->out(s_src))
|
||||
{
|
||||
unsigned edge_idx = aut->edge_number(s_edge);
|
||||
unsigned st2 = new_state(true, edge_idx, d_src);
|
||||
game->new_edge(cur, st2, bddtrue);
|
||||
for (const auto& d_edge : aut->out(d_src))
|
||||
if (bdd_implies(d_edge.cond, s_edge.cond)
|
||||
&& d_edge.acc.subset(s_edge.acc))
|
||||
{
|
||||
unsigned st3 = new_state(false, s_edge.dst, d_edge.dst);
|
||||
game->new_edge(st2, st3, bddtrue);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return i_simulates_j;
|
||||
}
|
||||
return game;
|
||||
}
|
||||
|
||||
std::vector<std::pair<int,int>>
|
||||
list_simulation(spot::const_twa_graph_ptr aut,
|
||||
unsigned i, unsigned j)
|
||||
{
|
||||
auto g = direct_sim_game(aut, i, j);
|
||||
spot::solve_safety_game(g);
|
||||
|
||||
const std::vector<bool>& winners = spot::get_state_winners(g);
|
||||
const std::vector<bool>& owners = spot::get_state_players(g);
|
||||
|
||||
typedef std::vector<std::pair<unsigned, unsigned>> names_t;
|
||||
auto names = *g->get_named_prop<names_t>("product-states");
|
||||
|
||||
std::vector<std::pair<int,int>> res;
|
||||
|
||||
unsigned n = g->num_states();
|
||||
for (unsigned i = 0; i < n; ++i)
|
||||
if (winners[i] && !owners[i])
|
||||
res.emplace_back(names[i]);
|
||||
return res;
|
||||
}
|
||||
#+END_SRC
|
||||
|
||||
#+RESULTS: cppCompute
|
||||
|
||||
Computes and print the simulations found when checking if 4 simulates 0.
|
||||
|
||||
#+NAME: cppCallCompute
|
||||
#+BEGIN_SRC text :exports none
|
||||
unsigned n = aut->num_states();
|
||||
std::vector<bool> can_sim(n*n);
|
||||
|
||||
compute_simulation(aut, 4, 0, can_sim);
|
||||
|
||||
for (unsigned i = 0; i < n; ++i)
|
||||
for (unsigned j = 0; j < n; ++j)
|
||||
if (can_sim[i * n + j])
|
||||
std::cout << i << " simulates " << j << '\n';
|
||||
#+END_SRC
|
||||
|
||||
#+BEGIN_SRC C++ :exports code :noweb yes
|
||||
<<cppCallCompute>>
|
||||
#+END_SRC
|
||||
|
||||
#+RESULTS: cppCallCompute
|
||||
Now to execute the above code on our example automaton, we just
|
||||
need to read the automaton from a file.
|
||||
|
||||
#+NAME: finalcpp
|
||||
#+BEGIN_SRC C++ :results verbatim :exports results :noweb yes
|
||||
#include <iostream>
|
||||
#include <spot/twa/twagraph.hh>
|
||||
|
||||
<<cppCompute>>
|
||||
|
||||
int main()
|
||||
{
|
||||
spot::bdd_dict_ptr dict = spot::make_bdd_dict();
|
||||
spot::twa_graph_ptr aut = make_twa_graph(dict);
|
||||
|
||||
bdd p1 = bdd_ithvar(aut->register_ap("p1"));
|
||||
bdd p2 = bdd_ithvar(aut->register_ap("p2"));
|
||||
|
||||
aut->set_acceptance(1, "t");
|
||||
aut->prop_state_acc(true);
|
||||
|
||||
aut->new_states(6);
|
||||
aut->set_init_state(0U);
|
||||
|
||||
aut->new_edge(0, 1, p2);
|
||||
aut->new_edge(1, 1, p1 & p2, {0});
|
||||
aut->new_edge(0, 2, p2);
|
||||
aut->new_edge(2, 3, p1);
|
||||
aut->new_edge(3, 3, p2);
|
||||
aut->new_edge(4, 5, p2);
|
||||
aut->new_edge(5, 5, p1);
|
||||
|
||||
unsigned n = aut->num_states();
|
||||
std::vector<bool> can_sim(n*n);
|
||||
|
||||
compute_simulation(aut, 4, 0, can_sim);
|
||||
|
||||
for (unsigned i = 0; i < n; ++i)
|
||||
for (unsigned j = 0; j < n; ++j)
|
||||
if (can_sim[i * n + j])
|
||||
std::cout << i << " simulates " << j << '\n';
|
||||
|
||||
return 0;
|
||||
}
|
||||
#+BEGIN_SRC C++ :results verbatim :exports both :noweb strip-export
|
||||
#include <iostream>
|
||||
#include <spot/twa/twagraph.hh>
|
||||
#include <spot/parseaut/public.hh>
|
||||
<<cppCompute>>
|
||||
int main()
|
||||
{
|
||||
spot::parsed_aut_ptr pa = parse_aut("tut40.hoa", spot::make_bdd_dict());
|
||||
if (pa->format_errors(std::cerr))
|
||||
return 1;
|
||||
if (pa->aborted)
|
||||
{
|
||||
std::cerr << "--ABORT-- read\n";
|
||||
return 1;
|
||||
}
|
||||
for (auto [i,j]: list_simulation(pa->aut, 4, 0))
|
||||
std::cout << i << " simulates " << j << '\n';
|
||||
return 0;
|
||||
}
|
||||
#+END_SRC
|
||||
|
||||
#+RESULTS: finalcpp
|
||||
: 4 simulates 0
|
||||
: 5 simulates 1
|
||||
|
||||
|
||||
#+BEGIN_SRC sh :results silent :exports results
|
||||
rm -f tut40.hoa
|
||||
#+END_SRC
|
||||
|
|
|
|||
|
|
@ -1031,7 +1031,10 @@ def _add_twa_graph(meth, name=None):
|
|||
|
||||
for meth in ('scc_filter', 'scc_filter_states',
|
||||
'is_deterministic', 'is_unambiguous',
|
||||
'contains'):
|
||||
'contains', 'get_strategy',
|
||||
'set_state_players', 'get_state_players',
|
||||
'set_state_player', 'get_state_player',
|
||||
'get_state_winners', 'get_state_winner'):
|
||||
_add_twa_graph(meth)
|
||||
_add_twa_graph('are_equivalent', 'equivalent_to')
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue