Repository: DES-Lab/AALpy Branch: master Commit: 1edacf63c4b3 Files: 225 Total size: 29.8 MB Directory structure: gitextract_arfvw_hu/ ├── .gitattributes ├── .github/ │ └── workflows/ │ ├── codeql-analysis.yml │ └── python-app.yml ├── .gitignore ├── Benchmarking/ │ ├── Benchmark_ErrorStop.py │ ├── CompleteStochasticBenchmarking.py │ ├── StochasticAlgComparison.py │ ├── StochasticBenchmarkingWPrism.py │ ├── StopWithErorrRate.py │ ├── all_results.pickle │ ├── benchmark.py │ ├── benchmark_alphabet_increase.py │ ├── benchmark_size_increase.py │ ├── cex_processing_benchmark.py │ ├── compare_lstar_and_kv.py │ ├── error_benchmark_statistics.py │ ├── evaluate_l_star_configurations.py │ ├── fm_benchmark.py │ ├── fm_plots.py │ ├── generate_plots.py │ ├── json_lbt.py │ ├── papni_sequences.pickle │ ├── papni_vs_rpni_benchmarking.py │ ├── passive_mdp_vs_smm.py │ ├── prism_eval_props/ │ │ ├── bluetooth.props │ │ ├── emqtt_two_client.props │ │ ├── first_eval.props │ │ ├── second_eval.props │ │ ├── shared_coin_eval.props │ │ ├── slot_machine_eval.props │ │ └── tcp_eval.props │ ├── rpni_papni_memory_footrpint.py │ ├── stochastic_benchmarking/ │ │ ├── Benchmark_ErrorStop.py │ │ ├── CompleteStochasticBenchmarking.py │ │ ├── StochasticBenchmarkingWPrism.py │ │ ├── passive_mdp_vs_smm.py │ │ ├── plot_error_steps.py │ │ ├── stochastic_benchmark_random_automata.py │ │ ├── strategy_comp.py │ │ └── unamb_error_plot.py │ ├── unamb_error_plot.py │ └── vpa_benchmarking/ │ └── benchmark_vpa.py ├── DotModels/ │ ├── Angluin_Mealy.dot │ ├── Angluin_Moore.dot │ ├── Bluetooth/ │ │ ├── CC2640R2-no-feature-req.dot │ │ ├── CC2640R2-no-feature-req_stochastic.dot │ │ ├── CC2640R2-no-pairing-req.dot │ │ ├── CC2650.dot │ │ ├── CYBLE-416045-02.dot │ │ ├── CYBLE-416045-02_Crash_No_Response_stochastic.dot │ │ ├── CYW43455.dot │ │ ├── CYW43455_stochastic.dot │ │ ├── bluetooth_model.dot │ │ ├── bluetooth_reduced.dot │ │ ├── cc2652r1.dot │ │ ├── convert_to_stochastic.py │ │ └── nRF52832.dot │ ├── MDPs/ │ │ ├── bluetooth.dot │ │ ├── faulty_car_alarm.dot │ │ ├── first_grid.dot │ │ ├── mqtt.dot │ │ ├── second_grid.dot │ │ ├── shared_coin.dot │ │ ├── slot_machine.dot │ │ └── tcp.dot │ ├── MQTT/ │ │ ├── ActiveMQ__two_client_will_retain.dot │ │ ├── VerneMQ__two_client_will_retain.dot │ │ ├── emqtt__two_client_will_retain.dot │ │ ├── hbmqtt__two_client_will_retain.dot │ │ └── mosquitto__two_client_will_retain.dot │ ├── SimpleABC/ │ │ ├── simple_abc_dfa.dot │ │ ├── simple_abc_mealy.dot │ │ └── simple_abc_moore.dot │ ├── TCP/ │ │ ├── TCP_Linux_Client.dot │ │ ├── tcp_server_bsd_trans.dot │ │ ├── tcp_server_ubuntu_trans.dot │ │ └── tcp_server_windows_trans.dot │ ├── TLS/ │ │ ├── JSSE_1.8.0_25_server_regular.dot │ │ ├── NSS_3.17.4_server_regular.dot │ │ ├── OpenSSL_1.0.2_server_regular.dot │ │ ├── RSA_BSAFE_C_4.0.4_server_regular.dot │ │ └── miTLS_0.1.3_server_regular.dot │ ├── arithmetics.dot │ ├── car_alarm.dot │ ├── coffee_mealy.dot │ ├── coffee_moore.dot │ ├── five_clients_mqtt_abstracted_onfsm.dot │ ├── mooreModel.dot │ ├── onfsm_0.dot │ ├── onfsm_1.dot │ ├── onfsm_2.dot │ ├── onfsm_3.dot │ ├── onfsm_4.dot │ ├── onfsm_5.dot │ └── tomitaGrammars/ │ ├── tomita_1.dot │ ├── tomita_2.dot │ ├── tomita_3.dot │ ├── tomita_4.dot │ ├── tomita_5.dot │ ├── tomita_6.dot │ └── tomita_7.dot ├── Examples.py ├── LICENCE.txt ├── README.md ├── aalpy/ │ ├── SULs/ │ │ ├── AutomataSUL.py │ │ ├── PyMethodSUL.py │ │ ├── RegexSUL.py │ │ ├── TomitaSUL.py │ │ └── __init__.py │ ├── __init__.py │ ├── automata/ │ │ ├── Dfa.py │ │ ├── MarkovChain.py │ │ ├── Mdp.py │ │ ├── MealyMachine.py │ │ ├── MooreMachine.py │ │ ├── NonDeterministicMooreMachine.py │ │ ├── Onfsm.py │ │ ├── Sevpa.py │ │ ├── StochasticMealyMachine.py │ │ ├── Vpa.py │ │ └── __init__.py │ ├── base/ │ │ ├── Automaton.py │ │ ├── CacheTree.py │ │ ├── Oracle.py │ │ ├── SUL.py │ │ └── __init__.py │ ├── learning_algs/ │ │ ├── __init__.py │ │ ├── adaptive/ │ │ │ ├── AdaptiveLSharp.py │ │ │ ├── AdaptiveObservationTree.py │ │ │ ├── StateMatching.py │ │ │ └── __init__.py │ │ ├── deterministic/ │ │ │ ├── ADS.py │ │ │ ├── Apartness.py │ │ │ ├── ClassificationTree.py │ │ │ ├── CounterExampleProcessing.py │ │ │ ├── KV.py │ │ │ ├── LSharp.py │ │ │ ├── LStar.py │ │ │ ├── ObservationTable.py │ │ │ ├── ObservationTree.py │ │ │ └── __init__.py │ │ ├── deterministic_passive/ │ │ │ ├── ClassicRPNI.py │ │ │ ├── GsmRPNI.py │ │ │ ├── PAPNI.py │ │ │ ├── RPNI.py │ │ │ ├── __init__.py │ │ │ ├── active_RPNI.py │ │ │ └── rpni_helper_functions.py │ │ ├── general_passive/ │ │ │ ├── GeneralizedStateMerging.py │ │ │ ├── GsmAlgorithms.py │ │ │ ├── GsmNode.py │ │ │ ├── Instrumentation.py │ │ │ ├── ScoreFunctionsGSM.py │ │ │ └── __init__.py │ │ ├── non_deterministic/ │ │ │ ├── AbstractedOnfsmLstar.py │ │ │ ├── AbstractedOnfsmObservationTable.py │ │ │ ├── NonDeterministicSULWrapper.py │ │ │ ├── OnfsmLstar.py │ │ │ ├── OnfsmObservationTable.py │ │ │ ├── TraceTree.py │ │ │ └── __init__.py │ │ ├── stochastic/ │ │ │ ├── DifferenceChecker.py │ │ │ ├── SamplingBasedObservationTable.py │ │ │ ├── StochasticCexProcessing.py │ │ │ ├── StochasticLStar.py │ │ │ ├── StochasticTeacher.py │ │ │ └── __init__.py │ │ └── stochastic_passive/ │ │ ├── ActiveAleriga.py │ │ ├── Alergia.py │ │ ├── CompatibilityChecker.py │ │ ├── FPTA.py │ │ └── __init__.py │ ├── oracles/ │ │ ├── BreadthFirstExplorationEqOracle.py │ │ ├── CacheBasedEqOracle.py │ │ ├── KWayStateCoverageEqOracle.py │ │ ├── KWayTransitionCoverageEqOracle.py │ │ ├── PacOracle.py │ │ ├── PerfectKnowledgeEqOracle.py │ │ ├── ProvidedSequencesOracleWrapper.py │ │ ├── RandomWalkEqOracle.py │ │ ├── RandomWordEqOracle.py │ │ ├── StatePrefixEqOracle.py │ │ ├── TransitionFocusOracle.py │ │ ├── UserInputEqOracle.py │ │ ├── WMethodEqOracle.py │ │ ├── WpMethodEqOracle.py │ │ └── __init__.py │ ├── paths.py │ └── utils/ │ ├── AutomatonGenerators.py │ ├── BenchmarkSULs.py │ ├── BenchmarkSevpaModels.py │ ├── BenchmarkVpaModels.py │ ├── DataHandler.py │ ├── FileHandler.py │ ├── HelperFunctions.py │ ├── ModelChecking.py │ ├── Sampling.py │ └── __init__.py ├── docs/ │ ├── README.md │ ├── _config.yml │ ├── google306875680a34d740.html │ └── instructions.txt ├── jAlergia/ │ ├── alergia.jar │ └── exampleMdpData.txt ├── notebooks/ │ ├── Abstracted_Non-Det_FSM.ipynb │ ├── AngluinExample.ipynb │ ├── MDP_Example.ipynb │ ├── MDP_and_SMM_Example.ipynb │ ├── ONFSM_Example.ipynb │ ├── RandomMealyExample.ipynb │ ├── RegexExample.ipynb │ └── Stochstic_Examples.ipynb ├── setup.py └── tests/ ├── oracles/ │ ├── test_baseOracle.py │ └── test_kWayTransitionCoverageEqOracle.py ├── test_charSet.py ├── test_deterministic.py ├── test_deterministic_passive.py ├── test_file_operations.py ├── test_non_deterministic.py ├── test_rwpmethod_oracle.py ├── test_stochastic.py ├── test_wmethod_oracle.py ├── test_wpmethod_oracle.py └── tests_imports.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitattributes ================================================ * linguist-vendored *.py linguist-vendored=false ================================================ FILE: .github/workflows/codeql-analysis.yml ================================================ # For most projects, this workflow file will not need changing; you simply need # to commit it to your repository. # # You may wish to alter this file to override the set of languages analyzed, # or to provide custom queries or build logic. # # ******** NOTE ******** # We have attempted to detect the languages in your repository. Please check # the `language` matrix defined below to confirm you have the correct set of # supported CodeQL languages. # name: "CodeQL" on: push: branches: [ master ] pull_request: # The branches below must be a subset of the branches above branches: [ master ] schedule: - cron: '17 10 * * 6' jobs: analyze: name: Analyze runs-on: ubuntu-latest permissions: actions: read contents: read security-events: write strategy: fail-fast: false matrix: language: [ 'python' ] # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ] # Learn more: # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed steps: - name: Checkout repository uses: actions/checkout@v2 # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL uses: github/codeql-action/init@v1 with: languages: ${{ matrix.language }} # If you wish to specify custom queries, you can do so here or in a config file. # By default, queries listed here will override any specified in a config file. # Prefix the list here with "+" to use these queries and those in the config file. # queries: ./path/to/local/query, your-org/your-repo/queries@main # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). # If this step fails, then you should remove it and run the build manually (see below) - name: Autobuild uses: github/codeql-action/autobuild@v1 # ℹ️ Command-line programs to run using the OS shell. # 📚 https://git.io/JvXDl # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines # and modify them (or add more) to build your code if your project # uses a compiled language #- run: | # make bootstrap # make release - name: Perform CodeQL Analysis uses: github/codeql-action/analyze@v1 ================================================ FILE: .github/workflows/python-app.yml ================================================ # This workflow will install Python dependencies, run tests and lint with a single version of Python # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions name: Python application on: push: branches: [ master ] pull_request: branches: [ master ] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python 3.9 uses: actions/setup-python@v2 with: python-version: 3.9 - name: Install dependencies run: | python -m pip install --upgrade pip pip install flake8 pytest if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so main.py LearnedModel.pdf # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ .vscode/settings.json # PyCharm .idea/ ================================================ FILE: Benchmarking/Benchmark_ErrorStop.py ================================================ import random import os import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles.RandomWordEqOracle import UnseenOutputRandomWordEqOracle from aalpy.utils import load_automaton_from_file, get_properties_file, get_correct_prop_values from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion seeds = [1212,4557,19059,468,43,654,235345,6546,76768,4563,543526,777676,5555,776767,87878787,98989,60967553,3866677,1555841,8638] path_to_dir = '../DotModels/MDPs/' files = ['first_grid.dot', 'second_grid.dot'] # 'slot_machine.dot' ,'shared_coin.dot' 'mqtt.dot', 'tcp.dot' prop_folder = 'prism_eval_props/' # TODO Change the path to your PRIMS executable and change the path_to_prism in the stop_based_on_confidence method in ModelChecking.py. prism_executable = "/home/mtappler/Programs/prism-4.4-linux64/bin/prism" aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "prism_eval_props/" n_c = 20 n_resample = 1000 min_rounds = 10 max_rounds = 300 experiment_repetition = 5 uniform_parameters = False strategy = ["normal"] # chi_square cex_sampling = [None] # random:100:0.15 cex_processing = [None] # add a single prefix for strat in strategy: for cex_stat in cex_sampling: for cex_proc in cex_processing: print(strat, cex_stat, cex_proc) benchmark_dir = f'FM_mdp_smm_error_based_stop/benchmark_{strat}_{cex_stat}_{cex_proc}/' if not os.path.exists(benchmark_dir): os.makedirs(benchmark_dir) for seed in range(experiment_repetition): print(seed) random.seed(seeds[seed]) text_file = open(f"{benchmark_dir}/exp_{seed}.csv", "w") for file in files: print(file) exp_name = file.split('.')[0] if uniform_parameters: if exp_name == 'first_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'second_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'shared_coin': n_c, n_resample = n_c, n_resample elif exp_name == 'slot_machine': n_c, n_resample = n_c, n_resample elif exp_name == 'mqtt': n_c, n_resample = n_c, n_resample elif exp_name == 'tcp': n_c, n_resample = n_c, n_resample else: if exp_name == 'first_grid': n_c, n_resample = 20, 1000 elif exp_name == 'second_grid': n_c, n_resample = 20, 2000 elif exp_name == 'shared_coin': n_c, n_resample = 25, 2500 elif exp_name == 'slot_machine': n_c, n_resample = 30, 5000 elif exp_name == 'mqtt': n_c, n_resample = 20, 1000 elif exp_name == 'tcp': n_c, n_resample = 20, 1000 stopping_data = (get_properties_file(exp_name), get_correct_prop_values(exp_name), 0.02) original_mdp = load_automaton_from_file(path_to_dir + file, automaton_type='mdp') input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) eq_oracle = UnseenOutputRandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=15, reset_after_cex=True) learned_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='mdp', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, property_based_stopping=stopping_data) del mdp_sul del eq_oracle random.seed(seeds[seed]) mdp_sul = AutomatonSUL(original_mdp) eq_oracle = UnseenOutputRandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=15, reset_after_cex=True) learned_smm, data_smm = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='smm', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, property_based_stopping=stopping_data) smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) properties_string_header = ",".join([f'{key}_val,{key}_err' for key in mdp_results.keys()]) property_string_mdp = ",".join([f'{str(mdp_results[p])},{str(mdp_err[p])}' for p in mdp_results.keys()]) property_string_smm = ",".join([f'{str(smm_results[p])},{str(smm_err[p])}' for p in smm_results.keys()]) text_file.write('Exp_Name, n_c, n_resample, Final Hypothesis Size, Learning time,' 'Eq. Query Time, Learning Rounds, #MQ Learning, # Steps Learning,' f'# MQ Eq.Queries, # Steps Eq.Queries , {properties_string_header}\n') text_file.write(f'learned_mdp_{exp_name},{n_c},{n_resample}, {data_mdp["automaton_size"]}, ' f'{data_mdp["learning_time"]}, {data_mdp["eq_oracle_time"]}, ' f'{data_mdp["learning_rounds"]}, {data_mdp["queries_learning"]}, {data_mdp["steps_learning"]},' f'{data_mdp["queries_eq_oracle"]}, {data_mdp["steps_eq_oracle"]},' f'{property_string_mdp}\n') text_file.write(f'learned_smm_{exp_name},{n_c},{n_resample}, {data_smm["automaton_size"]}, ' f'{data_smm["learning_time"]}, {data_smm["eq_oracle_time"]}, ' f'{data_smm["learning_rounds"]}, {data_smm["queries_learning"]}, {data_smm["steps_learning"]},' f'{data_smm["queries_eq_oracle"]}, {data_smm["steps_eq_oracle"]},' f'{property_string_smm}\n') text_file.flush() text_file.close() ================================================ FILE: Benchmarking/CompleteStochasticBenchmarking.py ================================================ import random import time import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles.RandomWordEqOracle import RandomWordEqOracle from aalpy.utils import load_automaton_from_file, get_properties_file, get_correct_prop_values from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion seeds = [29334,1554,9430459,92344168,55451679,569315,7776892,3875261,811,51,766603,778438967,9819877,6755560,52903,5257,4635,358,1441,838] path_to_dir = '../DotModels/MDPs/' #files = ['first_grid.dot', 'second_grid.dot', 'slot_machine.dot', 'mqtt.dot', 'tcp.dot'] # 'slot_machine.dot' ,'shared_coin.dot' files = ['second_grid.dot', 'mqtt.dot'] # 'slot_machine.dot' ,'shared_coin.dot' prop_folder = 'prism_eval_props/' # TODO Change the path to your PRIMS executable aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "prism_eval_props/" n_c = 10 n_resample = 1000 min_rounds = 25 max_rounds = 500 experiment_repetition = 10 uniform_parameters = False strategy = ["normal", "chi2"] # chi_square cex_sampling = ['bfs',] # random:100:0.15 cex_processing = [None, 'longest_prefix'] # add a single prefix start = time.time() model_dict = {m.split('.')[0] : load_automaton_from_file(path_to_dir + m, automaton_type='mdp') for m in files} for strat in strategy: for cex_stat in cex_sampling: for cex_proc in cex_processing: print(strat, cex_stat, cex_proc) benchmark_dir = f'FM_mdp_smm/benchmark_22_04_{strat}_{cex_proc}/' for seed in range(experiment_repetition): print(seed) random.seed(seeds[seed]) import os if not os.path.exists(benchmark_dir): os.makedirs(benchmark_dir) text_file = open(f"{benchmark_dir}/exp_{seed}.csv", "w") for file in files: print(file) exp_name = file.split('.')[0] original_mdp = model_dict[exp_name] input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=16, reset_after_cex=True) learned_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='mdp', min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, target_unambiguity=0.99) del mdp_sul del eq_oracle random.seed(seeds[seed]) mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=15, reset_after_cex=True) learned_smm, data_smm = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='smm', min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, target_unambiguity=0.99) smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) properties_string_header = ",".join([f'{key}_val,{key}_err' for key in mdp_results.keys()]) property_string_mdp = ",".join([f'{str(mdp_results[p])},{str(mdp_err[p])}' for p in mdp_results.keys()]) property_string_smm = ",".join([f'{str(smm_results[p])},{str(smm_err[p])}' for p in smm_results.keys()]) text_file.write('Exp_Name,n_c,n_resample,Final Hypothesis Size,Learning time,' 'Eq. Query Time,Learning Rounds,#MQ Learning,# Steps Learning,' f'# MQ Eq.Queries,# Steps Eq.Queries ,{properties_string_header}\n') text_file.write(f'learned_mdp_{exp_name},{n_c},{n_resample},{data_mdp["automaton_size"]},' f'{data_mdp["learning_time"]},{data_mdp["eq_oracle_time"]},' f'{data_mdp["learning_rounds"]},{data_mdp["queries_learning"]},{data_mdp["steps_learning"]},' f'{data_mdp["queries_eq_oracle"]},{data_mdp["steps_eq_oracle"]},' f'{property_string_mdp}\n') text_file.write(f'learned_smm_{exp_name},{n_c},{n_resample},{data_smm["automaton_size"]},' f'{data_smm["learning_time"]},{data_smm["eq_oracle_time"]}, ' f'{data_smm["learning_rounds"]},{data_smm["queries_learning"]},{data_smm["steps_learning"]},' f'{data_smm["queries_eq_oracle"]},{data_smm["steps_eq_oracle"]},' f'{property_string_smm}\n') text_file.flush() text_file.close() print('Exp duration', time.time() - start) ================================================ FILE: Benchmarking/StochasticAlgComparison.py ================================================ import random import time from statistics import mean import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar, run_Alergia from aalpy.oracles.RandomWordEqOracle import RandomWordEqOracle from aalpy.utils import load_automaton_from_file, get_properties_file, get_correct_prop_values from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion path_to_dir = '../DotModels/MDPs/' files = ['first_grid.dot', 'second_grid.dot', 'slot_machine.dot', 'mqtt.dot', 'tcp.dot', 'bluetooth.dot'] # prop_folder = 'prism_eval_props/' aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "prism_eval_props/" model_dict = {m.split('.')[0]: load_automaton_from_file(path_to_dir + m, automaton_type='mdp') for m in files} for file in files: print(file) exp_name = file.split('.')[0] print('--------------------------------------------------') print('Experiment:', exp_name) original_mdp = model_dict[exp_name] input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=500, min_walk_len=5, max_walk_len=16, reset_after_cex=True) learned_classic_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='mdp', min_rounds=10, strategy='classic', n_c=20, n_resample=2000, stopping_range_dict={}, max_rounds=200, return_data=True, target_unambiguity=0.98, print_level=1) del mdp_sul del eq_oracle mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=15, reset_after_cex=True) learned_smm, data_smm = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='smm', min_rounds=10, strategy='normal', max_rounds=200, return_data=True, target_unambiguity=0.98, print_level=1) smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_classic_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) num_alergia_samples = max([data_mdp["queries_learning"] + data_mdp["queries_eq_oracle"], data_smm["queries_learning"] + data_smm["queries_eq_oracle"]]) alergia_samples = [] for _ in range(num_alergia_samples): sample = [mdp_sul.pre()] for _ in range(random.randint(10, 30)): action = random.choice(input_alphabet) output = mdp_sul.step(action) sample.append((action, output)) alergia_samples.append(sample) alergia_model = run_Alergia(alergia_samples, automaton_type='mdp') alergia_results, alergia_error = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), alergia_model) print('Classic MDP learning', mean(mdp_err.values()), mdp_err) print('SMM learning', mean(smm_err.values()), smm_err) print('Alergia learning', mean(alergia_error.values()), alergia_error) print('Classic MDP traces', data_mdp["queries_learning"] + data_mdp["queries_eq_oracle"]) print('SMM learning traces', data_smm["queries_learning"] + data_smm["queries_eq_oracle"]) ================================================ FILE: Benchmarking/StochasticBenchmarkingWPrism.py ================================================ import random import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles.RandomWalkEqOracle import RandomWalkEqOracle from aalpy.utils import load_automaton_from_file, get_correct_prop_values, get_properties_file from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion path_to_dir = '../DotModels/MDPs/' files = ['first_grid.dot', 'second_grid.dot', 'slot_machine.dot', 'mqtt.dot', 'tcp.dot'] # 'shared_coin.dot' aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "prism_eval_props/" n_c = 20 n_resample = 1000 min_rounds = 10 max_rounds = 1000 strategy = "normal" for seed in range(1, 4): random.seed(seed) benchmark_dir = f"benchmark_complete_no_cq/benchmark_data_{seed}" import os if not os.path.exists(benchmark_dir): os.makedirs(benchmark_dir) text_file = open(f"{benchmark_dir}/StochasticExperiments.csv", "w") uniform_parameters = True for file in files: exp_name = file.split('.')[0] if uniform_parameters: if exp_name == 'first_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'second_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'shared_coin': n_c, n_resample = n_c, n_resample elif exp_name == 'slot_machine': n_c, n_resample = n_c, n_resample elif exp_name == 'mqtt': n_c, n_resample = n_c, n_resample elif exp_name == 'tcp': n_c, n_resample = n_c, n_resample else: if exp_name == 'first_grid': n_c, n_resample = 20, 1000 elif exp_name == 'second_grid': n_c, n_resample = 20, 1000 elif exp_name == 'shared_coin': n_c, n_resample = 30, 3000 elif exp_name == 'slot_machine': n_c, n_resample = 40, 5000 elif exp_name == 'mqtt': n_c, n_resample = 20, 1000 elif exp_name == 'tcp': n_c, n_resample = 20, 1000 original_mdp = load_automaton_from_file(path_to_dir + file, automaton_type='mdp') input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWalkEqOracle(input_alphabet, mdp_sul, num_steps=n_resample * (1 / 0.25), reset_after_cex=True, reset_prob=0.25) learned_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='mdp', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strategy, max_rounds=max_rounds, return_data=True, samples_cex_strategy="bfs") mdp_sul.num_steps = 0 mdp_sul.num_queries = 0 learned_smm, data_smm = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='smm', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strategy, max_rounds=max_rounds, return_data=True, samples_cex_strategy="bfs") smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) properties_string_header = ",".join([f'{key}_val,{key}_err' for key in mdp_results.keys()]) property_string_mdp = ",".join([f'{str(mdp_results[p])},{str(mdp_err[p])}' for p in mdp_results.keys()]) property_string_smm = ",".join([f'{str(smm_results[p])},{str(smm_err[p])}' for p in smm_results.keys()]) text_file.write('Exp_Name, n_c, n_resample, Final Hypothesis Size, Learning time,' 'Eq. Query Time, Learning Rounds, #MQ Learning, # Steps Learning,' f'# MQ Eq.Queries, # Steps Eq.Queries , {properties_string_header}\n') text_file.write(f'learned_mdp_{exp_name},{n_c},{n_resample}, {data_mdp["automaton_size"]}, ' f'{data_mdp["learning_time"]}, {data_mdp["eq_oracle_time"]}, ' f'{data_mdp["learning_rounds"]}, {data_mdp["queries_learning"]}, {data_mdp["steps_learning"]},' f'{data_mdp["queries_eq_oracle"]}, {data_mdp["steps_eq_oracle"]},' f'{property_string_mdp}\n') text_file.write(f'learned_smm_{exp_name},{n_c},{n_resample}, {data_smm["automaton_size"]}, ' f'{data_smm["learning_time"]}, {data_smm["eq_oracle_time"]}, ' f'{data_smm["learning_rounds"]}, {data_smm["queries_learning"]}, {data_smm["steps_learning"]},' f'{data_smm["queries_eq_oracle"]}, {data_smm["steps_eq_oracle"]},' f'{property_string_smm}\n') text_file.flush() text_file.close() ================================================ FILE: Benchmarking/StopWithErorrRate.py ================================================ import pickle import random import time from collections import defaultdict from statistics import mean import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar, run_Alergia from aalpy.oracles.RandomWordEqOracle import RandomWordEqOracle from aalpy.utils import load_automaton_from_file, get_properties_file, get_correct_prop_values, model_check_properties from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion path_to_dir = '../DotModels/MDPs/' files = ['slot_machine.dot', 'bluetooth.dot'] # files = ['first_grid.dot', 'second_grid.dot', 'tcp.dot', 'mqtt.dot', 'bluetooth.dot', 'slot_machine.dot'] prop_folder = 'prism_eval_props/' aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "prism_eval_props/" model_dict = {m.split('.')[0]: load_automaton_from_file(path_to_dir + m, automaton_type='mdp') for m in files} model_type = ['smm'] cex_processing = [None, 'longest_prefix', 'rs'] # model_type.reverse() res = defaultdict(list) # for file in files: # for mt in model_type: # for cp in cex_processing: # for _ in range(4): # # exp_name = file.split('.')[0] # # print('--------------------------------------------------') # print('Experiment:', exp_name, cp) # # original_mdp = model_dict[exp_name] # input_alphabet = original_mdp.get_input_alphabet() # # mdp_sul = AutomatonSUL(original_mdp) # # eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=500, min_walk_len=5, # max_walk_len=15, reset_after_cex=True) # # pbs = ((get_properties_file(exp_name), # get_correct_prop_values(exp_name), 0.02 if exp_name != 'bluetooth' else 0.03)) # learned_classic_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type=mt, # min_rounds=10, # #property_based_stopping=pbs, # cex_processing=cp, # samples_cex_strategy=None, # return_data=True, target_unambiguity=0.98, # print_level=1) # # res[exp_name].append((cp, data_mdp['queries_learning'] + data_mdp['queries_eq_oracle'])) # with open('cex_processing_res.pickle', 'wb') as handle: # pickle.dump(res, handle, protocol=pickle.HIGHEST_PROTOCOL) with open('cex_processing_res.pickle', 'rb') as handle: res = pickle.load(handle) for key, val in res.items(): print(key) sorted_by_cp = defaultdict(list) for cp, data in val: sorted_by_cp[cp].append(data) for cp_method, data in sorted_by_cp.items(): print(cp_method) print(mean(data), min(data), max(data)) ================================================ FILE: Benchmarking/benchmark.py ================================================ import os from statistics import mean from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_Lstar from aalpy.oracles import StatePrefixEqOracle from aalpy.utils import load_automaton_from_file dfa_1000_states_20_inputs = '../DotModels/DFA_1000_states_20_inp' dfa_2000_states_10_inputs = '../DotModels/DFA_2000_states_10_inp' moore_1000_states_20_inputs = '../DotModels/Moore_1000_states_20_inp_out' moore_2000_states_10_inputs = '../DotModels/Moore_2000_states_10_inp_out' run_times = [] # change on which folder to perform experiments exp = dfa_2000_states_10_inputs benchmarks = os.listdir(exp) benchmarks = benchmarks[:10] caching_opt = [True, False] closing_options = ['shortest_first', 'longest_first', 'single'] suffix_processing = ['all', 'single'] counter_example_processing = ['rs', 'longest_prefix', None] e_closedness = ['prefix', 'suffix'] for b in benchmarks: automaton = load_automaton_from_file(f'{exp}/{b}', automaton_type='dfa') input_al = automaton.get_input_alphabet() sul_dfa = AutomatonSUL(automaton) state_origin_eq_oracle = StatePrefixEqOracle(input_al, sul_dfa, walks_per_state=5, walk_len=25) learned_dfa, data = run_Lstar(input_al, sul_dfa, state_origin_eq_oracle, automaton_type='dfa', cache_and_non_det_check=False, cex_processing='rs', return_data=True, print_level=0) run_times.append(data['total_time']) print(run_times) print(mean(run_times)) ================================================ FILE: Benchmarking/benchmark_alphabet_increase.py ================================================ from statistics import mean import csv from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_Lstar from aalpy.oracles import RandomWalkEqOracle from aalpy.utils import generate_random_dfa, generate_random_mealy_machine, generate_random_moore_machine num_states = 1000 alph_size = 5 repeat = 10 num_increases = 20 states = ['alph_size', alph_size] times_dfa = ['dfa_pypy_rs'] times_mealy = ['mealy_pypy_rs'] times_moore = ['moore_pypyrs'] cex_processing = 'rs' for i in range(num_increases): print(i) total_time_dfa = [] total_time_mealy = [] total_time_moore = [] for _ in range(repeat): alphabet = list(range(alph_size)) dfa = generate_random_dfa(num_states, alphabet=alphabet, num_accepting_states=num_states // 2) sul = AutomatonSUL(dfa) # eq_oracle = StatePrefixEqOracle(alphabet, sul, walks_per_state=5, walk_len=40) eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=10000, reset_prob=0.09) _, data = run_Lstar(alphabet, sul, eq_oracle, cex_processing=cex_processing, cache_and_non_det_check=False, return_data=True, automaton_type='dfa') total_time_dfa.append(data['learning_time']) del dfa del sul del eq_oracle mealy = generate_random_mealy_machine(num_states, input_alphabet=alphabet, output_alphabet=alphabet) sul_mealy = AutomatonSUL(mealy) # eq_oracle = StatePrefixEqOracle(alphabet, sul_mealy, walks_per_state=5, walk_len=40) eq_oracle = RandomWalkEqOracle(alphabet, sul_mealy, num_steps=10000, reset_prob=0.09) _, data = run_Lstar(alphabet, sul_mealy, eq_oracle, cex_processing=cex_processing, cache_and_non_det_check=False, return_data=True, automaton_type='mealy') total_time_mealy.append(data['learning_time']) del mealy del sul_mealy del eq_oracle moore = generate_random_moore_machine(num_states, input_alphabet=alphabet, output_alphabet=alphabet) moore_sul = AutomatonSUL(moore) # eq_oracle = StatePrefixEqOracle(alphabet, moore_sul, walks_per_state=5, walk_len=40) eq_oracle = RandomWalkEqOracle(alphabet, moore_sul, num_steps=10000, reset_prob=0.09) _, data = run_Lstar(alphabet, moore_sul, eq_oracle, cex_processing=cex_processing, cache_and_non_det_check=False, return_data=True, automaton_type='moore') total_time_moore.append(data['learning_time']) alph_size += 5 states.append(alph_size) # save data and keep averages times_dfa.append(round(mean(total_time_dfa), 4)) times_mealy.append(round(mean(total_time_mealy), 4)) times_moore.append(round(mean(total_time_moore), 4)) with open('increasing_alphabet_experiments.csv', 'w') as f: wr = csv.writer(f, dialect='excel') wr.writerow(states) wr.writerow(times_dfa) wr.writerow(times_mealy) wr.writerow(times_moore) ================================================ FILE: Benchmarking/benchmark_size_increase.py ================================================ from statistics import mean import csv from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_Lstar from aalpy.oracles import RandomWalkEqOracle from aalpy.utils import generate_random_dfa, generate_random_mealy_machine, generate_random_moore_machine num_states = 100 alphabet = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] repeat = 15 num_increases = 50 states = ['num_states'] times_dfa = ['dfa_pypy_rs'] times_mealy = ['mealy_pypy_rs'] times_moore = ['moore_pypyrs'] total_dfa = ['dfa_total'] total_mealy = ['mealy_total'] total__moore = ['moore_total'] cex_processing = 'rs' for i in range(num_increases): print(i) learning_time_dfa = [] learning_time_mealy = [] learning_time_moore = [] total_time_dfa = [] total_time_mealy = [] total_time_moore = [] states.append(num_states) for _ in range(repeat): dfa = generate_random_dfa(num_states, alphabet=alphabet, num_accepting_states=num_states // 2) sul = AutomatonSUL(dfa) # eq_oracle = StatePrefixEqOracle(alphabet, sul, walks_per_state=5, walk_len=40) eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=9000, reset_prob=0.09) _, data = run_Lstar(alphabet, sul, eq_oracle, cex_processing=cex_processing, cache_and_non_det_check=False, return_data=True, automaton_type='dfa') learning_time_dfa.append(data['learning_time']) total_time_dfa.append(data['total_time']) del sul del eq_oracle del dfa mealy = generate_random_mealy_machine(num_states, input_alphabet=alphabet, output_alphabet=alphabet) sul_mealy = AutomatonSUL(mealy) # eq_oracle = StatePrefixEqOracle(alphabet, sul_mealy, walks_per_state=5, walk_len=40) eq_oracle = RandomWalkEqOracle(alphabet, sul_mealy, num_steps=9000, reset_prob=0.09) _, data = run_Lstar(alphabet, sul_mealy, eq_oracle, cex_processing=cex_processing, cache_and_non_det_check=False, return_data=True, automaton_type='mealy') learning_time_mealy.append(data['learning_time']) total_time_mealy.append(data['total_time']) del mealy del sul_mealy del eq_oracle moore = generate_random_moore_machine(num_states, input_alphabet=alphabet, output_alphabet=alphabet) moore_sul = AutomatonSUL(moore) # eq_oracle = StatePrefixEqOracle(alphabet, moore_sul, walks_per_state=5, walk_len=40) eq_oracle = RandomWalkEqOracle(alphabet, moore_sul, num_steps=9000, reset_prob=0.09) _, data = run_Lstar(alphabet, moore_sul, eq_oracle, cex_processing=cex_processing, cache_and_non_det_check=False, return_data=True, automaton_type='moore') learning_time_moore.append(data['learning_time']) total_time_moore.append(data['total_time']) # save data and keep averages times_dfa.append(round(mean(learning_time_dfa), 4)) times_mealy.append(round(mean(learning_time_mealy), 4)) times_moore.append(round(mean(learning_time_moore), 4)) total_dfa.append(round(mean(total_time_dfa), 4)) total_mealy.append(round(mean(total_time_mealy), 4)) total__moore.append(round(mean(total_time_moore), 4)) num_states += 100 with open('increasing_size_experiments.csv', 'w') as f: wr = csv.writer(f, dialect='excel') wr.writerow(states) wr.writerow(times_dfa) wr.writerow(times_mealy) wr.writerow(times_moore) wr.writerow(total_dfa) wr.writerow(total_mealy) wr.writerow(times_moore) ================================================ FILE: Benchmarking/cex_processing_benchmark.py ================================================ from collections import defaultdict from statistics import mean, stdev from aalpy.learning_algs import run_KV, run_Lstar from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWalkEqOracle from aalpy.utils import generate_random_deterministic_automata, bisimilar counterexample_processing_strategy = ['rs', 'linear_fwd', 'linear_bwd', 'exponential_fwd', 'exponential_bwd'] algorithms = ['l_star', 'kv'] model_sizes = [500] model_type = ['mealy', 'moore'] # alphabet_sizes = [(3,2), (3, 5), (3, 10), (5, 2), (5, 5), (5, 20)] alphabet_sizes = [(5, 3)] num_repetitions = 5 for learning_alg in algorithms: results = defaultdict(list) for model in model_type: for model_size in model_sizes: for input_size, output_size in alphabet_sizes: for cex_processing in counterexample_processing_strategy: for _ in range(num_repetitions): random_model = generate_random_deterministic_automata(model, num_states=model_size, input_alphabet_size=input_size, output_alphabet_size=output_size) sul = AutomatonSUL(random_model) input_al = random_model.get_input_alphabet() eq_oracle = RandomWalkEqOracle(input_al, sul, num_steps=20000, reset_prob=0.09) if learning_alg == 'kv': learned_model, info = run_KV(input_al, sul, eq_oracle, automaton_type=model, cex_processing=cex_processing, return_data=True, print_level=0) else: learned_model, info = run_Lstar(input_al, sul, eq_oracle, automaton_type=model, cex_processing=cex_processing, return_data=True, print_level=0) results[cex_processing].append(info['steps_learning']) if not bisimilar(learned_model, random_model): print(learning_alg, cex_processing, 'mismatch') print(learning_alg) for k, v in results.items(): print(k, mean(v), stdev(v), min(v), max(v)) ================================================ FILE: Benchmarking/compare_lstar_and_kv.py ================================================ from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_Lstar, run_KV from aalpy.oracles import RandomWordEqOracle from aalpy.utils import generate_random_deterministic_automata automata_type = ['dfa', 'mealy', 'moore'] automata_size = [10, 100, 500, 1000,] input_sizes = [2, 3] output_sizes = [2, 3, 5, 10] test_models = [] for model_type in automata_type: for size in automata_size: for i in input_sizes: for o in output_sizes: random_model = generate_random_deterministic_automata(model_type, size, i, o, num_accepting_states=size//8) input_al = random_model.get_input_alphabet() print('------------------------------------------') if model_type != 'dfa': print(f'Type: {model_type}, size: {size}, # inputs: {i}, # outputs: {o}') else: print(f'Type: {model_type}, size: {size}, # inputs: {i}, # accepting: {size//8}') # Lstar sul = AutomatonSUL(random_model) eq_oracle = RandomWordEqOracle(input_al, sul, num_walks=5000, min_walk_len=10, max_walk_len=40) l_star_model, l_star_info = run_Lstar(input_al, sul, eq_oracle, model_type, print_level=0, return_data=True) l_star_steps, l_star_queries = l_star_info['steps_learning'], l_star_info['queries_learning'] # KV sul = AutomatonSUL(random_model) eq_oracle = RandomWordEqOracle(input_al, sul, num_walks=5000, min_walk_len=10, max_walk_len=40) kv_model, kv_info = run_KV(input_al, sul, eq_oracle, model_type, print_level=0, return_data=True) kv_steps, kv_queries = kv_info['steps_learning'], kv_info['queries_learning'] if l_star_model.size != random_model.size: print('L* did not learn correctly.') if kv_model.size != random_model.size: print('KV did not learn correctly.') print(f'L* steps: {l_star_steps}') print(f'KV steps: {kv_steps}') if kv_steps < l_star_steps: print(f'KV is {round((l_star_steps / kv_steps) * 100 - 100, 2)}% more step efficient') else: print(f'L* is {round((kv_steps / l_star_steps) * 100 - 100, 2)}% more step efficient') ================================================ FILE: Benchmarking/error_benchmark_statistics.py ================================================ import csv import os from collections import defaultdict from statistics import mean directory = 'FM_mdp_smm_error_based_stop/benchmark_no_cq_bfs_longest_prefix/' benchmarks = os.listdir(directory)[:-1] benchmarks.remove('exp_14.csv') values = dict() for file in benchmarks: with open(directory + file, 'r') as f: reader = csv.reader(f) data = list(reader) for i in range(0, len(data), 3): header = data[i] mdp, smm = data[i + 1], data[i + 2] for formalism in [mdp, smm]: for i, val in enumerate(formalism[1:]): if formalism[0] not in values.keys(): values[formalism[0]] = defaultdict(list) values[formalism[0]][header[i + 1]].append(round(float(val), 2)) min_values_dict = dict() max_values_dict = dict() avr_values_dict = dict() for exp in values: exp_name = exp[12:] formalism = 'smm' if 'smm' in exp else 'mdp' name = f'{exp_name}_{formalism}' min_values_dict[name] = dict() max_values_dict[name] = dict() avr_values_dict[name] = dict() for category, value in values[exp].items(): min_values_dict[name][category] = min(value) max_values_dict[name][category] = max(value) avr_values_dict[name][category] = round(mean(value), 2) interesting_fields = [' Learning time', ' Learning Rounds', ' #MQ Learning', ' # Steps Learning'] print('ALL ERRORS ARE LESS THAN 2%. THAT WAS USED AS STOPPING CRITERION') experiments = list(min_values_dict.keys()) for e_index in range(0, len(experiments), 2): for i in interesting_fields: print(f'{experiments[e_index]} vs {experiments[e_index + 1]} = > {i}') min_eff = round(min_values_dict[experiments[e_index]][i] / min_values_dict[experiments[e_index + 1]][i]*100 , 2) print(f'Min : {min_values_dict[experiments[e_index]][i]} vs {min_values_dict[experiments[e_index + 1]][i]} | SMM efficiency : {min_eff}') max_eff = round(max_values_dict[experiments[e_index]][i] / max_values_dict[experiments[e_index + 1]][i]*100 , 2) print(f'Max : {max_values_dict[experiments[e_index]][i]} vs {max_values_dict[experiments[e_index + 1]][i]} | SMM efficiency : {max_eff}') avr_eff = round(avr_values_dict[experiments[e_index]][i] / avr_values_dict[experiments[e_index + 1]][i]*100 , 2) print(f'Avr : {avr_values_dict[experiments[e_index]][i]} vs {avr_values_dict[experiments[e_index + 1]][i]}| SMM efficiency : {avr_eff}') print('-------------------------------------------------') with open('error_benchmark.csv', 'w',newline='') as file: writer = csv.writer(file) experiments = list(min_values_dict.keys()) for e_index in range(0, len(experiments), 2): writer.writerow([experiments[e_index][:-4], 'mdp', 'smm', 'smm compared to mdp efficiency %']) for i in interesting_fields: print(f'{experiments[e_index]} vs {experiments[e_index + 1]} = > {i}') min_eff = round(min_values_dict[experiments[e_index]][i] / min_values_dict[experiments[e_index + 1]][i]*100 , 2) writer.writerow([i + '_min', min_values_dict[experiments[e_index]][i], min_values_dict[experiments[e_index + 1]][i], min_eff]) max_eff = round(max_values_dict[experiments[e_index]][i] / max_values_dict[experiments[e_index + 1]][i]*100 , 2) writer.writerow([i + '_max', max_values_dict[experiments[e_index]][i], max_values_dict[experiments[e_index + 1]][i], max_eff]) avr_eff = round(avr_values_dict[experiments[e_index]][i] / avr_values_dict[experiments[e_index + 1]][i]*100 , 2) writer.writerow([i + '_avr', avr_values_dict[experiments[e_index]][i], avr_values_dict[experiments[e_index + 1]][i], avr_eff]) writer.writerow([]) print('-------------------------------------------------') ================================================ FILE: Benchmarking/evaluate_l_star_configurations.py ================================================ import pickle from collections import defaultdict from random import seed from statistics import mean from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_Lstar from aalpy.oracles import StatePrefixEqOracle, RandomWMethodEqOracle, RandomWalkEqOracle, RandomWordEqOracle from aalpy.utils import generate_random_deterministic_automata #closing_strategies = ['shortest_first'] closing_strategies = ['shortest_first', 'longest_first', 'single', 'single_longest'] obs_table_cell_prefixes = [True, False] suffix_closed = [True, False] cex_processing = [None, 'longest_prefix', 'rs'] automata_size = [400 ] input_sizes = [2,] output_sizes = [3, ] num_repeats = 10 test_models = [] for size in automata_size: for i in input_sizes: for o in output_sizes: random_model = generate_random_deterministic_automata('dfa', size, i, o, num_accepting_states=30) test_models.append(random_model) tc = 0 num_exp = len(test_models) * len(closing_strategies) * len(suffix_closed) * num_repeats * len(cex_processing) * len(obs_table_cell_prefixes) stats = defaultdict(list) for test_model in test_models: input_al = test_model.get_input_alphabet() for closedness_type in suffix_closed: for closing_strategy in closing_strategies: for cex in cex_processing: for prefix_in_cell in obs_table_cell_prefixes: for _ in range(num_repeats): tc += 1 print(round(tc / num_exp * 100, 2)) # seed(tc) sul = AutomatonSUL(test_model) eq_oracle = RandomWordEqOracle(input_al, sul, num_walks=5000, min_walk_len=10, max_walk_len=40) model, info = run_Lstar(input_al, sul, eq_oracle, 'dfa', closing_strategy=closing_strategy, cex_processing=cex, e_set_suffix_closed=closedness_type, all_prefixes_in_obs_table=prefix_in_cell, print_level=0, return_data=True) config_name = f'suffix_closed:{closedness_type},closing_strategy:{closing_strategy},' \ f'cex:{cex},prefixes_in_cell_{prefix_in_cell}' stats[config_name].append( (info['queries_learning'], info['steps_learning'], model.size == test_model.size)) with open('stats.pickle', 'wb') as handle: pickle.dump(stats, handle, protocol=pickle.HIGHEST_PROTOCOL) # with open('stats.pickle', 'rb') as handle: # stats = pickle.load(handle) statistics_sorted = [] for k, v in stats.items(): mean_queries, mean_steps, num_correct = mean([x[0] for x in v]), mean([x[1] for x in v]), sum([x[2] for x in v]) statistics_sorted.append((k, mean_queries, mean_steps, num_correct)) statistics_sorted.sort(key=lambda x: x[2]) for k, q, s, c in statistics_sorted: print(k, int(q), int(s), c) # suffix_closed:True,closing_strategy:longest_first,cex:rs,prefixes_in_cell_True 6702 122491 10 # suffix_closed:False,closing_strategy:single,cex:rs,prefixes_in_cell_True 8339 150049 10 # suffix_closed:False,closing_strategy:longest_first,cex:rs,prefixes_in_cell_True 8087 154159 10 # suffix_closed:True,closing_strategy:single,cex:rs,prefixes_in_cell_True 8711 159552 10 # suffix_closed:False,closing_strategy:shortest_first,cex:longest_prefix,prefixes_in_cell_True 9032 162634 10 # suffix_closed:True,closing_strategy:shortest_first,cex:rs,prefixes_in_cell_True 9024 163924 10 # suffix_closed:False,closing_strategy:shortest_first,cex:rs,prefixes_in_cell_True 8685 164049 10 # suffix_closed:False,closing_strategy:longest_first,cex:longest_prefix,prefixes_in_cell_True 9011 167036 10 # suffix_closed:False,closing_strategy:single,cex:longest_prefix,prefixes_in_cell_True 9099 176007 10 # suffix_closed:True,closing_strategy:single,cex:longest_prefix,prefixes_in_cell_True 9291 178341 10 # suffix_closed:True,closing_strategy:shortest_first,cex:longest_prefix,prefixes_in_cell_True 9317 179006 10 # suffix_closed:False,closing_strategy:shortest_first,cex:None,prefixes_in_cell_True 10491 193517 10 # suffix_closed:True,closing_strategy:single,cex:None,prefixes_in_cell_True 11139 213554 10 # suffix_closed:True,closing_strategy:longest_first,cex:longest_prefix,prefixes_in_cell_True 10865 214022 10 # suffix_closed:True,closing_strategy:shortest_first,cex:None,prefixes_in_cell_True 12039 221108 10 # suffix_closed:False,closing_strategy:longest_first,cex:None,prefixes_in_cell_True 11107 222398 10 # suffix_closed:False,closing_strategy:single_longest,cex:rs,prefixes_in_cell_True 8998 227650 10 # suffix_closed:True,closing_strategy:longest_first,cex:None,prefixes_in_cell_True 12042 230262 10 # suffix_closed:False,closing_strategy:single,cex:None,prefixes_in_cell_True 12176 239711 10 # suffix_closed:True,closing_strategy:shortest_first,cex:rs,prefixes_in_cell_False 15891 319699 10 # suffix_closed:True,closing_strategy:single_longest,cex:None,prefixes_in_cell_True 10758 321832 10 # suffix_closed:False,closing_strategy:single_longest,cex:None,prefixes_in_cell_True 11331 330290 10 # suffix_closed:True,closing_strategy:single,cex:rs,prefixes_in_cell_False 16014 331618 10 # suffix_closed:False,closing_strategy:shortest_first,cex:rs,prefixes_in_cell_False 15315 335046 10 # suffix_closed:False,closing_strategy:single_longest,cex:rs,prefixes_in_cell_False 15289 339521 10 # suffix_closed:True,closing_strategy:longest_first,cex:rs,prefixes_in_cell_False 16933 343434 10 # suffix_closed:False,closing_strategy:single,cex:rs,prefixes_in_cell_False 16561 356579 10 # suffix_closed:False,closing_strategy:longest_first,cex:rs,prefixes_in_cell_False 17112 367300 10 # suffix_closed:False,closing_strategy:longest_first,cex:None,prefixes_in_cell_False 21305 421926 10 # suffix_closed:True,closing_strategy:single,cex:None,prefixes_in_cell_False 21228 439700 10 # suffix_closed:False,closing_strategy:single,cex:longest_prefix,prefixes_in_cell_False 21119 446616 10 # suffix_closed:True,closing_strategy:longest_first,cex:longest_prefix,prefixes_in_cell_False 20241 449658 10 # suffix_closed:False,closing_strategy:single,cex:None,prefixes_in_cell_False 22933 454032 10 # suffix_closed:False,closing_strategy:shortest_first,cex:longest_prefix,prefixes_in_cell_False 20601 456783 10 # suffix_closed:True,closing_strategy:longest_first,cex:None,prefixes_in_cell_False 21736 457516 10 # suffix_closed:False,closing_strategy:shortest_first,cex:None,prefixes_in_cell_False 22398 469775 10 # suffix_closed:True,closing_strategy:shortest_first,cex:longest_prefix,prefixes_in_cell_False 22365 477132 10 # suffix_closed:True,closing_strategy:single,cex:longest_prefix,prefixes_in_cell_False 21557 505135 10 # suffix_closed:True,closing_strategy:shortest_first,cex:None,prefixes_in_cell_False 24000 518014 10 # suffix_closed:False,closing_strategy:longest_first,cex:longest_prefix,prefixes_in_cell_False 23837 548645 10 # suffix_closed:False,closing_strategy:single_longest,cex:None,prefixes_in_cell_False 24994 583869 10 # suffix_closed:True,closing_strategy:single_longest,cex:None,prefixes_in_cell_False 24839 588329 10 # suffix_closed:False,closing_strategy:single_longest,cex:longest_prefix,prefixes_in_cell_True 10322 620607 10 # suffix_closed:True,closing_strategy:single_longest,cex:rs,prefixes_in_cell_True 9930 651615 10 # suffix_closed:True,closing_strategy:single_longest,cex:rs,prefixes_in_cell_False 16761 729912 10 # suffix_closed:True,closing_strategy:single_longest,cex:longest_prefix,prefixes_in_cell_True 11419 779192 10 # suffix_closed:True,closing_strategy:single_longest,cex:longest_prefix,prefixes_in_cell_False 19734 919089 10 # suffix_closed:False,closing_strategy:single_longest,cex:longest_prefix,prefixes_in_cell_False 19842 939866 10 ================================================ FILE: Benchmarking/fm_benchmark.py ================================================ import csv import os from collections import defaultdict from statistics import mean # directory = 'FM_mdp_smm/benchmark_no_cq_bfs_longest_prefix/' directory = 'FM_mdp_smm/benchmark_no_cq_merged_longest_prefix/' #directory = 'FM_mdp_smm/benchmark_new_chi2/' # directory = 'FM_mdp_smm/benchmark_no_cq_None_longest_prefix/' # directory = 'FM_mdp_smm/benchmark_chi_square_None_longest_prefix/' benchmarks = os.listdir(directory) values = dict() for file in benchmarks: with open(directory + file, 'r') as f: reader = csv.reader(f) data = list(reader) for i in range(0, len(data), 3): header = data[i] mdp,smm = data[i+1], data[i + 2] for formalism in [mdp, smm]: for i, val in enumerate(formalism[1:]): if formalism[0] not in values.keys(): values[formalism[0]] = defaultdict(list) values[formalism[0]][header[i+1]].append(round(float(val), 2)) min_values_dict = dict() max_values_dict = dict() avr_values_dict = dict() for exp in values: exp_name = exp[12:] formalism = 'smm' if 'smm' in exp else 'mdp' name = f'{exp_name}_{formalism}' min_values_dict[name] = dict() max_values_dict[name] = dict() avr_values_dict[name] = dict() for category, value in values[exp].items(): min_values_dict[name][category] = min(value) max_values_dict[name][category] = max(value) avr_values_dict[name][category] = round(mean(value), 2) import matplotlib.pyplot as plt import matplotlib matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) # TODO REMOVE WHITESPACES for new benchmarking results interesting_fields = ['Learning time', 'Learning Rounds', '#MQ Learning', '# Steps Learning', 'prob1_val','prob1_err','prob2_val','prob2_err','prob3_val','prob3_err','prob4_val','prob4_err','prob5_val','prob5_err'] experiments = list(min_values_dict.keys()) for e_index in range(0, len(experiments), 2): for i in interesting_fields: if i not in min_values_dict[experiments[e_index]].keys(): continue print(f'{experiments[e_index]} vs {experiments[e_index + 1]} = > {i}') if min_values_dict[experiments[e_index + 1]][i] != 0: min_eff = round(min_values_dict[experiments[e_index]][i] / min_values_dict[experiments[e_index + 1]][i]*100 , 2) else: min_eff = 0 print(f'Min : {min_values_dict[experiments[e_index]][i]} vs {min_values_dict[experiments[e_index + 1]][i]} | SMM efficiency : {min_eff}') if max_values_dict[experiments[e_index + 1]][i] != 0: max_eff = round(max_values_dict[experiments[e_index]][i] / max_values_dict[experiments[e_index + 1]][i]*100 , 2) else: max_eff = 0 print(f'Max : {max_values_dict[experiments[e_index]][i]} vs {max_values_dict[experiments[e_index + 1]][i]} | SMM efficiency : {max_eff}') if avr_values_dict[experiments[e_index + 1]][i] != 0: avr_eff = round(avr_values_dict[experiments[e_index]][i] / avr_values_dict[experiments[e_index + 1]][i]*100 , 2) else: avr_eff = 0 print(f'Avr : {avr_values_dict[experiments[e_index]][i]} vs {avr_values_dict[experiments[e_index + 1]][i]}| SMM efficiency : {avr_eff}') print('-------------------------------------------------') with open('fm_statistics_2204.csv', 'w',newline='') as file: writer = csv.writer(file) experiments = list(min_values_dict.keys()) for e_index in range(0, len(experiments), 2): writer.writerow([experiments[e_index][:-4], 'mdp', 'smm', 'smm compared to mdp efficiency %']) for i in interesting_fields: if i not in min_values_dict[experiments[e_index]].keys(): continue if min_values_dict[experiments[e_index + 1]][i] != 0: min_eff = round( min_values_dict[experiments[e_index]][i] / min_values_dict[experiments[e_index + 1]][i] * 100, 2) else: min_eff = 0 writer.writerow([i + '_min', min_values_dict[experiments[e_index]][i], min_values_dict[experiments[e_index + 1]][i], min_eff]) if max_values_dict[experiments[e_index + 1]][i] != 0: max_eff = round( max_values_dict[experiments[e_index]][i] / max_values_dict[experiments[e_index + 1]][i] * 100, 2) else: max_eff = 0 writer.writerow([i + '_max', max_values_dict[experiments[e_index]][i], max_values_dict[experiments[e_index + 1]][i], max_eff]) if avr_values_dict[experiments[e_index + 1]][i] != 0: avr_eff = round( avr_values_dict[experiments[e_index]][i] / avr_values_dict[experiments[e_index + 1]][i] * 100, 2) else: avr_eff = 0 writer.writerow([i + '_avr', avr_values_dict[experiments[e_index]][i], avr_values_dict[experiments[e_index + 1]][i], avr_eff]) writer.writerow([]) print('-------------------------------------------------') ================================================ FILE: Benchmarking/fm_plots.py ================================================ def plot_error(): import matplotlib.pyplot as plt import matplotlib matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) # MDP then SMM learning_time_data = [ [68.19, 140.31, 154.35, 116.8], [27.5, 98.31, 30.87, 68] ] num_mq_data = [ [81803.23, 153758.15, 560705.92, 248552.62, ], [36937.54, 91309.08, 51791.54, 92607] ] import numpy as np N = 4 ind = np.arange(N) # the x locations for the groups width = 0.25 # the width of the bars # fig = plt.figure() fig, (ax_time, ax_mq) = plt.subplots(1, 2, figsize=(10, 3)) ax_time.bar(ind, learning_time_data[0], width, label='MDP') ax_time.bar(ind + width, learning_time_data[1], width, label='SMM') # add some ax_time.set_ylabel('Learning Time (s)') ax_time.set_xticks(ind + width / 2) ax_time.set_xticklabels(('35 State\nGridworld', '72 State\nGridworld', 'MQTT', 'TCP',)) ax_time.grid(axis='y') ax_time.legend(loc='upper left') ax_mq.bar(ind, num_mq_data[0], width, label='MDP') ax_mq.bar(ind + width, num_mq_data[1], width, label='SMM') # add some ax_mq.set_ylabel('\# Membership Queries') ax_mq.ticklabel_format(axis='y', style='sci', scilimits=(1, 4)) ax_mq.set_xticks(ind + width / 2) ax_mq.set_xticklabels(('35 State\nGridworld', '72 State\nGridworld', 'MQTT', 'TCP',)) ax_mq.legend(loc='upper left') ax_mq.grid(axis='y') fig.tight_layout() # plt.show() plt.savefig("error_bench.pgf", bbox_inches='tight') import tikzplotlib tikzplotlib.save("error_bench.tex") def plot_benchmarks(): import matplotlib.pyplot as plt import matplotlib matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) # MDP then SMM num_mq_data = [ [81803.23, 153758.15, 560705.92, 248552.62, ], [36937.54, 91309.08, 51791.54, 92607] ] # TODO avr_cum_err = [ ] import numpy as np N = 4 ind = np.arange(N) # the x locations for the groups width = 0.25 # the width of the bars # fig = plt.figure() fig, (ax_time, ax_mq) = plt.subplots(1, 2, figsize=(10, 3)) ax_time.bar(ind, avr_cum_err[0], width, label='MDP') ax_time.bar(ind + width, avr_cum_err[1], width, label='SMM') # add some ax_time.set_ylabel('Learning Time (s)') ax_time.set_xticks(ind + width / 2) ax_time.set_xticklabels(('35 State\nGridworld', '72 State\nGridworld', 'MQTT', 'TCP',)) ax_time.grid(axis='y') ax_time.legend(loc='upper left') ax_mq.bar(ind, num_mq_data[0], width, label='MDP') ax_mq.bar(ind + width, num_mq_data[1], width, label='SMM') # add some ax_mq.set_ylabel('\# Membership Queries') ax_mq.ticklabel_format(axis='y', style='sci', scilimits=(1, 4)) ax_mq.set_xticks(ind + width / 2) ax_mq.set_xticklabels(('35 State\nGridworld', '72 State\nGridworld', 'MQTT', 'TCP',)) ax_mq.legend(loc='upper left') ax_mq.grid(axis='y') fig.tight_layout() # plt.show() plt.savefig("benchmarking.pgf", bbox_inches='tight') import tikzplotlib tikzplotlib.save("benchmarking.tex") if __name__ == '__main__': plot_error() ================================================ FILE: Benchmarking/generate_plots.py ================================================ from random import random, randint import matplotlib from matplotlib import pyplot as plt import csv def plot_increasing_size_exp(): data = [] with open('increasing_size_experiments.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0) x_axis.pop(0) # use total times only data = data[3:] x_axis = [int(x) for x in x_axis] #x_axis = list(range(2,51)) times = [] labels = ['DFA', 'Mealy', 'Moore'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) plt.plot(x_axis, [float(i) for i in r], label=labels.pop(0)) plt.legend() plt.xticks([100, 1000, 2000, 3000, 4000, 5000,]) plt.yticks([min(times), 0.5, 1, 1.5, max(times)]) #plt.yticks([min(times), 1, 2, max(times)]) #plt.yticks([min(times), 3, 6, 10 , 14]) plt.ylabel("Time (s)") plt.xlabel("Automaton Size") #plt.grid(axis='y') plt.grid() matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) plt.savefig('state_increase_runtime.pgf') #plt.show() def plot_increasing_alphabeth_exp(): data = [] with open('increasing_alphabet_experiments.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0)[:-1] x_axis.pop(0) # use total times only x_axis = [int(x) for x in x_axis] #x_axis = list(range(2,51)) times = [] labels = ['DFA', 'Mealy', 'Moore'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) plt.plot(x_axis, [float(i) for i in r], label=labels.pop(0)) plt.legend() plt.xticks([5,25,50,75,100]) plt.yticks([min(times), 1, 2.5, 4, max(times)]) #plt.yticks([min(times), 1, 2, max(times)]) #plt.yticks([min(times), 3, 6, 10 , 14]) plt.ylabel("Time (s)") plt.xlabel("Alphabet Size") #plt.grid(axis='y') plt.grid() matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) plt.savefig('alphabet_increase_runtime.pgf') #plt.show() def plot_together(): fig, (ax2, ax1) = plt.subplots(1, 2, figsize=(10, 3)) with open('increasing_alphabet_experiments.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0)[:-1] x_axis.pop(0) # use total times only x_axis = [int(x) for x in x_axis] #x_axis = list(range(2,51)) times = [] labels = ['DFA', 'Mealy', 'Moore'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) ax1.plot_side_by_side() ax1.legend() ax1.set_xticks([5,25,50,75,100], minor=False) ax1.set_yticks([min(times), 1, 2.5, 4, max(times)], minor=False) #plt.yticks([min(times), 1, 2, max(times)]) #plt.yticks([min(times), 3, 6, 10 , 14]) ax1.set_ylabel("Time (s)") ax1.set_xlabel("Alphabet Size") #plt.grid(axis='y') ax1.grid() with open('increasing_size_experiments.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0) x_axis.pop(0) # use total times only data = data[3:] x_axis = [int(x) for x in x_axis] #x_axis = list(range(2,51)) times = [] labels = ['DFA', 'Mealy', 'Moore'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) ax2.plot_side_by_side() ax2.legend() ax2.set_xticks([100, 1000, 2000, 3000, 4000, 5000,], minor=False) ax2.set_yticks([min(times), 0.5, 1, 1.5, max(times)], minor=False) #plt.yticks([min(times), 1, 2, max(times)]) #plt.yticks([min(times), 3, 6, 10 , 14]) ax2.set_ylabel("Time (s)") ax2.set_xlabel("Automaton Size") #plt.grid(axis='y') ax2.grid() # matplotlib.use("pgf") # matplotlib.rcParams.update({ # "pgf.texsystem": "pdflatex", # 'font.family': 'serif', # 'text.usetex': True, # 'pgf.rcfonts': False, # }) # # plt.savefig('state_increase_runtime.pgf') fig.tight_layout() matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) fig.savefig('both_images.pgf',bbox_inches='tight') def plot_together(): fig, (ax2, ax1) = plt.subplots(1, 2, figsize=(10, 3)) with open('increasing_alphabet_experiments.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0)[:-1] x_axis.pop(0) # use total times only x_axis = [int(x) for x in x_axis] # x_axis = list(range(2,51)) times = [] labels = ['DFA', 'Mealy', 'Moore'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) ax1.plot_side_by_side() ax1.legend() ax1.set_xticks([5, 25, 50, 75, 100], minor=False) ax1.set_yticks([min(times), 1, 2.5, 4, max(times)], minor=False) # plt.yticks([min(times), 1, 2, max(times)]) # plt.yticks([min(times), 3, 6, 10 , 14]) ax1.set_ylabel("Time (s)") ax1.set_xlabel("Alphabet Size") # plt.grid(axis='y') ax1.grid() with open('increasing_size_experiments.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0) x_axis.pop(0) # use total times only data = data[3:] x_axis = [int(x) for x in x_axis] # x_axis = list(range(2,51)) times = [] labels = ['DFA', 'Mealy', 'Moore'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) ax2.plot_side_by_side() ax2.legend() ax2.set_xticks([100, 1000, 2000, 3000, 4000, 5000, ], minor=False) ax2.set_yticks([min(times), 0.5, 1, 1.5, max(times)], minor=False) # plt.yticks([min(times), 1, 2, max(times)]) # plt.yticks([min(times), 3, 6, 10 , 14]) ax2.set_ylabel("Time (s)") ax2.set_xlabel("Automaton Size") # plt.grid(axis='y') ax2.grid() # matplotlib.use("pgf") # matplotlib.rcParams.update({ # "pgf.texsystem": "pdflatex", # 'font.family': 'serif', # 'text.usetex': True, # 'pgf.rcfonts': False, # }) # # plt.savefig('state_increase_runtime.pgf') fig.tight_layout() matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) fig.savefig('both_images.pgf', bbox_inches='tight') # queries_mealy_sizes, 100,500,1000,2000 # learnlib, 3356.14, 22316.74, 49037.52, 106613.32 # aalpy, 2255.1, 6025.8, 8037.85, 12372.5 def plot_together_learnlib_comp(): fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3)) with open('learnlib_com.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0)[:-1] x_axis.pop(0) x_axis = [int(x) for x in x_axis] # x_axis = list(range(2,51)) times = [] labels = ['DFA(AALpy)','DFA(LearnLib)', 'Mealy(AALpy)', 'Mealy(Learnlib)'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) ax1.plot_side_by_side() ax1.legend() ax1.set_xticks([100, 1000, 2000, 3000, 4000, 5000], minor=False) ax1.set_yticks([min(times), 0.5, 1, 1.5, max(times)], minor=False) # plt.yticks([min(times), 1, 2, max(times)]) # plt.yticks([min(times), 3, 6, 10 , 14]) ax1.set_ylabel("Time (s)") ax1.set_xlabel("Automaton Size") # plt.grid(axis='y') ax1.grid() with open('learnlib_alph_comp.csv', 'r') as f: reader = csv.reader(f) data = list(reader) x_axis = data.pop(0) x_axis.pop(0) # use total times only x_axis = [int(x) for x in x_axis] # x_axis = list(range(2,51)) times = [] labels = ['DFA(AALpy)', 'DFA(LearnLib)', 'Mealy(AALpy)', 'Mealy(Learnlib)'] for r in data: row_name = r.pop(0) times.extend([float(i) for i in r]) ax2.plot_side_by_side() ax2.legend() ax2.set_xticks([5, 25, 50, 75, 100], minor=False) ax2.set_yticks([min(times), 1, 2.5, 4, max(times)], minor=False) # plt.yticks([min(times), 1, 2, max(times)]) # plt.yticks([min(times), 3, 6, 10 , 14]) ax2.set_ylabel("Time (s)") ax2.set_xlabel("Alphabet Size") # plt.grid(axis='y') ax2.grid() # matplotlib.use("pgf") # matplotlib.rcParams.update({ # "pgf.texsystem": "pdflatex", # 'font.family': 'serif', # 'text.usetex': True, # 'pgf.rcfonts': False, # }) # # plt.savefig('state_increase_runtime.pgf') fig.tight_layout() matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) fig.savefig('learnlib_comp.pgf', bbox_inches='tight') def plot_runtime_steps(): automaton_sizes = list(range(2,100,2)) learnlib = [84.0, 84.0, 308.0, 452.0, 516.0, 564.0, 836.0, 996.0, 1188.0, 1268.0, 1870.5, 1604.0, 2358.46, 2004.0, 3189.12, 2699.32, 3756.52, 3617.64, 3925.64, 5144.98, 4278.78, 5198.06, 5452.32, 5272.12, 4841.46, 6046.92, 5864.2, 6043.34, 7501.06, 8291.58, 6976.76, 7722.48, 8498.26, 8021.4, 8409.82, 8427.62, 10348.0, 9017.5, 11266.08, 10496.48, 10948.76, 11697.94, 12539.88, 12573.5, 12266.44, 12788.36, 13619.44, 13204.7, 14218.14] aalpy = [80, 80, 304, 448, 512, 560, 832, 992, 1184, 1264, 1904.4666666666667, 1600, 2355, 2000, 3304.4, 2969, 3875.8, 3491.8, 3994.6666666666665, 5259.8, 4122.6, 5408.2, 5425.6, 5457.733333333334, 4700.6, 5575.266666666666, 5877.333333333333, 5643.333333333333, 7848, 8746.4, 6902, 6764.133333333333, 8968.266666666666, 8994.666666666666, 8548.066666666668, 8034.2, 10303.8, 9518.2, 11861.6, 10639.466666666667, 11049.466666666667, 12028.666666666666, 12798.4, 13055.733333333334, 12525.4, 13473.6, 13445.866666666667, 13654.866666666667, 15162.066666666668] learnlib_dfa_steps = [12, 176, 366, 817.517349244473, 729.3582188142311, 862.8326032572571, 1629.7225991534913, 2278.6612662357325, 1851.9775842649628, 2256.358673947518, 2877.186267318514, 3621.257762117678, 4309.52400476166, 3957.7026357704567, 5892.417280455997, 4340.934611960256, 5361.8053640093285, 5826.332838306764, 6170.173413693644, 6362.896456856769, 7467.5489502843675, 10647.813901891837, 7771.6990409704595, 10021.521929114147, 11682.322172805243, 13337.927737932912, 12739.746892560444, 14478.86166799318, 12871.644461597713, 13099.900598108483, 11801.712844210842, 14545.968533421541, 16248.345494832956, 16266.864422969868, 15963.905452213141, 19195.796973001674, 17252.921273408057, 18935.661631940457, 17732.29013734688, 19960.551029106155, 17375.00727356809, 18493.587556335762, 22476.68693665887, 20993.560784351062, 24736.797016941302, 23454.586313063137, 21647.451883062633, 29979.506261659953, 25118.61300496266] aalpy_dfa_steps = [12, 176.6, 366.93333333333334, 790.5333333333333, 840.5333333333333, 886.1333333333333, 1657.8, 2225.133333333333, 2076.3333333333335, 2649.3333333333335, 3065, 3218.0666666666666, 4783.466666666666, 3860.3333333333335, 5724.4, 4960.733333333334, 5226.733333333334, 6479.4, 5991.866666666667, 7096.2, 7241.866666666667, 9292.066666666668, 8487.6, 10789.4, 11765.2, 12693.666666666666, 11715.266666666666, 12860.6, 11887.466666666667, 12740.333333333334, 12882.133333333333, 15290.666666666666, 15572, 14563.066666666668, 14886.8, 17937.466666666667, 16332.533333333333, 20078.6, 18581.8, 18823.266666666666, 20331.733333333334, 20824.733333333334, 19940.8, 23263.666666666668, 21659.466666666667, 25069.933333333334, 24592.333333333332, 27892.2, 24161.066666666666] fig, ax1 = plt.subplots() ax1.set_xlabel('Automaton Size') ax1.set_ylabel('Number of Learning Steps') ax1.plot_side_by_side() ax1.plot_side_by_side() ax1.plot_side_by_side() ax1.plot_side_by_side() # ax1.tick_params(axis='y', labelcolor=color) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis # ax2.set_ylabel('Total Learning Time (ms)') # we already handled the x-label with ax1 ax2.plot_side_by_side() ax2.plot_side_by_side() ax2.plot_side_by_side() ax2.plot_side_by_side() fig.tight_layout() # otherwise the right y-label is slightly clipped plt.grid() plt.legend() # matplotlib.use("pgf") # matplotlib.rcParams.update({ # "pgf.texsystem": "pdflatex", # 'font.family': 'serif', # 'text.usetex': True, # 'pgf.rcfonts': False, # #'font.size': 42 # }) import tikzplotlib tikzplotlib.save("test.txt") #fig.savefig('runtime_comp.pgf', bbox_inches='tight') plot_runtime_steps() #plot_runtime_steps() # plot_together_learnlib_comp() #plot_increasing_size_exp() #plot_increasing_alphabeth_exp() ================================================ FILE: Benchmarking/json_lbt.py ================================================ import json import random from collections import defaultdict from aalpy import run_PAPNI, load_automaton_from_file from aalpy.automata import VpaAlphabet from aalpy.utils import generate_input_output_data_from_vpa def is_valid_json(s): try: json.loads(s) return True except json.JSONDecodeError: return False def to_json_string(json_tuple): json_str = '' for x in json_tuple: if x in {'key', 'val'}: json_str += f'\"{x}\"' else: json_str += x return json_str def generate_random_json(max_depth=3, max_elements=5): """ Generate a random valid JSON structure using abstract symbols. Args: max_depth (int): Maximum nesting depth max_elements (int): Maximum number of elements in arrays or objects Returns: list: List of symbols representing a valid JSON structure """ def generate_value(current_depth): if current_depth >= max_depth: return ['val'] # Choose between simple value, array, or object choice = random.choice(['simple', 'array', 'object']) if choice == 'simple': return ['val'] elif choice == 'array': return generate_array(current_depth) else: return generate_object(current_depth) def generate_array(current_depth): # Generate array with random number of elements num_elements = random.randint(1, max_elements) result = ['['] for i in range(num_elements): result.extend(generate_value(current_depth + 1)) if i < num_elements - 1: result.append(',') result.append(']') return result def generate_object(current_depth): # Generate object with random number of key-value pairs num_pairs = random.randint(1, max_elements) result = ['{'] for i in range(num_pairs): result.append('key') result.append(':') result.extend(generate_value(current_depth + 1)) if i < num_pairs - 1: result.append(',') result.append('}') return result # Start generation with an object at the root json_tuple = tuple(generate_object(0)) json_str = to_json_string(json_tuple) return json_str, json_tuple def corrupt_json(symbols): """ Take a valid JSON symbol list and make it invalid using various strategies. Returns corrupted symbols and description of corruption. """ corrupted = symbols.copy() strategies = [ 'bracket_mismatch', # Replace brackets with mismatched ones 'add_symbol', # Add inappropriate symbol 'drop_symbol', # Remove necessary symbol 'naked_value', # Just a lone value 'naked_key', # Just a lone key 'multiple_commas', # Add multiple consecutive commas 'trailing_comma', # Add comma at the end 'missing_value', # Remove value after key 'missing_key', # Remove key before value 'missing_colon', # Remove colon between key-value 'standalone_colon', # Just a colon 'comma_start_end', # Start or end with comma 'empty_structure', # Empty structure with internal comma 'multiple_colons' # Multiple colons in key-value pair ] strategy = random.choice(strategies) if strategy == 'bracket_mismatch': bracket_positions = [i for i, s in enumerate(corrupted) if s in ['{', '}', '[', ']']] if bracket_positions: pos = random.choice(bracket_positions) original = corrupted[pos] corruptions = { '{': random.choice(['}', ']']), '}': random.choice(['{', '[']), '[': random.choice(['}', ']']), ']': random.choice(['{', '[']) } corrupted[pos] = corruptions[original] reason = f"Replaced '{original}' with '{corrupted[pos]}'" elif strategy == 'naked_value': corrupted = ['val'] reason = "Created lone value without structure" elif strategy == 'naked_key': corrupted = ['key'] reason = "Created lone key without value or structure" elif strategy == 'multiple_commas': num_commas = random.randint(2, 4) corrupted = ['{'] + [',' for _ in range(num_commas)] + ['}'] reason = f"Created structure with {num_commas} consecutive commas" elif strategy == 'trailing_comma': if len(corrupted) > 2: # Need at least {} or [] if corrupted[-1] in ['}', ']']: corrupted.insert(-1, ',') reason = "Added trailing comma before closing bracket" else: corrupted.append(',') reason = "Added trailing comma at end" else: corrupted = ['{', ',', '}'] reason = "Added trailing comma in empty structure" elif strategy == 'missing_value': key_positions = [i for i, s in enumerate(corrupted) if s == 'key'] if key_positions: pos = random.choice(key_positions) if pos + 2 < len(corrupted) and corrupted[pos + 1] == ':': corrupted.pop(pos + 2) # Remove value reason = "Removed value after key" else: corrupted = ['{', 'key', ':', '}'] reason = "Created key without value" else: corrupted = ['{', 'key', ':', '}'] reason = "Created key without value" elif strategy == 'missing_key': corrupted = ['{', ':', 'val', '}'] reason = "Created value with missing key" elif strategy == 'missing_colon': colon_positions = [i for i, s in enumerate(corrupted) if s == ':'] if colon_positions: pos = random.choice(colon_positions) corrupted.pop(pos) reason = "Removed colon between key and value" else: corrupted = ['{', 'key', 'val', '}'] reason = "Created key-value pair without colon" elif strategy == 'standalone_colon': corrupted = [':'] reason = "Created standalone colon" elif strategy == 'comma_start_end': if random.choice([True, False]): corrupted = [','] + corrupted reason = "Added comma at start" else: corrupted.append(',') reason = "Added comma at end" elif strategy == 'empty_structure': structure_type = random.choice(['{', '[']) closing = '}' if structure_type == '{' else ']' corrupted = [structure_type, ',', closing] reason = f"Created empty {structure_type}{closing} with internal comma" elif strategy == 'multiple_colons': colon_count = random.randint(2, 3) corrupted = ['{', 'key'] + [':' for _ in range(colon_count)] + ['val', '}'] reason = f"Added {colon_count} colons between key and value" elif strategy == 'add_symbol': possible_additions = ['key', 'val', ',', ':', '{', '}', '[', ']'] symbol_to_add = random.choice(possible_additions) pos = random.randint(0, len(corrupted)) corrupted.insert(pos, symbol_to_add) reason = f"Added '{symbol_to_add}' at position {pos}" else: # drop_symbol if len(corrupted) > 1: pos = random.randint(0, len(corrupted) - 1) dropped = corrupted.pop(pos) reason = f"Dropped '{dropped}' from position {pos}" else: corrupted = [] reason = "Dropped all symbols" return corrupted # Define call, return, and internal symbols for JSON call_set = ['{', '['] return_set = ['}', ']'] internal_set = [':', ',', 'key', 'val'] # Define the input alphabet vpa_alphabet = VpaAlphabet( internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set ) def generate_dataset(num_sequences): dataset = set() while len(dataset) <= num_sequences: ts, tt = generate_random_json(max_depth=2, max_elements=3) assert is_valid_json(ts), ts dataset.add((tuple(tt), True)) for _ in range(5): ft = corrupt_json(list(tt)) json_str = ''.join(ft) if not is_valid_json(json_str): dataset.add((tuple(ft), False)) return dataset def validate_string_with_json_parser(json_str, json_parser): assert json_parser in {'json', 'ujson', 'orjson', 'simplejson', 'demjson', 'pyjson5'} import ujson import orjson import json5 import simplejson as sj import demjson import pyjson5 try: if json_parser == "json": json.loads(json_str) elif json_parser == "ujson": ujson.loads(json_str) elif json_parser == "orjson": orjson.loads(json_str) elif json_parser == "json5": json5.loads(json_str) elif json_parser == "simplejson": sj.loads(json_str) elif json_parser == "demjson": demjson.decode(json_str) elif json_parser == "pyjson5": pyjson5.loads(json_str) else: raise ValueError("Unsupported JSON parser") return True except (json.JSONDecodeError, ujson.JSONDecodeError, orjson.JSONDecodeError, demjson.JSONDecodeError, pyjson5.Json5Exception, ValueError) as e: return False use_learned_model = False model_learning_dataset = [] if not use_learned_model: model_learning_dataset = generate_dataset(num_sequences=20000) learned_json_model = run_PAPNI(model_learning_dataset, vpa_alphabet) # learned_json_model.visualize() learned_json_model.save('learned_json.dot') else: learned_json_model = load_automaton_from_file('learned_json.dot', 'vpa') parsers_under_test = ['json', 'ujson', 'orjson', 'simplejson', 'demjson', 'pyjson5'] num_learning_iterations = 3 disagreements = defaultdict(list) results = defaultdict(list) for _ in range(num_learning_iterations): disagreements.clear() test_dataset = generate_input_output_data_from_vpa(learned_json_model, num_sequences=10000, max_seq_len=16) print(f"Num well-matched tests: {len([x for x in test_dataset if learned_json_model.is_balanced(x[0])])}") num_new_sequences = 0 for seq, label in test_dataset: json_string = to_json_string(seq) for p in parsers_under_test: parser_output = validate_string_with_json_parser(json_string, p) if parser_output != label: disagreements[p].append(json_string) if json_string not in results.keys() or json_string in results.keys() and len(results[json_string]) != len(parsers_under_test): results[json_string].append(parser_output) add_to_test_set = all(json_string in x for x in disagreements.values()) if add_to_test_set: if (seq, label) in model_learning_dataset: model_learning_dataset.remove((seq, label)) model_learning_dataset.add((seq, not label)) num_new_sequences += 1 print(f'Added {num_new_sequences} to learning set, total size {len(model_learning_dataset)}') learned_json_model = run_PAPNI(model_learning_dataset, vpa_alphabet, print_info=False) print(f'Current model size: {learned_json_model.size}') comparison_results = {} for key, values in results.items(): true_indexes = [parsers_under_test[i] for i, v in enumerate(values) if v] false_indexes = [parsers_under_test[i] for i, v in enumerate(values) if not v] if true_indexes and false_indexes: # Ensure there are both True and False values comparison_results[key] = [true_indexes, false_indexes] for test_str, res in comparison_results.items(): print('---------------------') print(test_str) print('Postive ', res[0]) print('Negative', res[1]) # # for key, val in disagreements.items(): # print('----------------------------------------------------------') # print(key) # print(f'Total number of discrepancies: {len(val)}') # # # check which disagreements are not present in other parsers # values = set(val) # other_values = set() # for k, v in disagreements.items(): # if k != key: # other_values.update(v) # # unique = list(values - other_values) # # print(f'Unique discrepancies: {len(unique)}') # if unique: # print('Printing unique discrepancies') # for i in unique: # print("".join(i)) ================================================ FILE: Benchmarking/papni_sequences.pickle ================================================ [File too large to display: 24.3 MB] ================================================ FILE: Benchmarking/papni_vs_rpni_benchmarking.py ================================================ import pickle from collections import defaultdict from random import shuffle from aalpy import run_RPNI, run_PAPNI, AutomatonSUL from aalpy.utils import convert_i_o_traces_for_RPNI, generate_input_output_data_from_vpa, is_balanced from aalpy.utils.BenchmarkVpaModels import get_all_VPAs from statistics import mean, stdev # Load positive and negativ sequances for all models all_data = dict() with open('papni_sequences.pickle', 'rb') as handle: all_data = pickle.load(handle) def calculate_f1_score(precision, recall): if precision + recall == 0: return 0 return 2 * (precision * recall) / (precision + recall) def calculate_precision_recall_f1(true_positives, false_positives, false_negatives): precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0 recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0 f1 = calculate_f1_score(precision, recall) return precision, recall, f1 def compare_rpni_and_papni(test_data, rpni_model, papni_model): def evaluate_model(learned_model, test_data): true_positives = 0 false_positives = 0 false_negatives = 0 for input_seq, correct_output in test_data: learned_model.reset_to_initial() learned_output = learned_model.execute_sequence(learned_model.initial_state, input_seq)[-1] if learned_output and correct_output: true_positives += 1 elif learned_output and not correct_output: false_positives += 1 elif not learned_output and correct_output: false_negatives += 1 precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0 recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0 f1 = calculate_f1_score(precision, recall) return precision, recall, f1 rpni_error = evaluate_model(rpni_model, test_data) papni_error = evaluate_model(papni_model, test_data) # print(f'RPNI size {rpni_model.size} vs {papni_model.size} PAPNI size') # print(f'RPNI precision, recall, f1: {rpni_error}') # print(f'PAPNI precision, recall, f1: {papni_error}') return [rpni_model.size, papni_model.size, rpni_error, papni_error] # run KV for VPDA learning and record all sequences # these sequences should cover the whole characterizing set required to learn 1-SEVPA def get_sequences_from_active_sevpa(model, verbose=False): from aalpy import SUL, run_KV, RandomWordEqOracle, SevpaAlphabet class CustomSUL(SUL): def __init__(self, automatonSUL): super(CustomSUL, self).__init__() self.sul = automatonSUL self.sequences = [] def pre(self): self.tc = [] self.sul.pre() def post(self): self.sequences.append(self.tc) self.sul.post() def step(self, letter): output = self.sul.step(letter) if letter is not None: self.tc.append((letter, output)) return output vpa_alphabet = model.get_input_alphabet() alphabet = SevpaAlphabet(vpa_alphabet.internal_alphabet, vpa_alphabet.call_alphabet, vpa_alphabet.return_alphabet) sul = AutomatonSUL(model) sul = CustomSUL(sul) eq_oracle = RandomWordEqOracle(alphabet.get_merged_alphabet(), sul, num_walks=50000, min_walk_len=6, max_walk_len=30, reset_after_cex=True) # eq_oracle = BreadthFirstExplorationEqOracle(vpa_alphabet.get_merged_alphabet(), sul, 7) lm = run_KV(alphabet, sul, eq_oracle, automaton_type='vpa', print_level=3 if verbose else 0) return convert_i_o_traces_for_RPNI(sul.sequences), lm def split_data_to_learning_and_testing(data, learning_to_test_ratio=0.5): total_number_positive = len([x for x in data if x[1]]) total_number_negative = len(data) - total_number_positive num_learning_positive_seq = total_number_positive * learning_to_test_ratio num_learning_negative_seq = total_number_negative * learning_to_test_ratio # sorted(data, key=lambda x: len(x[0])) shuffle(data) learning_sequences, test_sequences = [], [] l_pos, l_neg = 0, 0 for seq, label in data: if label and l_pos <= num_learning_positive_seq: learning_sequences.append((seq, label)) l_pos += 1 elif not label and l_neg <= num_learning_negative_seq: learning_sequences.append((seq, label)) l_neg += 1 else: test_sequences.append((seq, label)) return learning_sequences, test_sequences def run_experiment(experiment_id, ground_truth_model, num_of_learning_seq, max_learning_seq_len, random_data_generation=True, learning_to_test_ratio=0.5): if random_data_generation: data = generate_input_output_data_from_vpa(ground_truth_model, num_sequences=num_of_learning_seq, max_seq_len=max_learning_seq_len, ) else: all_generated_data = all_data[experiment_id] # sorted(all_generated_data, key=lambda x: len(x)) shuffle(all_generated_data) positive_seq = [x for x in all_generated_data if x[1]] negative_seq = [x for x in all_generated_data if not x[1]] data = [] data += positive_seq[:5000] data += negative_seq[:10000 - len(data)] vpa_alphabet = ground_truth_model.get_input_alphabet() learning_data, test_data = split_data_to_learning_and_testing(data, learning_to_test_ratio=learning_to_test_ratio) num_positive_learning = len([x for x in learning_data if x[1]]) learning_set_size = (num_positive_learning, len(learning_data) - num_positive_learning) num_positive_test = len([x for x in test_data if x[1]]) num_test_size = (num_positive_test, len(test_data) - num_positive_test) rpni_model = run_RPNI(learning_data, 'dfa', print_info=False, input_completeness='sink_state') papni_model = run_PAPNI(learning_data, vpa_alphabet, print_info=False) comparison_results = compare_rpni_and_papni(test_data, rpni_model, papni_model) comparison_results = comparison_results + [learning_set_size, num_test_size] return comparison_results def run_experiments_multiple_times(test_models, num_times, learning_to_test_ratio=0.5): all_results = defaultdict(list) print(f'Running experiment on each model {num_times} times.') for idx, gt in enumerate(test_models): print('Experiment: ', idx) for _ in range(num_times): r = run_experiment(idx, gt, num_of_learning_seq=10000, max_learning_seq_len=50, random_data_generation=False, learning_to_test_ratio=learning_to_test_ratio) all_results[idx].append(r) rpni_results = [r[2] for r in all_results[idx]] papni_results = [r[3] for r in all_results[idx]] rpni_f1 = [r[2] for r in rpni_results] papni_f1 = [r[2] for r in papni_results] print(f'RPNI (F1) : min {min(rpni_f1)}, max {max(rpni_f1)}, mean {mean(rpni_f1)}, stddev {stdev(rpni_f1)}') print(f'PAPNI (F1) : min {min(papni_f1)}, max {max(papni_f1)}, mean {mean(papni_f1)}, stddev {stdev(papni_f1)}') print('----------------------------------------------------------------') import pickle with open('papni_rpni_eval_results.pickle', 'wb') as handle: pickle.dump(all_results, handle, protocol=pickle.HIGHEST_PROTOCOL) def test_papni_based_on_sevpa_dataset(): all_models = get_all_VPAs() for idx, gt in enumerate(all_models): sevpa_papni_mismatch, papni_error, sevpa_error = 0, 0, 0 input_al = gt.get_input_alphabet() sevpa_dataset, sevpa_model = get_sequences_from_active_sevpa(gt) sevpa_dataset_set = set(sevpa_dataset) papni_model = run_PAPNI(sevpa_dataset, input_al) balanced_counter = 0 not_in_learning = 0 in_learning = 0 for seq, label in all_data[idx]: if is_balanced(seq, input_al): balanced_counter += 1 if (seq, label) not in sevpa_dataset_set: not_in_learning += 1 else: in_learning += 1 sevpa_model.reset_to_initial() sevpa_output = sevpa_model.execute_sequence(sevpa_model.initial_state, seq) papni_model.reset_to_initial() papni_output = papni_model.execute_sequence(papni_model.initial_state, seq) if sevpa_output != papni_output: sevpa_papni_mismatch += 1 if papni_output[-1] != label: papni_error += 1 if sevpa_output[-1] != label: sevpa_error += 1 print('--------------------------------------') print(f'Model Index {idx}; # well-matched {balanced_counter}, # unique tests {not_in_learning}') print(f'Papni Error {papni_error}') print(f'Sevpa Error {sevpa_error}') print(f'Mismatch {sevpa_papni_mismatch}') assert in_learning + not_in_learning == balanced_counter #test_papni_based_on_sevpa_dataset() all_models = get_all_VPAs() run_experiments_multiple_times(all_models, num_times=20, learning_to_test_ratio=0.5) ================================================ FILE: Benchmarking/passive_mdp_vs_smm.py ================================================ import random import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion from aalpy.learning_algs import run_Alergia from aalpy.utils import load_automaton_from_file, get_correct_prop_values, get_properties_file from aalpy.utils import model_check_experiment path_to_dir = '../DotModels/MDPs/' files = ['first_grid.dot', 'second_grid.dot', 'slot_machine.dot', 'mqtt.dot', 'tcp.dot'] # 'shared_coin.dot' aalpy.paths.path_to_prism = "C:/Program Files/prism-4.7/bin/prism.bat" aalpy.paths.path_to_properties = "prism_eval_props/" def writeSamplesToFile(samples, path="alergiaSamples.txt"): isSMM = False if isinstance(samples[0][0], tuple): isSMM = True with open(path, 'a') as f: for sample in samples: s = "" if isSMM else f'{str(sample.pop(0))}' for i, o in sample: s += f',{i},{o}' f.write(s + '\n') f.close() # samples.clear() def deleteSampleFile(path="alergiaSamples.txt"): import os if os.path.exists(path): os.remove(path) num_traces = 100000 for file in ['first_grid.dot']: exp_name = file.split('.')[0] original_mdp = load_automaton_from_file(path_to_dir + file, automaton_type='mdp') input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) for _ in range(1): data = [] for _ in range(num_traces): sample = [mdp_sul.pre()] for _ in range(random.randint(10, 50)): i = random.choice(input_alphabet) o = mdp_sul.step(i) sample.append((i, o)) data.append(sample) mdp_sul.post() learned_mdp = run_Alergia(data, automaton_type='mdp') for s in data: s.pop(0) learned_smm = run_Alergia(data, automaton_type='smm') smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) print(learned_mdp.size, learned_smm.size, smm_2_mdp.size) print(f'-------{exp_name}---------') print(f'MDP Error: {mdp_err}') print(f'SMM Error: {smm_err}') smm_diff = {} for key, val in mdp_err.items(): if key not in smm_err.keys() or smm_err[key] == 0: continue smm_diff[key] = round(smm_err[key] - val, 2) print(f'SMM differance: {smm_diff}') ================================================ FILE: Benchmarking/prism_eval_props/bluetooth.props ================================================ Pmax=? [ F<5 ("crash") ] Pmax=? [ F<8 ("crash") ] Pmax=? [ F<11 ("crash") ] Pmax=? [ F<14 ("crash") ] Pmax=? [ F<17 ("crash") ] Pmax=? [ F<20 ("crash") ] Pmax=? [ F<3 ("no_response") ] Pmax=? [ F<5 ("no_response") ] Pmax=? [ F<8 ("no_response") ] Pmax=? [ F<11 ("no_response") ] Pmax=? [ F<14 ("no_response") ] Pmax=? [ F<17 ("no_response") ] Pmax=? [ F<20 ("no_response") ] ================================================ FILE: Benchmarking/prism_eval_props/emqtt_two_client.props ================================================ Pmax=? [ F<5 ("c2_Pub_c2_my_topic_bye") ] Pmax=? [ F<5 ("c1_crash") ] Pmax=? [ F<11 ("c1_crash") ] Pmax=? [ F<17 ("c1_crash") ] Pmax=? [(!("c2_crash")) U<12 ("c2_Pub_c2_my_topic_messageQos1") ] ================================================ FILE: Benchmarking/prism_eval_props/first_eval.props ================================================ Pmax=?[F<12("goal")] Pmax=? [ !("grass") U<=14 ("goal") ] Pmax=? [ !("sand") U<=16 ("goal") ] ================================================ FILE: Benchmarking/prism_eval_props/second_eval.props ================================================ Pmax=?[F<15("goal")] Pmax=?[F<13("goal")] Pmax=? [ !("mud") U<=18 ("goal") ] Pmax=? [ !("sand") U<=20 ("goal") ] ================================================ FILE: Benchmarking/prism_eval_props/shared_coin_eval.props ================================================ Pmax=?[F ("finished" & "c1_heads" & "c2_tails")] Pmax=?[F ("finished" & "c1_tails" & "c2_tails")] Pmax=?[!"five" U "finished"] Pmax=?[!"four" U "finished"] Pmax=?[F<40 ("finished" & "c1_heads" & "c2_tails")] Pmax=?[F<40 ("finished" & "c1_tails" & "c2_tails")] Pmax=?[!"five" U<40 "finished"] Pmax=?[!"four" U<40 "finished"] ================================================ FILE: Benchmarking/prism_eval_props/slot_machine_eval.props ================================================ Pmax=? [ F ("Pr10") ] Pmax=? [ F ("Pr2") ] Pmax=? [ F ("Pr0") ] Pmax=? [ X (X ("r220")) ] Pmax=? [ X(X (X ("r122"))) ] Pmax=? [ !(F<10 ("end")) ] Pmax=? [ X (X (X ("r111")))&(F ("Pr0")) ] ================================================ FILE: Benchmarking/prism_eval_props/tcp_eval.props ================================================ Pmax=? [ F<5 ("crash") ] Pmax=? [ F<11 ("crash") ] Pmax=? [ F<17 ("crash") ] Pmax=? [ F<23 ("crash") ] ================================================ FILE: Benchmarking/rpni_papni_memory_footrpint.py ================================================ import sys from random import randint, random import matplotlib.pyplot as plt # Data import tikzplotlib from Benchmarking.visualize_papni_rpni import tikzplotlib_fix_ncols from aalpy import load_automaton_from_file, run_PAPNI, run_RPNI from aalpy.utils import generate_input_output_data_from_vpa from aalpy.utils.BenchmarkVpaModels import get_all_VPAs from random import seed # def get_total_size(obj, seen=None): # """Recursively find the size of an object and all its referenced objects.""" # if seen is None: # seen = set() # # obj_id = id(obj) # if obj_id in seen: # Avoid processing the same object multiple times # return 0 # # seen.add(obj_id) # size = sys.getsizeof(obj) # # if isinstance(obj, dict): # size += sum(get_total_size(k, seen) + get_total_size(v, seen) for k, v in obj.items()) # elif isinstance(obj, (list, tuple, set, frozenset)): # size += sum(get_total_size(i, seen) for i in obj) # elif hasattr(obj, '__dict__'): # For objects with __dict__ attribute # size += get_total_size(vars(obj), seen) # elif hasattr(obj, '__slots__'): # For objects with __slots__ # size += sum(get_total_size(getattr(obj, s), seen) for s in obj.__slots__ if hasattr(obj, s)) # # return size # # # def size_in_mb(obj): # size_bytes = get_total_size(obj) # return size_bytes / (1024 ** 2) # # # #gt = load_automaton_from_file('../DotModels/arithmetics.dot', 'vpa') # gt = get_all_VPAs()[9] # vpa_alphabet = gt.get_input_alphabet() # # rpni_size = [] # papni_size = [] # for size in range(5000, 50001, 5000): # print(size) # data = generate_input_output_data_from_vpa(gt, # num_sequences=size, # max_seq_len=randint(6, 30)) # # y = run_RPNI(data, automaton_type='dfa', print_info=False) # x = run_PAPNI(data, vpa_alphabet, print_info=False) # # rpni_size.append(y) # papni_size.append(x) # # print(rpni_size) # print(papni_size) # runtime (pta, alg) papni, rpni rpni_runtime = [(0.02, 0.04), (0.06, 0.11), (0.11, 0.14), (0.11, 0.22), (0.14, 0.24), (0.12, 0.26), (0.15, 0.31), (0.26, 0.28), (0.21, 0.4), (0.25, 0.43)] papni_runtime = [(0.0, 0.01), (0.01, 0.04), (0.02, 0.04), (0.02, 0.05), (0.02, 0.06), (0.04, 0.07), (0.02, 0.06), (0.03, 0.06), (0.06, 0.1), (0.03, 0.09)] # size rpni papni in Mb rpni_size = [1.8873348236083984, 3.9477672576904297, 5.673147201538086, 7.70704460144043, 9.281957626342773, 12.503767013549805, 14.622617721557617, 15.591878890991211, 18.589590072631836, 20.439626693725586] papni_size = [0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312, 0.0034532546997070312] papni_size = [papni_size[0]] for i in range(len(rpni_runtime) - 1): papni_size.append(papni_size[-1] * (rpni_size[i+1]/rpni_size[i] )) print(papni_size) # Create subplots fig, axes = plt.subplots(1, 2, figsize=(12, 5)) ticks = range(5000, 50001, 5000) # Runtime plot axes[0].plot(ticks, [x + y for x,y in rpni_runtime], label="RPNI", marker='o') axes[0].plot(ticks, [x + y for x,y in papni_runtime], label="PAPNI", marker='s') axes[0].set_xlabel("Input Size") axes[0].set_ylabel("Runtime (s)") axes[0].set_title("Runtime Comparison") axes[0].legend() axes[0].grid(True) # Size plot axes[1].plot(ticks, rpni_size, label="RPNI", marker='o') axes[1].plot(ticks, papni_size, label="PAPNI", marker='s') axes[1].set_xlabel("Input Size") axes[1].set_ylabel("Size (MB)") axes[1].set_title("Size Comparison") axes[1].legend() axes[1].grid(True) # Layout adjustment plt.tight_layout() # plt.show() tikzplotlib_fix_ncols(fig) # plt.show() tikzplotlib.save("runtime_and_size_comparison.tex") ================================================ FILE: Benchmarking/stochastic_benchmarking/Benchmark_ErrorStop.py ================================================ import random import os import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles.RandomWordEqOracle import RandomWordEqOracle from aalpy.utils import load_automaton_from_file, get_properties_file, get_correct_prop_values from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion seeds = [1212,4557,19059,468,43,654,235345,6546,76768,4563,543526,777676,5555,776767,87878787,98989,60967553,3866677,1555841,8638] path_to_dir = '../DotModels/MDPs/' files = ['first_grid.dot', 'second_grid.dot'] # 'slot_machine.dot' ,'shared_coin.dot' 'mqtt.dot', 'tcp.dot' prop_folder = '../prism_eval_props/' # TODO Change the path to your PRIMS executable and change the path_to_prism in the stop_based_on_confidence method in ModelChecking.py. prism_executable = "/home/mtappler/Programs/prism-4.4-linux64/bin/prism" aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "prism_eval_props/" n_c = 20 n_resample = 1000 min_rounds = 10 max_rounds = 300 experiment_repetition = 5 uniform_parameters = False strategy = ["normal"] # chi_square cex_sampling = [None] # random:100:0.15 cex_processing = [None] # add a single prefix for strat in strategy: for cex_stat in cex_sampling: for cex_proc in cex_processing: print(strat, cex_stat, cex_proc) benchmark_dir = f'FM_mdp_smm_error_based_stop/benchmark_{strat}_{cex_stat}_{cex_proc}/' if not os.path.exists(benchmark_dir): os.makedirs(benchmark_dir) for seed in range(experiment_repetition): print(seed) random.seed(seeds[seed]) text_file = open(f"{benchmark_dir}/exp_{seed}.csv", "w") for file in files: print(file) exp_name = file.split('.')[0] if uniform_parameters: if exp_name == 'first_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'second_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'shared_coin': n_c, n_resample = n_c, n_resample elif exp_name == 'slot_machine': n_c, n_resample = n_c, n_resample elif exp_name == 'mqtt': n_c, n_resample = n_c, n_resample elif exp_name == 'tcp': n_c, n_resample = n_c, n_resample else: if exp_name == 'first_grid': n_c, n_resample = 20, 1000 elif exp_name == 'second_grid': n_c, n_resample = 20, 2000 elif exp_name == 'shared_coin': n_c, n_resample = 25, 2500 elif exp_name == 'slot_machine': n_c, n_resample = 30, 5000 elif exp_name == 'mqtt': n_c, n_resample = 20, 1000 elif exp_name == 'tcp': n_c, n_resample = 20, 1000 stopping_data = (get_properties_file(exp_name), get_correct_prop_values(exp_name), 0.02) original_mdp = load_automaton_from_file(path_to_dir + file, automaton_type='mdp') input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=15, reset_after_cex=True) learned_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='mdp', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, property_based_stopping=stopping_data) del mdp_sul del eq_oracle random.seed(seeds[seed]) mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=15, reset_after_cex=True) learned_smm, data_smm = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='smm', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, property_based_stopping=stopping_data) smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) properties_string_header = ",".join([f'{key}_val,{key}_err' for key in mdp_results.keys()]) property_string_mdp = ",".join([f'{str(mdp_results[p])},{str(mdp_err[p])}' for p in mdp_results.keys()]) property_string_smm = ",".join([f'{str(smm_results[p])},{str(smm_err[p])}' for p in smm_results.keys()]) text_file.write('Exp_Name, n_c, n_resample, Final Hypothesis Size, Learning time,' 'Eq. Query Time, Learning Rounds, #MQ Learning, # Steps Learning,' f'# MQ Eq.Queries, # Steps Eq.Queries , {properties_string_header}\n') text_file.write(f'learned_mdp_{exp_name},{n_c},{n_resample}, {data_mdp["automaton_size"]}, ' f'{data_mdp["learning_time"]}, {data_mdp["eq_oracle_time"]}, ' f'{data_mdp["learning_rounds"]}, {data_mdp["queries_learning"]}, {data_mdp["steps_learning"]},' f'{data_mdp["queries_eq_oracle"]}, {data_mdp["steps_eq_oracle"]},' f'{property_string_mdp}\n') text_file.write(f'learned_smm_{exp_name},{n_c},{n_resample}, {data_smm["automaton_size"]}, ' f'{data_smm["learning_time"]}, {data_smm["eq_oracle_time"]}, ' f'{data_smm["learning_rounds"]}, {data_smm["queries_learning"]}, {data_smm["steps_learning"]},' f'{data_smm["queries_eq_oracle"]}, {data_smm["steps_eq_oracle"]},' f'{property_string_smm}\n') text_file.flush() text_file.close() ================================================ FILE: Benchmarking/stochastic_benchmarking/CompleteStochasticBenchmarking.py ================================================ import random import time import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles.RandomWordEqOracle import RandomWordEqOracle from aalpy.utils import load_automaton_from_file, get_properties_file, get_correct_prop_values from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion seeds = [29334,1554,9430459,92344168,55451679,569315,7776892,3875261,811,51,766603,778438967,9819877,6755560,52903,5257,4635,358,1441,838] path_to_dir = '../DotModels/MDPs/' # files = ['first_grid.dot', 'second_grid.dot', 'slot_machine.dot', 'mqtt.dot', 'tcp.dot'] # 'slot_machine.dot' , # 'shared_coin.dot' files = ['second_grid.dot', 'mqtt.dot'] # 'slot_machine.dot' ,'shared_coin.dot' prop_folder = '../prism_eval_props/' # TODO Change the path to your PRIMS executable aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "../prism_eval_props/" n_c = 10 n_resample = 1000 min_rounds = 25 max_rounds = 500 experiment_repetition = 10 uniform_parameters = False strategy = ["normal", "chi2"] # chi_square cex_sampling = ['bfs',] # random:100:0.15 cex_processing = [None, 'longest_prefix'] # add a single prefix start = time.time() model_dict = {m.split('.')[0] : load_automaton_from_file(path_to_dir + m, automaton_type='mdp') for m in files} for strat in strategy: for cex_stat in cex_sampling: for cex_proc in cex_processing: print(strat, cex_stat, cex_proc) benchmark_dir = f'FM_mdp_smm/benchmark_22_04_{strat}_{cex_proc}/' for seed in range(experiment_repetition): print(seed) random.seed(seeds[seed]) import os if not os.path.exists(benchmark_dir): os.makedirs(benchmark_dir) text_file = open(f"{benchmark_dir}/exp_{seed}.csv", "w") for file in files: print(file) exp_name = file.split('.')[0] original_mdp = model_dict[exp_name] input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=16, reset_after_cex=True) learned_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='mdp', min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, target_unambiguity=0.99) del mdp_sul del eq_oracle random.seed(seeds[seed]) mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWordEqOracle(input_alphabet, mdp_sul, num_walks=150, min_walk_len=5, max_walk_len=15, reset_after_cex=True) learned_smm, data_smm = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='smm', min_rounds=min_rounds, strategy=strat, max_rounds=max_rounds, return_data=True, samples_cex_strategy=cex_stat, print_level=1, cex_processing=cex_proc, target_unambiguity=0.99) smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) properties_string_header = ",".join([f'{key}_val,{key}_err' for key in mdp_results.keys()]) property_string_mdp = ",".join([f'{str(mdp_results[p])},{str(mdp_err[p])}' for p in mdp_results.keys()]) property_string_smm = ",".join([f'{str(smm_results[p])},{str(smm_err[p])}' for p in smm_results.keys()]) text_file.write('Exp_Name,n_c,n_resample,Final Hypothesis Size,Learning time,' 'Eq. Query Time,Learning Rounds,#MQ Learning,# Steps Learning,' f'# MQ Eq.Queries,# Steps Eq.Queries ,{properties_string_header}\n') text_file.write(f'learned_mdp_{exp_name},{n_c},{n_resample},{data_mdp["automaton_size"]},' f'{data_mdp["learning_time"]},{data_mdp["eq_oracle_time"]},' f'{data_mdp["learning_rounds"]},{data_mdp["queries_learning"]},{data_mdp["steps_learning"]},' f'{data_mdp["queries_eq_oracle"]},{data_mdp["steps_eq_oracle"]},' f'{property_string_mdp}\n') text_file.write(f'learned_smm_{exp_name},{n_c},{n_resample},{data_smm["automaton_size"]},' f'{data_smm["learning_time"]},{data_smm["eq_oracle_time"]}, ' f'{data_smm["learning_rounds"]},{data_smm["queries_learning"]},{data_smm["steps_learning"]},' f'{data_smm["queries_eq_oracle"]},{data_smm["steps_eq_oracle"]},' f'{property_string_smm}\n') text_file.flush() text_file.close() print('Exp duration', time.time() - start) ================================================ FILE: Benchmarking/stochastic_benchmarking/StochasticBenchmarkingWPrism.py ================================================ import random import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles.RandomWalkEqOracle import RandomWalkEqOracle from aalpy.utils import load_automaton_from_file, get_correct_prop_values, get_properties_file from aalpy.utils import model_check_experiment from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion path_to_dir = '../DotModels/MDPs/' files = ['first_grid.dot', 'second_grid.dot', 'slot_machine.dot', 'mqtt.dot', 'tcp.dot'] # 'shared_coin.dot' aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "../prism_eval_props/" n_c = 20 n_resample = 1000 min_rounds = 10 max_rounds = 1000 strategy = "normal" for seed in range(1, 4): random.seed(seed) benchmark_dir = f"benchmark_complete_no_cq/benchmark_data_{seed}" import os if not os.path.exists(benchmark_dir): os.makedirs(benchmark_dir) text_file = open(f"{benchmark_dir}/StochasticExperiments.csv", "w") uniform_parameters = True for file in files: exp_name = file.split('.')[0] if uniform_parameters: if exp_name == 'first_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'second_grid': n_c, n_resample = n_c, n_resample elif exp_name == 'shared_coin': n_c, n_resample = n_c, n_resample elif exp_name == 'slot_machine': n_c, n_resample = n_c, n_resample elif exp_name == 'mqtt': n_c, n_resample = n_c, n_resample elif exp_name == 'tcp': n_c, n_resample = n_c, n_resample else: if exp_name == 'first_grid': n_c, n_resample = 20, 1000 elif exp_name == 'second_grid': n_c, n_resample = 20, 1000 elif exp_name == 'shared_coin': n_c, n_resample = 30, 3000 elif exp_name == 'slot_machine': n_c, n_resample = 40, 5000 elif exp_name == 'mqtt': n_c, n_resample = 20, 1000 elif exp_name == 'tcp': n_c, n_resample = 20, 1000 original_mdp = load_automaton_from_file(path_to_dir + file, automaton_type='mdp') input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) eq_oracle = RandomWalkEqOracle(input_alphabet, mdp_sul, num_steps=n_resample * (1 / 0.25), reset_after_cex=True, reset_prob=0.25) learned_mdp, data_mdp = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='mdp', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strategy, max_rounds=max_rounds, return_data=True, samples_cex_strategy="bfs") mdp_sul.num_steps = 0 mdp_sul.num_queries = 0 learned_smm, data_smm = run_stochastic_Lstar(input_alphabet, mdp_sul, eq_oracle, automaton_type='smm', n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, strategy=strategy, max_rounds=max_rounds, return_data=True, samples_cex_strategy="bfs") smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) properties_string_header = ",".join([f'{key}_val,{key}_err' for key in mdp_results.keys()]) property_string_mdp = ",".join([f'{str(mdp_results[p])},{str(mdp_err[p])}' for p in mdp_results.keys()]) property_string_smm = ",".join([f'{str(smm_results[p])},{str(smm_err[p])}' for p in smm_results.keys()]) text_file.write('Exp_Name, n_c, n_resample, Final Hypothesis Size, Learning time,' 'Eq. Query Time, Learning Rounds, #MQ Learning, # Steps Learning,' f'# MQ Eq.Queries, # Steps Eq.Queries , {properties_string_header}\n') text_file.write(f'learned_mdp_{exp_name},{n_c},{n_resample}, {data_mdp["automaton_size"]}, ' f'{data_mdp["learning_time"]}, {data_mdp["eq_oracle_time"]}, ' f'{data_mdp["learning_rounds"]}, {data_mdp["queries_learning"]}, {data_mdp["steps_learning"]},' f'{data_mdp["queries_eq_oracle"]}, {data_mdp["steps_eq_oracle"]},' f'{property_string_mdp}\n') text_file.write(f'learned_smm_{exp_name},{n_c},{n_resample}, {data_smm["automaton_size"]}, ' f'{data_smm["learning_time"]}, {data_smm["eq_oracle_time"]}, ' f'{data_smm["learning_rounds"]}, {data_smm["queries_learning"]}, {data_smm["steps_learning"]},' f'{data_smm["queries_eq_oracle"]}, {data_smm["steps_eq_oracle"]},' f'{property_string_smm}\n') text_file.flush() text_file.close() ================================================ FILE: Benchmarking/stochastic_benchmarking/passive_mdp_vs_smm.py ================================================ import random import os import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion from aalpy.learning_algs import run_Alergia, run_JAlergia from aalpy.utils import load_automaton_from_file, get_correct_prop_values, get_properties_file, visualize_automaton from aalpy.utils import model_check_experiment path_to_dir = '../DotModels/MDPs/' files = ['first_grid.dot', 'second_grid.dot', 'slot_machine.dot', 'mqtt.dot', 'tcp.dot'] # 'shared_coin.dot' aalpy.paths.path_to_prism = "C:/Program Files/prism-4.7/bin/prism.bat" aalpy.paths.path_to_properties = "../prism_eval_props/" def writeSamplesToFile(samples, path="alergiaSamples.txt"): isSMM = False if isinstance(samples[0][0], tuple): isSMM = True with open(path, 'a') as f: for sample in samples: s = "" if isSMM else f'{str(sample.pop(0))}' for i, o in sample: s += f',{i},{o}' f.write(s + '\n') f.close() # samples.clear() def deleteSampleFile(path="alergiaSamples.txt"): import os if os.path.exists(path): os.remove(path) num_traces = 100000 for file in ['first_grid.dot']: exp_name = file.split('.')[0] original_mdp = load_automaton_from_file(path_to_dir + file, automaton_type='mdp') input_alphabet = original_mdp.get_input_alphabet() mdp_sul = AutomatonSUL(original_mdp) for _ in range(1): data = [] for _ in range(num_traces): sample = [mdp_sul.pre()] for _ in range(random.randint(10, 50)): i = random.choice(input_alphabet) o = mdp_sul.step(i) sample.append((i, o)) data.append(sample) mdp_sul.post() learned_mdp = run_Alergia(data, automaton_type='mdp') for s in data: s.pop(0) learned_smm = run_Alergia(data, automaton_type='smm') smm_2_mdp = smm_to_mdp_conversion(learned_smm) mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), learned_mdp) smm_results, smm_err = model_check_experiment(get_properties_file(exp_name), get_correct_prop_values(exp_name), smm_2_mdp) print(learned_mdp.size, learned_smm.size, smm_2_mdp.size) print(f'-------{exp_name}---------') print(f'MDP Error: {mdp_err}') print(f'SMM Error: {smm_err}') smm_diff = {} for key, val in mdp_err.items(): if key not in smm_err.keys() or smm_err[key] == 0: continue smm_diff[key] = round(smm_err[key] - val, 2) print(f'SMM differance: {smm_diff}') ================================================ FILE: Benchmarking/stochastic_benchmarking/plot_error_steps.py ================================================ import matplotlib.pyplot as plt from aalpy.utils import load_automaton_from_file from aalpy.utils import statistical_model_checking model = load_automaton_from_file('../../DotModels/MDPs/bluetooth.dot', automaton_type='mdp') steps = [3, 5, 8, 11, 14, 17, 20] # statistical_tests= [] # for s in steps: # e = statistical_model_checking(model, {'no_response'}, s) # statistical_tests.append(e) # # print(statistical_tests) def crash_plot(): original_values_crash = [0, 0.16800000000000004, 0.3926480000000001, 0.5572338000000001, 0.6772233874640001, 0.7646958490393682, 0.8284632739463244] random_input_crash = [0, 0.001981749035076958, 0.0281691469985939, 0.03959723310087103, 0.046344616720299714, 0.04758085064218106, 0.05136504760916134] # smm learning took 34k queries smm_crash_property = [0, 0.174, 0.4046, 0.5714, 0.6915, 0.778, 0.8402, ] # mdp learning 180k queries mdp_crash_property = [0, 0.1899, 0.4624, 0.6471, 0.7684, 0.848, 0.9002, ] plt.plot(steps, original_values_crash, label='Correct Values') plt.plot(steps, smm_crash_property, label='SMM Values') plt.plot(steps, mdp_crash_property, label='MDP Values') plt.plot(steps, random_input_crash, label='Random Inputs') plt.xticks(steps) plt.grid() plt.legend() plt.show() def no_response(): original_values_no_response = [0.36000000000000004, 0.5904, 0.7902848, 0.8926258176000001, 0.9450244186112, 0.9718525023289344, 0.9855884811924145] mdp_no_response = [0.3993, 0.6407, 0.8315, 0.921, 0.9629, 0.9826, 0.9918] smm_no_response = [0.3652, 0.5966, 0.7956, 0.8965, 0.9476, 0.9734, 0.9865] random_input_no_response = [0.29130767125614576, 0.30205630054639654, 0.3069446148329197, 0.31200279332244946, 0.3154095142827484, 0.3145318825672143, 0.3125784442326385] plt.plot(steps, original_values_no_response, label='Correct Values') plt.plot(steps, smm_no_response, label='SMM Values') plt.plot(steps, mdp_no_response, label='MDP Values') plt.plot(steps, random_input_no_response, label='Random Inputs') plt.xticks(steps) plt.grid() plt.legend() plt.show() def side_by_side(): original_values_crash = [0, 0.16800000000000004, 0.3926480000000001, 0.5572338000000001, 0.6772233874640001, 0.7646958490393682, 0.8284632739463244] random_input_crash = [0, 0.001981749035076958, 0.0281691469985939, 0.03959723310087103, 0.046344616720299714, 0.04758085064218106, 0.05136504760916134] # smm learning took 34k queries smm_crash_property = [0, 0.174, 0.4046, 0.5714, 0.6915, 0.778, 0.8402, ] # mdp learning 180k queries mdp_crash_property = [0, 0.1899, 0.4624, 0.6471, 0.7684, 0.848, 0.9002, ] original_values_no_response = [0.36000000000000004, 0.5904, 0.7902848, 0.8926258176000001, 0.9450244186112, 0.9718525023289344, 0.9855884811924145] mdp_no_response = [0.3993, 0.6407, 0.8315, 0.921, 0.9629, 0.9826, 0.9918] smm_no_response = [0.3652, 0.5966, 0.7956, 0.8965, 0.9476, 0.9734, 0.9865] random_input_no_response = [0.29130767125614576, 0.30205630054639654, 0.3069446148329197, 0.31200279332244946, 0.3154095142827484, 0.3145318825672143, 0.3125784442326385] fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(5, 3)) axes[0].plot_side_by_side() axes[0].plot_side_by_side() axes[0].plot_side_by_side() axes[0].plot_side_by_side() axes[0].set_xlabel('Steps to \'crash\'') axes[0].set_ylabel('Property Value') axes[1].plot_side_by_side() axes[1].plot_side_by_side() axes[1].plot_side_by_side() axes[1].plot_side_by_side() axes[1].set_xlabel('Steps to \'no_response\'') axes[1].set_ylabel('Property Value') axes[0].set_xticks(steps) axes[1].set_xticks(steps) axes[0].grid() axes[1].grid() axes[0].legend() fig.tight_layout() plt.show() # import tikzplotlib # tikzplotlib.save("properties_over_time.tex") crash_plot() ================================================ FILE: Benchmarking/stochastic_benchmarking/stochastic_benchmark_random_automata.py ================================================ from itertools import product from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles import RandomWordEqOracle from aalpy.utils import generate_random_mdp, generate_random_smm automata_size = [5, 10, 15, 20, 30, 50, ] inputs_size = [2, 3, 5, 7, 9] outputs_size = [2, 5, 10, 15, 20] inputs_size = [7] outputs_size = [15] def learn(mdp, type): input_al = mdp.get_input_alphabet() sul = AutomatonSUL(mdp) eq_oracle = RandomWordEqOracle(input_al, sul, num_walks=1000, min_walk_len=4, max_walk_len=20) return run_stochastic_Lstar(input_al, sul, eq_oracle, automaton_type=type, cex_processing=None, print_level=0, return_data=True) num_queries_mdp = [] num_queries_smm = [] # i = 0 # for p in product(automata_size, inputs_size, outputs_size): # num_states, num_inputs, num_outputs = p # if num_inputs > num_outputs: # continue # # print(i) # i += 1 # # # random_mdp = generate_random_mdp(num_states=num_states, input_size=num_inputs, output_size=num_outputs) # random_smm = generate_random_mdp(num_states=num_states, input_size=num_inputs, output_size=num_outputs) # # random_smm = random_smm.to_mdp() # # _, mdp_data = learn(random_smm, 'mdp') # _, smm_data = learn(random_smm, 'smm') # # num_queries_mdp.append(mdp_data['queries_learning'] + mdp_data['queries_eq_oracle']) # num_queries_smm.append(smm_data['queries_learning'] + smm_data['queries_eq_oracle']) print(num_queries_mdp) print(num_queries_smm) num_queries_mdp_3_7 = [77115, 85440, 36326, 132485, 250055, 343526] num_queries_smm_3_7 = [23511, 14287, 17106, 55482, 50935, 99730] num_queries_mdp_4_10 = [54654, 265240, 245245, 238944, 320026, 1170086] num_queries_smm_4_10 = [7122, 42637, 32431, 51821, 75703, 204150] num_queries_mdp_7_15 = [237731, 397386, 924637, 2066456, 4117725, 4774201] num_queries_smm_7_15 = [15733, 19148, 52214, 106436, 157414, 605491] # mdp was used for data gen mdp_base_num_queries_mdp_3_7 = [6515, 13659, 42904, 31798, 129641, 128275] mdp_base_num_queries_smm_3_7 = [7383, 16985, 55428, 78679, 230936, 479493] mdp_base_num_queries_mdp_4_10 = [10110, 14032, 61815, 35108, 61489, 115270] mdp_base_num_queries_smm_4_10 = [8284, 11257, 38399, 49637, 74183, 145063] mdp_base_num_queries_mdp_7_15 = [7611, 16438, 12564, 33355, 76704, 348364] mdp_base_num_queries_smm_7_15 = [12132, 29568, 36804, 60763, 95613, 348675] pairs_smm_base = [(num_queries_mdp_3_7, num_queries_smm_3_7), (num_queries_mdp_4_10, num_queries_smm_4_10), (num_queries_mdp_7_15, num_queries_smm_7_15)] pairs_mdp_base = [(mdp_base_num_queries_mdp_3_7, mdp_base_num_queries_smm_3_7), (mdp_base_num_queries_mdp_4_10, mdp_base_num_queries_smm_4_10), (mdp_base_num_queries_mdp_7_15, mdp_base_num_queries_smm_7_15)] # for mdp, smm in pairs_mdp_base: save = [] for m, s in zip(mdp, smm): save.append(100 - round(s / m * 100, 2)) print(save) smm_save_3_7 = [69.51, 83.28, 52.91, 58.12, 79.63, 70.97] smm_save_4_10 = [86.97, 83.93, 86.78, 78.31, 76.34, 82.55] smm_save_7_15 = [93.38, 95.18, 94.35, 94.85, 96.18, 87.32] # # def plot_queries_smm_as_base(): # import matplotlib.pyplot as plt # # plt.plot(automata_size, smm_save_3_7, label='I:3,O: 7)') # plt.plot(automata_size, smm_save_4_10, label='I:4,O: 10') # plt.plot(automata_size, smm_save_7_15, label='I:7,O: 15') # # plt.xticks(automata_size) # # plt.grid() # plt.legend() # plt.show() # plot_queries_smm_as_base() ================================================ FILE: Benchmarking/stochastic_benchmarking/strategy_comp.py ================================================ from statistics import mean from aalpy.learning_algs.stochastic.DifferenceChecker import AdvancedHoeffdingChecker, HoeffdingChecker import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles import RandomWordEqOracle from aalpy.utils import load_automaton_from_file, model_check_experiment, get_properties_file, get_correct_prop_values aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "../../Benchmarking/prism_eval_props/" example = 'mqtt' mdp = load_automaton_from_file(f'../../DotModels/MDPs/{example}.dot', automaton_type='mdp') strategies = [AdvancedHoeffdingChecker(alpha=0.001), 'chi2'] def learn(strategy): input_al = mdp.get_input_alphabet() sul = AutomatonSUL(mdp) eq_oracle = RandomWordEqOracle(input_al, sul, num_walks=1000, min_walk_len=4, max_walk_len=20) model, data = run_stochastic_Lstar(input_al, sul, eq_oracle, automaton_type='smm', strategy=strategy, cex_processing=None, print_level=0, return_data=True) num_queries = data['queries_learning'] + data['queries_eq_oracle'] _, diff = model_check_experiment(get_properties_file(example), get_correct_prop_values(example), model.to_mdp()) avg_error = mean(diff.values()) return num_queries, avg_error # strategies = [HoeffdingChecker(alpha=0.001)] # for s in strategies: # plot_points = [] # for _ in range(10): # plot_points.append(learn(s)) # print(s) # print(plot_points) # mqtt normal_data_mqtt = [(38482, 0.01964), (31372, 0.01652), (31170, 0.00624), (31590, 0.01738), (30987, 0.01796), (46244, 0.0222), (48533, 0.01496), (31979, 0.02714), (33219, 0.00758), (32295, 0.015560000000000001)] chi2_data_mqtt = [(155242, 0.01054), (225776, 0.00348), (230385, 0.01838), (70482, 0.01374), (85096, 0.01284), (64644, 0.0354), (68898, 0.03556), (61591, 0.02464), (61773, 0.0067), (35876, 0.01562)] # # bluetooth normal_data_bt = [(51490, 0.011823076923076924), (66996, 0.015015384615384616), (96563, 0.003523076923076923), (41964, 0.030623076923076923), (40395, 0.026623076923076923), (62838, 0.012146153846153846), (123363, 0.03417692307692308), (100228, 0.029423076923076923), (51425, 0.009438461538461538), (67885, 0.0241)] chi2_data_bt = [(19523, 0.009261538461538462), (20467, 0.038446153846153845), (34288, 0.058253846153846156), (30030, 0.03462307692307692), (18773, 0.038215384615384616), (16949, 0.04113846153846154), (18195, 0.03705384615384615), (19437, 0.030615384615384614), (16834, 0.0487), (16699, 0.09320769230769231)] def plot(): from matplotlib import pyplot as plt normal_data, chi2_data = normal_data_mqtt, chi2_data_mqtt normal_x = [p[0] // 1000 for p in normal_data] normal_y = [p[1] for p in normal_data] chi2_x = [p[0] // 1000 for p in chi2_data] chi2_y = [p[1] for p in chi2_data] plt.figure() plt.scatter(normal_x, normal_y, label='Hoeffding') plt.scatter(chi2_x, chi2_y, label='Chi2') plt.title('Bluetooth') plt.xlabel('Number of Queries (in thousands)') plt.ylabel('Average Error') plt.legend() plt.grid() #plt.show() import tikzplotlib tikzplotlib.save('bluetooth_strategy_comp.tex') def plot_side_by_side(): from matplotlib import pyplot as plt fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 3)) normal_x = [p[0] // 1000 for p in normal_data_mqtt] normal_y = [p[1] for p in normal_data_mqtt] chi2_x = [p[0] // 1000 for p in chi2_data_mqtt] chi2_y = [p[1] for p in chi2_data_mqtt] axes[0].scatter(normal_x, normal_y, label='Hoeffding') axes[0].scatter(chi2_x, chi2_y, label='Chi2') normal_x = [p[0] // 1000 for p in normal_data_bt] normal_y = [p[1] for p in normal_data_bt] chi2_x = [p[0] // 1000 for p in chi2_data_bt] chi2_y = [p[1] for p in chi2_data_bt] axes[1].scatter(normal_x, normal_y, label='Hoeffding') axes[1].scatter(chi2_x, chi2_y, label='Chi2') axes[0].set_title('MQTT') axes[1].set_title('Bluetooth') axes[0].set_xlabel('Number of Queries (in thousands)') axes[0].set_ylabel('Average Error') axes[0].legend() axes[1].set_xlabel('Number of Queries (in thousands)') # axes[1].set_ylabel('Average Error') axes[1].legend() axes[0].grid() axes[1].grid() fig.tight_layout() plt.show() import tikzplotlib # tikzplotlib.save('strategy_comp_side_by_side.tex') plot_side_by_side() ================================================ FILE: Benchmarking/stochastic_benchmarking/unamb_error_plot.py ================================================ # max_err # acv_err # unamb import matplotlib.pyplot as plt import matplotlib mqtt_max_err_1 = [100, 100, 100, 100, 100, 100, 100, 12.25, 7.92, 3.87, 3.99, 6.0, 4.52, 2.99, 3.87, 4.29, 4.7, 4.26, 4.62, 6.41, 4.23, 3.38, 2.79, 2.2, 2.94, 3.04, 2.28, 2.11, 2.2, 2.45, 2.35, 2.12, 2.66, 2.27, 2.23, 2.21, 1.94, 1.82, 1.61, 1.45, 1.41, 1.83, 1.43, 1.42, 1.44, 1.41, 1.37, 1.31, 1.33, 1.3, 1.29, 1.27, 1.47, 1.78, 1.37, 1.28, 1.94, 1.64, 3.48, 3.11, 4.37, 3.93, 3.81, 4.28, 3.99, 4.25, 4.75, 5.14, 5.03, 4.72, 4.74, 4.42, 4.07, 3.72, 3.55, 3.21, 2.99, 2.84, 2.53, 2.75, 2.62, 2.26, 1.78, 1.9, 2.01, 2.09, 2.33, 2.63, 2.75, 2.53, 2.15, 2.48, 3.36, 3.03, 2.87, 3.02, 3.0, 3.04, 2.79, 2.84, 2.67, 3.11, 2.8, 2.52, 2.28, 2.0, 1.81, 1.7, 1.48, 1.74, 1.55, 1.64, 1.46, 1.23, 1.08, 1.26, 1.47, 1.65, 1.56, 1.76, 1.91, 1.97, 2.01, 1.94, 1.7, 1.65, 1.4, 1.15, 1.35, 1.29, 1.49, 1.76, 1.61, 1.66, 1.72, 1.9, 2.14, 1.96, 1.65, 1.56, 1.92, 1.77, 1.66, 1.37, 1.44, 1.41, 1.58, 1.33, 1.18, 1.28, 1.38, 1.17, 1.14, 1.25, 1.4, 1.22, 1.08, 1.09, 1.34, 1.2, 1.09, 1.19, 1.06, 1.07, 1.07, 1.06, 1.06, 1.06, 1.05, 1.05, 1.05, 1.05, 1.05, 1.04, 1.03, 1.02, 1.03, 1.03, 1.05, 1.04, 1.04, 1.04, 1.05, 1.05, 1.06, 1.06, 1.07, 1.07, 1.07, 1.08, 1.07, 1.08, 1.07, 1.06, 1.06, 1.06, 1.06, 1.05, 1.05, 1.03, 1.03, 1.02, 1.14, 1.16, 1.36, 1.18, 1.26, 1.16, 1.35, 1.52, 1.41, 1.46, 1.4, 1.47, 1.59, 1.65, 1.73, 1.8, 1.72, 1.66, 1.65, 1.75, 1.69, 1.72, 1.56, 1.6, 1.7, 1.62, 1.62, 1.61, 1.65, 1.86, 2.01, 1.93, 2.16, 2.11, 2.03, 1.93, 1.85, 1.78, 1.66, 1.67, 1.64, 1.76, 1.77, 1.67, 1.55, 1.59, 1.51, 1.46] mqtt_avr_err_1 = [100, 100, 100, 100, 100, 100, 100, 9.232, 4.548, 2.572, 2.592, 3.512, 2.89, 2.27, 2.606, 2.616, 2.754, 2.502, 2.6879999999999997, 3.42, 2.404, 2.148, 1.97, 1.672, 2.074, 2.084, 1.724, 1.472, 1.2, 1.066, 1.292, 1.348, 1.596, 1.506, 1.408, 0.91, 1.1219999999999999, 1.178, 1.276, 0.994, 0.918, 1.3519999999999999, 1.158, 1.068, 0.946, 0.708, 0.644, 0.56, 0.586, 0.562, 0.582, 0.764, 1.084, 1.242, 1.08, 1.056, 1.322, 1.184, 1.998, 1.8299999999999998, 2.394, 2.212, 2.112, 2.338, 2.206, 2.322, 2.4859999999999998, 2.634, 2.56, 2.4419999999999997, 2.412, 2.306, 2.166, 2.04, 1.936, 1.754, 1.658, 1.574, 1.428, 1.522, 1.476, 1.3559999999999999, 1.158, 1.19, 1.242, 1.294, 1.376, 1.474, 1.528, 1.432, 1.268, 1.3960000000000001, 1.786, 1.656, 1.616, 1.654, 1.6380000000000001, 1.64, 1.538, 1.556, 1.482, 1.682, 1.534, 1.4220000000000002, 1.318, 1.184, 1.104, 1.064, 0.98, 1.084, 0.994, 1.028, 0.9460000000000001, 0.822, 0.704, 0.832, 0.9440000000000001, 1.026, 0.99, 1.068, 1.134, 1.17, 1.182, 1.144, 1.03, 1.014, 0.912, 0.786, 0.872, 0.848, 0.942, 1.066, 0.992, 1.016, 1.048, 1.1280000000000001, 1.238, 1.164, 1.022, 0.984, 1.146, 1.098, 1.064, 0.934, 0.96, 0.954, 1.026, 0.906, 0.854, 0.928, 0.982, 0.896, 0.882, 0.9359999999999999, 1.008, 0.926, 0.8360000000000001, 0.866, 0.992, 0.922, 0.872, 0.9179999999999999, 0.8460000000000001, 0.796, 0.812, 0.868, 0.852, 0.734, 0.744, 0.702, 0.668, 0.654, 0.762, 0.654, 0.5780000000000001, 0.56, 0.558, 0.526, 0.47800000000000004, 0.462, 0.64, 0.612, 0.5640000000000001, 0.5720000000000001, 0.63, 0.624, 0.588, 0.716, 0.736, 0.6940000000000001, 0.602, 0.578, 0.542, 0.528, 0.554, 0.476, 0.542, 0.704, 0.722, 0.744, 0.728, 0.806, 0.87, 0.868, 0.958, 0.876, 0.918, 0.884, 0.97, 1.052, 1.0, 1.038, 1.016, 1.042, 1.094, 1.116, 1.168, 1.214, 1.19, 1.172, 1.188, 1.224, 1.206, 1.214, 1.1340000000000001, 1.1660000000000001, 1.196, 1.172, 1.178, 1.174, 1.186, 1.276, 1.3459999999999999, 1.318, 1.422, 1.4, 1.3699999999999999, 1.324, 1.296, 1.268, 1.216, 1.226, 1.222, 1.274, 1.274, 1.204, 1.1540000000000001, 1.1640000000000001, 1.1280000000000001, 1.1059999999999999] mqtt_unamb_1 = [83.33, 68.57, 70.21, 72.06, 73.42, 77.42, 85.22, 86.86, 90.74, 95.11, 97.28, 97.28, 97.28, 97.96, 99.46, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.59, 79.59, 79.59, 79.08, 78.57, 78.57, 79.59, 79.59, 79.59, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 80.61, 80.61, 81.12, 81.63, 81.63, 81.63, 81.63, 81.63, 81.12, 81.12, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.61, 80.1, 81.12, 81.12, 81.12, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 82.14, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.12, 81.12, 81.12, 82.14, 82.14, 82.14, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.16, 83.16, 83.16, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.16, 83.16, 83.67, 83.67, 83.67, 84.18, 84.18, 84.18, 84.18, 84.18, 84.69, 84.18, 84.18, 83.67, 84.69, 84.69, 85.2, 85.71, 85.71, 85.71, 85.71, 85.71, 85.71, 85.71, 85.71] mqtt_max_err_2 = [100, 100, 100, 100, 100, 100, 100, 9.76, 1.89, 28.08, 6.22, 3.56, 3.26, 3.51, 3.12, 2.79, 2.93, 2.61, 2.06, 2.14, 2.17, 2.06, 2.49, 2.2, 2.46, 2.37, 2.61, 2.67, 2.61, 2.67, 2.79, 2.84, 2.8, 2.92, 3.02, 3.01, 2.97, 3.26, 3.21, 3.04, 2.57, 3.15, 2.49, 2.66, 3.59, 3.66, 3.15, 3.55, 3.46, 3.52, 3.72, 3.42, 3.61, 3.14, 3.63, 3.51, 3.32, 2.92, 3.46, 3.61, 3.45, 3.54, 4.12, 4.81, 4.81, 4.53, 4.76, 4.41, 4.79, 5.14, 5.08, 5.31, 4.86, 4.66, 4.08, 4.29, 4.81, 4.43, 4.77, 4.58, 4.58, 4.77, 4.52, 4.36, 4.2, 4.45, 4.29, 4.23, 4.38, 4.57, 4.38, 4.34, 4.29, 4.05, 4.03, 3.83, 3.65, 3.43, 3.33, 3.78, 3.53, 3.55, 3.4, 3.34, 3.06, 3.43, 3.61, 3.61, 3.89, 3.58, 3.4, 3.23, 2.84, 2.57, 2.79, 2.8, 2.9, 2.68, 2.98, 2.76, 2.65, 2.4, 2.13, 2.38, 2.5, 2.53, 2.37, 2.47, 2.46, 2.23, 2.23, 2.35, 2.27, 2.1, 2.1, 1.86, 1.83, 1.93, 2.07, 1.76, 1.9, 1.63, 1.65, 1.52, 1.53, 1.93, 1.82, 1.63, 1.77, 1.57, 1.56, 1.57, 1.56, 1.76, 1.76, 2.02, 1.93, 1.92, 1.77, 1.96, 1.78, 1.72, 1.63, 1.4, 1.63, 1.49, 1.55, 1.64, 1.74, 1.5, 1.8, 1.66, 1.87, 2.05, 2.03, 2.35, 2.29, 2.34, 2.16, 2.05, 1.93, 1.93, 1.83, 1.84, 1.94, 2.17, 2.32, 2.7, 2.56, 2.45, 2.61, 2.63, 2.69, 2.73, 2.56, 2.56, 2.68, 2.53, 2.55, 2.66, 2.7, 2.56, 2.6, 2.68, 2.52, 2.44, 2.34, 2.23, 2.38, 2.34, 2.8, 2.84, 2.7, 2.65, 2.67, 2.73, 2.73, 2.82, 2.79, 2.77, 2.84, 2.78, 2.82, 2.9, 2.83, 2.82, 2.9, 2.81, 2.7, 2.7, 2.64, 2.57, 2.5, 2.58, 2.5, 2.61, 2.59, 2.71, 2.72, 2.67, 2.64, 2.76, 2.77, 2.75, 2.72, 2.74, 2.68, 2.6, 2.53, 2.49] mqtt_avr_err_2 = [100, 100, 100, 100, 100, 100, 100, 6.438, 0.984, 9.953999999999999, 3.312, 2.216, 1.238, 1.426, 1.19, 1.204, 1.62, 1.6139999999999999, 1.214, 1.252, 1.03, 0.9480000000000001, 1.4320000000000002, 1.368, 1.228, 1.086, 0.95, 1.1159999999999999, 1.028, 1.31, 1.236, 1.5899999999999999, 1.1139999999999999, 1.25, 1.354, 1.462, 1.9100000000000001, 2.152, 2.002, 1.8900000000000001, 1.97, 2.2439999999999998, 1.976, 2.048, 2.452, 2.536, 2.332, 2.454, 2.452, 2.5060000000000002, 2.552, 2.366, 2.294, 2.24, 2.436, 2.46, 2.316, 2.064, 2.32, 2.422, 2.328, 2.314, 2.548, 2.846, 2.856, 2.7439999999999998, 2.854, 2.634, 2.736, 2.868, 2.8779999999999997, 2.952, 2.726, 2.64, 2.38, 2.474, 2.686, 2.518, 2.638, 2.5100000000000002, 2.518, 2.606, 2.4699999999999998, 2.37, 2.284, 2.426, 2.346, 2.3280000000000003, 2.368, 2.45, 2.368, 2.36, 2.328, 2.252, 2.2520000000000002, 2.116, 2.082, 1.976, 1.97, 2.178, 2.038, 2.054, 2.004, 1.966, 1.832, 2.002, 2.09, 2.096, 2.214, 2.072, 1.974, 1.882, 1.698, 1.542, 1.6400000000000001, 1.652, 1.722, 1.6059999999999999, 1.746, 1.6159999999999999, 1.5899999999999999, 1.466, 1.356, 1.446, 1.304, 1.3379999999999999, 1.262, 1.302, 1.322, 1.22, 1.224, 1.294, 1.416, 1.324, 1.3439999999999999, 1.248, 1.22, 1.256, 1.3119999999999998, 1.182, 1.26, 1.1239999999999999, 1.128, 1.084, 1.124, 1.316, 1.26, 1.17, 1.226, 1.102, 1.1, 1.11, 1.106, 1.19, 1.19, 1.31, 1.274, 1.272, 1.214, 1.278, 1.198, 1.182, 1.128, 1.036, 1.154, 1.076, 1.104, 1.14, 1.18, 1.068, 1.188, 1.1159999999999999, 1.216, 1.3179999999999998, 1.296, 1.438, 1.412, 1.42, 1.34, 1.286, 1.232, 1.228, 0.996, 1.002, 1.054, 1.164, 1.24, 1.402, 1.342, 1.294, 1.3599999999999999, 1.3679999999999999, 1.39, 1.416, 1.336, 1.332, 1.3840000000000001, 1.314, 1.3439999999999999, 1.398, 1.42, 1.368, 1.3940000000000001, 1.426, 1.352, 1.316, 1.262, 1.22, 1.28, 1.264, 1.474, 1.5, 1.4460000000000002, 1.426, 1.426, 1.452, 1.452, 1.5, 1.482, 1.482, 1.51, 1.478, 1.492, 1.526, 1.494, 1.49, 1.518, 1.47, 1.422, 1.424, 1.4020000000000001, 1.3659999999999999, 1.328, 1.364, 1.324, 1.3760000000000001, 1.368, 1.416, 1.42, 1.396, 1.3840000000000001, 1.436, 1.44, 1.43, 1.4100000000000001, 1.4180000000000001, 1.4000000000000001, 1.36, 1.3359999999999999, 1.316] mqtt_unamb_2 = [83.33, 79.41, 73.91, 74.14, 75.0, 78.49, 84.48, 89.13, 91.36, 95.65, 96.2, 98.37, 98.37, 99.46, 99.46, 99.46, 100.0, 100.0, 99.46, 78.06, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.08, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.08, 79.08, 79.59, 79.08, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 79.59, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.61, 80.61, 80.61, 80.61, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 80.61, 80.61, 80.61, 80.61, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 82.14, 82.14, 82.14, 82.14, 81.63, 81.63, 81.63, 82.14, 82.14, 82.14, 82.14, 82.14, 82.65, 82.14, 82.14, 82.14, 82.14, 82.14, 82.14, 82.14, 82.14, 82.65, 82.65, 82.65, 82.14, 82.14, 82.14, 82.14, 82.14, 81.63, 81.63, 81.63, 82.14, 82.14, 82.65, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 82.65, 82.65, 82.65, 82.65, 83.16, 83.16, 83.16, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65] second_grid_max_err_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 97.43, 93.46, 85.46, 42.15, 24.14, 22.72, 18.37, 17.29, 19.52, 19.32, 16.22, 16.41, 16.87, 16.86, 17.05, 17.1, 17.31, 17.2, 0.74, 0.71, 0.77, 0.8, 0.63, 0.65, 0.71, 0.68, 0.75, 0.69, 0.78, 0.6, 0.64, 0.73, 0.72, 4.92, 4.59, 4.53, 4.55, 4.47, 1.28, 1.46, 1.39, 1.57, 2.48, 2.47, 2.08, 2.1, 1.9, 1.14, 1.28, 0.89, 0.94, 5.16, 5.21, 5.2, 5.16, 5.15, 5.48, 4.9, 4.84, 4.91, 5.14, 5.11, 5.15, 5.15, 5.1, 5.37, 5.36, 5.31, 5.27, 5.32, 5.27, 5.21, 5.22, 5.17, 5.19, 5.15, 5.14, 5.11, 5.25, 5.2, 5.4, 5.39, 5.34, 5.36, 5.29, 5.29, 5.28, 5.29, 0.47, 0.46, 0.46, 0.41, 0.41, 0.43, 0.38, 0.39, 0.42, 0.41, 0.42, 0.46, 0.41, 0.4, 0.43, 0.53, 0.48, 0.51, 0.55, 0.52, 0.61, 0.45, 0.47, 0.47, 0.3, 0.26, 0.31, 0.4, 0.33, 0.24, 0.27, 0.2, 0.27, 0.21, 0.28, 0.3, 0.36, 0.42, 0.34, 0.5, 0.65, 0.57, 0.44, 0.62, 0.7, 0.69, 0.61, 0.49, 0.46, 0.55, 0.47, 0.4, 0.43, 0.49, 0.41, 0.41, 0.4, 0.43, 0.35, 0.33, 0.39, 0.46, 0.48, 0.51, 0.57, 0.49, 0.61, 0.58, 0.6, 0.57, 0.48, 0.65, 0.54, 0.52, 0.57, 0.62, 0.57, 0.75, 0.72, 0.65, 0.66, 0.6, 0.55, 0.52, 0.48, 0.49, 0.54, 0.52, 0.51, 0.55, 0.4, 0.37, 0.38, 0.48, 0.65, 0.67, 0.64, 0.67, 0.64, 0.66, 0.78, 0.69, 0.64, 0.63, 0.55, 0.53, 0.61, 0.59, 0.56, 0.56, 0.57, 0.58, 0.64, 0.57, 0.6, 0.7, 0.64, 0.5, 0.48, 0.41, 0.41, 0.47, 0.45, 0.45, 0.42, 0.33, 0.36, 0.37, 0.4, 0.39, 0.35, 0.34, 0.36, 0.31, 0.34, 0.35, 0.32, 0.41, 0.46, 0.4, 0.41, 0.43, 0.39, 0.34, 0.41, 0.48, 0.54, 0.43, 0.37] second_grid_avr_err_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 66.4425, 58.0325, 40.832499999999996, 20.5425, 10.545, 10.945, 10.030000000000001, 7.035, 10.91, 10.6475, 5.755, 5.78, 6.0075, 5.4275, 5.455, 5.4350000000000005, 5.52, 5.6325, 0.5825, 0.5625, 0.495, 0.5225, 0.505, 0.5, 0.5125, 0.47750000000000004, 0.51, 0.51, 0.4625, 0.355, 0.34500000000000003, 0.4025, 0.4025, 1.765, 1.8175, 1.74, 1.6075, 1.6375, 0.84, 0.8825, 0.8674999999999999, 0.895, 1.19, 1.165, 1.0050000000000001, 1.02, 0.9375, 0.6375, 0.7050000000000001, 0.5125, 0.53, 1.8, 1.7575, 1.6975, 1.625, 1.7125000000000001, 1.7950000000000002, 1.705, 1.665, 1.53, 1.5725, 1.5550000000000002, 1.51, 1.52, 1.4849999999999999, 1.47, 1.435, 1.4224999999999999, 1.4175, 1.4075, 1.3975, 1.41, 1.3975, 1.365, 1.4025, 1.3900000000000001, 1.3824999999999998, 1.35, 1.46, 1.4200000000000002, 1.4975, 1.4874999999999998, 1.4575, 1.4575, 1.4425000000000001, 1.4575, 1.4375, 1.485, 0.3075, 0.3325, 0.33, 0.2475, 0.265, 0.31, 0.2325, 0.2625, 0.27, 0.24, 0.2625, 0.315, 0.22, 0.2225, 0.3075, 0.355, 0.3175, 0.325, 0.3325, 0.315, 0.335, 0.2625, 0.2675, 0.2675, 0.19, 0.1675, 0.185, 0.21000000000000002, 0.195, 0.16999999999999998, 0.175, 0.1575, 0.17, 0.165, 0.17, 0.18, 0.19, 0.1925, 0.17250000000000001, 0.24, 0.335, 0.3125, 0.23500000000000001, 0.3075, 0.3325, 0.33999999999999997, 0.28, 0.24, 0.20500000000000002, 0.24000000000000002, 0.2025, 0.1875, 0.185, 0.1875, 0.16749999999999998, 0.1825, 0.1875, 0.1925, 0.1825, 0.185, 0.20750000000000002, 0.2275, 0.235, 0.255, 0.27999999999999997, 0.2575, 0.3275, 0.295, 0.3075, 0.3025, 0.26, 0.3525, 0.29000000000000004, 0.2775, 0.2975, 0.315, 0.26999999999999996, 0.35250000000000004, 0.33749999999999997, 0.28750000000000003, 0.28500000000000003, 0.265, 0.24500000000000002, 0.2275, 0.21, 0.2175, 0.2275, 0.2325, 0.23500000000000001, 0.24250000000000002, 0.21500000000000002, 0.2075, 0.1975, 0.21, 0.2975, 0.315, 0.3075, 0.315, 0.3075, 0.3275, 0.3775, 0.33249999999999996, 0.3275, 0.315, 0.28500000000000003, 0.275, 0.3125, 0.2975, 0.2725, 0.2725, 0.285, 0.27249999999999996, 0.325, 0.2925, 0.3075, 0.3475, 0.32, 0.255, 0.245, 0.215, 0.2225, 0.22999999999999998, 0.2225, 0.2225, 0.21, 0.1925, 0.185, 0.195, 0.22, 0.2075, 0.1825, 0.1875, 0.1925, 0.175, 0.185, 0.1825, 0.175, 0.195, 0.20750000000000002, 0.185, 0.1975, 0.21, 0.195, 0.17750000000000002, 0.19499999999999998, 0.2225, 0.24000000000000002, 0.20249999999999999, 0.195] second_grid_unamb_1 = [60.0, 77.78, 71.05, 76.36, 73.61, 79.07, 70.43, 66.67, 74.12, 66.85, 71.2, 71.01, 71.75, 70.28, 67.55, 70.8, 73.08, 72.88, 74.51, 76.22, 76.43, 78.83, 79.8, 79.94, 80.78, 81.43, 82.74, 82.74, 83.06, 84.04, 84.08, 85.02, 85.03, 85.34, 85.99, 85.67, 85.67, 86.32, 84.08, 84.39, 88.85, 87.95, 85.67, 85.67, 85.67, 86.62, 86.94, 86.94, 87.26, 87.26, 87.58, 87.9, 87.9, 88.22, 88.54, 88.54, 88.54, 88.85, 88.85, 88.85, 88.85, 88.85, 88.85, 89.17, 89.49, 89.49, 89.49, 89.49, 89.81, 90.13, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.13, 88.51, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.76, 90.76, 90.45, 91.28, 91.08, 91.08, 91.08, 91.08, 91.08, 90.76, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 92.04, 92.04, 92.36, 92.04, 92.04, 92.04, 92.36, 92.04, 92.04, 92.04, 92.04, 92.68, 92.68, 92.36, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.36, 90.74, 92.68, 92.99, 92.99, 92.99, 92.99, 92.99, 92.68, 92.99, 93.31, 93.31, 93.31, 93.31, 92.99, 92.99, 92.99, 92.99, 92.68, 92.99, 92.68, 92.68, 91.36, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.36, 92.36, 92.68, 92.99, 92.68, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 93.31, 92.99, 93.31, 93.31, 93.31, 93.31, 92.99, 93.31, 93.31, 93.31, 93.63, 93.63, 93.63, 93.31, 93.63, 93.95, 93.95, 93.95, 94.27, 94.27, 94.27, 93.95, 94.27, 94.27, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.27, 94.27, 94.27, 94.27, 94.27, 94.59, 94.59, 94.59, 94.59, 94.59, 94.9, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.9, 94.9, 94.9, 94.9, 94.9, 94.9] second_grid_max_err_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 93.98, 97.43, 97.43, 95.88, 96.56, 92.01, 92.67, 91.36, 80.52, 76.18, 48.4, 46.6, 51.42, 6.01, 5.78, 5.81, 5.72, 5.5, 2.22, 2.32, 3.3, 1.98, 1.94, 1.51, 1.53, 1.55, 1.29, 1.12, 1.2, 1.89, 2.48, 2.34, 2.43, 4.38, 4.48, 1.67, 1.65, 1.57, 1.6, 1.48, 1.62, 1.7, 1.46, 1.57, 1.52, 1.33, 1.5, 1.57, 1.31, 1.29, 1.41, 1.35, 1.37, 1.23, 1.21, 1.26, 1.16, 1.12, 1.11, 1.35, 1.37, 1.34, 1.59, 1.74, 1.55, 1.07, 1.05, 1.09, 0.98, 1.25, 0.94, 1.06, 0.96, 0.92, 1.09, 1.14, 1.24, 1.2, 1.32, 1.31, 1.29, 1.18, 1.1, 1.05, 1.18, 1.15, 0.99, 1.17, 1.23, 1.26, 1.27, 1.21, 1.31, 1.24, 1.16, 1.15, 1.17, 1.2, 1.21, 1.16, 1.14, 1.13, 1.12, 1.09, 1.02, 1.01, 0.88, 0.92, 0.9, 0.91, 0.97, 0.98, 0.97, 0.97, 0.99, 1.0, 1.04, 1.0, 1.02, 1.02, 0.91, 0.89, 0.92, 0.96, 1.01, 0.95, 0.98, 0.97, 1.04, 1.08, 1.08, 1.09, 1.12, 1.21, 1.1, 1.09, 1.11, 1.14, 1.14, 1.19, 1.23, 1.19, 1.19, 1.24, 1.27, 1.28, 1.26, 1.26, 1.2, 1.2, 1.17, 1.25, 1.29, 1.25, 1.21, 1.19, 1.14, 1.18, 1.93, 2.21, 2.13, 2.18, 1.97, 1.97, 1.93, 1.9, 1.95, 1.91, 1.91, 1.88, 1.77, 1.77, 1.75, 1.77, 1.78, 1.8, 1.69, 1.62, 1.73, 1.8, 1.81, 1.7, 1.71, 1.64, 1.63, 1.61, 1.5, 1.52, 1.37, 1.31, 1.16, 1.24, 1.2, 1.23, 1.23, 1.13, 1.18, 1.13, 1.08, 1.11, 1.12, 1.14, 1.11, 1.05, 1.06, 1.06, 1.05, 1.0, 1.0, 0.96, 0.79, 0.81, 0.76, 0.81, 0.93, 0.89, 0.83, 0.9, 0.77, 0.78, 0.81, 0.78, 0.84, 0.79, 0.79, 0.82, 0.86, 0.83, 0.79, 0.85, 0.96, 1.06, 1.02, 1.03, 1.07] second_grid_avr_err_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 62.2975, 60.6325, 62.205000000000005, 59.2925, 60.722500000000004, 45.675, 45.425, 37.335, 27.685, 22.1, 15.67, 15.345, 15.9075, 3.045, 2.7525, 2.7849999999999997, 2.1999999999999997, 2.06, 1.1400000000000001, 1.355, 1.4024999999999999, 1.0825, 1.0025, 0.765, 0.7825, 0.765, 0.74, 0.835, 0.9025, 0.9975, 1.0875, 1.0725, 1.115, 1.9275, 1.9400000000000002, 1.0425, 1.03, 1.0025, 1.0125, 0.9924999999999999, 0.9175, 0.9149999999999999, 0.8925, 0.87, 0.875, 0.8925, 0.855, 0.8625, 0.875, 0.9, 0.9099999999999999, 0.9375, 0.93, 0.9175, 0.91, 0.8674999999999999, 0.85, 0.8700000000000001, 0.8350000000000001, 0.855, 0.8525, 0.8475, 0.8675, 0.845, 0.81, 0.8025, 0.8075, 0.7775000000000001, 0.77, 0.7525000000000001, 0.775, 0.775, 0.7175, 0.7175, 0.675, 0.7, 0.7375, 0.7275, 0.78, 0.7775, 0.735, 0.7425, 0.72, 0.6525, 0.65, 0.6749999999999999, 0.665, 0.705, 0.6849999999999999, 0.645, 0.625, 0.6175, 0.6325, 0.6275000000000001, 0.655, 0.6725, 0.64, 0.625, 0.6125, 0.595, 0.6, 0.6074999999999999, 0.605, 0.6375000000000001, 0.6, 0.5925, 0.6, 0.6224999999999999, 0.6074999999999999, 0.6275, 0.6475, 0.6575, 0.66, 0.67, 0.645, 0.635, 0.6375, 0.64, 0.64, 0.6425, 0.6575, 0.6799999999999999, 0.6575, 0.6699999999999999, 0.6825, 0.7050000000000001, 0.7025, 0.6925, 0.715, 0.73, 0.725, 0.7325, 0.7275, 0.7374999999999999, 0.7175, 0.7275, 0.7175, 0.7374999999999999, 0.7374999999999999, 0.74, 0.7525, 0.7825, 0.7725, 0.7875, 0.7825, 0.77, 0.775, 0.77, 0.7725, 0.78, 0.77, 0.7675, 0.7925, 0.8, 0.785, 0.78, 0.7775, 0.77, 1.2425, 1.3775, 1.3575, 1.3575, 1.265, 1.2874999999999999, 1.2725, 1.2625, 1.3125, 1.29, 1.2974999999999999, 1.2775, 1.19, 1.1875, 1.1775, 1.205, 1.215, 1.215, 1.1475, 1.0775000000000001, 1.1475, 1.19, 1.205, 1.1125, 1.125, 1.0625, 1.0374999999999999, 1.03, 0.9425, 0.9700000000000001, 0.855, 0.8075, 0.71, 0.78, 0.75, 0.7725, 0.7675, 0.72, 0.7324999999999999, 0.695, 0.6725, 0.7000000000000001, 0.7025, 0.7175, 0.6900000000000001, 0.6625, 0.6625, 0.6575, 0.6475, 0.6025, 0.6, 0.58, 0.46, 0.485, 0.44, 0.48, 0.5875, 0.5575, 0.5, 0.55, 0.455, 0.4625, 0.485, 0.46, 0.51, 0.4625, 0.475, 0.4875, 0.5, 0.47, 0.4325, 0.4775, 0.56, 0.625, 0.595, 0.5875, 0.625] second_grid_unamb_2 = [60.0, 72.22, 78.95, 80.0, 70.0, 74.12, 72.0, 67.74, 68.42, 69.18, 61.85, 66.14, 66.34, 66.2, 65.49, 63.9, 67.77, 65.49, 62.83, 66.9, 66.43, 64.03, 65.29, 68.73, 70.57, 72.31, 72.64, 73.94, 74.27, 74.92, 76.55, 77.52, 78.83, 79.8, 80.13, 80.78, 81.43, 81.43, 82.08, 82.28, 82.35, 83.71, 83.71, 83.39, 80.25, 80.89, 81.21, 79.39, 83.12, 83.44, 83.76, 84.08, 84.08, 84.08, 84.08, 84.08, 84.08, 83.76, 84.08, 84.08, 85.03, 85.03, 85.03, 84.71, 84.71, 85.35, 84.71, 84.08, 84.71, 84.71, 85.03, 85.35, 85.67, 85.99, 86.62, 86.62, 86.62, 86.94, 86.94, 87.26, 87.26, 87.26, 87.26, 87.26, 87.26, 87.26, 87.26, 87.58, 87.9, 87.9, 87.9, 87.9, 87.9, 87.9, 87.9, 88.22, 88.54, 88.22, 88.22, 88.54, 88.54, 89.17, 89.17, 89.17, 89.17, 89.49, 89.17, 89.81, 90.13, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 90.45, 90.97, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 90.45, 90.45, 90.76, 90.13, 90.13, 90.45, 90.45, 90.45, 90.45, 90.13, 90.45, 90.45, 90.45, 90.45, 90.76, 90.76, 90.76, 90.76, 90.76, 90.45, 90.76, 90.45, 90.76, 91.08, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.4, 91.72, 91.72, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.36, 92.36, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 93.31, 92.99, 93.31, 93.31, 93.63, 93.63, 93.31, 93.31, 93.31, 93.31, 93.31, 93.31, 93.63, 93.95, 93.63, 93.95, 93.95, 93.95, 94.27, 94.27, 94.27, 94.27, 94.27, 93.95, 93.95, 93.95, 93.95, 93.95, 94.27, 94.27, 94.27, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 94.27, 93.95, 94.59, 94.27, 94.27, 94.27, 94.27, 93.95, 93.95, 93.63, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 94.59, 94.59, 94.27, 94.27, 94.59, 94.59, 94.59, 94.9, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59] second_grid_max_err_3 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 90.41, 90.74, 90.48, 68.35, 37.94, 65.4, 27.09, 16.96, 17.02, 18.25, 18.23, 17.02, 29.13, 29.3, 28.79, 28.39, 26.64, 26.0, 74.46, 74.61, 70.84, 71.09, 71.1, 71.65, 71.52, 71.42, 71.42, 72.93, 72.56, 72.46, 84.49, 84.49, 84.59, 84.6, 84.69, 84.69, 4.43, 4.47, 4.45, 4.43, 4.33, 4.37, 4.44, 4.76, 5.19, 5.14, 5.39, 5.61, 5.62, 5.49, 5.34, 5.33, 5.46, 5.47, 5.67, 5.54, 5.58, 5.51, 5.44, 5.59, 5.42, 4.73, 4.66, 4.72, 4.69, 4.64, 4.79, 4.76, 5.11, 5.35, 5.82, 5.73, 5.55, 5.57, 5.57, 5.66, 5.65, 6.26, 6.26, 6.33, 6.25, 6.2, 6.11, 6.11, 6.32, 6.15, 6.13, 6.12, 6.14, 6.13, 6.18, 6.18, 5.83, 5.94, 5.94, 5.98, 6.0, 6.03, 6.02, 6.21, 6.15, 5.64, 5.61, 6.1, 6.1, 6.06, 6.11, 6.26, 6.14, 6.07, 6.01, 5.92, 5.93, 5.95, 5.94, 5.83, 5.33, 5.21, 5.25, 5.15, 5.07, 5.13, 5.21, 5.22, 5.25, 5.2, 5.25, 5.27, 5.24, 5.5, 5.49, 5.56, 5.04, 5.09, 4.63, 4.63, 4.59, 4.6, 4.79, 4.73, 4.29, 4.33, 4.33, 4.55, 4.51, 4.51, 4.47, 4.91, 4.96, 5.02, 5.25, 5.25, 5.35, 5.34, 5.36, 5.32, 5.37, 5.34, 5.42, 5.41, 5.4, 5.4, 5.76, 4.84, 4.81, 4.94, 4.94, 5.12, 5.06, 5.01, 5.08, 5.12, 5.07, 5.26, 5.5, 5.5, 5.55, 5.36, 5.4, 5.42, 5.41, 5.0, 4.95, 4.96, 5.02, 5.08, 5.04, 5.22, 5.23, 5.24, 5.47, 5.09, 5.02, 5.05, 5.04, 4.87, 5.16, 5.18, 5.2, 5.28, 5.26, 5.26, 5.61, 5.65, 5.62, 5.6, 5.65, 5.66, 5.63, 5.27, 5.23, 5.28, 5.25, 5.23, 5.12, 2.27, 2.28, 5.17, 5.21, 5.17, 2.32, 2.58, 5.44, 2.62, 2.64, 2.71, 2.66, 2.63, 2.64, 2.6] second_grid_avr_err_3 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 58.4325, 58.6325, 58.385000000000005, 28.154999999999998, 22.125, 25.87, 13.1875, 8.6725, 8.215, 8.55, 9.5825, 10.004999999999999, 13.0275, 13.2925, 13.225, 13.1675, 12.93, 12.81, 28.014999999999997, 28.015, 23.8675, 23.810000000000002, 23.81, 23.9025, 23.91, 23.9175, 23.875, 24.26, 24.3075, 24.287499999999998, 25.6775, 25.645, 25.905, 25.9425, 26.115, 26.0975, 1.6175, 1.755, 1.725, 1.7274999999999998, 1.6775, 1.67, 1.6775, 1.8125, 1.965, 1.9324999999999999, 1.9974999999999998, 2.0575, 2.035, 1.9775, 1.9275, 2.0275, 2.18, 2.1375, 2.1975, 2.13, 2.1475, 2.135, 2.0975, 2.2525, 2.185, 1.9075000000000002, 1.8525, 1.91, 1.8825, 1.8575, 2.095, 2.05, 2.1575, 2.2475, 2.5975, 2.5375, 2.4625, 2.4675000000000002, 2.4425, 2.4625, 2.5100000000000002, 2.7175, 2.7275, 2.835, 2.8175, 2.765, 2.725, 2.785, 2.845, 2.755, 2.7425, 2.73, 2.7925, 2.77, 2.7575, 2.7475, 2.605, 2.7275, 2.7175000000000002, 2.73, 2.735, 2.7225, 2.8, 2.8575, 2.8325, 2.6275, 2.5875, 2.7025, 2.6875, 2.655, 2.6425, 2.6875, 2.6375, 2.5925000000000002, 2.53, 2.48, 2.4475, 2.45, 2.4475000000000002, 2.375, 2.1875, 2.105, 2.12, 2.1475, 2.1, 2.1, 2.1075, 2.0925, 2.09, 2.075, 2.0925, 2.0974999999999997, 2.095, 2.2325, 2.2225, 2.21, 2.015, 2.0225, 1.795, 1.7875, 1.765, 1.7574999999999998, 1.8225, 1.8, 1.615, 1.725, 1.71, 1.8225, 1.7874999999999999, 1.7525, 1.7325, 1.8425, 1.8475, 1.9075, 1.975, 1.965, 1.9725, 1.945, 1.945, 1.98, 1.97, 1.9575, 2.0425, 2.0225, 2.025, 2.025, 2.105, 1.7675, 1.75, 1.7775, 1.7750000000000001, 1.8050000000000002, 1.78, 1.7625, 1.7850000000000001, 1.81, 1.8025, 1.8775, 1.965, 1.9925, 1.97, 1.9175, 1.935, 1.9625, 1.9525000000000001, 1.805, 1.7875, 1.825, 1.8074999999999999, 1.86, 1.805, 1.89, 1.8875000000000002, 1.875, 1.9575, 1.8175, 1.795, 1.8599999999999999, 1.855, 1.7850000000000001, 1.87, 1.8624999999999998, 1.8625, 1.8775, 1.8525, 1.8625, 2.0, 2.025, 2.0225, 2.0425, 2.0575, 2.0625, 2.0675, 1.9574999999999998, 1.9475, 1.9725000000000001, 1.96, 1.9725000000000001, 1.9525000000000001, 1.0675000000000001, 1.1, 2.0125, 2.0, 1.9925, 1.055, 1.1300000000000001, 2.035, 1.1525, 1.115, 1.165, 1.095, 1.1025, 1.1125, 1.09] second_grid_unamb_3 = [60.0, 66.67, 70.27, 80.0, 79.03, 66.67, 65.71, 69.64, 65.0, 67.11, 66.29, 68.62, 67.01, 71.92, 71.5, 67.86, 64.71, 64.9, 63.89, 65.64, 63.91, 68.42, 67.15, 65.87, 68.23, 66.67, 68.95, 70.92, 73.62, 75.24, 75.9, 77.2, 77.2, 77.52, 77.52, 78.18, 78.5, 79.8, 80.13, 81.11, 81.76, 81.76, 82.41, 83.06, 83.71, 84.36, 84.04, 83.39, 83.71, 84.04, 84.69, 85.02, 85.02, 85.02, 84.69, 85.34, 85.34, 85.99, 85.99, 85.99, 85.99, 85.99, 86.32, 86.64, 86.97, 87.3, 87.3, 84.08, 84.39, 84.39, 84.39, 84.08, 84.08, 84.08, 84.08, 84.08, 85.03, 85.03, 85.03, 85.35, 85.35, 85.35, 85.35, 85.99, 85.99, 86.31, 86.62, 86.62, 86.62, 86.62, 86.62, 86.94, 86.94, 86.94, 86.94, 87.58, 87.58, 87.9, 87.9, 87.9, 87.9, 87.9, 88.22, 88.22, 88.54, 88.54, 88.22, 88.54, 88.54, 88.85, 88.85, 88.85, 89.17, 89.17, 89.49, 89.49, 89.49, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 90.13, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 90.13, 90.13, 90.13, 90.13, 90.13, 90.13, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 90.76, 90.45, 88.54, 90.76, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.4, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.4, 91.4, 91.4, 91.72, 91.72, 91.72, 91.72, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.72, 91.72, 91.72, 92.04, 91.72, 91.72, 91.72, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.36, 92.68, 92.36, 92.36, 92.36, 92.99, 92.68, 92.36, 92.99, 92.99, 93.31, 93.31, 93.31, 93.31, 93.31, 92.99] second_grid_max_err_mdp_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 97.43, 97.43, 90.84, 62.94, 62.91, 63.98, 66.71, 66.64, 40.93, 43.14, 43.75, 22.0, 80.76, 83.9, 84.84, 84.59, 83.99, 84.62, 84.17, 85.44, 85.34, 85.27, 79.08, 79.17, 43.98, 37.44, 6.73, 5.35, 3.82, 2.69, 2.36, 2.24, 50.86, 50.78, 57.36, 2.49, 2.37, 2.35, 2.22, 1.99, 70.4, 70.51, 70.85, 1.45, 1.34, 1.49, 1.65, 1.47, 1.34, 1.31, 1.22, 1.26, 1.44, 1.47, 1.48, 1.73, 1.83, 2.03, 1.6, 1.47, 1.71, 1.42, 1.21, 1.08, 1.64, 1.37, 1.28, 2.32, 2.64, 2.47, 2.45, 2.23, 31.97, 32.27, 32.15, 33.57, 33.67, 84.09, 84.12, 84.31, 84.35, 84.31, 84.3, 85.35, 85.36, 85.36, 85.36, 85.37, 85.37, 85.37, 85.37, 85.35, 84.36, 84.38, 84.38, 84.38, 84.31, 84.31, 84.27, 0.93, 29.5, 29.61, 29.61, 28.77, 28.43, 28.53, 28.35, 28.21, 1.36, 1.43, 1.44, 1.39, 6.22, 6.22, 6.19, 6.13, 6.02, 6.02, 5.93, 5.89, 5.9, 5.88, 5.89, 5.94, 5.94, 5.91, 6.02, 5.98, 5.96, 5.88, 5.89, 5.82, 5.77, 5.77, 5.77, 5.76, 5.76, 5.8, 5.76, 5.72, 5.74, 5.73, 5.71, 5.72, 5.7, 5.63, 5.68, 5.62, 5.56, 5.53, 5.56, 5.55, 5.49, 5.5, 5.39, 5.28, 5.31, 5.28, 5.69, 5.71, 5.67, 5.65, 5.65, 5.54, 5.44, 5.35, 5.31, 5.32, 5.27, 5.24, 5.2, 5.16, 5.48, 5.47, 5.51, 5.54, 5.49, 5.51, 5.47, 5.49, 5.42, 5.37, 5.38, 5.35, 5.28, 5.26, 5.25, 5.23, 5.2, 5.24, 5.18, 5.19, 5.18, 5.16, 5.14, 5.13, 5.08, 5.06, 5.04, 5.01, 5.0, 4.97, 4.96, 4.95, 5.24, 5.23, 5.18, 5.16, 5.09, 5.06, 5.01, 5.04, 5.03, 5.05, 5.37, 5.35, 5.3, 5.28, 5.31, 5.33, 5.31, 5.27, 5.31, 5.66, 5.66] second_grid_avr_err_mdp_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 63.3325, 65.7775, 54.9275, 23.9625, 25.217499999999998, 32.199999999999996, 35.4375, 29.517500000000002, 23.5125, 21.87, 22.445, 12.545, 27.425, 25.8425, 27.2275, 32.205, 29.0325, 32.825, 32.2825, 31.4275, 30.295, 30.2025, 27.37, 27.4775, 14.165, 12.7975, 3.3625, 2.9425, 2.3525, 1.8025, 1.5325, 1.6675, 14.0725, 14.17, 15.655, 2.065, 1.585, 1.57, 1.57, 1.175, 17.990000000000002, 18.1125, 18.07, 1.035, 0.9875, 1.0325, 1.0975, 1.055, 0.92, 0.8624999999999999, 0.78, 0.89, 1.0175, 1.095, 1.12, 1.3775, 1.3725, 1.56, 0.725, 0.6025, 0.6325, 0.6, 0.585, 0.5575, 0.6475, 0.5225000000000001, 0.4975, 0.7525, 0.8575, 0.8350000000000001, 0.8475, 0.78, 8.219999999999999, 8.315000000000001, 8.3325, 8.6425, 11.11, 24.5, 24.5975, 24.657500000000002, 24.7425, 24.68, 24.6575, 25.325, 25.4125, 25.482499999999998, 25.5325, 25.565, 25.4475, 25.477500000000003, 25.545, 25.32, 24.7625, 24.805, 24.862499999999997, 24.8475, 24.68, 24.69, 24.732499999999998, 0.47000000000000003, 7.5975, 7.625, 7.6225, 7.415, 7.545, 7.5600000000000005, 7.550000000000001, 7.49, 0.74, 0.73, 0.7175, 0.745, 2.205, 2.2375, 2.27, 2.275, 2.2475, 2.2175, 2.255, 2.2375, 2.2375000000000003, 2.225, 2.205, 2.1975000000000002, 2.1825, 2.255, 2.3625, 2.3475, 2.3475, 2.2875, 2.3175, 2.39, 2.36, 2.3325, 2.295, 2.3375, 2.35, 2.3575, 2.36, 2.3125, 2.3425000000000002, 2.42, 2.3375, 2.33, 2.27, 2.23, 2.1475, 2.1575, 2.1475, 2.17, 2.1174999999999997, 2.105, 2.1, 2.09, 2.1075, 2.1350000000000002, 2.1125, 2.1325000000000003, 2.215, 2.1875, 2.26, 2.2725, 2.2125, 2.2, 2.21, 2.1625, 2.155, 2.12, 2.08, 2.0925000000000002, 2.0775, 2.095, 2.15, 2.185, 2.1774999999999998, 2.205, 2.21, 2.2125, 2.2675, 2.2, 2.18, 2.15, 2.13, 2.15, 2.1075, 2.0875, 2.12, 2.14, 2.1175, 2.1075, 2.1525, 2.185, 2.1799999999999997, 2.1675, 2.19, 2.215, 2.2475, 2.2475, 2.2225, 2.21, 2.1925, 2.1925, 2.185, 2.1975000000000002, 2.245, 2.2975000000000003, 2.2625, 2.2825, 2.2975, 2.3375, 2.3024999999999998, 2.2575, 2.2775, 2.3, 2.365, 2.38, 2.3674999999999997, 2.3875, 2.3449999999999998, 2.305, 2.3125, 2.3049999999999997, 2.2475, 2.2875, 2.3225000000000002] second_grid_unamb_mdp_1 = [80.0, 74.29, 77.59, 82.28, 72.28, 73.83, 69.84, 72.54, 70.2, 75.62, 77.5, 79.04, 75.28, 58.46, 62.56, 65.2, 71.81, 73.68, 75.95, 74.39, 73.18, 74.55, 68.44, 73.31, 74.78, 75.7, 70.35, 68.13, 71.83, 72.03, 73.4, 73.22, 76.6, 75.23, 75.69, 76.59, 77.1, 76.51, 77.85, 76.79, 71.01, 76.29, 79.57, 80.86, 80.0, 79.92, 81.42, 77.44, 78.46, 79.47, 80.28, 80.89, 81.5, 82.11, 82.32, 82.52, 83.13, 83.54, 83.94, 84.96, 85.16, 85.57, 86.38, 86.99, 86.59, 86.99, 86.99, 86.14, 86.55, 86.75, 87.15, 87.15, 88.15, 88.35, 88.35, 88.55, 88.76, 88.76, 89.16, 89.36, 89.38, 89.78, 90.18, 90.38, 90.58, 90.58, 90.58, 90.58, 90.98, 91.38, 91.58, 91.58, 89.88, 90.08, 90.28, 91.07, 91.07, 91.07, 91.27, 91.67, 91.67, 87.52, 87.52, 88.11, 88.11, 88.11, 88.11, 88.3, 88.5, 88.5, 88.69, 88.69, 89.11, 89.3, 88.91, 88.91, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.69, 89.69, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.27, 90.27, 90.66, 86.83, 87.02, 87.21, 87.4, 87.6, 87.98, 88.17, 88.17, 87.98, 88.17, 88.17, 88.17, 88.36, 88.55, 88.55, 88.55, 88.55, 88.55, 88.55, 88.74, 88.74, 88.55, 88.74, 88.74, 89.12, 89.12, 89.12, 89.5, 89.5, 89.5, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.5, 89.5, 89.69, 89.89, 89.69, 89.69, 89.69, 89.69, 89.69, 89.69, 89.69, 89.69, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 92.37, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.8, 92.8, 92.8, 92.8, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 92.8, 92.94, 93.19, 93.19, 93.19, 93.19, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.58, 93.39, 93.97, 93.97, 93.97, 93.97, 93.77, 93.77, 93.77, 93.77] second_grid_max_err_mdp_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 97.43, 97.43, 81.02, 82.12, 90.06, 87.85, 77.77, 55.47, 80.41, 71.61, 70.24, 63.23, 63.43, 65.83, 64.43, 64.26, 61.59, 62.82, 62.9, 60.64, 59.49, 59.59, 59.61, 59.61, 58.67, 57.58, 58.2, 5.67, 5.21, 5.06, 4.63, 4.91, 5.2, 4.66, 4.27, 4.42, 4.08, 4.32, 4.66, 4.68, 4.53, 4.12, 4.6, 4.37, 4.31, 4.1, 3.87, 4.35, 4.54, 4.59, 4.75, 4.72, 3.31, 3.11, 2.85, 2.76, 2.41, 2.24, 3.6, 3.51, 3.49, 3.6, 3.57, 3.57, 3.69, 3.74, 3.64, 3.58, 3.56, 3.46, 3.4, 3.34, 3.3, 3.36, 3.19, 3.23, 3.21, 3.29, 3.21, 3.4, 3.42, 3.39, 3.33, 3.29, 3.24, 3.88, 3.92, 4.06, 4.05, 4.08, 4.05, 4.04, 3.93, 4.08, 4.17, 4.29, 4.28, 4.3, 4.38, 4.54, 4.59, 4.55, 4.66, 4.62, 4.78, 4.71, 4.73, 4.72, 4.8, 4.74, 4.81, 4.85, 4.96, 4.97, 4.93, 4.89, 4.9, 4.9, 4.88, 4.77, 4.76, 4.83, 4.81, 4.77, 4.73, 4.66, 4.61, 4.58, 4.52, 4.44, 4.35, 4.35, 4.38, 4.42, 4.3, 4.38, 4.32, 4.35, 4.37, 4.39, 4.44, 4.47, 4.32, 4.3, 4.31, 4.31, 4.28, 5.13, 5.19, 5.23, 5.24, 5.23, 5.23, 5.23, 5.16, 5.16, 5.21, 5.32, 5.29, 5.32, 5.47, 5.42, 5.4, 5.54, 5.59, 5.52, 1.74, 0.6, 0.59, 0.58, 0.59, 60.12, 60.62, 1.07, 1.27, 0.58, 0.56, 0.76, 0.88, 0.76, 0.71, 0.94, 0.79, 0.67, 0.91, 0.98, 0.85, 0.76, 0.74, 0.73, 0.67, 0.64, 0.7, 0.81, 0.9, 0.87, 0.97, 0.89, 1.0, 0.93, 0.96, 1.04, 1.15, 1.1, 1.09, 1.11, 1.09, 1.07, 1.14, 1.13, 1.03, 0.96, 0.87, 0.85, 1.07, 1.01, 0.95, 0.9, 0.83, 1.02, 0.87, 0.75, 0.7, 0.67, 1.53, 0.9, 0.83, 0.78, 0.97] second_grid_avr_err_mdp_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 61.695, 66.6725, 54.12, 39.347500000000004, 42.305, 37.8925, 26.1875, 26.7275, 34.762499999999996, 34.05, 38.72, 36.715, 28.905, 23.6325, 22.150000000000002, 22.365000000000002, 20.5425, 21.535, 21.265, 16.767500000000002, 16.5425, 16.575, 16.445, 16.5625, 16.3325, 15.9725, 16.075, 2.985, 2.8025, 2.1599999999999997, 1.89, 1.7650000000000001, 1.8125, 1.705, 1.3224999999999998, 1.3675, 1.185, 1.23, 1.3, 1.2925, 1.615, 1.5875000000000001, 1.67, 1.55, 1.595, 1.4675, 1.6125, 2.215, 2.3375, 2.5225, 2.54, 2.455, 1.7725, 1.825, 1.5775, 1.3775, 1.22, 1.1400000000000001, 1.5050000000000001, 1.42, 1.4125, 1.505, 1.5, 1.52, 1.5625, 1.58, 1.4975, 1.3825, 1.36, 1.195, 1.0375, 1.085, 1.02, 1.09, 1.155, 1.12, 1.2675, 1.2825, 1.28, 1.0725, 1.205, 1.2925, 1.28, 1.2675, 1.2925, 1.4, 1.33, 1.3275, 1.3225, 1.3225, 1.325, 1.3125, 1.335, 1.5050000000000001, 1.47, 1.41, 1.385, 1.3675, 1.4075, 1.51, 1.5425, 1.4825, 1.5975000000000001, 1.5350000000000001, 1.6800000000000002, 1.575, 1.57, 1.5725, 1.5699999999999998, 1.53, 1.6024999999999998, 1.6749999999999998, 1.705, 1.7275, 1.6624999999999999, 1.5775, 1.5750000000000002, 1.53, 1.5, 1.4474999999999998, 1.4425, 1.4925, 1.505, 1.4725, 1.4300000000000002, 1.3900000000000001, 1.36, 1.33, 1.2674999999999998, 1.26, 1.2374999999999998, 1.22, 1.23, 1.24, 1.205, 1.2349999999999999, 1.2175, 1.2225, 1.2275, 1.2525, 1.2825000000000002, 1.29, 1.25, 1.26, 1.315, 1.3375, 1.3425, 1.54, 1.5425, 1.5525, 1.5225, 1.5050000000000001, 1.5075, 1.485, 1.46, 1.46, 1.4725, 1.49, 1.4875, 1.5150000000000001, 1.555, 1.53, 1.5025000000000002, 1.5375, 1.57, 1.5299999999999998, 0.595, 0.305, 0.295, 0.285, 0.285, 16.2025, 16.419999999999998, 0.435, 0.4775, 0.3575, 0.385, 0.505, 0.5075, 0.4775, 0.46499999999999997, 0.57, 0.4925, 0.44000000000000006, 0.385, 0.3475, 0.325, 0.28, 0.285, 0.345, 0.3425, 0.355, 0.365, 0.36, 0.3725, 0.3725, 0.355, 0.3, 0.3325, 0.3025, 0.31, 0.305, 0.3075, 0.335, 0.3325, 0.36750000000000005, 0.35000000000000003, 0.3775, 0.43, 0.4225, 0.405, 0.4125, 0.36, 0.4, 0.435, 0.355, 0.32, 0.2925, 0.295, 0.3575, 0.335, 0.275, 0.255, 0.23500000000000001, 0.595, 0.3275, 0.385, 0.395, 0.445] second_grid_unamb_mdp_2 = [80.0, 71.43, 70.97, 73.75, 73.0, 66.67, 59.44, 64.24, 69.81, 67.43, 64.1, 64.84, 72.52, 74.37, 72.84, 69.85, 68.44, 72.26, 70.99, 71.39, 72.36, 68.88, 74.67, 75.73, 75.82, 72.3, 74.24, 77.39, 77.05, 73.17, 77.37, 78.85, 76.05, 77.05, 72.5, 79.61, 81.44, 82.24, 82.63, 83.23, 83.03, 83.43, 83.63, 83.63, 83.83, 82.84, 84.22, 85.4, 85.8, 86.79, 87.57, 88.36, 89.55, 89.55, 90.34, 90.34, 90.53, 90.53, 90.73, 90.93, 91.12, 91.12, 91.32, 91.32, 91.32, 91.72, 91.72, 91.91, 92.31, 92.5, 92.31, 92.31, 92.5, 93.1, 93.1, 93.1, 93.49, 93.29, 92.25, 93.69, 93.69, 93.89, 93.89, 93.89, 93.89, 93.89, 93.89, 94.08, 89.65, 89.65, 89.45, 91.35, 93.58, 94.67, 94.67, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 94.87, 90.47, 90.47, 90.47, 90.66, 90.66, 90.86, 90.86, 90.86, 90.86, 91.05, 91.05, 91.05, 91.25, 91.44, 91.25, 88.31, 88.34, 90.25, 91.63, 91.83, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 91.83, 90.63, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.22, 92.41, 92.61, 92.41, 91.73, 92.22, 91.01, 92.41, 92.8, 92.8, 92.8, 92.8, 92.8, 92.8, 93.0, 92.8, 92.8, 93.0, 92.8, 92.8, 92.13, 93.19, 93.19, 93.0, 91.94, 89.6, 89.98, 90.17, 90.36, 90.36, 90.55, 90.36, 90.36, 90.36, 91.68, 92.51, 90.55, 92.44, 93.28, 93.28, 93.47, 93.47, 93.28, 91.3, 91.3, 92.63, 93.67, 93.67, 93.47, 93.67, 93.67, 93.67, 93.86, 93.67, 93.67, 93.86, 93.67, 93.86, 93.86, 93.86, 93.86, 94.05, 93.86, 93.86, 93.86, 94.05, 94.05, 94.05, 94.05, 94.05, 94.24, 94.24, 94.24, 94.24, 94.24, 94.24, 94.24, 94.05, 93.56, 94.43, 94.24, 94.43, 94.05] def single_plot(): matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) r = list(range(len(second_grid_max_err_2))) # plt.plot(r, second_grid_max_err_2, label = 'max_error_%') # plt.plot(r, second_grid_avr_err_2, label = 'avr_error_%') # plt.plot(r, second_grid_unamb_2, label='unamb_%') plt.plot(r, mqtt_max_err_1, label='Maximum Error') plt.plot(r, mqtt_avr_err_1, label='Average Error') plt.plot(r, mqtt_unamb_1, label='Unambiguous Rows') # second_grid_avr_err_2 looks nice # plt.plot(r, [100 - r for r in second_grid_unamb_2], label='unamb_%') plt.ylabel('Percentage') plt.xlabel('Learning Round') plt.title('MQTT') plt.grid(axis='y') plt.legend() #plt.show() plt.savefig("stoppingAndUnamb_mqtt.pgf") import tikzplotlib tikzplotlib.save("stoppingAndUnamb_mqtt.tex") def side_by_side(): matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) r = list(range(len(second_grid_max_err_2))) # plt.plot(r, second_grid_max_err_2, label = 'max_error_%') # plt.plot(r, second_grid_avr_err_2, label = 'avr_error_%') # plt.plot(r, second_grid_unamb_2, label='unamb_%') fig, (plt1, plt2) = plt.subplots(1, 2, figsize=(10, 3)) plt1.plot_side_by_side() plt1.plot_side_by_side() plt1.plot_side_by_side() # second_grid_avr_err_2 looks nice # plt.plot(r, [100 - r for r in second_grid_unamb_2], label='unamb_%') plt1.set_ylabel('Percentage') plt1.set_xlabel('Learning Round') plt1.set_title('72 State Gridworld') plt1.grid(axis='y') plt1.legend() plt2.plot_side_by_side() plt2.plot_side_by_side() plt2.plot_side_by_side() # second_grid_avr_err_2 looks nice # plt.plot(r, [100 - r for r in second_grid_unamb_2], label='unamb_%') plt2.set_ylabel('Percentage') plt2.set_xlabel('Learning Round') plt2.set_title('MQTT') plt2.grid(axis='y') plt2.legend() # plt.show() fig.tight_layout() fig.savefig("stoppingAndUnamb2plots.pgf") import tikzplotlib tikzplotlib.save("stoppingAndUnamb2plots.tex") if __name__ == '__main__': side_by_side() ================================================ FILE: Benchmarking/unamb_error_plot.py ================================================ # max_err # acv_err # unamb import matplotlib.pyplot as plt import matplotlib mqtt_max_err_1 = [100, 100, 100, 100, 100, 100, 100, 12.25, 7.92, 3.87, 3.99, 6.0, 4.52, 2.99, 3.87, 4.29, 4.7, 4.26, 4.62, 6.41, 4.23, 3.38, 2.79, 2.2, 2.94, 3.04, 2.28, 2.11, 2.2, 2.45, 2.35, 2.12, 2.66, 2.27, 2.23, 2.21, 1.94, 1.82, 1.61, 1.45, 1.41, 1.83, 1.43, 1.42, 1.44, 1.41, 1.37, 1.31, 1.33, 1.3, 1.29, 1.27, 1.47, 1.78, 1.37, 1.28, 1.94, 1.64, 3.48, 3.11, 4.37, 3.93, 3.81, 4.28, 3.99, 4.25, 4.75, 5.14, 5.03, 4.72, 4.74, 4.42, 4.07, 3.72, 3.55, 3.21, 2.99, 2.84, 2.53, 2.75, 2.62, 2.26, 1.78, 1.9, 2.01, 2.09, 2.33, 2.63, 2.75, 2.53, 2.15, 2.48, 3.36, 3.03, 2.87, 3.02, 3.0, 3.04, 2.79, 2.84, 2.67, 3.11, 2.8, 2.52, 2.28, 2.0, 1.81, 1.7, 1.48, 1.74, 1.55, 1.64, 1.46, 1.23, 1.08, 1.26, 1.47, 1.65, 1.56, 1.76, 1.91, 1.97, 2.01, 1.94, 1.7, 1.65, 1.4, 1.15, 1.35, 1.29, 1.49, 1.76, 1.61, 1.66, 1.72, 1.9, 2.14, 1.96, 1.65, 1.56, 1.92, 1.77, 1.66, 1.37, 1.44, 1.41, 1.58, 1.33, 1.18, 1.28, 1.38, 1.17, 1.14, 1.25, 1.4, 1.22, 1.08, 1.09, 1.34, 1.2, 1.09, 1.19, 1.06, 1.07, 1.07, 1.06, 1.06, 1.06, 1.05, 1.05, 1.05, 1.05, 1.05, 1.04, 1.03, 1.02, 1.03, 1.03, 1.05, 1.04, 1.04, 1.04, 1.05, 1.05, 1.06, 1.06, 1.07, 1.07, 1.07, 1.08, 1.07, 1.08, 1.07, 1.06, 1.06, 1.06, 1.06, 1.05, 1.05, 1.03, 1.03, 1.02, 1.14, 1.16, 1.36, 1.18, 1.26, 1.16, 1.35, 1.52, 1.41, 1.46, 1.4, 1.47, 1.59, 1.65, 1.73, 1.8, 1.72, 1.66, 1.65, 1.75, 1.69, 1.72, 1.56, 1.6, 1.7, 1.62, 1.62, 1.61, 1.65, 1.86, 2.01, 1.93, 2.16, 2.11, 2.03, 1.93, 1.85, 1.78, 1.66, 1.67, 1.64, 1.76, 1.77, 1.67, 1.55, 1.59, 1.51, 1.46] mqtt_avr_err_1 = [100, 100, 100, 100, 100, 100, 100, 9.232, 4.548, 2.572, 2.592, 3.512, 2.89, 2.27, 2.606, 2.616, 2.754, 2.502, 2.6879999999999997, 3.42, 2.404, 2.148, 1.97, 1.672, 2.074, 2.084, 1.724, 1.472, 1.2, 1.066, 1.292, 1.348, 1.596, 1.506, 1.408, 0.91, 1.1219999999999999, 1.178, 1.276, 0.994, 0.918, 1.3519999999999999, 1.158, 1.068, 0.946, 0.708, 0.644, 0.56, 0.586, 0.562, 0.582, 0.764, 1.084, 1.242, 1.08, 1.056, 1.322, 1.184, 1.998, 1.8299999999999998, 2.394, 2.212, 2.112, 2.338, 2.206, 2.322, 2.4859999999999998, 2.634, 2.56, 2.4419999999999997, 2.412, 2.306, 2.166, 2.04, 1.936, 1.754, 1.658, 1.574, 1.428, 1.522, 1.476, 1.3559999999999999, 1.158, 1.19, 1.242, 1.294, 1.376, 1.474, 1.528, 1.432, 1.268, 1.3960000000000001, 1.786, 1.656, 1.616, 1.654, 1.6380000000000001, 1.64, 1.538, 1.556, 1.482, 1.682, 1.534, 1.4220000000000002, 1.318, 1.184, 1.104, 1.064, 0.98, 1.084, 0.994, 1.028, 0.9460000000000001, 0.822, 0.704, 0.832, 0.9440000000000001, 1.026, 0.99, 1.068, 1.134, 1.17, 1.182, 1.144, 1.03, 1.014, 0.912, 0.786, 0.872, 0.848, 0.942, 1.066, 0.992, 1.016, 1.048, 1.1280000000000001, 1.238, 1.164, 1.022, 0.984, 1.146, 1.098, 1.064, 0.934, 0.96, 0.954, 1.026, 0.906, 0.854, 0.928, 0.982, 0.896, 0.882, 0.9359999999999999, 1.008, 0.926, 0.8360000000000001, 0.866, 0.992, 0.922, 0.872, 0.9179999999999999, 0.8460000000000001, 0.796, 0.812, 0.868, 0.852, 0.734, 0.744, 0.702, 0.668, 0.654, 0.762, 0.654, 0.5780000000000001, 0.56, 0.558, 0.526, 0.47800000000000004, 0.462, 0.64, 0.612, 0.5640000000000001, 0.5720000000000001, 0.63, 0.624, 0.588, 0.716, 0.736, 0.6940000000000001, 0.602, 0.578, 0.542, 0.528, 0.554, 0.476, 0.542, 0.704, 0.722, 0.744, 0.728, 0.806, 0.87, 0.868, 0.958, 0.876, 0.918, 0.884, 0.97, 1.052, 1.0, 1.038, 1.016, 1.042, 1.094, 1.116, 1.168, 1.214, 1.19, 1.172, 1.188, 1.224, 1.206, 1.214, 1.1340000000000001, 1.1660000000000001, 1.196, 1.172, 1.178, 1.174, 1.186, 1.276, 1.3459999999999999, 1.318, 1.422, 1.4, 1.3699999999999999, 1.324, 1.296, 1.268, 1.216, 1.226, 1.222, 1.274, 1.274, 1.204, 1.1540000000000001, 1.1640000000000001, 1.1280000000000001, 1.1059999999999999] mqtt_unamb_1 = [83.33, 68.57, 70.21, 72.06, 73.42, 77.42, 85.22, 86.86, 90.74, 95.11, 97.28, 97.28, 97.28, 97.96, 99.46, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 79.08, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.59, 79.59, 79.59, 79.08, 78.57, 78.57, 79.59, 79.59, 79.59, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 80.61, 80.61, 81.12, 81.63, 81.63, 81.63, 81.63, 81.63, 81.12, 81.12, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.61, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.61, 80.1, 81.12, 81.12, 81.12, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 82.14, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.63, 81.12, 81.12, 81.12, 82.14, 82.14, 82.14, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.16, 83.16, 83.16, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.67, 83.16, 83.16, 83.67, 83.67, 83.67, 84.18, 84.18, 84.18, 84.18, 84.18, 84.69, 84.18, 84.18, 83.67, 84.69, 84.69, 85.2, 85.71, 85.71, 85.71, 85.71, 85.71, 85.71, 85.71, 85.71] mqtt_max_err_2 = [100, 100, 100, 100, 100, 100, 100, 9.76, 1.89, 28.08, 6.22, 3.56, 3.26, 3.51, 3.12, 2.79, 2.93, 2.61, 2.06, 2.14, 2.17, 2.06, 2.49, 2.2, 2.46, 2.37, 2.61, 2.67, 2.61, 2.67, 2.79, 2.84, 2.8, 2.92, 3.02, 3.01, 2.97, 3.26, 3.21, 3.04, 2.57, 3.15, 2.49, 2.66, 3.59, 3.66, 3.15, 3.55, 3.46, 3.52, 3.72, 3.42, 3.61, 3.14, 3.63, 3.51, 3.32, 2.92, 3.46, 3.61, 3.45, 3.54, 4.12, 4.81, 4.81, 4.53, 4.76, 4.41, 4.79, 5.14, 5.08, 5.31, 4.86, 4.66, 4.08, 4.29, 4.81, 4.43, 4.77, 4.58, 4.58, 4.77, 4.52, 4.36, 4.2, 4.45, 4.29, 4.23, 4.38, 4.57, 4.38, 4.34, 4.29, 4.05, 4.03, 3.83, 3.65, 3.43, 3.33, 3.78, 3.53, 3.55, 3.4, 3.34, 3.06, 3.43, 3.61, 3.61, 3.89, 3.58, 3.4, 3.23, 2.84, 2.57, 2.79, 2.8, 2.9, 2.68, 2.98, 2.76, 2.65, 2.4, 2.13, 2.38, 2.5, 2.53, 2.37, 2.47, 2.46, 2.23, 2.23, 2.35, 2.27, 2.1, 2.1, 1.86, 1.83, 1.93, 2.07, 1.76, 1.9, 1.63, 1.65, 1.52, 1.53, 1.93, 1.82, 1.63, 1.77, 1.57, 1.56, 1.57, 1.56, 1.76, 1.76, 2.02, 1.93, 1.92, 1.77, 1.96, 1.78, 1.72, 1.63, 1.4, 1.63, 1.49, 1.55, 1.64, 1.74, 1.5, 1.8, 1.66, 1.87, 2.05, 2.03, 2.35, 2.29, 2.34, 2.16, 2.05, 1.93, 1.93, 1.83, 1.84, 1.94, 2.17, 2.32, 2.7, 2.56, 2.45, 2.61, 2.63, 2.69, 2.73, 2.56, 2.56, 2.68, 2.53, 2.55, 2.66, 2.7, 2.56, 2.6, 2.68, 2.52, 2.44, 2.34, 2.23, 2.38, 2.34, 2.8, 2.84, 2.7, 2.65, 2.67, 2.73, 2.73, 2.82, 2.79, 2.77, 2.84, 2.78, 2.82, 2.9, 2.83, 2.82, 2.9, 2.81, 2.7, 2.7, 2.64, 2.57, 2.5, 2.58, 2.5, 2.61, 2.59, 2.71, 2.72, 2.67, 2.64, 2.76, 2.77, 2.75, 2.72, 2.74, 2.68, 2.6, 2.53, 2.49] mqtt_avr_err_2 = [100, 100, 100, 100, 100, 100, 100, 6.438, 0.984, 9.953999999999999, 3.312, 2.216, 1.238, 1.426, 1.19, 1.204, 1.62, 1.6139999999999999, 1.214, 1.252, 1.03, 0.9480000000000001, 1.4320000000000002, 1.368, 1.228, 1.086, 0.95, 1.1159999999999999, 1.028, 1.31, 1.236, 1.5899999999999999, 1.1139999999999999, 1.25, 1.354, 1.462, 1.9100000000000001, 2.152, 2.002, 1.8900000000000001, 1.97, 2.2439999999999998, 1.976, 2.048, 2.452, 2.536, 2.332, 2.454, 2.452, 2.5060000000000002, 2.552, 2.366, 2.294, 2.24, 2.436, 2.46, 2.316, 2.064, 2.32, 2.422, 2.328, 2.314, 2.548, 2.846, 2.856, 2.7439999999999998, 2.854, 2.634, 2.736, 2.868, 2.8779999999999997, 2.952, 2.726, 2.64, 2.38, 2.474, 2.686, 2.518, 2.638, 2.5100000000000002, 2.518, 2.606, 2.4699999999999998, 2.37, 2.284, 2.426, 2.346, 2.3280000000000003, 2.368, 2.45, 2.368, 2.36, 2.328, 2.252, 2.2520000000000002, 2.116, 2.082, 1.976, 1.97, 2.178, 2.038, 2.054, 2.004, 1.966, 1.832, 2.002, 2.09, 2.096, 2.214, 2.072, 1.974, 1.882, 1.698, 1.542, 1.6400000000000001, 1.652, 1.722, 1.6059999999999999, 1.746, 1.6159999999999999, 1.5899999999999999, 1.466, 1.356, 1.446, 1.304, 1.3379999999999999, 1.262, 1.302, 1.322, 1.22, 1.224, 1.294, 1.416, 1.324, 1.3439999999999999, 1.248, 1.22, 1.256, 1.3119999999999998, 1.182, 1.26, 1.1239999999999999, 1.128, 1.084, 1.124, 1.316, 1.26, 1.17, 1.226, 1.102, 1.1, 1.11, 1.106, 1.19, 1.19, 1.31, 1.274, 1.272, 1.214, 1.278, 1.198, 1.182, 1.128, 1.036, 1.154, 1.076, 1.104, 1.14, 1.18, 1.068, 1.188, 1.1159999999999999, 1.216, 1.3179999999999998, 1.296, 1.438, 1.412, 1.42, 1.34, 1.286, 1.232, 1.228, 0.996, 1.002, 1.054, 1.164, 1.24, 1.402, 1.342, 1.294, 1.3599999999999999, 1.3679999999999999, 1.39, 1.416, 1.336, 1.332, 1.3840000000000001, 1.314, 1.3439999999999999, 1.398, 1.42, 1.368, 1.3940000000000001, 1.426, 1.352, 1.316, 1.262, 1.22, 1.28, 1.264, 1.474, 1.5, 1.4460000000000002, 1.426, 1.426, 1.452, 1.452, 1.5, 1.482, 1.482, 1.51, 1.478, 1.492, 1.526, 1.494, 1.49, 1.518, 1.47, 1.422, 1.424, 1.4020000000000001, 1.3659999999999999, 1.328, 1.364, 1.324, 1.3760000000000001, 1.368, 1.416, 1.42, 1.396, 1.3840000000000001, 1.436, 1.44, 1.43, 1.4100000000000001, 1.4180000000000001, 1.4000000000000001, 1.36, 1.3359999999999999, 1.316] mqtt_unamb_2 = [83.33, 79.41, 73.91, 74.14, 75.0, 78.49, 84.48, 89.13, 91.36, 95.65, 96.2, 98.37, 98.37, 99.46, 99.46, 99.46, 100.0, 100.0, 99.46, 78.06, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.08, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 78.57, 79.08, 79.08, 79.59, 79.08, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 79.59, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 79.59, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.61, 80.61, 80.61, 80.61, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 80.61, 80.61, 80.61, 80.61, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.1, 80.61, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 81.12, 82.14, 82.14, 82.14, 82.14, 81.63, 81.63, 81.63, 82.14, 82.14, 82.14, 82.14, 82.14, 82.65, 82.14, 82.14, 82.14, 82.14, 82.14, 82.14, 82.14, 82.14, 82.65, 82.65, 82.65, 82.14, 82.14, 82.14, 82.14, 82.14, 81.63, 81.63, 81.63, 82.14, 82.14, 82.65, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 82.65, 82.65, 82.65, 82.65, 83.16, 83.16, 83.16, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 83.16, 82.65, 82.65, 82.65, 82.65, 82.65, 82.65] second_grid_max_err_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 97.43, 93.46, 85.46, 42.15, 24.14, 22.72, 18.37, 17.29, 19.52, 19.32, 16.22, 16.41, 16.87, 16.86, 17.05, 17.1, 17.31, 17.2, 0.74, 0.71, 0.77, 0.8, 0.63, 0.65, 0.71, 0.68, 0.75, 0.69, 0.78, 0.6, 0.64, 0.73, 0.72, 4.92, 4.59, 4.53, 4.55, 4.47, 1.28, 1.46, 1.39, 1.57, 2.48, 2.47, 2.08, 2.1, 1.9, 1.14, 1.28, 0.89, 0.94, 5.16, 5.21, 5.2, 5.16, 5.15, 5.48, 4.9, 4.84, 4.91, 5.14, 5.11, 5.15, 5.15, 5.1, 5.37, 5.36, 5.31, 5.27, 5.32, 5.27, 5.21, 5.22, 5.17, 5.19, 5.15, 5.14, 5.11, 5.25, 5.2, 5.4, 5.39, 5.34, 5.36, 5.29, 5.29, 5.28, 5.29, 0.47, 0.46, 0.46, 0.41, 0.41, 0.43, 0.38, 0.39, 0.42, 0.41, 0.42, 0.46, 0.41, 0.4, 0.43, 0.53, 0.48, 0.51, 0.55, 0.52, 0.61, 0.45, 0.47, 0.47, 0.3, 0.26, 0.31, 0.4, 0.33, 0.24, 0.27, 0.2, 0.27, 0.21, 0.28, 0.3, 0.36, 0.42, 0.34, 0.5, 0.65, 0.57, 0.44, 0.62, 0.7, 0.69, 0.61, 0.49, 0.46, 0.55, 0.47, 0.4, 0.43, 0.49, 0.41, 0.41, 0.4, 0.43, 0.35, 0.33, 0.39, 0.46, 0.48, 0.51, 0.57, 0.49, 0.61, 0.58, 0.6, 0.57, 0.48, 0.65, 0.54, 0.52, 0.57, 0.62, 0.57, 0.75, 0.72, 0.65, 0.66, 0.6, 0.55, 0.52, 0.48, 0.49, 0.54, 0.52, 0.51, 0.55, 0.4, 0.37, 0.38, 0.48, 0.65, 0.67, 0.64, 0.67, 0.64, 0.66, 0.78, 0.69, 0.64, 0.63, 0.55, 0.53, 0.61, 0.59, 0.56, 0.56, 0.57, 0.58, 0.64, 0.57, 0.6, 0.7, 0.64, 0.5, 0.48, 0.41, 0.41, 0.47, 0.45, 0.45, 0.42, 0.33, 0.36, 0.37, 0.4, 0.39, 0.35, 0.34, 0.36, 0.31, 0.34, 0.35, 0.32, 0.41, 0.46, 0.4, 0.41, 0.43, 0.39, 0.34, 0.41, 0.48, 0.54, 0.43, 0.37] second_grid_avr_err_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 66.4425, 58.0325, 40.832499999999996, 20.5425, 10.545, 10.945, 10.030000000000001, 7.035, 10.91, 10.6475, 5.755, 5.78, 6.0075, 5.4275, 5.455, 5.4350000000000005, 5.52, 5.6325, 0.5825, 0.5625, 0.495, 0.5225, 0.505, 0.5, 0.5125, 0.47750000000000004, 0.51, 0.51, 0.4625, 0.355, 0.34500000000000003, 0.4025, 0.4025, 1.765, 1.8175, 1.74, 1.6075, 1.6375, 0.84, 0.8825, 0.8674999999999999, 0.895, 1.19, 1.165, 1.0050000000000001, 1.02, 0.9375, 0.6375, 0.7050000000000001, 0.5125, 0.53, 1.8, 1.7575, 1.6975, 1.625, 1.7125000000000001, 1.7950000000000002, 1.705, 1.665, 1.53, 1.5725, 1.5550000000000002, 1.51, 1.52, 1.4849999999999999, 1.47, 1.435, 1.4224999999999999, 1.4175, 1.4075, 1.3975, 1.41, 1.3975, 1.365, 1.4025, 1.3900000000000001, 1.3824999999999998, 1.35, 1.46, 1.4200000000000002, 1.4975, 1.4874999999999998, 1.4575, 1.4575, 1.4425000000000001, 1.4575, 1.4375, 1.485, 0.3075, 0.3325, 0.33, 0.2475, 0.265, 0.31, 0.2325, 0.2625, 0.27, 0.24, 0.2625, 0.315, 0.22, 0.2225, 0.3075, 0.355, 0.3175, 0.325, 0.3325, 0.315, 0.335, 0.2625, 0.2675, 0.2675, 0.19, 0.1675, 0.185, 0.21000000000000002, 0.195, 0.16999999999999998, 0.175, 0.1575, 0.17, 0.165, 0.17, 0.18, 0.19, 0.1925, 0.17250000000000001, 0.24, 0.335, 0.3125, 0.23500000000000001, 0.3075, 0.3325, 0.33999999999999997, 0.28, 0.24, 0.20500000000000002, 0.24000000000000002, 0.2025, 0.1875, 0.185, 0.1875, 0.16749999999999998, 0.1825, 0.1875, 0.1925, 0.1825, 0.185, 0.20750000000000002, 0.2275, 0.235, 0.255, 0.27999999999999997, 0.2575, 0.3275, 0.295, 0.3075, 0.3025, 0.26, 0.3525, 0.29000000000000004, 0.2775, 0.2975, 0.315, 0.26999999999999996, 0.35250000000000004, 0.33749999999999997, 0.28750000000000003, 0.28500000000000003, 0.265, 0.24500000000000002, 0.2275, 0.21, 0.2175, 0.2275, 0.2325, 0.23500000000000001, 0.24250000000000002, 0.21500000000000002, 0.2075, 0.1975, 0.21, 0.2975, 0.315, 0.3075, 0.315, 0.3075, 0.3275, 0.3775, 0.33249999999999996, 0.3275, 0.315, 0.28500000000000003, 0.275, 0.3125, 0.2975, 0.2725, 0.2725, 0.285, 0.27249999999999996, 0.325, 0.2925, 0.3075, 0.3475, 0.32, 0.255, 0.245, 0.215, 0.2225, 0.22999999999999998, 0.2225, 0.2225, 0.21, 0.1925, 0.185, 0.195, 0.22, 0.2075, 0.1825, 0.1875, 0.1925, 0.175, 0.185, 0.1825, 0.175, 0.195, 0.20750000000000002, 0.185, 0.1975, 0.21, 0.195, 0.17750000000000002, 0.19499999999999998, 0.2225, 0.24000000000000002, 0.20249999999999999, 0.195] second_grid_unamb_1 = [60.0, 77.78, 71.05, 76.36, 73.61, 79.07, 70.43, 66.67, 74.12, 66.85, 71.2, 71.01, 71.75, 70.28, 67.55, 70.8, 73.08, 72.88, 74.51, 76.22, 76.43, 78.83, 79.8, 79.94, 80.78, 81.43, 82.74, 82.74, 83.06, 84.04, 84.08, 85.02, 85.03, 85.34, 85.99, 85.67, 85.67, 86.32, 84.08, 84.39, 88.85, 87.95, 85.67, 85.67, 85.67, 86.62, 86.94, 86.94, 87.26, 87.26, 87.58, 87.9, 87.9, 88.22, 88.54, 88.54, 88.54, 88.85, 88.85, 88.85, 88.85, 88.85, 88.85, 89.17, 89.49, 89.49, 89.49, 89.49, 89.81, 90.13, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.13, 88.51, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.76, 90.76, 90.45, 91.28, 91.08, 91.08, 91.08, 91.08, 91.08, 90.76, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 92.04, 92.04, 92.36, 92.04, 92.04, 92.04, 92.36, 92.04, 92.04, 92.04, 92.04, 92.68, 92.68, 92.36, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.36, 90.74, 92.68, 92.99, 92.99, 92.99, 92.99, 92.99, 92.68, 92.99, 93.31, 93.31, 93.31, 93.31, 92.99, 92.99, 92.99, 92.99, 92.68, 92.99, 92.68, 92.68, 91.36, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.68, 92.36, 92.36, 92.68, 92.99, 92.68, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 93.31, 92.99, 93.31, 93.31, 93.31, 93.31, 92.99, 93.31, 93.31, 93.31, 93.63, 93.63, 93.63, 93.31, 93.63, 93.95, 93.95, 93.95, 94.27, 94.27, 94.27, 93.95, 94.27, 94.27, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.27, 94.27, 94.27, 94.27, 94.27, 94.59, 94.59, 94.59, 94.59, 94.59, 94.9, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.9, 94.9, 94.9, 94.9, 94.9, 94.9] second_grid_max_err_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 93.98, 97.43, 97.43, 95.88, 96.56, 92.01, 92.67, 91.36, 80.52, 76.18, 48.4, 46.6, 51.42, 6.01, 5.78, 5.81, 5.72, 5.5, 2.22, 2.32, 3.3, 1.98, 1.94, 1.51, 1.53, 1.55, 1.29, 1.12, 1.2, 1.89, 2.48, 2.34, 2.43, 4.38, 4.48, 1.67, 1.65, 1.57, 1.6, 1.48, 1.62, 1.7, 1.46, 1.57, 1.52, 1.33, 1.5, 1.57, 1.31, 1.29, 1.41, 1.35, 1.37, 1.23, 1.21, 1.26, 1.16, 1.12, 1.11, 1.35, 1.37, 1.34, 1.59, 1.74, 1.55, 1.07, 1.05, 1.09, 0.98, 1.25, 0.94, 1.06, 0.96, 0.92, 1.09, 1.14, 1.24, 1.2, 1.32, 1.31, 1.29, 1.18, 1.1, 1.05, 1.18, 1.15, 0.99, 1.17, 1.23, 1.26, 1.27, 1.21, 1.31, 1.24, 1.16, 1.15, 1.17, 1.2, 1.21, 1.16, 1.14, 1.13, 1.12, 1.09, 1.02, 1.01, 0.88, 0.92, 0.9, 0.91, 0.97, 0.98, 0.97, 0.97, 0.99, 1.0, 1.04, 1.0, 1.02, 1.02, 0.91, 0.89, 0.92, 0.96, 1.01, 0.95, 0.98, 0.97, 1.04, 1.08, 1.08, 1.09, 1.12, 1.21, 1.1, 1.09, 1.11, 1.14, 1.14, 1.19, 1.23, 1.19, 1.19, 1.24, 1.27, 1.28, 1.26, 1.26, 1.2, 1.2, 1.17, 1.25, 1.29, 1.25, 1.21, 1.19, 1.14, 1.18, 1.93, 2.21, 2.13, 2.18, 1.97, 1.97, 1.93, 1.9, 1.95, 1.91, 1.91, 1.88, 1.77, 1.77, 1.75, 1.77, 1.78, 1.8, 1.69, 1.62, 1.73, 1.8, 1.81, 1.7, 1.71, 1.64, 1.63, 1.61, 1.5, 1.52, 1.37, 1.31, 1.16, 1.24, 1.2, 1.23, 1.23, 1.13, 1.18, 1.13, 1.08, 1.11, 1.12, 1.14, 1.11, 1.05, 1.06, 1.06, 1.05, 1.0, 1.0, 0.96, 0.79, 0.81, 0.76, 0.81, 0.93, 0.89, 0.83, 0.9, 0.77, 0.78, 0.81, 0.78, 0.84, 0.79, 0.79, 0.82, 0.86, 0.83, 0.79, 0.85, 0.96, 1.06, 1.02, 1.03, 1.07] second_grid_avr_err_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 62.2975, 60.6325, 62.205000000000005, 59.2925, 60.722500000000004, 45.675, 45.425, 37.335, 27.685, 22.1, 15.67, 15.345, 15.9075, 3.045, 2.7525, 2.7849999999999997, 2.1999999999999997, 2.06, 1.1400000000000001, 1.355, 1.4024999999999999, 1.0825, 1.0025, 0.765, 0.7825, 0.765, 0.74, 0.835, 0.9025, 0.9975, 1.0875, 1.0725, 1.115, 1.9275, 1.9400000000000002, 1.0425, 1.03, 1.0025, 1.0125, 0.9924999999999999, 0.9175, 0.9149999999999999, 0.8925, 0.87, 0.875, 0.8925, 0.855, 0.8625, 0.875, 0.9, 0.9099999999999999, 0.9375, 0.93, 0.9175, 0.91, 0.8674999999999999, 0.85, 0.8700000000000001, 0.8350000000000001, 0.855, 0.8525, 0.8475, 0.8675, 0.845, 0.81, 0.8025, 0.8075, 0.7775000000000001, 0.77, 0.7525000000000001, 0.775, 0.775, 0.7175, 0.7175, 0.675, 0.7, 0.7375, 0.7275, 0.78, 0.7775, 0.735, 0.7425, 0.72, 0.6525, 0.65, 0.6749999999999999, 0.665, 0.705, 0.6849999999999999, 0.645, 0.625, 0.6175, 0.6325, 0.6275000000000001, 0.655, 0.6725, 0.64, 0.625, 0.6125, 0.595, 0.6, 0.6074999999999999, 0.605, 0.6375000000000001, 0.6, 0.5925, 0.6, 0.6224999999999999, 0.6074999999999999, 0.6275, 0.6475, 0.6575, 0.66, 0.67, 0.645, 0.635, 0.6375, 0.64, 0.64, 0.6425, 0.6575, 0.6799999999999999, 0.6575, 0.6699999999999999, 0.6825, 0.7050000000000001, 0.7025, 0.6925, 0.715, 0.73, 0.725, 0.7325, 0.7275, 0.7374999999999999, 0.7175, 0.7275, 0.7175, 0.7374999999999999, 0.7374999999999999, 0.74, 0.7525, 0.7825, 0.7725, 0.7875, 0.7825, 0.77, 0.775, 0.77, 0.7725, 0.78, 0.77, 0.7675, 0.7925, 0.8, 0.785, 0.78, 0.7775, 0.77, 1.2425, 1.3775, 1.3575, 1.3575, 1.265, 1.2874999999999999, 1.2725, 1.2625, 1.3125, 1.29, 1.2974999999999999, 1.2775, 1.19, 1.1875, 1.1775, 1.205, 1.215, 1.215, 1.1475, 1.0775000000000001, 1.1475, 1.19, 1.205, 1.1125, 1.125, 1.0625, 1.0374999999999999, 1.03, 0.9425, 0.9700000000000001, 0.855, 0.8075, 0.71, 0.78, 0.75, 0.7725, 0.7675, 0.72, 0.7324999999999999, 0.695, 0.6725, 0.7000000000000001, 0.7025, 0.7175, 0.6900000000000001, 0.6625, 0.6625, 0.6575, 0.6475, 0.6025, 0.6, 0.58, 0.46, 0.485, 0.44, 0.48, 0.5875, 0.5575, 0.5, 0.55, 0.455, 0.4625, 0.485, 0.46, 0.51, 0.4625, 0.475, 0.4875, 0.5, 0.47, 0.4325, 0.4775, 0.56, 0.625, 0.595, 0.5875, 0.625] second_grid_unamb_2 = [60.0, 72.22, 78.95, 80.0, 70.0, 74.12, 72.0, 67.74, 68.42, 69.18, 61.85, 66.14, 66.34, 66.2, 65.49, 63.9, 67.77, 65.49, 62.83, 66.9, 66.43, 64.03, 65.29, 68.73, 70.57, 72.31, 72.64, 73.94, 74.27, 74.92, 76.55, 77.52, 78.83, 79.8, 80.13, 80.78, 81.43, 81.43, 82.08, 82.28, 82.35, 83.71, 83.71, 83.39, 80.25, 80.89, 81.21, 79.39, 83.12, 83.44, 83.76, 84.08, 84.08, 84.08, 84.08, 84.08, 84.08, 83.76, 84.08, 84.08, 85.03, 85.03, 85.03, 84.71, 84.71, 85.35, 84.71, 84.08, 84.71, 84.71, 85.03, 85.35, 85.67, 85.99, 86.62, 86.62, 86.62, 86.94, 86.94, 87.26, 87.26, 87.26, 87.26, 87.26, 87.26, 87.26, 87.26, 87.58, 87.9, 87.9, 87.9, 87.9, 87.9, 87.9, 87.9, 88.22, 88.54, 88.22, 88.22, 88.54, 88.54, 89.17, 89.17, 89.17, 89.17, 89.49, 89.17, 89.81, 90.13, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 90.45, 90.97, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 90.45, 90.45, 90.76, 90.13, 90.13, 90.45, 90.45, 90.45, 90.45, 90.13, 90.45, 90.45, 90.45, 90.45, 90.76, 90.76, 90.76, 90.76, 90.76, 90.45, 90.76, 90.45, 90.76, 91.08, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.4, 91.72, 91.72, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.36, 92.36, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 92.99, 93.31, 92.99, 93.31, 93.31, 93.63, 93.63, 93.31, 93.31, 93.31, 93.31, 93.31, 93.31, 93.63, 93.95, 93.63, 93.95, 93.95, 93.95, 94.27, 94.27, 94.27, 94.27, 94.27, 93.95, 93.95, 93.95, 93.95, 93.95, 94.27, 94.27, 94.27, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 94.27, 93.95, 94.59, 94.27, 94.27, 94.27, 94.27, 93.95, 93.95, 93.63, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 93.95, 94.59, 94.59, 94.27, 94.27, 94.59, 94.59, 94.59, 94.9, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59, 94.59] second_grid_max_err_3 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 90.41, 90.74, 90.48, 68.35, 37.94, 65.4, 27.09, 16.96, 17.02, 18.25, 18.23, 17.02, 29.13, 29.3, 28.79, 28.39, 26.64, 26.0, 74.46, 74.61, 70.84, 71.09, 71.1, 71.65, 71.52, 71.42, 71.42, 72.93, 72.56, 72.46, 84.49, 84.49, 84.59, 84.6, 84.69, 84.69, 4.43, 4.47, 4.45, 4.43, 4.33, 4.37, 4.44, 4.76, 5.19, 5.14, 5.39, 5.61, 5.62, 5.49, 5.34, 5.33, 5.46, 5.47, 5.67, 5.54, 5.58, 5.51, 5.44, 5.59, 5.42, 4.73, 4.66, 4.72, 4.69, 4.64, 4.79, 4.76, 5.11, 5.35, 5.82, 5.73, 5.55, 5.57, 5.57, 5.66, 5.65, 6.26, 6.26, 6.33, 6.25, 6.2, 6.11, 6.11, 6.32, 6.15, 6.13, 6.12, 6.14, 6.13, 6.18, 6.18, 5.83, 5.94, 5.94, 5.98, 6.0, 6.03, 6.02, 6.21, 6.15, 5.64, 5.61, 6.1, 6.1, 6.06, 6.11, 6.26, 6.14, 6.07, 6.01, 5.92, 5.93, 5.95, 5.94, 5.83, 5.33, 5.21, 5.25, 5.15, 5.07, 5.13, 5.21, 5.22, 5.25, 5.2, 5.25, 5.27, 5.24, 5.5, 5.49, 5.56, 5.04, 5.09, 4.63, 4.63, 4.59, 4.6, 4.79, 4.73, 4.29, 4.33, 4.33, 4.55, 4.51, 4.51, 4.47, 4.91, 4.96, 5.02, 5.25, 5.25, 5.35, 5.34, 5.36, 5.32, 5.37, 5.34, 5.42, 5.41, 5.4, 5.4, 5.76, 4.84, 4.81, 4.94, 4.94, 5.12, 5.06, 5.01, 5.08, 5.12, 5.07, 5.26, 5.5, 5.5, 5.55, 5.36, 5.4, 5.42, 5.41, 5.0, 4.95, 4.96, 5.02, 5.08, 5.04, 5.22, 5.23, 5.24, 5.47, 5.09, 5.02, 5.05, 5.04, 4.87, 5.16, 5.18, 5.2, 5.28, 5.26, 5.26, 5.61, 5.65, 5.62, 5.6, 5.65, 5.66, 5.63, 5.27, 5.23, 5.28, 5.25, 5.23, 5.12, 2.27, 2.28, 5.17, 5.21, 5.17, 2.32, 2.58, 5.44, 2.62, 2.64, 2.71, 2.66, 2.63, 2.64, 2.6] second_grid_avr_err_3 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 58.4325, 58.6325, 58.385000000000005, 28.154999999999998, 22.125, 25.87, 13.1875, 8.6725, 8.215, 8.55, 9.5825, 10.004999999999999, 13.0275, 13.2925, 13.225, 13.1675, 12.93, 12.81, 28.014999999999997, 28.015, 23.8675, 23.810000000000002, 23.81, 23.9025, 23.91, 23.9175, 23.875, 24.26, 24.3075, 24.287499999999998, 25.6775, 25.645, 25.905, 25.9425, 26.115, 26.0975, 1.6175, 1.755, 1.725, 1.7274999999999998, 1.6775, 1.67, 1.6775, 1.8125, 1.965, 1.9324999999999999, 1.9974999999999998, 2.0575, 2.035, 1.9775, 1.9275, 2.0275, 2.18, 2.1375, 2.1975, 2.13, 2.1475, 2.135, 2.0975, 2.2525, 2.185, 1.9075000000000002, 1.8525, 1.91, 1.8825, 1.8575, 2.095, 2.05, 2.1575, 2.2475, 2.5975, 2.5375, 2.4625, 2.4675000000000002, 2.4425, 2.4625, 2.5100000000000002, 2.7175, 2.7275, 2.835, 2.8175, 2.765, 2.725, 2.785, 2.845, 2.755, 2.7425, 2.73, 2.7925, 2.77, 2.7575, 2.7475, 2.605, 2.7275, 2.7175000000000002, 2.73, 2.735, 2.7225, 2.8, 2.8575, 2.8325, 2.6275, 2.5875, 2.7025, 2.6875, 2.655, 2.6425, 2.6875, 2.6375, 2.5925000000000002, 2.53, 2.48, 2.4475, 2.45, 2.4475000000000002, 2.375, 2.1875, 2.105, 2.12, 2.1475, 2.1, 2.1, 2.1075, 2.0925, 2.09, 2.075, 2.0925, 2.0974999999999997, 2.095, 2.2325, 2.2225, 2.21, 2.015, 2.0225, 1.795, 1.7875, 1.765, 1.7574999999999998, 1.8225, 1.8, 1.615, 1.725, 1.71, 1.8225, 1.7874999999999999, 1.7525, 1.7325, 1.8425, 1.8475, 1.9075, 1.975, 1.965, 1.9725, 1.945, 1.945, 1.98, 1.97, 1.9575, 2.0425, 2.0225, 2.025, 2.025, 2.105, 1.7675, 1.75, 1.7775, 1.7750000000000001, 1.8050000000000002, 1.78, 1.7625, 1.7850000000000001, 1.81, 1.8025, 1.8775, 1.965, 1.9925, 1.97, 1.9175, 1.935, 1.9625, 1.9525000000000001, 1.805, 1.7875, 1.825, 1.8074999999999999, 1.86, 1.805, 1.89, 1.8875000000000002, 1.875, 1.9575, 1.8175, 1.795, 1.8599999999999999, 1.855, 1.7850000000000001, 1.87, 1.8624999999999998, 1.8625, 1.8775, 1.8525, 1.8625, 2.0, 2.025, 2.0225, 2.0425, 2.0575, 2.0625, 2.0675, 1.9574999999999998, 1.9475, 1.9725000000000001, 1.96, 1.9725000000000001, 1.9525000000000001, 1.0675000000000001, 1.1, 2.0125, 2.0, 1.9925, 1.055, 1.1300000000000001, 2.035, 1.1525, 1.115, 1.165, 1.095, 1.1025, 1.1125, 1.09] second_grid_unamb_3 = [60.0, 66.67, 70.27, 80.0, 79.03, 66.67, 65.71, 69.64, 65.0, 67.11, 66.29, 68.62, 67.01, 71.92, 71.5, 67.86, 64.71, 64.9, 63.89, 65.64, 63.91, 68.42, 67.15, 65.87, 68.23, 66.67, 68.95, 70.92, 73.62, 75.24, 75.9, 77.2, 77.2, 77.52, 77.52, 78.18, 78.5, 79.8, 80.13, 81.11, 81.76, 81.76, 82.41, 83.06, 83.71, 84.36, 84.04, 83.39, 83.71, 84.04, 84.69, 85.02, 85.02, 85.02, 84.69, 85.34, 85.34, 85.99, 85.99, 85.99, 85.99, 85.99, 86.32, 86.64, 86.97, 87.3, 87.3, 84.08, 84.39, 84.39, 84.39, 84.08, 84.08, 84.08, 84.08, 84.08, 85.03, 85.03, 85.03, 85.35, 85.35, 85.35, 85.35, 85.99, 85.99, 86.31, 86.62, 86.62, 86.62, 86.62, 86.62, 86.94, 86.94, 86.94, 86.94, 87.58, 87.58, 87.9, 87.9, 87.9, 87.9, 87.9, 88.22, 88.22, 88.54, 88.54, 88.22, 88.54, 88.54, 88.85, 88.85, 88.85, 89.17, 89.17, 89.49, 89.49, 89.49, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 90.13, 89.81, 89.81, 89.81, 89.81, 89.81, 89.81, 90.13, 90.13, 90.13, 90.13, 90.13, 90.13, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.45, 90.76, 90.76, 90.76, 90.76, 90.76, 90.76, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 90.76, 90.45, 88.54, 90.76, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.08, 91.4, 91.4, 91.4, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.72, 91.4, 91.4, 91.4, 91.72, 91.72, 91.72, 91.72, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.4, 91.72, 91.72, 91.72, 92.04, 91.72, 91.72, 91.72, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.04, 92.36, 92.68, 92.36, 92.36, 92.36, 92.99, 92.68, 92.36, 92.99, 92.99, 93.31, 93.31, 93.31, 93.31, 93.31, 92.99] second_grid_max_err_mdp_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 97.43, 97.43, 90.84, 62.94, 62.91, 63.98, 66.71, 66.64, 40.93, 43.14, 43.75, 22.0, 80.76, 83.9, 84.84, 84.59, 83.99, 84.62, 84.17, 85.44, 85.34, 85.27, 79.08, 79.17, 43.98, 37.44, 6.73, 5.35, 3.82, 2.69, 2.36, 2.24, 50.86, 50.78, 57.36, 2.49, 2.37, 2.35, 2.22, 1.99, 70.4, 70.51, 70.85, 1.45, 1.34, 1.49, 1.65, 1.47, 1.34, 1.31, 1.22, 1.26, 1.44, 1.47, 1.48, 1.73, 1.83, 2.03, 1.6, 1.47, 1.71, 1.42, 1.21, 1.08, 1.64, 1.37, 1.28, 2.32, 2.64, 2.47, 2.45, 2.23, 31.97, 32.27, 32.15, 33.57, 33.67, 84.09, 84.12, 84.31, 84.35, 84.31, 84.3, 85.35, 85.36, 85.36, 85.36, 85.37, 85.37, 85.37, 85.37, 85.35, 84.36, 84.38, 84.38, 84.38, 84.31, 84.31, 84.27, 0.93, 29.5, 29.61, 29.61, 28.77, 28.43, 28.53, 28.35, 28.21, 1.36, 1.43, 1.44, 1.39, 6.22, 6.22, 6.19, 6.13, 6.02, 6.02, 5.93, 5.89, 5.9, 5.88, 5.89, 5.94, 5.94, 5.91, 6.02, 5.98, 5.96, 5.88, 5.89, 5.82, 5.77, 5.77, 5.77, 5.76, 5.76, 5.8, 5.76, 5.72, 5.74, 5.73, 5.71, 5.72, 5.7, 5.63, 5.68, 5.62, 5.56, 5.53, 5.56, 5.55, 5.49, 5.5, 5.39, 5.28, 5.31, 5.28, 5.69, 5.71, 5.67, 5.65, 5.65, 5.54, 5.44, 5.35, 5.31, 5.32, 5.27, 5.24, 5.2, 5.16, 5.48, 5.47, 5.51, 5.54, 5.49, 5.51, 5.47, 5.49, 5.42, 5.37, 5.38, 5.35, 5.28, 5.26, 5.25, 5.23, 5.2, 5.24, 5.18, 5.19, 5.18, 5.16, 5.14, 5.13, 5.08, 5.06, 5.04, 5.01, 5.0, 4.97, 4.96, 4.95, 5.24, 5.23, 5.18, 5.16, 5.09, 5.06, 5.01, 5.04, 5.03, 5.05, 5.37, 5.35, 5.3, 5.28, 5.31, 5.33, 5.31, 5.27, 5.31, 5.66, 5.66] second_grid_avr_err_mdp_1 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 63.3325, 65.7775, 54.9275, 23.9625, 25.217499999999998, 32.199999999999996, 35.4375, 29.517500000000002, 23.5125, 21.87, 22.445, 12.545, 27.425, 25.8425, 27.2275, 32.205, 29.0325, 32.825, 32.2825, 31.4275, 30.295, 30.2025, 27.37, 27.4775, 14.165, 12.7975, 3.3625, 2.9425, 2.3525, 1.8025, 1.5325, 1.6675, 14.0725, 14.17, 15.655, 2.065, 1.585, 1.57, 1.57, 1.175, 17.990000000000002, 18.1125, 18.07, 1.035, 0.9875, 1.0325, 1.0975, 1.055, 0.92, 0.8624999999999999, 0.78, 0.89, 1.0175, 1.095, 1.12, 1.3775, 1.3725, 1.56, 0.725, 0.6025, 0.6325, 0.6, 0.585, 0.5575, 0.6475, 0.5225000000000001, 0.4975, 0.7525, 0.8575, 0.8350000000000001, 0.8475, 0.78, 8.219999999999999, 8.315000000000001, 8.3325, 8.6425, 11.11, 24.5, 24.5975, 24.657500000000002, 24.7425, 24.68, 24.6575, 25.325, 25.4125, 25.482499999999998, 25.5325, 25.565, 25.4475, 25.477500000000003, 25.545, 25.32, 24.7625, 24.805, 24.862499999999997, 24.8475, 24.68, 24.69, 24.732499999999998, 0.47000000000000003, 7.5975, 7.625, 7.6225, 7.415, 7.545, 7.5600000000000005, 7.550000000000001, 7.49, 0.74, 0.73, 0.7175, 0.745, 2.205, 2.2375, 2.27, 2.275, 2.2475, 2.2175, 2.255, 2.2375, 2.2375000000000003, 2.225, 2.205, 2.1975000000000002, 2.1825, 2.255, 2.3625, 2.3475, 2.3475, 2.2875, 2.3175, 2.39, 2.36, 2.3325, 2.295, 2.3375, 2.35, 2.3575, 2.36, 2.3125, 2.3425000000000002, 2.42, 2.3375, 2.33, 2.27, 2.23, 2.1475, 2.1575, 2.1475, 2.17, 2.1174999999999997, 2.105, 2.1, 2.09, 2.1075, 2.1350000000000002, 2.1125, 2.1325000000000003, 2.215, 2.1875, 2.26, 2.2725, 2.2125, 2.2, 2.21, 2.1625, 2.155, 2.12, 2.08, 2.0925000000000002, 2.0775, 2.095, 2.15, 2.185, 2.1774999999999998, 2.205, 2.21, 2.2125, 2.2675, 2.2, 2.18, 2.15, 2.13, 2.15, 2.1075, 2.0875, 2.12, 2.14, 2.1175, 2.1075, 2.1525, 2.185, 2.1799999999999997, 2.1675, 2.19, 2.215, 2.2475, 2.2475, 2.2225, 2.21, 2.1925, 2.1925, 2.185, 2.1975000000000002, 2.245, 2.2975000000000003, 2.2625, 2.2825, 2.2975, 2.3375, 2.3024999999999998, 2.2575, 2.2775, 2.3, 2.365, 2.38, 2.3674999999999997, 2.3875, 2.3449999999999998, 2.305, 2.3125, 2.3049999999999997, 2.2475, 2.2875, 2.3225000000000002] second_grid_unamb_mdp_1 = [80.0, 74.29, 77.59, 82.28, 72.28, 73.83, 69.84, 72.54, 70.2, 75.62, 77.5, 79.04, 75.28, 58.46, 62.56, 65.2, 71.81, 73.68, 75.95, 74.39, 73.18, 74.55, 68.44, 73.31, 74.78, 75.7, 70.35, 68.13, 71.83, 72.03, 73.4, 73.22, 76.6, 75.23, 75.69, 76.59, 77.1, 76.51, 77.85, 76.79, 71.01, 76.29, 79.57, 80.86, 80.0, 79.92, 81.42, 77.44, 78.46, 79.47, 80.28, 80.89, 81.5, 82.11, 82.32, 82.52, 83.13, 83.54, 83.94, 84.96, 85.16, 85.57, 86.38, 86.99, 86.59, 86.99, 86.99, 86.14, 86.55, 86.75, 87.15, 87.15, 88.15, 88.35, 88.35, 88.55, 88.76, 88.76, 89.16, 89.36, 89.38, 89.78, 90.18, 90.38, 90.58, 90.58, 90.58, 90.58, 90.98, 91.38, 91.58, 91.58, 89.88, 90.08, 90.28, 91.07, 91.07, 91.07, 91.27, 91.67, 91.67, 87.52, 87.52, 88.11, 88.11, 88.11, 88.11, 88.3, 88.5, 88.5, 88.69, 88.69, 89.11, 89.3, 88.91, 88.91, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.3, 89.69, 89.69, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.08, 90.27, 90.27, 90.66, 86.83, 87.02, 87.21, 87.4, 87.6, 87.98, 88.17, 88.17, 87.98, 88.17, 88.17, 88.17, 88.36, 88.55, 88.55, 88.55, 88.55, 88.55, 88.55, 88.74, 88.74, 88.55, 88.74, 88.74, 89.12, 89.12, 89.12, 89.5, 89.5, 89.5, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.31, 89.5, 89.5, 89.69, 89.89, 89.69, 89.69, 89.69, 89.69, 89.69, 89.69, 89.69, 89.69, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 89.89, 92.37, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.61, 92.8, 92.8, 92.8, 92.8, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 92.8, 92.94, 93.19, 93.19, 93.19, 93.19, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.58, 93.39, 93.97, 93.97, 93.97, 93.97, 93.77, 93.77, 93.77, 93.77] second_grid_max_err_mdp_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 97.43, 97.43, 81.02, 82.12, 90.06, 87.85, 77.77, 55.47, 80.41, 71.61, 70.24, 63.23, 63.43, 65.83, 64.43, 64.26, 61.59, 62.82, 62.9, 60.64, 59.49, 59.59, 59.61, 59.61, 58.67, 57.58, 58.2, 5.67, 5.21, 5.06, 4.63, 4.91, 5.2, 4.66, 4.27, 4.42, 4.08, 4.32, 4.66, 4.68, 4.53, 4.12, 4.6, 4.37, 4.31, 4.1, 3.87, 4.35, 4.54, 4.59, 4.75, 4.72, 3.31, 3.11, 2.85, 2.76, 2.41, 2.24, 3.6, 3.51, 3.49, 3.6, 3.57, 3.57, 3.69, 3.74, 3.64, 3.58, 3.56, 3.46, 3.4, 3.34, 3.3, 3.36, 3.19, 3.23, 3.21, 3.29, 3.21, 3.4, 3.42, 3.39, 3.33, 3.29, 3.24, 3.88, 3.92, 4.06, 4.05, 4.08, 4.05, 4.04, 3.93, 4.08, 4.17, 4.29, 4.28, 4.3, 4.38, 4.54, 4.59, 4.55, 4.66, 4.62, 4.78, 4.71, 4.73, 4.72, 4.8, 4.74, 4.81, 4.85, 4.96, 4.97, 4.93, 4.89, 4.9, 4.9, 4.88, 4.77, 4.76, 4.83, 4.81, 4.77, 4.73, 4.66, 4.61, 4.58, 4.52, 4.44, 4.35, 4.35, 4.38, 4.42, 4.3, 4.38, 4.32, 4.35, 4.37, 4.39, 4.44, 4.47, 4.32, 4.3, 4.31, 4.31, 4.28, 5.13, 5.19, 5.23, 5.24, 5.23, 5.23, 5.23, 5.16, 5.16, 5.21, 5.32, 5.29, 5.32, 5.47, 5.42, 5.4, 5.54, 5.59, 5.52, 1.74, 0.6, 0.59, 0.58, 0.59, 60.12, 60.62, 1.07, 1.27, 0.58, 0.56, 0.76, 0.88, 0.76, 0.71, 0.94, 0.79, 0.67, 0.91, 0.98, 0.85, 0.76, 0.74, 0.73, 0.67, 0.64, 0.7, 0.81, 0.9, 0.87, 0.97, 0.89, 1.0, 0.93, 0.96, 1.04, 1.15, 1.1, 1.09, 1.11, 1.09, 1.07, 1.14, 1.13, 1.03, 0.96, 0.87, 0.85, 1.07, 1.01, 0.95, 0.9, 0.83, 1.02, 0.87, 0.75, 0.7, 0.67, 1.53, 0.9, 0.83, 0.78, 0.97] second_grid_avr_err_mdp_2 = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 61.695, 66.6725, 54.12, 39.347500000000004, 42.305, 37.8925, 26.1875, 26.7275, 34.762499999999996, 34.05, 38.72, 36.715, 28.905, 23.6325, 22.150000000000002, 22.365000000000002, 20.5425, 21.535, 21.265, 16.767500000000002, 16.5425, 16.575, 16.445, 16.5625, 16.3325, 15.9725, 16.075, 2.985, 2.8025, 2.1599999999999997, 1.89, 1.7650000000000001, 1.8125, 1.705, 1.3224999999999998, 1.3675, 1.185, 1.23, 1.3, 1.2925, 1.615, 1.5875000000000001, 1.67, 1.55, 1.595, 1.4675, 1.6125, 2.215, 2.3375, 2.5225, 2.54, 2.455, 1.7725, 1.825, 1.5775, 1.3775, 1.22, 1.1400000000000001, 1.5050000000000001, 1.42, 1.4125, 1.505, 1.5, 1.52, 1.5625, 1.58, 1.4975, 1.3825, 1.36, 1.195, 1.0375, 1.085, 1.02, 1.09, 1.155, 1.12, 1.2675, 1.2825, 1.28, 1.0725, 1.205, 1.2925, 1.28, 1.2675, 1.2925, 1.4, 1.33, 1.3275, 1.3225, 1.3225, 1.325, 1.3125, 1.335, 1.5050000000000001, 1.47, 1.41, 1.385, 1.3675, 1.4075, 1.51, 1.5425, 1.4825, 1.5975000000000001, 1.5350000000000001, 1.6800000000000002, 1.575, 1.57, 1.5725, 1.5699999999999998, 1.53, 1.6024999999999998, 1.6749999999999998, 1.705, 1.7275, 1.6624999999999999, 1.5775, 1.5750000000000002, 1.53, 1.5, 1.4474999999999998, 1.4425, 1.4925, 1.505, 1.4725, 1.4300000000000002, 1.3900000000000001, 1.36, 1.33, 1.2674999999999998, 1.26, 1.2374999999999998, 1.22, 1.23, 1.24, 1.205, 1.2349999999999999, 1.2175, 1.2225, 1.2275, 1.2525, 1.2825000000000002, 1.29, 1.25, 1.26, 1.315, 1.3375, 1.3425, 1.54, 1.5425, 1.5525, 1.5225, 1.5050000000000001, 1.5075, 1.485, 1.46, 1.46, 1.4725, 1.49, 1.4875, 1.5150000000000001, 1.555, 1.53, 1.5025000000000002, 1.5375, 1.57, 1.5299999999999998, 0.595, 0.305, 0.295, 0.285, 0.285, 16.2025, 16.419999999999998, 0.435, 0.4775, 0.3575, 0.385, 0.505, 0.5075, 0.4775, 0.46499999999999997, 0.57, 0.4925, 0.44000000000000006, 0.385, 0.3475, 0.325, 0.28, 0.285, 0.345, 0.3425, 0.355, 0.365, 0.36, 0.3725, 0.3725, 0.355, 0.3, 0.3325, 0.3025, 0.31, 0.305, 0.3075, 0.335, 0.3325, 0.36750000000000005, 0.35000000000000003, 0.3775, 0.43, 0.4225, 0.405, 0.4125, 0.36, 0.4, 0.435, 0.355, 0.32, 0.2925, 0.295, 0.3575, 0.335, 0.275, 0.255, 0.23500000000000001, 0.595, 0.3275, 0.385, 0.395, 0.445] second_grid_unamb_mdp_2 = [80.0, 71.43, 70.97, 73.75, 73.0, 66.67, 59.44, 64.24, 69.81, 67.43, 64.1, 64.84, 72.52, 74.37, 72.84, 69.85, 68.44, 72.26, 70.99, 71.39, 72.36, 68.88, 74.67, 75.73, 75.82, 72.3, 74.24, 77.39, 77.05, 73.17, 77.37, 78.85, 76.05, 77.05, 72.5, 79.61, 81.44, 82.24, 82.63, 83.23, 83.03, 83.43, 83.63, 83.63, 83.83, 82.84, 84.22, 85.4, 85.8, 86.79, 87.57, 88.36, 89.55, 89.55, 90.34, 90.34, 90.53, 90.53, 90.73, 90.93, 91.12, 91.12, 91.32, 91.32, 91.32, 91.72, 91.72, 91.91, 92.31, 92.5, 92.31, 92.31, 92.5, 93.1, 93.1, 93.1, 93.49, 93.29, 92.25, 93.69, 93.69, 93.89, 93.89, 93.89, 93.89, 93.89, 93.89, 94.08, 89.65, 89.65, 89.45, 91.35, 93.58, 94.67, 94.67, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 94.87, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 95.07, 94.87, 90.47, 90.47, 90.47, 90.66, 90.66, 90.86, 90.86, 90.86, 90.86, 91.05, 91.05, 91.05, 91.25, 91.44, 91.25, 88.31, 88.34, 90.25, 91.63, 91.83, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 91.83, 90.63, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.02, 92.22, 92.41, 92.61, 92.41, 91.73, 92.22, 91.01, 92.41, 92.8, 92.8, 92.8, 92.8, 92.8, 92.8, 93.0, 92.8, 92.8, 93.0, 92.8, 92.8, 92.13, 93.19, 93.19, 93.0, 91.94, 89.6, 89.98, 90.17, 90.36, 90.36, 90.55, 90.36, 90.36, 90.36, 91.68, 92.51, 90.55, 92.44, 93.28, 93.28, 93.47, 93.47, 93.28, 91.3, 91.3, 92.63, 93.67, 93.67, 93.47, 93.67, 93.67, 93.67, 93.86, 93.67, 93.67, 93.86, 93.67, 93.86, 93.86, 93.86, 93.86, 94.05, 93.86, 93.86, 93.86, 94.05, 94.05, 94.05, 94.05, 94.05, 94.24, 94.24, 94.24, 94.24, 94.24, 94.24, 94.24, 94.05, 93.56, 94.43, 94.24, 94.43, 94.05] def single_plot(): matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) r = list(range(len(second_grid_max_err_2))) # plt.plot(r, second_grid_max_err_2, label = 'max_error_%') # plt.plot(r, second_grid_avr_err_2, label = 'avr_error_%') # plt.plot(r, second_grid_unamb_2, label='unamb_%') plt.plot(r, mqtt_max_err_1, label='Maximum Error') plt.plot(r, mqtt_avr_err_1, label='Average Error') plt.plot(r, mqtt_unamb_1, label='Unambiguous Rows') # second_grid_avr_err_2 looks nice # plt.plot(r, [100 - r for r in second_grid_unamb_2], label='unamb_%') plt.ylabel('Percentage') plt.xlabel('Learning Round') plt.title('MQTT') plt.grid(axis='y') plt.legend() #plt.show() plt.savefig("stoppingAndUnamb_mqtt.pgf") import tikzplotlib tikzplotlib.save("stoppingAndUnamb_mqtt.tex") def side_by_side(): matplotlib.use("pgf") matplotlib.rcParams.update({ "pgf.texsystem": "pdflatex", 'font.family': 'serif', 'text.usetex': True, 'pgf.rcfonts': False, }) r = list(range(len(second_grid_max_err_2))) # plt.plot(r, second_grid_max_err_2, label = 'max_error_%') # plt.plot(r, second_grid_avr_err_2, label = 'avr_error_%') # plt.plot(r, second_grid_unamb_2, label='unamb_%') fig, (plt1, plt2) = plt.subplots(1, 2, figsize=(10, 3)) plt1.plot_side_by_side() plt1.plot_side_by_side() plt1.plot_side_by_side() # second_grid_avr_err_2 looks nice # plt.plot(r, [100 - r for r in second_grid_unamb_2], label='unamb_%') plt1.set_ylabel('Percentage') plt1.set_xlabel('Learning Round') plt1.set_title('72 State Gridworld') plt1.grid(axis='y') plt1.legend() plt2.plot_side_by_side() plt2.plot_side_by_side() plt2.plot_side_by_side() # second_grid_avr_err_2 looks nice # plt.plot(r, [100 - r for r in second_grid_unamb_2], label='unamb_%') plt2.set_ylabel('Percentage') plt2.set_xlabel('Learning Round') plt2.set_title('MQTT') plt2.grid(axis='y') plt2.legend() # plt.show() fig.tight_layout() fig.savefig("stoppingAndUnamb2plots.pgf") import tikzplotlib tikzplotlib.save("stoppingAndUnamb2plots.tex") if __name__ == '__main__': side_by_side() ================================================ FILE: Benchmarking/vpa_benchmarking/benchmark_vpa.py ================================================ from collections import defaultdict import matplotlib.pyplot as plt import numpy as np import pickle from aalpy.SULs.AutomataSUL import SevpaSUL, DfaSUL from aalpy.automata import SevpaAlphabet from aalpy.learning_algs import run_KV from aalpy.oracles import RandomWordEqOracle from aalpy.utils import generate_random_sevpa, visualize_automaton from aalpy.utils.BenchmarkVpaModels import * def state_increasing(): print("Benchmarking for increasing state size") max_number_states = 100 step_size = 10 repeats = 10 cex_processing = ['rs', 'linear_fwd', 'linear_bwd', 'exponential_fwd', 'exponential_bwd'] # cex_processing = ['rs'] data_dict = defaultdict(tuple) for cex in cex_processing: states_data_median = [] query_data_median = [] for number_states in range(10, max_number_states + 1, step_size): print(number_states) states_data = [] query_data = [] for x in range(repeats): random_svepa = generate_random_sevpa(num_states=number_states, internal_alphabet_size=3, call_alphabet_size=3, return_alphabet_size=3, acceptance_prob=0.4, return_transition_prob=0.5) alphabet = random_svepa.input_alphabet sul = SevpaSUL(random_svepa) eq_oracle = RandomWordEqOracle(alphabet=alphabet.get_merged_alphabet(), sul=sul, num_walks=10000, min_walk_len=10, max_walk_len=30) model, data = run_KV(alphabet=alphabet, sul=sul, eq_oracle=eq_oracle, automaton_type='vpa', print_level=0, cex_processing=cex, return_data=True) states_data.append(number_states) query_data.append(data['queries_learning']) states_data_median.append(np.median(states_data)) query_data_median.append(np.median(query_data)) data_dict[cex] = (states_data_median, query_data_median) # Save data_dict to a pickle file with open('state_increasing.pickle', 'wb') as file: pickle.dump(data_dict, file) # plot plt.figure() plt.xlabel('Number of states') plt.ylabel('Number of membership queries') plt.title('Query growth of a random SEVPA with increasing state size') for key in data_dict: plt.plot(data_dict[key][0], data_dict[key][1], label=key) plt.legend() plt.savefig('state_increasing.png') def alphabet_increasing(): print("Benchmarking for increasing alphabet size") repeats = 10 max_alphabet_size = 15 cex_processing = ['rs', 'linear_fwd', 'linear_bwd', 'exponential_fwd', 'exponential_bwd'] # cex_processing = ['rs'] data_dict = defaultdict(tuple) for cex in cex_processing: states_data_median = [] query_data_median = [] for alphabet_size in range(1, max_alphabet_size): print(alphabet_size) for x in range(repeats): random_svepa = generate_random_sevpa(num_states=100, internal_alphabet_size=alphabet_size, call_alphabet_size=alphabet_size, return_alphabet_size=alphabet_size, acceptance_prob=0.4, return_transition_prob=0.5) alphabet = random_svepa.input_alphabet sul = SevpaSUL(random_svepa) eq_oracle = RandomWordEqOracle(alphabet=alphabet.get_merged_alphabet(), sul=sul, num_walks=10000, min_walk_len=10, max_walk_len=30) states_data = [] query_data = [] model, data = run_KV(alphabet=alphabet, sul=sul, eq_oracle=eq_oracle, automaton_type='vpa', print_level=0, cex_processing=cex, return_data=True) states_data.append(alphabet_size * 3) query_data.append(data['queries_learning']) states_data_median.append(np.median(states_data)) query_data_median.append(np.median(query_data)) data_dict[cex] = (states_data_median, query_data_median) # Save data_dict to a pickle file with open('alphabet_increasing.pickle', 'wb') as file: pickle.dump(data_dict, file) # plot plt.figure() plt.xlabel('Size of the input alphabet') plt.ylabel('Number of membership queries') plt.title('Query growth of a random SEVPA with increasing alphabet size') for key in data_dict: plt.plot(data_dict[key][0], data_dict[key][1], label=key) plt.legend() plt.savefig('alphabet_increasing.png') def alphabet_increasing_variable(): print("Benchmarking for variably increasing alphabet size") repeats = 10 max_alphabet_size = 15 data_dict = defaultdict(tuple) alphabet_types = ['int', 'call', 'ret'] for alphabet_type in alphabet_types: states_data_median = [] query_data_median = [] for alphabet_size in range(1, max_alphabet_size): print(alphabet_size) for x in range(repeats): if alphabet_type == 'int': random_svepa = generate_random_sevpa(num_states=100, internal_alphabet_size=alphabet_size, call_alphabet_size=1, return_alphabet_size=1, acceptance_prob=0.4, return_transition_prob=0.5) elif alphabet_type == 'call': random_svepa = generate_random_sevpa(num_states=100, internal_alphabet_size=alphabet_size, call_alphabet_size=1, return_alphabet_size=1, acceptance_prob=0.4, return_transition_prob=0.5) elif alphabet_type == 'ret': random_svepa = generate_random_sevpa(num_states=100, internal_alphabet_size=alphabet_size, call_alphabet_size=1, return_alphabet_size=1, acceptance_prob=0.4, return_transition_prob=0.5) alphabet = random_svepa.input_alphabet sul = SevpaSUL(random_svepa) eq_oracle = RandomWordEqOracle(alphabet=alphabet.get_merged_alphabet(), sul=sul, num_walks=10000, min_walk_len=10, max_walk_len=30) states_data = [] query_data = [] model, data = run_KV(alphabet=alphabet, sul=sul, eq_oracle=eq_oracle, automaton_type='vpa', print_level=0, cex_processing='rs', return_data=True) states_data.append(alphabet_size) query_data.append(data['queries_learning']) states_data_median.append(np.median(states_data)) query_data_median.append(np.median(query_data)) data_dict[alphabet_type] = (states_data_median, query_data_median) # Save data_dict to a pickle file with open('alphabet_increasing_variable.pickle', 'wb') as file: pickle.dump(data_dict, file) # plot plt.figure() plt.xlabel('Size of the input alphabet') plt.ylabel('Number of membership queries') plt.title('Query growth of a random SEVPA with increasing alphabet size') for key in data_dict: plt.plot(data_dict[key][0], data_dict[key][1], label=key) plt.legend() plt.savefig('alphabet_increasing_variable.png') def benchmark_vpa_dfa(): max_learning_rounds = 100 data_dict = defaultdict(tuple) label_data = [] for i, vpa in enumerate( [vpa_L1(), vpa_L2(), vpa_for_L3(), vpa_L3(), vpa_for_L5(), vpa_L4(), vpa_L6(), vpa_L8(), vpa_for_L10(), vpa_for_L11(), vpa_L9(), vpa_L10(), vpa_L11(), vpa_L12()]): print(f'VPA {i + 1 if i < 6 else i + 2}') label_data.append(f'VPA {i + 1 if i < 6 else i + 2}') model_under_learning = vpa alphabet_sevpa = SevpaAlphabet(list(model_under_learning.internal_set), list(model_under_learning.call_set), list(model_under_learning.return_set)) alphabet_dfa = model_under_learning.input_alphabet.get_merged_alphabet() sul_vpa = VpaSUL(vpa) sul_dfa = DfaSUL(vpa) eq_oracle_vpa = RandomWordEqOracle(alphabet=alphabet_sevpa.get_merged_alphabet(), sul=sul_vpa, num_walks=10000, min_walk_len=10, max_walk_len=30) eq_oracle_dfa = RandomWordEqOracle(alphabet=alphabet_sevpa.get_merged_alphabet(), sul=sul_vpa, num_walks=10000, min_walk_len=10, max_walk_len=30) model_vpa, data_vpa = run_KV(alphabet=alphabet_sevpa, sul=sul_vpa, eq_oracle=eq_oracle_vpa, automaton_type='vpa', print_level=0, cex_processing='rs', return_data=True, max_learning_rounds=max_learning_rounds) model_dfa, data_dfa = run_KV(alphabet=alphabet_dfa, sul=sul_dfa, eq_oracle=eq_oracle_dfa, automaton_type='dfa', print_level=0, cex_processing='rs', return_data=True, max_learning_rounds=max_learning_rounds) print(data_dfa['queries_learning']) data_dict[vpa] = (data_vpa['queries_learning'], data_dfa['queries_learning']) # Save data_dict to a pickle file with open('benchmark_vpa_dfa.pickle', 'wb') as file: pickle.dump(data_dict, file) #plotting keys = list(data_dict.keys()) values = list(data_dict.values()) data1, data2 = zip(*values) # Creating bar graph bar_width = 0.35 index = np.arange(len(keys)) plt.bar(index, data1, bar_width, label='Data VPA', align='center') plt.bar(index + bar_width, data2, bar_width, label='Data DFA', align='center') plt.xlabel('VPA Instances') plt.ylabel('Number of Queries') plt.title('Bar Graph of Queries for VPA and DFA') plt.xticks(index + bar_width / 2, label_data) plt.legend() plt.show() # choose which benchmark to execute state_increasing() alphabet_increasing() alphabet_increasing_variable() benchmark_vpa_dfa() ================================================ FILE: DotModels/Angluin_Mealy.dot ================================================ digraph Angluin_Mealy { s0 [label="s0"]; s1 [label="s1"]; s2 [label="s2"]; s3 [label="s3"]; s0 -> s2 [label="a/0"]; s0 -> s1 [label="b/0"]; s1 -> s3 [label="a/0"]; s1 -> s0 [label="b/1"]; s2 -> s0 [label="a/1"]; s2 -> s3 [label="b/0"]; s3 -> s1 [label="a/0"]; s3 -> s2 [label="b/0"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Angluin_Moore.dot ================================================ digraph Angluin_Moore { s0 [label="s0|1", shape=record, style=rounded]; s1 [label="s1|0", shape=record, style=rounded]; s2 [label="s2|0", shape=record, style=rounded]; s3 [label="s3|0", shape=record, style=rounded]; s0 -> s2 [label=a]; s0 -> s1 [label=b]; s1 -> s3 [label=a]; s1 -> s0 [label=b]; s2 -> s0 [label=a]; s2 -> s3 [label=b]; s3 -> s1 [label=a]; s3 -> s2 [label=b]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CC2640R2-no-feature-req.dot ================================================ digraph "CC2640R2-no-feature" { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4]; s5 [label=s5]; s6 [label=s6]; s7 [label=s7]; s8 [label=s8]; s9 [label=s9]; s10 [label=s10]; s0 -> s0 [label="scan_req/Adv"]; s0 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s0 -> s0 [label="length_req/Empty"]; s0 -> s0 [label="length_rsp/Empty"]; s0 -> s0 [label="feature_rsp/Empty"]; s0 -> s0 [label="version_req/Empty"]; s0 -> s0 [label="mtu_req/Empty"]; s0 -> s0 [label="pairing_req/Empty"]; s1 -> s0 [label="scan_req/Adv"]; s1 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s1 -> s4 [label="length_rsp/BTLE|BTLE_DATA"]; s1 -> s1 [label="feature_rsp/BTLE|BTLE_DATA"]; s1 -> s3 [label="version_req/BTLE|BTLE_DATA"]; s1 -> s1 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s1 -> s2 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s2 -> s0 [label="scan_req/Adv"]; s2 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s2 -> s8 [label="length_rsp/BTLE|BTLE_DATA"]; s2 -> s2 [label="feature_rsp/BTLE|BTLE_DATA"]; s2 -> s7 [label="version_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s2 -> s1 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s3 -> s0 [label="scan_req/Adv"]; s3 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s3 -> s3 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s3 -> s6 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s3 -> s3 [label="feature_rsp/BTLE|BTLE_DATA"]; s3 -> s5 [label="version_req/BTLE|BTLE_DATA"]; s3 -> s3 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s3 -> s7 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s4 -> s0 [label="scan_req/Adv"]; s4 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s4 -> s4 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s4 -> s4 [label="length_rsp/BTLE|BTLE_DATA"]; s4 -> s4 [label="feature_rsp/BTLE|BTLE_DATA"]; s4 -> s6 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s4 -> s4 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s4 -> s8 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s5 -> s0 [label="scan_req/Adv"]; s5 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s5 -> s5 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s5 -> s6 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP|LL_VERSION_IND"]; s5 -> s5 [label="feature_rsp/BTLE|BTLE_DATA"]; s5 -> s5 [label="version_req/BTLE|BTLE_DATA"]; s5 -> s5 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s5 -> s10 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s6 -> s0 [label="scan_req/Adv"]; s6 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s6 -> s6 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s6 -> s6 [label="length_rsp/BTLE|BTLE_DATA"]; s6 -> s6 [label="feature_rsp/BTLE|BTLE_DATA"]; s6 -> s6 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s6 -> s6 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s6 -> s9 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s7 -> s0 [label="scan_req/Adv"]; s7 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s7 -> s7 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s7 -> s9 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s7 -> s7 [label="feature_rsp/BTLE|BTLE_DATA"]; s7 -> s10 [label="version_req/BTLE|BTLE_DATA"]; s7 -> s7 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s7 -> s3 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s8 -> s0 [label="scan_req/Adv"]; s8 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s8 -> s8 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s8 -> s8 [label="length_rsp/BTLE|BTLE_DATA"]; s8 -> s8 [label="feature_rsp/BTLE|BTLE_DATA"]; s8 -> s9 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s8 -> s8 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s8 -> s4 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s9 -> s0 [label="scan_req/Adv"]; s9 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s9 -> s9 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s9 -> s9 [label="length_rsp/BTLE|BTLE_DATA"]; s9 -> s9 [label="feature_rsp/BTLE|BTLE_DATA"]; s9 -> s9 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s9 -> s9 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s9 -> s6 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s10 -> s0 [label="scan_req/Adv"]; s10 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s10 -> s10 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s10 -> s9 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP|LL_VERSION_IND"]; s10 -> s10 [label="feature_rsp/BTLE|BTLE_DATA"]; s10 -> s10 [label="version_req/BTLE|BTLE_DATA"]; s10 -> s10 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s10 -> s5 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CC2640R2-no-feature-req_stochastic.dot ================================================ digraph "CC2640R2-no-feature-req-stochastic" { s0 [label=init]; s1 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s2 [label=Empty]; s3 [label=Adv]; s4 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s5 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s6 [label="BTLE|BTLE_DATA"]; s7 [label="BTLE|BTLE_DATA"]; s8 [label="BTLE|BTLE_DATA"]; s9 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s10 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s11 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s12 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s13 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s14 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s15 [label="BTLE|BTLE_DATA"]; s16 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s17 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s18 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s19 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s20 [label="BTLE|BTLE_DATA"]; s21 [label="BTLE|BTLE_DATA"]; s22 [label="BTLE|BTLE_DATA"]; s23 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s24 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s25 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s26 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s27 [label="BTLE|BTLE_DATA"]; s28 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s29 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s30 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s31 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP|LL_VERSION_IND"]; s32 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s33 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s34 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s35 [label="BTLE|BTLE_DATA"]; s36 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s37 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s38 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s39 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s40 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s41 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s42 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s43 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP|LL_VERSION_IND"]; s44 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s45 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s46 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s47 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s48 [label="BTLE|BTLE_DATA"]; s49 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s0 -> s3 [label="scan_req:1.0"]; s0 -> s2 [label="length_req:1.0"]; s0 -> s2 [label="length_rsp:1.0"]; s0 -> s2 [label="feature_rsp:1.0"]; s0 -> s2 [label="version_req:1.0"]; s0 -> s0 [label="mtu_req:0.3"]; s0 -> s2 [label="mtu_req:0.7"]; s0 -> s0 [label="pairing_req:0.3"]; s0 -> s2 [label="pairing_req:0.7"]; s0 -> s1 [label="connection_req:1.0"]; s1 -> s3 [label="scan_req:1.0"]; s1 -> s9 [label="length_req:1.0"]; s1 -> s8 [label="length_rsp:1.0"]; s1 -> s7 [label="feature_rsp:1.0"]; s1 -> s6 [label="version_req:1.0"]; s1 -> s0 [label="mtu_req:0.3"]; s1 -> s5 [label="mtu_req:0.7"]; s1 -> s0 [label="pairing_req:0.3"]; s1 -> s4 [label="pairing_req:0.7"]; s1 -> s1 [label="connection_req:1.0"]; s2 -> s3 [label="scan_req:1.0"]; s2 -> s2 [label="length_req:1.0"]; s2 -> s2 [label="length_rsp:1.0"]; s2 -> s2 [label="feature_rsp:1.0"]; s2 -> s2 [label="version_req:1.0"]; s2 -> s0 [label="mtu_req:0.3"]; s2 -> s2 [label="mtu_req:0.7"]; s2 -> s0 [label="pairing_req:0.3"]; s2 -> s2 [label="pairing_req:0.7"]; s2 -> s1 [label="connection_req:1.0"]; s3 -> s3 [label="scan_req:1.0"]; s3 -> s2 [label="length_req:1.0"]; s3 -> s2 [label="length_rsp:1.0"]; s3 -> s2 [label="feature_rsp:1.0"]; s3 -> s2 [label="version_req:1.0"]; s3 -> s0 [label="mtu_req:0.3"]; s3 -> s2 [label="mtu_req:0.7"]; s3 -> s0 [label="pairing_req:0.3"]; s3 -> s2 [label="pairing_req:0.7"]; s3 -> s1 [label="connection_req:1.0"]; s4 -> s3 [label="scan_req:1.0"]; s4 -> s23 [label="length_req:1.0"]; s4 -> s22 [label="length_rsp:1.0"]; s4 -> s21 [label="feature_rsp:1.0"]; s4 -> s20 [label="version_req:1.0"]; s4 -> s0 [label="mtu_req:0.3"]; s4 -> s19 [label="mtu_req:0.7"]; s4 -> s0 [label="pairing_req:0.3"]; s4 -> s18 [label="pairing_req:0.7"]; s4 -> s1 [label="connection_req:1.0"]; s5 -> s3 [label="scan_req:1.0"]; s5 -> s9 [label="length_req:1.0"]; s5 -> s8 [label="length_rsp:1.0"]; s5 -> s7 [label="feature_rsp:1.0"]; s5 -> s6 [label="version_req:1.0"]; s5 -> s0 [label="mtu_req:0.3"]; s5 -> s5 [label="mtu_req:0.7"]; s5 -> s0 [label="pairing_req:0.3"]; s5 -> s4 [label="pairing_req:0.7"]; s5 -> s1 [label="connection_req:1.0"]; s6 -> s3 [label="scan_req:1.0"]; s6 -> s17 [label="length_req:1.0"]; s6 -> s16 [label="length_rsp:1.0"]; s6 -> s6 [label="feature_rsp:1.0"]; s6 -> s15 [label="version_req:1.0"]; s6 -> s0 [label="mtu_req:0.3"]; s6 -> s14 [label="mtu_req:0.7"]; s6 -> s0 [label="pairing_req:0.3"]; s6 -> s13 [label="pairing_req:0.7"]; s6 -> s1 [label="connection_req:1.0"]; s7 -> s3 [label="scan_req:1.0"]; s7 -> s9 [label="length_req:1.0"]; s7 -> s8 [label="length_rsp:1.0"]; s7 -> s7 [label="feature_rsp:1.0"]; s7 -> s6 [label="version_req:1.0"]; s7 -> s0 [label="mtu_req:0.3"]; s7 -> s5 [label="mtu_req:0.7"]; s7 -> s0 [label="pairing_req:0.3"]; s7 -> s4 [label="pairing_req:0.7"]; s7 -> s1 [label="connection_req:1.0"]; s8 -> s3 [label="scan_req:1.0"]; s8 -> s12 [label="length_req:1.0"]; s8 -> s8 [label="length_rsp:1.0"]; s8 -> s8 [label="feature_rsp:1.0"]; s8 -> s16 [label="version_req:1.0"]; s8 -> s0 [label="mtu_req:0.3"]; s8 -> s11 [label="mtu_req:0.7"]; s8 -> s0 [label="pairing_req:0.3"]; s8 -> s10 [label="pairing_req:0.7"]; s8 -> s1 [label="connection_req:1.0"]; s9 -> s3 [label="scan_req:1.0"]; s9 -> s9 [label="length_req:1.0"]; s9 -> s8 [label="length_rsp:1.0"]; s9 -> s7 [label="feature_rsp:1.0"]; s9 -> s6 [label="version_req:1.0"]; s9 -> s0 [label="mtu_req:0.3"]; s9 -> s5 [label="mtu_req:0.7"]; s9 -> s0 [label="pairing_req:0.3"]; s9 -> s4 [label="pairing_req:0.7"]; s9 -> s1 [label="connection_req:1.0"]; s10 -> s3 [label="scan_req:1.0"]; s10 -> s40 [label="length_req:1.0"]; s10 -> s22 [label="length_rsp:1.0"]; s10 -> s22 [label="feature_rsp:1.0"]; s10 -> s39 [label="version_req:1.0"]; s10 -> s0 [label="mtu_req:0.3"]; s10 -> s38 [label="mtu_req:0.7"]; s10 -> s0 [label="pairing_req:0.3"]; s10 -> s37 [label="pairing_req:0.7"]; s10 -> s1 [label="connection_req:1.0"]; s11 -> s3 [label="scan_req:1.0"]; s11 -> s12 [label="length_req:1.0"]; s11 -> s8 [label="length_rsp:1.0"]; s11 -> s8 [label="feature_rsp:1.0"]; s11 -> s16 [label="version_req:1.0"]; s11 -> s0 [label="mtu_req:0.3"]; s11 -> s11 [label="mtu_req:0.7"]; s11 -> s0 [label="pairing_req:0.3"]; s11 -> s10 [label="pairing_req:0.7"]; s11 -> s1 [label="connection_req:1.0"]; s12 -> s3 [label="scan_req:1.0"]; s12 -> s12 [label="length_req:1.0"]; s12 -> s8 [label="length_rsp:1.0"]; s12 -> s8 [label="feature_rsp:1.0"]; s12 -> s16 [label="version_req:1.0"]; s12 -> s0 [label="mtu_req:0.3"]; s12 -> s11 [label="mtu_req:0.7"]; s12 -> s0 [label="pairing_req:0.3"]; s12 -> s10 [label="pairing_req:0.7"]; s12 -> s1 [label="connection_req:1.0"]; s13 -> s3 [label="scan_req:1.0"]; s13 -> s36 [label="length_req:1.0"]; s13 -> s39 [label="length_rsp:1.0"]; s13 -> s20 [label="feature_rsp:1.0"]; s13 -> s35 [label="version_req:1.0"]; s13 -> s0 [label="mtu_req:0.3"]; s13 -> s34 [label="mtu_req:0.7"]; s13 -> s0 [label="pairing_req:0.3"]; s13 -> s33 [label="pairing_req:0.7"]; s13 -> s1 [label="connection_req:1.0"]; s14 -> s3 [label="scan_req:1.0"]; s14 -> s17 [label="length_req:1.0"]; s14 -> s16 [label="length_rsp:1.0"]; s14 -> s6 [label="feature_rsp:1.0"]; s14 -> s15 [label="version_req:1.0"]; s14 -> s0 [label="mtu_req:0.3"]; s14 -> s14 [label="mtu_req:0.7"]; s14 -> s0 [label="pairing_req:0.3"]; s14 -> s13 [label="pairing_req:0.7"]; s14 -> s1 [label="connection_req:1.0"]; s15 -> s3 [label="scan_req:1.0"]; s15 -> s32 [label="length_req:1.0"]; s15 -> s31 [label="length_rsp:1.0"]; s15 -> s15 [label="feature_rsp:1.0"]; s15 -> s15 [label="version_req:1.0"]; s15 -> s0 [label="mtu_req:0.3"]; s15 -> s30 [label="mtu_req:0.7"]; s15 -> s0 [label="pairing_req:0.3"]; s15 -> s29 [label="pairing_req:0.7"]; s15 -> s1 [label="connection_req:1.0"]; s16 -> s3 [label="scan_req:1.0"]; s16 -> s28 [label="length_req:1.0"]; s16 -> s27 [label="length_rsp:1.0"]; s16 -> s27 [label="feature_rsp:1.0"]; s16 -> s26 [label="version_req:1.0"]; s16 -> s0 [label="mtu_req:0.3"]; s16 -> s25 [label="mtu_req:0.7"]; s16 -> s0 [label="pairing_req:0.3"]; s16 -> s24 [label="pairing_req:0.7"]; s16 -> s1 [label="connection_req:1.0"]; s17 -> s3 [label="scan_req:1.0"]; s17 -> s17 [label="length_req:1.0"]; s17 -> s16 [label="length_rsp:1.0"]; s17 -> s6 [label="feature_rsp:1.0"]; s17 -> s15 [label="version_req:1.0"]; s17 -> s0 [label="mtu_req:0.3"]; s17 -> s14 [label="mtu_req:0.7"]; s17 -> s0 [label="pairing_req:0.3"]; s17 -> s13 [label="pairing_req:0.7"]; s17 -> s1 [label="connection_req:1.0"]; s18 -> s3 [label="scan_req:1.0"]; s18 -> s9 [label="length_req:1.0"]; s18 -> s8 [label="length_rsp:1.0"]; s18 -> s7 [label="feature_rsp:1.0"]; s18 -> s6 [label="version_req:1.0"]; s18 -> s0 [label="mtu_req:0.3"]; s18 -> s5 [label="mtu_req:0.7"]; s18 -> s0 [label="pairing_req:0.3"]; s18 -> s4 [label="pairing_req:0.7"]; s18 -> s1 [label="connection_req:1.0"]; s19 -> s3 [label="scan_req:1.0"]; s19 -> s23 [label="length_req:1.0"]; s19 -> s22 [label="length_rsp:1.0"]; s19 -> s21 [label="feature_rsp:1.0"]; s19 -> s20 [label="version_req:1.0"]; s19 -> s0 [label="mtu_req:0.3"]; s19 -> s19 [label="mtu_req:0.7"]; s19 -> s0 [label="pairing_req:0.3"]; s19 -> s18 [label="pairing_req:0.7"]; s19 -> s1 [label="connection_req:1.0"]; s20 -> s3 [label="scan_req:1.0"]; s20 -> s36 [label="length_req:1.0"]; s20 -> s39 [label="length_rsp:1.0"]; s20 -> s20 [label="feature_rsp:1.0"]; s20 -> s35 [label="version_req:1.0"]; s20 -> s0 [label="mtu_req:0.3"]; s20 -> s34 [label="mtu_req:0.7"]; s20 -> s0 [label="pairing_req:0.3"]; s20 -> s33 [label="pairing_req:0.7"]; s20 -> s1 [label="connection_req:1.0"]; s21 -> s3 [label="scan_req:1.0"]; s21 -> s23 [label="length_req:1.0"]; s21 -> s22 [label="length_rsp:1.0"]; s21 -> s21 [label="feature_rsp:1.0"]; s21 -> s20 [label="version_req:1.0"]; s21 -> s0 [label="mtu_req:0.3"]; s21 -> s19 [label="mtu_req:0.7"]; s21 -> s0 [label="pairing_req:0.3"]; s21 -> s18 [label="pairing_req:0.7"]; s21 -> s1 [label="connection_req:1.0"]; s22 -> s3 [label="scan_req:1.0"]; s22 -> s40 [label="length_req:1.0"]; s22 -> s22 [label="length_rsp:1.0"]; s22 -> s22 [label="feature_rsp:1.0"]; s22 -> s39 [label="version_req:1.0"]; s22 -> s0 [label="mtu_req:0.3"]; s22 -> s38 [label="mtu_req:0.7"]; s22 -> s0 [label="pairing_req:0.3"]; s22 -> s37 [label="pairing_req:0.7"]; s22 -> s1 [label="connection_req:1.0"]; s23 -> s3 [label="scan_req:1.0"]; s23 -> s23 [label="length_req:1.0"]; s23 -> s22 [label="length_rsp:1.0"]; s23 -> s21 [label="feature_rsp:1.0"]; s23 -> s20 [label="version_req:1.0"]; s23 -> s0 [label="mtu_req:0.3"]; s23 -> s19 [label="mtu_req:0.7"]; s23 -> s0 [label="pairing_req:0.3"]; s23 -> s18 [label="pairing_req:0.7"]; s23 -> s1 [label="connection_req:1.0"]; s24 -> s3 [label="scan_req:1.0"]; s24 -> s49 [label="length_req:1.0"]; s24 -> s48 [label="length_rsp:1.0"]; s24 -> s48 [label="feature_rsp:1.0"]; s24 -> s47 [label="version_req:1.0"]; s24 -> s0 [label="mtu_req:0.3"]; s24 -> s46 [label="mtu_req:0.7"]; s24 -> s0 [label="pairing_req:0.3"]; s24 -> s45 [label="pairing_req:0.7"]; s24 -> s1 [label="connection_req:1.0"]; s25 -> s3 [label="scan_req:1.0"]; s25 -> s28 [label="length_req:1.0"]; s25 -> s27 [label="length_rsp:1.0"]; s25 -> s27 [label="feature_rsp:1.0"]; s25 -> s26 [label="version_req:1.0"]; s25 -> s0 [label="mtu_req:0.3"]; s25 -> s25 [label="mtu_req:0.7"]; s25 -> s0 [label="pairing_req:0.3"]; s25 -> s24 [label="pairing_req:0.7"]; s25 -> s1 [label="connection_req:1.0"]; s26 -> s3 [label="scan_req:1.0"]; s26 -> s28 [label="length_req:1.0"]; s26 -> s27 [label="length_rsp:1.0"]; s26 -> s27 [label="feature_rsp:1.0"]; s26 -> s26 [label="version_req:1.0"]; s26 -> s0 [label="mtu_req:0.3"]; s26 -> s25 [label="mtu_req:0.7"]; s26 -> s0 [label="pairing_req:0.3"]; s26 -> s24 [label="pairing_req:0.7"]; s26 -> s1 [label="connection_req:1.0"]; s27 -> s3 [label="scan_req:1.0"]; s27 -> s28 [label="length_req:1.0"]; s27 -> s27 [label="length_rsp:1.0"]; s27 -> s27 [label="feature_rsp:1.0"]; s27 -> s26 [label="version_req:1.0"]; s27 -> s0 [label="mtu_req:0.3"]; s27 -> s25 [label="mtu_req:0.7"]; s27 -> s0 [label="pairing_req:0.3"]; s27 -> s24 [label="pairing_req:0.7"]; s27 -> s1 [label="connection_req:1.0"]; s28 -> s3 [label="scan_req:1.0"]; s28 -> s28 [label="length_req:1.0"]; s28 -> s27 [label="length_rsp:1.0"]; s28 -> s27 [label="feature_rsp:1.0"]; s28 -> s26 [label="version_req:1.0"]; s28 -> s0 [label="mtu_req:0.3"]; s28 -> s25 [label="mtu_req:0.7"]; s28 -> s0 [label="pairing_req:0.3"]; s28 -> s24 [label="pairing_req:0.7"]; s28 -> s1 [label="connection_req:1.0"]; s29 -> s3 [label="scan_req:1.0"]; s29 -> s44 [label="length_req:1.0"]; s29 -> s43 [label="length_rsp:1.0"]; s29 -> s35 [label="feature_rsp:1.0"]; s29 -> s35 [label="version_req:1.0"]; s29 -> s0 [label="mtu_req:0.3"]; s29 -> s42 [label="mtu_req:0.7"]; s29 -> s0 [label="pairing_req:0.3"]; s29 -> s41 [label="pairing_req:0.7"]; s29 -> s1 [label="connection_req:1.0"]; s30 -> s3 [label="scan_req:1.0"]; s30 -> s32 [label="length_req:1.0"]; s30 -> s31 [label="length_rsp:1.0"]; s30 -> s15 [label="feature_rsp:1.0"]; s30 -> s15 [label="version_req:1.0"]; s30 -> s0 [label="mtu_req:0.3"]; s30 -> s30 [label="mtu_req:0.7"]; s30 -> s0 [label="pairing_req:0.3"]; s30 -> s29 [label="pairing_req:0.7"]; s30 -> s1 [label="connection_req:1.0"]; s31 -> s3 [label="scan_req:1.0"]; s31 -> s28 [label="length_req:1.0"]; s31 -> s27 [label="length_rsp:1.0"]; s31 -> s27 [label="feature_rsp:1.0"]; s31 -> s26 [label="version_req:1.0"]; s31 -> s0 [label="mtu_req:0.3"]; s31 -> s25 [label="mtu_req:0.7"]; s31 -> s0 [label="pairing_req:0.3"]; s31 -> s24 [label="pairing_req:0.7"]; s31 -> s1 [label="connection_req:1.0"]; s32 -> s3 [label="scan_req:1.0"]; s32 -> s32 [label="length_req:1.0"]; s32 -> s31 [label="length_rsp:1.0"]; s32 -> s15 [label="feature_rsp:1.0"]; s32 -> s15 [label="version_req:1.0"]; s32 -> s0 [label="mtu_req:0.3"]; s32 -> s30 [label="mtu_req:0.7"]; s32 -> s0 [label="pairing_req:0.3"]; s32 -> s29 [label="pairing_req:0.7"]; s32 -> s1 [label="connection_req:1.0"]; s33 -> s3 [label="scan_req:1.0"]; s33 -> s17 [label="length_req:1.0"]; s33 -> s16 [label="length_rsp:1.0"]; s33 -> s6 [label="feature_rsp:1.0"]; s33 -> s15 [label="version_req:1.0"]; s33 -> s0 [label="mtu_req:0.3"]; s33 -> s14 [label="mtu_req:0.7"]; s33 -> s0 [label="pairing_req:0.3"]; s33 -> s13 [label="pairing_req:0.7"]; s33 -> s1 [label="connection_req:1.0"]; s34 -> s3 [label="scan_req:1.0"]; s34 -> s36 [label="length_req:1.0"]; s34 -> s39 [label="length_rsp:1.0"]; s34 -> s20 [label="feature_rsp:1.0"]; s34 -> s35 [label="version_req:1.0"]; s34 -> s0 [label="mtu_req:0.3"]; s34 -> s34 [label="mtu_req:0.7"]; s34 -> s0 [label="pairing_req:0.3"]; s34 -> s33 [label="pairing_req:0.7"]; s34 -> s1 [label="connection_req:1.0"]; s35 -> s3 [label="scan_req:1.0"]; s35 -> s44 [label="length_req:1.0"]; s35 -> s43 [label="length_rsp:1.0"]; s35 -> s35 [label="feature_rsp:1.0"]; s35 -> s35 [label="version_req:1.0"]; s35 -> s0 [label="mtu_req:0.3"]; s35 -> s42 [label="mtu_req:0.7"]; s35 -> s0 [label="pairing_req:0.3"]; s35 -> s41 [label="pairing_req:0.7"]; s35 -> s1 [label="connection_req:1.0"]; s36 -> s3 [label="scan_req:1.0"]; s36 -> s36 [label="length_req:1.0"]; s36 -> s39 [label="length_rsp:1.0"]; s36 -> s20 [label="feature_rsp:1.0"]; s36 -> s35 [label="version_req:1.0"]; s36 -> s0 [label="mtu_req:0.3"]; s36 -> s34 [label="mtu_req:0.7"]; s36 -> s0 [label="pairing_req:0.3"]; s36 -> s33 [label="pairing_req:0.7"]; s36 -> s1 [label="connection_req:1.0"]; s37 -> s3 [label="scan_req:1.0"]; s37 -> s12 [label="length_req:1.0"]; s37 -> s8 [label="length_rsp:1.0"]; s37 -> s8 [label="feature_rsp:1.0"]; s37 -> s16 [label="version_req:1.0"]; s37 -> s0 [label="mtu_req:0.3"]; s37 -> s11 [label="mtu_req:0.7"]; s37 -> s0 [label="pairing_req:0.3"]; s37 -> s10 [label="pairing_req:0.7"]; s37 -> s1 [label="connection_req:1.0"]; s38 -> s3 [label="scan_req:1.0"]; s38 -> s40 [label="length_req:1.0"]; s38 -> s22 [label="length_rsp:1.0"]; s38 -> s22 [label="feature_rsp:1.0"]; s38 -> s39 [label="version_req:1.0"]; s38 -> s0 [label="mtu_req:0.3"]; s38 -> s38 [label="mtu_req:0.7"]; s38 -> s0 [label="pairing_req:0.3"]; s38 -> s37 [label="pairing_req:0.7"]; s38 -> s1 [label="connection_req:1.0"]; s39 -> s3 [label="scan_req:1.0"]; s39 -> s49 [label="length_req:1.0"]; s39 -> s48 [label="length_rsp:1.0"]; s39 -> s48 [label="feature_rsp:1.0"]; s39 -> s47 [label="version_req:1.0"]; s39 -> s0 [label="mtu_req:0.3"]; s39 -> s46 [label="mtu_req:0.7"]; s39 -> s0 [label="pairing_req:0.3"]; s39 -> s45 [label="pairing_req:0.7"]; s39 -> s1 [label="connection_req:1.0"]; s40 -> s3 [label="scan_req:1.0"]; s40 -> s40 [label="length_req:1.0"]; s40 -> s22 [label="length_rsp:1.0"]; s40 -> s22 [label="feature_rsp:1.0"]; s40 -> s39 [label="version_req:1.0"]; s40 -> s0 [label="mtu_req:0.3"]; s40 -> s38 [label="mtu_req:0.7"]; s40 -> s0 [label="pairing_req:0.3"]; s40 -> s37 [label="pairing_req:0.7"]; s40 -> s1 [label="connection_req:1.0"]; s41 -> s3 [label="scan_req:1.0"]; s41 -> s32 [label="length_req:1.0"]; s41 -> s31 [label="length_rsp:1.0"]; s41 -> s15 [label="feature_rsp:1.0"]; s41 -> s15 [label="version_req:1.0"]; s41 -> s0 [label="mtu_req:0.3"]; s41 -> s30 [label="mtu_req:0.7"]; s41 -> s0 [label="pairing_req:0.3"]; s41 -> s29 [label="pairing_req:0.7"]; s41 -> s1 [label="connection_req:1.0"]; s42 -> s3 [label="scan_req:1.0"]; s42 -> s44 [label="length_req:1.0"]; s42 -> s43 [label="length_rsp:1.0"]; s42 -> s35 [label="feature_rsp:1.0"]; s42 -> s35 [label="version_req:1.0"]; s42 -> s0 [label="mtu_req:0.3"]; s42 -> s42 [label="mtu_req:0.7"]; s42 -> s0 [label="pairing_req:0.3"]; s42 -> s41 [label="pairing_req:0.7"]; s42 -> s1 [label="connection_req:1.0"]; s43 -> s3 [label="scan_req:1.0"]; s43 -> s49 [label="length_req:1.0"]; s43 -> s48 [label="length_rsp:1.0"]; s43 -> s48 [label="feature_rsp:1.0"]; s43 -> s47 [label="version_req:1.0"]; s43 -> s0 [label="mtu_req:0.3"]; s43 -> s46 [label="mtu_req:0.7"]; s43 -> s0 [label="pairing_req:0.3"]; s43 -> s45 [label="pairing_req:0.7"]; s43 -> s1 [label="connection_req:1.0"]; s44 -> s3 [label="scan_req:1.0"]; s44 -> s44 [label="length_req:1.0"]; s44 -> s43 [label="length_rsp:1.0"]; s44 -> s35 [label="feature_rsp:1.0"]; s44 -> s35 [label="version_req:1.0"]; s44 -> s0 [label="mtu_req:0.3"]; s44 -> s42 [label="mtu_req:0.7"]; s44 -> s0 [label="pairing_req:0.3"]; s44 -> s41 [label="pairing_req:0.7"]; s44 -> s1 [label="connection_req:1.0"]; s45 -> s3 [label="scan_req:1.0"]; s45 -> s28 [label="length_req:1.0"]; s45 -> s27 [label="length_rsp:1.0"]; s45 -> s27 [label="feature_rsp:1.0"]; s45 -> s26 [label="version_req:1.0"]; s45 -> s0 [label="mtu_req:0.3"]; s45 -> s25 [label="mtu_req:0.7"]; s45 -> s0 [label="pairing_req:0.3"]; s45 -> s24 [label="pairing_req:0.7"]; s45 -> s1 [label="connection_req:1.0"]; s46 -> s3 [label="scan_req:1.0"]; s46 -> s49 [label="length_req:1.0"]; s46 -> s48 [label="length_rsp:1.0"]; s46 -> s48 [label="feature_rsp:1.0"]; s46 -> s47 [label="version_req:1.0"]; s46 -> s0 [label="mtu_req:0.3"]; s46 -> s46 [label="mtu_req:0.7"]; s46 -> s0 [label="pairing_req:0.3"]; s46 -> s45 [label="pairing_req:0.7"]; s46 -> s1 [label="connection_req:1.0"]; s47 -> s3 [label="scan_req:1.0"]; s47 -> s49 [label="length_req:1.0"]; s47 -> s48 [label="length_rsp:1.0"]; s47 -> s48 [label="feature_rsp:1.0"]; s47 -> s47 [label="version_req:1.0"]; s47 -> s0 [label="mtu_req:0.3"]; s47 -> s46 [label="mtu_req:0.7"]; s47 -> s0 [label="pairing_req:0.3"]; s47 -> s45 [label="pairing_req:0.7"]; s47 -> s1 [label="connection_req:1.0"]; s48 -> s3 [label="scan_req:1.0"]; s48 -> s49 [label="length_req:1.0"]; s48 -> s48 [label="length_rsp:1.0"]; s48 -> s48 [label="feature_rsp:1.0"]; s48 -> s47 [label="version_req:1.0"]; s48 -> s0 [label="mtu_req:0.3"]; s48 -> s46 [label="mtu_req:0.7"]; s48 -> s0 [label="pairing_req:0.3"]; s48 -> s45 [label="pairing_req:0.7"]; s48 -> s1 [label="connection_req:1.0"]; s49 -> s3 [label="scan_req:1.0"]; s49 -> s49 [label="length_req:1.0"]; s49 -> s48 [label="length_rsp:1.0"]; s49 -> s48 [label="feature_rsp:1.0"]; s49 -> s47 [label="version_req:1.0"]; s49 -> s0 [label="mtu_req:0.3"]; s49 -> s46 [label="mtu_req:0.7"]; s49 -> s0 [label="pairing_req:0.3"]; s49 -> s45 [label="pairing_req:0.7"]; s49 -> s1 [label="connection_req:1.0"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CC2640R2-no-pairing-req.dot ================================================ digraph CC2640R2 { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4]; s5 [label=s5]; s0 -> s0 [label="scan_req/Adv"]; s0 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s0 -> s0 [label="length_req/Empty"]; s0 -> s0 [label="length_rsp/Empty"]; s0 -> s0 [label="feature_rsp/Empty"]; s0 -> s0 [label="feature_req/Empty"]; s0 -> s0 [label="version_req/Empty"]; s0 -> s0 [label="mtu_req/Empty"]; s1 -> s0 [label="scan_req/Adv"]; s1 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s1 -> s3 [label="length_rsp/BTLE|BTLE_DATA"]; s1 -> s1 [label="feature_rsp/BTLE|BTLE_DATA"]; s1 -> s1 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s1 -> s2 [label="version_req/BTLE|BTLE_DATA"]; s1 -> s1 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s2 -> s0 [label="scan_req/Adv"]; s2 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s2 -> s5 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s2 -> s2 [label="feature_rsp/BTLE|BTLE_DATA"]; s2 -> s2 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s2 -> s4 [label="version_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s3 -> s0 [label="scan_req/Adv"]; s3 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s3 -> s3 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s3 -> s3 [label="length_rsp/BTLE|BTLE_DATA"]; s3 -> s3 [label="feature_rsp/BTLE|BTLE_DATA"]; s3 -> s3 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s3 -> s5 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s3 -> s3 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s4 -> s0 [label="scan_req/Adv"]; s4 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s4 -> s4 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s4 -> s5 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP|LL_VERSION_IND"]; s4 -> s4 [label="feature_rsp/BTLE|BTLE_DATA"]; s4 -> s4 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s4 -> s4 [label="version_req/BTLE|BTLE_DATA"]; s4 -> s4 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s5 -> s0 [label="scan_req/Adv"]; s5 -> s1 [label="connection_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s5 -> s5 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s5 -> s5 [label="length_rsp/BTLE|BTLE_DATA"]; s5 -> s5 [label="feature_rsp/BTLE|BTLE_DATA"]; s5 -> s5 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s5 -> s5 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s5 -> s5 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CC2650.dot ================================================ digraph "cc2650" { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4]; s0 -> s0 [label="scan_req/Adv"]; s0 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s0 -> s0 [label="length_req/Empty"]; s0 -> s0 [label="length_rsp/Empty"]; s0 -> s0 [label="feature_rsp/Empty"]; s0 -> s0 [label="feature_req/Empty"]; s0 -> s0 [label="version_req/Empty"]; s0 -> s0 [label="mtu_req/Empty"]; s0 -> s0 [label="pairing_req/Empty"]; s1 -> s0 [label="scan_req/Adv"]; s1 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s1 -> s1 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s1 -> s1 [label="feature_rsp/BTLE|BTLE_DATA"]; s1 -> s1 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s1 -> s3 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s1 -> s1 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s1 -> s2 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s2 -> s0 [label="scan_req/Adv"]; s2 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s2 -> s2 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s2 -> s2 [label="feature_rsp/BTLE|BTLE_DATA"]; s2 -> s2 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s2 -> s4 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s2 -> s2 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s2 -> s1 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s3 -> s0 [label="scan_req/Adv"]; s3 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s3 -> s3 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s3 -> s3 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s3 -> s3 [label="feature_rsp/BTLE|BTLE_DATA"]; s3 -> s3 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s3 -> s3 [label="version_req/BTLE|BTLE_DATA"]; s3 -> s3 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s3 -> s4 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s4 -> s0 [label="scan_req/Adv"]; s4 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s4 -> s4 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s4 -> s4 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s4 -> s4 [label="feature_rsp/BTLE|BTLE_DATA"]; s4 -> s4 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s4 -> s4 [label="version_req/BTLE|BTLE_DATA"]; s4 -> s4 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s4 -> s3 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CYBLE-416045-02.dot ================================================ digraph "cyble-416045-02" { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s0 -> s0 [label="scan_req/Adv"]; s0 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s0 -> s0 [label="length_req/Empty"]; s0 -> s0 [label="length_rsp/Empty"]; s0 -> s0 [label="feature_rsp/Empty"]; s0 -> s0 [label="feature_req/Empty"]; s0 -> s0 [label="version_req/Empty"]; s0 -> s0 [label="mtu_req/Empty"]; s0 -> s0 [label="pairing_req/Empty"]; s1 -> s0 [label="scan_req/Adv"]; s1 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s1 -> s1 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s1 -> s1 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s1 -> s1 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s1 -> s2 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s1 -> s1 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s1 -> s1 [label="pairing_req/BTLE|BTLE_DATA"]; s2 -> s0 [label="scan_req/Adv"]; s2 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s2 -> s2 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s2 -> s2 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s2 -> s2 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s2 -> s2 [label="version_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s2 -> s2 [label="pairing_req/BTLE|BTLE_DATA"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CYBLE-416045-02_Crash_No_Response_stochastic.dot ================================================ digraph "CYBLE-416045-02_Crash_No_Response_stochastic" { 0 [label=___start___]; s00 [label=E]; s01 [label=Adv]; s02 [label=SYSTEM_CRASH]; s03 [label=Empty]; s10 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s11 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s12 [label="BTLE|BTLE_DATA"]; s13 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s14 [label=E]; s15 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s20 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s21 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s22 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s23 [label="BTLE|BTLE_DATA"]; s24 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s25 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; 0 -> s01 [label="scan_req:1.0"]; 0 -> s03 [label="length_req:1.0"]; 0 -> s03 [label="length_rsp:1.0"]; 0 -> s03 [label="feature_rsp:1.0"]; 0 -> s03 [label="feature_req:1.0"]; 0 -> s03 [label="version_req:1.0"]; 0 -> s00 [label="mtu_req:0.2"]; 0 -> s03 [label="mtu_req:0.8"]; 0 -> s03 [label="mtu_req:1.0"]; 0 -> s00 [label="pairing_req:0.2"]; 0 -> s03 [label="pairing_req:0.8"]; 0 -> s03 [label="pairing_req:1.0"]; 0 -> s12 [label="connection_req:1.0"]; s00 -> s01 [label="scan_req:1.0"]; s00 -> s03 [label="length_req:1.0"]; s00 -> s03 [label="length_rsp:1.0"]; s00 -> s03 [label="feature_rsp:1.0"]; s00 -> s03 [label="feature_req:1.0"]; s00 -> s03 [label="version_req:1.0"]; s00 -> s00 [label="mtu_req:0.2"]; s00 -> s03 [label="mtu_req:0.8"]; s00 -> s03 [label="mtu_req:1.0"]; s00 -> s00 [label="pairing_req:0.2"]; s00 -> s03 [label="pairing_req:0.8"]; s00 -> s03 [label="pairing_req:1.0"]; s00 -> s12 [label="connection_req:1.0"]; s01 -> s01 [label="scan_req:1.0"]; s01 -> s03 [label="length_req:1.0"]; s01 -> s03 [label="length_rsp:1.0"]; s01 -> s03 [label="feature_rsp:1.0"]; s01 -> s03 [label="feature_req:1.0"]; s01 -> s03 [label="version_req:1.0"]; s01 -> s00 [label="mtu_req:0.2"]; s01 -> s03 [label="mtu_req:0.8"]; s01 -> s03 [label="mtu_req:1.0"]; s01 -> s00 [label="pairing_req:0.2"]; s01 -> s03 [label="pairing_req:0.8"]; s01 -> s03 [label="pairing_req:1.0"]; s01 -> s12 [label="connection_req:1.0"]; s02 -> s01 [label="scan_req:1.0"]; s02 -> s03 [label="length_req:1.0"]; s02 -> s03 [label="length_rsp:1.0"]; s02 -> s03 [label="feature_rsp:1.0"]; s02 -> s03 [label="feature_req:1.0"]; s02 -> s03 [label="version_req:1.0"]; s02 -> s00 [label="mtu_req:0.2"]; s02 -> s03 [label="mtu_req:0.8"]; s02 -> s03 [label="mtu_req:1.0"]; s02 -> s00 [label="pairing_req:0.2"]; s02 -> s03 [label="pairing_req:0.8"]; s02 -> s03 [label="pairing_req:1.0"]; s02 -> s12 [label="connection_req:1.0"]; s03 -> s01 [label="scan_req:1.0"]; s03 -> s03 [label="length_req:1.0"]; s03 -> s03 [label="length_rsp:1.0"]; s03 -> s03 [label="feature_rsp:1.0"]; s03 -> s03 [label="feature_req:1.0"]; s03 -> s03 [label="version_req:1.0"]; s03 -> s00 [label="mtu_req:0.2"]; s03 -> s03 [label="mtu_req:0.8"]; s03 -> s03 [label="mtu_req:1.0"]; s03 -> s00 [label="pairing_req:0.2"]; s03 -> s03 [label="pairing_req:0.8"]; s03 -> s03 [label="pairing_req:1.0"]; s03 -> s12 [label="connection_req:1.0"]; s10 -> s01 [label="scan_req:1.0"]; s10 -> s11 [label="length_req:1.0"]; s10 -> s11 [label="length_rsp:1.0"]; s10 -> s13 [label="feature_rsp:1.0"]; s10 -> s15 [label="feature_req:1.0"]; s10 -> s22 [label="version_req:1.0"]; s10 -> s14 [label="mtu_req:0.2"]; s10 -> s10 [label="mtu_req:0.8"]; s10 -> s10 [label="mtu_req:1.0"]; s10 -> s14 [label="pairing_req:0.2"]; s10 -> s12 [label="pairing_req:0.8"]; s10 -> s12 [label="pairing_req:1.0"]; s10 -> s12 [label="connection_req:1.0"]; s11 -> s01 [label="scan_req:1.0"]; s11 -> s11 [label="length_req:1.0"]; s11 -> s11 [label="length_rsp:1.0"]; s11 -> s13 [label="feature_rsp:1.0"]; s11 -> s15 [label="feature_req:1.0"]; s11 -> s22 [label="version_req:1.0"]; s11 -> s14 [label="mtu_req:0.2"]; s11 -> s10 [label="mtu_req:0.8"]; s11 -> s10 [label="mtu_req:1.0"]; s11 -> s14 [label="pairing_req:0.2"]; s11 -> s12 [label="pairing_req:0.8"]; s11 -> s12 [label="pairing_req:1.0"]; s11 -> s12 [label="connection_req:1.0"]; s12 -> s01 [label="scan_req:1.0"]; s12 -> s11 [label="length_req:1.0"]; s12 -> s11 [label="length_rsp:1.0"]; s12 -> s13 [label="feature_rsp:1.0"]; s12 -> s15 [label="feature_req:1.0"]; s12 -> s22 [label="version_req:1.0"]; s12 -> s14 [label="mtu_req:0.2"]; s12 -> s10 [label="mtu_req:0.8"]; s12 -> s10 [label="mtu_req:1.0"]; s12 -> s14 [label="pairing_req:0.2"]; s12 -> s12 [label="pairing_req:0.8"]; s12 -> s12 [label="pairing_req:1.0"]; s12 -> s12 [label="connection_req:1.0"]; s13 -> s01 [label="scan_req:1.0"]; s13 -> s11 [label="length_req:1.0"]; s13 -> s11 [label="length_rsp:1.0"]; s13 -> s13 [label="feature_rsp:1.0"]; s13 -> s15 [label="feature_req:1.0"]; s13 -> s22 [label="version_req:1.0"]; s13 -> s14 [label="mtu_req:0.2"]; s13 -> s10 [label="mtu_req:0.8"]; s13 -> s10 [label="mtu_req:1.0"]; s13 -> s14 [label="pairing_req:0.2"]; s13 -> s12 [label="pairing_req:0.8"]; s13 -> s12 [label="pairing_req:1.0"]; s13 -> s12 [label="connection_req:1.0"]; s14 -> s01 [label="scan_req:1.0"]; s14 -> s11 [label="length_req:1.0"]; s14 -> s11 [label="length_rsp:1.0"]; s14 -> s13 [label="feature_rsp:1.0"]; s14 -> s15 [label="feature_req:1.0"]; s14 -> s22 [label="version_req:1.0"]; s14 -> s14 [label="mtu_req:0.2"]; s14 -> s10 [label="mtu_req:0.8"]; s14 -> s10 [label="mtu_req:1.0"]; s14 -> s14 [label="pairing_req:0.2"]; s14 -> s12 [label="pairing_req:0.8"]; s14 -> s12 [label="pairing_req:1.0"]; s14 -> s12 [label="connection_req:1.0"]; s15 -> s01 [label="scan_req:1.0"]; s15 -> s11 [label="length_req:1.0"]; s15 -> s11 [label="length_rsp:1.0"]; s15 -> s13 [label="feature_rsp:1.0"]; s15 -> s15 [label="feature_req:1.0"]; s15 -> s22 [label="version_req:1.0"]; s15 -> s14 [label="mtu_req:0.2"]; s15 -> s10 [label="mtu_req:0.8"]; s15 -> s10 [label="mtu_req:1.0"]; s15 -> s14 [label="pairing_req:0.2"]; s15 -> s12 [label="pairing_req:0.8"]; s15 -> s12 [label="pairing_req:1.0"]; s15 -> s12 [label="connection_req:1.0"]; s20 -> s01 [label="scan_req:1.0"]; s20 -> s02 [label="length_req:0.1"]; s20 -> s21 [label="length_req:0.9"]; s20 -> s02 [label="length_rsp:0.1"]; s20 -> s21 [label="length_rsp:0.9"]; s20 -> s02 [label="feature_rsp:0.1"]; s20 -> s24 [label="feature_rsp:0.9"]; s20 -> s25 [label="feature_req:1.0"]; s20 -> s23 [label="version_req:1.0"]; s20 -> s23 [label="mtu_req:0.2"]; s20 -> s20 [label="mtu_req:0.8"]; s20 -> s20 [label="mtu_req:1.0"]; s20 -> s23 [label="pairing_req:0.2"]; s20 -> s23 [label="pairing_req:0.8"]; s20 -> s23 [label="pairing_req:1.0"]; s20 -> s12 [label="connection_req:1.0"]; s21 -> s01 [label="scan_req:1.0"]; s21 -> s02 [label="length_req:0.1"]; s21 -> s21 [label="length_req:0.9"]; s21 -> s02 [label="length_rsp:0.1"]; s21 -> s21 [label="length_rsp:0.9"]; s21 -> s02 [label="feature_rsp:0.1"]; s21 -> s24 [label="feature_rsp:0.9"]; s21 -> s25 [label="feature_req:1.0"]; s21 -> s23 [label="version_req:1.0"]; s21 -> s23 [label="mtu_req:0.2"]; s21 -> s20 [label="mtu_req:0.8"]; s21 -> s20 [label="mtu_req:1.0"]; s21 -> s23 [label="pairing_req:0.2"]; s21 -> s23 [label="pairing_req:0.8"]; s21 -> s23 [label="pairing_req:1.0"]; s21 -> s12 [label="connection_req:1.0"]; s22 -> s01 [label="scan_req:1.0"]; s22 -> s02 [label="length_req:0.1"]; s22 -> s21 [label="length_req:0.9"]; s22 -> s02 [label="length_rsp:0.1"]; s22 -> s21 [label="length_rsp:0.9"]; s22 -> s02 [label="feature_rsp:0.1"]; s22 -> s24 [label="feature_rsp:0.9"]; s22 -> s25 [label="feature_req:1.0"]; s22 -> s23 [label="version_req:1.0"]; s22 -> s23 [label="mtu_req:0.2"]; s22 -> s20 [label="mtu_req:0.8"]; s22 -> s20 [label="mtu_req:1.0"]; s22 -> s23 [label="pairing_req:0.2"]; s22 -> s23 [label="pairing_req:0.8"]; s22 -> s23 [label="pairing_req:1.0"]; s22 -> s12 [label="connection_req:1.0"]; s23 -> s01 [label="scan_req:1.0"]; s23 -> s02 [label="length_req:0.1"]; s23 -> s21 [label="length_req:0.9"]; s23 -> s02 [label="length_rsp:0.1"]; s23 -> s21 [label="length_rsp:0.9"]; s23 -> s02 [label="feature_rsp:0.1"]; s23 -> s24 [label="feature_rsp:0.9"]; s23 -> s25 [label="feature_req:1.0"]; s23 -> s23 [label="version_req:1.0"]; s23 -> s23 [label="mtu_req:0.2"]; s23 -> s20 [label="mtu_req:0.8"]; s23 -> s20 [label="mtu_req:1.0"]; s23 -> s23 [label="pairing_req:0.2"]; s23 -> s23 [label="pairing_req:0.8"]; s23 -> s23 [label="pairing_req:1.0"]; s23 -> s12 [label="connection_req:1.0"]; s24 -> s01 [label="scan_req:1.0"]; s24 -> s02 [label="length_req:0.1"]; s24 -> s21 [label="length_req:0.9"]; s24 -> s02 [label="length_rsp:0.1"]; s24 -> s21 [label="length_rsp:0.9"]; s24 -> s02 [label="feature_rsp:0.1"]; s24 -> s24 [label="feature_rsp:0.9"]; s24 -> s25 [label="feature_req:1.0"]; s24 -> s23 [label="version_req:1.0"]; s24 -> s23 [label="mtu_req:0.2"]; s24 -> s20 [label="mtu_req:0.8"]; s24 -> s20 [label="mtu_req:1.0"]; s24 -> s23 [label="pairing_req:0.2"]; s24 -> s23 [label="pairing_req:0.8"]; s24 -> s23 [label="pairing_req:1.0"]; s24 -> s12 [label="connection_req:1.0"]; s25 -> s01 [label="scan_req:1.0"]; s25 -> s02 [label="length_req:0.1"]; s25 -> s21 [label="length_req:0.9"]; s25 -> s02 [label="length_rsp:0.1"]; s25 -> s21 [label="length_rsp:0.9"]; s25 -> s02 [label="feature_rsp:0.1"]; s25 -> s24 [label="feature_rsp:0.9"]; s25 -> s25 [label="feature_req:1.0"]; s25 -> s23 [label="version_req:1.0"]; s25 -> s23 [label="mtu_req:0.2"]; s25 -> s20 [label="mtu_req:0.8"]; s25 -> s20 [label="mtu_req:1.0"]; s25 -> s23 [label="pairing_req:0.2"]; s25 -> s23 [label="pairing_req:0.8"]; s25 -> s23 [label="pairing_req:1.0"]; s25 -> s12 [label="connection_req:1.0"]; __start0 [label="", shape=none]; __start0 -> 0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CYW43455.dot ================================================ digraph cyw43455 { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4]; s5 [label=s5]; s6 [label=s6]; s7 [label=s7]; s8 [label=s8]; s9 [label=s9]; s10 [label=s10]; s11 [label=s11]; s12 [label=s12]; s13 [label=s13]; s14 [label=s14]; s15 [label=s15]; s0 -> s0 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s0 -> s0 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s0 -> s4 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s0 -> s0 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s0 -> s3 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s0 -> s2 [label="mtu_req/BTLE|BTLE_DATA"]; s0 -> s1 [label="pairing_req/BTLE|BTLE_DATA"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s1 -> s1 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s1 -> s10 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s1 -> s1 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s1 -> s9 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s1 -> s8 [label="mtu_req/BTLE|BTLE_DATA"]; s1 -> s1 [label="pairing_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s2 -> s2 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s2 -> s4 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s2 -> s2 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s2 -> s7 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s2 -> s2 [label="mtu_req/BTLE|BTLE_DATA"]; s2 -> s8 [label="pairing_req/BTLE|BTLE_DATA"]; s3 -> s3 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s3 -> s3 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s3 -> s6 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s3 -> s3 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s3 -> s3 [label="version_req/BTLE|BTLE_DATA"]; s3 -> s7 [label="mtu_req/BTLE|BTLE_DATA"]; s3 -> s9 [label="pairing_req/BTLE|BTLE_DATA"]; s4 -> s4 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s4 -> s5 [label="length_rsp/BTLE|BTLE_DATA"]; s4 -> s4 [label="feature_rsp/BTLE|BTLE_DATA"]; s4 -> s4 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s4 -> s6 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s4 -> s4 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s4 -> s10 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s5 -> s5 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s5 -> s5 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s5 -> s5 [label="feature_rsp/BTLE|BTLE_DATA"]; s5 -> s5 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s5 -> s14 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s5 -> s5 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s5 -> s13 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s6 -> s6 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s6 -> s14 [label="length_rsp/BTLE|BTLE_DATA"]; s6 -> s6 [label="feature_rsp/BTLE|BTLE_DATA"]; s6 -> s6 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s6 -> s6 [label="version_req/BTLE|BTLE_DATA"]; s6 -> s6 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s6 -> s12 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s7 -> s7 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s7 -> s7 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s7 -> s6 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s7 -> s7 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s7 -> s7 [label="version_req/BTLE|BTLE_DATA"]; s7 -> s7 [label="mtu_req/BTLE|BTLE_DATA"]; s7 -> s11 [label="pairing_req/BTLE|BTLE_DATA"]; s8 -> s8 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s8 -> s2 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s8 -> s10 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s8 -> s8 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s8 -> s11 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s8 -> s8 [label="mtu_req/BTLE|BTLE_DATA"]; s8 -> s8 [label="pairing_req/BTLE|BTLE_DATA"]; s9 -> s9 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s9 -> s9 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s9 -> s12 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s9 -> s9 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s9 -> s9 [label="version_req/BTLE|BTLE_DATA"]; s9 -> s11 [label="mtu_req/BTLE|BTLE_DATA"]; s9 -> s9 [label="pairing_req/BTLE|BTLE_DATA"]; s10 -> s10 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s10 -> s13 [label="length_rsp/BTLE|BTLE_DATA"]; s10 -> s10 [label="feature_rsp/BTLE|BTLE_DATA"]; s10 -> s10 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s10 -> s12 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s10 -> s10 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s10 -> s10 [label="pairing_req/BTLE|BTLE_DATA"]; s11 -> s11 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s11 -> s11 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s11 -> s12 [label="feature_rsp/ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s11 -> s11 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s11 -> s11 [label="version_req/BTLE|BTLE_DATA"]; s11 -> s11 [label="mtu_req/BTLE|BTLE_DATA"]; s11 -> s11 [label="pairing_req/BTLE|BTLE_DATA"]; s12 -> s12 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s12 -> s15 [label="length_rsp/BTLE|BTLE_DATA"]; s12 -> s12 [label="feature_rsp/BTLE|BTLE_DATA"]; s12 -> s12 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s12 -> s12 [label="version_req/BTLE|BTLE_DATA"]; s12 -> s12 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s12 -> s12 [label="pairing_req/BTLE|BTLE_DATA"]; s13 -> s13 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s13 -> s13 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s13 -> s13 [label="feature_rsp/BTLE|BTLE_DATA"]; s13 -> s13 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s13 -> s15 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s13 -> s13 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s13 -> s13 [label="pairing_req/BTLE|BTLE_DATA"]; s14 -> s14 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s14 -> s14 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s14 -> s14 [label="feature_rsp/BTLE|BTLE_DATA"]; s14 -> s14 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s14 -> s14 [label="version_req/BTLE|BTLE_DATA"]; s14 -> s14 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s14 -> s15 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s15 -> s15 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s15 -> s15 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s15 -> s15 [label="feature_rsp/BTLE|BTLE_DATA"]; s15 -> s15 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s15 -> s15 [label="version_req/BTLE|BTLE_DATA"]; s15 -> s15 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s15 -> s15 [label="pairing_req/BTLE|BTLE_DATA"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/CYW43455_stochastic.dot ================================================ digraph CYW43455_stochastic { 0 [label=___start___]; s00 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s01 [label=E]; s02 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s03 [label=SYSTEM_CRASH]; s04 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s10 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s11 [label="BTLE|BTLE_DATA"]; s12 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s13 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s20 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s21 [label="BTLE|BTLE_DATA"]; s22 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s23 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s30 [label="BTLE|BTLE_DATA"]; s31 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s32 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s33 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s34 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s40 [label="BTLE|BTLE_DATA"]; s41 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s42 [label="ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s43 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s44 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s45 [label="ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s50 [label="BTLE|BTLE_DATA"]; s51 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s52 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s53 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s54 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s60 [label="BTLE|BTLE_DATA"]; s61 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s62 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s63 [label="ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s64 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s65 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s66 [label="ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ"]; s70 [label="BTLE|BTLE_DATA"]; s71 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s72 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s73 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s74 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s80 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s81 [label="BTLE|BTLE_DATA"]; s82 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s90 [label="BTLE|BTLE_DATA"]; s91 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s92 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s93 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s94 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s100 [label="BTLE|BTLE_DATA"]; s101 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s102 [label="ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s103 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s104 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s105 [label="ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s106 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s110 [label="BTLE|BTLE_DATA"]; s111 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s112 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s113 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s114 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s120 [label="BTLE|BTLE_DATA"]; s121 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s122 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s123 [label="ATT_Exchange_MTU_Request|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s124 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s125 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s126 [label="ATT_Exchange_MTU_Request|ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_CTRL|BTLE_DATA|L2CAP_CmdHdr|L2CAP_Connection_Parameter_Update_Request|L2CAP_Hdr|LL_LENGTH_REQ|SM_Hdr|SM_Pairing_Response"]; s127 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s130 [label="BTLE|BTLE_DATA"]; s131 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s132 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s133 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s134 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s135 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s140 [label="BTLE|BTLE_DATA"]; s141 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s142 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s143 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s144 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s145 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s150 [label="BTLE|BTLE_DATA"]; s151 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s152 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s153 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s154 [label="ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s155 [label="BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s156 [label="BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; 0 -> s01 [label="length_req:0.2"]; 0 -> s00 [label="length_req:0.8"]; 0 -> s00 [label="length_req:1.0"]; 0 -> s01 [label="length_rsp:0.2"]; 0 -> s04 [label="length_rsp:0.8"]; 0 -> s04 [label="length_rsp:1.0"]; 0 -> s01 [label="feature_req:0.2"]; 0 -> s02 [label="feature_req:0.8"]; 0 -> s02 [label="feature_req:1.0"]; 0 -> s01 [label="feature_rsp:0.2"]; 0 -> s45 [label="feature_rsp:0.8"]; 0 -> s45 [label="feature_rsp:1.0"]; 0 -> s01 [label="version_req:0.2"]; 0 -> s32 [label="version_req:0.8"]; 0 -> s32 [label="version_req:1.0"]; 0 -> s01 [label="mtu_req:0.2"]; 0 -> s21 [label="mtu_req:0.8"]; 0 -> s21 [label="mtu_req:1.0"]; 0 -> s01 [label="pairing_req:0.2"]; 0 -> s11 [label="pairing_req:0.8"]; 0 -> s11 [label="pairing_req:1.0"]; s00 -> s01 [label="length_req:0.2"]; s00 -> s00 [label="length_req:0.8"]; s00 -> s00 [label="length_req:1.0"]; s00 -> s01 [label="length_rsp:0.2"]; s00 -> s04 [label="length_rsp:0.8"]; s00 -> s04 [label="length_rsp:1.0"]; s00 -> s01 [label="feature_req:0.2"]; s00 -> s02 [label="feature_req:0.8"]; s00 -> s02 [label="feature_req:1.0"]; s00 -> s01 [label="feature_rsp:0.2"]; s00 -> s45 [label="feature_rsp:0.8"]; s00 -> s45 [label="feature_rsp:1.0"]; s00 -> s01 [label="version_req:0.2"]; s00 -> s32 [label="version_req:0.8"]; s00 -> s32 [label="version_req:1.0"]; s00 -> s01 [label="mtu_req:0.2"]; s00 -> s21 [label="mtu_req:0.8"]; s00 -> s21 [label="mtu_req:1.0"]; s00 -> s01 [label="pairing_req:0.2"]; s00 -> s11 [label="pairing_req:0.8"]; s00 -> s11 [label="pairing_req:1.0"]; s01 -> s01 [label="length_req:0.2"]; s01 -> s00 [label="length_req:0.8"]; s01 -> s00 [label="length_req:1.0"]; s01 -> s01 [label="length_rsp:0.2"]; s01 -> s04 [label="length_rsp:0.8"]; s01 -> s04 [label="length_rsp:1.0"]; s01 -> s01 [label="feature_req:0.2"]; s01 -> s02 [label="feature_req:0.8"]; s01 -> s02 [label="feature_req:1.0"]; s01 -> s01 [label="feature_rsp:0.2"]; s01 -> s45 [label="feature_rsp:0.8"]; s01 -> s45 [label="feature_rsp:1.0"]; s01 -> s01 [label="version_req:0.2"]; s01 -> s32 [label="version_req:0.8"]; s01 -> s32 [label="version_req:1.0"]; s01 -> s01 [label="mtu_req:0.2"]; s01 -> s21 [label="mtu_req:0.8"]; s01 -> s21 [label="mtu_req:1.0"]; s01 -> s01 [label="pairing_req:0.2"]; s01 -> s11 [label="pairing_req:0.8"]; s01 -> s11 [label="pairing_req:1.0"]; s02 -> s01 [label="length_req:0.2"]; s02 -> s00 [label="length_req:0.8"]; s02 -> s00 [label="length_req:1.0"]; s02 -> s01 [label="length_rsp:0.2"]; s02 -> s04 [label="length_rsp:0.8"]; s02 -> s04 [label="length_rsp:1.0"]; s02 -> s01 [label="feature_req:0.2"]; s02 -> s02 [label="feature_req:0.8"]; s02 -> s02 [label="feature_req:1.0"]; s02 -> s01 [label="feature_rsp:0.2"]; s02 -> s45 [label="feature_rsp:0.8"]; s02 -> s45 [label="feature_rsp:1.0"]; s02 -> s01 [label="version_req:0.2"]; s02 -> s32 [label="version_req:0.8"]; s02 -> s32 [label="version_req:1.0"]; s02 -> s01 [label="mtu_req:0.2"]; s02 -> s21 [label="mtu_req:0.8"]; s02 -> s21 [label="mtu_req:1.0"]; s02 -> s01 [label="pairing_req:0.2"]; s02 -> s11 [label="pairing_req:0.8"]; s02 -> s11 [label="pairing_req:1.0"]; s03 -> s01 [label="length_req:0.2"]; s03 -> s00 [label="length_req:0.8"]; s03 -> s00 [label="length_req:1.0"]; s03 -> s01 [label="length_rsp:0.2"]; s03 -> s04 [label="length_rsp:0.8"]; s03 -> s04 [label="length_rsp:1.0"]; s03 -> s01 [label="feature_req:0.2"]; s03 -> s02 [label="feature_req:0.8"]; s03 -> s02 [label="feature_req:1.0"]; s03 -> s01 [label="feature_rsp:0.2"]; s03 -> s45 [label="feature_rsp:0.8"]; s03 -> s45 [label="feature_rsp:1.0"]; s03 -> s01 [label="version_req:0.2"]; s03 -> s32 [label="version_req:0.8"]; s03 -> s32 [label="version_req:1.0"]; s03 -> s01 [label="mtu_req:0.2"]; s03 -> s21 [label="mtu_req:0.8"]; s03 -> s21 [label="mtu_req:1.0"]; s03 -> s01 [label="pairing_req:0.2"]; s03 -> s11 [label="pairing_req:0.8"]; s03 -> s11 [label="pairing_req:1.0"]; s04 -> s01 [label="length_req:0.2"]; s04 -> s00 [label="length_req:0.8"]; s04 -> s00 [label="length_req:1.0"]; s04 -> s01 [label="length_rsp:0.2"]; s04 -> s04 [label="length_rsp:0.8"]; s04 -> s04 [label="length_rsp:1.0"]; s04 -> s01 [label="feature_req:0.2"]; s04 -> s02 [label="feature_req:0.8"]; s04 -> s02 [label="feature_req:1.0"]; s04 -> s01 [label="feature_rsp:0.2"]; s04 -> s45 [label="feature_rsp:0.8"]; s04 -> s45 [label="feature_rsp:1.0"]; s04 -> s01 [label="version_req:0.2"]; s04 -> s32 [label="version_req:0.8"]; s04 -> s32 [label="version_req:1.0"]; s04 -> s01 [label="mtu_req:0.2"]; s04 -> s21 [label="mtu_req:0.8"]; s04 -> s21 [label="mtu_req:1.0"]; s04 -> s01 [label="pairing_req:0.2"]; s04 -> s11 [label="pairing_req:0.8"]; s04 -> s11 [label="pairing_req:1.0"]; s10 -> s12 [label="length_req:1.0"]; s10 -> s13 [label="length_rsp:1.0"]; s10 -> s10 [label="feature_req:1.0"]; s10 -> s102 [label="feature_rsp:1.0"]; s10 -> s92 [label="version_req:1.0"]; s10 -> s81 [label="mtu_req:1.0"]; s10 -> s11 [label="pairing_req:1.0"]; s11 -> s12 [label="length_req:1.0"]; s11 -> s13 [label="length_rsp:1.0"]; s11 -> s10 [label="feature_req:1.0"]; s11 -> s102 [label="feature_rsp:1.0"]; s11 -> s92 [label="version_req:1.0"]; s11 -> s81 [label="mtu_req:1.0"]; s11 -> s11 [label="pairing_req:1.0"]; s12 -> s12 [label="length_req:1.0"]; s12 -> s13 [label="length_rsp:1.0"]; s12 -> s10 [label="feature_req:1.0"]; s12 -> s102 [label="feature_rsp:1.0"]; s12 -> s92 [label="version_req:1.0"]; s12 -> s81 [label="mtu_req:1.0"]; s12 -> s11 [label="pairing_req:1.0"]; s13 -> s12 [label="length_req:1.0"]; s13 -> s13 [label="length_rsp:1.0"]; s13 -> s10 [label="feature_req:1.0"]; s13 -> s102 [label="feature_rsp:1.0"]; s13 -> s92 [label="version_req:1.0"]; s13 -> s81 [label="mtu_req:1.0"]; s13 -> s11 [label="pairing_req:1.0"]; s20 -> s22 [label="length_req:1.0"]; s20 -> s23 [label="length_rsp:1.0"]; s20 -> s20 [label="feature_req:1.0"]; s20 -> s42 [label="feature_rsp:1.0"]; s20 -> s72 [label="version_req:1.0"]; s20 -> s21 [label="mtu_req:1.0"]; s20 -> s81 [label="pairing_req:1.0"]; s21 -> s22 [label="length_req:1.0"]; s21 -> s23 [label="length_rsp:1.0"]; s21 -> s20 [label="feature_req:1.0"]; s21 -> s42 [label="feature_rsp:1.0"]; s21 -> s72 [label="version_req:1.0"]; s21 -> s21 [label="mtu_req:1.0"]; s21 -> s81 [label="pairing_req:1.0"]; s22 -> s22 [label="length_req:1.0"]; s22 -> s23 [label="length_rsp:1.0"]; s22 -> s20 [label="feature_req:1.0"]; s22 -> s42 [label="feature_rsp:1.0"]; s22 -> s72 [label="version_req:1.0"]; s22 -> s21 [label="mtu_req:1.0"]; s22 -> s81 [label="pairing_req:1.0"]; s23 -> s22 [label="length_req:1.0"]; s23 -> s23 [label="length_rsp:1.0"]; s23 -> s20 [label="feature_req:1.0"]; s23 -> s42 [label="feature_rsp:1.0"]; s23 -> s72 [label="version_req:1.0"]; s23 -> s21 [label="mtu_req:1.0"]; s23 -> s81 [label="pairing_req:1.0"]; s30 -> s31 [label="length_req:1.0"]; s30 -> s34 [label="length_rsp:1.0"]; s30 -> s33 [label="feature_req:1.0"]; s30 -> s66 [label="feature_rsp:1.0"]; s30 -> s30 [label="version_req:1.0"]; s30 -> s70 [label="mtu_req:1.0"]; s30 -> s90 [label="pairing_req:1.0"]; s31 -> s31 [label="length_req:1.0"]; s31 -> s34 [label="length_rsp:1.0"]; s31 -> s33 [label="feature_req:1.0"]; s31 -> s66 [label="feature_rsp:1.0"]; s31 -> s30 [label="version_req:1.0"]; s31 -> s70 [label="mtu_req:1.0"]; s31 -> s90 [label="pairing_req:1.0"]; s32 -> s31 [label="length_req:1.0"]; s32 -> s34 [label="length_rsp:1.0"]; s32 -> s33 [label="feature_req:1.0"]; s32 -> s66 [label="feature_rsp:1.0"]; s32 -> s30 [label="version_req:1.0"]; s32 -> s70 [label="mtu_req:1.0"]; s32 -> s90 [label="pairing_req:1.0"]; s33 -> s31 [label="length_req:1.0"]; s33 -> s34 [label="length_rsp:1.0"]; s33 -> s33 [label="feature_req:1.0"]; s33 -> s66 [label="feature_rsp:1.0"]; s33 -> s30 [label="version_req:1.0"]; s33 -> s70 [label="mtu_req:1.0"]; s33 -> s90 [label="pairing_req:1.0"]; s34 -> s31 [label="length_req:1.0"]; s34 -> s34 [label="length_rsp:1.0"]; s34 -> s33 [label="feature_req:1.0"]; s34 -> s66 [label="feature_rsp:1.0"]; s34 -> s30 [label="version_req:1.0"]; s34 -> s70 [label="mtu_req:1.0"]; s34 -> s90 [label="pairing_req:1.0"]; s40 -> s41 [label="length_req:1.0"]; s40 -> s50 [label="length_rsp:1.0"]; s40 -> s43 [label="feature_req:1.0"]; s40 -> s40 [label="feature_rsp:1.0"]; s40 -> s62 [label="version_req:1.0"]; s40 -> s44 [label="mtu_req:1.0"]; s40 -> s106 [label="pairing_req:1.0"]; s41 -> s41 [label="length_req:1.0"]; s41 -> s50 [label="length_rsp:1.0"]; s41 -> s43 [label="feature_req:1.0"]; s41 -> s40 [label="feature_rsp:1.0"]; s41 -> s62 [label="version_req:1.0"]; s41 -> s44 [label="mtu_req:1.0"]; s41 -> s106 [label="pairing_req:1.0"]; s42 -> s41 [label="length_req:1.0"]; s42 -> s50 [label="length_rsp:1.0"]; s42 -> s43 [label="feature_req:1.0"]; s42 -> s40 [label="feature_rsp:1.0"]; s42 -> s62 [label="version_req:1.0"]; s42 -> s44 [label="mtu_req:1.0"]; s42 -> s106 [label="pairing_req:1.0"]; s43 -> s41 [label="length_req:1.0"]; s43 -> s50 [label="length_rsp:1.0"]; s43 -> s43 [label="feature_req:1.0"]; s43 -> s40 [label="feature_rsp:1.0"]; s43 -> s62 [label="version_req:1.0"]; s43 -> s44 [label="mtu_req:1.0"]; s43 -> s106 [label="pairing_req:1.0"]; s44 -> s41 [label="length_req:1.0"]; s44 -> s50 [label="length_rsp:1.0"]; s44 -> s43 [label="feature_req:1.0"]; s44 -> s40 [label="feature_rsp:1.0"]; s44 -> s62 [label="version_req:1.0"]; s44 -> s44 [label="mtu_req:1.0"]; s44 -> s106 [label="pairing_req:1.0"]; s45 -> s41 [label="length_req:1.0"]; s45 -> s50 [label="length_rsp:1.0"]; s45 -> s43 [label="feature_req:1.0"]; s45 -> s40 [label="feature_rsp:1.0"]; s45 -> s62 [label="version_req:1.0"]; s45 -> s44 [label="mtu_req:1.0"]; s45 -> s106 [label="pairing_req:1.0"]; s50 -> s03 [label="length_req:0.1"]; s50 -> s51 [label="length_req:0.9"]; s50 -> s03 [label="length_rsp:0.1"]; s50 -> s54 [label="length_rsp:0.9"]; s50 -> s52 [label="feature_req:1.0"]; s50 -> s03 [label="feature_rsp:0.1"]; s50 -> s50 [label="feature_rsp:0.9"]; s50 -> s142 [label="version_req:1.0"]; s50 -> s53 [label="mtu_req:1.0"]; s50 -> s135 [label="pairing_req:1.0"]; s51 -> s03 [label="length_req:0.1"]; s51 -> s51 [label="length_req:0.9"]; s51 -> s03 [label="length_rsp:0.1"]; s51 -> s54 [label="length_rsp:0.9"]; s51 -> s52 [label="feature_req:1.0"]; s51 -> s03 [label="feature_rsp:0.1"]; s51 -> s50 [label="feature_rsp:0.9"]; s51 -> s142 [label="version_req:1.0"]; s51 -> s53 [label="mtu_req:1.0"]; s51 -> s135 [label="pairing_req:1.0"]; s52 -> s03 [label="length_req:0.1"]; s52 -> s51 [label="length_req:0.9"]; s52 -> s03 [label="length_rsp:0.1"]; s52 -> s54 [label="length_rsp:0.9"]; s52 -> s52 [label="feature_req:1.0"]; s52 -> s03 [label="feature_rsp:0.1"]; s52 -> s50 [label="feature_rsp:0.9"]; s52 -> s142 [label="version_req:1.0"]; s52 -> s53 [label="mtu_req:1.0"]; s52 -> s135 [label="pairing_req:1.0"]; s53 -> s03 [label="length_req:0.1"]; s53 -> s51 [label="length_req:0.9"]; s53 -> s03 [label="length_rsp:0.1"]; s53 -> s54 [label="length_rsp:0.9"]; s53 -> s52 [label="feature_req:1.0"]; s53 -> s03 [label="feature_rsp:0.1"]; s53 -> s50 [label="feature_rsp:0.9"]; s53 -> s142 [label="version_req:1.0"]; s53 -> s53 [label="mtu_req:1.0"]; s53 -> s135 [label="pairing_req:1.0"]; s54 -> s03 [label="length_req:0.1"]; s54 -> s51 [label="length_req:0.9"]; s54 -> s03 [label="length_rsp:0.1"]; s54 -> s54 [label="length_rsp:0.9"]; s54 -> s52 [label="feature_req:1.0"]; s54 -> s03 [label="feature_rsp:0.1"]; s54 -> s50 [label="feature_rsp:0.9"]; s54 -> s142 [label="version_req:1.0"]; s54 -> s53 [label="mtu_req:1.0"]; s54 -> s135 [label="pairing_req:1.0"]; s60 -> s66 [label="length_req:0.2"]; s60 -> s61 [label="length_req:0.8"]; s60 -> s61 [label="length_req:1.0"]; s60 -> s66 [label="length_rsp:0.2"]; s60 -> s140 [label="length_rsp:0.8"]; s60 -> s140 [label="length_rsp:1.0"]; s60 -> s66 [label="feature_req:0.2"]; s60 -> s64 [label="feature_req:0.8"]; s60 -> s64 [label="feature_req:1.0"]; s60 -> s66 [label="feature_rsp:0.2"]; s60 -> s60 [label="feature_rsp:0.8"]; s60 -> s60 [label="feature_rsp:1.0"]; s60 -> s66 [label="version_req:0.2"]; s60 -> s60 [label="version_req:0.8"]; s60 -> s60 [label="version_req:1.0"]; s60 -> s66 [label="mtu_req:0.2"]; s60 -> s65 [label="mtu_req:0.8"]; s60 -> s65 [label="mtu_req:1.0"]; s60 -> s66 [label="pairing_req:0.2"]; s60 -> s127 [label="pairing_req:0.8"]; s60 -> s127 [label="pairing_req:1.0"]; s61 -> s66 [label="length_req:0.2"]; s61 -> s61 [label="length_req:0.8"]; s61 -> s61 [label="length_req:1.0"]; s61 -> s66 [label="length_rsp:0.2"]; s61 -> s140 [label="length_rsp:0.8"]; s61 -> s140 [label="length_rsp:1.0"]; s61 -> s66 [label="feature_req:0.2"]; s61 -> s64 [label="feature_req:0.8"]; s61 -> s64 [label="feature_req:1.0"]; s61 -> s66 [label="feature_rsp:0.2"]; s61 -> s60 [label="feature_rsp:0.8"]; s61 -> s60 [label="feature_rsp:1.0"]; s61 -> s66 [label="version_req:0.2"]; s61 -> s60 [label="version_req:0.8"]; s61 -> s60 [label="version_req:1.0"]; s61 -> s66 [label="mtu_req:0.2"]; s61 -> s65 [label="mtu_req:0.8"]; s61 -> s65 [label="mtu_req:1.0"]; s61 -> s66 [label="pairing_req:0.2"]; s61 -> s127 [label="pairing_req:0.8"]; s61 -> s127 [label="pairing_req:1.0"]; s62 -> s66 [label="length_req:0.2"]; s62 -> s61 [label="length_req:0.8"]; s62 -> s61 [label="length_req:1.0"]; s62 -> s66 [label="length_rsp:0.2"]; s62 -> s140 [label="length_rsp:0.8"]; s62 -> s140 [label="length_rsp:1.0"]; s62 -> s66 [label="feature_req:0.2"]; s62 -> s64 [label="feature_req:0.8"]; s62 -> s64 [label="feature_req:1.0"]; s62 -> s66 [label="feature_rsp:0.2"]; s62 -> s60 [label="feature_rsp:0.8"]; s62 -> s60 [label="feature_rsp:1.0"]; s62 -> s66 [label="version_req:0.2"]; s62 -> s60 [label="version_req:0.8"]; s62 -> s60 [label="version_req:1.0"]; s62 -> s66 [label="mtu_req:0.2"]; s62 -> s65 [label="mtu_req:0.8"]; s62 -> s65 [label="mtu_req:1.0"]; s62 -> s66 [label="pairing_req:0.2"]; s62 -> s127 [label="pairing_req:0.8"]; s62 -> s127 [label="pairing_req:1.0"]; s63 -> s66 [label="length_req:0.2"]; s63 -> s61 [label="length_req:0.8"]; s63 -> s61 [label="length_req:1.0"]; s63 -> s66 [label="length_rsp:0.2"]; s63 -> s140 [label="length_rsp:0.8"]; s63 -> s140 [label="length_rsp:1.0"]; s63 -> s66 [label="feature_req:0.2"]; s63 -> s64 [label="feature_req:0.8"]; s63 -> s64 [label="feature_req:1.0"]; s63 -> s66 [label="feature_rsp:0.2"]; s63 -> s60 [label="feature_rsp:0.8"]; s63 -> s60 [label="feature_rsp:1.0"]; s63 -> s66 [label="version_req:0.2"]; s63 -> s60 [label="version_req:0.8"]; s63 -> s60 [label="version_req:1.0"]; s63 -> s66 [label="mtu_req:0.2"]; s63 -> s65 [label="mtu_req:0.8"]; s63 -> s65 [label="mtu_req:1.0"]; s63 -> s66 [label="pairing_req:0.2"]; s63 -> s127 [label="pairing_req:0.8"]; s63 -> s127 [label="pairing_req:1.0"]; s64 -> s66 [label="length_req:0.2"]; s64 -> s61 [label="length_req:0.8"]; s64 -> s61 [label="length_req:1.0"]; s64 -> s66 [label="length_rsp:0.2"]; s64 -> s140 [label="length_rsp:0.8"]; s64 -> s140 [label="length_rsp:1.0"]; s64 -> s66 [label="feature_req:0.2"]; s64 -> s64 [label="feature_req:0.8"]; s64 -> s64 [label="feature_req:1.0"]; s64 -> s66 [label="feature_rsp:0.2"]; s64 -> s60 [label="feature_rsp:0.8"]; s64 -> s60 [label="feature_rsp:1.0"]; s64 -> s66 [label="version_req:0.2"]; s64 -> s60 [label="version_req:0.8"]; s64 -> s60 [label="version_req:1.0"]; s64 -> s66 [label="mtu_req:0.2"]; s64 -> s65 [label="mtu_req:0.8"]; s64 -> s65 [label="mtu_req:1.0"]; s64 -> s66 [label="pairing_req:0.2"]; s64 -> s127 [label="pairing_req:0.8"]; s64 -> s127 [label="pairing_req:1.0"]; s65 -> s66 [label="length_req:0.2"]; s65 -> s61 [label="length_req:0.8"]; s65 -> s61 [label="length_req:1.0"]; s65 -> s66 [label="length_rsp:0.2"]; s65 -> s140 [label="length_rsp:0.8"]; s65 -> s140 [label="length_rsp:1.0"]; s65 -> s66 [label="feature_req:0.2"]; s65 -> s64 [label="feature_req:0.8"]; s65 -> s64 [label="feature_req:1.0"]; s65 -> s66 [label="feature_rsp:0.2"]; s65 -> s60 [label="feature_rsp:0.8"]; s65 -> s60 [label="feature_rsp:1.0"]; s65 -> s66 [label="version_req:0.2"]; s65 -> s60 [label="version_req:0.8"]; s65 -> s60 [label="version_req:1.0"]; s65 -> s66 [label="mtu_req:0.2"]; s65 -> s65 [label="mtu_req:0.8"]; s65 -> s65 [label="mtu_req:1.0"]; s65 -> s66 [label="pairing_req:0.2"]; s65 -> s127 [label="pairing_req:0.8"]; s65 -> s127 [label="pairing_req:1.0"]; s66 -> s66 [label="length_req:0.2"]; s66 -> s61 [label="length_req:0.8"]; s66 -> s61 [label="length_req:1.0"]; s66 -> s66 [label="length_rsp:0.2"]; s66 -> s140 [label="length_rsp:0.8"]; s66 -> s140 [label="length_rsp:1.0"]; s66 -> s66 [label="feature_req:0.2"]; s66 -> s64 [label="feature_req:0.8"]; s66 -> s64 [label="feature_req:1.0"]; s66 -> s66 [label="feature_rsp:0.2"]; s66 -> s60 [label="feature_rsp:0.8"]; s66 -> s60 [label="feature_rsp:1.0"]; s66 -> s66 [label="version_req:0.2"]; s66 -> s60 [label="version_req:0.8"]; s66 -> s60 [label="version_req:1.0"]; s66 -> s66 [label="mtu_req:0.2"]; s66 -> s65 [label="mtu_req:0.8"]; s66 -> s65 [label="mtu_req:1.0"]; s66 -> s66 [label="pairing_req:0.2"]; s66 -> s127 [label="pairing_req:0.8"]; s66 -> s127 [label="pairing_req:1.0"]; s70 -> s71 [label="length_req:1.0"]; s70 -> s74 [label="length_rsp:1.0"]; s70 -> s73 [label="feature_req:1.0"]; s70 -> s63 [label="feature_rsp:1.0"]; s70 -> s70 [label="version_req:1.0"]; s70 -> s70 [label="mtu_req:1.0"]; s70 -> s110 [label="pairing_req:1.0"]; s71 -> s71 [label="length_req:1.0"]; s71 -> s74 [label="length_rsp:1.0"]; s71 -> s73 [label="feature_req:1.0"]; s71 -> s63 [label="feature_rsp:1.0"]; s71 -> s70 [label="version_req:1.0"]; s71 -> s70 [label="mtu_req:1.0"]; s71 -> s110 [label="pairing_req:1.0"]; s72 -> s71 [label="length_req:1.0"]; s72 -> s74 [label="length_rsp:1.0"]; s72 -> s73 [label="feature_req:1.0"]; s72 -> s63 [label="feature_rsp:1.0"]; s72 -> s70 [label="version_req:1.0"]; s72 -> s70 [label="mtu_req:1.0"]; s72 -> s110 [label="pairing_req:1.0"]; s73 -> s71 [label="length_req:1.0"]; s73 -> s74 [label="length_rsp:1.0"]; s73 -> s73 [label="feature_req:1.0"]; s73 -> s63 [label="feature_rsp:1.0"]; s73 -> s70 [label="version_req:1.0"]; s73 -> s70 [label="mtu_req:1.0"]; s73 -> s110 [label="pairing_req:1.0"]; s74 -> s71 [label="length_req:1.0"]; s74 -> s74 [label="length_rsp:1.0"]; s74 -> s73 [label="feature_req:1.0"]; s74 -> s63 [label="feature_rsp:1.0"]; s74 -> s70 [label="version_req:1.0"]; s74 -> s70 [label="mtu_req:1.0"]; s74 -> s110 [label="pairing_req:1.0"]; s80 -> s82 [label="length_req:1.0"]; s80 -> s23 [label="length_rsp:1.0"]; s80 -> s80 [label="feature_req:1.0"]; s80 -> s105 [label="feature_rsp:1.0"]; s80 -> s112 [label="version_req:1.0"]; s80 -> s81 [label="mtu_req:1.0"]; s80 -> s81 [label="pairing_req:1.0"]; s81 -> s82 [label="length_req:1.0"]; s81 -> s23 [label="length_rsp:1.0"]; s81 -> s80 [label="feature_req:1.0"]; s81 -> s105 [label="feature_rsp:1.0"]; s81 -> s112 [label="version_req:1.0"]; s81 -> s81 [label="mtu_req:1.0"]; s81 -> s81 [label="pairing_req:1.0"]; s82 -> s82 [label="length_req:1.0"]; s82 -> s23 [label="length_rsp:1.0"]; s82 -> s80 [label="feature_req:1.0"]; s82 -> s105 [label="feature_rsp:1.0"]; s82 -> s112 [label="version_req:1.0"]; s82 -> s81 [label="mtu_req:1.0"]; s82 -> s81 [label="pairing_req:1.0"]; s90 -> s91 [label="length_req:1.0"]; s90 -> s94 [label="length_rsp:1.0"]; s90 -> s93 [label="feature_req:1.0"]; s90 -> s123 [label="feature_rsp:1.0"]; s90 -> s90 [label="version_req:1.0"]; s90 -> s110 [label="mtu_req:1.0"]; s90 -> s90 [label="pairing_req:1.0"]; s91 -> s91 [label="length_req:1.0"]; s91 -> s94 [label="length_rsp:1.0"]; s91 -> s93 [label="feature_req:1.0"]; s91 -> s123 [label="feature_rsp:1.0"]; s91 -> s90 [label="version_req:1.0"]; s91 -> s110 [label="mtu_req:1.0"]; s91 -> s90 [label="pairing_req:1.0"]; s92 -> s91 [label="length_req:1.0"]; s92 -> s94 [label="length_rsp:1.0"]; s92 -> s93 [label="feature_req:1.0"]; s92 -> s123 [label="feature_rsp:1.0"]; s92 -> s90 [label="version_req:1.0"]; s92 -> s110 [label="mtu_req:1.0"]; s92 -> s90 [label="pairing_req:1.0"]; s93 -> s91 [label="length_req:1.0"]; s93 -> s94 [label="length_rsp:1.0"]; s93 -> s93 [label="feature_req:1.0"]; s93 -> s123 [label="feature_rsp:1.0"]; s93 -> s90 [label="version_req:1.0"]; s93 -> s110 [label="mtu_req:1.0"]; s93 -> s90 [label="pairing_req:1.0"]; s94 -> s91 [label="length_req:1.0"]; s94 -> s94 [label="length_rsp:1.0"]; s94 -> s93 [label="feature_req:1.0"]; s94 -> s123 [label="feature_rsp:1.0"]; s94 -> s90 [label="version_req:1.0"]; s94 -> s110 [label="mtu_req:1.0"]; s94 -> s90 [label="pairing_req:1.0"]; s100 -> s03 [label="length_req:0.1"]; s100 -> s101 [label="length_req:0.9"]; s100 -> s03 [label="length_rsp:0.1"]; s100 -> s130 [label="length_rsp:0.9"]; s100 -> s103 [label="feature_req:1.0"]; s100 -> s03 [label="feature_rsp:0.1"]; s100 -> s100 [label="feature_rsp:0.9"]; s100 -> s122 [label="version_req:1.0"]; s100 -> s104 [label="mtu_req:1.0"]; s100 -> s100 [label="pairing_req:1.0"]; s101 -> s03 [label="length_req:0.1"]; s101 -> s101 [label="length_req:0.9"]; s101 -> s03 [label="length_rsp:0.1"]; s101 -> s130 [label="length_rsp:0.9"]; s101 -> s103 [label="feature_req:1.0"]; s101 -> s03 [label="feature_rsp:0.1"]; s101 -> s100 [label="feature_rsp:0.9"]; s101 -> s122 [label="version_req:1.0"]; s101 -> s104 [label="mtu_req:1.0"]; s101 -> s100 [label="pairing_req:1.0"]; s102 -> s03 [label="length_req:0.1"]; s102 -> s101 [label="length_req:0.9"]; s102 -> s03 [label="length_rsp:0.1"]; s102 -> s130 [label="length_rsp:0.9"]; s102 -> s103 [label="feature_req:1.0"]; s102 -> s03 [label="feature_rsp:0.1"]; s102 -> s100 [label="feature_rsp:0.9"]; s102 -> s122 [label="version_req:1.0"]; s102 -> s104 [label="mtu_req:1.0"]; s102 -> s100 [label="pairing_req:1.0"]; s103 -> s03 [label="length_req:0.1"]; s103 -> s101 [label="length_req:0.9"]; s103 -> s03 [label="length_rsp:0.1"]; s103 -> s130 [label="length_rsp:0.9"]; s103 -> s103 [label="feature_req:1.0"]; s103 -> s03 [label="feature_rsp:0.1"]; s103 -> s100 [label="feature_rsp:0.9"]; s103 -> s122 [label="version_req:1.0"]; s103 -> s104 [label="mtu_req:1.0"]; s103 -> s100 [label="pairing_req:1.0"]; s104 -> s03 [label="length_req:0.1"]; s104 -> s101 [label="length_req:0.9"]; s104 -> s03 [label="length_rsp:0.1"]; s104 -> s130 [label="length_rsp:0.9"]; s104 -> s103 [label="feature_req:1.0"]; s104 -> s03 [label="feature_rsp:0.1"]; s104 -> s100 [label="feature_rsp:0.9"]; s104 -> s122 [label="version_req:1.0"]; s104 -> s104 [label="mtu_req:1.0"]; s104 -> s100 [label="pairing_req:1.0"]; s105 -> s03 [label="length_req:0.1"]; s105 -> s101 [label="length_req:0.9"]; s105 -> s03 [label="length_rsp:0.1"]; s105 -> s130 [label="length_rsp:0.9"]; s105 -> s103 [label="feature_req:1.0"]; s105 -> s03 [label="feature_rsp:0.1"]; s105 -> s100 [label="feature_rsp:0.9"]; s105 -> s122 [label="version_req:1.0"]; s105 -> s104 [label="mtu_req:1.0"]; s105 -> s100 [label="pairing_req:1.0"]; s106 -> s03 [label="length_req:0.1"]; s106 -> s101 [label="length_req:0.9"]; s106 -> s03 [label="length_rsp:0.1"]; s106 -> s130 [label="length_rsp:0.9"]; s106 -> s103 [label="feature_req:1.0"]; s106 -> s03 [label="feature_rsp:0.1"]; s106 -> s100 [label="feature_rsp:0.9"]; s106 -> s122 [label="version_req:1.0"]; s106 -> s104 [label="mtu_req:1.0"]; s106 -> s100 [label="pairing_req:1.0"]; s110 -> s111 [label="length_req:1.0"]; s110 -> s114 [label="length_rsp:1.0"]; s110 -> s113 [label="feature_req:1.0"]; s110 -> s126 [label="feature_rsp:1.0"]; s110 -> s110 [label="version_req:1.0"]; s110 -> s110 [label="mtu_req:1.0"]; s110 -> s110 [label="pairing_req:1.0"]; s111 -> s111 [label="length_req:1.0"]; s111 -> s114 [label="length_rsp:1.0"]; s111 -> s113 [label="feature_req:1.0"]; s111 -> s126 [label="feature_rsp:1.0"]; s111 -> s110 [label="version_req:1.0"]; s111 -> s110 [label="mtu_req:1.0"]; s111 -> s110 [label="pairing_req:1.0"]; s112 -> s111 [label="length_req:1.0"]; s112 -> s114 [label="length_rsp:1.0"]; s112 -> s113 [label="feature_req:1.0"]; s112 -> s126 [label="feature_rsp:1.0"]; s112 -> s110 [label="version_req:1.0"]; s112 -> s110 [label="mtu_req:1.0"]; s112 -> s110 [label="pairing_req:1.0"]; s113 -> s111 [label="length_req:1.0"]; s113 -> s114 [label="length_rsp:1.0"]; s113 -> s113 [label="feature_req:1.0"]; s113 -> s126 [label="feature_rsp:1.0"]; s113 -> s110 [label="version_req:1.0"]; s113 -> s110 [label="mtu_req:1.0"]; s113 -> s110 [label="pairing_req:1.0"]; s114 -> s111 [label="length_req:1.0"]; s114 -> s114 [label="length_rsp:1.0"]; s114 -> s113 [label="feature_req:1.0"]; s114 -> s126 [label="feature_rsp:1.0"]; s114 -> s110 [label="version_req:1.0"]; s114 -> s110 [label="mtu_req:1.0"]; s114 -> s110 [label="pairing_req:1.0"]; s120 -> s122 [label="length_req:0.2"]; s120 -> s121 [label="length_req:0.8"]; s120 -> s121 [label="length_req:1.0"]; s120 -> s122 [label="length_rsp:0.2"]; s120 -> s150 [label="length_rsp:0.8"]; s120 -> s150 [label="length_rsp:1.0"]; s120 -> s122 [label="feature_req:0.2"]; s120 -> s124 [label="feature_req:0.8"]; s120 -> s124 [label="feature_req:1.0"]; s120 -> s122 [label="feature_rsp:0.2"]; s120 -> s120 [label="feature_rsp:0.8"]; s120 -> s120 [label="feature_rsp:1.0"]; s120 -> s122 [label="version_req:0.2"]; s120 -> s120 [label="version_req:0.8"]; s120 -> s120 [label="version_req:1.0"]; s120 -> s122 [label="mtu_req:0.2"]; s120 -> s125 [label="mtu_req:0.8"]; s120 -> s125 [label="mtu_req:1.0"]; s120 -> s122 [label="pairing_req:0.2"]; s120 -> s120 [label="pairing_req:0.8"]; s120 -> s120 [label="pairing_req:1.0"]; s121 -> s122 [label="length_req:0.2"]; s121 -> s121 [label="length_req:0.8"]; s121 -> s121 [label="length_req:1.0"]; s121 -> s122 [label="length_rsp:0.2"]; s121 -> s150 [label="length_rsp:0.8"]; s121 -> s150 [label="length_rsp:1.0"]; s121 -> s122 [label="feature_req:0.2"]; s121 -> s124 [label="feature_req:0.8"]; s121 -> s124 [label="feature_req:1.0"]; s121 -> s122 [label="feature_rsp:0.2"]; s121 -> s120 [label="feature_rsp:0.8"]; s121 -> s120 [label="feature_rsp:1.0"]; s121 -> s122 [label="version_req:0.2"]; s121 -> s120 [label="version_req:0.8"]; s121 -> s120 [label="version_req:1.0"]; s121 -> s122 [label="mtu_req:0.2"]; s121 -> s125 [label="mtu_req:0.8"]; s121 -> s125 [label="mtu_req:1.0"]; s121 -> s122 [label="pairing_req:0.2"]; s121 -> s120 [label="pairing_req:0.8"]; s121 -> s120 [label="pairing_req:1.0"]; s122 -> s122 [label="length_req:0.2"]; s122 -> s121 [label="length_req:0.8"]; s122 -> s121 [label="length_req:1.0"]; s122 -> s122 [label="length_rsp:0.2"]; s122 -> s150 [label="length_rsp:0.8"]; s122 -> s150 [label="length_rsp:1.0"]; s122 -> s122 [label="feature_req:0.2"]; s122 -> s124 [label="feature_req:0.8"]; s122 -> s124 [label="feature_req:1.0"]; s122 -> s122 [label="feature_rsp:0.2"]; s122 -> s120 [label="feature_rsp:0.8"]; s122 -> s120 [label="feature_rsp:1.0"]; s122 -> s122 [label="version_req:0.2"]; s122 -> s120 [label="version_req:0.8"]; s122 -> s120 [label="version_req:1.0"]; s122 -> s122 [label="mtu_req:0.2"]; s122 -> s125 [label="mtu_req:0.8"]; s122 -> s125 [label="mtu_req:1.0"]; s122 -> s122 [label="pairing_req:0.2"]; s122 -> s120 [label="pairing_req:0.8"]; s122 -> s120 [label="pairing_req:1.0"]; s123 -> s122 [label="length_req:0.2"]; s123 -> s121 [label="length_req:0.8"]; s123 -> s121 [label="length_req:1.0"]; s123 -> s122 [label="length_rsp:0.2"]; s123 -> s150 [label="length_rsp:0.8"]; s123 -> s150 [label="length_rsp:1.0"]; s123 -> s122 [label="feature_req:0.2"]; s123 -> s124 [label="feature_req:0.8"]; s123 -> s124 [label="feature_req:1.0"]; s123 -> s122 [label="feature_rsp:0.2"]; s123 -> s120 [label="feature_rsp:0.8"]; s123 -> s120 [label="feature_rsp:1.0"]; s123 -> s122 [label="version_req:0.2"]; s123 -> s120 [label="version_req:0.8"]; s123 -> s120 [label="version_req:1.0"]; s123 -> s122 [label="mtu_req:0.2"]; s123 -> s125 [label="mtu_req:0.8"]; s123 -> s125 [label="mtu_req:1.0"]; s123 -> s122 [label="pairing_req:0.2"]; s123 -> s120 [label="pairing_req:0.8"]; s123 -> s120 [label="pairing_req:1.0"]; s124 -> s122 [label="length_req:0.2"]; s124 -> s121 [label="length_req:0.8"]; s124 -> s121 [label="length_req:1.0"]; s124 -> s122 [label="length_rsp:0.2"]; s124 -> s150 [label="length_rsp:0.8"]; s124 -> s150 [label="length_rsp:1.0"]; s124 -> s122 [label="feature_req:0.2"]; s124 -> s124 [label="feature_req:0.8"]; s124 -> s124 [label="feature_req:1.0"]; s124 -> s122 [label="feature_rsp:0.2"]; s124 -> s120 [label="feature_rsp:0.8"]; s124 -> s120 [label="feature_rsp:1.0"]; s124 -> s122 [label="version_req:0.2"]; s124 -> s120 [label="version_req:0.8"]; s124 -> s120 [label="version_req:1.0"]; s124 -> s122 [label="mtu_req:0.2"]; s124 -> s125 [label="mtu_req:0.8"]; s124 -> s125 [label="mtu_req:1.0"]; s124 -> s122 [label="pairing_req:0.2"]; s124 -> s120 [label="pairing_req:0.8"]; s124 -> s120 [label="pairing_req:1.0"]; s125 -> s122 [label="length_req:0.2"]; s125 -> s121 [label="length_req:0.8"]; s125 -> s121 [label="length_req:1.0"]; s125 -> s122 [label="length_rsp:0.2"]; s125 -> s150 [label="length_rsp:0.8"]; s125 -> s150 [label="length_rsp:1.0"]; s125 -> s122 [label="feature_req:0.2"]; s125 -> s124 [label="feature_req:0.8"]; s125 -> s124 [label="feature_req:1.0"]; s125 -> s122 [label="feature_rsp:0.2"]; s125 -> s120 [label="feature_rsp:0.8"]; s125 -> s120 [label="feature_rsp:1.0"]; s125 -> s122 [label="version_req:0.2"]; s125 -> s120 [label="version_req:0.8"]; s125 -> s120 [label="version_req:1.0"]; s125 -> s122 [label="mtu_req:0.2"]; s125 -> s125 [label="mtu_req:0.8"]; s125 -> s125 [label="mtu_req:1.0"]; s125 -> s122 [label="pairing_req:0.2"]; s125 -> s120 [label="pairing_req:0.8"]; s125 -> s120 [label="pairing_req:1.0"]; s126 -> s122 [label="length_req:0.2"]; s126 -> s121 [label="length_req:0.8"]; s126 -> s121 [label="length_req:1.0"]; s126 -> s122 [label="length_rsp:0.2"]; s126 -> s150 [label="length_rsp:0.8"]; s126 -> s150 [label="length_rsp:1.0"]; s126 -> s122 [label="feature_req:0.2"]; s126 -> s124 [label="feature_req:0.8"]; s126 -> s124 [label="feature_req:1.0"]; s126 -> s122 [label="feature_rsp:0.2"]; s126 -> s120 [label="feature_rsp:0.8"]; s126 -> s120 [label="feature_rsp:1.0"]; s126 -> s122 [label="version_req:0.2"]; s126 -> s120 [label="version_req:0.8"]; s126 -> s120 [label="version_req:1.0"]; s126 -> s122 [label="mtu_req:0.2"]; s126 -> s125 [label="mtu_req:0.8"]; s126 -> s125 [label="mtu_req:1.0"]; s126 -> s122 [label="pairing_req:0.2"]; s126 -> s120 [label="pairing_req:0.8"]; s126 -> s120 [label="pairing_req:1.0"]; s127 -> s122 [label="length_req:0.2"]; s127 -> s121 [label="length_req:0.8"]; s127 -> s121 [label="length_req:1.0"]; s127 -> s122 [label="length_rsp:0.2"]; s127 -> s150 [label="length_rsp:0.8"]; s127 -> s150 [label="length_rsp:1.0"]; s127 -> s122 [label="feature_req:0.2"]; s127 -> s124 [label="feature_req:0.8"]; s127 -> s124 [label="feature_req:1.0"]; s127 -> s122 [label="feature_rsp:0.2"]; s127 -> s120 [label="feature_rsp:0.8"]; s127 -> s120 [label="feature_rsp:1.0"]; s127 -> s122 [label="version_req:0.2"]; s127 -> s120 [label="version_req:0.8"]; s127 -> s120 [label="version_req:1.0"]; s127 -> s122 [label="mtu_req:0.2"]; s127 -> s125 [label="mtu_req:0.8"]; s127 -> s125 [label="mtu_req:1.0"]; s127 -> s122 [label="pairing_req:0.2"]; s127 -> s120 [label="pairing_req:0.8"]; s127 -> s120 [label="pairing_req:1.0"]; s130 -> s131 [label="length_req:1.0"]; s130 -> s134 [label="length_rsp:1.0"]; s130 -> s132 [label="feature_req:1.0"]; s130 -> s130 [label="feature_rsp:1.0"]; s130 -> s152 [label="version_req:1.0"]; s130 -> s133 [label="mtu_req:1.0"]; s130 -> s130 [label="pairing_req:1.0"]; s131 -> s131 [label="length_req:1.0"]; s131 -> s134 [label="length_rsp:1.0"]; s131 -> s132 [label="feature_req:1.0"]; s131 -> s130 [label="feature_rsp:1.0"]; s131 -> s152 [label="version_req:1.0"]; s131 -> s133 [label="mtu_req:1.0"]; s131 -> s130 [label="pairing_req:1.0"]; s132 -> s131 [label="length_req:1.0"]; s132 -> s134 [label="length_rsp:1.0"]; s132 -> s132 [label="feature_req:1.0"]; s132 -> s130 [label="feature_rsp:1.0"]; s132 -> s152 [label="version_req:1.0"]; s132 -> s133 [label="mtu_req:1.0"]; s132 -> s130 [label="pairing_req:1.0"]; s133 -> s131 [label="length_req:1.0"]; s133 -> s134 [label="length_rsp:1.0"]; s133 -> s132 [label="feature_req:1.0"]; s133 -> s130 [label="feature_rsp:1.0"]; s133 -> s152 [label="version_req:1.0"]; s133 -> s133 [label="mtu_req:1.0"]; s133 -> s130 [label="pairing_req:1.0"]; s134 -> s131 [label="length_req:1.0"]; s134 -> s134 [label="length_rsp:1.0"]; s134 -> s132 [label="feature_req:1.0"]; s134 -> s130 [label="feature_rsp:1.0"]; s134 -> s152 [label="version_req:1.0"]; s134 -> s133 [label="mtu_req:1.0"]; s134 -> s130 [label="pairing_req:1.0"]; s135 -> s131 [label="length_req:1.0"]; s135 -> s134 [label="length_rsp:1.0"]; s135 -> s132 [label="feature_req:1.0"]; s135 -> s130 [label="feature_rsp:1.0"]; s135 -> s152 [label="version_req:1.0"]; s135 -> s133 [label="mtu_req:1.0"]; s135 -> s130 [label="pairing_req:1.0"]; s140 -> s141 [label="length_req:1.0"]; s140 -> s145 [label="length_rsp:1.0"]; s140 -> s143 [label="feature_req:1.0"]; s140 -> s140 [label="feature_rsp:1.0"]; s140 -> s140 [label="version_req:1.0"]; s140 -> s144 [label="mtu_req:1.0"]; s140 -> s156 [label="pairing_req:1.0"]; s141 -> s141 [label="length_req:1.0"]; s141 -> s145 [label="length_rsp:1.0"]; s141 -> s143 [label="feature_req:1.0"]; s141 -> s140 [label="feature_rsp:1.0"]; s141 -> s140 [label="version_req:1.0"]; s141 -> s144 [label="mtu_req:1.0"]; s141 -> s156 [label="pairing_req:1.0"]; s142 -> s141 [label="length_req:1.0"]; s142 -> s145 [label="length_rsp:1.0"]; s142 -> s143 [label="feature_req:1.0"]; s142 -> s140 [label="feature_rsp:1.0"]; s142 -> s140 [label="version_req:1.0"]; s142 -> s144 [label="mtu_req:1.0"]; s142 -> s156 [label="pairing_req:1.0"]; s143 -> s141 [label="length_req:1.0"]; s143 -> s145 [label="length_rsp:1.0"]; s143 -> s143 [label="feature_req:1.0"]; s143 -> s140 [label="feature_rsp:1.0"]; s143 -> s140 [label="version_req:1.0"]; s143 -> s144 [label="mtu_req:1.0"]; s143 -> s156 [label="pairing_req:1.0"]; s144 -> s141 [label="length_req:1.0"]; s144 -> s145 [label="length_rsp:1.0"]; s144 -> s143 [label="feature_req:1.0"]; s144 -> s140 [label="feature_rsp:1.0"]; s144 -> s140 [label="version_req:1.0"]; s144 -> s144 [label="mtu_req:1.0"]; s144 -> s156 [label="pairing_req:1.0"]; s145 -> s141 [label="length_req:1.0"]; s145 -> s145 [label="length_rsp:1.0"]; s145 -> s143 [label="feature_req:1.0"]; s145 -> s140 [label="feature_rsp:1.0"]; s145 -> s140 [label="version_req:1.0"]; s145 -> s144 [label="mtu_req:1.0"]; s145 -> s156 [label="pairing_req:1.0"]; s150 -> s151 [label="length_req:1.0"]; s150 -> s155 [label="length_rsp:1.0"]; s150 -> s153 [label="feature_req:1.0"]; s150 -> s150 [label="feature_rsp:1.0"]; s150 -> s150 [label="version_req:1.0"]; s150 -> s154 [label="mtu_req:1.0"]; s150 -> s150 [label="pairing_req:1.0"]; s151 -> s151 [label="length_req:1.0"]; s151 -> s155 [label="length_rsp:1.0"]; s151 -> s153 [label="feature_req:1.0"]; s151 -> s150 [label="feature_rsp:1.0"]; s151 -> s150 [label="version_req:1.0"]; s151 -> s154 [label="mtu_req:1.0"]; s151 -> s150 [label="pairing_req:1.0"]; s152 -> s151 [label="length_req:1.0"]; s152 -> s155 [label="length_rsp:1.0"]; s152 -> s153 [label="feature_req:1.0"]; s152 -> s150 [label="feature_rsp:1.0"]; s152 -> s150 [label="version_req:1.0"]; s152 -> s154 [label="mtu_req:1.0"]; s152 -> s150 [label="pairing_req:1.0"]; s153 -> s151 [label="length_req:1.0"]; s153 -> s155 [label="length_rsp:1.0"]; s153 -> s153 [label="feature_req:1.0"]; s153 -> s150 [label="feature_rsp:1.0"]; s153 -> s150 [label="version_req:1.0"]; s153 -> s154 [label="mtu_req:1.0"]; s153 -> s150 [label="pairing_req:1.0"]; s154 -> s151 [label="length_req:1.0"]; s154 -> s155 [label="length_rsp:1.0"]; s154 -> s153 [label="feature_req:1.0"]; s154 -> s150 [label="feature_rsp:1.0"]; s154 -> s150 [label="version_req:1.0"]; s154 -> s154 [label="mtu_req:1.0"]; s154 -> s150 [label="pairing_req:1.0"]; s155 -> s151 [label="length_req:1.0"]; s155 -> s155 [label="length_rsp:1.0"]; s155 -> s153 [label="feature_req:1.0"]; s155 -> s150 [label="feature_rsp:1.0"]; s155 -> s150 [label="version_req:1.0"]; s155 -> s154 [label="mtu_req:1.0"]; s155 -> s150 [label="pairing_req:1.0"]; s156 -> s151 [label="length_req:1.0"]; s156 -> s155 [label="length_rsp:1.0"]; s156 -> s153 [label="feature_req:1.0"]; s156 -> s150 [label="feature_rsp:1.0"]; s156 -> s150 [label="version_req:1.0"]; s156 -> s154 [label="mtu_req:1.0"]; s156 -> s150 [label="pairing_req:1.0"]; __start0 [label="", shape=none]; __start0 -> 0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/bluetooth_model.dot ================================================ digraph LearnedModel { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s0 -> s0 [label="i0/o0"]; s0 -> s1 [label="i1/o1"]; s0 -> s0 [label="i2/o2"]; s0 -> s0 [label="i3/o2"]; s0 -> s0 [label="i4/o2"]; s0 -> s0 [label="i5/o2"]; s0 -> s0 [label="i6/o2"]; s0 -> s0 [label="i7/o2"]; s0 -> s0 [label="i8/o2"]; s1 -> s0 [label="i0/o0"]; s1 -> s1 [label="i1/o1"]; s1 -> s1 [label="i2/o3"]; s1 -> s1 [label="i3/o3"]; s1 -> s1 [label="i4/o4"]; s1 -> s1 [label="i5/o5"]; s1 -> s2 [label="i6/o6"]; s1 -> s1 [label="i7/o7"]; s1 -> s1 [label="i8/o8"]; s2 -> s0 [label="i0/o0"]; s2 -> s1 [label="i1/o1"]; s2 -> s2 [label="i2/o3"]; s2 -> s2 [label="i3/o3"]; s2 -> s2 [label="i4/o4"]; s2 -> s2 [label="i5/o5"]; s2 -> s2 [label="i6/o1"]; s2 -> s2 [label="i7/o7"]; s2 -> s2 [label="i8/o8"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/bluetooth_reduced.dot ================================================ digraph LearnedModel { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s0 -> s0 [label="scan_req/Adv"]; s0 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s0 -> s0 [label="length_req/Empty"]; s0 -> s0 [label="length_rsp/Empty"]; s0 -> s0 [label="feature_rsp/Empty"]; s0 -> s0 [label="feature_req/Empty"]; s0 -> s0 [label="version_req/Empty"]; s0 -> s0 [label="mtu_req/Empty"]; s0 -> s0 [label="pairing_req/Empty"]; s1 -> s0 [label="scan_req/Adv"]; s1 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s1 -> s1 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s1 -> s1 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s1 -> s1 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s1 -> s2 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s1 -> s1 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s1 -> s1 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s2 -> s0 [label="scan_req/Adv"]; s2 -> s1 [label="connection_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s2 -> s2 [label="length_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s2 -> s2 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_REJECT_IND"]; s2 -> s2 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s2 -> s2 [label="version_req/BTLE|BTLE_DATA"]; s2 -> s2 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s2 -> s2 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/cc2652r1.dot ================================================ digraph "CC2652R1" { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s0 -> s0 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s0 -> s0 [label="length_rsp/BTLE|BTLE_DATA"]; s0 -> s2 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s0 -> s0 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s0 -> s0 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s0 -> s0 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s0 -> s1 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s1 -> s1 [label="length_rsp/BTLE|BTLE_DATA"]; s1 -> s3 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_REQ"]; s1 -> s1 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s1 -> s1 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s1 -> s1 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s1 -> s0 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s2 -> s0 [label="length_rsp/BTLE|BTLE_DATA"]; s2 -> s2 [label="feature_rsp/BTLE|BTLE_DATA"]; s2 -> s2 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s2 -> s2 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s2 -> s2 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s2 -> s3 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s3 -> s3 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s3 -> s1 [label="length_rsp/BTLE|BTLE_DATA"]; s3 -> s3 [label="feature_rsp/BTLE|BTLE_DATA"]; s3 -> s3 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s3 -> s3 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s3 -> s3 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s3 -> s2 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Failed|SM_Hdr"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/Bluetooth/convert_to_stochastic.py ================================================ import random from aalpy.SULs import AutomatonSUL from aalpy.base import SUL from aalpy.automata import Mdp, MdpState, StochasticMealyState, StochasticMealyMachine from aalpy.learning_algs import run_Lstar, run_stochastic_Lstar from aalpy.oracles import RandomWMethodEqOracle, RandomWordEqOracle from aalpy.utils import load_automaton_from_file model = load_automaton_from_file('CYW43455.dot', automaton_type='mealy', compute_prefixes=True) alphabet = model.get_input_alphabet() print(alphabet) class ModelSUL(SUL): def __init__(self, model): super().__init__() self.model = model self.last_output = 'init' def pre(self): self.model.reset_to_initial() self.last_output = 'init' def post(self): pass def step(self, letter): if not letter: return self.last_output self.last_output = self.model.step(letter) return self.last_output model_sul = ModelSUL(model) def to_mdp(): eq_oracle = RandomWMethodEqOracle(alphabet, model_sul) learned_model = run_Lstar(alphabet, model_sul, eq_oracle, 'moore') # CC2640R2-no-feature-req.dot # {'mtu_req', 'pairing_req',} have 0.3 percent chance of looping to initial state moore_mdp_state_map = dict() initial_mdp_state = None for state in learned_model.states: mdp_state = MdpState(state.state_id, state.output) moore_mdp_state_map[state.prefix] = mdp_state if not state.prefix: initial_mdp_state = mdp_state # moore_mdp_state_map['sink'] = MdpState('sink', 'NO_RESPONSE') assert initial_mdp_state for state in learned_model.states: mdp_state = moore_mdp_state_map[state.prefix] state_num = int(mdp_state.state_id[1:]) for i in alphabet: reached_moore = state.transitions[i].prefix # if i in {'pairing_req', 'mtu_req'} and mdp_state.output != moore_mdp_state_map[reached_moore].output: if state_num % 2 == 0 and mdp_state.output != moore_mdp_state_map[reached_moore].output: mdp_state.transitions[i].append((mdp_state, 0.2)) mdp_state.transitions[i].append((moore_mdp_state_map[reached_moore], 0.8)) else: mdp_state.transitions[i].append((moore_mdp_state_map[reached_moore], 1.)) mdp = Mdp(initial_mdp_state, list(moore_mdp_state_map.values())) return mdp def to_smm(): # CC2640R2-no-feature-req.dot # {'mtu_req', 'pairing_req',} have 0.3 percent chance of looping to initial state moore_mdp_state_map = dict() initial_mdp_state = None for state in model.states: mdp_state = StochasticMealyState(state.state_id) moore_mdp_state_map[state.prefix] = mdp_state if not state.prefix: initial_mdp_state = mdp_state assert initial_mdp_state for state in model.states: mdp_state = moore_mdp_state_map[state.prefix] state_num = int(mdp_state.state_id[1:]) for i in alphabet: reached_state = state.transitions[i].prefix correct_output = state.output_fun[i] # if i in {'pairing_req', 'mtu_req'} and mdp_state.output != moore_mdp_state_map[reached_moore].output: if state_num % 6 == 0: last_out = model.compute_output_seq(model.initial_state, state.prefix[:-1]) if state.prefix else "NO_RESPONSE" if not last_out or last_out == correct_output: last_out = 'NO_RESPONSE' mdp_state.transitions[i].append((mdp_state, last_out[-1], 0.2)) mdp_state.transitions[i].append((moore_mdp_state_map[reached_state], correct_output, 0.8)) if state_num % 5 == 0 and i in {'length_req', 'length_rsp', 'feature_rsp'} and len(state.prefix) == 2: mdp_state.transitions[i].append((moore_mdp_state_map[model.initial_state.prefix], 'SYSTEM_CRASH', 0.1)) mdp_state.transitions[i].append((moore_mdp_state_map[reached_state], correct_output, 0.9)) else: mdp_state.transitions[i].append((moore_mdp_state_map[reached_state], correct_output, 1.)) mdp = StochasticMealyMachine(initial_mdp_state, list(moore_mdp_state_map.values())) return mdp mdp = to_smm() # mdp.visualize() # exit() # mdp.save(file_path='CC2640R2-no-feature-req-stochastic') # exit() # mdp.make_input_complete('self_loop') # mdp_sul = StochasticMealySUL(mdp) mdp_sul = AutomatonSUL(mdp.to_mdp()) eq_oracle = RandomWordEqOracle(alphabet, model_sul, num_walks=10000, min_walk_len=10, max_walk_len=100) stochastic_model = run_stochastic_Lstar(alphabet, mdp_sul, eq_oracle, automaton_type='mdp') # mdp = mdp.to_mdp() # mdp.save('CYW43455_stochastic') ================================================ FILE: DotModels/Bluetooth/nRF52832.dot ================================================ digraph "nRF52832" { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4]; s0 -> s0 [label="scan_req/Adv"]; s0 -> s1 [label="connection_req/BTLE|BTLE_DATA|L2CAP_Hdr|Raw|SM_Hdr"]; s0 -> s0 [label="length_req/Empty"]; s0 -> s0 [label="length_rsp/Empty"]; s0 -> s0 [label="feature_rsp/Empty"]; s0 -> s0 [label="feature_req/Empty"]; s0 -> s0 [label="version_req/Empty"]; s0 -> s0 [label="mtu_req/Empty"]; s0 -> s0 [label="pairing_req/Empty"]; s1 -> s0 [label="scan_req/Adv"]; s1 -> s1 [label="connection_req/BTLE|BTLE_DATA|L2CAP_Hdr|Raw|SM_Hdr"]; s1 -> s1 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s1 -> s0 [label="length_rsp/BTLE|BTLE_DATA"]; s1 -> s1 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s1 -> s1 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s1 -> s3 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s1 -> s2 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s1 -> s1 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s2 -> s0 [label="scan_req/Adv"]; s2 -> s1 [label="connection_req/BTLE|BTLE_DATA|L2CAP_Hdr|Raw|SM_Hdr"]; s2 -> s2 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s2 -> s0 [label="length_rsp/BTLE|BTLE_DATA"]; s2 -> s2 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s2 -> s2 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s2 -> s4 [label="version_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_VERSION_IND"]; s2 -> s2 [label="mtu_req/ATT_Error_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s2 -> s2 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s3 -> s0 [label="scan_req/Adv"]; s3 -> s1 [label="connection_req/BTLE|BTLE_DATA|L2CAP_Hdr|Raw|SM_Hdr"]; s3 -> s3 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s3 -> s0 [label="length_rsp/BTLE|BTLE_DATA"]; s3 -> s3 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s3 -> s3 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s3 -> s3 [label="version_req/BTLE|BTLE_DATA"]; s3 -> s4 [label="mtu_req/ATT_Exchange_MTU_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s3 -> s3 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; s4 -> s0 [label="scan_req/Adv"]; s4 -> s1 [label="connection_req/BTLE|BTLE_DATA|L2CAP_Hdr|Raw|SM_Hdr"]; s4 -> s4 [label="length_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_LENGTH_RSP"]; s4 -> s0 [label="length_rsp/BTLE|BTLE_DATA"]; s4 -> s4 [label="feature_rsp/BTLE|BTLE_CTRL|BTLE_DATA|LL_UNKNOWN_RSP"]; s4 -> s4 [label="feature_req/BTLE|BTLE_CTRL|BTLE_DATA|LL_FEATURE_RSP"]; s4 -> s4 [label="version_req/BTLE|BTLE_DATA"]; s4 -> s4 [label="mtu_req/ATT_Error_Response|ATT_Hdr|BTLE|BTLE_DATA|L2CAP_Hdr"]; s4 -> s4 [label="pairing_req/BTLE|BTLE_DATA|L2CAP_Hdr|SM_Hdr|SM_Pairing_Response"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/MDPs/bluetooth.dot ================================================ digraph bluetooth { 0 [label="___start___"]; s00 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s01 [label="crash"]; s02 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s03 [label="no_response"]; s04 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s10 [label="BTLE_BTLE_DATA"]; s11 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s12 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s13 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s20 [label="BTLE_BTLE_DATA"]; s21 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s22 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s23 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s30 [label="BTLE_BTLE_DATA"]; s31 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s32 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s33 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s34 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s40 [label="BTLE_BTLE_DATA"]; s41 [label="ATT_Exchange_MTU_Request_ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ"]; s42 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s43 [label="ATT_Exchange_MTU_Request_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ"]; s44 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s45 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s50 [label="BTLE_BTLE_DATA"]; s51 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s52 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s53 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s54 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s60 [label="BTLE_BTLE_DATA"]; s61 [label="ATT_Exchange_MTU_Request_ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ"]; s62 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s63 [label="ATT_Exchange_MTU_Request_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ"]; s64 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s65 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s66 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s70 [label="BTLE_BTLE_DATA"]; s71 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s72 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s73 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s74 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s80 [label="BTLE_BTLE_DATA"]; s81 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s82 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s90 [label="BTLE_BTLE_DATA"]; s91 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s92 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s93 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s94 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s100 [label="BTLE_BTLE_DATA"]; s101 [label="BTLE_BTLE_DATA_L2CAP_Hdr_SM_Hdr_SM_Pairing_Response"]; s102 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s103 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s104 [label="ATT_Exchange_MTU_Request_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ_SM_Hdr_SM_Pairing_Response"]; s105 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s106 [label="ATT_Exchange_MTU_Request_ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ_SM_Hdr_SM_Pairing_Response"]; s110 [label="BTLE_BTLE_DATA"]; s111 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s112 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s113 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s114 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s120 [label="BTLE_BTLE_DATA"]; s121 [label="BTLE_BTLE_DATA_L2CAP_Hdr_SM_Hdr_SM_Pairing_Response"]; s122 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s123 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s124 [label="ATT_Exchange_MTU_Request_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ_SM_Hdr_SM_Pairing_Response"]; s125 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s126 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s127 [label="ATT_Exchange_MTU_Request_ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_CTRL_BTLE_DATA_L2CAP_CmdHdr_L2CAP_Connection_Parameter_Update_Request_L2CAP_Hdr_LL_LENGTH_REQ_SM_Hdr_SM_Pairing_Response"]; s130 [label="BTLE_BTLE_DATA"]; s131 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s132 [label="BTLE_BTLE_DATA_L2CAP_Hdr_SM_Hdr_SM_Pairing_Response"]; s133 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s134 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s135 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s140 [label="BTLE_BTLE_DATA"]; s141 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s142 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s143 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s144 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s145 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; s150 [label="BTLE_BTLE_DATA"]; s151 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_REJECT_IND"]; s152 [label="BTLE_BTLE_DATA_L2CAP_Hdr_SM_Hdr_SM_Pairing_Response"]; s153 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_LENGTH_RSP"]; s154 [label="ATT_Exchange_MTU_Response_ATT_Hdr_BTLE_BTLE_DATA_L2CAP_Hdr"]; s155 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_VERSION_IND"]; s156 [label="BTLE_BTLE_CTRL_BTLE_DATA_LL_FEATURE_RSP"]; 0 -> s03 [label="length_req:0.2"]; 0 -> s02 [label="length_req:0.8"]; 0 -> s03 [label="length_rsp:0.2"]; 0 -> s00 [label="length_rsp:0.8"]; 0 -> s03 [label="feature_req:0.2"]; 0 -> s04 [label="feature_req:0.8"]; 0 -> s03 [label="feature_rsp:0.2"]; 0 -> s43 [label="feature_rsp:0.8"]; 0 -> s03 [label="version_req:0.2"]; 0 -> s33 [label="version_req:0.8"]; 0 -> s03 [label="mtu_req:0.2"]; 0 -> s20 [label="mtu_req:0.8"]; 0 -> s03 [label="pairing_req:0.2"]; 0 -> s10 [label="pairing_req:0.8"]; s00 -> s03 [label="length_req:0.2"]; s00 -> s02 [label="length_req:0.8"]; s00 -> s03 [label="length_rsp:0.2"]; s00 -> s00 [label="length_rsp:0.8"]; s00 -> s03 [label="feature_req:0.2"]; s00 -> s04 [label="feature_req:0.8"]; s00 -> s03 [label="feature_rsp:0.2"]; s00 -> s43 [label="feature_rsp:0.8"]; s00 -> s03 [label="version_req:0.2"]; s00 -> s33 [label="version_req:0.8"]; s00 -> s03 [label="mtu_req:0.2"]; s00 -> s20 [label="mtu_req:0.8"]; s00 -> s03 [label="pairing_req:0.2"]; s00 -> s10 [label="pairing_req:0.8"]; s01 -> s03 [label="length_req:0.2"]; s01 -> s02 [label="length_req:0.8"]; s01 -> s03 [label="length_rsp:0.2"]; s01 -> s00 [label="length_rsp:0.8"]; s01 -> s03 [label="feature_req:0.2"]; s01 -> s04 [label="feature_req:0.8"]; s01 -> s03 [label="feature_rsp:0.2"]; s01 -> s43 [label="feature_rsp:0.8"]; s01 -> s03 [label="version_req:0.2"]; s01 -> s33 [label="version_req:0.8"]; s01 -> s03 [label="mtu_req:0.2"]; s01 -> s20 [label="mtu_req:0.8"]; s01 -> s03 [label="pairing_req:0.2"]; s01 -> s10 [label="pairing_req:0.8"]; s02 -> s03 [label="length_req:0.2"]; s02 -> s02 [label="length_req:0.8"]; s02 -> s03 [label="length_rsp:0.2"]; s02 -> s00 [label="length_rsp:0.8"]; s02 -> s03 [label="feature_req:0.2"]; s02 -> s04 [label="feature_req:0.8"]; s02 -> s03 [label="feature_rsp:0.2"]; s02 -> s43 [label="feature_rsp:0.8"]; s02 -> s03 [label="version_req:0.2"]; s02 -> s33 [label="version_req:0.8"]; s02 -> s03 [label="mtu_req:0.2"]; s02 -> s20 [label="mtu_req:0.8"]; s02 -> s03 [label="pairing_req:0.2"]; s02 -> s10 [label="pairing_req:0.8"]; s03 -> s03 [label="length_req:0.2"]; s03 -> s02 [label="length_req:0.8"]; s03 -> s03 [label="length_rsp:0.2"]; s03 -> s00 [label="length_rsp:0.8"]; s03 -> s03 [label="feature_req:0.2"]; s03 -> s04 [label="feature_req:0.8"]; s03 -> s03 [label="feature_rsp:0.2"]; s03 -> s43 [label="feature_rsp:0.8"]; s03 -> s03 [label="version_req:0.2"]; s03 -> s33 [label="version_req:0.8"]; s03 -> s03 [label="mtu_req:0.2"]; s03 -> s20 [label="mtu_req:0.8"]; s03 -> s03 [label="pairing_req:0.2"]; s03 -> s10 [label="pairing_req:0.8"]; s04 -> s03 [label="length_req:0.2"]; s04 -> s02 [label="length_req:0.8"]; s04 -> s03 [label="length_rsp:0.2"]; s04 -> s00 [label="length_rsp:0.8"]; s04 -> s03 [label="feature_req:0.2"]; s04 -> s04 [label="feature_req:0.8"]; s04 -> s03 [label="feature_rsp:0.2"]; s04 -> s43 [label="feature_rsp:0.8"]; s04 -> s03 [label="version_req:0.2"]; s04 -> s33 [label="version_req:0.8"]; s04 -> s03 [label="mtu_req:0.2"]; s04 -> s20 [label="mtu_req:0.8"]; s04 -> s03 [label="pairing_req:0.2"]; s04 -> s10 [label="pairing_req:0.8"]; s10 -> s12 [label="length_req:1.0"]; s10 -> s13 [label="length_rsp:1.0"]; s10 -> s11 [label="feature_req:1.0"]; s10 -> s104 [label="feature_rsp:1.0"]; s10 -> s93 [label="version_req:1.0"]; s10 -> s80 [label="mtu_req:1.0"]; s10 -> s10 [label="pairing_req:1.0"]; s11 -> s12 [label="length_req:1.0"]; s11 -> s13 [label="length_rsp:1.0"]; s11 -> s11 [label="feature_req:1.0"]; s11 -> s104 [label="feature_rsp:1.0"]; s11 -> s93 [label="version_req:1.0"]; s11 -> s80 [label="mtu_req:1.0"]; s11 -> s10 [label="pairing_req:1.0"]; s12 -> s12 [label="length_req:1.0"]; s12 -> s13 [label="length_rsp:1.0"]; s12 -> s11 [label="feature_req:1.0"]; s12 -> s104 [label="feature_rsp:1.0"]; s12 -> s93 [label="version_req:1.0"]; s12 -> s80 [label="mtu_req:1.0"]; s12 -> s10 [label="pairing_req:1.0"]; s13 -> s12 [label="length_req:1.0"]; s13 -> s13 [label="length_rsp:1.0"]; s13 -> s11 [label="feature_req:1.0"]; s13 -> s104 [label="feature_rsp:1.0"]; s13 -> s93 [label="version_req:1.0"]; s13 -> s80 [label="mtu_req:1.0"]; s13 -> s10 [label="pairing_req:1.0"]; s20 -> s22 [label="length_req:1.0"]; s20 -> s23 [label="length_rsp:1.0"]; s20 -> s21 [label="feature_req:1.0"]; s20 -> s41 [label="feature_rsp:1.0"]; s20 -> s73 [label="version_req:1.0"]; s20 -> s20 [label="mtu_req:1.0"]; s20 -> s80 [label="pairing_req:1.0"]; s21 -> s22 [label="length_req:1.0"]; s21 -> s23 [label="length_rsp:1.0"]; s21 -> s21 [label="feature_req:1.0"]; s21 -> s41 [label="feature_rsp:1.0"]; s21 -> s73 [label="version_req:1.0"]; s21 -> s20 [label="mtu_req:1.0"]; s21 -> s80 [label="pairing_req:1.0"]; s22 -> s22 [label="length_req:1.0"]; s22 -> s23 [label="length_rsp:1.0"]; s22 -> s21 [label="feature_req:1.0"]; s22 -> s41 [label="feature_rsp:1.0"]; s22 -> s73 [label="version_req:1.0"]; s22 -> s20 [label="mtu_req:1.0"]; s22 -> s80 [label="pairing_req:1.0"]; s23 -> s22 [label="length_req:1.0"]; s23 -> s23 [label="length_rsp:1.0"]; s23 -> s21 [label="feature_req:1.0"]; s23 -> s41 [label="feature_rsp:1.0"]; s23 -> s73 [label="version_req:1.0"]; s23 -> s20 [label="mtu_req:1.0"]; s23 -> s80 [label="pairing_req:1.0"]; s30 -> s32 [label="length_req:1.0"]; s30 -> s31 [label="length_rsp:1.0"]; s30 -> s34 [label="feature_req:1.0"]; s30 -> s63 [label="feature_rsp:1.0"]; s30 -> s30 [label="version_req:1.0"]; s30 -> s70 [label="mtu_req:1.0"]; s30 -> s90 [label="pairing_req:1.0"]; s31 -> s32 [label="length_req:1.0"]; s31 -> s31 [label="length_rsp:1.0"]; s31 -> s34 [label="feature_req:1.0"]; s31 -> s63 [label="feature_rsp:1.0"]; s31 -> s30 [label="version_req:1.0"]; s31 -> s70 [label="mtu_req:1.0"]; s31 -> s90 [label="pairing_req:1.0"]; s32 -> s32 [label="length_req:1.0"]; s32 -> s31 [label="length_rsp:1.0"]; s32 -> s34 [label="feature_req:1.0"]; s32 -> s63 [label="feature_rsp:1.0"]; s32 -> s30 [label="version_req:1.0"]; s32 -> s70 [label="mtu_req:1.0"]; s32 -> s90 [label="pairing_req:1.0"]; s33 -> s32 [label="length_req:1.0"]; s33 -> s31 [label="length_rsp:1.0"]; s33 -> s34 [label="feature_req:1.0"]; s33 -> s63 [label="feature_rsp:1.0"]; s33 -> s30 [label="version_req:1.0"]; s33 -> s70 [label="mtu_req:1.0"]; s33 -> s90 [label="pairing_req:1.0"]; s34 -> s32 [label="length_req:1.0"]; s34 -> s31 [label="length_rsp:1.0"]; s34 -> s34 [label="feature_req:1.0"]; s34 -> s63 [label="feature_rsp:1.0"]; s34 -> s30 [label="version_req:1.0"]; s34 -> s70 [label="mtu_req:1.0"]; s34 -> s90 [label="pairing_req:1.0"]; s40 -> s42 [label="length_req:1.0"]; s40 -> s50 [label="length_rsp:1.0"]; s40 -> s45 [label="feature_req:1.0"]; s40 -> s40 [label="feature_rsp:1.0"]; s40 -> s65 [label="version_req:1.0"]; s40 -> s44 [label="mtu_req:1.0"]; s40 -> s101 [label="pairing_req:1.0"]; s41 -> s42 [label="length_req:1.0"]; s41 -> s50 [label="length_rsp:1.0"]; s41 -> s45 [label="feature_req:1.0"]; s41 -> s40 [label="feature_rsp:1.0"]; s41 -> s65 [label="version_req:1.0"]; s41 -> s44 [label="mtu_req:1.0"]; s41 -> s101 [label="pairing_req:1.0"]; s42 -> s42 [label="length_req:1.0"]; s42 -> s50 [label="length_rsp:1.0"]; s42 -> s45 [label="feature_req:1.0"]; s42 -> s40 [label="feature_rsp:1.0"]; s42 -> s65 [label="version_req:1.0"]; s42 -> s44 [label="mtu_req:1.0"]; s42 -> s101 [label="pairing_req:1.0"]; s43 -> s42 [label="length_req:1.0"]; s43 -> s50 [label="length_rsp:1.0"]; s43 -> s45 [label="feature_req:1.0"]; s43 -> s40 [label="feature_rsp:1.0"]; s43 -> s65 [label="version_req:1.0"]; s43 -> s44 [label="mtu_req:1.0"]; s43 -> s101 [label="pairing_req:1.0"]; s44 -> s42 [label="length_req:1.0"]; s44 -> s50 [label="length_rsp:1.0"]; s44 -> s45 [label="feature_req:1.0"]; s44 -> s40 [label="feature_rsp:1.0"]; s44 -> s65 [label="version_req:1.0"]; s44 -> s44 [label="mtu_req:1.0"]; s44 -> s101 [label="pairing_req:1.0"]; s45 -> s42 [label="length_req:1.0"]; s45 -> s50 [label="length_rsp:1.0"]; s45 -> s45 [label="feature_req:1.0"]; s45 -> s40 [label="feature_rsp:1.0"]; s45 -> s65 [label="version_req:1.0"]; s45 -> s44 [label="mtu_req:1.0"]; s45 -> s101 [label="pairing_req:1.0"]; s50 -> s01 [label="length_req:0.1"]; s50 -> s52 [label="length_req:0.9"]; s50 -> s01 [label="length_rsp:0.1"]; s50 -> s51 [label="length_rsp:0.9"]; s50 -> s54 [label="feature_req:1.0"]; s50 -> s01 [label="feature_rsp:0.1"]; s50 -> s50 [label="feature_rsp:0.9"]; s50 -> s144 [label="version_req:1.0"]; s50 -> s53 [label="mtu_req:1.0"]; s50 -> s132 [label="pairing_req:1.0"]; s51 -> s01 [label="length_req:0.1"]; s51 -> s52 [label="length_req:0.9"]; s51 -> s01 [label="length_rsp:0.1"]; s51 -> s51 [label="length_rsp:0.9"]; s51 -> s54 [label="feature_req:1.0"]; s51 -> s01 [label="feature_rsp:0.1"]; s51 -> s50 [label="feature_rsp:0.9"]; s51 -> s144 [label="version_req:1.0"]; s51 -> s53 [label="mtu_req:1.0"]; s51 -> s132 [label="pairing_req:1.0"]; s52 -> s01 [label="length_req:0.1"]; s52 -> s52 [label="length_req:0.9"]; s52 -> s01 [label="length_rsp:0.1"]; s52 -> s51 [label="length_rsp:0.9"]; s52 -> s54 [label="feature_req:1.0"]; s52 -> s01 [label="feature_rsp:0.1"]; s52 -> s50 [label="feature_rsp:0.9"]; s52 -> s144 [label="version_req:1.0"]; s52 -> s53 [label="mtu_req:1.0"]; s52 -> s132 [label="pairing_req:1.0"]; s53 -> s01 [label="length_req:0.1"]; s53 -> s52 [label="length_req:0.9"]; s53 -> s01 [label="length_rsp:0.1"]; s53 -> s51 [label="length_rsp:0.9"]; s53 -> s54 [label="feature_req:1.0"]; s53 -> s01 [label="feature_rsp:0.1"]; s53 -> s50 [label="feature_rsp:0.9"]; s53 -> s144 [label="version_req:1.0"]; s53 -> s53 [label="mtu_req:1.0"]; s53 -> s132 [label="pairing_req:1.0"]; s54 -> s01 [label="length_req:0.1"]; s54 -> s52 [label="length_req:0.9"]; s54 -> s01 [label="length_rsp:0.1"]; s54 -> s51 [label="length_rsp:0.9"]; s54 -> s54 [label="feature_req:1.0"]; s54 -> s01 [label="feature_rsp:0.1"]; s54 -> s50 [label="feature_rsp:0.9"]; s54 -> s144 [label="version_req:1.0"]; s54 -> s53 [label="mtu_req:1.0"]; s54 -> s132 [label="pairing_req:1.0"]; s60 -> s63 [label="length_req:0.2"]; s60 -> s62 [label="length_req:0.8"]; s60 -> s63 [label="length_rsp:0.2"]; s60 -> s140 [label="length_rsp:0.8"]; s60 -> s63 [label="feature_req:0.2"]; s60 -> s66 [label="feature_req:0.8"]; s60 -> s63 [label="feature_rsp:0.2"]; s60 -> s60 [label="feature_rsp:0.8"]; s60 -> s63 [label="version_req:0.2"]; s60 -> s60 [label="version_req:0.8"]; s60 -> s63 [label="mtu_req:0.2"]; s60 -> s64 [label="mtu_req:0.8"]; s60 -> s63 [label="pairing_req:0.2"]; s60 -> s121 [label="pairing_req:0.8"]; s61 -> s63 [label="length_req:0.2"]; s61 -> s62 [label="length_req:0.8"]; s61 -> s63 [label="length_rsp:0.2"]; s61 -> s140 [label="length_rsp:0.8"]; s61 -> s63 [label="feature_req:0.2"]; s61 -> s66 [label="feature_req:0.8"]; s61 -> s63 [label="feature_rsp:0.2"]; s61 -> s60 [label="feature_rsp:0.8"]; s61 -> s63 [label="version_req:0.2"]; s61 -> s60 [label="version_req:0.8"]; s61 -> s63 [label="mtu_req:0.2"]; s61 -> s64 [label="mtu_req:0.8"]; s61 -> s63 [label="pairing_req:0.2"]; s61 -> s121 [label="pairing_req:0.8"]; s62 -> s63 [label="length_req:0.2"]; s62 -> s62 [label="length_req:0.8"]; s62 -> s63 [label="length_rsp:0.2"]; s62 -> s140 [label="length_rsp:0.8"]; s62 -> s63 [label="feature_req:0.2"]; s62 -> s66 [label="feature_req:0.8"]; s62 -> s63 [label="feature_rsp:0.2"]; s62 -> s60 [label="feature_rsp:0.8"]; s62 -> s63 [label="version_req:0.2"]; s62 -> s60 [label="version_req:0.8"]; s62 -> s63 [label="mtu_req:0.2"]; s62 -> s64 [label="mtu_req:0.8"]; s62 -> s63 [label="pairing_req:0.2"]; s62 -> s121 [label="pairing_req:0.8"]; s63 -> s63 [label="length_req:0.2"]; s63 -> s62 [label="length_req:0.8"]; s63 -> s63 [label="length_rsp:0.2"]; s63 -> s140 [label="length_rsp:0.8"]; s63 -> s63 [label="feature_req:0.2"]; s63 -> s66 [label="feature_req:0.8"]; s63 -> s63 [label="feature_rsp:0.2"]; s63 -> s60 [label="feature_rsp:0.8"]; s63 -> s63 [label="version_req:0.2"]; s63 -> s60 [label="version_req:0.8"]; s63 -> s63 [label="mtu_req:0.2"]; s63 -> s64 [label="mtu_req:0.8"]; s63 -> s63 [label="pairing_req:0.2"]; s63 -> s121 [label="pairing_req:0.8"]; s64 -> s63 [label="length_req:0.2"]; s64 -> s62 [label="length_req:0.8"]; s64 -> s63 [label="length_rsp:0.2"]; s64 -> s140 [label="length_rsp:0.8"]; s64 -> s63 [label="feature_req:0.2"]; s64 -> s66 [label="feature_req:0.8"]; s64 -> s63 [label="feature_rsp:0.2"]; s64 -> s60 [label="feature_rsp:0.8"]; s64 -> s63 [label="version_req:0.2"]; s64 -> s60 [label="version_req:0.8"]; s64 -> s63 [label="mtu_req:0.2"]; s64 -> s64 [label="mtu_req:0.8"]; s64 -> s63 [label="pairing_req:0.2"]; s64 -> s121 [label="pairing_req:0.8"]; s65 -> s63 [label="length_req:0.2"]; s65 -> s62 [label="length_req:0.8"]; s65 -> s63 [label="length_rsp:0.2"]; s65 -> s140 [label="length_rsp:0.8"]; s65 -> s63 [label="feature_req:0.2"]; s65 -> s66 [label="feature_req:0.8"]; s65 -> s63 [label="feature_rsp:0.2"]; s65 -> s60 [label="feature_rsp:0.8"]; s65 -> s63 [label="version_req:0.2"]; s65 -> s60 [label="version_req:0.8"]; s65 -> s63 [label="mtu_req:0.2"]; s65 -> s64 [label="mtu_req:0.8"]; s65 -> s63 [label="pairing_req:0.2"]; s65 -> s121 [label="pairing_req:0.8"]; s66 -> s63 [label="length_req:0.2"]; s66 -> s62 [label="length_req:0.8"]; s66 -> s63 [label="length_rsp:0.2"]; s66 -> s140 [label="length_rsp:0.8"]; s66 -> s63 [label="feature_req:0.2"]; s66 -> s66 [label="feature_req:0.8"]; s66 -> s63 [label="feature_rsp:0.2"]; s66 -> s60 [label="feature_rsp:0.8"]; s66 -> s63 [label="version_req:0.2"]; s66 -> s60 [label="version_req:0.8"]; s66 -> s63 [label="mtu_req:0.2"]; s66 -> s64 [label="mtu_req:0.8"]; s66 -> s63 [label="pairing_req:0.2"]; s66 -> s121 [label="pairing_req:0.8"]; s70 -> s72 [label="length_req:1.0"]; s70 -> s71 [label="length_rsp:1.0"]; s70 -> s74 [label="feature_req:1.0"]; s70 -> s61 [label="feature_rsp:1.0"]; s70 -> s70 [label="version_req:1.0"]; s70 -> s70 [label="mtu_req:1.0"]; s70 -> s110 [label="pairing_req:1.0"]; s71 -> s72 [label="length_req:1.0"]; s71 -> s71 [label="length_rsp:1.0"]; s71 -> s74 [label="feature_req:1.0"]; s71 -> s61 [label="feature_rsp:1.0"]; s71 -> s70 [label="version_req:1.0"]; s71 -> s70 [label="mtu_req:1.0"]; s71 -> s110 [label="pairing_req:1.0"]; s72 -> s72 [label="length_req:1.0"]; s72 -> s71 [label="length_rsp:1.0"]; s72 -> s74 [label="feature_req:1.0"]; s72 -> s61 [label="feature_rsp:1.0"]; s72 -> s70 [label="version_req:1.0"]; s72 -> s70 [label="mtu_req:1.0"]; s72 -> s110 [label="pairing_req:1.0"]; s73 -> s72 [label="length_req:1.0"]; s73 -> s71 [label="length_rsp:1.0"]; s73 -> s74 [label="feature_req:1.0"]; s73 -> s61 [label="feature_rsp:1.0"]; s73 -> s70 [label="version_req:1.0"]; s73 -> s70 [label="mtu_req:1.0"]; s73 -> s110 [label="pairing_req:1.0"]; s74 -> s72 [label="length_req:1.0"]; s74 -> s71 [label="length_rsp:1.0"]; s74 -> s74 [label="feature_req:1.0"]; s74 -> s61 [label="feature_rsp:1.0"]; s74 -> s70 [label="version_req:1.0"]; s74 -> s70 [label="mtu_req:1.0"]; s74 -> s110 [label="pairing_req:1.0"]; s80 -> s82 [label="length_req:1.0"]; s80 -> s23 [label="length_rsp:1.0"]; s80 -> s81 [label="feature_req:1.0"]; s80 -> s106 [label="feature_rsp:1.0"]; s80 -> s113 [label="version_req:1.0"]; s80 -> s80 [label="mtu_req:1.0"]; s80 -> s80 [label="pairing_req:1.0"]; s81 -> s82 [label="length_req:1.0"]; s81 -> s23 [label="length_rsp:1.0"]; s81 -> s81 [label="feature_req:1.0"]; s81 -> s106 [label="feature_rsp:1.0"]; s81 -> s113 [label="version_req:1.0"]; s81 -> s80 [label="mtu_req:1.0"]; s81 -> s80 [label="pairing_req:1.0"]; s82 -> s82 [label="length_req:1.0"]; s82 -> s23 [label="length_rsp:1.0"]; s82 -> s81 [label="feature_req:1.0"]; s82 -> s106 [label="feature_rsp:1.0"]; s82 -> s113 [label="version_req:1.0"]; s82 -> s80 [label="mtu_req:1.0"]; s82 -> s80 [label="pairing_req:1.0"]; s90 -> s92 [label="length_req:1.0"]; s90 -> s91 [label="length_rsp:1.0"]; s90 -> s94 [label="feature_req:1.0"]; s90 -> s124 [label="feature_rsp:1.0"]; s90 -> s90 [label="version_req:1.0"]; s90 -> s110 [label="mtu_req:1.0"]; s90 -> s90 [label="pairing_req:1.0"]; s91 -> s92 [label="length_req:1.0"]; s91 -> s91 [label="length_rsp:1.0"]; s91 -> s94 [label="feature_req:1.0"]; s91 -> s124 [label="feature_rsp:1.0"]; s91 -> s90 [label="version_req:1.0"]; s91 -> s110 [label="mtu_req:1.0"]; s91 -> s90 [label="pairing_req:1.0"]; s92 -> s92 [label="length_req:1.0"]; s92 -> s91 [label="length_rsp:1.0"]; s92 -> s94 [label="feature_req:1.0"]; s92 -> s124 [label="feature_rsp:1.0"]; s92 -> s90 [label="version_req:1.0"]; s92 -> s110 [label="mtu_req:1.0"]; s92 -> s90 [label="pairing_req:1.0"]; s93 -> s92 [label="length_req:1.0"]; s93 -> s91 [label="length_rsp:1.0"]; s93 -> s94 [label="feature_req:1.0"]; s93 -> s124 [label="feature_rsp:1.0"]; s93 -> s90 [label="version_req:1.0"]; s93 -> s110 [label="mtu_req:1.0"]; s93 -> s90 [label="pairing_req:1.0"]; s94 -> s92 [label="length_req:1.0"]; s94 -> s91 [label="length_rsp:1.0"]; s94 -> s94 [label="feature_req:1.0"]; s94 -> s124 [label="feature_rsp:1.0"]; s94 -> s90 [label="version_req:1.0"]; s94 -> s110 [label="mtu_req:1.0"]; s94 -> s90 [label="pairing_req:1.0"]; s100 -> s01 [label="length_req:0.1"]; s100 -> s102 [label="length_req:0.9"]; s100 -> s01 [label="length_rsp:0.1"]; s100 -> s130 [label="length_rsp:0.9"]; s100 -> s103 [label="feature_req:1.0"]; s100 -> s01 [label="feature_rsp:0.1"]; s100 -> s100 [label="feature_rsp:0.9"]; s100 -> s126 [label="version_req:1.0"]; s100 -> s105 [label="mtu_req:1.0"]; s100 -> s100 [label="pairing_req:1.0"]; s101 -> s01 [label="length_req:0.1"]; s101 -> s102 [label="length_req:0.9"]; s101 -> s01 [label="length_rsp:0.1"]; s101 -> s130 [label="length_rsp:0.9"]; s101 -> s103 [label="feature_req:1.0"]; s101 -> s01 [label="feature_rsp:0.1"]; s101 -> s100 [label="feature_rsp:0.9"]; s101 -> s126 [label="version_req:1.0"]; s101 -> s105 [label="mtu_req:1.0"]; s101 -> s100 [label="pairing_req:1.0"]; s102 -> s01 [label="length_req:0.1"]; s102 -> s102 [label="length_req:0.9"]; s102 -> s01 [label="length_rsp:0.1"]; s102 -> s130 [label="length_rsp:0.9"]; s102 -> s103 [label="feature_req:1.0"]; s102 -> s01 [label="feature_rsp:0.1"]; s102 -> s100 [label="feature_rsp:0.9"]; s102 -> s126 [label="version_req:1.0"]; s102 -> s105 [label="mtu_req:1.0"]; s102 -> s100 [label="pairing_req:1.0"]; s103 -> s01 [label="length_req:0.1"]; s103 -> s102 [label="length_req:0.9"]; s103 -> s01 [label="length_rsp:0.1"]; s103 -> s130 [label="length_rsp:0.9"]; s103 -> s103 [label="feature_req:1.0"]; s103 -> s01 [label="feature_rsp:0.1"]; s103 -> s100 [label="feature_rsp:0.9"]; s103 -> s126 [label="version_req:1.0"]; s103 -> s105 [label="mtu_req:1.0"]; s103 -> s100 [label="pairing_req:1.0"]; s104 -> s01 [label="length_req:0.1"]; s104 -> s102 [label="length_req:0.9"]; s104 -> s01 [label="length_rsp:0.1"]; s104 -> s130 [label="length_rsp:0.9"]; s104 -> s103 [label="feature_req:1.0"]; s104 -> s01 [label="feature_rsp:0.1"]; s104 -> s100 [label="feature_rsp:0.9"]; s104 -> s126 [label="version_req:1.0"]; s104 -> s105 [label="mtu_req:1.0"]; s104 -> s100 [label="pairing_req:1.0"]; s105 -> s01 [label="length_req:0.1"]; s105 -> s102 [label="length_req:0.9"]; s105 -> s01 [label="length_rsp:0.1"]; s105 -> s130 [label="length_rsp:0.9"]; s105 -> s103 [label="feature_req:1.0"]; s105 -> s01 [label="feature_rsp:0.1"]; s105 -> s100 [label="feature_rsp:0.9"]; s105 -> s126 [label="version_req:1.0"]; s105 -> s105 [label="mtu_req:1.0"]; s105 -> s100 [label="pairing_req:1.0"]; s106 -> s01 [label="length_req:0.1"]; s106 -> s102 [label="length_req:0.9"]; s106 -> s01 [label="length_rsp:0.1"]; s106 -> s130 [label="length_rsp:0.9"]; s106 -> s103 [label="feature_req:1.0"]; s106 -> s01 [label="feature_rsp:0.1"]; s106 -> s100 [label="feature_rsp:0.9"]; s106 -> s126 [label="version_req:1.0"]; s106 -> s105 [label="mtu_req:1.0"]; s106 -> s100 [label="pairing_req:1.0"]; s110 -> s112 [label="length_req:1.0"]; s110 -> s111 [label="length_rsp:1.0"]; s110 -> s114 [label="feature_req:1.0"]; s110 -> s127 [label="feature_rsp:1.0"]; s110 -> s110 [label="version_req:1.0"]; s110 -> s110 [label="mtu_req:1.0"]; s110 -> s110 [label="pairing_req:1.0"]; s111 -> s112 [label="length_req:1.0"]; s111 -> s111 [label="length_rsp:1.0"]; s111 -> s114 [label="feature_req:1.0"]; s111 -> s127 [label="feature_rsp:1.0"]; s111 -> s110 [label="version_req:1.0"]; s111 -> s110 [label="mtu_req:1.0"]; s111 -> s110 [label="pairing_req:1.0"]; s112 -> s112 [label="length_req:1.0"]; s112 -> s111 [label="length_rsp:1.0"]; s112 -> s114 [label="feature_req:1.0"]; s112 -> s127 [label="feature_rsp:1.0"]; s112 -> s110 [label="version_req:1.0"]; s112 -> s110 [label="mtu_req:1.0"]; s112 -> s110 [label="pairing_req:1.0"]; s113 -> s112 [label="length_req:1.0"]; s113 -> s111 [label="length_rsp:1.0"]; s113 -> s114 [label="feature_req:1.0"]; s113 -> s127 [label="feature_rsp:1.0"]; s113 -> s110 [label="version_req:1.0"]; s113 -> s110 [label="mtu_req:1.0"]; s113 -> s110 [label="pairing_req:1.0"]; s114 -> s112 [label="length_req:1.0"]; s114 -> s111 [label="length_rsp:1.0"]; s114 -> s114 [label="feature_req:1.0"]; s114 -> s127 [label="feature_rsp:1.0"]; s114 -> s110 [label="version_req:1.0"]; s114 -> s110 [label="mtu_req:1.0"]; s114 -> s110 [label="pairing_req:1.0"]; s120 -> s126 [label="length_req:0.2"]; s120 -> s122 [label="length_req:0.8"]; s120 -> s126 [label="length_rsp:0.2"]; s120 -> s150 [label="length_rsp:0.8"]; s120 -> s126 [label="feature_req:0.2"]; s120 -> s123 [label="feature_req:0.8"]; s120 -> s126 [label="feature_rsp:0.2"]; s120 -> s120 [label="feature_rsp:0.8"]; s120 -> s126 [label="version_req:0.2"]; s120 -> s120 [label="version_req:0.8"]; s120 -> s126 [label="mtu_req:0.2"]; s120 -> s125 [label="mtu_req:0.8"]; s120 -> s126 [label="pairing_req:0.2"]; s120 -> s120 [label="pairing_req:0.8"]; s121 -> s126 [label="length_req:0.2"]; s121 -> s122 [label="length_req:0.8"]; s121 -> s126 [label="length_rsp:0.2"]; s121 -> s150 [label="length_rsp:0.8"]; s121 -> s126 [label="feature_req:0.2"]; s121 -> s123 [label="feature_req:0.8"]; s121 -> s126 [label="feature_rsp:0.2"]; s121 -> s120 [label="feature_rsp:0.8"]; s121 -> s126 [label="version_req:0.2"]; s121 -> s120 [label="version_req:0.8"]; s121 -> s126 [label="mtu_req:0.2"]; s121 -> s125 [label="mtu_req:0.8"]; s121 -> s126 [label="pairing_req:0.2"]; s121 -> s120 [label="pairing_req:0.8"]; s122 -> s126 [label="length_req:0.2"]; s122 -> s122 [label="length_req:0.8"]; s122 -> s126 [label="length_rsp:0.2"]; s122 -> s150 [label="length_rsp:0.8"]; s122 -> s126 [label="feature_req:0.2"]; s122 -> s123 [label="feature_req:0.8"]; s122 -> s126 [label="feature_rsp:0.2"]; s122 -> s120 [label="feature_rsp:0.8"]; s122 -> s126 [label="version_req:0.2"]; s122 -> s120 [label="version_req:0.8"]; s122 -> s126 [label="mtu_req:0.2"]; s122 -> s125 [label="mtu_req:0.8"]; s122 -> s126 [label="pairing_req:0.2"]; s122 -> s120 [label="pairing_req:0.8"]; s123 -> s126 [label="length_req:0.2"]; s123 -> s122 [label="length_req:0.8"]; s123 -> s126 [label="length_rsp:0.2"]; s123 -> s150 [label="length_rsp:0.8"]; s123 -> s126 [label="feature_req:0.2"]; s123 -> s123 [label="feature_req:0.8"]; s123 -> s126 [label="feature_rsp:0.2"]; s123 -> s120 [label="feature_rsp:0.8"]; s123 -> s126 [label="version_req:0.2"]; s123 -> s120 [label="version_req:0.8"]; s123 -> s126 [label="mtu_req:0.2"]; s123 -> s125 [label="mtu_req:0.8"]; s123 -> s126 [label="pairing_req:0.2"]; s123 -> s120 [label="pairing_req:0.8"]; s124 -> s126 [label="length_req:0.2"]; s124 -> s122 [label="length_req:0.8"]; s124 -> s126 [label="length_rsp:0.2"]; s124 -> s150 [label="length_rsp:0.8"]; s124 -> s126 [label="feature_req:0.2"]; s124 -> s123 [label="feature_req:0.8"]; s124 -> s126 [label="feature_rsp:0.2"]; s124 -> s120 [label="feature_rsp:0.8"]; s124 -> s126 [label="version_req:0.2"]; s124 -> s120 [label="version_req:0.8"]; s124 -> s126 [label="mtu_req:0.2"]; s124 -> s125 [label="mtu_req:0.8"]; s124 -> s126 [label="pairing_req:0.2"]; s124 -> s120 [label="pairing_req:0.8"]; s125 -> s126 [label="length_req:0.2"]; s125 -> s122 [label="length_req:0.8"]; s125 -> s126 [label="length_rsp:0.2"]; s125 -> s150 [label="length_rsp:0.8"]; s125 -> s126 [label="feature_req:0.2"]; s125 -> s123 [label="feature_req:0.8"]; s125 -> s126 [label="feature_rsp:0.2"]; s125 -> s120 [label="feature_rsp:0.8"]; s125 -> s126 [label="version_req:0.2"]; s125 -> s120 [label="version_req:0.8"]; s125 -> s126 [label="mtu_req:0.2"]; s125 -> s125 [label="mtu_req:0.8"]; s125 -> s126 [label="pairing_req:0.2"]; s125 -> s120 [label="pairing_req:0.8"]; s126 -> s126 [label="length_req:0.2"]; s126 -> s122 [label="length_req:0.8"]; s126 -> s126 [label="length_rsp:0.2"]; s126 -> s150 [label="length_rsp:0.8"]; s126 -> s126 [label="feature_req:0.2"]; s126 -> s123 [label="feature_req:0.8"]; s126 -> s126 [label="feature_rsp:0.2"]; s126 -> s120 [label="feature_rsp:0.8"]; s126 -> s126 [label="version_req:0.2"]; s126 -> s120 [label="version_req:0.8"]; s126 -> s126 [label="mtu_req:0.2"]; s126 -> s125 [label="mtu_req:0.8"]; s126 -> s126 [label="pairing_req:0.2"]; s126 -> s120 [label="pairing_req:0.8"]; s127 -> s126 [label="length_req:0.2"]; s127 -> s122 [label="length_req:0.8"]; s127 -> s126 [label="length_rsp:0.2"]; s127 -> s150 [label="length_rsp:0.8"]; s127 -> s126 [label="feature_req:0.2"]; s127 -> s123 [label="feature_req:0.8"]; s127 -> s126 [label="feature_rsp:0.2"]; s127 -> s120 [label="feature_rsp:0.8"]; s127 -> s126 [label="version_req:0.2"]; s127 -> s120 [label="version_req:0.8"]; s127 -> s126 [label="mtu_req:0.2"]; s127 -> s125 [label="mtu_req:0.8"]; s127 -> s126 [label="pairing_req:0.2"]; s127 -> s120 [label="pairing_req:0.8"]; s130 -> s133 [label="length_req:1.0"]; s130 -> s131 [label="length_rsp:1.0"]; s130 -> s135 [label="feature_req:1.0"]; s130 -> s130 [label="feature_rsp:1.0"]; s130 -> s155 [label="version_req:1.0"]; s130 -> s134 [label="mtu_req:1.0"]; s130 -> s130 [label="pairing_req:1.0"]; s131 -> s133 [label="length_req:1.0"]; s131 -> s131 [label="length_rsp:1.0"]; s131 -> s135 [label="feature_req:1.0"]; s131 -> s130 [label="feature_rsp:1.0"]; s131 -> s155 [label="version_req:1.0"]; s131 -> s134 [label="mtu_req:1.0"]; s131 -> s130 [label="pairing_req:1.0"]; s132 -> s133 [label="length_req:1.0"]; s132 -> s131 [label="length_rsp:1.0"]; s132 -> s135 [label="feature_req:1.0"]; s132 -> s130 [label="feature_rsp:1.0"]; s132 -> s155 [label="version_req:1.0"]; s132 -> s134 [label="mtu_req:1.0"]; s132 -> s130 [label="pairing_req:1.0"]; s133 -> s133 [label="length_req:1.0"]; s133 -> s131 [label="length_rsp:1.0"]; s133 -> s135 [label="feature_req:1.0"]; s133 -> s130 [label="feature_rsp:1.0"]; s133 -> s155 [label="version_req:1.0"]; s133 -> s134 [label="mtu_req:1.0"]; s133 -> s130 [label="pairing_req:1.0"]; s134 -> s133 [label="length_req:1.0"]; s134 -> s131 [label="length_rsp:1.0"]; s134 -> s135 [label="feature_req:1.0"]; s134 -> s130 [label="feature_rsp:1.0"]; s134 -> s155 [label="version_req:1.0"]; s134 -> s134 [label="mtu_req:1.0"]; s134 -> s130 [label="pairing_req:1.0"]; s135 -> s133 [label="length_req:1.0"]; s135 -> s131 [label="length_rsp:1.0"]; s135 -> s135 [label="feature_req:1.0"]; s135 -> s130 [label="feature_rsp:1.0"]; s135 -> s155 [label="version_req:1.0"]; s135 -> s134 [label="mtu_req:1.0"]; s135 -> s130 [label="pairing_req:1.0"]; s140 -> s142 [label="length_req:1.0"]; s140 -> s141 [label="length_rsp:1.0"]; s140 -> s145 [label="feature_req:1.0"]; s140 -> s140 [label="feature_rsp:1.0"]; s140 -> s140 [label="version_req:1.0"]; s140 -> s143 [label="mtu_req:1.0"]; s140 -> s152 [label="pairing_req:1.0"]; s141 -> s142 [label="length_req:1.0"]; s141 -> s141 [label="length_rsp:1.0"]; s141 -> s145 [label="feature_req:1.0"]; s141 -> s140 [label="feature_rsp:1.0"]; s141 -> s140 [label="version_req:1.0"]; s141 -> s143 [label="mtu_req:1.0"]; s141 -> s152 [label="pairing_req:1.0"]; s142 -> s142 [label="length_req:1.0"]; s142 -> s141 [label="length_rsp:1.0"]; s142 -> s145 [label="feature_req:1.0"]; s142 -> s140 [label="feature_rsp:1.0"]; s142 -> s140 [label="version_req:1.0"]; s142 -> s143 [label="mtu_req:1.0"]; s142 -> s152 [label="pairing_req:1.0"]; s143 -> s142 [label="length_req:1.0"]; s143 -> s141 [label="length_rsp:1.0"]; s143 -> s145 [label="feature_req:1.0"]; s143 -> s140 [label="feature_rsp:1.0"]; s143 -> s140 [label="version_req:1.0"]; s143 -> s143 [label="mtu_req:1.0"]; s143 -> s152 [label="pairing_req:1.0"]; s144 -> s142 [label="length_req:1.0"]; s144 -> s141 [label="length_rsp:1.0"]; s144 -> s145 [label="feature_req:1.0"]; s144 -> s140 [label="feature_rsp:1.0"]; s144 -> s140 [label="version_req:1.0"]; s144 -> s143 [label="mtu_req:1.0"]; s144 -> s152 [label="pairing_req:1.0"]; s145 -> s142 [label="length_req:1.0"]; s145 -> s141 [label="length_rsp:1.0"]; s145 -> s145 [label="feature_req:1.0"]; s145 -> s140 [label="feature_rsp:1.0"]; s145 -> s140 [label="version_req:1.0"]; s145 -> s143 [label="mtu_req:1.0"]; s145 -> s152 [label="pairing_req:1.0"]; s150 -> s153 [label="length_req:1.0"]; s150 -> s151 [label="length_rsp:1.0"]; s150 -> s156 [label="feature_req:1.0"]; s150 -> s150 [label="feature_rsp:1.0"]; s150 -> s150 [label="version_req:1.0"]; s150 -> s154 [label="mtu_req:1.0"]; s150 -> s150 [label="pairing_req:1.0"]; s151 -> s153 [label="length_req:1.0"]; s151 -> s151 [label="length_rsp:1.0"]; s151 -> s156 [label="feature_req:1.0"]; s151 -> s150 [label="feature_rsp:1.0"]; s151 -> s150 [label="version_req:1.0"]; s151 -> s154 [label="mtu_req:1.0"]; s151 -> s150 [label="pairing_req:1.0"]; s152 -> s153 [label="length_req:1.0"]; s152 -> s151 [label="length_rsp:1.0"]; s152 -> s156 [label="feature_req:1.0"]; s152 -> s150 [label="feature_rsp:1.0"]; s152 -> s150 [label="version_req:1.0"]; s152 -> s154 [label="mtu_req:1.0"]; s152 -> s150 [label="pairing_req:1.0"]; s153 -> s153 [label="length_req:1.0"]; s153 -> s151 [label="length_rsp:1.0"]; s153 -> s156 [label="feature_req:1.0"]; s153 -> s150 [label="feature_rsp:1.0"]; s153 -> s150 [label="version_req:1.0"]; s153 -> s154 [label="mtu_req:1.0"]; s153 -> s150 [label="pairing_req:1.0"]; s154 -> s153 [label="length_req:1.0"]; s154 -> s151 [label="length_rsp:1.0"]; s154 -> s156 [label="feature_req:1.0"]; s154 -> s150 [label="feature_rsp:1.0"]; s154 -> s150 [label="version_req:1.0"]; s154 -> s154 [label="mtu_req:1.0"]; s154 -> s150 [label="pairing_req:1.0"]; s155 -> s153 [label="length_req:1.0"]; s155 -> s151 [label="length_rsp:1.0"]; s155 -> s156 [label="feature_req:1.0"]; s155 -> s150 [label="feature_rsp:1.0"]; s155 -> s150 [label="version_req:1.0"]; s155 -> s154 [label="mtu_req:1.0"]; s155 -> s150 [label="pairing_req:1.0"]; s156 -> s153 [label="length_req:1.0"]; s156 -> s151 [label="length_rsp:1.0"]; s156 -> s156 [label="feature_req:1.0"]; s156 -> s150 [label="feature_rsp:1.0"]; s156 -> s150 [label="version_req:1.0"]; s156 -> s154 [label="mtu_req:1.0"]; s156 -> s150 [label="pairing_req:1.0"]; __start0 [label="", shape=none]; __start0 -> 0 [label=""]; } ================================================ FILE: DotModels/MDPs/faulty_car_alarm.dot ================================================ digraph faulty_car_alarm { q1_locked_closed [label="N"]; q2_locked_open [label="A"]; q3_locked_closed [label="A"]; q5_unlocked_closed [label="N"]; q6_unlocked_open [label="N"]; q7_locked_open [label="N"]; q4_faulty [label="N"]; q1_locked_closed -> q2_locked_open [label="d:1"]; q1_locked_closed -> q5_unlocked_closed [label="l:1"]; q2_locked_open -> q3_locked_closed [label="d:1"]; q2_locked_open -> q6_unlocked_open [label="l:1"]; q3_locked_closed -> q2_locked_open [label="d:1"]; q3_locked_closed -> q5_unlocked_closed [label="l:1"]; q5_unlocked_closed -> q6_unlocked_open [label="d:1"]; q5_unlocked_closed -> q1_locked_closed [label="l:1"]; q6_unlocked_open -> q5_unlocked_closed [label="d:1"]; q6_unlocked_open -> q7_locked_open [label="l:1"]; q7_locked_open -> q4_faulty [label="d:1"]; q7_locked_open -> q6_unlocked_open [label="l:1"]; q4_faulty -> q2_locked_open [label="d:0.9"]; q4_faulty -> q7_locked_open [label="d:0.1"]; q4_faulty -> q5_unlocked_closed [label="l:1"]; __start0 [label="", shape=none]; __start0 -> q1_locked_closed [label=""]; } ================================================ FILE: DotModels/MDPs/first_grid.dot ================================================ digraph first_grid { 28 [label="mud"]; 17 [label="grass"]; 25 [label="wall"]; 24 [label="concrete"]; 13 [label="wall"]; 26 [label="concrete"]; 4 [label="wall"]; 6 [label="grass__goal"]; 10 [label="wall"]; 8 [label="concrete"]; 27 [label="wall"]; 31 [label="wall"]; 2 [label="wall"]; 0 [label="concrete"]; 20 [label="wall"]; 14 [label="sand"]; 22 [label="wall"]; 23 [label="mud"]; 21 [label="mud"]; 12 [label="grass"]; 16 [label="wall"]; 18 [label="wall"]; 11 [label="mud"]; 15 [label="concrete"]; 3 [label="sand"]; 33 [label="grass"]; 5 [label="mud"]; 9 [label="wall"]; 19 [label="concrete"]; 29 [label="grass"]; 30 [label="wall"]; 7 [label="wall__goal"]; 32 [label="mud"]; 1 [label="wall"]; 34 [label="wall"]; 28 -> 27 [label="East:1.0"]; 28 -> 26 [label="North:1.0"]; 28 -> 29 [label="South:0.8"]; 28 -> 21 [label="South:0.2"]; 28 -> 19 [label="West:1.0"]; 17 -> 26 [label="East:1.0"]; 17 -> 18 [label="North:1.0"]; 17 -> 19 [label="South:1.0"]; 17 -> 11 [label="West:0.6"]; 17 -> 12 [label="West:0.4"]; 25 -> 33 [label="East:0.4"]; 25 -> 32 [label="East:0.6"]; 25 -> 15 [label="North:0.4"]; 25 -> 23 [label="North:0.6"]; 25 -> 26 [label="South:1.0"]; 25 -> 25 [label="West:1.0"]; 24 -> 32 [label="East:0.6"]; 24 -> 33 [label="East:0.4"]; 24 -> 15 [label="North:0.4"]; 24 -> 23 [label="North:0.6"]; 24 -> 26 [label="South:1.0"]; 24 -> 25 [label="West:1.0"]; 13 -> 21 [label="East:0.6"]; 13 -> 19 [label="East:0.4"]; 13 -> 5 [label="North:0.1"]; 13 -> 12 [label="North:0.8"]; 13 -> 19 [label="North:0.1"]; 13 -> 13 [label="South:1.0"]; 13 -> 6 [label="West:0.8"]; 13 -> 5 [label="West:0.2"]; 26 -> 32 [label="East:0.2"]; 26 -> 33 [label="East:0.8"]; 26 -> 24 [label="North:1.0"]; 26 -> 19 [label="South:0.4"]; 26 -> 28 [label="South:0.6"]; 26 -> 19 [label="West:0.2"]; 26 -> 17 [label="West:0.8"]; 4 -> 11 [label="East:0.1"]; 4 -> 12 [label="East:0.8"]; 4 -> 14 [label="East:0.1"]; 4 -> 3 [label="North:0.75"]; 4 -> 11 [label="North:0.25"]; 4 -> 6 [label="South:0.8"]; 4 -> 14 [label="South:0.2"]; 4 -> 4 [label="West:1.0"]; 6 -> 12 [label="East:0.25"]; 6 -> 14 [label="East:0.75"]; 6 -> 12 [label="North:0.4"]; 6 -> 5 [label="North:0.6"]; 6 -> 7 [label="South:1.0"]; 6 -> 7 [label="West:1.0"]; 10 -> 17 [label="East:0.8"]; 10 -> 19 [label="East:0.2"]; 10 -> 10 [label="North:1.0"]; 10 -> 5 [label="South:0.1"]; 10 -> 12 [label="South:0.8"]; 10 -> 19 [label="South:0.1"]; 10 -> 5 [label="West:0.25"]; 10 -> 3 [label="West:0.75"]; 8 -> 15 [label="East:1.0"]; 8 -> 9 [label="North:1.0"]; 8 -> 9 [label="South:1.0"]; 8 -> 0 [label="West:1.0"]; 27 -> 27 [label="East:1.0"]; 27 -> 26 [label="North:1.0"]; 27 -> 29 [label="South:0.8"]; 27 -> 21 [label="South:0.2"]; 27 -> 19 [label="West:1.0"]; 31 -> 31 [label="East:1.0"]; 31 -> 31 [label="North:1.0"]; 31 -> 33 [label="South:0.8"]; 31 -> 26 [label="South:0.2"]; 31 -> 24 [label="West:1.0"]; 2 -> 11 [label="East:0.6"]; 2 -> 12 [label="East:0.4"]; 2 -> 2 [label="North:1.0"]; 2 -> 12 [label="South:0.4"]; 2 -> 5 [label="South:0.6"]; 2 -> 2 [label="West:1.0"]; 0 -> 8 [label="East:1.0"]; 0 -> 1 [label="North:1.0"]; 0 -> 1 [label="South:1.0"]; 0 -> 1 [label="West:1.0"]; 20 -> 28 [label="East:0.2"]; 20 -> 29 [label="East:0.8"]; 20 -> 19 [label="North:1.0"]; 20 -> 20 [label="South:1.0"]; 20 -> 14 [label="West:0.75"]; 20 -> 12 [label="West:0.25"]; 14 -> 21 [label="East:0.6"]; 14 -> 19 [label="East:0.4"]; 14 -> 12 [label="North:0.8"]; 14 -> 19 [label="North:0.1"]; 14 -> 5 [label="North:0.1"]; 14 -> 13 [label="South:1.0"]; 14 -> 6 [label="West:0.8"]; 14 -> 5 [label="West:0.2"]; 22 -> 22 [label="East:1.0"]; 22 -> 22 [label="North:1.0"]; 22 -> 24 [label="South:1.0"]; 22 -> 15 [label="West:1.0"]; 23 -> 22 [label="East:1.0"]; 23 -> 22 [label="North:1.0"]; 23 -> 24 [label="South:1.0"]; 23 -> 15 [label="West:1.0"]; 21 -> 29 [label="East:0.8"]; 21 -> 28 [label="East:0.2"]; 21 -> 19 [label="North:1.0"]; 21 -> 20 [label="South:1.0"]; 21 -> 14 [label="West:0.75"]; 21 -> 12 [label="West:0.25"]; 12 -> 19 [label="East:1.0"]; 12 -> 3 [label="North:0.2"]; 12 -> 17 [label="North:0.2"]; 12 -> 11 [label="North:0.6"]; 12 -> 6 [label="South:0.125"]; 12 -> 14 [label="South:0.75"]; 12 -> 21 [label="South:0.125"]; 12 -> 5 [label="West:0.6"]; 12 -> 6 [label="West:0.2"]; 12 -> 3 [label="West:0.2"]; 16 -> 24 [label="East:0.4"]; 16 -> 23 [label="East:0.6"]; 16 -> 16 [label="North:1.0"]; 16 -> 16 [label="South:1.0"]; 16 -> 8 [label="West:1.0"]; 18 -> 26 [label="East:1.0"]; 18 -> 18 [label="North:1.0"]; 18 -> 19 [label="South:1.0"]; 18 -> 12 [label="West:0.4"]; 18 -> 11 [label="West:0.6"]; 11 -> 17 [label="East:0.8"]; 11 -> 19 [label="East:0.2"]; 11 -> 10 [label="North:1.0"]; 11 -> 5 [label="South:0.1"]; 11 -> 12 [label="South:0.8"]; 11 -> 19 [label="South:0.1"]; 11 -> 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[label="spin1:1.0"]; 116 -> 142 [label="spin2:1.0"]; 116 -> 142 [label="spin3:1.0"]; 116 -> 142 [label="stop:1.0"]; 243 -> 269 [label="spin1:1.0"]; 243 -> 269 [label="spin2:1.0"]; 243 -> 269 [label="spin3:1.0"]; 243 -> 87 [label="stop:0.5"]; 243 -> 269 [label="stop:0.5"]; 247 -> 273 [label="spin1:1.0"]; 247 -> 273 [label="spin2:1.0"]; 247 -> 273 [label="spin3:1.0"]; 247 -> 91 [label="stop:0.5"]; 247 -> 273 [label="stop:0.5"]; 265 -> 291 [label="spin1:1.0"]; 265 -> 291 [label="spin2:1.0"]; 265 -> 291 [label="spin3:1.0"]; 265 -> 291 [label="stop:1.0"]; 310 -> 310 [label="spin1:1.0"]; 310 -> 310 [label="spin2:1.0"]; 310 -> 310 [label="spin3:1.0"]; 310 -> 310 [label="stop:1.0"]; 199 -> 225 [label="spin1:1.0"]; 199 -> 225 [label="spin2:1.0"]; 199 -> 225 [label="spin3:1.0"]; 199 -> 225 [label="stop:1.0"]; 211 -> 211 [label="spin1:1.0"]; 211 -> 211 [label="spin2:1.0"]; 211 -> 211 [label="spin3:1.0"]; 211 -> 211 [label="stop:1.0"]; 1 -> 90 [label="spin1:0.7"]; 1 -> 99 [label="spin1:0.3"]; 1 -> 84 [label="spin2:0.7"]; 1 -> 87 [label="spin2:0.3"]; 1 -> 81 [label="spin3:0.7"]; 1 -> 82 [label="spin3:0.3"]; 1 -> 1 [label="stop:0.5"]; 1 -> 28 [label="stop:0.5"]; 145 -> 145 [label="spin1:1.0"]; 145 -> 145 [label="spin2:1.0"]; 145 -> 145 [label="spin3:1.0"]; 145 -> 145 [label="stop:1.0"]; 164 -> 251 [label="spin1:0.23"]; 164 -> 260 [label="spin1:0.77"]; 164 -> 242 [label="spin2:0.77"]; 164 -> 239 [label="spin2:0.23"]; 164 -> 243 [label="spin3:0.23"]; 164 -> 244 [label="spin3:0.77"]; 164 -> 6 [label="stop:0.5"]; 164 -> 190 [label="stop:0.5"]; 168 -> 246 [label="spin1:0.23"]; 168 -> 255 [label="spin1:0.77"]; 168 -> 249 [label="spin2:0.23"]; 168 -> 252 [label="spin2:0.77"]; 168 -> 246 [label="spin3:0.23"]; 168 -> 247 [label="spin3:0.77"]; 168 -> 10 [label="stop:0.5"]; 168 -> 194 [label="stop:0.5"]; 34 -> 61 [label="spin1:1.0"]; 34 -> 61 [label="spin2:1.0"]; 34 -> 61 [label="spin3:1.0"]; 34 -> 61 [label="stop:1.0"]; 79 -> 79 [label="spin1:1.0"]; 79 -> 79 [label="spin2:1.0"]; 79 -> 79 [label="spin3:1.0"]; 79 -> 79 [label="stop:1.0"]; 103 -> 172 [label="spin1:0.47"]; 103 -> 181 [label="spin1:0.53"]; 103 -> 181 [label="spin2:0.47"]; 103 -> 184 [label="spin2:0.53"]; 103 -> 180 [label="spin3:0.47"]; 103 -> 181 [label="spin3:0.53"]; 103 -> 129 [label="stop:0.5"]; 103 -> 23 [label="stop:0.5"]; 249 -> 275 [label="spin1:1.0"]; 249 -> 275 [label="spin2:1.0"]; 249 -> 275 [label="spin3:1.0"]; 249 -> 93 [label="stop:0.5"]; 249 -> 275 [label="stop:0.5"]; 294 -> 294 [label="spin1:1.0"]; 294 -> 294 [label="spin2:1.0"]; 294 -> 294 [label="spin3:1.0"]; 294 -> 294 [label="stop:1.0"]; 282 -> 308 [label="spin1:1.0"]; 282 -> 308 [label="spin2:1.0"]; 282 -> 308 [label="spin3:1.0"]; 282 -> 308 [label="stop:1.0"]; 228 -> 228 [label="spin1:1.0"]; 228 -> 228 [label="spin2:1.0"]; 228 -> 228 [label="spin3:1.0"]; 228 -> 228 [label="stop:1.0"]; 117 -> 143 [label="spin1:1.0"]; 117 -> 143 [label="spin2:1.0"]; 117 -> 143 [label="spin3:1.0"]; 117 -> 143 [label="stop:1.0"]; 17 -> 97 [label="spin1:0.7"]; 17 -> 106 [label="spin1:0.3"]; 17 -> 94 [label="spin2:0.7"]; 17 -> 97 [label="spin2:0.3"]; 17 -> 96 [label="spin3:0.7"]; 17 -> 97 [label="spin3:0.3"]; 17 -> 17 [label="stop:0.5"]; 17 -> 44 [label="stop:0.5"]; 51 -> 78 [label="spin1:1.0"]; 51 -> 78 [label="spin2:1.0"]; 51 -> 78 [label="spin3:1.0"]; 51 -> 78 [label="stop:1.0"]; 184 -> 262 [label="spin1:0.77"]; 184 -> 253 [label="spin1:0.23"]; 184 -> 259 [label="spin2:0.23"]; 184 -> 262 [label="spin2:0.77"]; 184 -> 261 [label="spin3:0.23"]; 184 -> 262 [label="spin3:0.77"]; 184 -> 210 [label="stop:0.5"]; 184 -> 26 [label="stop:0.5"]; 63 -> 63 [label="spin1:1.0"]; 63 -> 63 [label="spin2:1.0"]; 63 -> 63 [label="spin3:1.0"]; 63 -> 63 [label="stop:1.0"]; 40 -> 67 [label="spin1:1.0"]; 40 -> 67 [label="spin2:1.0"]; 40 -> 67 [label="spin3:1.0"]; 40 -> 67 [label="stop:1.0"]; 311 -> 311 [label="spin1:1.0"]; 311 -> 311 [label="spin2:1.0"]; 311 -> 311 [label="spin3:1.0"]; 311 -> 311 [label="stop:1.0"]; 212 -> 212 [label="spin1:1.0"]; 212 -> 212 [label="spin2:1.0"]; 212 -> 212 [label="spin3:1.0"]; 212 -> 212 [label="stop:1.0"]; 266 -> 292 [label="spin1:1.0"]; 266 -> 292 [label="spin2:1.0"]; 266 -> 292 [label="spin3:1.0"]; 266 -> 292 [label="stop:1.0"]; 146 -> 146 [label="spin1:1.0"]; 146 -> 146 [label="spin2:1.0"]; 146 -> 146 [label="spin3:1.0"]; 146 -> 146 [label="stop:1.0"]; 200 -> 226 [label="spin1:1.0"]; 200 -> 226 [label="spin2:1.0"]; 200 -> 226 [label="spin3:1.0"]; 200 -> 226 [label="stop:1.0"]; 0 -> 89 [label="spin1:0.7"]; 0 -> 98 [label="spin1:0.3"]; 0 -> 83 [label="spin2:0.7"]; 0 -> 86 [label="spin2:0.3"]; 0 -> 81 [label="spin3:0.7"]; 0 -> 82 [label="spin3:0.3"]; 0 -> 0 [label="stop:0.5"]; 0 -> 27 [label="stop:0.5"]; 35 -> 62 [label="spin1:1.0"]; 35 -> 62 [label="spin2:1.0"]; 35 -> 62 [label="spin3:1.0"]; 35 -> 62 [label="stop:1.0"]; 80 -> 80 [label="spin1:1.0"]; 80 -> 80 [label="spin2:1.0"]; 80 -> 80 [label="spin3:1.0"]; 80 -> 80 [label="stop:1.0"]; 167 -> 245 [label="spin1:0.23"]; 167 -> 254 [label="spin1:0.77"]; 167 -> 248 [label="spin2:0.23"]; 167 -> 251 [label="spin2:0.77"]; 167 -> 246 [label="spin3:0.23"]; 167 -> 247 [label="spin3:0.77"]; 167 -> 9 [label="stop:0.5"]; 167 -> 193 [label="stop:0.5"]; 102 -> 171 [label="spin1:0.47"]; 102 -> 180 [label="spin1:0.53"]; 102 -> 180 [label="spin2:0.47"]; 102 -> 183 [label="spin2:0.53"]; 102 -> 180 [label="spin3:0.47"]; 102 -> 181 [label="spin3:0.53"]; 102 -> 128 [label="stop:0.5"]; 102 -> 22 [label="stop:0.5"]; 248 -> 274 [label="spin1:1.0"]; 248 -> 274 [label="spin2:1.0"]; 248 -> 274 [label="spin3:1.0"]; 248 -> 92 [label="stop:0.5"]; 248 -> 274 [label="stop:0.5"]; 283 -> 309 [label="spin1:1.0"]; 283 -> 309 [label="spin2:1.0"]; 283 -> 309 [label="spin3:1.0"]; 283 -> 309 [label="stop:1.0"]; 295 -> 295 [label="spin1:1.0"]; 295 -> 295 [label="spin2:1.0"]; 295 -> 295 [label="spin3:1.0"]; 295 -> 295 [label="stop:1.0"]; 229 -> 229 [label="spin1:1.0"]; 229 -> 229 [label="spin2:1.0"]; 229 -> 229 [label="spin3:1.0"]; 229 -> 229 [label="stop:1.0"]; 16 -> 96 [label="spin1:0.7"]; 16 -> 105 [label="spin1:0.3"]; 16 -> 93 [label="spin2:0.7"]; 16 -> 96 [label="spin2:0.3"]; 16 -> 96 [label="spin3:0.7"]; 16 -> 97 [label="spin3:0.3"]; 16 -> 16 [label="stop:0.5"]; 16 -> 43 [label="stop:0.5"]; 20 -> 91 [label="spin1:0.7"]; 20 -> 100 [label="spin1:0.3"]; 20 -> 103 [label="spin2:0.7"]; 20 -> 106 [label="spin2:0.3"]; 20 -> 99 [label="spin3:0.7"]; 20 -> 100 [label="spin3:0.3"]; 20 -> 20 [label="stop:0.5"]; 20 -> 47 [label="stop:0.5"]; 64 -> 64 [label="spin1:1.0"]; 64 -> 64 [label="spin2:1.0"]; 64 -> 64 [label="spin3:1.0"]; 64 -> 64 [label="stop:1.0"]; 82 -> 169 [label="spin1:0.47"]; 82 -> 178 [label="spin1:0.53"]; 82 -> 163 [label="spin2:0.47"]; 82 -> 166 [label="spin2:0.53"]; 82 -> 159 [label="spin3:0.47"]; 82 -> 160 [label="spin3:0.53"]; 82 -> 2 [label="stop:0.5"]; 82 -> 108 [label="stop:0.5"]; 52 -> 79 [label="spin1:1.0"]; 52 -> 79 [label="spin2:1.0"]; 52 -> 79 [label="spin3:1.0"]; 52 -> 79 [label="stop:1.0"]; 118 -> 144 [label="spin1:1.0"]; 118 -> 144 [label="spin2:1.0"]; 118 -> 144 [label="spin3:1.0"]; 118 -> 144 [label="stop:1.0"]; 183 -> 261 [label="spin1:0.77"]; 183 -> 252 [label="spin1:0.23"]; 183 -> 258 [label="spin2:0.23"]; 183 -> 261 [label="spin2:0.77"]; 183 -> 261 [label="spin3:0.23"]; 183 -> 262 [label="spin3:0.77"]; 183 -> 25 [label="stop:0.5"]; 183 -> 209 [label="stop:0.5"]; 104 -> 173 [label="spin1:0.47"]; 104 -> 182 [label="spin1:0.53"]; 104 -> 179 [label="spin2:0.47"]; 104 -> 182 [label="spin2:0.53"]; 104 -> 183 [label="spin3:0.47"]; 104 -> 184 [label="spin3:0.53"]; 104 -> 130 [label="stop:0.5"]; 104 -> 24 [label="stop:0.5"]; 267 -> 293 [label="spin1:1.0"]; 267 -> 293 [label="spin2:1.0"]; 267 -> 293 [label="spin3:1.0"]; 267 -> 293 [label="stop:1.0"]; 312 -> 312 [label="spin1:1.0"]; 312 -> 312 [label="spin2:1.0"]; 312 -> 312 [label="spin3:1.0"]; 312 -> 312 [label="stop:1.0"]; 201 -> 227 [label="spin1:1.0"]; 201 -> 227 [label="spin2:1.0"]; 201 -> 227 [label="spin3:1.0"]; 201 -> 227 [label="stop:1.0"]; 213 -> 213 [label="spin1:1.0"]; 213 -> 213 [label="spin2:1.0"]; 213 -> 213 [label="spin3:1.0"]; 213 -> 213 [label="stop:1.0"]; 147 -> 147 [label="spin1:1.0"]; 147 -> 147 [label="spin2:1.0"]; 147 -> 147 [label="spin3:1.0"]; 147 -> 147 [label="stop:1.0"]; 36 -> 63 [label="spin1:1.0"]; 36 -> 63 [label="spin2:1.0"]; 36 -> 63 [label="spin3:1.0"]; 36 -> 63 [label="stop:1.0"]; 166 -> 253 [label="spin1:0.23"]; 166 -> 262 [label="spin1:0.77"]; 166 -> 244 [label="spin2:0.77"]; 166 -> 241 [label="spin2:0.23"]; 166 -> 243 [label="spin3:0.23"]; 166 -> 244 [label="spin3:0.77"]; 166 -> 8 [label="stop:0.5"]; 166 -> 192 [label="stop:0.5"]; 296 -> 296 [label="spin1:1.0"]; 296 -> 296 [label="spin2:1.0"]; 296 -> 296 [label="spin3:1.0"]; 296 -> 296 [label="stop:1.0"]; 185 -> 211 [label="spin1:1.0"]; 185 -> 211 [label="spin2:1.0"]; 185 -> 211 [label="spin3:1.0"]; 185 -> 211 [label="stop:1.0"]; 230 -> 230 [label="spin1:1.0"]; 230 -> 230 [label="spin2:1.0"]; 230 -> 230 [label="spin3:1.0"]; 230 -> 230 [label="stop:1.0"]; 284 -> 310 [label="spin1:1.0"]; 284 -> 310 [label="spin2:1.0"]; 284 -> 310 [label="spin3:1.0"]; 284 -> 310 [label="stop:1.0"]; 254 -> 280 [label="spin1:1.0"]; 254 -> 280 [label="spin2:1.0"]; 254 -> 280 [label="spin3:1.0"]; 254 -> 98 [label="stop:0.5"]; 254 -> 280 [label="stop:0.5"]; 53 -> 80 [label="spin1:1.0"]; 53 -> 80 [label="spin2:1.0"]; 53 -> 80 [label="spin3:1.0"]; 53 -> 80 [label="stop:1.0"]; 65 -> 65 [label="spin1:1.0"]; 65 -> 65 [label="spin2:1.0"]; 65 -> 65 [label="spin3:1.0"]; 65 -> 65 [label="stop:1.0"]; 22 -> 93 [label="spin1:0.7"]; 22 -> 102 [label="spin1:0.3"]; 22 -> 102 [label="spin2:0.7"]; 22 -> 105 [label="spin2:0.3"]; 22 -> 102 [label="spin3:0.7"]; 22 -> 103 [label="spin3:0.3"]; 22 -> 22 [label="stop:0.5"]; 22 -> 49 [label="stop:0.5"]; 84 -> 171 [label="spin1:0.47"]; 84 -> 180 [label="spin1:0.53"]; 84 -> 162 [label="spin2:0.47"]; 84 -> 165 [label="spin2:0.53"]; 84 -> 162 [label="spin3:0.47"]; 84 -> 163 [label="spin3:0.53"]; 84 -> 4 [label="stop:0.5"]; 84 -> 110 [label="stop:0.5"]; 268 -> 294 [label="spin1:1.0"]; 268 -> 294 [label="spin2:1.0"]; 268 -> 294 [label="spin3:1.0"]; 268 -> 294 [label="stop:1.0"]; 313 -> 313 [label="spin1:1.0"]; 313 -> 313 [label="spin2:1.0"]; 313 -> 313 [label="spin3:1.0"]; 313 -> 313 [label="stop:1.0"]; 214 -> 214 [label="spin1:1.0"]; 214 -> 214 [label="spin2:1.0"]; 214 -> 214 [label="spin3:1.0"]; 214 -> 214 [label="stop:1.0"]; 202 -> 228 [label="spin1:1.0"]; 202 -> 228 [label="spin2:1.0"]; 202 -> 228 [label="spin3:1.0"]; 202 -> 228 [label="stop:1.0"]; 148 -> 148 [label="spin1:1.0"]; 148 -> 148 [label="spin2:1.0"]; 148 -> 148 [label="spin3:1.0"]; 148 -> 148 [label="stop:1.0"]; 2 -> 91 [label="spin1:0.7"]; 2 -> 100 [label="spin1:0.3"]; 2 -> 85 [label="spin2:0.7"]; 2 -> 88 [label="spin2:0.3"]; 2 -> 81 [label="spin3:0.7"]; 2 -> 82 [label="spin3:0.3"]; 2 -> 2 [label="stop:0.5"]; 2 -> 29 [label="stop:0.5"]; 37 -> 64 [label="spin1:1.0"]; 37 -> 64 [label="spin2:1.0"]; 37 -> 64 [label="spin3:1.0"]; 37 -> 64 [label="stop:1.0"]; 165 -> 252 [label="spin1:0.23"]; 165 -> 261 [label="spin1:0.77"]; 165 -> 243 [label="spin2:0.77"]; 165 -> 240 [label="spin2:0.23"]; 165 -> 243 [label="spin3:0.23"]; 165 -> 244 [label="spin3:0.77"]; 165 -> 7 [label="stop:0.5"]; 165 -> 191 [label="stop:0.5"]; 169 -> 247 [label="spin1:0.23"]; 169 -> 256 [label="spin1:0.77"]; 169 -> 250 [label="spin2:0.23"]; 169 -> 253 [label="spin2:0.77"]; 169 -> 246 [label="spin3:0.23"]; 169 -> 247 [label="spin3:0.77"]; 169 -> 195 [label="stop:0.5"]; 169 -> 11 [label="stop:0.5"]; 285 -> 311 [label="spin1:1.0"]; 285 -> 311 [label="spin2:1.0"]; 285 -> 311 [label="spin3:1.0"]; 285 -> 311 [label="stop:1.0"]; 250 -> 276 [label="spin1:1.0"]; 250 -> 276 [label="spin2:1.0"]; 250 -> 276 [label="spin3:1.0"]; 250 -> 94 [label="stop:0.5"]; 250 -> 276 [label="stop:0.5"]; 297 -> 297 [label="spin1:1.0"]; 297 -> 297 [label="spin2:1.0"]; 297 -> 297 [label="spin3:1.0"]; 297 -> 297 [label="stop:1.0"]; 186 -> 212 [label="spin1:1.0"]; 186 -> 212 [label="spin2:1.0"]; 186 -> 212 [label="spin3:1.0"]; 186 -> 212 [label="stop:1.0"]; 231 -> 231 [label="spin1:1.0"]; 231 -> 231 [label="spin2:1.0"]; 231 -> 231 [label="spin3:1.0"]; 231 -> 231 [label="stop:1.0"]; 66 -> 66 [label="spin1:1.0"]; 66 -> 66 [label="spin2:1.0"]; 66 -> 66 [label="spin3:1.0"]; 66 -> 66 [label="stop:1.0"]; 120 -> 146 [label="spin1:1.0"]; 120 -> 146 [label="spin2:1.0"]; 120 -> 146 [label="spin3:1.0"]; 120 -> 146 [label="stop:1.0"]; 21 -> 92 [label="spin1:0.7"]; 21 -> 101 [label="spin1:0.3"]; 21 -> 101 [label="spin2:0.7"]; 21 -> 104 [label="spin2:0.3"]; 21 -> 102 [label="spin3:0.7"]; 21 -> 103 [label="spin3:0.3"]; 21 -> 21 [label="stop:0.5"]; 21 -> 48 [label="stop:0.5"]; 83 -> 170 [label="spin1:0.47"]; 83 -> 179 [label="spin1:0.53"]; 83 -> 161 [label="spin2:0.47"]; 83 -> 164 [label="spin2:0.53"]; 83 -> 162 [label="spin3:0.47"]; 83 -> 163 [label="spin3:0.53"]; 83 -> 3 [label="stop:0.5"]; 83 -> 109 [label="stop:0.5"]; 171 -> 249 [label="spin1:0.23"]; 171 -> 258 [label="spin1:0.77"]; 171 -> 249 [label="spin2:0.23"]; 171 -> 252 [label="spin2:0.77"]; 171 -> 249 [label="spin3:0.23"]; 171 -> 250 [label="spin3:0.77"]; 171 -> 197 [label="stop:0.5"]; 171 -> 13 [label="stop:0.5"]; 269 -> 295 [label="spin1:1.0"]; 269 -> 295 [label="spin2:1.0"]; 269 -> 295 [label="spin3:1.0"]; 269 -> 295 [label="stop:1.0"]; 314 -> 314 [label="spin1:1.0"]; 314 -> 314 [label="spin2:1.0"]; 314 -> 314 [label="spin3:1.0"]; 314 -> 314 [label="stop:1.0"]; 106 -> 175 [label="spin1:0.47"]; 106 -> 184 [label="spin1:0.53"]; 106 -> 181 [label="spin2:0.47"]; 106 -> 184 [label="spin2:0.53"]; 106 -> 183 [label="spin3:0.47"]; 106 -> 184 [label="spin3:0.53"]; 106 -> 26 [label="stop:0.5"]; 106 -> 132 [label="stop:0.5"]; 203 -> 229 [label="spin1:1.0"]; 203 -> 229 [label="spin2:1.0"]; 203 -> 229 [label="spin3:1.0"]; 203 -> 229 [label="stop:1.0"]; 215 -> 215 [label="spin1:1.0"]; 215 -> 215 [label="spin2:1.0"]; 215 -> 215 [label="spin3:1.0"]; 215 -> 215 [label="stop:1.0"]; 149 -> 149 [label="spin1:1.0"]; 149 -> 149 [label="spin2:1.0"]; 149 -> 149 [label="spin3:1.0"]; 149 -> 149 [label="stop:1.0"]; 275 -> 301 [label="spin1:1.0"]; 275 -> 301 [label="spin2:1.0"]; 275 -> 301 [label="spin3:1.0"]; 275 -> 301 [label="stop:1.0"]; 38 -> 65 [label="spin1:1.0"]; 38 -> 65 [label="spin2:1.0"]; 38 -> 65 [label="spin3:1.0"]; 38 -> 65 [label="stop:1.0"]; 4 -> 93 [label="spin1:0.7"]; 4 -> 102 [label="spin1:0.3"]; 4 -> 84 [label="spin2:0.7"]; 4 -> 87 [label="spin2:0.3"]; 4 -> 84 [label="spin3:0.7"]; 4 -> 85 [label="spin3:0.3"]; 4 -> 4 [label="stop:0.5"]; 4 -> 31 [label="stop:0.5"]; 298 -> 298 [label="spin1:1.0"]; 298 -> 298 [label="spin2:1.0"]; 298 -> 298 [label="spin3:1.0"]; 298 -> 298 [label="stop:1.0"]; 286 -> 312 [label="spin1:1.0"]; 286 -> 312 [label="spin2:1.0"]; 286 -> 312 [label="spin3:1.0"]; 286 -> 312 [label="stop:1.0"]; 89 -> 167 [label="spin1:0.47"]; 89 -> 176 [label="spin1:0.53"]; 89 -> 173 [label="spin2:0.53"]; 89 -> 170 [label="spin2:0.47"]; 89 -> 168 [label="spin3:0.47"]; 89 -> 169 [label="spin3:0.53"]; 89 -> 9 [label="stop:0.5"]; 89 -> 115 [label="stop:0.5"]; 187 -> 213 [label="spin1:1.0"]; 187 -> 213 [label="spin2:1.0"]; 187 -> 213 [label="spin3:1.0"]; 187 -> 213 [label="stop:1.0"]; 232 -> 232 [label="spin1:1.0"]; 232 -> 232 [label="spin2:1.0"]; 232 -> 232 [label="spin3:1.0"]; 232 -> 232 [label="stop:1.0"]; 121 -> 147 [label="spin1:1.0"]; 121 -> 147 [label="spin2:1.0"]; 121 -> 147 [label="spin3:1.0"]; 121 -> 147 [label="stop:1.0"]; 252 -> 278 [label="spin1:1.0"]; 252 -> 278 [label="spin2:1.0"]; 252 -> 278 [label="spin3:1.0"]; 252 -> 96 [label="stop:0.5"]; 252 -> 278 [label="stop:0.5"]; 256 -> 282 [label="spin1:1.0"]; 256 -> 282 [label="spin2:1.0"]; 256 -> 282 [label="spin3:1.0"]; 256 -> 100 [label="stop:0.5"]; 256 -> 282 [label="stop:0.5"]; 133 -> 133 [label="spin1:1.0"]; 133 -> 133 [label="spin2:1.0"]; 133 -> 133 [label="spin3:1.0"]; 133 -> 133 [label="stop:1.0"]; 67 -> 67 [label="spin1:1.0"]; 67 -> 67 [label="spin2:1.0"]; 67 -> 67 [label="spin3:1.0"]; 67 -> 67 [label="stop:1.0"]; 170 -> 248 [label="spin1:0.23"]; 170 -> 257 [label="spin1:0.77"]; 170 -> 248 [label="spin2:0.23"]; 170 -> 251 [label="spin2:0.77"]; 170 -> 249 [label="spin3:0.23"]; 170 -> 250 [label="spin3:0.77"]; 170 -> 12 [label="stop:0.5"]; 170 -> 196 [label="stop:0.5"]; 270 -> 296 [label="spin1:1.0"]; 270 -> 296 [label="spin2:1.0"]; 270 -> 296 [label="spin3:1.0"]; 270 -> 296 [label="stop:1.0"]; 105 -> 174 [label="spin1:0.47"]; 105 -> 183 [label="spin1:0.53"]; 105 -> 180 [label="spin2:0.47"]; 105 -> 183 [label="spin2:0.53"]; 105 -> 183 [label="spin3:0.47"]; 105 -> 184 [label="spin3:0.53"]; 105 -> 25 [label="stop:0.5"]; 105 -> 131 [label="stop:0.5"]; 216 -> 216 [label="spin1:1.0"]; 216 -> 216 [label="spin2:1.0"]; 216 -> 216 [label="spin3:1.0"]; 216 -> 216 [label="stop:1.0"]; 204 -> 230 [label="spin1:1.0"]; 204 -> 230 [label="spin2:1.0"]; 204 -> 230 [label="spin3:1.0"]; 204 -> 230 [label="stop:1.0"]; 150 -> 150 [label="spin1:1.0"]; 150 -> 150 [label="spin2:1.0"]; 150 -> 150 [label="spin3:1.0"]; 150 -> 150 [label="stop:1.0"]; 39 -> 66 [label="spin1:1.0"]; 39 -> 66 [label="spin2:1.0"]; 39 -> 66 [label="spin3:1.0"]; 39 -> 66 [label="stop:1.0"]; 3 -> 92 [label="spin1:0.7"]; 3 -> 101 [label="spin1:0.3"]; 3 -> 83 [label="spin2:0.7"]; 3 -> 86 [label="spin2:0.3"]; 3 -> 84 [label="spin3:0.7"]; 3 -> 85 [label="spin3:0.3"]; 3 -> 3 [label="stop:0.5"]; 3 -> 30 [label="stop:0.5"]; 287 -> 313 [label="spin1:1.0"]; 287 -> 313 [label="spin2:1.0"]; 287 -> 313 [label="spin3:1.0"]; 287 -> 313 [label="stop:1.0"]; 299 -> 299 [label="spin1:1.0"]; 299 -> 299 [label="spin2:1.0"]; 299 -> 299 [label="spin3:1.0"]; 299 -> 299 [label="stop:1.0"]; 233 -> 233 [label="spin1:1.0"]; 233 -> 233 [label="spin2:1.0"]; 233 -> 233 [label="spin3:1.0"]; 233 -> 233 [label="stop:1.0"]; 134 -> 134 [label="spin1:1.0"]; 134 -> 134 [label="spin2:1.0"]; 134 -> 134 [label="spin3:1.0"]; 134 -> 134 [label="stop:1.0"]; 188 -> 214 [label="spin1:1.0"]; 188 -> 214 [label="spin2:1.0"]; 188 -> 214 [label="spin3:1.0"]; 188 -> 214 [label="stop:1.0"]; 68 -> 68 [label="spin1:1.0"]; 68 -> 68 [label="spin2:1.0"]; 68 -> 68 [label="spin3:1.0"]; 68 -> 68 [label="stop:1.0"]; 251 -> 277 [label="spin1:1.0"]; 251 -> 277 [label="spin2:1.0"]; 251 -> 277 [label="spin3:1.0"]; 251 -> 95 [label="stop:0.5"]; 251 -> 277 [label="stop:0.5"]; 255 -> 281 [label="spin1:1.0"]; 255 -> 281 [label="spin2:1.0"]; 255 -> 281 [label="spin3:1.0"]; 255 -> 99 [label="stop:0.5"]; 255 -> 281 [label="stop:0.5"]; 122 -> 148 [label="spin1:1.0"]; 122 -> 148 [label="spin2:1.0"]; 122 -> 148 [label="spin3:1.0"]; 122 -> 148 [label="stop:1.0"]; 23 -> 94 [label="spin1:0.7"]; 23 -> 103 [label="spin1:0.3"]; 23 -> 103 [label="spin2:0.7"]; 23 -> 106 [label="spin2:0.3"]; 23 -> 102 [label="spin3:0.7"]; 23 -> 103 [label="spin3:0.3"]; 23 -> 50 [label="stop:0.5"]; 23 -> 23 [label="stop:0.5"]; 85 -> 172 [label="spin1:0.47"]; 85 -> 181 [label="spin1:0.53"]; 85 -> 163 [label="spin2:0.47"]; 85 -> 166 [label="spin2:0.53"]; 85 -> 162 [label="spin3:0.47"]; 85 -> 163 [label="spin3:0.53"]; 85 -> 5 [label="stop:0.5"]; 85 -> 111 [label="stop:0.5"]; 271 -> 297 [label="spin1:1.0"]; 271 -> 297 [label="spin2:1.0"]; 271 -> 297 [label="spin3:1.0"]; 271 -> 297 [label="stop:1.0"]; 205 -> 231 [label="spin1:1.0"]; 205 -> 231 [label="spin2:1.0"]; 205 -> 231 [label="spin3:1.0"]; 205 -> 231 [label="stop:1.0"]; 9 -> 89 [label="spin1:0.7"]; 9 -> 98 [label="spin1:0.3"]; 9 -> 92 [label="spin2:0.7"]; 9 -> 95 [label="spin2:0.3"]; 9 -> 90 [label="spin3:0.7"]; 9 -> 91 [label="spin3:0.3"]; 9 -> 9 [label="stop:0.5"]; 9 -> 36 [label="stop:0.5"]; 217 -> 217 [label="spin1:1.0"]; 217 -> 217 [label="spin2:1.0"]; 217 -> 217 [label="spin3:1.0"]; 217 -> 217 [label="stop:1.0"]; 176 -> 254 [label="spin1:0.77"]; 176 -> 245 [label="spin1:0.23"]; 176 -> 257 [label="spin2:0.23"]; 176 -> 260 [label="spin2:0.77"]; 176 -> 255 [label="spin3:0.23"]; 176 -> 256 [label="spin3:0.77"]; 176 -> 202 [label="stop:0.5"]; 176 -> 18 [label="stop:0.5"]; 151 -> 151 [label="spin1:1.0"]; 151 -> 151 [label="spin2:1.0"]; 151 -> 151 [label="spin3:1.0"]; 151 -> 151 [label="stop:1.0"]; 238 -> 264 [label="spin1:1.0"]; 238 -> 264 [label="spin2:1.0"]; 238 -> 264 [label="spin3:1.0"]; 238 -> 264 [label="stop:0.5"]; 238 -> 82 [label="stop:0.5"]; 258 -> 284 [label="spin1:1.0"]; 258 -> 284 [label="spin2:1.0"]; 258 -> 284 [label="spin3:1.0"]; 258 -> 102 [label="stop:0.5"]; 258 -> 284 [label="stop:0.5"]; 300 -> 300 [label="spin1:1.0"]; 300 -> 300 [label="spin2:1.0"]; 300 -> 300 [label="spin3:1.0"]; 300 -> 300 [label="stop:1.0"]; 288 -> 314 [label="spin1:1.0"]; 288 -> 314 [label="spin2:1.0"]; 288 -> 314 [label="spin3:1.0"]; 288 -> 314 [label="stop:1.0"]; 189 -> 215 [label="spin1:1.0"]; 189 -> 215 [label="spin2:1.0"]; 189 -> 215 [label="spin3:1.0"]; 189 -> 215 [label="stop:1.0"]; 234 -> 234 [label="spin1:1.0"]; 234 -> 234 [label="spin2:1.0"]; 234 -> 234 [label="spin3:1.0"]; 234 -> 234 [label="stop:1.0"]; 25 -> 96 [label="spin1:0.7"]; 25 -> 105 [label="spin1:0.3"]; 25 -> 102 [label="spin2:0.7"]; 25 -> 105 [label="spin2:0.3"]; 25 -> 105 [label="spin3:0.7"]; 25 -> 106 [label="spin3:0.3"]; 25 -> 25 [label="stop:0.5"]; 25 -> 52 [label="stop:0.5"]; 87 -> 174 [label="spin1:0.47"]; 87 -> 183 [label="spin1:0.53"]; 87 -> 162 [label="spin2:0.47"]; 87 -> 165 [label="spin2:0.53"]; 87 -> 165 [label="spin3:0.47"]; 87 -> 166 [label="spin3:0.53"]; 87 -> 7 [label="stop:0.5"]; 87 -> 113 [label="stop:0.5"]; 91 -> 169 [label="spin1:0.47"]; 91 -> 178 [label="spin1:0.53"]; 91 -> 175 [label="spin2:0.53"]; 91 -> 172 [label="spin2:0.47"]; 91 -> 168 [label="spin3:0.47"]; 91 -> 169 [label="spin3:0.53"]; 91 -> 11 [label="stop:0.5"]; 91 -> 117 [label="stop:0.5"]; 123 -> 149 [label="spin1:1.0"]; 123 -> 149 [label="spin2:1.0"]; 123 -> 149 [label="spin3:1.0"]; 123 -> 149 [label="stop:1.0"]; 135 -> 135 [label="spin1:1.0"]; 135 -> 135 [label="spin2:1.0"]; 135 -> 135 [label="spin3:1.0"]; 135 -> 135 [label="stop:1.0"]; 69 -> 69 [label="spin1:1.0"]; 69 -> 69 [label="spin2:1.0"]; 69 -> 69 [label="spin3:1.0"]; 69 -> 69 [label="stop:1.0"]; 172 -> 250 [label="spin1:0.23"]; 172 -> 259 [label="spin1:0.77"]; 172 -> 250 [label="spin2:0.23"]; 172 -> 253 [label="spin2:0.77"]; 172 -> 249 [label="spin3:0.23"]; 172 -> 250 [label="spin3:0.77"]; 172 -> 14 [label="stop:0.5"]; 172 -> 198 [label="stop:0.5"]; 218 -> 218 [label="spin1:1.0"]; 218 -> 218 [label="spin2:1.0"]; 218 -> 218 [label="spin3:1.0"]; 218 -> 218 [label="stop:1.0"]; 272 -> 298 [label="spin1:1.0"]; 272 -> 298 [label="spin2:1.0"]; 272 -> 298 [label="spin3:1.0"]; 272 -> 298 [label="stop:1.0"]; 107 -> 133 [label="spin1:1.0"]; 107 -> 133 [label="spin2:1.0"]; 107 -> 133 [label="spin3:1.0"]; 107 -> 133 [label="stop:1.0"]; 152 -> 152 [label="spin1:1.0"]; 152 -> 152 [label="spin2:1.0"]; 152 -> 152 [label="spin3:1.0"]; 152 -> 152 [label="stop:1.0"]; 206 -> 232 [label="spin1:1.0"]; 206 -> 232 [label="spin2:1.0"]; 206 -> 232 [label="spin3:1.0"]; 206 -> 232 [label="stop:1.0"]; 41 -> 68 [label="spin1:1.0"]; 41 -> 68 [label="spin2:1.0"]; 41 -> 68 [label="spin3:1.0"]; 41 -> 68 [label="stop:1.0"]; 237 -> 263 [label="spin1:1.0"]; 237 -> 263 [label="spin2:1.0"]; 237 -> 263 [label="spin3:1.0"]; 237 -> 263 [label="stop:0.5"]; 237 -> 81 [label="stop:0.5"]; 257 -> 283 [label="spin1:1.0"]; 257 -> 283 [label="spin2:1.0"]; 257 -> 283 [label="spin3:1.0"]; 257 -> 101 [label="stop:0.5"]; 257 -> 283 [label="stop:0.5"]; 5 -> 94 [label="spin1:0.7"]; 5 -> 103 [label="spin1:0.3"]; 5 -> 85 [label="spin2:0.7"]; 5 -> 88 [label="spin2:0.3"]; 5 -> 84 [label="spin3:0.7"]; 5 -> 85 [label="spin3:0.3"]; 5 -> 5 [label="stop:0.5"]; 5 -> 32 [label="stop:0.5"]; 301 -> 301 [label="spin1:1.0"]; 301 -> 301 [label="spin2:1.0"]; 301 -> 301 [label="spin3:1.0"]; 301 -> 301 [label="stop:1.0"]; 190 -> 216 [label="spin1:1.0"]; 190 -> 216 [label="spin2:1.0"]; 190 -> 216 [label="spin3:1.0"]; 190 -> 216 [label="stop:1.0"]; 235 -> 235 [label="spin1:1.0"]; 235 -> 235 [label="spin2:1.0"]; 235 -> 235 [label="spin3:1.0"]; 235 -> 235 [label="stop:1.0"]; 24 -> 95 [label="spin1:0.7"]; 24 -> 104 [label="spin1:0.3"]; 24 -> 101 [label="spin2:0.7"]; 24 -> 104 [label="spin2:0.3"]; 24 -> 105 [label="spin3:0.7"]; 24 -> 106 [label="spin3:0.3"]; 24 -> 24 [label="stop:0.5"]; 24 -> 51 [label="stop:0.5"]; 86 -> 173 [label="spin1:0.47"]; 86 -> 182 [label="spin1:0.53"]; 86 -> 161 [label="spin2:0.47"]; 86 -> 164 [label="spin2:0.53"]; 86 -> 165 [label="spin3:0.47"]; 86 -> 166 [label="spin3:0.53"]; 86 -> 6 [label="stop:0.5"]; 86 -> 112 [label="stop:0.5"]; 90 -> 168 [label="spin1:0.47"]; 90 -> 177 [label="spin1:0.53"]; 90 -> 174 [label="spin2:0.53"]; 90 -> 171 [label="spin2:0.47"]; 90 -> 168 [label="spin3:0.47"]; 90 -> 169 [label="spin3:0.53"]; 90 -> 116 [label="stop:0.5"]; 90 -> 10 [label="stop:0.5"]; 136 -> 136 [label="spin1:1.0"]; 136 -> 136 [label="spin2:1.0"]; 136 -> 136 [label="spin3:1.0"]; 136 -> 136 [label="stop:1.0"]; 124 -> 150 [label="spin1:1.0"]; 124 -> 150 [label="spin2:1.0"]; 124 -> 150 [label="spin3:1.0"]; 124 -> 150 [label="stop:1.0"]; 70 -> 70 [label="spin1:1.0"]; 70 -> 70 [label="spin2:1.0"]; 70 -> 70 [label="spin3:1.0"]; 70 -> 70 [label="stop:1.0"]; 253 -> 279 [label="spin1:1.0"]; 253 -> 279 [label="spin2:1.0"]; 253 -> 279 [label="spin3:1.0"]; 253 -> 97 [label="stop:0.5"]; 253 -> 279 [label="stop:0.5"]; 273 -> 299 [label="spin1:1.0"]; 273 -> 299 [label="spin2:1.0"]; 273 -> 299 [label="spin3:1.0"]; 273 -> 299 [label="stop:1.0"]; 207 -> 233 [label="spin1:1.0"]; 207 -> 233 [label="spin2:1.0"]; 207 -> 233 [label="spin3:1.0"]; 207 -> 233 [label="stop:1.0"]; 219 -> 219 [label="spin1:1.0"]; 219 -> 219 [label="spin2:1.0"]; 219 -> 219 [label="spin3:1.0"]; 219 -> 219 [label="stop:1.0"]; 7 -> 96 [label="spin1:0.7"]; 7 -> 105 [label="spin1:0.3"]; 7 -> 84 [label="spin2:0.7"]; 7 -> 87 [label="spin2:0.3"]; 7 -> 87 [label="spin3:0.7"]; 7 -> 88 [label="spin3:0.3"]; 7 -> 7 [label="stop:0.5"]; 7 -> 34 [label="stop:0.5"]; 11 -> 91 [label="spin1:0.7"]; 11 -> 100 [label="spin1:0.3"]; 11 -> 94 [label="spin2:0.7"]; 11 -> 97 [label="spin2:0.3"]; 11 -> 90 [label="spin3:0.7"]; 11 -> 91 [label="spin3:0.3"]; 11 -> 11 [label="stop:0.5"]; 11 -> 38 [label="stop:0.5"]; 108 -> 134 [label="spin1:1.0"]; 108 -> 134 [label="spin2:1.0"]; 108 -> 134 [label="spin3:1.0"]; 108 -> 134 [label="stop:1.0"]; 153 -> 153 [label="spin1:1.0"]; 153 -> 153 [label="spin2:1.0"]; 153 -> 153 [label="spin3:1.0"]; 153 -> 153 [label="stop:1.0"]; 54 -> 54 [label="spin1:1.0"]; 54 -> 54 [label="spin2:1.0"]; 54 -> 54 [label="spin3:1.0"]; 54 -> 54 [label="stop:1.0"]; 174 -> 252 [label="spin1:0.23"]; 174 -> 261 [label="spin1:0.77"]; 174 -> 249 [label="spin2:0.23"]; 174 -> 252 [label="spin2:0.77"]; 174 -> 252 [label="spin3:0.23"]; 174 -> 253 [label="spin3:0.77"]; 174 -> 16 [label="stop:0.5"]; 174 -> 200 [label="stop:0.5"]; 178 -> 256 [label="spin1:0.77"]; 178 -> 247 [label="spin1:0.23"]; 178 -> 259 [label="spin2:0.23"]; 178 -> 262 [label="spin2:0.77"]; 178 -> 255 [label="spin3:0.23"]; 178 -> 256 [label="spin3:0.77"]; 178 -> 20 [label="stop:0.5"]; 178 -> 204 [label="stop:0.5"]; 42 -> 69 [label="spin1:1.0"]; 42 -> 69 [label="spin2:1.0"]; 42 -> 69 [label="spin3:1.0"]; 42 -> 69 [label="stop:1.0"]; 302 -> 302 [label="spin1:1.0"]; 302 -> 302 [label="spin2:1.0"]; 302 -> 302 [label="spin3:1.0"]; 302 -> 302 [label="stop:1.0"]; 191 -> 217 [label="spin1:1.0"]; 191 -> 217 [label="spin2:1.0"]; 191 -> 217 [label="spin3:1.0"]; 191 -> 217 [label="stop:1.0"]; 236 -> 236 [label="spin1:1.0"]; 236 -> 236 [label="spin2:1.0"]; 236 -> 236 [label="spin3:1.0"]; 236 -> 236 [label="stop:1.0"]; 125 -> 151 [label="spin1:1.0"]; 125 -> 151 [label="spin2:1.0"]; 125 -> 151 [label="spin3:1.0"]; 125 -> 151 [label="stop:1.0"]; 137 -> 137 [label="spin1:1.0"]; 137 -> 137 [label="spin2:1.0"]; 137 -> 137 [label="spin3:1.0"]; 137 -> 137 [label="stop:1.0"]; 71 -> 71 [label="spin1:1.0"]; 71 -> 71 [label="spin2:1.0"]; 71 -> 71 [label="spin3:1.0"]; 71 -> 71 [label="stop:1.0"]; 92 -> 170 [label="spin1:0.47"]; 92 -> 179 [label="spin1:0.53"]; 92 -> 170 [label="spin2:0.47"]; 92 -> 173 [label="spin2:0.53"]; 92 -> 171 [label="spin3:0.47"]; 92 -> 172 [label="spin3:0.53"]; 92 -> 12 [label="stop:0.5"]; 92 -> 118 [label="stop:0.5"]; 197 -> 223 [label="spin1:1.0"]; 197 -> 223 [label="spin2:1.0"]; 197 -> 223 [label="spin3:1.0"]; 197 -> 223 [label="stop:1.0"]; 239 -> 265 [label="spin1:1.0"]; 239 -> 265 [label="spin2:1.0"]; 239 -> 265 [label="spin3:1.0"]; 239 -> 83 [label="stop:0.5"]; 239 -> 265 [label="stop:0.5"]; 220 -> 220 [label="spin1:1.0"]; 220 -> 220 [label="spin2:1.0"]; 220 -> 220 [label="spin3:1.0"]; 220 -> 220 [label="stop:1.0"]; 208 -> 234 [label="spin1:1.0"]; 208 -> 234 [label="spin2:1.0"]; 208 -> 234 [label="spin3:1.0"]; 208 -> 234 [label="stop:1.0"]; 274 -> 300 [label="spin1:1.0"]; 274 -> 300 [label="spin2:1.0"]; 274 -> 300 [label="spin3:1.0"]; 274 -> 300 [label="stop:1.0"]; 109 -> 135 [label="spin1:1.0"]; 109 -> 135 [label="spin2:1.0"]; 109 -> 135 [label="spin3:1.0"]; 109 -> 135 [label="stop:1.0"]; 154 -> 154 [label="spin1:1.0"]; 154 -> 154 [label="spin2:1.0"]; 154 -> 154 [label="spin3:1.0"]; 154 -> 154 [label="stop:1.0"]; 6 -> 95 [label="spin1:0.7"]; 6 -> 104 [label="spin1:0.3"]; 6 -> 83 [label="spin2:0.7"]; 6 -> 86 [label="spin2:0.3"]; 6 -> 87 [label="spin3:0.7"]; 6 -> 88 [label="spin3:0.3"]; 6 -> 6 [label="stop:0.5"]; 6 -> 33 [label="stop:0.5"]; 10 -> 90 [label="spin1:0.7"]; 10 -> 99 [label="spin1:0.3"]; 10 -> 93 [label="spin2:0.7"]; 10 -> 96 [label="spin2:0.3"]; 10 -> 90 [label="spin3:0.7"]; 10 -> 91 [label="spin3:0.3"]; 10 -> 10 [label="stop:0.5"]; 10 -> 37 [label="stop:0.5"]; 43 -> 70 [label="spin1:1.0"]; 43 -> 70 [label="spin2:1.0"]; 43 -> 70 [label="spin3:1.0"]; 43 -> 70 [label="stop:1.0"]; 173 -> 251 [label="spin1:0.23"]; 173 -> 260 [label="spin1:0.77"]; 173 -> 248 [label="spin2:0.23"]; 173 -> 251 [label="spin2:0.77"]; 173 -> 252 [label="spin3:0.23"]; 173 -> 253 [label="spin3:0.77"]; 173 -> 199 [label="stop:0.5"]; 173 -> 15 [label="stop:0.5"]; 177 -> 255 [label="spin1:0.77"]; 177 -> 246 [label="spin1:0.23"]; 177 -> 258 [label="spin2:0.23"]; 177 -> 261 [label="spin2:0.77"]; 177 -> 255 [label="spin3:0.23"]; 177 -> 256 [label="spin3:0.77"]; 177 -> 19 [label="stop:0.5"]; 177 -> 203 [label="stop:0.5"]; 55 -> 55 [label="spin1:1.0"]; 55 -> 55 [label="spin2:1.0"]; 55 -> 55 [label="spin3:1.0"]; 55 -> 55 [label="stop:1.0"]; 259 -> 285 [label="spin1:1.0"]; 259 -> 285 [label="spin2:1.0"]; 259 -> 285 [label="spin3:1.0"]; 259 -> 103 [label="stop:0.5"]; 259 -> 285 [label="stop:0.5"]; 303 -> 303 [label="spin1:1.0"]; 303 -> 303 [label="spin2:1.0"]; 303 -> 303 [label="spin3:1.0"]; 303 -> 303 [label="stop:1.0"]; 192 -> 218 [label="spin1:1.0"]; 192 -> 218 [label="spin2:1.0"]; 192 -> 218 [label="spin3:1.0"]; 192 -> 218 [label="stop:1.0"]; 138 -> 138 [label="spin1:1.0"]; 138 -> 138 [label="spin2:1.0"]; 138 -> 138 [label="spin3:1.0"]; 138 -> 138 [label="stop:1.0"]; 126 -> 152 [label="spin1:1.0"]; 126 -> 152 [label="spin2:1.0"]; 126 -> 152 [label="spin3:1.0"]; 126 -> 152 [label="stop:1.0"]; 26 -> 97 [label="spin1:0.7"]; 26 -> 106 [label="spin1:0.3"]; 26 -> 103 [label="spin2:0.7"]; 26 -> 106 [label="spin2:0.3"]; 26 -> 105 [label="spin3:0.7"]; 26 -> 106 [label="spin3:0.3"]; 26 -> 26 [label="stop:0.5"]; 26 -> 53 [label="stop:0.5"]; 27 -> 54 [label="spin1:1.0"]; 27 -> 54 [label="spin2:1.0"]; 27 -> 54 [label="spin3:1.0"]; 27 -> 54 [label="stop:1.0"]; 72 -> 72 [label="spin1:1.0"]; 72 -> 72 [label="spin2:1.0"]; 72 -> 72 [label="spin3:1.0"]; 72 -> 72 [label="stop:1.0"]; 88 -> 175 [label="spin1:0.47"]; 88 -> 184 [label="spin1:0.53"]; 88 -> 163 [label="spin2:0.47"]; 88 -> 166 [label="spin2:0.53"]; 88 -> 165 [label="spin3:0.47"]; 88 -> 166 [label="spin3:0.53"]; 88 -> 114 [label="stop:0.5"]; 88 -> 8 [label="stop:0.5"]; 209 -> 235 [label="spin1:1.0"]; 209 -> 235 [label="spin2:1.0"]; 209 -> 235 [label="spin3:1.0"]; 209 -> 235 [label="stop:1.0"]; 221 -> 221 [label="spin1:1.0"]; 221 -> 221 [label="spin2:1.0"]; 221 -> 221 [label="spin3:1.0"]; 221 -> 221 [label="stop:1.0"]; 155 -> 155 [label="spin1:1.0"]; 155 -> 155 [label="spin2:1.0"]; 155 -> 155 [label="spin3:1.0"]; 155 -> 155 [label="stop:1.0"]; 56 -> 56 [label="spin1:1.0"]; 56 -> 56 [label="spin2:1.0"]; 56 -> 56 [label="spin3:1.0"]; 56 -> 56 [label="stop:1.0"]; 44 -> 71 [label="spin1:1.0"]; 44 -> 71 [label="spin2:1.0"]; 44 -> 71 [label="spin3:1.0"]; 44 -> 71 [label="stop:1.0"]; 110 -> 136 [label="spin1:1.0"]; 110 -> 136 [label="spin2:1.0"]; 110 -> 136 [label="spin3:1.0"]; 110 -> 136 [label="stop:1.0"]; 12 -> 92 [label="spin1:0.7"]; 12 -> 101 [label="spin1:0.3"]; 12 -> 92 [label="spin2:0.7"]; 12 -> 95 [label="spin2:0.3"]; 12 -> 93 [label="spin3:0.7"]; 12 -> 94 [label="spin3:0.3"]; 12 -> 12 [label="stop:0.5"]; 12 -> 39 [label="stop:0.5"]; 98 -> 167 [label="spin1:0.47"]; 98 -> 176 [label="spin1:0.53"]; 98 -> 179 [label="spin2:0.47"]; 98 -> 182 [label="spin2:0.53"]; 98 -> 177 [label="spin3:0.47"]; 98 -> 178 [label="spin3:0.53"]; 98 -> 18 [label="stop:0.5"]; 98 -> 124 [label="stop:0.5"]; 304 -> 304 [label="spin1:1.0"]; 304 -> 304 [label="spin2:1.0"]; 304 -> 304 [label="spin3:1.0"]; 304 -> 304 [label="stop:1.0"]; 193 -> 219 [label="spin1:1.0"]; 193 -> 219 [label="spin2:1.0"]; 193 -> 219 [label="spin3:1.0"]; 193 -> 219 [label="stop:1.0"]; 127 -> 153 [label="spin1:1.0"]; 127 -> 153 [label="spin2:1.0"]; 127 -> 153 [label="spin3:1.0"]; 127 -> 153 [label="stop:1.0"]; 261 -> 287 [label="spin1:1.0"]; 261 -> 287 [label="spin2:1.0"]; 261 -> 287 [label="spin3:1.0"]; 261 -> 105 [label="stop:0.5"]; 261 -> 287 [label="stop:0.5"]; 139 -> 139 [label="spin1:1.0"]; 139 -> 139 [label="spin2:1.0"]; 139 -> 139 [label="spin3:1.0"]; 139 -> 139 [label="stop:1.0"]; 28 -> 55 [label="spin1:1.0"]; 28 -> 55 [label="spin2:1.0"]; 28 -> 55 [label="spin3:1.0"]; 28 -> 55 [label="stop:1.0"]; 73 -> 73 [label="spin1:1.0"]; 73 -> 73 [label="spin2:1.0"]; 73 -> 73 [label="spin3:1.0"]; 73 -> 73 [label="stop:1.0"]; 159 -> 246 [label="spin1:0.23"]; 159 -> 255 [label="spin1:0.77"]; 159 -> 243 [label="spin2:0.77"]; 159 -> 240 [label="spin2:0.23"]; 159 -> 237 [label="spin3:0.23"]; 159 -> 238 [label="spin3:0.77"]; 159 -> 1 [label="stop:0.5"]; 159 -> 185 [label="stop:0.5"]; 94 -> 172 [label="spin1:0.47"]; 94 -> 181 [label="spin1:0.53"]; 94 -> 172 [label="spin2:0.47"]; 94 -> 175 [label="spin2:0.53"]; 94 -> 171 [label="spin3:0.47"]; 94 -> 172 [label="spin3:0.53"]; 94 -> 120 [label="stop:0.5"]; 94 -> 14 [label="stop:0.5"]; 179 -> 257 [label="spin1:0.77"]; 179 -> 248 [label="spin1:0.23"]; 179 -> 257 [label="spin2:0.23"]; 179 -> 260 [label="spin2:0.77"]; 179 -> 258 [label="spin3:0.23"]; 179 -> 259 [label="spin3:0.77"]; 179 -> 21 [label="stop:0.5"]; 179 -> 205 [label="stop:0.5"]; 241 -> 267 [label="spin1:1.0"]; 241 -> 267 [label="spin2:1.0"]; 241 -> 267 [label="spin3:1.0"]; 241 -> 267 [label="stop:0.5"]; 241 -> 85 [label="stop:0.5"]; 222 -> 222 [label="spin1:1.0"]; 222 -> 222 [label="spin2:1.0"]; 222 -> 222 [label="spin3:1.0"]; 222 -> 222 [label="stop:1.0"]; 210 -> 236 [label="spin1:1.0"]; 210 -> 236 [label="spin2:1.0"]; 210 -> 236 [label="spin3:1.0"]; 210 -> 236 [label="stop:1.0"]; 276 -> 302 [label="spin1:1.0"]; 276 -> 302 [label="spin2:1.0"]; 276 -> 302 [label="spin3:1.0"]; 276 -> 302 [label="stop:1.0"]; 111 -> 137 [label="spin1:1.0"]; 111 -> 137 [label="spin2:1.0"]; 111 -> 137 [label="spin3:1.0"]; 111 -> 137 [label="stop:1.0"]; 156 -> 156 [label="spin1:1.0"]; 156 -> 156 [label="spin2:1.0"]; 156 -> 156 [label="spin3:1.0"]; 156 -> 156 [label="stop:1.0"]; 45 -> 72 [label="spin1:1.0"]; 45 -> 72 [label="spin2:1.0"]; 45 -> 72 [label="spin3:1.0"]; 45 -> 72 [label="stop:1.0"]; 8 -> 97 [label="spin1:0.7"]; 8 -> 106 [label="spin1:0.3"]; 8 -> 85 [label="spin2:0.7"]; 8 -> 88 [label="spin2:0.3"]; 8 -> 87 [label="spin3:0.7"]; 8 -> 88 [label="spin3:0.3"]; 8 -> 8 [label="stop:0.5"]; 8 -> 35 [label="stop:0.5"]; 57 -> 57 [label="spin1:1.0"]; 57 -> 57 [label="spin2:1.0"]; 57 -> 57 [label="spin3:1.0"]; 57 -> 57 [label="stop:1.0"]; 175 -> 253 [label="spin1:0.23"]; 175 -> 262 [label="spin1:0.77"]; 175 -> 250 [label="spin2:0.23"]; 175 -> 253 [label="spin2:0.77"]; 175 -> 252 [label="spin3:0.23"]; 175 -> 253 [label="spin3:0.77"]; 175 -> 17 [label="stop:0.5"]; 175 -> 201 [label="stop:0.5"]; 305 -> 305 [label="spin1:1.0"]; 305 -> 305 [label="spin2:1.0"]; 305 -> 305 [label="spin3:1.0"]; 305 -> 305 [label="stop:1.0"]; 140 -> 140 [label="spin1:1.0"]; 140 -> 140 [label="spin2:1.0"]; 140 -> 140 [label="spin3:1.0"]; 140 -> 140 [label="stop:1.0"]; 260 -> 286 [label="spin1:1.0"]; 260 -> 286 [label="spin2:1.0"]; 260 -> 286 [label="spin3:1.0"]; 260 -> 104 [label="stop:0.5"]; 260 -> 286 [label="stop:0.5"]; 194 -> 220 [label="spin1:1.0"]; 194 -> 220 [label="spin2:1.0"]; 194 -> 220 [label="spin3:1.0"]; 194 -> 220 [label="stop:1.0"]; 29 -> 56 [label="spin1:1.0"]; 29 -> 56 [label="spin2:1.0"]; 29 -> 56 [label="spin3:1.0"]; 29 -> 56 [label="stop:1.0"]; 74 -> 74 [label="spin1:1.0"]; 74 -> 74 [label="spin2:1.0"]; 74 -> 74 [label="spin3:1.0"]; 74 -> 74 [label="stop:1.0"]; 128 -> 154 [label="spin1:1.0"]; 128 -> 154 [label="spin2:1.0"]; 128 -> 154 [label="spin3:1.0"]; 128 -> 154 [label="stop:1.0"]; 93 -> 171 [label="spin1:0.47"]; 93 -> 180 [label="spin1:0.53"]; 93 -> 171 [label="spin2:0.47"]; 93 -> 174 [label="spin2:0.53"]; 93 -> 171 [label="spin3:0.47"]; 93 -> 172 [label="spin3:0.53"]; 93 -> 13 [label="stop:0.5"]; 93 -> 119 [label="stop:0.5"]; 277 -> 303 [label="spin1:1.0"]; 277 -> 303 [label="spin2:1.0"]; 277 -> 303 [label="spin3:1.0"]; 277 -> 303 [label="stop:1.0"]; 240 -> 266 [label="spin1:1.0"]; 240 -> 266 [label="spin2:1.0"]; 240 -> 266 [label="spin3:1.0"]; 240 -> 266 [label="stop:0.5"]; 240 -> 84 [label="stop:0.5"]; 289 -> 289 [label="spin1:1.0"]; 289 -> 289 [label="spin2:1.0"]; 289 -> 289 [label="spin3:1.0"]; 289 -> 289 [label="stop:1.0"]; 223 -> 223 [label="spin1:1.0"]; 223 -> 223 [label="spin2:1.0"]; 223 -> 223 [label="spin3:1.0"]; 223 -> 223 [label="stop:1.0"]; 112 -> 138 [label="spin1:1.0"]; 112 -> 138 [label="spin2:1.0"]; 112 -> 138 [label="spin3:1.0"]; 112 -> 138 [label="stop:1.0"]; 157 -> 157 [label="spin1:1.0"]; 157 -> 157 [label="spin2:1.0"]; 157 -> 157 [label="spin3:1.0"]; 157 -> 157 [label="stop:1.0"]; 58 -> 58 [label="spin1:1.0"]; 58 -> 58 [label="spin2:1.0"]; 58 -> 58 [label="spin3:1.0"]; 58 -> 58 [label="stop:1.0"]; 46 -> 73 [label="spin1:1.0"]; 46 -> 73 [label="spin2:1.0"]; 46 -> 73 [label="spin3:1.0"]; 46 -> 73 [label="stop:1.0"]; 14 -> 94 [label="spin1:0.7"]; 14 -> 103 [label="spin1:0.3"]; 14 -> 94 [label="spin2:0.7"]; 14 -> 97 [label="spin2:0.3"]; 14 -> 93 [label="spin3:0.7"]; 14 -> 94 [label="spin3:0.3"]; 14 -> 14 [label="stop:0.5"]; 14 -> 41 [label="stop:0.5"]; 161 -> 248 [label="spin1:0.23"]; 161 -> 257 [label="spin1:0.77"]; 161 -> 242 [label="spin2:0.77"]; 161 -> 239 [label="spin2:0.23"]; 161 -> 240 [label="spin3:0.23"]; 161 -> 241 [label="spin3:0.77"]; 161 -> 3 [label="stop:0.5"]; 161 -> 187 [label="stop:0.5"]; 306 -> 306 [label="spin1:1.0"]; 306 -> 306 [label="spin2:1.0"]; 306 -> 306 [label="spin3:1.0"]; 306 -> 306 [label="stop:1.0"]; 96 -> 174 [label="spin1:0.47"]; 96 -> 183 [label="spin1:0.53"]; 96 -> 171 [label="spin2:0.47"]; 96 -> 174 [label="spin2:0.53"]; 96 -> 174 [label="spin3:0.47"]; 96 -> 175 [label="spin3:0.53"]; 96 -> 16 [label="stop:0.5"]; 96 -> 122 [label="stop:0.5"]; 100 -> 169 [label="spin1:0.47"]; 100 -> 178 [label="spin1:0.53"]; 100 -> 181 [label="spin2:0.47"]; 100 -> 184 [label="spin2:0.53"]; 100 -> 177 [label="spin3:0.47"]; 100 -> 178 [label="spin3:0.53"]; 100 -> 126 [label="stop:0.5"]; 100 -> 20 [label="stop:0.5"]; 195 -> 221 [label="spin1:1.0"]; 195 -> 221 [label="spin2:1.0"]; 195 -> 221 [label="spin3:1.0"]; 195 -> 221 [label="stop:1.0"]; 129 -> 155 [label="spin1:1.0"]; 129 -> 155 [label="spin2:1.0"]; 129 -> 155 [label="spin3:1.0"]; 129 -> 155 [label="stop:1.0"]; 141 -> 141 [label="spin1:1.0"]; 141 -> 141 [label="spin2:1.0"]; 141 -> 141 [label="spin3:1.0"]; 141 -> 141 [label="stop:1.0"]; 30 -> 57 [label="spin1:1.0"]; 30 -> 57 [label="spin2:1.0"]; 30 -> 57 [label="spin3:1.0"]; 30 -> 57 [label="stop:1.0"]; 75 -> 75 [label="spin1:1.0"]; 75 -> 75 [label="spin2:1.0"]; 75 -> 75 [label="spin3:1.0"]; 75 -> 75 [label="stop:1.0"]; 181 -> 259 [label="spin1:0.77"]; 181 -> 250 [label="spin1:0.23"]; 181 -> 259 [label="spin2:0.23"]; 181 -> 262 [label="spin2:0.77"]; 181 -> 258 [label="spin3:0.23"]; 181 -> 259 [label="spin3:0.77"]; 181 -> 23 [label="stop:0.5"]; 181 -> 207 [label="stop:0.5"]; 290 -> 290 [label="spin1:1.0"]; 290 -> 290 [label="spin2:1.0"]; 290 -> 290 [label="spin3:1.0"]; 290 -> 290 [label="stop:1.0"]; 224 -> 224 [label="spin1:1.0"]; 224 -> 224 [label="spin2:1.0"]; 224 -> 224 [label="spin3:1.0"]; 224 -> 224 [label="stop:1.0"]; 278 -> 304 [label="spin1:1.0"]; 278 -> 304 [label="spin2:1.0"]; 278 -> 304 [label="spin3:1.0"]; 278 -> 304 [label="stop:1.0"]; 113 -> 139 [label="spin1:1.0"]; 113 -> 139 [label="spin2:1.0"]; 113 -> 139 [label="spin3:1.0"]; 113 -> 139 [label="stop:1.0"]; 158 -> 158 [label="spin1:1.0"]; 158 -> 158 [label="spin2:1.0"]; 158 -> 158 [label="spin3:1.0"]; 158 -> 158 [label="stop:1.0"]; 47 -> 74 [label="spin1:1.0"]; 47 -> 74 [label="spin2:1.0"]; 47 -> 74 [label="spin3:1.0"]; 47 -> 74 [label="stop:1.0"]; 242 -> 268 [label="spin1:1.0"]; 242 -> 268 [label="spin2:1.0"]; 242 -> 268 [label="spin3:1.0"]; 242 -> 268 [label="stop:0.5"]; 242 -> 86 [label="stop:0.5"]; 246 -> 272 [label="spin1:1.0"]; 246 -> 272 [label="spin2:1.0"]; 246 -> 272 [label="spin3:1.0"]; 246 -> 90 [label="stop:0.5"]; 246 -> 272 [label="stop:0.5"]; 59 -> 59 [label="spin1:1.0"]; 59 -> 59 [label="spin2:1.0"]; 59 -> 59 [label="spin3:1.0"]; 59 -> 59 [label="stop:1.0"]; 13 -> 93 [label="spin1:0.7"]; 13 -> 102 [label="spin1:0.3"]; 13 -> 93 [label="spin2:0.7"]; 13 -> 96 [label="spin2:0.3"]; 13 -> 93 [label="spin3:0.7"]; 13 -> 94 [label="spin3:0.3"]; 13 -> 13 [label="stop:0.5"]; 13 -> 40 [label="stop:0.5"]; 307 -> 307 [label="spin1:1.0"]; 307 -> 307 [label="spin2:1.0"]; 307 -> 307 [label="spin3:1.0"]; 307 -> 307 [label="stop:1.0"]; 95 -> 173 [label="spin1:0.47"]; 95 -> 182 [label="spin1:0.53"]; 95 -> 170 [label="spin2:0.47"]; 95 -> 173 [label="spin2:0.53"]; 95 -> 174 [label="spin3:0.47"]; 95 -> 175 [label="spin3:0.53"]; 95 -> 121 [label="stop:0.5"]; 95 -> 15 [label="stop:0.5"]; 99 -> 168 [label="spin1:0.47"]; 99 -> 177 [label="spin1:0.53"]; 99 -> 180 [label="spin2:0.47"]; 99 -> 183 [label="spin2:0.53"]; 99 -> 177 [label="spin3:0.47"]; 99 -> 178 [label="spin3:0.53"]; 99 -> 125 [label="stop:0.5"]; 99 -> 19 [label="stop:0.5"]; 142 -> 142 [label="spin1:1.0"]; 142 -> 142 [label="spin2:1.0"]; 142 -> 142 [label="spin3:1.0"]; 142 -> 142 [label="stop:1.0"]; 130 -> 156 [label="spin1:1.0"]; 130 -> 156 [label="spin2:1.0"]; 130 -> 156 [label="spin3:1.0"]; 130 -> 156 [label="stop:1.0"]; 196 -> 222 [label="spin1:1.0"]; 196 -> 222 [label="spin2:1.0"]; 196 -> 222 [label="spin3:1.0"]; 196 -> 222 [label="stop:1.0"]; 76 -> 76 [label="spin1:1.0"]; 76 -> 76 [label="spin2:1.0"]; 76 -> 76 [label="spin3:1.0"]; 76 -> 76 [label="stop:1.0"]; 262 -> 288 [label="spin1:1.0"]; 262 -> 288 [label="spin2:1.0"]; 262 -> 288 [label="spin3:1.0"]; 262 -> 106 [label="stop:0.5"]; 262 -> 288 [label="stop:0.5"]; 31 -> 58 [label="spin1:1.0"]; 31 -> 58 [label="spin2:1.0"]; 31 -> 58 [label="spin3:1.0"]; 31 -> 58 [label="stop:1.0"]; 160 -> 247 [label="spin1:0.23"]; 160 -> 256 [label="spin1:0.77"]; 160 -> 244 [label="spin2:0.77"]; 160 -> 241 [label="spin2:0.23"]; 160 -> 237 [label="spin3:0.23"]; 160 -> 238 [label="spin3:0.77"]; 160 -> 2 [label="stop:0.5"]; 160 -> 186 [label="stop:0.5"]; 279 -> 305 [label="spin1:1.0"]; 279 -> 305 [label="spin2:1.0"]; 279 -> 305 [label="spin3:1.0"]; 279 -> 305 [label="stop:1.0"]; 180 -> 258 [label="spin1:0.77"]; 180 -> 249 [label="spin1:0.23"]; 180 -> 258 [label="spin2:0.23"]; 180 -> 261 [label="spin2:0.77"]; 180 -> 258 [label="spin3:0.23"]; 180 -> 259 [label="spin3:0.77"]; 180 -> 22 [label="stop:0.5"]; 180 -> 206 [label="stop:0.5"]; 291 -> 291 [label="spin1:1.0"]; 291 -> 291 [label="spin2:1.0"]; 291 -> 291 [label="spin3:1.0"]; 291 -> 291 [label="stop:1.0"]; 225 -> 225 [label="spin1:1.0"]; 225 -> 225 [label="spin2:1.0"]; 225 -> 225 [label="spin3:1.0"]; 225 -> 225 [label="stop:1.0"]; 114 -> 140 [label="spin1:1.0"]; 114 -> 140 [label="spin2:1.0"]; 114 -> 140 [label="spin3:1.0"]; 114 -> 140 [label="stop:1.0"]; 60 -> 60 [label="spin1:1.0"]; 60 -> 60 [label="spin2:1.0"]; 60 -> 60 [label="spin3:1.0"]; 60 -> 60 [label="stop:1.0"]; 245 -> 271 [label="spin1:1.0"]; 245 -> 271 [label="spin2:1.0"]; 245 -> 271 [label="spin3:1.0"]; 245 -> 89 [label="stop:0.5"]; 245 -> 271 [label="stop:0.5"]; 48 -> 75 [label="spin1:1.0"]; 48 -> 75 [label="spin2:1.0"]; 48 -> 75 [label="spin3:1.0"]; 48 -> 75 [label="stop:1.0"]; __start0 [label="", shape=none]; __start0 -> 0 [label=""]; } ================================================ FILE: DotModels/MDPs/tcp.dot ================================================ digraph tcp { 8 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 25 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 107 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 19 [label="start"]; 132 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 69 [label="TIMEOUT"]; 36 [label="TIMEOUT"]; 43 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 143 [label="TIMEOUT"]; 16 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 11 [label="TIMEOUT"]; 24 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 154 [label="ACK_plus_FIN_p_NEXT_c_CURRENT_c_0_p"]; 90 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 44 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 27 [label="TIMEOUT"]; 34 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 60 [label="TIMEOUT"]; 39 [label="ACK_plus_FIN_p_NEXT_c_CURRENT_c_0_p"]; 112 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 35 [label="TIMEOUT"]; 46 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 81 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 114 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 10 [label="TIMEOUT"]; 22 [label="ACK_plus_SYN_p_FRESH_c_NEXT_c_0_p"]; 109 [label="TIMEOUT"]; 76 [label="TIMEOUT"]; 150 [label="TIMEOUT"]; 18 [label="TIMEOUT"]; 155 [label="ACK_plus_FIN_p_NEXT_c_CURRENT_c_0_p"]; 84 [label="TIMEOUT"]; 64 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 1 [label="TIMEOUT"]; 116 [label="crash"]; 130 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 49 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 115 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 9 [label="TIMEOUT"]; 138 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 29 [label="ACK_plus_SYN_p_FRESH_c_NEXT_c_0_p"]; 140 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 100 [label="TIMEOUT"]; 74 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 21 [label="ACK_plus_SYN_p_FRESH_c_NEXT_c_0_p"]; 42 [label="TIMEOUT"]; 108 [label="TIMEOUT"]; 135 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 32 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 149 [label="TIMEOUT"]; 96 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 33 [label="TIMEOUT"]; 0 [label="TIMEOUT"]; 30 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 124 [label="TIMEOUT"]; 41 [label="TIMEOUT"]; 5 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 66 [label="TIMEOUT"]; 71 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 38 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 40 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 13 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 152 [label="ACK_plus_SYN_p_FRESH_c_NEXT_c_0_p"]; 153 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 57 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 78 [label="ACK_plus_SYN_p_FRESH_c_NEXT_c_0_p"]; 82 [label="TIMEOUT"]; 31 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 128 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 65 [label="TIMEOUT"]; 12 [label="ACK_plus_SYN_p_FRESH_c_NEXT_c_0_p"]; 58 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 123 [label="TIMEOUT"]; 7 [label="TIMEOUT"]; 54 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 73 [label="TIMEOUT"]; 37 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 47 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 98 [label="TIMEOUT"]; 48 [label="TIMEOUT"]; 101 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 106 [label="TIMEOUT"]; 134 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 23 [label="TIMEOUT"]; 89 [label="TIMEOUT"]; 125 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 147 [label="TIMEOUT"]; 92 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 3 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 59 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 97 [label="TIMEOUT"]; 26 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 88 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 122 [label="TIMEOUT"]; 17 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 6 [label="TIMEOUT"]; 105 [label="TIMEOUT"]; 50 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 72 [label="TIMEOUT"]; 15 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 28 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 111 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 14 [label="TIMEOUT"]; 75 [label="ACK_plus_RST_p_NEXT_c_CURRENT_c_0_p"]; 113 [label="TIMEOUT"]; 80 [label="TIMEOUT"]; 77 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 133 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 51 [label="ACK_plus_FIN_p_NEXT_c_CURRENT_c_0_p"]; 110 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 55 [label="TIMEOUT"]; 146 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 121 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 118 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 129 [label="TIMEOUT"]; 2 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 93 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 4 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 126 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 137 [label="TIMEOUT"]; 45 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 68 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 79 [label="TIMEOUT"]; 63 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 142 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 87 [label="TIMEOUT"]; 120 [label="TIMEOUT"]; 117 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 139 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 127 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 95 [label="TIMEOUT"]; 62 [label="TIMEOUT"]; 67 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 53 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 131 [label="ACK_plus_RST_p_ZERO_c_NEXT_c_0_p"]; 145 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 103 [label="TIMEOUT"]; 136 [label="TIMEOUT"]; 70 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 83 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 144 [label="TIMEOUT"]; 141 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 104 [label="RST_p_NEXT_c_ZERO_c_0_p"]; 102 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 119 [label="TIMEOUT"]; 20 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 91 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 148 [label="ACK_plus_SYN_p_FRESH_c_NEXT_c_0_p"]; 86 [label="RST_p_ZERO_c_ZERO_c_0_p"]; 94 [label="TIMEOUT"]; 99 [label="ACK_p_NEXT_c_CURRENT_c_0_p"]; 85 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 52 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 61 [label="TIMEOUT"]; 151 [label="ACK_p_NEXT_c_NEXT_c_0_p"]; 56 [label="ACK_plus_RST_p_NEXT_c_CURRENT_c_0_p"]; 8 -> 9 [label="ACCEPT:1.0"]; 8 -> 76 [label="ACK_p_V_c_V_c_0_p:1.0"]; 8 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 8 -> 74 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 8 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 8 -> 56 [label="CLOSE:1.0"]; 8 -> 76 [label="CLOSECONNECTION:1.0"]; 8 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 8 -> 49 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 8 -> 76 [label="LISTEN:1.0"]; 8 -> 76 [label="RCV:1.0"]; 8 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 8 -> 76 [label="SYN_p_V_c_V_c_0_p:1.0"]; 8 -> 8 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 25 -> 103 [label="ACCEPT:1.0"]; 25 -> 146 [label="ACK_p_V_c_V_c_0_p:1.0"]; 25 -> 146 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 25 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 25 -> 66 [label="CLOSE:1.0"]; 25 -> 72 [label="CLOSECONNECTION:1.0"]; 25 -> 146 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 25 -> 72 [label="LISTEN:1.0"]; 25 -> 72 [label="RCV:1.0"]; 25 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 25 -> 152 [label="SYN_p_V_c_V_c_0_p:1.0"]; 25 -> 146 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 107 -> 69 [label="ACCEPT:1.0"]; 107 -> 69 [label="ACK_p_V_c_V_c_0_p:1.0"]; 107 -> 102 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 107 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 107 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 107 -> 69 [label="CLOSE:1.0"]; 107 -> 155 [label="CLOSECONNECTION:1.0"]; 107 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 107 -> 140 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 107 -> 69 [label="LISTEN:1.0"]; 107 -> 7 [label="RCV:1.0"]; 107 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 107 -> 69 [label="SYN_p_V_c_V_c_0_p:1.0"]; 107 -> 107 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 19 -> 97 [label="ACCEPT:1.0"]; 19 -> 70 [label="ACK_p_V_c_V_c_0_p:1.0"]; 19 -> 70 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 19 -> 97 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 19 -> 66 [label="CLOSE:1.0"]; 19 -> 97 [label="CLOSECONNECTION:1.0"]; 19 -> 70 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 19 -> 73 [label="LISTEN:1.0"]; 19 -> 97 [label="RCV:1.0"]; 19 -> 97 [label="RST_p_V_c_V_c_0_p:1.0"]; 19 -> 34 [label="SYN_p_V_c_V_c_0_p:1.0"]; 19 -> 70 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 132 -> 9 [label="ACCEPT:1.0"]; 132 -> 9 [label="ACK_p_V_c_V_c_0_p:1.0"]; 132 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 132 -> 45 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 132 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 132 -> 56 [label="CLOSE:1.0"]; 132 -> 120 [label="CLOSECONNECTION:1.0"]; 132 -> 92 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 132 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 132 -> 9 [label="LISTEN:1.0"]; 132 -> 9 [label="RCV:1.0"]; 132 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 132 -> 9 [label="SYN_p_V_c_V_c_0_p:1.0"]; 132 -> 132 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 69 -> 69 [label="ACCEPT:1.0"]; 69 -> 69 [label="ACK_p_V_c_V_c_0_p:1.0"]; 69 -> 102 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 69 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 69 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 69 -> 69 [label="CLOSE:1.0"]; 69 -> 155 [label="CLOSECONNECTION:1.0"]; 69 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 69 -> 140 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 69 -> 69 [label="LISTEN:1.0"]; 69 -> 7 [label="RCV:1.0"]; 69 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 69 -> 69 [label="SYN_p_V_c_V_c_0_p:1.0"]; 69 -> 107 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 36 -> 98 [label="ACCEPT:1.0"]; 36 -> 36 [label="ACK_p_V_c_V_c_0_p:1.0"]; 36 -> 85 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 36 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 36 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 36 -> 56 [label="CLOSE:1.0"]; 36 -> 36 [label="CLOSECONNECTION:1.0"]; 36 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 36 -> 127 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 36 -> 36 [label="LISTEN:1.0"]; 36 -> 36 [label="RCV:1.0"]; 36 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 36 -> 36 [label="SYN_p_V_c_V_c_0_p:1.0"]; 36 -> 16 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 43 -> 94 [label="ACCEPT:1.0"]; 43 -> 20 [label="ACK_p_V_c_V_c_0_p:1.0"]; 43 -> 20 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 43 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 43 -> 66 [label="CLOSE:1.0"]; 43 -> 87 [label="CLOSECONNECTION:1.0"]; 43 -> 20 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 43 -> 94 [label="LISTEN:1.0"]; 43 -> 94 [label="RCV:1.0"]; 43 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 43 -> 78 [label="SYN_p_V_c_V_c_0_p:1.0"]; 43 -> 20 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 143 -> 143 [label="ACCEPT:1.0"]; 143 -> 143 [label="ACK_p_V_c_V_c_0_p:1.0"]; 143 -> 143 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 143 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 143 -> 56 [label="CLOSE:1.0"]; 143 -> 109 [label="CLOSECONNECTION:1.0"]; 143 -> 143 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 143 -> 143 [label="LISTEN:1.0"]; 143 -> 143 [label="RCV:1.0"]; 143 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 143 -> 143 [label="SYN_p_V_c_V_c_0_p:1.0"]; 143 -> 30 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 16 -> 98 [label="ACCEPT:1.0"]; 16 -> 36 [label="ACK_p_V_c_V_c_0_p:1.0"]; 16 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 16 -> 85 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 16 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 16 -> 56 [label="CLOSE:1.0"]; 16 -> 36 [label="CLOSECONNECTION:1.0"]; 16 -> 127 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 16 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 16 -> 36 [label="LISTEN:1.0"]; 16 -> 36 [label="RCV:1.0"]; 16 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 16 -> 36 [label="SYN_p_V_c_V_c_0_p:1.0"]; 16 -> 16 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 11 -> 11 [label="ACCEPT:1.0"]; 11 -> 11 [label="ACK_p_V_c_V_c_0_p:1.0"]; 11 -> 90 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 11 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 11 -> 11 [label="CLOSE:1.0"]; 11 -> 11 [label="CLOSECONNECTION:1.0"]; 11 -> 90 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 11 -> 11 [label="LISTEN:1.0"]; 11 -> 11 [label="RCV:1.0"]; 11 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 11 -> 90 [label="SYN_p_V_c_V_c_0_p:1.0"]; 11 -> 90 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 24 -> 136 [label="ACCEPT:1.0"]; 24 -> 136 [label="ACK_p_V_c_V_c_0_p:1.0"]; 24 -> 136 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 24 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 24 -> 123 [label="CLOSE:1.0"]; 24 -> 51 [label="CLOSECONNECTION:1.0"]; 24 -> 136 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 24 -> 136 [label="LISTEN:1.0"]; 24 -> 136 [label="RCV:1.0"]; 24 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 24 -> 136 [label="SYN_p_V_c_V_c_0_p:1.0"]; 24 -> 24 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 154 -> 18 [label="ACCEPT:1.0"]; 154 -> 147 [label="ACK_p_V_c_V_c_0_p:1.0"]; 154 -> 147 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 154 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 154 -> 18 [label="CLOSE:1.0"]; 154 -> 18 [label="CLOSECONNECTION:1.0"]; 154 -> 147 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 154 -> 18 [label="LISTEN:1.0"]; 154 -> 18 [label="RCV:1.0"]; 154 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 154 -> 18 [label="SYN_p_V_c_V_c_0_p:1.0"]; 154 -> 99 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 90 -> 11 [label="ACCEPT:1.0"]; 90 -> 11 [label="ACK_p_V_c_V_c_0_p:1.0"]; 90 -> 90 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 90 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 90 -> 11 [label="CLOSE:1.0"]; 90 -> 11 [label="CLOSECONNECTION:1.0"]; 90 -> 90 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 90 -> 11 [label="LISTEN:1.0"]; 90 -> 11 [label="RCV:1.0"]; 90 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 90 -> 90 [label="SYN_p_V_c_V_c_0_p:1.0"]; 90 -> 90 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 44 -> 66 [label="ACCEPT:1.0"]; 44 -> 44 [label="ACK_p_V_c_V_c_0_p:1.0"]; 44 -> 44 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 44 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 44 -> 66 [label="CLOSE:1.0"]; 44 -> 66 [label="CLOSECONNECTION:1.0"]; 44 -> 44 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 44 -> 66 [label="LISTEN:1.0"]; 44 -> 66 [label="RCV:1.0"]; 44 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 44 -> 4 [label="SYN_p_V_c_V_c_0_p:1.0"]; 44 -> 44 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 27 -> 82 [label="ACCEPT:1.0"]; 27 -> 106 [label="ACK_p_V_c_V_c_0_p:1.0"]; 27 -> 106 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 27 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 27 -> 18 [label="CLOSE:1.0"]; 27 -> 27 [label="CLOSECONNECTION:1.0"]; 27 -> 106 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 27 -> 27 [label="LISTEN:1.0"]; 27 -> 27 [label="RCV:1.0"]; 27 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 27 -> 27 [label="SYN_p_V_c_V_c_0_p:1.0"]; 27 -> 126 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 34 -> 97 [label="ACCEPT:1.0"]; 34 -> 70 [label="ACK_p_V_c_V_c_0_p:1.0"]; 34 -> 70 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 34 -> 97 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 34 -> 66 [label="CLOSE:1.0"]; 34 -> 97 [label="CLOSECONNECTION:1.0"]; 34 -> 70 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 34 -> 73 [label="LISTEN:1.0"]; 34 -> 97 [label="RCV:1.0"]; 34 -> 97 [label="RST_p_V_c_V_c_0_p:1.0"]; 34 -> 34 [label="SYN_p_V_c_V_c_0_p:1.0"]; 34 -> 70 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 60 -> 60 [label="ACCEPT:1.0"]; 60 -> 60 [label="ACK_p_V_c_V_c_0_p:1.0"]; 60 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 60 -> 101 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 60 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 60 -> 56 [label="CLOSE:1.0"]; 60 -> 76 [label="CLOSECONNECTION:1.0"]; 60 -> 52 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 60 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 60 -> 60 [label="LISTEN:1.0"]; 60 -> 60 [label="RCV:1.0"]; 60 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 60 -> 60 [label="SYN_p_V_c_V_c_0_p:1.0"]; 60 -> 153 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 39 -> 89 [label="ACCEPT:1.0"]; 39 -> 105 [label="ACK_p_V_c_V_c_0_p:1.0"]; 39 -> 32 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 39 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 39 -> 35 [label="CLOSE:1.0"]; 39 -> 122 [label="CLOSECONNECTION:1.0"]; 39 -> 46 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 39 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 39 -> 122 [label="LISTEN:1.0"]; 39 -> 122 [label="RCV:1.0"]; 39 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 39 -> 122 [label="SYN_p_V_c_V_c_0_p:1.0"]; 39 -> 67 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 112 -> 11 [label="ACCEPT:1.0"]; 112 -> 11 [label="ACK_p_V_c_V_c_0_p:1.0"]; 112 -> 90 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 112 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 112 -> 11 [label="CLOSE:1.0"]; 112 -> 11 [label="CLOSECONNECTION:1.0"]; 112 -> 90 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 112 -> 11 [label="LISTEN:1.0"]; 112 -> 11 [label="RCV:1.0"]; 112 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 112 -> 90 [label="SYN_p_V_c_V_c_0_p:1.0"]; 112 -> 90 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 35 -> 35 [label="ACCEPT:1.0"]; 35 -> 23 [label="ACK_p_V_c_V_c_0_p:1.0"]; 35 -> 47 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 35 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 35 -> 35 [label="CLOSE:1.0"]; 35 -> 35 [label="CLOSECONNECTION:1.0"]; 35 -> 112 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 35 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 35 -> 35 [label="LISTEN:1.0"]; 35 -> 35 [label="RCV:1.0"]; 35 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 35 -> 35 [label="SYN_p_V_c_V_c_0_p:1.0"]; 35 -> 96 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 46 -> 84 [label="ACCEPT:1.0"]; 46 -> 41 [label="ACK_p_V_c_V_c_0_p:1.0"]; 46 -> 64 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 46 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 46 -> 11 [label="CLOSE:1.0"]; 46 -> 41 [label="CLOSECONNECTION:1.0"]; 46 -> 64 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 46 -> 41 [label="LISTEN:1.0"]; 46 -> 41 [label="RCV:1.0"]; 46 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 46 -> 64 [label="SYN_p_V_c_V_c_0_p:1.0"]; 46 -> 64 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 81 -> 113 [label="ACCEPT:1.0"]; 81 -> 113 [label="ACK_p_V_c_V_c_0_p:1.0"]; 81 -> 113 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 81 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 81 -> 56 [label="CLOSE:1.0"]; 81 -> 61 [label="CLOSECONNECTION:1.0"]; 81 -> 113 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 81 -> 113 [label="LISTEN:1.0"]; 81 -> 113 [label="RCV:1.0"]; 81 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 81 -> 113 [label="SYN_p_V_c_V_c_0_p:1.0"]; 81 -> 81 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 114 -> 1 [label="ACCEPT:1.0"]; 114 -> 61 [label="ACK_p_V_c_V_c_0_p:1.0"]; 114 -> 61 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 114 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 114 -> 56 [label="CLOSE:1.0"]; 114 -> 61 [label="CLOSECONNECTION:1.0"]; 114 -> 61 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 114 -> 61 [label="LISTEN:1.0"]; 114 -> 61 [label="RCV:1.0"]; 114 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 114 -> 61 [label="SYN_p_V_c_V_c_0_p:1.0"]; 114 -> 114 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 10 -> 10 [label="ACCEPT:1.0"]; 10 -> 10 [label="ACK_p_V_c_V_c_0_p:1.0"]; 10 -> 101 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 10 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 10 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 10 -> 56 [label="CLOSE:1.0"]; 10 -> 95 [label="CLOSECONNECTION:1.0"]; 10 -> 53 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 10 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 10 -> 10 [label="LISTEN:1.0"]; 10 -> 10 [label="RCV:1.0"]; 10 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 10 -> 10 [label="SYN_p_V_c_V_c_0_p:1.0"]; 10 -> 142 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 22 -> 129 [label="ACCEPT:1.0"]; 22 -> 9 [label="ACK_p_V_c_V_c_0_p:1.0"]; 22 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 22 -> 45 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 22 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 22 -> 147 [label="CLOSE:1.0"]; 22 -> 137 [label="CLOSECONNECTION:1.0"]; 22 -> 92 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 22 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 22 -> 129 [label="LISTEN:1.0"]; 22 -> 129 [label="RCV:1.0"]; 22 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 22 -> 28 [label="SYN_p_V_c_V_c_0_p:1.0"]; 22 -> 88 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 109 -> 113 [label="ACCEPT:1.0"]; 109 -> 109 [label="ACK_p_V_c_V_c_0_p:1.0"]; 109 -> 109 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 109 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 109 -> 56 [label="CLOSE:1.0"]; 109 -> 109 [label="CLOSECONNECTION:1.0"]; 109 -> 109 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 109 -> 109 [label="LISTEN:1.0"]; 109 -> 109 [label="RCV:1.0"]; 109 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 109 -> 109 [label="SYN_p_V_c_V_c_0_p:1.0"]; 109 -> 38 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 76 -> 9 [label="ACCEPT:1.0"]; 76 -> 76 [label="ACK_p_V_c_V_c_0_p:1.0"]; 76 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 76 -> 74 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 76 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 76 -> 56 [label="CLOSE:1.0"]; 76 -> 76 [label="CLOSECONNECTION:1.0"]; 76 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 76 -> 49 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 76 -> 76 [label="LISTEN:1.0"]; 76 -> 76 [label="RCV:1.0"]; 76 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 76 -> 76 [label="SYN_p_V_c_V_c_0_p:1.0"]; 76 -> 8 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 150 -> 150 [label="ACCEPT:1.0"]; 150 -> 60 [label="ACK_p_V_c_V_c_0_p:1.0"]; 150 -> 101 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 150 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 150 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 150 -> 147 [label="CLOSE:1.0"]; 150 -> 79 [label="CLOSECONNECTION:1.0"]; 150 -> 52 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 150 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 150 -> 150 [label="LISTEN:1.0"]; 150 -> 150 [label="RCV:1.0"]; 150 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 150 -> 111 [label="SYN_p_V_c_V_c_0_p:1.0"]; 150 -> 115 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 18 -> 18 [label="ACCEPT:1.0"]; 18 -> 147 [label="ACK_p_V_c_V_c_0_p:1.0"]; 18 -> 147 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 18 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 18 -> 18 [label="CLOSE:1.0"]; 18 -> 18 [label="CLOSECONNECTION:1.0"]; 18 -> 147 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 18 -> 18 [label="LISTEN:1.0"]; 18 -> 18 [label="RCV:1.0"]; 18 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 18 -> 18 [label="SYN_p_V_c_V_c_0_p:1.0"]; 18 -> 99 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 155 -> 35 [label="ACCEPT:1.0"]; 155 -> 23 [label="ACK_p_V_c_V_c_0_p:1.0"]; 155 -> 47 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 155 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 155 -> 35 [label="CLOSE:1.0"]; 155 -> 35 [label="CLOSECONNECTION:1.0"]; 155 -> 112 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 155 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 155 -> 35 [label="LISTEN:1.0"]; 155 -> 35 [label="RCV:1.0"]; 155 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 155 -> 35 [label="SYN_p_V_c_V_c_0_p:1.0"]; 155 -> 96 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 84 -> 84 [label="ACCEPT:1.0"]; 84 -> 84 [label="ACK_p_V_c_V_c_0_p:1.0"]; 84 -> 68 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 84 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 84 -> 11 [label="CLOSE:1.0"]; 84 -> 41 [label="CLOSECONNECTION:1.0"]; 84 -> 68 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 84 -> 84 [label="LISTEN:1.0"]; 84 -> 84 [label="RCV:1.0"]; 84 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 84 -> 68 [label="SYN_p_V_c_V_c_0_p:1.0"]; 84 -> 68 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 64 -> 84 [label="ACCEPT:1.0"]; 64 -> 41 [label="ACK_p_V_c_V_c_0_p:1.0"]; 64 -> 64 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 64 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 64 -> 11 [label="CLOSE:1.0"]; 64 -> 41 [label="CLOSECONNECTION:1.0"]; 64 -> 64 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 64 -> 41 [label="LISTEN:1.0"]; 64 -> 41 [label="RCV:1.0"]; 64 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 64 -> 64 [label="SYN_p_V_c_V_c_0_p:1.0"]; 64 -> 64 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 1 -> 1 [label="ACCEPT:1.0"]; 1 -> 1 [label="ACK_p_V_c_V_c_0_p:1.0"]; 1 -> 1 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 1 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 1 -> 6 [label="CLOSE:1.0"]; 1 -> 75 [label="CLOSECONNECTION:1.0"]; 1 -> 1 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 1 -> 1 [label="LISTEN:1.0"]; 1 -> 136 [label="RCV:1.0"]; 1 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 1 -> 1 [label="SYN_p_V_c_V_c_0_p:1.0"]; 1 -> 37 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 116 -> 97 [label="ACCEPT:1.0"]; 116 -> 70 [label="ACK_p_V_c_V_c_0_p:1.0"]; 116 -> 70 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 116 -> 97 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 116 -> 66 [label="CLOSE:1.0"]; 116 -> 97 [label="CLOSECONNECTION:1.0"]; 116 -> 70 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 116 -> 73 [label="LISTEN:1.0"]; 116 -> 97 [label="RCV:1.0"]; 116 -> 97 [label="RST_p_V_c_V_c_0_p:1.0"]; 116 -> 34 [label="SYN_p_V_c_V_c_0_p:1.0"]; 116 -> 70 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 130 -> 149 [label="ACCEPT:1.0"]; 130 -> 149 [label="ACK_p_V_c_V_c_0_p:1.0"]; 130 -> 149 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 130 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 130 -> 56 [label="CLOSE:1.0"]; 130 -> 80 [label="CLOSECONNECTION:1.0"]; 130 -> 149 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 130 -> 149 [label="LISTEN:1.0"]; 130 -> 149 [label="RCV:1.0"]; 130 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 130 -> 149 [label="SYN_p_V_c_V_c_0_p:1.0"]; 130 -> 130 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 49 -> 149 [label="ACCEPT:1.0"]; 49 -> 65 [label="ACK_p_V_c_V_c_0_p:1.0"]; 49 -> 65 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 49 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 49 -> 56 [label="CLOSE:1.0"]; 49 -> 65 [label="CLOSECONNECTION:1.0"]; 49 -> 65 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 49 -> 65 [label="LISTEN:1.0"]; 49 -> 65 [label="RCV:1.0"]; 49 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 49 -> 65 [label="SYN_p_V_c_V_c_0_p:1.0"]; 49 -> 110 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 115 -> 14 [label="ACCEPT:1.0"]; 115 -> 63 [label="ACK_p_V_c_V_c_0_p:1.0"]; 115 -> 63 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 115 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 115 -> 66 [label="CLOSE:1.0"]; 115 -> 72 [label="CLOSECONNECTION:1.0"]; 115 -> 63 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 115 -> 14 [label="LISTEN:1.0"]; 115 -> 14 [label="RCV:1.0"]; 115 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 115 -> 29 [label="SYN_p_V_c_V_c_0_p:1.0"]; 115 -> 63 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 9 -> 9 [label="ACCEPT:1.0"]; 9 -> 9 [label="ACK_p_V_c_V_c_0_p:1.0"]; 9 -> 45 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 9 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 9 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 9 -> 56 [label="CLOSE:1.0"]; 9 -> 120 [label="CLOSECONNECTION:1.0"]; 9 -> 92 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 9 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 9 -> 9 [label="LISTEN:1.0"]; 9 -> 9 [label="RCV:1.0"]; 9 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 9 -> 9 [label="SYN_p_V_c_V_c_0_p:1.0"]; 9 -> 132 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 138 -> 6 [label="ACCEPT:1.0"]; 138 -> 6 [label="ACK_p_V_c_V_c_0_p:1.0"]; 138 -> 6 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 138 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 138 -> 6 [label="CLOSE:1.0"]; 138 -> 56 [label="CLOSECONNECTION:1.0"]; 138 -> 6 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 138 -> 6 [label="LISTEN:1.0"]; 138 -> 123 [label="RCV:1.0"]; 138 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 138 -> 6 [label="SYN_p_V_c_V_c_0_p:1.0"]; 138 -> 138 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 29 -> 150 [label="ACCEPT:1.0"]; 29 -> 60 [label="ACK_p_V_c_V_c_0_p:1.0"]; 29 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 29 -> 101 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 29 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 29 -> 147 [label="CLOSE:1.0"]; 29 -> 79 [label="CLOSECONNECTION:1.0"]; 29 -> 52 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 29 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 29 -> 150 [label="LISTEN:1.0"]; 29 -> 150 [label="RCV:1.0"]; 29 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 29 -> 111 [label="SYN_p_V_c_V_c_0_p:1.0"]; 29 -> 115 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 140 -> 123 [label="ACCEPT:1.0"]; 140 -> 123 [label="ACK_p_V_c_V_c_0_p:1.0"]; 140 -> 123 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 140 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 140 -> 123 [label="CLOSE:1.0"]; 140 -> 154 [label="CLOSECONNECTION:1.0"]; 140 -> 123 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 140 -> 123 [label="LISTEN:1.0"]; 140 -> 123 [label="RCV:1.0"]; 140 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 140 -> 123 [label="SYN_p_V_c_V_c_0_p:1.0"]; 140 -> 83 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 100 -> 100 [label="ACCEPT:1.0"]; 100 -> 58 [label="ACK_p_V_c_V_c_0_p:1.0"]; 100 -> 58 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 100 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 100 -> 147 [label="CLOSE:1.0"]; 100 -> 106 [label="CLOSECONNECTION:1.0"]; 100 -> 58 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 100 -> 100 [label="LISTEN:1.0"]; 100 -> 100 [label="RCV:1.0"]; 100 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 100 -> 21 [label="SYN_p_V_c_V_c_0_p:1.0"]; 100 -> 58 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 74 -> 62 [label="ACCEPT:1.0"]; 74 -> 95 [label="ACK_p_V_c_V_c_0_p:1.0"]; 74 -> 74 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 74 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 74 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 74 -> 56 [label="CLOSE:1.0"]; 74 -> 95 [label="CLOSECONNECTION:1.0"]; 74 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 74 -> 59 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 74 -> 95 [label="LISTEN:1.0"]; 74 -> 95 [label="RCV:1.0"]; 74 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 74 -> 95 [label="SYN_p_V_c_V_c_0_p:1.0"]; 74 -> 93 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 21 -> 55 [label="ACCEPT:1.0"]; 21 -> 48 [label="ACK_p_V_c_V_c_0_p:1.0"]; 21 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 21 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 21 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 21 -> 147 [label="CLOSE:1.0"]; 21 -> 137 [label="CLOSECONNECTION:1.0"]; 21 -> 40 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 21 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 21 -> 55 [label="LISTEN:1.0"]; 21 -> 55 [label="RCV:1.0"]; 21 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 21 -> 131 [label="SYN_p_V_c_V_c_0_p:1.0"]; 21 -> 58 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 42 -> 42 [label="ACCEPT:1.0"]; 42 -> 42 [label="ACK_p_V_c_V_c_0_p:1.0"]; 42 -> 42 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 42 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 42 -> 147 [label="CLOSE:1.0"]; 42 -> 144 [label="CLOSECONNECTION:1.0"]; 42 -> 42 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 42 -> 42 [label="LISTEN:1.0"]; 42 -> 42 [label="RCV:1.0"]; 42 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 42 -> 135 [label="SYN_p_V_c_V_c_0_p:1.0"]; 42 -> 43 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 108 -> 108 [label="ACCEPT:1.0"]; 108 -> 108 [label="ACK_p_V_c_V_c_0_p:1.0"]; 108 -> 58 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 108 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 108 -> 23 [label="CLOSE:1.0"]; 108 -> 105 [label="CLOSECONNECTION:1.0"]; 108 -> 151 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 108 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 108 -> 108 [label="LISTEN:1.0"]; 108 -> 108 [label="RCV:1.0"]; 108 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 108 -> 131 [label="SYN_p_V_c_V_c_0_p:1.0"]; 108 -> 58 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 135 -> 94 [label="ACCEPT:1.0"]; 135 -> 20 [label="ACK_p_V_c_V_c_0_p:1.0"]; 135 -> 20 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 135 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 135 -> 66 [label="CLOSE:1.0"]; 135 -> 87 [label="CLOSECONNECTION:1.0"]; 135 -> 20 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 135 -> 94 [label="LISTEN:1.0"]; 135 -> 94 [label="RCV:1.0"]; 135 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 135 -> 78 [label="SYN_p_V_c_V_c_0_p:1.0"]; 135 -> 20 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 32 -> 0 [label="ACCEPT:1.0"]; 32 -> 121 [label="ACK_p_V_c_V_c_0_p:1.0"]; 32 -> 121 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 32 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 32 -> 66 [label="CLOSE:1.0"]; 32 -> 73 [label="CLOSECONNECTION:1.0"]; 32 -> 121 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 32 -> 73 [label="LISTEN:1.0"]; 32 -> 73 [label="RCV:1.0"]; 32 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 32 -> 148 [label="SYN_p_V_c_V_c_0_p:1.0"]; 32 -> 121 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 149 -> 149 [label="ACCEPT:1.0"]; 149 -> 149 [label="ACK_p_V_c_V_c_0_p:1.0"]; 149 -> 149 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 149 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 149 -> 56 [label="CLOSE:1.0"]; 149 -> 80 [label="CLOSECONNECTION:1.0"]; 149 -> 149 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 149 -> 149 [label="LISTEN:1.0"]; 149 -> 149 [label="RCV:1.0"]; 149 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 149 -> 149 [label="SYN_p_V_c_V_c_0_p:1.0"]; 149 -> 130 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 96 -> 35 [label="ACCEPT:1.0"]; 96 -> 23 [label="ACK_p_V_c_V_c_0_p:1.0"]; 96 -> 47 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 96 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 96 -> 35 [label="CLOSE:1.0"]; 96 -> 35 [label="CLOSECONNECTION:1.0"]; 96 -> 112 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 96 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 96 -> 35 [label="LISTEN:1.0"]; 96 -> 35 [label="RCV:1.0"]; 96 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 96 -> 35 [label="SYN_p_V_c_V_c_0_p:1.0"]; 96 -> 96 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 33 -> 33 [label="ACCEPT:1.0"]; 33 -> 33 [label="ACK_p_V_c_V_c_0_p:1.0"]; 33 -> 33 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 33 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 33 -> 56 [label="CLOSE:1.0"]; 33 -> 65 [label="CLOSECONNECTION:1.0"]; 33 -> 33 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 33 -> 33 [label="LISTEN:1.0"]; 33 -> 33 [label="RCV:1.0"]; 33 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 33 -> 33 [label="SYN_p_V_c_V_c_0_p:1.0"]; 33 -> 128 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 0 -> 0 [label="ACCEPT:1.0"]; 0 -> 86 [label="ACK_p_V_c_V_c_0_p:1.0"]; 0 -> 86 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 0 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 0 -> 66 [label="CLOSE:1.0"]; 0 -> 73 [label="CLOSECONNECTION:1.0"]; 0 -> 86 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 0 -> 0 [label="LISTEN:1.0"]; 0 -> 0 [label="RCV:1.0"]; 0 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 0 -> 21 [label="SYN_p_V_c_V_c_0_p:1.0"]; 0 -> 86 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 30 -> 143 [label="ACCEPT:1.0"]; 30 -> 143 [label="ACK_p_V_c_V_c_0_p:1.0"]; 30 -> 143 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 30 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 30 -> 56 [label="CLOSE:1.0"]; 30 -> 109 [label="CLOSECONNECTION:1.0"]; 30 -> 143 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 30 -> 143 [label="LISTEN:1.0"]; 30 -> 143 [label="RCV:1.0"]; 30 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 30 -> 143 [label="SYN_p_V_c_V_c_0_p:1.0"]; 30 -> 30 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 124 -> 124 [label="ACCEPT:1.0"]; 124 -> 124 [label="ACK_p_V_c_V_c_0_p:1.0"]; 124 -> 102 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 124 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 124 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 124 -> 124 [label="CLOSE:1.0"]; 124 -> 56 [label="CLOSECONNECTION:1.0"]; 124 -> 17 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 124 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 124 -> 124 [label="LISTEN:1.0"]; 124 -> 69 [label="RCV:1.0"]; 124 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 124 -> 124 [label="SYN_p_V_c_V_c_0_p:1.0"]; 124 -> 77 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 41 -> 84 [label="ACCEPT:1.0"]; 41 -> 41 [label="ACK_p_V_c_V_c_0_p:1.0"]; 41 -> 64 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 41 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 41 -> 11 [label="CLOSE:1.0"]; 41 -> 41 [label="CLOSECONNECTION:1.0"]; 41 -> 64 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 41 -> 41 [label="LISTEN:1.0"]; 41 -> 41 [label="RCV:1.0"]; 41 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 41 -> 64 [label="SYN_p_V_c_V_c_0_p:1.0"]; 41 -> 64 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 5 -> 119 [label="ACCEPT:1.0"]; 5 -> 119 [label="ACK_p_V_c_V_c_0_p:1.0"]; 5 -> 133 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 5 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 5 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 5 -> 69 [label="CLOSE:1.0"]; 5 -> 39 [label="CLOSECONNECTION:1.0"]; 5 -> 40 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 5 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 5 -> 119 [label="LISTEN:1.0"]; 5 -> 119 [label="RCV:1.0"]; 5 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 5 -> 119 [label="SYN_p_V_c_V_c_0_p:1.0"]; 5 -> 5 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 66 -> 66 [label="ACCEPT:1.0"]; 66 -> 44 [label="ACK_p_V_c_V_c_0_p:1.0"]; 66 -> 44 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 66 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 66 -> 66 [label="CLOSE:1.0"]; 66 -> 66 [label="CLOSECONNECTION:1.0"]; 66 -> 44 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 66 -> 66 [label="LISTEN:1.0"]; 66 -> 66 [label="RCV:1.0"]; 66 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 66 -> 4 [label="SYN_p_V_c_V_c_0_p:1.0"]; 66 -> 44 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 71 -> 89 [label="ACCEPT:1.0"]; 71 -> 108 [label="ACK_p_V_c_V_c_0_p:1.0"]; 71 -> 58 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 71 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 71 -> 35 [label="CLOSE:1.0"]; 71 -> 122 [label="CLOSECONNECTION:1.0"]; 71 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 71 -> 151 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 71 -> 89 [label="LISTEN:1.0"]; 71 -> 89 [label="RCV:1.0"]; 71 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 71 -> 89 [label="SYN_p_V_c_V_c_0_p:1.0"]; 71 -> 71 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 38 -> 113 [label="ACCEPT:1.0"]; 38 -> 109 [label="ACK_p_V_c_V_c_0_p:1.0"]; 38 -> 109 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 38 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 38 -> 56 [label="CLOSE:1.0"]; 38 -> 109 [label="CLOSECONNECTION:1.0"]; 38 -> 109 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 38 -> 109 [label="LISTEN:1.0"]; 38 -> 109 [label="RCV:1.0"]; 38 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 38 -> 109 [label="SYN_p_V_c_V_c_0_p:1.0"]; 38 -> 38 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 40 -> 136 [label="ACCEPT:1.0"]; 40 -> 136 [label="ACK_p_V_c_V_c_0_p:1.0"]; 40 -> 136 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 40 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 40 -> 123 [label="CLOSE:1.0"]; 40 -> 51 [label="CLOSECONNECTION:1.0"]; 40 -> 136 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 40 -> 136 [label="LISTEN:1.0"]; 40 -> 136 [label="RCV:1.0"]; 40 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 40 -> 136 [label="SYN_p_V_c_V_c_0_p:1.0"]; 40 -> 24 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 13 -> 7 [label="ACCEPT:1.0"]; 13 -> 7 [label="ACK_p_V_c_V_c_0_p:1.0"]; 13 -> 50 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 13 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 13 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 13 -> 69 [label="CLOSE:1.0"]; 13 -> 155 [label="CLOSECONNECTION:1.0"]; 13 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 13 -> 140 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 13 -> 7 [label="LISTEN:1.0"]; 13 -> 7 [label="RCV:1.0"]; 13 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 13 -> 7 [label="SYN_p_V_c_V_c_0_p:1.0"]; 13 -> 13 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 152 -> 129 [label="ACCEPT:1.0"]; 152 -> 76 [label="ACK_p_V_c_V_c_0_p:1.0"]; 152 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 152 -> 74 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 152 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 152 -> 147 [label="CLOSE:1.0"]; 152 -> 79 [label="CLOSECONNECTION:1.0"]; 152 -> 49 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 152 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 152 -> 79 [label="LISTEN:1.0"]; 152 -> 79 [label="RCV:1.0"]; 152 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 152 -> 25 [label="SYN_p_V_c_V_c_0_p:1.0"]; 152 -> 15 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 153 -> 60 [label="ACCEPT:1.0"]; 153 -> 60 [label="ACK_p_V_c_V_c_0_p:1.0"]; 153 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 153 -> 101 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 153 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 153 -> 56 [label="CLOSE:1.0"]; 153 -> 76 [label="CLOSECONNECTION:1.0"]; 153 -> 52 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 153 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 153 -> 60 [label="LISTEN:1.0"]; 153 -> 60 [label="RCV:1.0"]; 153 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 153 -> 60 [label="SYN_p_V_c_V_c_0_p:1.0"]; 153 -> 153 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 57 -> 14 [label="ACCEPT:1.0"]; 57 -> 57 [label="ACK_p_V_c_V_c_0_p:1.0"]; 57 -> 57 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 57 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 57 -> 66 [label="CLOSE:1.0"]; 57 -> 87 [label="CLOSECONNECTION:1.0"]; 57 -> 57 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 57 -> 87 [label="LISTEN:1.0"]; 57 -> 87 [label="RCV:1.0"]; 57 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 57 -> 12 [label="SYN_p_V_c_V_c_0_p:1.0"]; 57 -> 57 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 78 -> 42 [label="ACCEPT:1.0"]; 78 -> 42 [label="ACK_p_V_c_V_c_0_p:1.0"]; 78 -> 42 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 78 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 78 -> 147 [label="CLOSE:1.0"]; 78 -> 144 [label="CLOSECONNECTION:1.0"]; 78 -> 42 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 78 -> 42 [label="LISTEN:1.0"]; 78 -> 42 [label="RCV:1.0"]; 78 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 78 -> 135 [label="SYN_p_V_c_V_c_0_p:1.0"]; 78 -> 43 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 82 -> 82 [label="ACCEPT:1.0"]; 82 -> 100 [label="ACK_p_V_c_V_c_0_p:1.0"]; 82 -> 100 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 82 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 82 -> 18 [label="CLOSE:1.0"]; 82 -> 27 [label="CLOSECONNECTION:1.0"]; 82 -> 100 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 82 -> 82 [label="LISTEN:1.0"]; 82 -> 82 [label="RCV:1.0"]; 82 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 82 -> 82 [label="SYN_p_V_c_V_c_0_p:1.0"]; 82 -> 3 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 31 -> 98 [label="ACCEPT:1.0"]; 31 -> 98 [label="ACK_p_V_c_V_c_0_p:1.0"]; 31 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 31 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 31 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 31 -> 124 [label="CLOSE:1.0"]; 31 -> 75 [label="CLOSECONNECTION:1.0"]; 31 -> 125 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 31 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 31 -> 98 [label="LISTEN:1.0"]; 31 -> 48 [label="RCV:1.0"]; 31 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 31 -> 98 [label="SYN_p_V_c_V_c_0_p:1.0"]; 31 -> 2 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 128 -> 33 [label="ACCEPT:1.0"]; 128 -> 33 [label="ACK_p_V_c_V_c_0_p:1.0"]; 128 -> 33 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 128 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 128 -> 56 [label="CLOSE:1.0"]; 128 -> 65 [label="CLOSECONNECTION:1.0"]; 128 -> 33 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 128 -> 33 [label="LISTEN:1.0"]; 128 -> 33 [label="RCV:1.0"]; 128 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 128 -> 33 [label="SYN_p_V_c_V_c_0_p:1.0"]; 128 -> 128 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 65 -> 149 [label="ACCEPT:1.0"]; 65 -> 65 [label="ACK_p_V_c_V_c_0_p:1.0"]; 65 -> 65 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 65 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 65 -> 56 [label="CLOSE:1.0"]; 65 -> 65 [label="CLOSECONNECTION:1.0"]; 65 -> 65 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 65 -> 65 [label="LISTEN:1.0"]; 65 -> 65 [label="RCV:1.0"]; 65 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 65 -> 65 [label="SYN_p_V_c_V_c_0_p:1.0"]; 65 -> 110 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 12 -> 150 [label="ACCEPT:1.0"]; 12 -> 144 [label="ACK_p_V_c_V_c_0_p:1.0"]; 12 -> 144 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 12 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 12 -> 147 [label="CLOSE:1.0"]; 12 -> 144 [label="CLOSECONNECTION:1.0"]; 12 -> 144 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 12 -> 144 [label="LISTEN:1.0"]; 12 -> 144 [label="RCV:1.0"]; 12 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 12 -> 54 [label="SYN_p_V_c_V_c_0_p:1.0"]; 12 -> 104 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 58 -> 0 [label="ACCEPT:1.0"]; 58 -> 86 [label="ACK_p_V_c_V_c_0_p:1.0"]; 58 -> 86 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 58 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 58 -> 66 [label="CLOSE:1.0"]; 58 -> 73 [label="CLOSECONNECTION:1.0"]; 58 -> 86 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 58 -> 0 [label="LISTEN:1.0"]; 58 -> 0 [label="RCV:1.0"]; 58 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 58 -> 21 [label="SYN_p_V_c_V_c_0_p:1.0"]; 58 -> 86 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 123 -> 123 [label="ACCEPT:1.0"]; 123 -> 123 [label="ACK_p_V_c_V_c_0_p:1.0"]; 123 -> 123 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 123 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 123 -> 123 [label="CLOSE:1.0"]; 123 -> 154 [label="CLOSECONNECTION:1.0"]; 123 -> 123 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 123 -> 123 [label="LISTEN:1.0"]; 123 -> 123 [label="RCV:1.0"]; 123 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 123 -> 123 [label="SYN_p_V_c_V_c_0_p:1.0"]; 123 -> 83 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 7 -> 7 [label="ACCEPT:1.0"]; 7 -> 7 [label="ACK_p_V_c_V_c_0_p:1.0"]; 7 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 7 -> 50 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 7 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 7 -> 69 [label="CLOSE:1.0"]; 7 -> 155 [label="CLOSECONNECTION:1.0"]; 7 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 7 -> 140 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 7 -> 7 [label="LISTEN:1.0"]; 7 -> 7 [label="RCV:1.0"]; 7 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 7 -> 7 [label="SYN_p_V_c_V_c_0_p:1.0"]; 7 -> 13 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 54 -> 14 [label="ACCEPT:1.0"]; 54 -> 57 [label="ACK_p_V_c_V_c_0_p:1.0"]; 54 -> 57 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 54 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 54 -> 66 [label="CLOSE:1.0"]; 54 -> 87 [label="CLOSECONNECTION:1.0"]; 54 -> 57 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 54 -> 87 [label="LISTEN:1.0"]; 54 -> 87 [label="RCV:1.0"]; 54 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 54 -> 12 [label="SYN_p_V_c_V_c_0_p:1.0"]; 54 -> 57 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 73 -> 0 [label="ACCEPT:1.0"]; 73 -> 121 [label="ACK_p_V_c_V_c_0_p:1.0"]; 73 -> 121 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 73 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 73 -> 66 [label="CLOSE:1.0"]; 73 -> 73 [label="CLOSECONNECTION:1.0"]; 73 -> 121 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 73 -> 73 [label="LISTEN:1.0"]; 73 -> 73 [label="RCV:1.0"]; 73 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 73 -> 148 [label="SYN_p_V_c_V_c_0_p:1.0"]; 73 -> 121 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 37 -> 1 [label="ACCEPT:1.0"]; 37 -> 1 [label="ACK_p_V_c_V_c_0_p:1.0"]; 37 -> 1 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 37 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 37 -> 6 [label="CLOSE:1.0"]; 37 -> 75 [label="CLOSECONNECTION:1.0"]; 37 -> 1 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 37 -> 1 [label="LISTEN:1.0"]; 37 -> 136 [label="RCV:1.0"]; 37 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 37 -> 1 [label="SYN_p_V_c_V_c_0_p:1.0"]; 37 -> 37 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 47 -> 66 [label="ACCEPT:1.0"]; 47 -> 44 [label="ACK_p_V_c_V_c_0_p:1.0"]; 47 -> 44 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 47 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 47 -> 66 [label="CLOSE:1.0"]; 47 -> 66 [label="CLOSECONNECTION:1.0"]; 47 -> 44 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 47 -> 66 [label="LISTEN:1.0"]; 47 -> 66 [label="RCV:1.0"]; 47 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 47 -> 4 [label="SYN_p_V_c_V_c_0_p:1.0"]; 47 -> 44 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 98 -> 98 [label="ACCEPT:1.0"]; 98 -> 98 [label="ACK_p_V_c_V_c_0_p:1.0"]; 98 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 98 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 98 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 98 -> 124 [label="CLOSE:1.0"]; 98 -> 75 [label="CLOSECONNECTION:1.0"]; 98 -> 125 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 98 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 98 -> 98 [label="LISTEN:1.0"]; 98 -> 48 [label="RCV:1.0"]; 98 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 98 -> 98 [label="SYN_p_V_c_V_c_0_p:1.0"]; 98 -> 2 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 48 -> 48 [label="ACCEPT:1.0"]; 48 -> 48 [label="ACK_p_V_c_V_c_0_p:1.0"]; 48 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 48 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 48 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 48 -> 69 [label="CLOSE:1.0"]; 48 -> 39 [label="CLOSECONNECTION:1.0"]; 48 -> 40 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 48 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 48 -> 48 [label="LISTEN:1.0"]; 48 -> 119 [label="RCV:1.0"]; 48 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 48 -> 48 [label="SYN_p_V_c_V_c_0_p:1.0"]; 48 -> 91 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 101 -> 10 [label="ACCEPT:1.0"]; 101 -> 10 [label="ACK_p_V_c_V_c_0_p:1.0"]; 101 -> 101 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 101 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 101 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 101 -> 56 [label="CLOSE:1.0"]; 101 -> 95 [label="CLOSECONNECTION:1.0"]; 101 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 101 -> 53 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 101 -> 10 [label="LISTEN:1.0"]; 101 -> 10 [label="RCV:1.0"]; 101 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 101 -> 10 [label="SYN_p_V_c_V_c_0_p:1.0"]; 101 -> 142 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 106 -> 100 [label="ACCEPT:1.0"]; 106 -> 32 [label="ACK_p_V_c_V_c_0_p:1.0"]; 106 -> 32 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 106 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 106 -> 147 [label="CLOSE:1.0"]; 106 -> 106 [label="CLOSECONNECTION:1.0"]; 106 -> 32 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 106 -> 106 [label="LISTEN:1.0"]; 106 -> 106 [label="RCV:1.0"]; 106 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 106 -> 148 [label="SYN_p_V_c_V_c_0_p:1.0"]; 106 -> 32 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 134 -> 136 [label="ACCEPT:1.0"]; 134 -> 80 [label="ACK_p_V_c_V_c_0_p:1.0"]; 134 -> 80 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 134 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 134 -> 56 [label="CLOSE:1.0"]; 134 -> 80 [label="CLOSECONNECTION:1.0"]; 134 -> 80 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 134 -> 80 [label="LISTEN:1.0"]; 134 -> 80 [label="RCV:1.0"]; 134 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 134 -> 80 [label="SYN_p_V_c_V_c_0_p:1.0"]; 134 -> 141 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 23 -> 23 [label="ACCEPT:1.0"]; 23 -> 23 [label="ACK_p_V_c_V_c_0_p:1.0"]; 23 -> 47 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 23 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 23 -> 23 [label="CLOSE:1.0"]; 23 -> 23 [label="CLOSECONNECTION:1.0"]; 23 -> 112 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 23 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 23 -> 23 [label="LISTEN:1.0"]; 23 -> 23 [label="RCV:1.0"]; 23 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 23 -> 4 [label="SYN_p_V_c_V_c_0_p:1.0"]; 23 -> 47 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 89 -> 89 [label="ACCEPT:1.0"]; 89 -> 108 [label="ACK_p_V_c_V_c_0_p:1.0"]; 89 -> 58 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 89 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 89 -> 35 [label="CLOSE:1.0"]; 89 -> 122 [label="CLOSECONNECTION:1.0"]; 89 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 89 -> 151 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 89 -> 89 [label="LISTEN:1.0"]; 89 -> 89 [label="RCV:1.0"]; 89 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 89 -> 89 [label="SYN_p_V_c_V_c_0_p:1.0"]; 89 -> 71 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 125 -> 1 [label="ACCEPT:1.0"]; 125 -> 1 [label="ACK_p_V_c_V_c_0_p:1.0"]; 125 -> 1 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 125 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 125 -> 6 [label="CLOSE:1.0"]; 125 -> 75 [label="CLOSECONNECTION:1.0"]; 125 -> 1 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 125 -> 1 [label="LISTEN:1.0"]; 125 -> 136 [label="RCV:1.0"]; 125 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 125 -> 1 [label="SYN_p_V_c_V_c_0_p:1.0"]; 125 -> 37 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 147 -> 147 [label="ACCEPT:1.0"]; 147 -> 47 [label="ACK_p_V_c_V_c_0_p:1.0"]; 147 -> 47 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 147 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 147 -> 147 [label="CLOSE:1.0"]; 147 -> 147 [label="CLOSECONNECTION:1.0"]; 147 -> 47 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 147 -> 147 [label="LISTEN:1.0"]; 147 -> 147 [label="RCV:1.0"]; 147 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 147 -> 4 [label="SYN_p_V_c_V_c_0_p:1.0"]; 147 -> 47 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 92 -> 149 [label="ACCEPT:1.0"]; 92 -> 149 [label="ACK_p_V_c_V_c_0_p:1.0"]; 92 -> 149 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 92 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 92 -> 56 [label="CLOSE:1.0"]; 92 -> 80 [label="CLOSECONNECTION:1.0"]; 92 -> 149 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 92 -> 149 [label="LISTEN:1.0"]; 92 -> 149 [label="RCV:1.0"]; 92 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 92 -> 149 [label="SYN_p_V_c_V_c_0_p:1.0"]; 92 -> 130 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 3 -> 82 [label="ACCEPT:1.0"]; 3 -> 100 [label="ACK_p_V_c_V_c_0_p:1.0"]; 3 -> 100 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 3 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 3 -> 18 [label="CLOSE:1.0"]; 3 -> 27 [label="CLOSECONNECTION:1.0"]; 3 -> 100 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 3 -> 82 [label="LISTEN:1.0"]; 3 -> 82 [label="RCV:1.0"]; 3 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 3 -> 82 [label="SYN_p_V_c_V_c_0_p:1.0"]; 3 -> 3 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 59 -> 113 [label="ACCEPT:1.0"]; 59 -> 109 [label="ACK_p_V_c_V_c_0_p:1.0"]; 59 -> 109 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 59 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 59 -> 56 [label="CLOSE:1.0"]; 59 -> 109 [label="CLOSECONNECTION:1.0"]; 59 -> 109 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 59 -> 109 [label="LISTEN:1.0"]; 59 -> 109 [label="RCV:1.0"]; 59 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 59 -> 109 [label="SYN_p_V_c_V_c_0_p:1.0"]; 59 -> 38 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 97 -> 97 [label="ACCEPT:1.0"]; 97 -> 70 [label="ACK_p_V_c_V_c_0_p:1.0"]; 97 -> 70 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 97 -> 97 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 97 -> 66 [label="CLOSE:1.0"]; 97 -> 97 [label="CLOSECONNECTION:1.0"]; 97 -> 70 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 97 -> 73 [label="LISTEN:1.0"]; 97 -> 97 [label="RCV:1.0"]; 97 -> 97 [label="RST_p_V_c_V_c_0_p:1.0"]; 97 -> 34 [label="SYN_p_V_c_V_c_0_p:1.0"]; 97 -> 70 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 26 -> 113 [label="ACCEPT:1.0"]; 26 -> 113 [label="ACK_p_V_c_V_c_0_p:1.0"]; 26 -> 113 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 26 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 26 -> 56 [label="CLOSE:1.0"]; 26 -> 61 [label="CLOSECONNECTION:1.0"]; 26 -> 113 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 26 -> 113 [label="LISTEN:1.0"]; 26 -> 113 [label="RCV:1.0"]; 26 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 26 -> 113 [label="SYN_p_V_c_V_c_0_p:1.0"]; 26 -> 81 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 88 -> 103 [label="ACCEPT:1.0"]; 88 -> 145 [label="ACK_p_V_c_V_c_0_p:1.0"]; 88 -> 145 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 88 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 88 -> 66 [label="CLOSE:1.0"]; 88 -> 73 [label="CLOSECONNECTION:1.0"]; 88 -> 145 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 88 -> 103 [label="LISTEN:1.0"]; 88 -> 103 [label="RCV:1.0"]; 88 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 88 -> 22 [label="SYN_p_V_c_V_c_0_p:1.0"]; 88 -> 145 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 122 -> 89 [label="ACCEPT:1.0"]; 122 -> 105 [label="ACK_p_V_c_V_c_0_p:1.0"]; 122 -> 32 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 122 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 122 -> 35 [label="CLOSE:1.0"]; 122 -> 122 [label="CLOSECONNECTION:1.0"]; 122 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 122 -> 46 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 122 -> 122 [label="LISTEN:1.0"]; 122 -> 122 [label="RCV:1.0"]; 122 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 122 -> 122 [label="SYN_p_V_c_V_c_0_p:1.0"]; 122 -> 67 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 17 -> 6 [label="ACCEPT:1.0"]; 17 -> 6 [label="ACK_p_V_c_V_c_0_p:1.0"]; 17 -> 6 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 17 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 17 -> 6 [label="CLOSE:1.0"]; 17 -> 56 [label="CLOSECONNECTION:1.0"]; 17 -> 6 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 17 -> 6 [label="LISTEN:1.0"]; 17 -> 123 [label="RCV:1.0"]; 17 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 17 -> 6 [label="SYN_p_V_c_V_c_0_p:1.0"]; 17 -> 138 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 6 -> 6 [label="ACCEPT:1.0"]; 6 -> 6 [label="ACK_p_V_c_V_c_0_p:1.0"]; 6 -> 6 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 6 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 6 -> 6 [label="CLOSE:1.0"]; 6 -> 56 [label="CLOSECONNECTION:1.0"]; 6 -> 6 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 6 -> 6 [label="LISTEN:1.0"]; 6 -> 123 [label="RCV:1.0"]; 6 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 6 -> 6 [label="SYN_p_V_c_V_c_0_p:1.0"]; 6 -> 138 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 105 -> 108 [label="ACCEPT:1.0"]; 105 -> 105 [label="ACK_p_V_c_V_c_0_p:1.0"]; 105 -> 32 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 105 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 105 -> 23 [label="CLOSE:1.0"]; 105 -> 105 [label="CLOSECONNECTION:1.0"]; 105 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 105 -> 46 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 105 -> 105 [label="LISTEN:1.0"]; 105 -> 105 [label="RCV:1.0"]; 105 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 105 -> 139 [label="SYN_p_V_c_V_c_0_p:1.0"]; 105 -> 32 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 50 -> 69 [label="ACCEPT:1.0"]; 50 -> 69 [label="ACK_p_V_c_V_c_0_p:1.0"]; 50 -> 102 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 50 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 50 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 50 -> 69 [label="CLOSE:1.0"]; 50 -> 155 [label="CLOSECONNECTION:1.0"]; 50 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 50 -> 140 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 50 -> 69 [label="LISTEN:1.0"]; 50 -> 7 [label="RCV:1.0"]; 50 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 50 -> 69 [label="SYN_p_V_c_V_c_0_p:1.0"]; 50 -> 107 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 72 -> 103 [label="ACCEPT:1.0"]; 72 -> 146 [label="ACK_p_V_c_V_c_0_p:1.0"]; 72 -> 146 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 72 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 72 -> 66 [label="CLOSE:1.0"]; 72 -> 72 [label="CLOSECONNECTION:1.0"]; 72 -> 146 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 72 -> 72 [label="LISTEN:1.0"]; 72 -> 72 [label="RCV:1.0"]; 72 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 72 -> 152 [label="SYN_p_V_c_V_c_0_p:1.0"]; 72 -> 146 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 15 -> 103 [label="ACCEPT:1.0"]; 15 -> 146 [label="ACK_p_V_c_V_c_0_p:1.0"]; 15 -> 146 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 15 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 15 -> 66 [label="CLOSE:1.0"]; 15 -> 72 [label="CLOSECONNECTION:1.0"]; 15 -> 146 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 15 -> 72 [label="LISTEN:1.0"]; 15 -> 72 [label="RCV:1.0"]; 15 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 15 -> 152 [label="SYN_p_V_c_V_c_0_p:1.0"]; 15 -> 146 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 28 -> 103 [label="ACCEPT:1.0"]; 28 -> 145 [label="ACK_p_V_c_V_c_0_p:1.0"]; 28 -> 145 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 28 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 28 -> 66 [label="CLOSE:1.0"]; 28 -> 73 [label="CLOSECONNECTION:1.0"]; 28 -> 145 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 28 -> 103 [label="LISTEN:1.0"]; 28 -> 103 [label="RCV:1.0"]; 28 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 28 -> 22 [label="SYN_p_V_c_V_c_0_p:1.0"]; 28 -> 145 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 111 -> 14 [label="ACCEPT:1.0"]; 111 -> 63 [label="ACK_p_V_c_V_c_0_p:1.0"]; 111 -> 63 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 111 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 111 -> 66 [label="CLOSE:1.0"]; 111 -> 72 [label="CLOSECONNECTION:1.0"]; 111 -> 63 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 111 -> 14 [label="LISTEN:1.0"]; 111 -> 14 [label="RCV:1.0"]; 111 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 111 -> 29 [label="SYN_p_V_c_V_c_0_p:1.0"]; 111 -> 63 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 14 -> 14 [label="ACCEPT:1.0"]; 14 -> 63 [label="ACK_p_V_c_V_c_0_p:1.0"]; 14 -> 63 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 14 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 14 -> 66 [label="CLOSE:1.0"]; 14 -> 72 [label="CLOSECONNECTION:1.0"]; 14 -> 63 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 14 -> 14 [label="LISTEN:1.0"]; 14 -> 14 [label="RCV:1.0"]; 14 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 14 -> 29 [label="SYN_p_V_c_V_c_0_p:1.0"]; 14 -> 63 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 75 -> 0 [label="ACCEPT:1.0"]; 75 -> 121 [label="ACK_p_V_c_V_c_0_p:1.0"]; 75 -> 121 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 75 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 75 -> 66 [label="CLOSE:1.0"]; 75 -> 73 [label="CLOSECONNECTION:1.0"]; 75 -> 121 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 75 -> 73 [label="LISTEN:1.0"]; 75 -> 73 [label="RCV:1.0"]; 75 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 75 -> 148 [label="SYN_p_V_c_V_c_0_p:1.0"]; 75 -> 121 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 113 -> 113 [label="ACCEPT:1.0"]; 113 -> 113 [label="ACK_p_V_c_V_c_0_p:1.0"]; 113 -> 113 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 113 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 113 -> 56 [label="CLOSE:1.0"]; 113 -> 61 [label="CLOSECONNECTION:1.0"]; 113 -> 113 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 113 -> 113 [label="LISTEN:1.0"]; 113 -> 113 [label="RCV:1.0"]; 113 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 113 -> 113 [label="SYN_p_V_c_V_c_0_p:1.0"]; 113 -> 81 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 80 -> 136 [label="ACCEPT:1.0"]; 80 -> 80 [label="ACK_p_V_c_V_c_0_p:1.0"]; 80 -> 80 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 80 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 80 -> 56 [label="CLOSE:1.0"]; 80 -> 80 [label="CLOSECONNECTION:1.0"]; 80 -> 80 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 80 -> 80 [label="LISTEN:1.0"]; 80 -> 80 [label="RCV:1.0"]; 80 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 80 -> 80 [label="SYN_p_V_c_V_c_0_p:1.0"]; 80 -> 141 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 77 -> 124 [label="ACCEPT:1.0"]; 77 -> 124 [label="ACK_p_V_c_V_c_0_p:1.0"]; 77 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 77 -> 102 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 77 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 77 -> 124 [label="CLOSE:1.0"]; 77 -> 56 [label="CLOSECONNECTION:1.0"]; 77 -> 17 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 77 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 77 -> 124 [label="LISTEN:1.0"]; 77 -> 69 [label="RCV:1.0"]; 77 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 77 -> 124 [label="SYN_p_V_c_V_c_0_p:1.0"]; 77 -> 77 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 133 -> 48 [label="ACCEPT:1.0"]; 133 -> 48 [label="ACK_p_V_c_V_c_0_p:1.0"]; 133 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 133 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 133 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 133 -> 69 [label="CLOSE:1.0"]; 133 -> 39 [label="CLOSECONNECTION:1.0"]; 133 -> 40 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 133 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 133 -> 48 [label="LISTEN:1.0"]; 133 -> 119 [label="RCV:1.0"]; 133 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 133 -> 48 [label="SYN_p_V_c_V_c_0_p:1.0"]; 133 -> 91 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 51 -> 82 [label="ACCEPT:1.0"]; 51 -> 106 [label="ACK_p_V_c_V_c_0_p:1.0"]; 51 -> 106 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 51 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 51 -> 18 [label="CLOSE:1.0"]; 51 -> 27 [label="CLOSECONNECTION:1.0"]; 51 -> 106 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 51 -> 27 [label="LISTEN:1.0"]; 51 -> 27 [label="RCV:1.0"]; 51 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 51 -> 27 [label="SYN_p_V_c_V_c_0_p:1.0"]; 51 -> 126 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 110 -> 149 [label="ACCEPT:1.0"]; 110 -> 65 [label="ACK_p_V_c_V_c_0_p:1.0"]; 110 -> 65 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 110 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 110 -> 56 [label="CLOSE:1.0"]; 110 -> 65 [label="CLOSECONNECTION:1.0"]; 110 -> 65 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 110 -> 65 [label="LISTEN:1.0"]; 110 -> 65 [label="RCV:1.0"]; 110 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 110 -> 65 [label="SYN_p_V_c_V_c_0_p:1.0"]; 110 -> 110 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 55 -> 55 [label="ACCEPT:1.0"]; 55 -> 48 [label="ACK_p_V_c_V_c_0_p:1.0"]; 55 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 55 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 55 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 55 -> 147 [label="CLOSE:1.0"]; 55 -> 137 [label="CLOSECONNECTION:1.0"]; 55 -> 40 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 55 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 55 -> 55 [label="LISTEN:1.0"]; 55 -> 55 [label="RCV:1.0"]; 55 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 55 -> 131 [label="SYN_p_V_c_V_c_0_p:1.0"]; 55 -> 58 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 146 -> 103 [label="ACCEPT:1.0"]; 146 -> 146 [label="ACK_p_V_c_V_c_0_p:1.0"]; 146 -> 146 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 146 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 146 -> 66 [label="CLOSE:1.0"]; 146 -> 72 [label="CLOSECONNECTION:1.0"]; 146 -> 146 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 146 -> 72 [label="LISTEN:1.0"]; 146 -> 72 [label="RCV:1.0"]; 146 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 146 -> 152 [label="SYN_p_V_c_V_c_0_p:1.0"]; 146 -> 146 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 121 -> 0 [label="ACCEPT:1.0"]; 121 -> 121 [label="ACK_p_V_c_V_c_0_p:1.0"]; 121 -> 121 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 121 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 121 -> 66 [label="CLOSE:1.0"]; 121 -> 73 [label="CLOSECONNECTION:1.0"]; 121 -> 121 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 121 -> 73 [label="LISTEN:1.0"]; 121 -> 73 [label="RCV:1.0"]; 121 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 121 -> 148 [label="SYN_p_V_c_V_c_0_p:1.0"]; 121 -> 121 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 118 -> 62 [label="ACCEPT:1.0"]; 118 -> 62 [label="ACK_p_V_c_V_c_0_p:1.0"]; 118 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 118 -> 45 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 118 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 118 -> 56 [label="CLOSE:1.0"]; 118 -> 36 [label="CLOSECONNECTION:1.0"]; 118 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 118 -> 26 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 118 -> 62 [label="LISTEN:1.0"]; 118 -> 62 [label="RCV:1.0"]; 118 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 118 -> 62 [label="SYN_p_V_c_V_c_0_p:1.0"]; 118 -> 118 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 129 -> 129 [label="ACCEPT:1.0"]; 129 -> 9 [label="ACK_p_V_c_V_c_0_p:1.0"]; 129 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 129 -> 45 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 129 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 129 -> 147 [label="CLOSE:1.0"]; 129 -> 137 [label="CLOSECONNECTION:1.0"]; 129 -> 92 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 129 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 129 -> 129 [label="LISTEN:1.0"]; 129 -> 129 [label="RCV:1.0"]; 129 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 129 -> 28 [label="SYN_p_V_c_V_c_0_p:1.0"]; 129 -> 88 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 2 -> 98 [label="ACCEPT:1.0"]; 2 -> 98 [label="ACK_p_V_c_V_c_0_p:1.0"]; 2 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 2 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 2 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 2 -> 124 [label="CLOSE:1.0"]; 2 -> 75 [label="CLOSECONNECTION:1.0"]; 2 -> 125 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 2 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 2 -> 98 [label="LISTEN:1.0"]; 2 -> 48 [label="RCV:1.0"]; 2 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 2 -> 98 [label="SYN_p_V_c_V_c_0_p:1.0"]; 2 -> 2 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 93 -> 62 [label="ACCEPT:1.0"]; 93 -> 95 [label="ACK_p_V_c_V_c_0_p:1.0"]; 93 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 93 -> 74 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 93 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 93 -> 56 [label="CLOSE:1.0"]; 93 -> 95 [label="CLOSECONNECTION:1.0"]; 93 -> 59 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 93 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 93 -> 95 [label="LISTEN:1.0"]; 93 -> 95 [label="RCV:1.0"]; 93 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 93 -> 95 [label="SYN_p_V_c_V_c_0_p:1.0"]; 93 -> 93 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 4 -> 66 [label="ACCEPT:1.0"]; 4 -> 44 [label="ACK_p_V_c_V_c_0_p:1.0"]; 4 -> 44 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 4 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 4 -> 66 [label="CLOSE:1.0"]; 4 -> 66 [label="CLOSECONNECTION:1.0"]; 4 -> 44 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 4 -> 66 [label="LISTEN:1.0"]; 4 -> 66 [label="RCV:1.0"]; 4 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 4 -> 4 [label="SYN_p_V_c_V_c_0_p:1.0"]; 4 -> 44 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 126 -> 82 [label="ACCEPT:1.0"]; 126 -> 106 [label="ACK_p_V_c_V_c_0_p:1.0"]; 126 -> 106 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 126 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 126 -> 18 [label="CLOSE:1.0"]; 126 -> 27 [label="CLOSECONNECTION:1.0"]; 126 -> 106 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 126 -> 27 [label="LISTEN:1.0"]; 126 -> 27 [label="RCV:1.0"]; 126 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 126 -> 27 [label="SYN_p_V_c_V_c_0_p:1.0"]; 126 -> 126 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 137 -> 55 [label="ACCEPT:1.0"]; 137 -> 120 [label="ACK_p_V_c_V_c_0_p:1.0"]; 137 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 137 -> 85 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 137 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 137 -> 147 [label="CLOSE:1.0"]; 137 -> 137 [label="CLOSECONNECTION:1.0"]; 137 -> 134 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 137 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 137 -> 137 [label="LISTEN:1.0"]; 137 -> 137 [label="RCV:1.0"]; 137 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 137 -> 139 [label="SYN_p_V_c_V_c_0_p:1.0"]; 137 -> 32 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 45 -> 62 [label="ACCEPT:1.0"]; 45 -> 62 [label="ACK_p_V_c_V_c_0_p:1.0"]; 45 -> 45 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 45 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 45 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 45 -> 56 [label="CLOSE:1.0"]; 45 -> 36 [label="CLOSECONNECTION:1.0"]; 45 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 45 -> 26 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 45 -> 62 [label="LISTEN:1.0"]; 45 -> 62 [label="RCV:1.0"]; 45 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 45 -> 62 [label="SYN_p_V_c_V_c_0_p:1.0"]; 45 -> 118 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 68 -> 84 [label="ACCEPT:1.0"]; 68 -> 84 [label="ACK_p_V_c_V_c_0_p:1.0"]; 68 -> 68 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 68 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 68 -> 11 [label="CLOSE:1.0"]; 68 -> 41 [label="CLOSECONNECTION:1.0"]; 68 -> 68 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 68 -> 84 [label="LISTEN:1.0"]; 68 -> 84 [label="RCV:1.0"]; 68 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 68 -> 68 [label="SYN_p_V_c_V_c_0_p:1.0"]; 68 -> 68 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 79 -> 129 [label="ACCEPT:1.0"]; 79 -> 76 [label="ACK_p_V_c_V_c_0_p:1.0"]; 79 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 79 -> 74 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 79 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 79 -> 147 [label="CLOSE:1.0"]; 79 -> 79 [label="CLOSECONNECTION:1.0"]; 79 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 79 -> 49 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 79 -> 79 [label="LISTEN:1.0"]; 79 -> 79 [label="RCV:1.0"]; 79 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 79 -> 25 [label="SYN_p_V_c_V_c_0_p:1.0"]; 79 -> 15 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 63 -> 14 [label="ACCEPT:1.0"]; 63 -> 63 [label="ACK_p_V_c_V_c_0_p:1.0"]; 63 -> 63 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 63 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 63 -> 66 [label="CLOSE:1.0"]; 63 -> 72 [label="CLOSECONNECTION:1.0"]; 63 -> 63 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 63 -> 14 [label="LISTEN:1.0"]; 63 -> 14 [label="RCV:1.0"]; 63 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 63 -> 29 [label="SYN_p_V_c_V_c_0_p:1.0"]; 63 -> 63 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 142 -> 10 [label="ACCEPT:1.0"]; 142 -> 10 [label="ACK_p_V_c_V_c_0_p:1.0"]; 142 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 142 -> 101 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 142 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 142 -> 56 [label="CLOSE:1.0"]; 142 -> 95 [label="CLOSECONNECTION:1.0"]; 142 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 142 -> 53 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 142 -> 10 [label="LISTEN:1.0"]; 142 -> 10 [label="RCV:1.0"]; 142 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 142 -> 10 [label="SYN_p_V_c_V_c_0_p:1.0"]; 142 -> 142 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 87 -> 14 [label="ACCEPT:1.0"]; 87 -> 57 [label="ACK_p_V_c_V_c_0_p:1.0"]; 87 -> 57 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 87 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 87 -> 66 [label="CLOSE:1.0"]; 87 -> 87 [label="CLOSECONNECTION:1.0"]; 87 -> 57 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 87 -> 87 [label="LISTEN:1.0"]; 87 -> 87 [label="RCV:1.0"]; 87 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 87 -> 12 [label="SYN_p_V_c_V_c_0_p:1.0"]; 87 -> 57 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 120 -> 48 [label="ACCEPT:1.0"]; 120 -> 120 [label="ACK_p_V_c_V_c_0_p:1.0"]; 120 -> 85 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 120 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 120 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 120 -> 56 [label="CLOSE:1.0"]; 120 -> 120 [label="CLOSECONNECTION:1.0"]; 120 -> 134 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 120 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 120 -> 120 [label="LISTEN:1.0"]; 120 -> 120 [label="RCV:1.0"]; 120 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 120 -> 120 [label="SYN_p_V_c_V_c_0_p:1.0"]; 120 -> 117 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 117 -> 48 [label="ACCEPT:1.0"]; 117 -> 120 [label="ACK_p_V_c_V_c_0_p:1.0"]; 117 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 117 -> 85 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 117 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 117 -> 56 [label="CLOSE:1.0"]; 117 -> 120 [label="CLOSECONNECTION:1.0"]; 117 -> 134 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 117 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 117 -> 120 [label="LISTEN:1.0"]; 117 -> 120 [label="RCV:1.0"]; 117 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 117 -> 120 [label="SYN_p_V_c_V_c_0_p:1.0"]; 117 -> 117 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 139 -> 0 [label="ACCEPT:1.0"]; 139 -> 121 [label="ACK_p_V_c_V_c_0_p:1.0"]; 139 -> 121 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 139 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 139 -> 66 [label="CLOSE:1.0"]; 139 -> 73 [label="CLOSECONNECTION:1.0"]; 139 -> 121 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 139 -> 73 [label="LISTEN:1.0"]; 139 -> 73 [label="RCV:1.0"]; 139 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 139 -> 148 [label="SYN_p_V_c_V_c_0_p:1.0"]; 139 -> 121 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 127 -> 1 [label="ACCEPT:1.0"]; 127 -> 61 [label="ACK_p_V_c_V_c_0_p:1.0"]; 127 -> 61 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 127 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 127 -> 56 [label="CLOSE:1.0"]; 127 -> 61 [label="CLOSECONNECTION:1.0"]; 127 -> 61 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 127 -> 61 [label="LISTEN:1.0"]; 127 -> 61 [label="RCV:1.0"]; 127 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 127 -> 61 [label="SYN_p_V_c_V_c_0_p:1.0"]; 127 -> 114 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 95 -> 62 [label="ACCEPT:1.0"]; 95 -> 95 [label="ACK_p_V_c_V_c_0_p:1.0"]; 95 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 95 -> 74 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 95 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 95 -> 56 [label="CLOSE:1.0"]; 95 -> 95 [label="CLOSECONNECTION:1.0"]; 95 -> 59 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 95 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 95 -> 95 [label="LISTEN:1.0"]; 95 -> 95 [label="RCV:1.0"]; 95 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 95 -> 95 [label="SYN_p_V_c_V_c_0_p:1.0"]; 95 -> 93 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 62 -> 62 [label="ACCEPT:1.0"]; 62 -> 62 [label="ACK_p_V_c_V_c_0_p:1.0"]; 62 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 62 -> 45 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 62 -> 14 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 62 -> 56 [label="CLOSE:1.0"]; 62 -> 36 [label="CLOSECONNECTION:1.0"]; 62 -> 26 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 62 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 62 -> 62 [label="LISTEN:1.0"]; 62 -> 62 [label="RCV:1.0"]; 62 -> 14 [label="RST_p_V_c_V_c_0_p:1.0"]; 62 -> 62 [label="SYN_p_V_c_V_c_0_p:1.0"]; 62 -> 118 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 67 -> 89 [label="ACCEPT:1.0"]; 67 -> 105 [label="ACK_p_V_c_V_c_0_p:1.0"]; 67 -> 32 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 67 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 67 -> 35 [label="CLOSE:1.0"]; 67 -> 122 [label="CLOSECONNECTION:1.0"]; 67 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 67 -> 46 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 67 -> 122 [label="LISTEN:1.0"]; 67 -> 122 [label="RCV:1.0"]; 67 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 67 -> 122 [label="SYN_p_V_c_V_c_0_p:1.0"]; 67 -> 67 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 53 -> 143 [label="ACCEPT:1.0"]; 53 -> 143 [label="ACK_p_V_c_V_c_0_p:1.0"]; 53 -> 143 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 53 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 53 -> 56 [label="CLOSE:1.0"]; 53 -> 109 [label="CLOSECONNECTION:1.0"]; 53 -> 143 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 53 -> 143 [label="LISTEN:1.0"]; 53 -> 143 [label="RCV:1.0"]; 53 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 53 -> 143 [label="SYN_p_V_c_V_c_0_p:1.0"]; 53 -> 30 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 131 -> 0 [label="ACCEPT:1.0"]; 131 -> 86 [label="ACK_p_V_c_V_c_0_p:1.0"]; 131 -> 86 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 131 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 131 -> 66 [label="CLOSE:1.0"]; 131 -> 73 [label="CLOSECONNECTION:1.0"]; 131 -> 86 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 131 -> 0 [label="LISTEN:1.0"]; 131 -> 0 [label="RCV:1.0"]; 131 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 131 -> 21 [label="SYN_p_V_c_V_c_0_p:1.0"]; 131 -> 86 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 145 -> 103 [label="ACCEPT:1.0"]; 145 -> 145 [label="ACK_p_V_c_V_c_0_p:1.0"]; 145 -> 145 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 145 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 145 -> 66 [label="CLOSE:1.0"]; 145 -> 73 [label="CLOSECONNECTION:1.0"]; 145 -> 145 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 145 -> 103 [label="LISTEN:1.0"]; 145 -> 103 [label="RCV:1.0"]; 145 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 145 -> 22 [label="SYN_p_V_c_V_c_0_p:1.0"]; 145 -> 145 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 103 -> 103 [label="ACCEPT:1.0"]; 103 -> 145 [label="ACK_p_V_c_V_c_0_p:1.0"]; 103 -> 145 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 103 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 103 -> 66 [label="CLOSE:1.0"]; 103 -> 73 [label="CLOSECONNECTION:1.0"]; 103 -> 145 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 103 -> 103 [label="LISTEN:1.0"]; 103 -> 103 [label="RCV:1.0"]; 103 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 103 -> 22 [label="SYN_p_V_c_V_c_0_p:1.0"]; 103 -> 145 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 136 -> 136 [label="ACCEPT:1.0"]; 136 -> 136 [label="ACK_p_V_c_V_c_0_p:1.0"]; 136 -> 136 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 136 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 136 -> 123 [label="CLOSE:1.0"]; 136 -> 51 [label="CLOSECONNECTION:1.0"]; 136 -> 136 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 136 -> 136 [label="LISTEN:1.0"]; 136 -> 136 [label="RCV:1.0"]; 136 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 136 -> 136 [label="SYN_p_V_c_V_c_0_p:1.0"]; 136 -> 24 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 70 -> 97 [label="ACCEPT:1.0"]; 70 -> 70 [label="ACK_p_V_c_V_c_0_p:1.0"]; 70 -> 70 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 70 -> 97 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 70 -> 66 [label="CLOSE:1.0"]; 70 -> 97 [label="CLOSECONNECTION:1.0"]; 70 -> 70 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 70 -> 73 [label="LISTEN:1.0"]; 70 -> 97 [label="RCV:1.0"]; 70 -> 97 [label="RST_p_V_c_V_c_0_p:1.0"]; 70 -> 34 [label="SYN_p_V_c_V_c_0_p:1.0"]; 70 -> 70 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 83 -> 123 [label="ACCEPT:1.0"]; 83 -> 123 [label="ACK_p_V_c_V_c_0_p:1.0"]; 83 -> 123 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 83 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 83 -> 123 [label="CLOSE:1.0"]; 83 -> 154 [label="CLOSECONNECTION:1.0"]; 83 -> 123 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 83 -> 123 [label="LISTEN:1.0"]; 83 -> 123 [label="RCV:1.0"]; 83 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 83 -> 123 [label="SYN_p_V_c_V_c_0_p:1.0"]; 83 -> 83 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 144 -> 150 [label="ACCEPT:1.0"]; 144 -> 144 [label="ACK_p_V_c_V_c_0_p:1.0"]; 144 -> 144 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 144 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 144 -> 147 [label="CLOSE:1.0"]; 144 -> 144 [label="CLOSECONNECTION:1.0"]; 144 -> 144 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 144 -> 144 [label="LISTEN:1.0"]; 144 -> 144 [label="RCV:1.0"]; 144 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 144 -> 54 [label="SYN_p_V_c_V_c_0_p:1.0"]; 144 -> 104 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 141 -> 136 [label="ACCEPT:1.0"]; 141 -> 80 [label="ACK_p_V_c_V_c_0_p:1.0"]; 141 -> 80 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 141 -> 72 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 141 -> 56 [label="CLOSE:1.0"]; 141 -> 80 [label="CLOSECONNECTION:1.0"]; 141 -> 80 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 141 -> 80 [label="LISTEN:1.0"]; 141 -> 80 [label="RCV:1.0"]; 141 -> 72 [label="RST_p_V_c_V_c_0_p:1.0"]; 141 -> 80 [label="SYN_p_V_c_V_c_0_p:1.0"]; 141 -> 141 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 104 -> 14 [label="ACCEPT:1.0"]; 104 -> 57 [label="ACK_p_V_c_V_c_0_p:1.0"]; 104 -> 57 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 104 -> 87 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 104 -> 66 [label="CLOSE:1.0"]; 104 -> 87 [label="CLOSECONNECTION:1.0"]; 104 -> 57 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 104 -> 87 [label="LISTEN:1.0"]; 104 -> 87 [label="RCV:1.0"]; 104 -> 87 [label="RST_p_V_c_V_c_0_p:1.0"]; 104 -> 12 [label="SYN_p_V_c_V_c_0_p:1.0"]; 104 -> 57 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 102 -> 124 [label="ACCEPT:1.0"]; 102 -> 124 [label="ACK_p_V_c_V_c_0_p:1.0"]; 102 -> 102 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 102 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 102 -> 66 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 102 -> 124 [label="CLOSE:1.0"]; 102 -> 56 [label="CLOSECONNECTION:1.0"]; 102 -> 17 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 102 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 102 -> 124 [label="LISTEN:1.0"]; 102 -> 69 [label="RCV:1.0"]; 102 -> 66 [label="RST_p_V_c_V_c_0_p:1.0"]; 102 -> 124 [label="SYN_p_V_c_V_c_0_p:1.0"]; 102 -> 77 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 119 -> 119 [label="ACCEPT:1.0"]; 119 -> 119 [label="ACK_p_V_c_V_c_0_p:1.0"]; 119 -> 133 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 119 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 119 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 119 -> 69 [label="CLOSE:1.0"]; 119 -> 39 [label="CLOSECONNECTION:1.0"]; 119 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 119 -> 40 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 119 -> 119 [label="LISTEN:1.0"]; 119 -> 119 [label="RCV:1.0"]; 119 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 119 -> 119 [label="SYN_p_V_c_V_c_0_p:1.0"]; 119 -> 5 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 20 -> 94 [label="ACCEPT:1.0"]; 20 -> 20 [label="ACK_p_V_c_V_c_0_p:1.0"]; 20 -> 20 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 20 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 20 -> 66 [label="CLOSE:1.0"]; 20 -> 87 [label="CLOSECONNECTION:1.0"]; 20 -> 20 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 20 -> 94 [label="LISTEN:1.0"]; 20 -> 94 [label="RCV:1.0"]; 20 -> 94 [label="RST_p_V_c_V_c_0_p:1.0"]; 20 -> 78 [label="SYN_p_V_c_V_c_0_p:1.0"]; 20 -> 20 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 91 -> 48 [label="ACCEPT:1.0"]; 91 -> 48 [label="ACK_p_V_c_V_c_0_p:1.0"]; 91 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 91 -> 31 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 91 -> 103 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 91 -> 69 [label="CLOSE:1.0"]; 91 -> 39 [label="CLOSECONNECTION:1.0"]; 91 -> 40 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 91 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 91 -> 48 [label="LISTEN:1.0"]; 91 -> 119 [label="RCV:1.0"]; 91 -> 103 [label="RST_p_V_c_V_c_0_p:1.0"]; 91 -> 48 [label="SYN_p_V_c_V_c_0_p:1.0"]; 91 -> 91 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 148 -> 55 [label="ACCEPT:1.0"]; 148 -> 120 [label="ACK_p_V_c_V_c_0_p:1.0"]; 148 -> 116 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.1"]; 148 -> 85 [label="ACK_plus_PSH_p_V_c_V_c_1_p:0.9"]; 148 -> 73 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 148 -> 147 [label="CLOSE:1.0"]; 148 -> 137 [label="CLOSECONNECTION:1.0"]; 148 -> 134 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.9"]; 148 -> 116 [label="FIN_plus_ACK_p_V_c_V_c_0_p:0.1"]; 148 -> 137 [label="LISTEN:1.0"]; 148 -> 137 [label="RCV:1.0"]; 148 -> 73 [label="RST_p_V_c_V_c_0_p:1.0"]; 148 -> 139 [label="SYN_p_V_c_V_c_0_p:1.0"]; 148 -> 32 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 86 -> 0 [label="ACCEPT:1.0"]; 86 -> 86 [label="ACK_p_V_c_V_c_0_p:1.0"]; 86 -> 86 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 86 -> 0 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 86 -> 66 [label="CLOSE:1.0"]; 86 -> 73 [label="CLOSECONNECTION:1.0"]; 86 -> 86 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 86 -> 0 [label="LISTEN:1.0"]; 86 -> 0 [label="RCV:1.0"]; 86 -> 0 [label="RST_p_V_c_V_c_0_p:1.0"]; 86 -> 21 [label="SYN_p_V_c_V_c_0_p:1.0"]; 86 -> 86 [label="SYN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 94 -> 94 [label="ACCEPT:1.0"]; 94 -> 20 [label="ACK_p_V_c_V_c_0_p:1.0"]; 94 -> 20 [label="ACK_plus_PSH_p_V_c_V_c_1_p:1.0"]; 94 -> 94 [label="ACK_plus_RST_p_V_c_V_c_0_p:1.0"]; 94 -> 66 [label="CLOSE:1.0"]; 94 -> 87 [label="CLOSECONNECTION:1.0"]; 94 -> 20 [label="FIN_plus_ACK_p_V_c_V_c_0_p:1.0"]; 94 -> 94 [label="LISTEN:1.0"]; 94 -> 94 [label="RCV:1.0"]; 94 -> 94 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[label="DisconnectC1 / c1_ConnectionClosed__Empty"]; s12 -> s8 [label="ConnectC2 / c1_ConnectionClosed__c2_ConnectionClosed"]; s12 -> s15 [label="ConnectC1WithWill / c1_ConnAck__Empty"]; s12 -> s13 [label="ConnectC1WithWillRetain / c1_ConnAck__Empty"]; s12 -> s12 [label="DeleteRetainedC1 / c1_ConnectionClosed__Empty"]; s12 -> s4 [label="DeleteRetainedC2 / c1_ConnectionClosed__Pub(c2,my_topic,)__c2_PubAck"]; s12 -> s12 [label="SubscribeC2 / c1_ConnectionClosed__c2_SubAck__Pub(c2,my_topic,bye)"]; s12 -> s9 [label="UnSubScribeC2 / c1_ConnectionClosed__c2_UnSubAck"]; s12 -> s12 [label="DisconnectTCPC1 / c1_ConnectionClosed__Empty"]; s12 -> s12 [label="DisconnectC1 / c1_ConnectionClosed__Empty"]; s13 -> s16 [label="ConnectC2 / Empty__c2_ConnectionClosed"]; s13 -> s12 [label="ConnectC1WithWill / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s13 -> s12 [label="ConnectC1WithWillRetain / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s13 -> s5 [label="DeleteRetainedC1 / c1_PubAck__Pub(c2,my_topic,)"]; s13 -> s5 [label="DeleteRetainedC2 / Empty__Pub(c2,my_topic,)__c2_PubAck"]; s13 -> s13 [label="SubscribeC2 / Empty__c2_SubAck__Pub(c2,my_topic,bye)"]; s13 -> s17 [label="UnSubScribeC2 / Empty__c2_UnSubAck"]; s13 -> s12 [label="DisconnectTCPC1 / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s13 -> s12 [label="DisconnectC1 / c1_ConnectionClosed__Empty"]; s14 -> s3 [label="ConnectC2 / Empty__c2_ConnectionClosed"]; s14 -> s4 [label="ConnectC1WithWill / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s14 -> s4 [label="ConnectC1WithWillRetain / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s14 -> s14 [label="DeleteRetainedC1 / c1_PubAck__Pub(c2,my_topic,)"]; s14 -> s14 [label="DeleteRetainedC2 / Empty__Pub(c2,my_topic,)__c2_PubAck"]; s14 -> s14 [label="SubscribeC2 / Empty__c2_SubAck"]; s14 -> s2 [label="UnSubScribeC2 / Empty__c2_UnSubAck"]; s14 -> s4 [label="DisconnectTCPC1 / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s14 -> s4 [label="DisconnectC1 / c1_ConnectionClosed__Empty"]; s15 -> s10 [label="ConnectC2 / Empty__c2_ConnectionClosed"]; s15 -> s12 [label="ConnectC1WithWill / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s15 -> s12 [label="ConnectC1WithWillRetain / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s15 -> s14 [label="DeleteRetainedC1 / c1_PubAck__Pub(c2,my_topic,)"]; s15 -> s14 [label="DeleteRetainedC2 / Empty__Pub(c2,my_topic,)__c2_PubAck"]; s15 -> s15 [label="SubscribeC2 / Empty__c2_SubAck__Pub(c2,my_topic,bye)"]; s15 -> s11 [label="UnSubScribeC2 / Empty__c2_UnSubAck"]; s15 -> s12 [label="DisconnectTCPC1 / c1_ConnectionClosed__Pub(c2,my_topic,bye)"]; s15 -> s12 [label="DisconnectC1 / c1_ConnectionClosed__Empty"]; s16 -> s17 [label="ConnectC2 / Empty__c2_ConnAck"]; s16 -> s8 [label="ConnectC1WithWill / c1_ConnectionClosed__c2_ConnectionClosed"]; s16 -> s8 [label="ConnectC1WithWillRetain / c1_ConnectionClosed__c2_ConnectionClosed"]; s16 -> s7 [label="DeleteRetainedC1 / c1_PubAck__c2_ConnectionClosed"]; s16 -> s16 [label="DeleteRetainedC2 / Empty__c2_ConnectionClosed"]; s16 -> s16 [label="SubscribeC2 / Empty__c2_ConnectionClosed"]; s16 -> s16 [label="UnSubScribeC2 / Empty__c2_ConnectionClosed"]; s16 -> s8 [label="DisconnectTCPC1 / c1_ConnectionClosed__c2_ConnectionClosed"]; s16 -> s8 [label="DisconnectC1 / c1_ConnectionClosed__c2_ConnectionClosed"]; s17 -> s16 [label="ConnectC2 / Empty__c2_ConnectionClosed"]; s17 -> s9 [label="ConnectC1WithWill / c1_ConnectionClosed__Empty"]; s17 -> s9 [label="ConnectC1WithWillRetain / c1_ConnectionClosed__Empty"]; s17 -> s6 [label="DeleteRetainedC1 / c1_PubAck__Empty"]; s17 -> s6 [label="DeleteRetainedC2 / Empty__c2_PubAck"]; s17 -> s13 [label="SubscribeC2 / Empty__c2_SubAck__Pub(c2,my_topic,bye)"]; s17 -> s17 [label="UnSubScribeC2 / Empty__c2_UnSubAck"]; s17 -> s9 [label="DisconnectTCPC1 / c1_ConnectionClosed__Empty"]; s17 -> s9 [label="DisconnectC1 / c1_ConnectionClosed__Empty"]; __start0 -> s0; } ================================================ FILE: DotModels/SimpleABC/simple_abc_dfa.dot ================================================ digraph simpleABCDfa { s0 [label="s0"]; s1 [label="s1", shape=doublecircle]; s0 -> s1 [label="a"]; s0 -> s0 [label="b"]; s0 -> s0 [label="c"]; s1 -> s1 [label="a"]; s1 -> s0 [label="b"]; s1 -> s0 [label="c"]; __start0 [shape=none, label=""]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/SimpleABC/simple_abc_mealy.dot ================================================ digraph simpleABCmealy{ s0 [label="s0"]; s0 -> s0 [label="a/1"]; s0 -> s0 [label="b/2"]; s0 -> s0 [label="c/3"]; __start0 [shape=none, label=""]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/SimpleABC/simple_abc_moore.dot ================================================ digraph simpleABCmoore{ s0 [label="s0|0", shape=record, style=rounded]; s1 [label="s1|1", shape=record, style=rounded]; s2 [label="s2|2", shape=record, style=rounded]; s3 [label="s3|3", shape=record, style=rounded]; s0 -> s1 [label="a"]; s0 -> s2 [label="b"]; s0 -> s3 [label="c"]; s1 -> s1 [label="a"]; s1 -> s2 [label="b"]; s1 -> s3 [label="c"]; s2 -> s1 [label="a"]; s2 -> s2 [label="b"]; s2 -> s3 [label="c"]; s3 -> s1 [label="a"]; s3 -> s2 [label="b"]; s3 -> s3 [label="c"]; __start0 [shape=none, label=""]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/TCP/TCP_Linux_Client.dot ================================================ digraph "TrainingDataAndAutomata/TCP_Linux_Client1.dot" { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4]; s5 [label=s5]; s6 [label=s6]; s7 [label=s7]; s8 [label=s8]; s9 [label=s9]; s10 [label=s10]; s11 [label=s11]; s12 [label=s12]; s13 [label=s13]; s14 [label=s14]; s0 -> s0 [label="ACK+RST(V,V,0)/TIMEOUT"]; s0 -> s0 [label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s0 -> s0 [label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s0 -> s0 [label="RST(V,V,0)/TIMEOUT"]; s0 -> s0 [label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s0 -> s0 [label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s0 -> s0 [label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s0 -> s0 [label="RCV/TIMEOUT"]; s0 -> s1 [label="CLOSE/TIMEOUT"]; s0 -> s2 [label="CONNECT/SYN(FRESH,ZERO,0)"]; s1 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s1 -> s1 [label="CLOSE/TIMEOUT"]; s1 -> s1 [label="CONNECT/TIMEOUT"]; s1 -> s1 [label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s1 -> s1 [label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s1 -> s1 [label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 -> s1 [label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 -> s1 [label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s1 -> s1 [label="RCV/TIMEOUT"]; s2 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s2 -> s5 [label="CLOSE/TIMEOUT"]; s2 -> s2 [label="CONNECT/TIMEOUT"]; s2 -> s2 [label="ACK+PSH(V,V,1)/TIMEOUT"]; s2 -> s2 [label="ACK(V,V,0)/TIMEOUT"]; s2 -> s2 [label="FIN+ACK(V,V,0)/TIMEOUT"]; s2 -> s2 [label="RCV/TIMEOUT"]; s2 -> s4 [label="SYN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s2 -> s6 [label="RST(V,V,0)/TIMEOUT"]; s2 -> s3 [label="SYN(V,V,0)/ACK+SYN(CURRENT,NEXT,0)"]; s3 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s3 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s3 -> s8 [label="CLOSE/ACK+FIN(NEXT,CURRENT,0)"]; s3 -> s3 [label="CONNECT/TIMEOUT"]; s3 -> s3 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s3 -> s3 [label="SYN(V,V,0)/TIMEOUT"]; s3 -> s3 [label="RCV/TIMEOUT"]; s3 -> s7 [label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s3 -> s4 [label="ACK(V,V,0)/TIMEOUT"]; s3 -> s9 [label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s4 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s4 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s4 -> s8 [label="CLOSE/ACK+FIN(NEXT,CURRENT,0)"]; s4 -> s4 [label="CONNECT/TIMEOUT"]; s4 -> s4 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s4 -> s4 [label="ACK(V,V,0)/TIMEOUT"]; s4 -> s4 [label="SYN(V,V,0)/TIMEOUT"]; s4 -> s7 [label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s4 -> s9 [label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s4 -> s10 [label="RCV/TIMEOUT"]; s5 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s5 -> s1 [label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s5 -> s1 [label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s5 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s5 -> s1 [label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s5 -> s1 [label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s5 -> s1 [label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s5 -> s5 [label="CLOSE/TIMEOUT"]; s5 -> s5 [label="CONNECT/TIMEOUT"]; s5 -> s5 [label="RCV/TIMEOUT"]; s6 -> s6 [label="ACK+RST(V,V,0)/TIMEOUT"]; s6 -> s6 [label="CONNECT/TIMEOUT"]; s6 -> s6 [label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s6 -> s6 [label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s6 -> s6 [label="RST(V,V,0)/TIMEOUT"]; s6 -> s6 [label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s6 -> s6 [label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s6 -> s6 [label="RCV/TIMEOUT"]; s6 -> s1 [label="CLOSE/TIMEOUT"]; s6 -> s3 [label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s7 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s7 -> s1 [label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s7 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s7 -> s7 [label="CONNECT/TIMEOUT"]; s7 -> s7 [label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s7 -> s7 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s7 -> s7 [label="ACK(V,V,0)/TIMEOUT"]; s7 -> s7 [label="SYN(V,V,0)/TIMEOUT"]; s7 -> s11 [label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s7 -> s4 [label="RCV/TIMEOUT"]; s8 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s8 -> s1 [label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s8 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s8 -> s8 [label="CLOSE/TIMEOUT"]; s8 -> s8 [label="CONNECT/TIMEOUT"]; s8 -> s8 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s8 -> s8 [label="SYN(V,V,0)/TIMEOUT"]; s8 -> s8 [label="RCV/TIMEOUT"]; s8 -> s12 [label="ACK(V,V,0)/TIMEOUT"]; s8 -> s13 [label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s9 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s9 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s9 -> s14 [label="CLOSE/ACK+FIN(NEXT,CURRENT,0)"]; s9 -> s9 [label="CONNECT/TIMEOUT"]; s9 -> s9 [label="ACK+PSH(V,V,1)/TIMEOUT"]; s9 -> s9 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s9 -> s9 [label="ACK(V,V,0)/TIMEOUT"]; s9 -> s9 [label="FIN+ACK(V,V,0)/TIMEOUT"]; s9 -> s9 [label="SYN(V,V,0)/TIMEOUT"]; s9 -> s9 [label="RCV/TIMEOUT"]; s10 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s10 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s10 -> s8 [label="CLOSE/ACK+FIN(NEXT,CURRENT,0)"]; s10 -> s10 [label="CONNECT/TIMEOUT"]; s10 -> s10 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s10 -> s10 [label="ACK(V,V,0)/TIMEOUT"]; s10 -> s10 [label="SYN(V,V,0)/TIMEOUT"]; s10 -> s10 [label="RCV/TIMEOUT"]; s10 -> s4 [label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s10 -> s9 [label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s11 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s11 -> s1 [label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s11 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s11 -> s11 [label="CONNECT/TIMEOUT"]; s11 -> s11 [label="ACK+PSH(V,V,1)/TIMEOUT"]; s11 -> s11 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s11 -> s11 [label="ACK(V,V,0)/TIMEOUT"]; s11 -> s11 [label="FIN+ACK(V,V,0)/TIMEOUT"]; s11 -> s11 [label="SYN(V,V,0)/TIMEOUT"]; s11 -> s9 [label="RCV/TIMEOUT"]; s12 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s12 -> s1 [label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s12 -> s1 [label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s12 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s12 -> s1 [label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s12 -> s12 [label="CLOSE/TIMEOUT"]; s12 -> s12 [label="CONNECT/TIMEOUT"]; s12 -> s12 [label="ACK(V,V,0)/TIMEOUT"]; s12 -> s12 [label="RCV/TIMEOUT"]; s12 -> s13 [label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s13 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s13 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s13 -> s13 [label="CLOSE/TIMEOUT"]; s13 -> s13 [label="CONNECT/TIMEOUT"]; s13 -> s13 [label="ACK+PSH(V,V,1)/ACK(NEXT,CURRENT,0)"]; s13 -> s13 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s13 -> s13 [label="ACK(V,V,0)/TIMEOUT"]; s13 -> s13 [label="FIN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s13 -> s13 [label="SYN(V,V,0)/ACK(NEXT,CURRENT,0)"]; s13 -> s13 [label="RCV/TIMEOUT"]; s14 -> s1 [label="ACK+RST(V,V,0)/TIMEOUT"]; s14 -> s1 [label="RST(V,V,0)/TIMEOUT"]; s14 -> s14 [label="CLOSE/TIMEOUT"]; s14 -> s14 [label="CONNECT/TIMEOUT"]; s14 -> s14 [label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s14 -> s14 [label="SYN(V,V,0)/TIMEOUT"]; s14 -> s14 [label="RCV/TIMEOUT"]; s14 -> s5 [label="ACK+PSH(V,V,1)/TIMEOUT"]; s14 -> s5 [label="ACK(V,V,0)/TIMEOUT"]; s14 -> s5 [label="FIN+ACK(V,V,0)/TIMEOUT"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/TCP/tcp_server_bsd_trans.dot ================================================ digraph G { __start0 [label="" shape="none"]; s0 [shape="circle" label="s0"]; s0 -> s0[label="CLOSECONNECTION/TIMEOUT"]; s0 -> s0[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s0 -> s0[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s0 -> s0[label="RST(V,V,0)/TIMEOUT"]; s0 -> s0[label="ACCEPT/TIMEOUT"]; s0 -> s0[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s0 -> s1[label="LISTEN/TIMEOUT"]; s0 -> s0[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s0 -> s0[label="RCV/TIMEOUT"]; s0 -> s0[label="ACK+RST(V,V,0)/TIMEOUT"]; s0 -> s2[label="CLOSE/TIMEOUT"]; s0 -> s0[label="SEND/TIMEOUT"]; s0 -> s0[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 [shape="circle" label="s1"]; s1 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s1 -> s1[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s1 -> s1[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 -> s1[label="RST(V,V,0)/TIMEOUT"]; s1 -> s4[label="ACCEPT/TIMEOUT"]; s1 -> s1[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 -> s1[label="LISTEN/TIMEOUT"]; s1 -> s3[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s1 -> s1[label="RCV/TIMEOUT"]; s1 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s1 -> s2[label="CLOSE/TIMEOUT"]; s1 -> s1[label="SEND/TIMEOUT"]; s1 -> s1[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s2 [shape="circle" label="s2"]; s2 -> s2[label="CLOSECONNECTION/TIMEOUT"]; s2 -> s2[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s2 -> s2[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s2 -> s2[label="RST(V,V,0)/TIMEOUT"]; s2 -> s2[label="ACCEPT/TIMEOUT"]; s2 -> s2[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s2 -> s2[label="LISTEN/TIMEOUT"]; s2 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s2 -> s2[label="RCV/TIMEOUT"]; s2 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s2 -> s2[label="CLOSE/TIMEOUT"]; s2 -> s2[label="SEND/TIMEOUT"]; s2 -> s2[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s3 [shape="circle" label="s3"]; s3 -> s3[label="CLOSECONNECTION/TIMEOUT"]; s3 -> s8[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s3 -> s6[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s3 -> s1[label="RST(V,V,0)/TIMEOUT"]; s3 -> s9[label="ACCEPT/TIMEOUT"]; s3 -> s7[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s3 -> s3[label="LISTEN/TIMEOUT"]; s3 -> s3[label="SYN(V,V,0)/TIMEOUT"]; s3 -> s3[label="RCV/TIMEOUT"]; s3 -> s10[label="ACK+RST(V,V,0)/TIMEOUT"]; s3 -> s5[label="CLOSE/TIMEOUT"]; s3 -> s3[label="SEND/TIMEOUT"]; s3 -> s8[label="ACK(V,V,0)/TIMEOUT"]; s4 [shape="circle" label="s4"]; s4 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s4 -> s4[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s4 -> s4[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s4 -> s4[label="RST(V,V,0)/TIMEOUT"]; s4 -> s4[label="ACCEPT/TIMEOUT"]; s4 -> s4[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s4 -> s4[label="LISTEN/TIMEOUT"]; s4 -> s9[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s4 -> s4[label="RCV/TIMEOUT"]; s4 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s4 -> s2[label="CLOSE/TIMEOUT"]; s4 -> s4[label="SEND/TIMEOUT"]; s4 -> s4[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s5 [shape="circle" label="s5"]; s5 -> s5[label="CLOSECONNECTION/TIMEOUT"]; s5 -> s2[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s5 -> s5[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s5 -> s2[label="RST(V,V,0)/TIMEOUT"]; s5 -> s5[label="ACCEPT/TIMEOUT"]; s5 -> s2[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s5 -> s5[label="LISTEN/TIMEOUT"]; s5 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s5 -> s5[label="RCV/TIMEOUT"]; s5 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s5 -> s5[label="CLOSE/TIMEOUT"]; s5 -> s5[label="SEND/TIMEOUT"]; s5 -> s2[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s6 [shape="circle" label="s6"]; s6 -> s6[label="CLOSECONNECTION/TIMEOUT"]; s6 -> s1[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s6 -> s6[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s6 -> s1[label="RST(V,V,0)/TIMEOUT"]; s6 -> s11[label="ACCEPT/TIMEOUT"]; s6 -> s1[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s6 -> s6[label="LISTEN/TIMEOUT"]; s6 -> s3[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s6 -> s6[label="RCV/TIMEOUT"]; s6 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s6 -> s5[label="CLOSE/TIMEOUT"]; s6 -> s6[label="SEND/TIMEOUT"]; s6 -> s1[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s7 [shape="circle" label="s7"]; s7 -> s7[label="CLOSECONNECTION/TIMEOUT"]; s7 -> s7[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s7 -> s12[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s7 -> s12[label="RST(V,V,0)/TIMEOUT"]; s7 -> s13[label="ACCEPT/TIMEOUT"]; s7 -> s7[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s7 -> s7[label="LISTEN/TIMEOUT"]; s7 -> s12[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s7 -> s7[label="RCV/TIMEOUT"]; s7 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s7 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s7 -> s7[label="SEND/TIMEOUT"]; s7 -> s7[label="ACK(V,V,0)/TIMEOUT"]; s8 [shape="circle" label="s8"]; s8 -> s8[label="CLOSECONNECTION/TIMEOUT"]; s8 -> s8[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s8 -> s12[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s8 -> s12[label="RST(V,V,0)/TIMEOUT"]; s8 -> s14[label="ACCEPT/TIMEOUT"]; s8 -> s7[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s8 -> s8[label="LISTEN/TIMEOUT"]; s8 -> s12[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s8 -> s8[label="RCV/TIMEOUT"]; s8 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s8 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s8 -> s8[label="SEND/TIMEOUT"]; s8 -> s8[label="ACK(V,V,0)/TIMEOUT"]; s9 [shape="circle" label="s9"]; s9 -> s3[label="CLOSECONNECTION/TIMEOUT"]; s9 -> s14[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s9 -> s11[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s9 -> s4[label="RST(V,V,0)/TIMEOUT"]; s9 -> s9[label="ACCEPT/TIMEOUT"]; s9 -> s13[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s9 -> s9[label="LISTEN/TIMEOUT"]; s9 -> s9[label="SYN(V,V,0)/TIMEOUT"]; s9 -> s9[label="RCV/TIMEOUT"]; s9 -> s15[label="ACK+RST(V,V,0)/TIMEOUT"]; s9 -> s5[label="CLOSE/TIMEOUT"]; s9 -> s9[label="SEND/TIMEOUT"]; s9 -> s14[label="ACK(V,V,0)/TIMEOUT"]; s10 [shape="circle" label="s10"]; s10 -> s10[label="CLOSECONNECTION/TIMEOUT"]; s10 -> s1[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s10 -> s1[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s10 -> s10[label="RST(V,V,0)/TIMEOUT"]; s10 -> s15[label="ACCEPT/TIMEOUT"]; s10 -> s1[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s10 -> s10[label="LISTEN/TIMEOUT"]; s10 -> s10[label="SYN(V,V,0)/TIMEOUT"]; s10 -> s10[label="RCV/TIMEOUT"]; s10 -> s10[label="ACK+RST(V,V,0)/TIMEOUT"]; s10 -> s2[label="CLOSE/TIMEOUT"]; s10 -> s10[label="SEND/TIMEOUT"]; s10 -> s1[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s11 [shape="circle" label="s11"]; s11 -> s6[label="CLOSECONNECTION/TIMEOUT"]; s11 -> s4[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s11 -> s11[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s11 -> s4[label="RST(V,V,0)/TIMEOUT"]; s11 -> s11[label="ACCEPT/TIMEOUT"]; s11 -> s4[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s11 -> s11[label="LISTEN/TIMEOUT"]; s11 -> s9[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s11 -> s11[label="RCV/TIMEOUT"]; s11 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s11 -> s5[label="CLOSE/TIMEOUT"]; s11 -> s11[label="SEND/TIMEOUT"]; s11 -> s4[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s12 [shape="circle" label="s12"]; s12 -> s12[label="CLOSECONNECTION/TIMEOUT"]; s12 -> s12[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s12 -> s12[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s12 -> s12[label="RST(V,V,0)/TIMEOUT"]; s12 -> s1[label="ACCEPT/TIMEOUT"]; s12 -> s12[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s12 -> s12[label="LISTEN/TIMEOUT"]; s12 -> s16[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s12 -> s12[label="RCV/TIMEOUT"]; s12 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s12 -> s2[label="CLOSE/TIMEOUT"]; s12 -> s12[label="SEND/TIMEOUT"]; s12 -> s12[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s13 [shape="circle" label="s13"]; s13 -> s18[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s13 -> s13[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s13 -> s19[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s13 -> s19[label="RST(V,V,0)/TIMEOUT"]; s13 -> s13[label="ACCEPT/TIMEOUT"]; s13 -> s13[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s13 -> s13[label="LISTEN/TIMEOUT"]; s13 -> s19[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s13 -> s13[label="RCV/TIMEOUT"]; s13 -> s19[label="ACK+RST(V,V,0)/TIMEOUT"]; s13 -> s17[label="CLOSE/TIMEOUT"]; s13 -> s13[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s13 -> s13[label="ACK(V,V,0)/TIMEOUT"]; s14 [shape="circle" label="s14"]; s14 -> s21[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s14 -> s14[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s14 -> s19[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s14 -> s19[label="RST(V,V,0)/TIMEOUT"]; s14 -> s14[label="ACCEPT/TIMEOUT"]; s14 -> s13[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s14 -> s14[label="LISTEN/TIMEOUT"]; s14 -> s19[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s14 -> s14[label="RCV/TIMEOUT"]; s14 -> s19[label="ACK+RST(V,V,0)/TIMEOUT"]; s14 -> s20[label="CLOSE/TIMEOUT"]; s14 -> s14[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s14 -> s14[label="ACK(V,V,0)/TIMEOUT"]; s15 [shape="circle" label="s15"]; s15 -> s10[label="CLOSECONNECTION/TIMEOUT"]; s15 -> s4[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s15 -> s4[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s15 -> s15[label="RST(V,V,0)/TIMEOUT"]; s15 -> s15[label="ACCEPT/TIMEOUT"]; s15 -> s4[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s15 -> s15[label="LISTEN/TIMEOUT"]; s15 -> s15[label="SYN(V,V,0)/TIMEOUT"]; s15 -> s15[label="RCV/TIMEOUT"]; s15 -> s15[label="ACK+RST(V,V,0)/TIMEOUT"]; s15 -> s2[label="CLOSE/TIMEOUT"]; s15 -> s15[label="SEND/TIMEOUT"]; s15 -> s4[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s16 [shape="circle" label="s16"]; s16 -> s16[label="CLOSECONNECTION/TIMEOUT"]; s16 -> s23[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s16 -> s22[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s16 -> s12[label="RST(V,V,0)/TIMEOUT"]; s16 -> s3[label="ACCEPT/TIMEOUT"]; s16 -> s24[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s16 -> s16[label="LISTEN/TIMEOUT"]; s16 -> s16[label="SYN(V,V,0)/TIMEOUT"]; s16 -> s16[label="RCV/TIMEOUT"]; s16 -> s25[label="ACK+RST(V,V,0)/TIMEOUT"]; s16 -> s5[label="CLOSE/TIMEOUT"]; s16 -> s16[label="SEND/TIMEOUT"]; s16 -> s23[label="ACK(V,V,0)/TIMEOUT"]; s17 [shape="circle" label="s17"]; s17 -> s26[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s17 -> s17[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s17 -> s2[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s17 -> s2[label="RST(V,V,0)/TIMEOUT"]; s17 -> s17[label="ACCEPT/TIMEOUT"]; s17 -> s17[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s17 -> s17[label="LISTEN/TIMEOUT"]; s17 -> s2[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s17 -> s17[label="RCV/TIMEOUT"]; s17 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s17 -> s17[label="CLOSE/TIMEOUT"]; s17 -> s17[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s17 -> s17[label="ACK(V,V,0)/TIMEOUT"]; s18 [shape="circle" label="s18"]; s18 -> s18[label="CLOSECONNECTION/TIMEOUT"]; s18 -> s1[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s18 -> s1[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s18 -> s1[label="RST(V,V,0)/TIMEOUT"]; s18 -> s27[label="ACCEPT/TIMEOUT"]; s18 -> s6[label="FIN+ACK(V,V,0)/TIMEOUT"]; s18 -> s18[label="LISTEN/TIMEOUT"]; s18 -> s1[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s18 -> s18[label="RCV/TIMEOUT"]; s18 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s18 -> s26[label="CLOSE/TIMEOUT"]; s18 -> s18[label="SEND/TIMEOUT"]; s18 -> s6[label="ACK(V,V,0)/TIMEOUT"]; s19 [shape="circle" label="s19"]; s19 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s19 -> s19[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s19 -> s19[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s19 -> s19[label="RST(V,V,0)/TIMEOUT"]; s19 -> s19[label="ACCEPT/TIMEOUT"]; s19 -> s19[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s19 -> s19[label="LISTEN/TIMEOUT"]; s19 -> s28[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s19 -> s19[label="RCV/TIMEOUT"]; s19 -> s19[label="ACK+RST(V,V,0)/TIMEOUT"]; s19 -> s2[label="CLOSE/TIMEOUT"]; s19 -> s19[label="SEND/TIMEOUT"]; s19 -> s19[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s20 [shape="circle" label="s20"]; s20 -> s29[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s20 -> s20[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s20 -> s2[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s20 -> s2[label="RST(V,V,0)/TIMEOUT"]; s20 -> s20[label="ACCEPT/TIMEOUT"]; s20 -> s17[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s20 -> s20[label="LISTEN/TIMEOUT"]; s20 -> s2[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s20 -> s20[label="RCV/TIMEOUT"]; s20 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s20 -> s20[label="CLOSE/TIMEOUT"]; s20 -> s20[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s20 -> s20[label="ACK(V,V,0)/TIMEOUT"]; s21 [shape="circle" label="s21"]; s21 -> s21[label="CLOSECONNECTION/TIMEOUT"]; s21 -> s1[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s21 -> s1[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s21 -> s1[label="RST(V,V,0)/TIMEOUT"]; s21 -> s30[label="ACCEPT/TIMEOUT"]; s21 -> s31[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s21 -> s21[label="LISTEN/TIMEOUT"]; s21 -> s1[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s21 -> s21[label="RCV/TIMEOUT"]; s21 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s21 -> s29[label="CLOSE/TIMEOUT"]; s21 -> s21[label="SEND/TIMEOUT"]; s21 -> s21[label="ACK(V,V,0)/TIMEOUT"]; s22 [shape="circle" label="s22"]; s22 -> s22[label="CLOSECONNECTION/TIMEOUT"]; s22 -> s12[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s22 -> s22[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s22 -> s12[label="RST(V,V,0)/TIMEOUT"]; s22 -> s6[label="ACCEPT/TIMEOUT"]; s22 -> s12[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s22 -> s22[label="LISTEN/TIMEOUT"]; s22 -> s16[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s22 -> s22[label="RCV/TIMEOUT"]; s22 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s22 -> s5[label="CLOSE/TIMEOUT"]; s22 -> s22[label="SEND/TIMEOUT"]; s22 -> s12[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s23 [shape="circle" label="s23"]; s23 -> s23[label="CLOSECONNECTION/TIMEOUT"]; s23 -> s23[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s23 -> s32[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s23 -> s32[label="RST(V,V,0)/TIMEOUT"]; s23 -> s8[label="ACCEPT/TIMEOUT"]; s23 -> s24[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s23 -> s23[label="LISTEN/TIMEOUT"]; s23 -> s32[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s23 -> s23[label="RCV/TIMEOUT"]; s23 -> s32[label="ACK+RST(V,V,0)/TIMEOUT"]; s23 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s23 -> s23[label="SEND/TIMEOUT"]; s23 -> s23[label="ACK(V,V,0)/TIMEOUT"]; s24 [shape="circle" label="s24"]; s24 -> s24[label="CLOSECONNECTION/TIMEOUT"]; s24 -> s24[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s24 -> s32[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s24 -> s32[label="RST(V,V,0)/TIMEOUT"]; s24 -> s7[label="ACCEPT/TIMEOUT"]; s24 -> s24[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s24 -> s24[label="LISTEN/TIMEOUT"]; s24 -> s32[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s24 -> s24[label="RCV/TIMEOUT"]; s24 -> s32[label="ACK+RST(V,V,0)/TIMEOUT"]; s24 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s24 -> s24[label="SEND/TIMEOUT"]; s24 -> s24[label="ACK(V,V,0)/TIMEOUT"]; s25 [shape="circle" label="s25"]; s25 -> s25[label="CLOSECONNECTION/TIMEOUT"]; s25 -> s12[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s25 -> s12[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s25 -> s25[label="RST(V,V,0)/TIMEOUT"]; s25 -> s10[label="ACCEPT/TIMEOUT"]; s25 -> s12[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s25 -> s25[label="LISTEN/TIMEOUT"]; s25 -> s25[label="SYN(V,V,0)/TIMEOUT"]; s25 -> s25[label="RCV/TIMEOUT"]; s25 -> s25[label="ACK+RST(V,V,0)/TIMEOUT"]; s25 -> s2[label="CLOSE/TIMEOUT"]; s25 -> s25[label="SEND/TIMEOUT"]; s25 -> s12[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s26 [shape="circle" label="s26"]; s26 -> s26[label="CLOSECONNECTION/TIMEOUT"]; s26 -> s2[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s26 -> s2[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s26 -> s2[label="RST(V,V,0)/TIMEOUT"]; s26 -> s26[label="ACCEPT/TIMEOUT"]; s26 -> s5[label="FIN+ACK(V,V,0)/TIMEOUT"]; s26 -> s26[label="LISTEN/TIMEOUT"]; s26 -> s2[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s26 -> s26[label="RCV/TIMEOUT"]; s26 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s26 -> s26[label="CLOSE/TIMEOUT"]; s26 -> s26[label="SEND/TIMEOUT"]; s26 -> s5[label="ACK(V,V,0)/TIMEOUT"]; s27 [shape="circle" label="s27"]; s27 -> s18[label="CLOSECONNECTION/TIMEOUT"]; s27 -> s4[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s27 -> s4[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s27 -> s4[label="RST(V,V,0)/TIMEOUT"]; s27 -> s27[label="ACCEPT/TIMEOUT"]; s27 -> s11[label="FIN+ACK(V,V,0)/TIMEOUT"]; s27 -> s27[label="LISTEN/TIMEOUT"]; s27 -> s4[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s27 -> s27[label="RCV/TIMEOUT"]; s27 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s27 -> s26[label="CLOSE/TIMEOUT"]; s27 -> s27[label="SEND/TIMEOUT"]; s27 -> s11[label="ACK(V,V,0)/TIMEOUT"]; s28 [shape="circle" label="s28"]; s28 -> s3[label="CLOSECONNECTION/TIMEOUT"]; s28 -> s34[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s28 -> s36[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s28 -> s19[label="RST(V,V,0)/TIMEOUT"]; s28 -> s28[label="ACCEPT/TIMEOUT"]; s28 -> s33[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s28 -> s28[label="LISTEN/TIMEOUT"]; s28 -> s28[label="SYN(V,V,0)/TIMEOUT"]; s28 -> s28[label="RCV/TIMEOUT"]; s28 -> s35[label="ACK+RST(V,V,0)/TIMEOUT"]; s28 -> s5[label="CLOSE/TIMEOUT"]; s28 -> s28[label="SEND/TIMEOUT"]; s28 -> s34[label="ACK(V,V,0)/TIMEOUT"]; s29 [shape="circle" label="s29"]; s29 -> s29[label="CLOSECONNECTION/TIMEOUT"]; s29 -> s2[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s29 -> s2[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s29 -> s2[label="RST(V,V,0)/TIMEOUT"]; s29 -> s29[label="ACCEPT/TIMEOUT"]; s29 -> s37[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s29 -> s29[label="LISTEN/TIMEOUT"]; s29 -> s2[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s29 -> s29[label="RCV/TIMEOUT"]; s29 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s29 -> s29[label="CLOSE/TIMEOUT"]; s29 -> s29[label="SEND/TIMEOUT"]; s29 -> s29[label="ACK(V,V,0)/TIMEOUT"]; s30 [shape="circle" label="s30"]; s30 -> s21[label="CLOSECONNECTION/TIMEOUT"]; s30 -> s4[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s30 -> s4[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s30 -> s4[label="RST(V,V,0)/TIMEOUT"]; s30 -> s30[label="ACCEPT/TIMEOUT"]; s30 -> s38[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s30 -> s30[label="LISTEN/TIMEOUT"]; s30 -> s4[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s30 -> s30[label="RCV/TIMEOUT"]; s30 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s30 -> s29[label="CLOSE/TIMEOUT"]; s30 -> s30[label="SEND/TIMEOUT"]; s30 -> s30[label="ACK(V,V,0)/TIMEOUT"]; s31 [shape="circle" label="s31"]; s31 -> s31[label="CLOSECONNECTION/TIMEOUT"]; s31 -> s31[label="ACK+PSH(V,V,1)/ACK(NEXT,CURRENT,0)"]; s31 -> s31[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s31 -> s39[label="RST(V,V,0)/TIMEOUT"]; s31 -> s38[label="ACCEPT/TIMEOUT"]; s31 -> s31[label="FIN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s31 -> s31[label="LISTEN/TIMEOUT"]; s31 -> s31[label="SYN(V,V,0)/TIMEOUT"]; s31 -> s31[label="RCV/TIMEOUT"]; s31 -> s39[label="ACK+RST(V,V,0)/TIMEOUT"]; s31 -> s37[label="CLOSE/TIMEOUT"]; s31 -> s31[label="SEND/TIMEOUT"]; s31 -> s31[label="ACK(V,V,0)/TIMEOUT"]; s32 [shape="circle" label="s32"]; s32 -> s32[label="CLOSECONNECTION/TIMEOUT"]; s32 -> s32[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s32 -> s32[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s32 -> s32[label="RST(V,V,0)/TIMEOUT"]; s32 -> s12[label="ACCEPT/TIMEOUT"]; s32 -> s32[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s32 -> s32[label="LISTEN/TIMEOUT"]; s32 -> s40[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s32 -> s32[label="RCV/TIMEOUT"]; s32 -> s32[label="ACK+RST(V,V,0)/TIMEOUT"]; s32 -> s2[label="CLOSE/TIMEOUT"]; s32 -> s32[label="SEND/TIMEOUT"]; s32 -> s32[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s33 [shape="circle" label="s33"]; s33 -> s7[label="CLOSECONNECTION/TIMEOUT"]; s33 -> s33[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s33 -> s41[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s33 -> s41[label="RST(V,V,0)/TIMEOUT"]; s33 -> s33[label="ACCEPT/TIMEOUT"]; s33 -> s33[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s33 -> s33[label="LISTEN/TIMEOUT"]; s33 -> s41[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s33 -> s33[label="RCV/TIMEOUT"]; s33 -> s41[label="ACK+RST(V,V,0)/TIMEOUT"]; s33 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s33 -> s33[label="SEND/TIMEOUT"]; s33 -> s33[label="ACK(V,V,0)/TIMEOUT"]; s34 [shape="circle" label="s34"]; s34 -> s8[label="CLOSECONNECTION/TIMEOUT"]; s34 -> s34[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s34 -> s41[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s34 -> s41[label="RST(V,V,0)/TIMEOUT"]; s34 -> s34[label="ACCEPT/TIMEOUT"]; s34 -> s33[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s34 -> s34[label="LISTEN/TIMEOUT"]; s34 -> s41[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s34 -> s34[label="RCV/TIMEOUT"]; s34 -> s41[label="ACK+RST(V,V,0)/TIMEOUT"]; s34 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s34 -> s34[label="SEND/TIMEOUT"]; s34 -> s34[label="ACK(V,V,0)/TIMEOUT"]; s35 [shape="circle" label="s35"]; s35 -> s10[label="CLOSECONNECTION/TIMEOUT"]; s35 -> s19[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s35 -> s19[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s35 -> s35[label="RST(V,V,0)/TIMEOUT"]; s35 -> s35[label="ACCEPT/TIMEOUT"]; s35 -> s19[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s35 -> s35[label="LISTEN/TIMEOUT"]; s35 -> s35[label="SYN(V,V,0)/TIMEOUT"]; s35 -> s35[label="RCV/TIMEOUT"]; s35 -> s35[label="ACK+RST(V,V,0)/TIMEOUT"]; s35 -> s2[label="CLOSE/TIMEOUT"]; s35 -> s35[label="SEND/TIMEOUT"]; s35 -> s19[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s36 [shape="circle" label="s36"]; s36 -> s6[label="CLOSECONNECTION/TIMEOUT"]; s36 -> s19[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s36 -> s36[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s36 -> s19[label="RST(V,V,0)/TIMEOUT"]; s36 -> s36[label="ACCEPT/TIMEOUT"]; s36 -> s19[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s36 -> s36[label="LISTEN/TIMEOUT"]; s36 -> s28[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s36 -> s36[label="RCV/TIMEOUT"]; s36 -> s19[label="ACK+RST(V,V,0)/TIMEOUT"]; s36 -> s5[label="CLOSE/TIMEOUT"]; s36 -> s36[label="SEND/TIMEOUT"]; s36 -> s19[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s37 [shape="circle" label="s37"]; s37 -> s37[label="CLOSECONNECTION/TIMEOUT"]; s37 -> s37[label="ACK+PSH(V,V,1)/ACK(NEXT,CURRENT,0)"]; s37 -> s37[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s37 -> s42[label="RST(V,V,0)/TIMEOUT"]; s37 -> s37[label="ACCEPT/TIMEOUT"]; s37 -> s37[label="FIN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s37 -> s37[label="LISTEN/TIMEOUT"]; s37 -> s37[label="SYN(V,V,0)/TIMEOUT"]; s37 -> s37[label="RCV/TIMEOUT"]; s37 -> s42[label="ACK+RST(V,V,0)/TIMEOUT"]; s37 -> s37[label="CLOSE/TIMEOUT"]; s37 -> s37[label="SEND/TIMEOUT"]; s37 -> s37[label="ACK(V,V,0)/TIMEOUT"]; s38 [shape="circle" label="s38"]; s38 -> s31[label="CLOSECONNECTION/TIMEOUT"]; s38 -> s38[label="ACK+PSH(V,V,1)/ACK(NEXT,CURRENT,0)"]; s38 -> s38[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s38 -> s43[label="RST(V,V,0)/TIMEOUT"]; s38 -> s38[label="ACCEPT/TIMEOUT"]; s38 -> s38[label="FIN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s38 -> s38[label="LISTEN/TIMEOUT"]; s38 -> s38[label="SYN(V,V,0)/TIMEOUT"]; s38 -> s38[label="RCV/TIMEOUT"]; s38 -> s43[label="ACK+RST(V,V,0)/TIMEOUT"]; s38 -> s37[label="CLOSE/TIMEOUT"]; s38 -> s38[label="SEND/TIMEOUT"]; s38 -> s38[label="ACK(V,V,0)/TIMEOUT"]; s39 [shape="circle" label="s39"]; s39 -> s39[label="CLOSECONNECTION/TIMEOUT"]; s39 -> s39[label="ACK+PSH(V,V,1)/ACK(FRESH,FRESH,0)"]; s39 -> s39[label="SYN+ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s39 -> s39[label="RST(V,V,0)/TIMEOUT"]; s39 -> s43[label="ACCEPT/TIMEOUT"]; s39 -> s39[label="FIN+ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s39 -> s39[label="LISTEN/TIMEOUT"]; s39 -> s3[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s39 -> s39[label="RCV/TIMEOUT"]; s39 -> s39[label="ACK+RST(V,V,0)/TIMEOUT"]; s39 -> s42[label="CLOSE/TIMEOUT"]; s39 -> s39[label="SEND/TIMEOUT"]; s39 -> s39[label="ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s40 [shape="circle" label="s40"]; s40 -> s40[label="CLOSECONNECTION/TIMEOUT"]; s40 -> s32[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s40 -> s44[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s40 -> s32[label="RST(V,V,0)/TIMEOUT"]; s40 -> s16[label="ACCEPT/TIMEOUT"]; s40 -> s32[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s40 -> s40[label="LISTEN/TIMEOUT"]; s40 -> s40[label="SYN(V,V,0)/TIMEOUT"]; s40 -> s40[label="RCV/TIMEOUT"]; s40 -> s45[label="ACK+RST(V,V,0)/TIMEOUT"]; s40 -> s5[label="CLOSE/TIMEOUT"]; s40 -> s40[label="SEND/TIMEOUT"]; s40 -> s32[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s41 [shape="circle" label="s41"]; s41 -> s12[label="CLOSECONNECTION/TIMEOUT"]; s41 -> s41[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s41 -> s41[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s41 -> s41[label="RST(V,V,0)/TIMEOUT"]; s41 -> s41[label="ACCEPT/TIMEOUT"]; s41 -> s41[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s41 -> s41[label="LISTEN/TIMEOUT"]; s41 -> s46[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s41 -> s41[label="RCV/TIMEOUT"]; s41 -> s41[label="ACK+RST(V,V,0)/TIMEOUT"]; s41 -> s2[label="CLOSE/TIMEOUT"]; s41 -> s41[label="SEND/TIMEOUT"]; s41 -> s41[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s42 [shape="circle" label="s42"]; s42 -> s42[label="CLOSECONNECTION/TIMEOUT"]; s42 -> s42[label="ACK+PSH(V,V,1)/ACK(FRESH,FRESH,0)"]; s42 -> s42[label="SYN+ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s42 -> s42[label="RST(V,V,0)/TIMEOUT"]; s42 -> s42[label="ACCEPT/TIMEOUT"]; s42 -> s42[label="FIN+ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s42 -> s42[label="LISTEN/TIMEOUT"]; s42 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s42 -> s42[label="RCV/TIMEOUT"]; s42 -> s42[label="ACK+RST(V,V,0)/TIMEOUT"]; s42 -> s42[label="CLOSE/TIMEOUT"]; s42 -> s42[label="SEND/TIMEOUT"]; s42 -> s42[label="ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s43 [shape="circle" label="s43"]; s43 -> s39[label="CLOSECONNECTION/TIMEOUT"]; s43 -> s43[label="ACK+PSH(V,V,1)/ACK(FRESH,FRESH,0)"]; s43 -> s43[label="SYN+ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s43 -> s43[label="RST(V,V,0)/TIMEOUT"]; s43 -> s43[label="ACCEPT/TIMEOUT"]; s43 -> s43[label="FIN+ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s43 -> s43[label="LISTEN/TIMEOUT"]; s43 -> s9[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s43 -> s43[label="RCV/TIMEOUT"]; s43 -> s43[label="ACK+RST(V,V,0)/TIMEOUT"]; s43 -> s42[label="CLOSE/TIMEOUT"]; s43 -> s43[label="SEND/TIMEOUT"]; s43 -> s43[label="ACK(V,V,0)/ACK(FRESH,FRESH,0)"]; s44 [shape="circle" label="s44"]; s44 -> s44[label="CLOSECONNECTION/TIMEOUT"]; s44 -> s32[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s44 -> s44[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s44 -> s32[label="RST(V,V,0)/TIMEOUT"]; s44 -> s22[label="ACCEPT/TIMEOUT"]; s44 -> s32[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s44 -> s44[label="LISTEN/TIMEOUT"]; s44 -> s40[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s44 -> s44[label="RCV/TIMEOUT"]; s44 -> s32[label="ACK+RST(V,V,0)/TIMEOUT"]; s44 -> s5[label="CLOSE/TIMEOUT"]; s44 -> s44[label="SEND/TIMEOUT"]; s44 -> s32[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s45 [shape="circle" label="s45"]; s45 -> s45[label="CLOSECONNECTION/TIMEOUT"]; s45 -> s32[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s45 -> s32[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s45 -> s45[label="RST(V,V,0)/TIMEOUT"]; s45 -> s25[label="ACCEPT/TIMEOUT"]; s45 -> s32[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s45 -> s45[label="LISTEN/TIMEOUT"]; s45 -> s45[label="SYN(V,V,0)/TIMEOUT"]; s45 -> s45[label="RCV/TIMEOUT"]; s45 -> s45[label="ACK+RST(V,V,0)/TIMEOUT"]; s45 -> s2[label="CLOSE/TIMEOUT"]; s45 -> s45[label="SEND/TIMEOUT"]; s45 -> s32[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s46 [shape="circle" label="s46"]; s46 -> s16[label="CLOSECONNECTION/TIMEOUT"]; s46 -> s48[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s46 -> s49[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s46 -> s41[label="RST(V,V,0)/TIMEOUT"]; s46 -> s46[label="ACCEPT/TIMEOUT"]; s46 -> s50[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s46 -> s46[label="LISTEN/TIMEOUT"]; s46 -> s46[label="SYN(V,V,0)/TIMEOUT"]; s46 -> s46[label="RCV/TIMEOUT"]; s46 -> s47[label="ACK+RST(V,V,0)/TIMEOUT"]; s46 -> s5[label="CLOSE/TIMEOUT"]; s46 -> s46[label="SEND/TIMEOUT"]; s46 -> s48[label="ACK(V,V,0)/TIMEOUT"]; s47 [shape="circle" label="s47"]; s47 -> s25[label="CLOSECONNECTION/TIMEOUT"]; s47 -> s41[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s47 -> s41[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s47 -> s47[label="RST(V,V,0)/TIMEOUT"]; s47 -> s47[label="ACCEPT/TIMEOUT"]; s47 -> s41[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s47 -> s47[label="LISTEN/TIMEOUT"]; s47 -> s47[label="SYN(V,V,0)/TIMEOUT"]; s47 -> s47[label="RCV/TIMEOUT"]; s47 -> s47[label="ACK+RST(V,V,0)/TIMEOUT"]; s47 -> s2[label="CLOSE/TIMEOUT"]; s47 -> s47[label="SEND/TIMEOUT"]; s47 -> s41[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s48 [shape="circle" label="s48"]; s48 -> s23[label="CLOSECONNECTION/TIMEOUT"]; s48 -> s48[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s48 -> s51[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s48 -> s51[label="RST(V,V,0)/TIMEOUT"]; s48 -> s48[label="ACCEPT/TIMEOUT"]; s48 -> s50[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s48 -> s48[label="LISTEN/TIMEOUT"]; s48 -> s51[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s48 -> s48[label="RCV/TIMEOUT"]; s48 -> s51[label="ACK+RST(V,V,0)/TIMEOUT"]; s48 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s48 -> s48[label="SEND/TIMEOUT"]; s48 -> s48[label="ACK(V,V,0)/TIMEOUT"]; s49 [shape="circle" label="s49"]; s49 -> s22[label="CLOSECONNECTION/TIMEOUT"]; s49 -> s41[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s49 -> s49[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s49 -> s41[label="RST(V,V,0)/TIMEOUT"]; s49 -> s49[label="ACCEPT/TIMEOUT"]; s49 -> s41[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s49 -> s49[label="LISTEN/TIMEOUT"]; s49 -> s46[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s49 -> s49[label="RCV/TIMEOUT"]; s49 -> s41[label="ACK+RST(V,V,0)/TIMEOUT"]; s49 -> s5[label="CLOSE/TIMEOUT"]; s49 -> s49[label="SEND/TIMEOUT"]; s49 -> s41[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s50 [shape="circle" label="s50"]; s50 -> s24[label="CLOSECONNECTION/TIMEOUT"]; s50 -> s50[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s50 -> s51[label="SYN+ACK(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s50 -> s51[label="RST(V,V,0)/TIMEOUT"]; s50 -> s50[label="ACCEPT/TIMEOUT"]; s50 -> s50[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s50 -> s50[label="LISTEN/TIMEOUT"]; s50 -> s51[label="SYN(V,V,0)/ACK+RST(NEXT,CURRENT,0)"]; s50 -> s50[label="RCV/TIMEOUT"]; s50 -> s51[label="ACK+RST(V,V,0)/TIMEOUT"]; s50 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s50 -> s50[label="SEND/TIMEOUT"]; s50 -> s50[label="ACK(V,V,0)/TIMEOUT"]; s51 [shape="circle" label="s51"]; s51 -> s32[label="CLOSECONNECTION/TIMEOUT"]; s51 -> s51[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s51 -> s51[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s51 -> s51[label="RST(V,V,0)/TIMEOUT"]; s51 -> s51[label="ACCEPT/TIMEOUT"]; s51 -> s51[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s51 -> s51[label="LISTEN/TIMEOUT"]; s51 -> s52[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s51 -> s51[label="RCV/TIMEOUT"]; s51 -> s51[label="ACK+RST(V,V,0)/TIMEOUT"]; s51 -> s2[label="CLOSE/TIMEOUT"]; s51 -> s51[label="SEND/TIMEOUT"]; s51 -> s51[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s52 [shape="circle" label="s52"]; s52 -> s40[label="CLOSECONNECTION/TIMEOUT"]; s52 -> s51[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s52 -> s53[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s52 -> s51[label="RST(V,V,0)/TIMEOUT"]; s52 -> s52[label="ACCEPT/TIMEOUT"]; s52 -> s51[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s52 -> s52[label="LISTEN/TIMEOUT"]; s52 -> s52[label="SYN(V,V,0)/TIMEOUT"]; s52 -> s52[label="RCV/TIMEOUT"]; s52 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s52 -> s5[label="CLOSE/TIMEOUT"]; s52 -> s52[label="SEND/TIMEOUT"]; s52 -> s51[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s53 [shape="circle" label="s53"]; s53 -> s44[label="CLOSECONNECTION/TIMEOUT"]; s53 -> s51[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s53 -> s53[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s53 -> s51[label="RST(V,V,0)/TIMEOUT"]; s53 -> s53[label="ACCEPT/TIMEOUT"]; s53 -> s51[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s53 -> s53[label="LISTEN/TIMEOUT"]; s53 -> s52[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s53 -> s53[label="RCV/TIMEOUT"]; s53 -> s51[label="ACK+RST(V,V,0)/TIMEOUT"]; s53 -> s5[label="CLOSE/TIMEOUT"]; s53 -> s53[label="SEND/TIMEOUT"]; s53 -> s51[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s54 [shape="circle" label="s54"]; s54 -> s45[label="CLOSECONNECTION/TIMEOUT"]; s54 -> s51[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s54 -> s51[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s54 -> s54[label="RST(V,V,0)/TIMEOUT"]; s54 -> s54[label="ACCEPT/TIMEOUT"]; s54 -> s51[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s54 -> s54[label="LISTEN/TIMEOUT"]; s54 -> s54[label="SYN(V,V,0)/TIMEOUT"]; s54 -> s54[label="RCV/TIMEOUT"]; s54 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s54 -> s2[label="CLOSE/TIMEOUT"]; s54 -> s54[label="SEND/TIMEOUT"]; s54 -> s51[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; __start0 -> s0; } ================================================ FILE: DotModels/TCP/tcp_server_ubuntu_trans.dot ================================================ digraph G { __start0 [label="" shape="none"]; s0 [shape="circle" label="s0"]; s0 -> s0[label="CLOSECONNECTION/TIMEOUT"]; s0 -> s0[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s0 -> s0[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s0 -> s0[label="RST(V,V,0)/TIMEOUT"]; s0 -> s0[label="ACCEPT/TIMEOUT"]; s0 -> s0[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s0 -> s1[label="LISTEN/TIMEOUT"]; s0 -> s0[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s0 -> s0[label="RCV/TIMEOUT"]; s0 -> s0[label="ACK+RST(V,V,0)/TIMEOUT"]; s0 -> s2[label="CLOSE/TIMEOUT"]; s0 -> s0[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 [shape="circle" label="s1"]; s1 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s1 -> s1[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s1 -> s1[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 -> s1[label="RST(V,V,0)/TIMEOUT"]; s1 -> s4[label="ACCEPT/TIMEOUT"]; s1 -> s1[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s1 -> s1[label="LISTEN/TIMEOUT"]; s1 -> s3[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s1 -> s1[label="RCV/TIMEOUT"]; s1 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s1 -> s2[label="CLOSE/TIMEOUT"]; s1 -> s1[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s2 [shape="circle" label="s2"]; s2 -> s2[label="CLOSECONNECTION/TIMEOUT"]; s2 -> s2[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s2 -> s2[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s2 -> s2[label="RST(V,V,0)/TIMEOUT"]; s2 -> s2[label="ACCEPT/TIMEOUT"]; s2 -> s2[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s2 -> s2[label="LISTEN/TIMEOUT"]; s2 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s2 -> s2[label="RCV/TIMEOUT"]; s2 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s2 -> s2[label="CLOSE/TIMEOUT"]; s2 -> s2[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s3 [shape="circle" label="s3"]; s3 -> s3[label="CLOSECONNECTION/TIMEOUT"]; s3 -> s5[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s3 -> s1[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s3 -> s1[label="RST(V,V,0)/TIMEOUT"]; s3 -> s9[label="ACCEPT/TIMEOUT"]; s3 -> s7[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s3 -> s3[label="LISTEN/TIMEOUT"]; s3 -> s1[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s3 -> s3[label="RCV/TIMEOUT"]; s3 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s3 -> s8[label="CLOSE/TIMEOUT"]; s3 -> s6[label="ACK(V,V,0)/TIMEOUT"]; s4 [shape="circle" label="s4"]; s4 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s4 -> s4[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s4 -> s4[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s4 -> s4[label="RST(V,V,0)/TIMEOUT"]; s4 -> s4[label="ACCEPT/TIMEOUT"]; s4 -> s4[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s4 -> s4[label="LISTEN/TIMEOUT"]; s4 -> s9[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s4 -> s4[label="RCV/TIMEOUT"]; s4 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s4 -> s2[label="CLOSE/TIMEOUT"]; s4 -> s4[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s5 [shape="circle" label="s5"]; s5 -> s5[label="CLOSECONNECTION/TIMEOUT"]; s5 -> s5[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s5 -> s5[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s5 -> s12[label="RST(V,V,0)/TIMEOUT"]; s5 -> s11[label="ACCEPT/TIMEOUT"]; s5 -> s10[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s5 -> s5[label="LISTEN/TIMEOUT"]; s5 -> s5[label="SYN(V,V,0)/TIMEOUT"]; s5 -> s5[label="RCV/TIMEOUT"]; s5 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s5 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s5 -> s5[label="ACK(V,V,0)/TIMEOUT"]; s6 [shape="circle" label="s6"]; s6 -> s6[label="CLOSECONNECTION/TIMEOUT"]; s6 -> s5[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s6 -> s6[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s6 -> s12[label="RST(V,V,0)/TIMEOUT"]; s6 -> s13[label="ACCEPT/TIMEOUT"]; s6 -> s7[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s6 -> s6[label="LISTEN/TIMEOUT"]; s6 -> s6[label="SYN(V,V,0)/TIMEOUT"]; s6 -> s6[label="RCV/TIMEOUT"]; s6 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s6 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s6 -> s6[label="ACK(V,V,0)/TIMEOUT"]; s7 [shape="circle" label="s7"]; s7 -> s7[label="CLOSECONNECTION/TIMEOUT"]; s7 -> s7[label="ACK+PSH(V,V,1)/TIMEOUT"]; s7 -> s7[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s7 -> s12[label="RST(V,V,0)/TIMEOUT"]; s7 -> s14[label="ACCEPT/TIMEOUT"]; s7 -> s7[label="FIN+ACK(V,V,0)/TIMEOUT"]; s7 -> s7[label="LISTEN/TIMEOUT"]; s7 -> s7[label="SYN(V,V,0)/TIMEOUT"]; s7 -> s7[label="RCV/TIMEOUT"]; s7 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s7 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s7 -> s7[label="ACK(V,V,0)/TIMEOUT"]; s8 [shape="circle" label="s8"]; s8 -> s8[label="CLOSECONNECTION/TIMEOUT"]; s8 -> s2[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s8 -> s2[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s8 -> s2[label="RST(V,V,0)/TIMEOUT"]; s8 -> s8[label="ACCEPT/TIMEOUT"]; s8 -> s2[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s8 -> s8[label="LISTEN/TIMEOUT"]; s8 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s8 -> s8[label="RCV/TIMEOUT"]; s8 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s8 -> s8[label="CLOSE/TIMEOUT"]; s8 -> s2[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s9 [shape="circle" label="s9"]; s9 -> s3[label="CLOSECONNECTION/TIMEOUT"]; s9 -> s11[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s9 -> s4[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s9 -> s4[label="RST(V,V,0)/TIMEOUT"]; s9 -> s9[label="ACCEPT/TIMEOUT"]; s9 -> s14[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s9 -> s9[label="LISTEN/TIMEOUT"]; s9 -> s4[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s9 -> s9[label="RCV/TIMEOUT"]; s9 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s9 -> s8[label="CLOSE/TIMEOUT"]; s9 -> s13[label="ACK(V,V,0)/TIMEOUT"]; s10 [shape="circle" label="s10"]; s10 -> s10[label="CLOSECONNECTION/TIMEOUT"]; s10 -> s10[label="ACK+PSH(V,V,1)/TIMEOUT"]; s10 -> s10[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s10 -> s12[label="RST(V,V,0)/TIMEOUT"]; s10 -> s15[label="ACCEPT/TIMEOUT"]; s10 -> s10[label="FIN+ACK(V,V,0)/TIMEOUT"]; s10 -> s10[label="LISTEN/TIMEOUT"]; s10 -> s10[label="SYN(V,V,0)/TIMEOUT"]; s10 -> s10[label="RCV/TIMEOUT"]; s10 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s10 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s10 -> s10[label="ACK(V,V,0)/TIMEOUT"]; s11 [shape="circle" label="s11"]; s11 -> s1[label="CLOSECONNECTION/ACK+RST(NEXT,CURRENT,0)"]; s11 -> s11[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s11 -> s11[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s11 -> s17[label="RST(V,V,0)/TIMEOUT"]; s11 -> s11[label="ACCEPT/TIMEOUT"]; s11 -> s15[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s11 -> s11[label="LISTEN/TIMEOUT"]; s11 -> s11[label="SYN(V,V,0)/TIMEOUT"]; s11 -> s13[label="RCV/TIMEOUT"]; s11 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s11 -> s16[label="CLOSE/TIMEOUT"]; s11 -> s11[label="ACK(V,V,0)/TIMEOUT"]; s12 [shape="circle" label="s12"]; s12 -> s12[label="CLOSECONNECTION/TIMEOUT"]; s12 -> s12[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s12 -> s12[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s12 -> s12[label="RST(V,V,0)/TIMEOUT"]; s12 -> s17[label="ACCEPT/TIMEOUT"]; s12 -> s12[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s12 -> s12[label="LISTEN/TIMEOUT"]; s12 -> s18[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s12 -> s12[label="RCV/TIMEOUT"]; s12 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s12 -> s2[label="CLOSE/TIMEOUT"]; s12 -> s12[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s13 [shape="circle" label="s13"]; s13 -> s19[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s13 -> s11[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s13 -> s13[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s13 -> s17[label="RST(V,V,0)/TIMEOUT"]; s13 -> s13[label="ACCEPT/TIMEOUT"]; s13 -> s14[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s13 -> s13[label="LISTEN/TIMEOUT"]; s13 -> s13[label="SYN(V,V,0)/TIMEOUT"]; s13 -> s20[label="RCV/TIMEOUT"]; s13 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s13 -> s21[label="CLOSE/TIMEOUT"]; s13 -> s13[label="ACK(V,V,0)/TIMEOUT"]; s14 [shape="circle" label="s14"]; s14 -> s23[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s14 -> s14[label="ACK+PSH(V,V,1)/TIMEOUT"]; s14 -> s14[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s14 -> s17[label="RST(V,V,0)/TIMEOUT"]; s14 -> s14[label="ACCEPT/TIMEOUT"]; s14 -> s14[label="FIN+ACK(V,V,0)/TIMEOUT"]; s14 -> s14[label="LISTEN/TIMEOUT"]; s14 -> s14[label="SYN(V,V,0)/TIMEOUT"]; s14 -> s14[label="RCV/TIMEOUT"]; s14 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s14 -> s22[label="CLOSE/TIMEOUT"]; s14 -> s14[label="ACK(V,V,0)/TIMEOUT"]; s15 [shape="circle" label="s15"]; s15 -> s1[label="CLOSECONNECTION/ACK+RST(NEXT,CURRENT,0)"]; s15 -> s15[label="ACK+PSH(V,V,1)/TIMEOUT"]; s15 -> s15[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s15 -> s17[label="RST(V,V,0)/TIMEOUT"]; s15 -> s15[label="ACCEPT/TIMEOUT"]; s15 -> s15[label="FIN+ACK(V,V,0)/TIMEOUT"]; s15 -> s15[label="LISTEN/TIMEOUT"]; s15 -> s15[label="SYN(V,V,0)/TIMEOUT"]; s15 -> s14[label="RCV/TIMEOUT"]; s15 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s15 -> s24[label="CLOSE/TIMEOUT"]; s15 -> s15[label="ACK(V,V,0)/TIMEOUT"]; s16 [shape="circle" label="s16"]; s16 -> s2[label="CLOSECONNECTION/ACK+RST(NEXT,CURRENT,0)"]; s16 -> s16[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s16 -> s16[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s16 -> s2[label="RST(V,V,0)/TIMEOUT"]; s16 -> s16[label="ACCEPT/TIMEOUT"]; s16 -> s24[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s16 -> s16[label="LISTEN/TIMEOUT"]; s16 -> s16[label="SYN(V,V,0)/TIMEOUT"]; s16 -> s21[label="RCV/TIMEOUT"]; s16 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s16 -> s16[label="CLOSE/TIMEOUT"]; s16 -> s16[label="ACK(V,V,0)/TIMEOUT"]; s17 [shape="circle" label="s17"]; s17 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s17 -> s17[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s17 -> s17[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s17 -> s17[label="RST(V,V,0)/TIMEOUT"]; s17 -> s17[label="ACCEPT/TIMEOUT"]; s17 -> s17[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s17 -> s17[label="LISTEN/TIMEOUT"]; s17 -> s25[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s17 -> s17[label="RCV/TIMEOUT"]; s17 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s17 -> s2[label="CLOSE/TIMEOUT"]; s17 -> s17[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s18 [shape="circle" label="s18"]; s18 -> s18[label="CLOSECONNECTION/TIMEOUT"]; s18 -> s28[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s18 -> s12[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s18 -> s12[label="RST(V,V,0)/TIMEOUT"]; s18 -> s25[label="ACCEPT/TIMEOUT"]; s18 -> s26[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s18 -> s18[label="LISTEN/TIMEOUT"]; s18 -> s12[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s18 -> s18[label="RCV/TIMEOUT"]; s18 -> s12[label="ACK+RST(V,V,0)/TIMEOUT"]; s18 -> s8[label="CLOSE/TIMEOUT"]; s18 -> s27[label="ACK(V,V,0)/TIMEOUT"]; s19 [shape="circle" label="s19"]; s19 -> s19[label="CLOSECONNECTION/TIMEOUT"]; s19 -> s1[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s19 -> s19[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s19 -> s1[label="RST(V,V,0)/TIMEOUT"]; s19 -> s31[label="ACCEPT/TIMEOUT"]; s19 -> s32[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s19 -> s19[label="LISTEN/TIMEOUT"]; s19 -> s19[label="SYN(V,V,0)/TIMEOUT"]; s19 -> s19[label="RCV/TIMEOUT"]; s19 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s19 -> s29[label="CLOSE/TIMEOUT"]; s19 -> s30[label="ACK(V,V,0)/TIMEOUT"]; s20 [shape="circle" label="s20"]; s20 -> s19[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s20 -> s13[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s20 -> s20[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s20 -> s17[label="RST(V,V,0)/TIMEOUT"]; s20 -> s20[label="ACCEPT/TIMEOUT"]; s20 -> s14[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s20 -> s20[label="LISTEN/TIMEOUT"]; s20 -> s20[label="SYN(V,V,0)/TIMEOUT"]; s20 -> s20[label="RCV/TIMEOUT"]; s20 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s20 -> s21[label="CLOSE/TIMEOUT"]; s20 -> s20[label="ACK(V,V,0)/TIMEOUT"]; s21 [shape="circle" label="s21"]; s21 -> s29[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s21 -> s16[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s21 -> s21[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s21 -> s2[label="RST(V,V,0)/TIMEOUT"]; s21 -> s21[label="ACCEPT/TIMEOUT"]; s21 -> s22[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s21 -> s21[label="LISTEN/TIMEOUT"]; s21 -> s21[label="SYN(V,V,0)/TIMEOUT"]; s21 -> s33[label="RCV/TIMEOUT"]; s21 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s21 -> s21[label="CLOSE/TIMEOUT"]; s21 -> s21[label="ACK(V,V,0)/TIMEOUT"]; s22 [shape="circle" label="s22"]; s22 -> s34[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s22 -> s22[label="ACK+PSH(V,V,1)/TIMEOUT"]; s22 -> s22[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s22 -> s2[label="RST(V,V,0)/TIMEOUT"]; s22 -> s22[label="ACCEPT/TIMEOUT"]; s22 -> s22[label="FIN+ACK(V,V,0)/TIMEOUT"]; s22 -> s22[label="LISTEN/TIMEOUT"]; s22 -> s22[label="SYN(V,V,0)/TIMEOUT"]; s22 -> s22[label="RCV/TIMEOUT"]; s22 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s22 -> s22[label="CLOSE/TIMEOUT"]; s22 -> s22[label="ACK(V,V,0)/TIMEOUT"]; s23 [shape="circle" label="s23"]; s23 -> s23[label="CLOSECONNECTION/TIMEOUT"]; s23 -> s35[label="ACK+PSH(V,V,1)/TIMEOUT"]; s23 -> s23[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s23 -> s1[label="RST(V,V,0)/TIMEOUT"]; s23 -> s36[label="ACCEPT/TIMEOUT"]; s23 -> s35[label="FIN+ACK(V,V,0)/TIMEOUT"]; s23 -> s23[label="LISTEN/TIMEOUT"]; s23 -> s23[label="SYN(V,V,0)/TIMEOUT"]; s23 -> s23[label="RCV/TIMEOUT"]; s23 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s23 -> s34[label="CLOSE/TIMEOUT"]; s23 -> s35[label="ACK(V,V,0)/TIMEOUT"]; s24 [shape="circle" label="s24"]; s24 -> s2[label="CLOSECONNECTION/ACK+RST(NEXT,CURRENT,0)"]; s24 -> s24[label="ACK+PSH(V,V,1)/TIMEOUT"]; s24 -> s24[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s24 -> s2[label="RST(V,V,0)/TIMEOUT"]; s24 -> s24[label="ACCEPT/TIMEOUT"]; s24 -> s24[label="FIN+ACK(V,V,0)/TIMEOUT"]; s24 -> s24[label="LISTEN/TIMEOUT"]; s24 -> s24[label="SYN(V,V,0)/TIMEOUT"]; s24 -> s22[label="RCV/TIMEOUT"]; s24 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s24 -> s24[label="CLOSE/TIMEOUT"]; s24 -> s24[label="ACK(V,V,0)/TIMEOUT"]; s25 [shape="circle" label="s25"]; s25 -> s3[label="CLOSECONNECTION/TIMEOUT"]; s25 -> s37[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s25 -> s17[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s25 -> s17[label="RST(V,V,0)/TIMEOUT"]; s25 -> s25[label="ACCEPT/TIMEOUT"]; s25 -> s38[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s25 -> s25[label="LISTEN/TIMEOUT"]; s25 -> s17[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s25 -> s25[label="RCV/TIMEOUT"]; s25 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s25 -> s8[label="CLOSE/TIMEOUT"]; s25 -> s39[label="ACK(V,V,0)/TIMEOUT"]; s26 [shape="circle" label="s26"]; s26 -> s26[label="CLOSECONNECTION/TIMEOUT"]; s26 -> s26[label="ACK+PSH(V,V,1)/TIMEOUT"]; s26 -> s26[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s26 -> s40[label="RST(V,V,0)/TIMEOUT"]; s26 -> s38[label="ACCEPT/TIMEOUT"]; s26 -> s26[label="FIN+ACK(V,V,0)/TIMEOUT"]; s26 -> s26[label="LISTEN/TIMEOUT"]; s26 -> s26[label="SYN(V,V,0)/TIMEOUT"]; s26 -> s26[label="RCV/TIMEOUT"]; s26 -> s40[label="ACK+RST(V,V,0)/TIMEOUT"]; s26 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s26 -> s26[label="ACK(V,V,0)/TIMEOUT"]; s27 [shape="circle" label="s27"]; s27 -> s27[label="CLOSECONNECTION/TIMEOUT"]; s27 -> s28[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s27 -> s27[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s27 -> s40[label="RST(V,V,0)/TIMEOUT"]; s27 -> s39[label="ACCEPT/TIMEOUT"]; s27 -> s26[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s27 -> s27[label="LISTEN/TIMEOUT"]; s27 -> s27[label="SYN(V,V,0)/TIMEOUT"]; s27 -> s27[label="RCV/TIMEOUT"]; s27 -> s40[label="ACK+RST(V,V,0)/TIMEOUT"]; s27 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s27 -> s27[label="ACK(V,V,0)/TIMEOUT"]; s28 [shape="circle" label="s28"]; s28 -> s28[label="CLOSECONNECTION/TIMEOUT"]; s28 -> s28[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s28 -> s28[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s28 -> s40[label="RST(V,V,0)/TIMEOUT"]; s28 -> s37[label="ACCEPT/TIMEOUT"]; s28 -> s41[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s28 -> s28[label="LISTEN/TIMEOUT"]; s28 -> s28[label="SYN(V,V,0)/TIMEOUT"]; s28 -> s28[label="RCV/TIMEOUT"]; s28 -> s40[label="ACK+RST(V,V,0)/TIMEOUT"]; s28 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s28 -> s28[label="ACK(V,V,0)/TIMEOUT"]; s29 [shape="circle" label="s29"]; s29 -> s29[label="CLOSECONNECTION/TIMEOUT"]; s29 -> s2[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s29 -> s29[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s29 -> s2[label="RST(V,V,0)/TIMEOUT"]; s29 -> s29[label="ACCEPT/TIMEOUT"]; s29 -> s43[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s29 -> s29[label="LISTEN/TIMEOUT"]; s29 -> s29[label="SYN(V,V,0)/TIMEOUT"]; s29 -> s29[label="RCV/TIMEOUT"]; s29 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s29 -> s29[label="CLOSE/TIMEOUT"]; s29 -> s42[label="ACK(V,V,0)/TIMEOUT"]; s30 [shape="circle" label="s30"]; s30 -> s30[label="CLOSECONNECTION/TIMEOUT"]; s30 -> s1[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s30 -> s1[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s30 -> s1[label="RST(V,V,0)/TIMEOUT"]; s30 -> s44[label="ACCEPT/TIMEOUT"]; s30 -> s32[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s30 -> s30[label="LISTEN/TIMEOUT"]; s30 -> s1[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s30 -> s30[label="RCV/TIMEOUT"]; s30 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s30 -> s42[label="CLOSE/TIMEOUT"]; s30 -> s30[label="ACK(V,V,0)/TIMEOUT"]; s31 [shape="circle" label="s31"]; s31 -> s19[label="CLOSECONNECTION/TIMEOUT"]; s31 -> s4[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s31 -> s31[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s31 -> s4[label="RST(V,V,0)/TIMEOUT"]; s31 -> s31[label="ACCEPT/TIMEOUT"]; s31 -> s45[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s31 -> s31[label="LISTEN/TIMEOUT"]; s31 -> s31[label="SYN(V,V,0)/TIMEOUT"]; s31 -> s31[label="RCV/TIMEOUT"]; s31 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s31 -> s29[label="CLOSE/TIMEOUT"]; s31 -> s44[label="ACK(V,V,0)/TIMEOUT"]; s32 [shape="circle" label="s32"]; s32 -> s32[label="CLOSECONNECTION/TIMEOUT"]; s32 -> s32[label="ACK+PSH(V,V,1)/ACK(NEXT,CURRENT,0)"]; s32 -> s32[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s32 -> s1[label="RST(V,V,0)/TIMEOUT"]; s32 -> s45[label="ACCEPT/TIMEOUT"]; s32 -> s32[label="FIN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s32 -> s32[label="LISTEN/TIMEOUT"]; s32 -> s32[label="SYN(V,V,0)/ACK(NEXT,CURRENT,0)"]; s32 -> s32[label="RCV/TIMEOUT"]; s32 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s32 -> s43[label="CLOSE/TIMEOUT"]; s32 -> s32[label="ACK(V,V,0)/TIMEOUT"]; s33 [shape="circle" label="s33"]; s33 -> s29[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s33 -> s21[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s33 -> s33[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s33 -> s2[label="RST(V,V,0)/TIMEOUT"]; s33 -> s33[label="ACCEPT/TIMEOUT"]; s33 -> s22[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s33 -> s33[label="LISTEN/TIMEOUT"]; s33 -> s33[label="SYN(V,V,0)/TIMEOUT"]; s33 -> s33[label="RCV/TIMEOUT"]; s33 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s33 -> s21[label="CLOSE/TIMEOUT"]; s33 -> s33[label="ACK(V,V,0)/TIMEOUT"]; s34 [shape="circle" label="s34"]; s34 -> s34[label="CLOSECONNECTION/TIMEOUT"]; s34 -> s8[label="ACK+PSH(V,V,1)/TIMEOUT"]; s34 -> s34[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s34 -> s2[label="RST(V,V,0)/TIMEOUT"]; s34 -> s34[label="ACCEPT/TIMEOUT"]; s34 -> s8[label="FIN+ACK(V,V,0)/TIMEOUT"]; s34 -> s34[label="LISTEN/TIMEOUT"]; s34 -> s34[label="SYN(V,V,0)/TIMEOUT"]; s34 -> s34[label="RCV/TIMEOUT"]; s34 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s34 -> s34[label="CLOSE/TIMEOUT"]; s34 -> s8[label="ACK(V,V,0)/TIMEOUT"]; s35 [shape="circle" label="s35"]; s35 -> s35[label="CLOSECONNECTION/TIMEOUT"]; s35 -> s1[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s35 -> s1[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s35 -> s1[label="RST(V,V,0)/TIMEOUT"]; s35 -> s46[label="ACCEPT/TIMEOUT"]; s35 -> s1[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s35 -> s35[label="LISTEN/TIMEOUT"]; s35 -> s3[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s35 -> s35[label="RCV/TIMEOUT"]; s35 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s35 -> s8[label="CLOSE/TIMEOUT"]; s35 -> s1[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s36 [shape="circle" label="s36"]; s36 -> s23[label="CLOSECONNECTION/TIMEOUT"]; s36 -> s46[label="ACK+PSH(V,V,1)/TIMEOUT"]; s36 -> s36[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s36 -> s4[label="RST(V,V,0)/TIMEOUT"]; s36 -> s36[label="ACCEPT/TIMEOUT"]; s36 -> s46[label="FIN+ACK(V,V,0)/TIMEOUT"]; s36 -> s36[label="LISTEN/TIMEOUT"]; s36 -> s36[label="SYN(V,V,0)/TIMEOUT"]; s36 -> s36[label="RCV/TIMEOUT"]; s36 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s36 -> s34[label="CLOSE/TIMEOUT"]; s36 -> s46[label="ACK(V,V,0)/TIMEOUT"]; s37 [shape="circle" label="s37"]; s37 -> s5[label="CLOSECONNECTION/TIMEOUT"]; s37 -> s37[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s37 -> s37[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s37 -> s48[label="RST(V,V,0)/TIMEOUT"]; s37 -> s37[label="ACCEPT/TIMEOUT"]; s37 -> s47[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s37 -> s37[label="LISTEN/TIMEOUT"]; s37 -> s37[label="SYN(V,V,0)/TIMEOUT"]; s37 -> s37[label="RCV/TIMEOUT"]; s37 -> s48[label="ACK+RST(V,V,0)/TIMEOUT"]; s37 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s37 -> s37[label="ACK(V,V,0)/TIMEOUT"]; s38 [shape="circle" label="s38"]; s38 -> s7[label="CLOSECONNECTION/TIMEOUT"]; s38 -> s38[label="ACK+PSH(V,V,1)/TIMEOUT"]; s38 -> s38[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s38 -> s48[label="RST(V,V,0)/TIMEOUT"]; s38 -> s38[label="ACCEPT/TIMEOUT"]; s38 -> s38[label="FIN+ACK(V,V,0)/TIMEOUT"]; s38 -> s38[label="LISTEN/TIMEOUT"]; s38 -> s38[label="SYN(V,V,0)/TIMEOUT"]; s38 -> s38[label="RCV/TIMEOUT"]; s38 -> s48[label="ACK+RST(V,V,0)/TIMEOUT"]; s38 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s38 -> s38[label="ACK(V,V,0)/TIMEOUT"]; s39 [shape="circle" label="s39"]; s39 -> s6[label="CLOSECONNECTION/TIMEOUT"]; s39 -> s37[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s39 -> s39[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s39 -> s48[label="RST(V,V,0)/TIMEOUT"]; s39 -> s39[label="ACCEPT/TIMEOUT"]; s39 -> s38[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s39 -> s39[label="LISTEN/TIMEOUT"]; s39 -> s39[label="SYN(V,V,0)/TIMEOUT"]; s39 -> s39[label="RCV/TIMEOUT"]; s39 -> s48[label="ACK+RST(V,V,0)/TIMEOUT"]; s39 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s39 -> s39[label="ACK(V,V,0)/TIMEOUT"]; s40 [shape="circle" label="s40"]; s40 -> s40[label="CLOSECONNECTION/TIMEOUT"]; s40 -> s40[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s40 -> s40[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s40 -> s40[label="RST(V,V,0)/TIMEOUT"]; s40 -> s48[label="ACCEPT/TIMEOUT"]; s40 -> s40[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s40 -> s40[label="LISTEN/TIMEOUT"]; s40 -> s49[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s40 -> s40[label="RCV/TIMEOUT"]; s40 -> s40[label="ACK+RST(V,V,0)/TIMEOUT"]; s40 -> s2[label="CLOSE/TIMEOUT"]; s40 -> s40[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s41 [shape="circle" label="s41"]; s41 -> s41[label="CLOSECONNECTION/TIMEOUT"]; s41 -> s41[label="ACK+PSH(V,V,1)/TIMEOUT"]; s41 -> s41[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s41 -> s40[label="RST(V,V,0)/TIMEOUT"]; s41 -> s47[label="ACCEPT/TIMEOUT"]; s41 -> s41[label="FIN+ACK(V,V,0)/TIMEOUT"]; s41 -> s41[label="LISTEN/TIMEOUT"]; s41 -> s41[label="SYN(V,V,0)/TIMEOUT"]; s41 -> s41[label="RCV/TIMEOUT"]; s41 -> s40[label="ACK+RST(V,V,0)/TIMEOUT"]; s41 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s41 -> s41[label="ACK(V,V,0)/TIMEOUT"]; s42 [shape="circle" label="s42"]; s42 -> s42[label="CLOSECONNECTION/TIMEOUT"]; s42 -> s2[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s42 -> s2[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s42 -> s2[label="RST(V,V,0)/TIMEOUT"]; s42 -> s42[label="ACCEPT/TIMEOUT"]; s42 -> s43[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s42 -> s42[label="LISTEN/TIMEOUT"]; s42 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s42 -> s42[label="RCV/TIMEOUT"]; s42 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s42 -> s42[label="CLOSE/TIMEOUT"]; s42 -> s42[label="ACK(V,V,0)/TIMEOUT"]; s43 [shape="circle" label="s43"]; s43 -> s43[label="CLOSECONNECTION/TIMEOUT"]; s43 -> s43[label="ACK+PSH(V,V,1)/ACK(NEXT,CURRENT,0)"]; s43 -> s43[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s43 -> s2[label="RST(V,V,0)/TIMEOUT"]; s43 -> s43[label="ACCEPT/TIMEOUT"]; s43 -> s43[label="FIN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s43 -> s43[label="LISTEN/TIMEOUT"]; s43 -> s43[label="SYN(V,V,0)/ACK(NEXT,CURRENT,0)"]; s43 -> s43[label="RCV/TIMEOUT"]; s43 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s43 -> s43[label="CLOSE/TIMEOUT"]; s43 -> s43[label="ACK(V,V,0)/TIMEOUT"]; s44 [shape="circle" label="s44"]; s44 -> s30[label="CLOSECONNECTION/TIMEOUT"]; s44 -> s4[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s44 -> s4[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s44 -> s4[label="RST(V,V,0)/TIMEOUT"]; s44 -> s44[label="ACCEPT/TIMEOUT"]; s44 -> s45[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s44 -> s44[label="LISTEN/TIMEOUT"]; s44 -> s4[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s44 -> s44[label="RCV/TIMEOUT"]; s44 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s44 -> s42[label="CLOSE/TIMEOUT"]; s44 -> s44[label="ACK(V,V,0)/TIMEOUT"]; s45 [shape="circle" label="s45"]; s45 -> s32[label="CLOSECONNECTION/TIMEOUT"]; s45 -> s45[label="ACK+PSH(V,V,1)/ACK(NEXT,CURRENT,0)"]; s45 -> s45[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s45 -> s4[label="RST(V,V,0)/TIMEOUT"]; s45 -> s45[label="ACCEPT/TIMEOUT"]; s45 -> s45[label="FIN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s45 -> s45[label="LISTEN/TIMEOUT"]; s45 -> s45[label="SYN(V,V,0)/ACK(NEXT,CURRENT,0)"]; s45 -> s45[label="RCV/TIMEOUT"]; s45 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s45 -> s43[label="CLOSE/TIMEOUT"]; s45 -> s45[label="ACK(V,V,0)/TIMEOUT"]; s46 [shape="circle" label="s46"]; s46 -> s35[label="CLOSECONNECTION/TIMEOUT"]; s46 -> s4[label="ACK+PSH(V,V,1)/RST(NEXT,ZERO,0)"]; s46 -> s4[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s46 -> s4[label="RST(V,V,0)/TIMEOUT"]; s46 -> s46[label="ACCEPT/TIMEOUT"]; s46 -> s4[label="FIN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s46 -> s46[label="LISTEN/TIMEOUT"]; s46 -> s9[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s46 -> s46[label="RCV/TIMEOUT"]; s46 -> s4[label="ACK+RST(V,V,0)/TIMEOUT"]; s46 -> s8[label="CLOSE/TIMEOUT"]; s46 -> s4[label="ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s47 [shape="circle" label="s47"]; s47 -> s10[label="CLOSECONNECTION/TIMEOUT"]; s47 -> s47[label="ACK+PSH(V,V,1)/TIMEOUT"]; s47 -> s47[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s47 -> s48[label="RST(V,V,0)/TIMEOUT"]; s47 -> s47[label="ACCEPT/TIMEOUT"]; s47 -> s47[label="FIN+ACK(V,V,0)/TIMEOUT"]; s47 -> s47[label="LISTEN/TIMEOUT"]; s47 -> s47[label="SYN(V,V,0)/TIMEOUT"]; s47 -> s47[label="RCV/TIMEOUT"]; s47 -> s48[label="ACK+RST(V,V,0)/TIMEOUT"]; s47 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s47 -> s47[label="ACK(V,V,0)/TIMEOUT"]; s48 [shape="circle" label="s48"]; s48 -> s12[label="CLOSECONNECTION/TIMEOUT"]; s48 -> s48[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s48 -> s48[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s48 -> s48[label="RST(V,V,0)/TIMEOUT"]; s48 -> s48[label="ACCEPT/TIMEOUT"]; s48 -> s48[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s48 -> s48[label="LISTEN/TIMEOUT"]; s48 -> s50[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s48 -> s48[label="RCV/TIMEOUT"]; s48 -> s48[label="ACK+RST(V,V,0)/TIMEOUT"]; s48 -> s2[label="CLOSE/TIMEOUT"]; s48 -> s48[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s49 [shape="circle" label="s49"]; s49 -> s49[label="CLOSECONNECTION/TIMEOUT"]; s49 -> s49[label="ACK+PSH(V,V,1)/TIMEOUT"]; s49 -> s40[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s49 -> s40[label="RST(V,V,0)/TIMEOUT"]; s49 -> s50[label="ACCEPT/TIMEOUT"]; s49 -> s49[label="FIN+ACK(V,V,0)/TIMEOUT"]; s49 -> s49[label="LISTEN/TIMEOUT"]; s49 -> s40[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s49 -> s49[label="RCV/TIMEOUT"]; s49 -> s40[label="ACK+RST(V,V,0)/TIMEOUT"]; s49 -> s8[label="CLOSE/TIMEOUT"]; s49 -> s49[label="ACK(V,V,0)/TIMEOUT"]; s50 [shape="circle" label="s50"]; s50 -> s18[label="CLOSECONNECTION/TIMEOUT"]; s50 -> s52[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s50 -> s48[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s50 -> s48[label="RST(V,V,0)/TIMEOUT"]; s50 -> s50[label="ACCEPT/TIMEOUT"]; s50 -> s53[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s50 -> s50[label="LISTEN/TIMEOUT"]; s50 -> s48[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s50 -> s50[label="RCV/TIMEOUT"]; s50 -> s48[label="ACK+RST(V,V,0)/TIMEOUT"]; s50 -> s8[label="CLOSE/TIMEOUT"]; s50 -> s51[label="ACK(V,V,0)/TIMEOUT"]; s51 [shape="circle" label="s51"]; s51 -> s27[label="CLOSECONNECTION/TIMEOUT"]; s51 -> s52[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s51 -> s51[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s51 -> s54[label="RST(V,V,0)/TIMEOUT"]; s51 -> s51[label="ACCEPT/TIMEOUT"]; s51 -> s53[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s51 -> s51[label="LISTEN/TIMEOUT"]; s51 -> s51[label="SYN(V,V,0)/TIMEOUT"]; s51 -> s51[label="RCV/TIMEOUT"]; s51 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s51 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s51 -> s51[label="ACK(V,V,0)/TIMEOUT"]; s52 [shape="circle" label="s52"]; s52 -> s28[label="CLOSECONNECTION/TIMEOUT"]; s52 -> s52[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s52 -> s52[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s52 -> s54[label="RST(V,V,0)/TIMEOUT"]; s52 -> s52[label="ACCEPT/TIMEOUT"]; s52 -> s55[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s52 -> s52[label="LISTEN/TIMEOUT"]; s52 -> s52[label="SYN(V,V,0)/TIMEOUT"]; s52 -> s52[label="RCV/TIMEOUT"]; s52 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s52 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s52 -> s52[label="ACK(V,V,0)/TIMEOUT"]; s53 [shape="circle" label="s53"]; s53 -> s26[label="CLOSECONNECTION/TIMEOUT"]; s53 -> s53[label="ACK+PSH(V,V,1)/TIMEOUT"]; s53 -> s53[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s53 -> s54[label="RST(V,V,0)/TIMEOUT"]; s53 -> s53[label="ACCEPT/TIMEOUT"]; s53 -> s53[label="FIN+ACK(V,V,0)/TIMEOUT"]; s53 -> s53[label="LISTEN/TIMEOUT"]; s53 -> s53[label="SYN(V,V,0)/TIMEOUT"]; s53 -> s53[label="RCV/TIMEOUT"]; s53 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s53 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s53 -> s53[label="ACK(V,V,0)/TIMEOUT"]; s54 [shape="circle" label="s54"]; s54 -> s40[label="CLOSECONNECTION/TIMEOUT"]; s54 -> s54[label="ACK+PSH(V,V,1)/RST(ZERO,ZERO,0)"]; s54 -> s54[label="SYN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s54 -> s54[label="RST(V,V,0)/TIMEOUT"]; s54 -> s54[label="ACCEPT/TIMEOUT"]; s54 -> s54[label="FIN+ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s54 -> s54[label="LISTEN/TIMEOUT"]; s54 -> s56[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s54 -> s54[label="RCV/TIMEOUT"]; s54 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s54 -> s2[label="CLOSE/TIMEOUT"]; s54 -> s54[label="ACK(V,V,0)/RST(ZERO,ZERO,0)"]; s55 [shape="circle" label="s55"]; s55 -> s41[label="CLOSECONNECTION/TIMEOUT"]; s55 -> s55[label="ACK+PSH(V,V,1)/TIMEOUT"]; s55 -> s55[label="SYN+ACK(V,V,0)/ACK(NEXT,CURRENT,0)"]; s55 -> s54[label="RST(V,V,0)/TIMEOUT"]; s55 -> s55[label="ACCEPT/TIMEOUT"]; s55 -> s55[label="FIN+ACK(V,V,0)/TIMEOUT"]; s55 -> s55[label="LISTEN/TIMEOUT"]; s55 -> s55[label="SYN(V,V,0)/TIMEOUT"]; s55 -> s55[label="RCV/TIMEOUT"]; s55 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s55 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s55 -> s55[label="ACK(V,V,0)/TIMEOUT"]; s56 [shape="circle" label="s56"]; s56 -> s49[label="CLOSECONNECTION/TIMEOUT"]; s56 -> s56[label="ACK+PSH(V,V,1)/TIMEOUT"]; s56 -> s54[label="SYN+ACK(V,V,0)/RST(NEXT,ZERO,0)"]; s56 -> s54[label="RST(V,V,0)/TIMEOUT"]; s56 -> s56[label="ACCEPT/TIMEOUT"]; s56 -> s56[label="FIN+ACK(V,V,0)/TIMEOUT"]; s56 -> s56[label="LISTEN/TIMEOUT"]; s56 -> s54[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s56 -> s56[label="RCV/TIMEOUT"]; s56 -> s54[label="ACK+RST(V,V,0)/TIMEOUT"]; s56 -> s8[label="CLOSE/TIMEOUT"]; s56 -> s56[label="ACK(V,V,0)/TIMEOUT"]; __start0 -> s0; } ================================================ FILE: DotModels/TCP/tcp_server_windows_trans.dot ================================================ digraph G { __start0 [label="" shape="none"]; s0 [shape="circle" label="s0"]; s0 -> s0[label="CLOSECONNECTION/TIMEOUT"]; s0 -> s0[label="ACK+PSH(V,V,1)/TIMEOUT"]; s0 -> s0[label="SYN+ACK(V,V,0)/TIMEOUT"]; s0 -> s0[label="RST(V,V,0)/TIMEOUT"]; s0 -> s0[label="ACCEPT/TIMEOUT"]; s0 -> s0[label="FIN+ACK(V,V,0)/TIMEOUT"]; s0 -> s1[label="LISTEN/TIMEOUT"]; s0 -> s0[label="SYN(V,V,0)/TIMEOUT"]; s0 -> s0[label="RCV/TIMEOUT"]; s0 -> s0[label="ACK+RST(V,V,0)/TIMEOUT"]; s0 -> s2[label="CLOSE/TIMEOUT"]; s0 -> s0[label="SEND/TIMEOUT"]; s0 -> s0[label="ACK(V,V,0)/TIMEOUT"]; s1 [shape="circle" label="s1"]; s1 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s1 -> s1[label="ACK+PSH(V,V,1)/TIMEOUT"]; s1 -> s1[label="SYN+ACK(V,V,0)/TIMEOUT"]; s1 -> s1[label="RST(V,V,0)/TIMEOUT"]; s1 -> s3[label="ACCEPT/TIMEOUT"]; s1 -> s1[label="FIN+ACK(V,V,0)/TIMEOUT"]; s1 -> s1[label="LISTEN/TIMEOUT"]; s1 -> s4[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s1 -> s1[label="RCV/TIMEOUT"]; s1 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s1 -> s2[label="CLOSE/TIMEOUT"]; s1 -> s1[label="SEND/TIMEOUT"]; s1 -> s1[label="ACK(V,V,0)/TIMEOUT"]; s2 [shape="circle" label="s2"]; s2 -> s2[label="CLOSECONNECTION/TIMEOUT"]; s2 -> s2[label="ACK+PSH(V,V,1)/TIMEOUT"]; s2 -> s2[label="SYN+ACK(V,V,0)/TIMEOUT"]; s2 -> s2[label="RST(V,V,0)/TIMEOUT"]; s2 -> s2[label="ACCEPT/TIMEOUT"]; s2 -> s2[label="FIN+ACK(V,V,0)/TIMEOUT"]; s2 -> s2[label="LISTEN/TIMEOUT"]; s2 -> s2[label="SYN(V,V,0)/TIMEOUT"]; s2 -> s2[label="RCV/TIMEOUT"]; s2 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s2 -> s2[label="CLOSE/TIMEOUT"]; s2 -> s2[label="SEND/TIMEOUT"]; s2 -> s2[label="ACK(V,V,0)/TIMEOUT"]; s3 [shape="circle" label="s3"]; s3 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s3 -> s3[label="ACK+PSH(V,V,1)/TIMEOUT"]; s3 -> s3[label="SYN+ACK(V,V,0)/TIMEOUT"]; s3 -> s3[label="RST(V,V,0)/TIMEOUT"]; s3 -> s3[label="ACCEPT/TIMEOUT"]; s3 -> s3[label="FIN+ACK(V,V,0)/TIMEOUT"]; s3 -> s3[label="LISTEN/TIMEOUT"]; s3 -> s5[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s3 -> s3[label="RCV/TIMEOUT"]; s3 -> s3[label="ACK+RST(V,V,0)/TIMEOUT"]; s3 -> s2[label="CLOSE/TIMEOUT"]; s3 -> s3[label="SEND/TIMEOUT"]; s3 -> s3[label="ACK(V,V,0)/TIMEOUT"]; s4 [shape="circle" label="s4"]; s4 -> s4[label="CLOSECONNECTION/TIMEOUT"]; s4 -> s7[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s4 -> s4[label="SYN+ACK(V,V,0)/TIMEOUT"]; s4 -> s1[label="RST(V,V,0)/TIMEOUT"]; s4 -> s5[label="ACCEPT/TIMEOUT"]; s4 -> s9[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s4 -> s4[label="LISTEN/TIMEOUT"]; s4 -> s1[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s4 -> s4[label="RCV/TIMEOUT"]; s4 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s4 -> s8[label="CLOSE/TIMEOUT"]; s4 -> s4[label="SEND/TIMEOUT"]; s4 -> s6[label="ACK(V,V,0)/TIMEOUT"]; s5 [shape="circle" label="s5"]; s5 -> s4[label="CLOSECONNECTION/TIMEOUT"]; s5 -> s12[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s5 -> s5[label="SYN+ACK(V,V,0)/TIMEOUT"]; s5 -> s3[label="RST(V,V,0)/TIMEOUT"]; s5 -> s5[label="ACCEPT/TIMEOUT"]; s5 -> s10[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s5 -> s5[label="LISTEN/TIMEOUT"]; s5 -> s3[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s5 -> s5[label="RCV/TIMEOUT"]; s5 -> s3[label="ACK+RST(V,V,0)/TIMEOUT"]; s5 -> s8[label="CLOSE/TIMEOUT"]; s5 -> s5[label="SEND/TIMEOUT"]; s5 -> s11[label="ACK(V,V,0)/TIMEOUT"]; s6 [shape="circle" label="s6"]; s6 -> s6[label="CLOSECONNECTION/TIMEOUT"]; s6 -> s7[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s6 -> s13[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s6 -> s13[label="RST(V,V,0)/TIMEOUT"]; s6 -> s11[label="ACCEPT/TIMEOUT"]; s6 -> s9[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s6 -> s6[label="LISTEN/TIMEOUT"]; s6 -> s13[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s6 -> s6[label="RCV/TIMEOUT"]; s6 -> s13[label="ACK+RST(V,V,0)/TIMEOUT"]; s6 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s6 -> s6[label="SEND/TIMEOUT"]; s6 -> s6[label="ACK(V,V,0)/TIMEOUT"]; s7 [shape="circle" label="s7"]; s7 -> s7[label="CLOSECONNECTION/TIMEOUT"]; s7 -> s7[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s7 -> s13[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s7 -> s13[label="RST(V,V,0)/TIMEOUT"]; s7 -> s12[label="ACCEPT/TIMEOUT"]; s7 -> s14[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s7 -> s7[label="LISTEN/TIMEOUT"]; s7 -> s13[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s7 -> s7[label="RCV/TIMEOUT"]; s7 -> s13[label="ACK+RST(V,V,0)/TIMEOUT"]; s7 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s7 -> s7[label="SEND/TIMEOUT"]; s7 -> s7[label="ACK(V,V,0)/TIMEOUT"]; s8 [shape="circle" label="s8"]; s8 -> s8[label="CLOSECONNECTION/TIMEOUT"]; s8 -> s2[label="ACK+PSH(V,V,1)/RST(NEXT,CURRENT,0)"]; s8 -> s8[label="SYN+ACK(V,V,0)/TIMEOUT"]; s8 -> s2[label="RST(V,V,0)/TIMEOUT"]; s8 -> s8[label="ACCEPT/TIMEOUT"]; s8 -> s2[label="FIN+ACK(V,V,0)/RST(NEXT,CURRENT,0)"]; s8 -> s8[label="LISTEN/TIMEOUT"]; s8 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s8 -> s8[label="RCV/TIMEOUT"]; s8 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s8 -> s8[label="CLOSE/TIMEOUT"]; s8 -> s8[label="SEND/TIMEOUT"]; s8 -> s2[label="ACK(V,V,0)/RST(NEXT,CURRENT,0)"]; s9 [shape="circle" label="s9"]; s9 -> s9[label="CLOSECONNECTION/TIMEOUT"]; s9 -> s9[label="ACK+PSH(V,V,1)/TIMEOUT"]; s9 -> s13[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s9 -> s13[label="RST(V,V,0)/TIMEOUT"]; s9 -> s10[label="ACCEPT/TIMEOUT"]; s9 -> s9[label="FIN+ACK(V,V,0)/TIMEOUT"]; s9 -> s9[label="LISTEN/TIMEOUT"]; s9 -> s13[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s9 -> s9[label="RCV/TIMEOUT"]; s9 -> s13[label="ACK+RST(V,V,0)/TIMEOUT"]; s9 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s9 -> s9[label="SEND/TIMEOUT"]; s9 -> s9[label="ACK(V,V,0)/TIMEOUT"]; s10 [shape="circle" label="s10"]; s10 -> s15[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s10 -> s10[label="ACK+PSH(V,V,1)/TIMEOUT"]; s10 -> s17[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s10 -> s17[label="RST(V,V,0)/TIMEOUT"]; s10 -> s10[label="ACCEPT/TIMEOUT"]; s10 -> s10[label="FIN+ACK(V,V,0)/TIMEOUT"]; s10 -> s10[label="LISTEN/TIMEOUT"]; s10 -> s17[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s10 -> s10[label="RCV/TIMEOUT"]; s10 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s10 -> s16[label="CLOSE/TIMEOUT"]; s10 -> s10[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s10 -> s10[label="ACK(V,V,0)/TIMEOUT"]; s11 [shape="circle" label="s11"]; s11 -> s19[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s11 -> s12[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s11 -> s17[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s11 -> s17[label="RST(V,V,0)/TIMEOUT"]; s11 -> s11[label="ACCEPT/TIMEOUT"]; s11 -> s10[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s11 -> s11[label="LISTEN/TIMEOUT"]; s11 -> s17[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s11 -> s18[label="RCV/TIMEOUT"]; s11 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s11 -> s20[label="CLOSE/TIMEOUT"]; s11 -> s11[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s11 -> s11[label="ACK(V,V,0)/TIMEOUT"]; s12 [shape="circle" label="s12"]; s12 -> s1[label="CLOSECONNECTION/ACK+RST(NEXT,CURRENT,0)"]; s12 -> s12[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s12 -> s17[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s12 -> s17[label="RST(V,V,0)/TIMEOUT"]; s12 -> s12[label="ACCEPT/TIMEOUT"]; s12 -> s22[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s12 -> s12[label="LISTEN/TIMEOUT"]; s12 -> s17[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s12 -> s11[label="RCV/TIMEOUT"]; s12 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s12 -> s21[label="CLOSE/TIMEOUT"]; s12 -> s12[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s12 -> s12[label="ACK(V,V,0)/TIMEOUT"]; s13 [shape="circle" label="s13"]; s13 -> s13[label="CLOSECONNECTION/TIMEOUT"]; s13 -> s13[label="ACK+PSH(V,V,1)/TIMEOUT"]; s13 -> s13[label="SYN+ACK(V,V,0)/TIMEOUT"]; s13 -> s13[label="RST(V,V,0)/TIMEOUT"]; s13 -> s17[label="ACCEPT/TIMEOUT"]; s13 -> s13[label="FIN+ACK(V,V,0)/TIMEOUT"]; s13 -> s13[label="LISTEN/TIMEOUT"]; s13 -> s13[label="SYN(V,V,0)/TIMEOUT"]; s13 -> s13[label="RCV/TIMEOUT"]; s13 -> s13[label="ACK+RST(V,V,0)/TIMEOUT"]; s13 -> s2[label="CLOSE/TIMEOUT"]; s13 -> s13[label="SEND/TIMEOUT"]; s13 -> s13[label="ACK(V,V,0)/TIMEOUT"]; s14 [shape="circle" label="s14"]; s14 -> s14[label="CLOSECONNECTION/TIMEOUT"]; s14 -> s14[label="ACK+PSH(V,V,1)/TIMEOUT"]; s14 -> s13[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s14 -> s13[label="RST(V,V,0)/TIMEOUT"]; s14 -> s22[label="ACCEPT/TIMEOUT"]; s14 -> s14[label="FIN+ACK(V,V,0)/TIMEOUT"]; s14 -> s14[label="LISTEN/TIMEOUT"]; s14 -> s13[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s14 -> s14[label="RCV/TIMEOUT"]; s14 -> s13[label="ACK+RST(V,V,0)/TIMEOUT"]; s14 -> s2[label="CLOSE/ACK+RST(NEXT,CURRENT,0)"]; s14 -> s14[label="SEND/TIMEOUT"]; s14 -> s14[label="ACK(V,V,0)/TIMEOUT"]; s15 [shape="circle" label="s15"]; s15 -> s15[label="CLOSECONNECTION/TIMEOUT"]; s15 -> s1[label="ACK+PSH(V,V,1)/TIMEOUT"]; s15 -> s1[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s15 -> s1[label="RST(V,V,0)/TIMEOUT"]; s15 -> s24[label="ACCEPT/TIMEOUT"]; s15 -> s1[label="FIN+ACK(V,V,0)/TIMEOUT"]; s15 -> s15[label="LISTEN/TIMEOUT"]; s15 -> s1[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s15 -> s15[label="RCV/TIMEOUT"]; s15 -> s1[label="ACK+RST(V,V,0)/TIMEOUT"]; s15 -> s23[label="CLOSE/TIMEOUT"]; s15 -> s15[label="SEND/TIMEOUT"]; s15 -> s1[label="ACK(V,V,0)/TIMEOUT"]; s16 [shape="circle" label="s16"]; s16 -> s23[label="CLOSECONNECTION/ACK+FIN(NEXT,CURRENT,0)"]; s16 -> s16[label="ACK+PSH(V,V,1)/TIMEOUT"]; s16 -> s2[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s16 -> s2[label="RST(V,V,0)/TIMEOUT"]; s16 -> s16[label="ACCEPT/TIMEOUT"]; s16 -> s16[label="FIN+ACK(V,V,0)/TIMEOUT"]; s16 -> s16[label="LISTEN/TIMEOUT"]; s16 -> s2[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s16 -> s16[label="RCV/TIMEOUT"]; s16 -> s2[label="ACK+RST(V,V,0)/TIMEOUT"]; s16 -> s16[label="CLOSE/TIMEOUT"]; s16 -> s16[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s16 -> s16[label="ACK(V,V,0)/TIMEOUT"]; s17 [shape="circle" label="s17"]; s17 -> s1[label="CLOSECONNECTION/TIMEOUT"]; s17 -> s17[label="ACK+PSH(V,V,1)/TIMEOUT"]; s17 -> s17[label="SYN+ACK(V,V,0)/TIMEOUT"]; s17 -> s17[label="RST(V,V,0)/TIMEOUT"]; s17 -> s17[label="ACCEPT/TIMEOUT"]; s17 -> s17[label="FIN+ACK(V,V,0)/TIMEOUT"]; s17 -> s17[label="LISTEN/TIMEOUT"]; s17 -> s25[label="SYN(V,V,0)/ACK+SYN(FRESH,NEXT,0)"]; s17 -> s17[label="RCV/TIMEOUT"]; s17 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s17 -> s2[label="CLOSE/TIMEOUT"]; s17 -> s17[label="SEND/TIMEOUT"]; s17 -> s17[label="ACK(V,V,0)/TIMEOUT"]; s18 [shape="circle" label="s18"]; s18 -> s1[label="CLOSECONNECTION/ACK+RST(NEXT,CURRENT,0)"]; s18 -> s11[label="ACK+PSH(V,V,1)/ACK(NEXT,NEXT,0)"]; s18 -> s17[label="SYN+ACK(V,V,0)/RST(NEXT,FRESH,0)"]; s18 -> s17[label="RST(V,V,0)/TIMEOUT"]; s18 -> s18[label="ACCEPT/TIMEOUT"]; s18 -> s10[label="FIN+ACK(V,V,0)/ACK(NEXT,NEXT,0)"]; s18 -> s18[label="LISTEN/TIMEOUT"]; s18 -> s17[label="SYN(V,V,0)/ACK+RST(ZERO,NEXT,0)"]; s18 -> s18[label="RCV/TIMEOUT"]; s18 -> s17[label="ACK+RST(V,V,0)/TIMEOUT"]; s18 -> s20[label="CLOSE/TIMEOUT"]; s18 -> s18[label="SEND/ACK+PSH(NEXT,CURRENT,1)"]; s18 -> s18[label="ACK(V,V,0)/TIMEOUT"]; s19 [shape="circle" label="s19"]; s19 -> s19[label="CLOSECONNECTION/TIMEOUT"]; s19 -> s1[label="ACK+PSH(V,V,1)/ACK+RST(NEXT,NEXT,0)"]; s19 -> 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[shape="circle" margin=0 label="q2"]; q1 [shape="circle" margin=0 label="q1"]; q0 -> q2 [label="b/1"]; q0 -> q0 [label="a/0"]; q0 -> q0 [label="b/2"]; q0 -> q2 [label="a/1"]; q2 -> q1 [label="b/0"]; q2 -> q0 [label="a/2"]; q1 -> q1 [label="a/2"]; q1 -> q2 [label="b/0"]; __start0 -> q1; } ================================================ FILE: DotModels/onfsm_2.dot ================================================ digraph g { __start0 [label="" shape="none"]; q1 [shape="circle" margin=0 label="q1"]; q0 [shape="circle" margin=0 label="q0"]; q2 [shape="circle" margin=0 label="q2"]; q2 -> q0 [label="b/O"]; q0 -> q1 [label="a/2"]; q2 -> q1 [label="b/0"]; q2 -> q2 [label="a/1"]; q1 -> q2 [label="b/0"]; q1 -> q0 [label="a/2"]; q0 -> q0 [label="b/0"]; __start0 -> q0; } ================================================ FILE: DotModels/onfsm_3.dot ================================================ digraph g { __start0 [label="" shape="none"]; q5 [shape="circle" margin=0 label="q5"]; q2 [shape="circle" margin=0 label="q2"]; q7 [shape="circle" margin=0 label="q7"]; q8 [shape="circle" margin=0 label="q8"]; q4 [shape="circle" margin=0 label="q4"]; q6 [shape="circle" margin=0 label="q6"]; q0 [shape="circle" margin=0 label="q0"]; q1 [shape="circle" margin=0 label="q1"]; q3 [shape="circle" margin=0 label="q3"]; q8 -> q6 [label="b/0"]; q5 -> q1 [label="b/O"]; q1 -> q3 [label="a/2"]; q7 -> q2 [label="b/0"]; q2 -> q8 [label="b/0"]; q8 -> q8 [label="a/1"]; q6 -> q5 [label="b/0"]; q2 -> q4 [label="a/2"]; q4 -> q4 [label="b/0"]; q1 -> q1 [label="b/0"]; q5 -> q5 [label="a/1"]; q3 -> q7 [label="b/0"]; q6 -> q0 [label="a/2"]; q0 -> q6 [label="a/2"]; q4 -> q2 [label="a/2"]; q3 -> q1 [label="a/2"]; q0 -> q0 [label="b/0"]; q7 -> q7 [label="a/1"]; __start0 -> q1; } ================================================ FILE: DotModels/onfsm_4.dot ================================================ digraph Angluin_Mealy { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s0 -> s1 [label="a/x"]; s0 -> s2 [label="a/y"]; s0 -> s3 [label="a/z"]; s1 -> s1 [label="a/0"]; s2 -> s2 [label="a/0"]; s3 -> s3 [label="a/0"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/onfsm_5.dot ================================================ digraph Angluin_Mealy { s0 [label=s0]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4]; s0 -> s1 [label="a/X"]; s0 -> s2 [label="a/Y"]; s0 -> s0 [label="b/Z"]; s1 -> s1 [label="a/X"]; s1 -> s3 [label="b/Z"]; s2 -> s2 [label="a/Y"]; s2 -> s4 [label="b/W"]; s3 -> s0 [label="a/V"]; s3 -> s3 [label="b/Z"]; s4 -> s0 [label="a/V"]; s4 -> s4 [label="b/W"]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/tomitaGrammars/tomita_1.dot ================================================ digraph "tomita_1" { s0 [label=s0, shape=doublecircle]; s1 [label=s1]; s0 -> s1 [label=0]; s0 -> s0 [label=1]; s1 -> s1 [label=0]; s1 -> s1 [label=1]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/tomitaGrammars/tomita_2.dot ================================================ digraph "tomita_2" { s0 [label=s0]; s1 [label=s1]; s2 [label=s2, shape=doublecircle]; s3 [label=s3]; s0 -> s3 [label=0]; s0 -> s1 [label=1]; s1 -> s2 [label=0]; s1 -> s3 [label=1]; s2 -> s3 [label=0]; s2 -> s1 [label=1]; s3 -> s3 [label=0]; s3 -> s3 [label=1]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/tomitaGrammars/tomita_3.dot ================================================ digraph "tomita_3" { s0 [label=s0, shape=doublecircle]; s1 [label=s1, shape=doublecircle]; s2 [label=s2]; s3 [label=s3]; s4 [label=s4, shape=doublecircle]; s0 -> s0 [label=0]; s0 -> s1 [label=1]; s1 -> s2 [label=0]; s1 -> s0 [label=1]; s2 -> s4 [label=0]; s2 -> s3 [label=1]; s3 -> s3 [label=0]; s3 -> s3 [label=1]; s4 -> s2 [label=0]; s4 -> s4 [label=1]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/tomitaGrammars/tomita_4.dot ================================================ digraph "tomita_4" { s0 [label=s0, shape=doublecircle]; s1 [label=s1, shape=doublecircle]; s2 [label=s2, shape=doublecircle]; s3 [label=s3]; s0 -> s1 [label=0]; s0 -> s0 [label=1]; s1 -> s2 [label=0]; s1 -> s0 [label=1]; s2 -> s3 [label=0]; s2 -> s0 [label=1]; s3 -> s3 [label=0]; s3 -> s3 [label=1]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/tomitaGrammars/tomita_5.dot ================================================ digraph "tomita_5" { s0 [label=s0, shape=doublecircle]; s1 [label=s1]; s2 [label=s2]; s3 [label=s3]; s0 -> s1 [label=0]; s0 -> s2 [label=1]; s1 -> s0 [label=0]; s1 -> s3 [label=1]; s2 -> s3 [label=0]; s2 -> s0 [label=1]; s3 -> s2 [label=0]; s3 -> s1 [label=1]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/tomitaGrammars/tomita_6.dot ================================================ digraph "tomita_6" { s0 [label=s0, shape=doublecircle]; s1 [label=s1]; s2 [label=s2]; s0 -> s1 [label=0]; s0 -> s2 [label=1]; s1 -> s2 [label=0]; s1 -> s0 [label=1]; s2 -> s0 [label=0]; s2 -> s1 [label=1]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: DotModels/tomitaGrammars/tomita_7.dot ================================================ digraph "tomita_7" { s0 [label=s0, shape=doublecircle]; s1 [label=s1, shape=doublecircle]; s2 [label=s2, shape=doublecircle]; s3 [label=s3, shape=doublecircle]; s4 [label=s4]; s0 -> s0 [label=0]; s0 -> s1 [label=1]; s1 -> s2 [label=0]; s1 -> s1 [label=1]; s2 -> s2 [label=0]; s2 -> s3 [label=1]; s3 -> s4 [label=0]; s3 -> s3 [label=1]; s4 -> s4 [label=0]; s4 -> s4 [label=1]; __start0 [label="", shape=none]; __start0 -> s0 [label=""]; } ================================================ FILE: Examples.py ================================================ def random_deterministic_model_example(): from aalpy.utils import generate_random_deterministic_automata from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWMethodEqOracle from aalpy.learning_algs import run_KV model_type = 'mealy' # or 'moore', 'dfa' # for random dfa's you can also define num_accepting_states random_model = generate_random_deterministic_automata(automaton_type=model_type, num_states=100, input_alphabet_size=3, output_alphabet_size=4) sul = AutomatonSUL(random_model) input_alphabet = random_model.get_input_alphabet() # select any of the oracles eq_oracle = RandomWMethodEqOracle(input_alphabet, sul, walks_per_state=10, walk_len=20) learned_model = run_KV(input_alphabet, sul, eq_oracle, model_type) assert learned_model == random_model return learned_model def angluin_seminal_example(): """ Example automaton from Angluin's seminal paper. :return: learned DFA """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWalkEqOracle from aalpy.learning_algs import run_Lstar from aalpy.utils import get_Angluin_dfa dfa = get_Angluin_dfa() alphabet = dfa.get_input_alphabet() sul = AutomatonSUL(dfa) eq_oracle = RandomWalkEqOracle(alphabet, sul, 500) learned_dfa = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa', cache_and_non_det_check=True, cex_processing=None, print_level=3) assert learned_dfa == dfa return learned_dfa def angluin_seminal_example_lsharp(): """ Example automaton from Angluin's seminal paper. :return: learned DFA """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWalkEqOracle from aalpy.learning_algs import run_Lstar, run_Lsharp from aalpy.utils import get_Angluin_dfa dfa = get_Angluin_dfa() alphabet = dfa.get_input_alphabet() sul = AutomatonSUL(dfa) eq_oracle = RandomWalkEqOracle(alphabet, sul, 500) learned_dfa = run_Lsharp(alphabet, sul, eq_oracle, automaton_type='dfa', extension_rule="SepSeq", separation_rule="ADS", max_learning_rounds=50, print_level=3) assert learned_dfa == dfa return learned_dfa def tomita_example(tomita_number=3): """ Pass a tomita function to this example and learn it. :param: function of the desired tomita grammar :rtype: Dfa :return DFA representing tomita grammar """ from aalpy.SULs import TomitaSUL from aalpy.learning_algs import run_Lstar from aalpy.oracles import StatePrefixEqOracle tomita_sul = TomitaSUL(tomita_number) alphabet = [0, 1] state_origin_eq_oracle = StatePrefixEqOracle(alphabet, tomita_sul, walks_per_state=50, walk_len=10) # or replace run_Lstar with run_KV learned_dfa = run_Lstar(alphabet, tomita_sul, state_origin_eq_oracle, automaton_type='dfa', ) return learned_dfa def regex_example(regex, alphabet): """ Learn a regular expression. :param regex: regex to learn :param alphabet: alphabet of the regex :return: DFA representing the regex """ from aalpy.SULs import RegexSUL from aalpy.oracles import StatePrefixEqOracle from aalpy.learning_algs import run_Lstar regex_sul = RegexSUL(regex) eq_oracle = StatePrefixEqOracle(alphabet, regex_sul, walks_per_state=2000, walk_len=15) # or replace run_Lstar with run_KV learned_regex = run_Lstar(alphabet, regex_sul, eq_oracle, automaton_type='dfa') return learned_regex def learn_date_validator(): from aalpy.base import SUL from aalpy.utils import DateValidator from aalpy.oracles import StatePrefixEqOracle from aalpy.learning_algs import run_Lstar class DateSUL(SUL): """ An example implementation of a system under learning that can be used to learn the behavior of the date validator. """ def __init__(self): super().__init__() # DateValidator is a black-box class used for date string verification # The format of the dates is %d/%m/%Y' # Its method is_date_accepted returns True if date is accepted, False otherwise self.dv = DateValidator() self.string = "" def pre(self): # reset the string used for testing self.string = "" pass def post(self): pass def step(self, letter): # add the input to the current string if letter is not None: self.string += str(letter) # test if the current sting is accepted return self.dv.is_date_accepted(self.string) # instantiate the SUL sul = DateSUL() # define the input alphabet alphabet = list(range(0, 9)) + ['/'] # define a equivalence oracle eq_oracle = StatePrefixEqOracle(alphabet, sul, walks_per_state=500, walk_len=15) # run the learning algorithm learned_model = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa') # visualize the automaton learned_model.visualize() def bluetooth_Lsharp(): from aalpy.utils import load_automaton_from_file from aalpy.SULs import MealySUL from aalpy.oracles import WpMethodEqOracle from aalpy.learning_algs import run_Lsharp mealy_machine = load_automaton_from_file(f'./DotModels/Bluetooth/CYW43455.dot', automaton_type='mealy') input_alphabet = mealy_machine.get_input_alphabet() sul_mealy = MealySUL(mealy_machine) eq_oracle = WpMethodEqOracle(input_alphabet, sul_mealy, len(mealy_machine.states)) # Extension rule options: {"Nothing", "SepSeq", "ADS"} # Separation rule options: {"SepSeq", "ADS"} learned_mealy = run_Lsharp(input_alphabet, sul_mealy, eq_oracle, automaton_type='mealy', extension_rule=None, separation_rule="SepSeq", max_learning_rounds=50, print_level=3) def bluetooth_adaptive_Lsharp(): from aalpy.utils import load_automaton_from_file from aalpy.SULs import MealySUL from aalpy.oracles import WpMethodEqOracle from aalpy.learning_algs import run_adaptive_Lsharp reference1 = load_automaton_from_file(f'./DotModels/Bluetooth/CC2650.dot', automaton_type='mealy') reference2 = load_automaton_from_file(f'./DotModels/Bluetooth/nRF52832.dot', automaton_type='mealy') reference3 = load_automaton_from_file(f'./DotModels/Bluetooth/CC2640R2-no-feature-req.dot', automaton_type='mealy') target = load_automaton_from_file(f'./DotModels/Bluetooth/CYW43455.dot', automaton_type='mealy') input_alphabet = target.get_input_alphabet() sul_mealy = MealySUL(target) eq_oracle = WpMethodEqOracle(input_alphabet, sul_mealy, len(target.states)) # Rebuilding options: {True, False} # State Matching options: {None, "Total", "Approximate"} learned_mealy = run_adaptive_Lsharp(input_alphabet, sul_mealy, [reference1, reference2, reference3], eq_oracle, automaton_type='mealy', extension_rule='SepSeq', separation_rule="ADS", rebuilding=True, state_matching="Approximate", print_level=2) def random_deterministic_example_with_provided_sequences(): from random import choice, randint from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_Lstar from aalpy.utils import generate_random_deterministic_automata random_mealy = generate_random_deterministic_automata('mealy', num_states=10, input_alphabet_size=4, output_alphabet_size=3) input_alphabet = random_mealy.get_input_alphabet() sul_mealy = AutomatonSUL(random_mealy) # samples obtained form somewhere else samples = [] for _ in range(1000): inputs = tuple(choice(input_alphabet) for _ in range(randint(4, 12))) outputs = sul_mealy.query(inputs) input_output_pair = (inputs, outputs) samples.append(input_output_pair) from aalpy.oracles import RandomWalkEqOracle random_walk_eq_oracle = RandomWalkEqOracle(input_alphabet, sul_mealy, 5000) learned_mealy = run_Lstar(input_alphabet, sul_mealy, random_walk_eq_oracle, automaton_type='mealy', samples=samples) def big_input_alphabet_example(): """ Small example where input alphabet can be huge and outputs are just true and false (DFA). """ from aalpy.base import SUL from aalpy.learning_algs import run_Lstar from aalpy.oracles import RandomWMethodEqOracle input_alphabet_size = 1000 automaton_depth = 4 class alternatingSUL(SUL): def __init__(self): super().__init__() self.counter = 0 def pre(self): self.counter = 0 def post(self): pass def step(self, letter): if letter is None: return False out = letter % 2 self.counter = min(self.counter + 1, automaton_depth) if self.counter % 2 == 1: return not out return out input_al = list(range(input_alphabet_size)) sul = alternatingSUL() eq_oracle = RandomWMethodEqOracle(input_al, sul) model = run_Lstar(input_al, sul, eq_oracle, 'dfa', cache_and_non_det_check=False) return model def random_onfsm_example(num_states, input_size, output_size, n_sampling): """ Generate and learn random ONFSM. :param num_states: number of states of the randomly generated automaton :param input_size: size of the input alphabet :param output_size: size of the output alphabet :param n_sampling: number of times each query will be repeated to ensure that all non-determinist outputs are observed :return: learned ONFSM """ from aalpy.SULs import AutomatonSUL from aalpy.utils import generate_random_ONFSM from aalpy.oracles import RandomWalkEqOracle, RandomWordEqOracle from aalpy.learning_algs import run_non_det_Lstar onfsm = generate_random_ONFSM(num_states=num_states, num_inputs=input_size, num_outputs=output_size) alphabet = onfsm.get_input_alphabet() sul = AutomatonSUL(onfsm) eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=500, reset_prob=0.15, reset_after_cex=True) eq_oracle = RandomWordEqOracle(alphabet, sul, num_walks=500, min_walk_len=8, max_walk_len=20) learned_model = run_non_det_Lstar(alphabet, sul, eq_oracle=eq_oracle, n_sampling=n_sampling, print_level=2) return learned_model def random_mdp_example(): from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWalkEqOracle from aalpy.learning_algs import run_stochastic_Lstar from aalpy.utils import generate_random_mdp mdp = generate_random_mdp(num_states=10, input_size=3, output_size=3) input_alphabet = mdp.get_input_alphabet() sul = AutomatonSUL(mdp) eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=5000, reset_prob=0.11, reset_after_cex=False) learned_mdp = run_stochastic_Lstar(input_alphabet, sul, eq_oracle, min_rounds=5, max_rounds=50) return learned_mdp def learn_python_class(): """ Learn a Mealy machine where inputs are methods and arguments of the class that serves as SUL. :return: Mealy machine """ # class from aalpy.SULs import PyClassSUL, FunctionDecorator from aalpy.oracles import StatePrefixEqOracle from aalpy.learning_algs import run_Lstar from aalpy.utils import MockMqttExample mqtt = MockMqttExample input_al = [FunctionDecorator(mqtt.connect), FunctionDecorator(mqtt.disconnect), FunctionDecorator(mqtt.subscribe, 'topic'), FunctionDecorator(mqtt.unsubscribe, 'topic'), FunctionDecorator(mqtt.publish, 'topic')] sul = PyClassSUL(mqtt) eq_oracle = StatePrefixEqOracle(input_al, sul, walks_per_state=20, walk_len=20) mealy = run_Lstar(input_al, sul, eq_oracle=eq_oracle, automaton_type='mealy', cache_and_non_det_check=True) mealy.visualize() def mqtt_example(): from aalpy.base import SUL from aalpy.oracles import RandomWalkEqOracle from aalpy.learning_algs import run_Lstar from aalpy.utils import MockMqttExample class MQTT_SUL(SUL): def __init__(self): super().__init__() self.mqtt = MockMqttExample() def pre(self): self.mqtt.state = 'CONCLOSED' def post(self): self.mqtt.topics.clear() def step(self, letter): if letter == 'connect': return self.mqtt.connect() elif letter == 'disconnect': return self.mqtt.disconnect() elif letter == 'publish': return self.mqtt.publish(topic='test') elif letter == 'subscribe': return self.mqtt.subscribe(topic='test') else: return self.mqtt.unsubscribe(topic='test') sul = MQTT_SUL() input_al = ['connect', 'disconnect', 'publish', 'subscribe', 'unsubscribe'] eq_oracle = RandomWalkEqOracle(input_al, sul, num_steps=2000, reset_after_cex=True, reset_prob=0.15) mealy = run_Lstar(input_al, sul, eq_oracle=eq_oracle, automaton_type='mealy', cache_and_non_det_check=True, print_level=3) mealy.visualize() def onfsm_mealy_paper_example(): """ Learning a ONFSM presented in 'Learning Finite State Models of Observable Nondeterministic Systems in a Testing Context'. :return: learned ONFSM """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWordEqOracle from aalpy.learning_algs import run_non_det_Lstar from aalpy.utils import get_benchmark_ONFSM onfsm = get_benchmark_ONFSM() alphabet = onfsm.get_input_alphabet() sul = AutomatonSUL(onfsm) eq_oracle = RandomWordEqOracle(alphabet, sul, num_walks=500, min_walk_len=5, max_walk_len=12) learned_onfsm = run_non_det_Lstar(alphabet, sul, eq_oracle, n_sampling=10, print_level=2) return learned_onfsm def multi_client_mqtt_example(): """ Example from paper P'Learning Abstracted Non-deterministic Finite State Machines'. https://link.springer.com/chapter/10.1007/978-3-030-64881-7_4 Returns: learned automaton """ import random from aalpy.base import SUL from aalpy.oracles import RandomWalkEqOracle from aalpy.learning_algs import run_abstracted_ONFSM_Lstar from aalpy.SULs import AutomatonSUL from aalpy.utils import load_automaton_from_file class Multi_Client_MQTT_Mapper(SUL): def __init__(self): super().__init__() five_clients_mqtt_mealy = load_automaton_from_file('DotModels/five_clients_mqtt_abstracted_onfsm.dot', automaton_type='mealy') self.five_client_mqtt = AutomatonSUL(five_clients_mqtt_mealy) self.connected_clients = set() self.subscribed_clients = set() self.clients = ('c0', 'c1', 'c2', 'c3', 'c4') def get_input_alphabet(self): return ['connect', 'disconnect', 'subscribe', 'unsubscribe', 'publish'] def pre(self): self.five_client_mqtt.pre() def post(self): self.five_client_mqtt.post() self.connected_clients = set() self.subscribed_clients = set() def step(self, letter): client = random.choice(self.clients) inp = client + '_' + letter concrete_output = self.five_client_mqtt.step(inp) all_out = '' if letter == 'connect': if client not in self.connected_clients: self.connected_clients.add(client) elif client in self.connected_clients: self.connected_clients.remove(client) if client in self.subscribed_clients: self.subscribed_clients.remove(client) if len(self.subscribed_clients) == 0: all_out = '_UNSUB_ALL' elif letter == 'subscribe' and client in self.connected_clients: self.subscribed_clients.add(client) elif letter == 'disconnect' and client in self.connected_clients: self.connected_clients.remove(client) if client in self.subscribed_clients: self.subscribed_clients.remove(client) if len(self.subscribed_clients) == 0: all_out = '_UNSUB_ALL' elif letter == 'unsubscribe' and client in self.connected_clients: if client in self.subscribed_clients: self.subscribed_clients.remove(client) if len(self.subscribed_clients) == 0: all_out = '_ALL' concrete_outputs = concrete_output.split('__') abstract_outputs = set([e[3:] for e in concrete_outputs]) if 'Empty' in abstract_outputs: abstract_outputs.remove('Empty') if abstract_outputs == {'CONCLOSED'}: if len(self.connected_clients) == 0: all_out = '_ALL' return 'CONCLOSED' + all_out else: if 'CONCLOSED' in abstract_outputs: abstract_outputs.remove('CONCLOSED') abstract_outputs = sorted(list(abstract_outputs)) output = '_'.join(abstract_outputs) return '_'.join(set(output.split('_'))) + all_out sul = Multi_Client_MQTT_Mapper() alphabet = sul.get_input_alphabet() eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=5000, reset_prob=0.09, reset_after_cex=True) abstraction_mapping = { 'CONCLOSED': 'CONCLOSED', 'CONCLOSED_UNSUB_ALL': 'CONCLOSED', 'CONCLOSED_ALL': 'CONCLOSED', 'UNSUBACK': 'UNSUBACK', 'UNSUBACK_ALL': 'UNSUBACK' } learned_onfsm = run_abstracted_ONFSM_Lstar(alphabet, sul, eq_oracle, abstraction_mapping=abstraction_mapping, n_sampling=200, print_level=3) return learned_onfsm def abstracted_onfsm_example(): """ Learning an abstracted ONFSM. The original ONFSM has 9 states. The learned abstracted ONFSM only has 3 states. :return: learned abstracted ONFSM """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWordEqOracle from aalpy.learning_algs import run_abstracted_ONFSM_Lstar from aalpy.utils import get_ONFSM onfsm = get_ONFSM() alphabet = onfsm.get_input_alphabet() sul = AutomatonSUL(onfsm) eq_oracle = RandomWordEqOracle(alphabet, sul, num_walks=500, min_walk_len=4, max_walk_len=8, reset_after_cex=True) abstraction_mapping = {0: 0, 'O': 0} learned_onfsm = run_abstracted_ONFSM_Lstar(alphabet, sul, eq_oracle=eq_oracle, abstraction_mapping=abstraction_mapping, n_sampling=50, print_level=3) return learned_onfsm def faulty_coffee_machine_mdp_example(automaton_type='mdp'): """ Learning faulty coffee machine that can be found in Chapter 5 and Chapter 7 of Martin's Tappler PhD thesis. :automaton_type either mdp or smm :return learned MDP """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWalkEqOracle from aalpy.learning_algs import run_stochastic_Lstar from aalpy.utils import get_faulty_coffee_machine_MDP mdp = get_faulty_coffee_machine_MDP() input_alphabet = mdp.get_input_alphabet() sul = AutomatonSUL(mdp) eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=500, reset_prob=0.11, reset_after_cex=False) learned_mdp = run_stochastic_Lstar(input_alphabet, sul, automaton_type=automaton_type, eq_oracle=eq_oracle, n_c=20, n_resample=100, min_rounds=3, max_rounds=50, print_level=3, cex_processing='longest_prefix', samples_cex_strategy='bfs') return learned_mdp def weird_coffee_machine_mdp_example(): """ Learning faulty coffee machine that can be found in Chapter 5 and Chapter 7 of Martin's Tappler PhD thesis. :return learned MDP """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWordEqOracle from aalpy.learning_algs import run_stochastic_Lstar from aalpy.utils import get_weird_coffee_machine_MDP mdp = get_weird_coffee_machine_MDP() input_alphabet = mdp.get_input_alphabet() sul = AutomatonSUL(mdp) eq_oracle = RandomWordEqOracle(input_alphabet, sul=sul, num_walks=2000, min_walk_len=4, max_walk_len=10, reset_after_cex=True) learned_mdp = run_stochastic_Lstar(input_alphabet, sul, eq_oracle, n_c=20, n_resample=1000, min_rounds=10, max_rounds=500, strategy='normal', cex_processing=None, samples_cex_strategy=None, automaton_type='smm') return learned_mdp def benchmark_stochastic_example(example, automaton_type='smm', n_c=20, n_resample=1000, min_rounds=10, max_rounds=500, strategy='normal', cex_processing='longest_prefix', stopping_based_on_prop=None, samples_cex_strategy=None): """ Learning the stochastic Mealy Machine(SMM) various benchmarking examples found in Chapter 7 of Martin's Tappler PhD thesis. :param n_c: cutoff for a state to be considered complete :param automaton_type: either smm (stochastic mealy machine) or mdp (Markov decision process) :param n_resample: resampling size :param example: One of ['first_grid', 'second_grid', 'shared_coin', 'slot_machine'] :param min_rounds: minimum number of learning rounds :param max_rounds: maximum number of learning rounds :param strategy: normal, classic or chi2 :param cex_processing: counterexample processing strategy :stopping_based_on_prop: a tuple (path to properties, correct values, error bound) :param samples_cex_strategy: strategy to sample cex in the trace tree :return: learned SMM """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWalkEqOracle, RandomWordEqOracle from aalpy.learning_algs import run_stochastic_Lstar from aalpy.utils import load_automaton_from_file # Specify the path to the dot file containing a MDP mdp = load_automaton_from_file(f'./DotModels/MDPs/{example}.dot', automaton_type='mdp') input_alphabet = mdp.get_input_alphabet() sul = AutomatonSUL(mdp) eq_oracle = RandomWordEqOracle(input_alphabet, sul, num_walks=100, min_walk_len=5, max_walk_len=15, reset_after_cex=True) eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=2000, reset_prob=0.25, reset_after_cex=True) learned_mdp = run_stochastic_Lstar(input_alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul, n_c=n_c, n_resample=n_resample, min_rounds=min_rounds, max_rounds=max_rounds, automaton_type=automaton_type, strategy=strategy, cex_processing=cex_processing, samples_cex_strategy=samples_cex_strategy, target_unambiguity=0.99, property_based_stopping=stopping_based_on_prop) return learned_mdp def custom_stochastic_example(stochastic_machine, learning_type='smm', min_rounds=10, max_rounds=500): """ Learning custom SMM. :param stochastic_machine: stochastic Mealy machine or MDP to learn :param learning_type: 'smm' or 'mdp' :param min_rounds: minimum number of learning rounds :param max_rounds: maximum number of learning rounds :return: learned model """ from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWordEqOracle from aalpy.learning_algs import run_stochastic_Lstar input_al = stochastic_machine.get_input_alphabet() sul = AutomatonSUL(stochastic_machine) eq_oracle = RandomWordEqOracle(alphabet=input_al, sul=sul, num_walks=1000, min_walk_len=10, max_walk_len=30, reset_after_cex=True) learned_model = run_stochastic_Lstar(input_al, sul, eq_oracle, automaton_type=learning_type, min_rounds=min_rounds, max_rounds=max_rounds, print_level=2) return learned_model def learn_stochastic_system_and_do_model_checking(example, automaton_type='smm', n_c=20, n_resample=1000, min_rounds=10, max_rounds=500, strategy='normal', cex_processing='longest_prefix', stopping_based_on_prop=None, samples_cex_strategy=None): import aalpy.paths from aalpy.automata import StochasticMealyMachine from aalpy.utils import model_check_experiment, get_properties_file, get_correct_prop_values from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "Benchmarking/prism_eval_props/" learned_model = benchmark_stochastic_example(example, automaton_type, n_c, n_resample, min_rounds, max_rounds, strategy, cex_processing, stopping_based_on_prop, samples_cex_strategy) if isinstance(learned_model, StochasticMealyMachine): mdp = smm_to_mdp_conversion(learned_model) else: mdp = learned_model values, diff = model_check_experiment(get_properties_file(example), get_correct_prop_values(example), mdp) print('Value for each property:', [round(d * 100, 2) for d in values.values()]) print('Error for each property:', [round(d * 100, 2) for d in diff.values()]) def alergia_mdp_example(): from aalpy.SULs import AutomatonSUL from random import randint, choice from aalpy.learning_algs import run_Alergia from aalpy.utils import generate_random_mdp mdp = generate_random_mdp(5, 2, 3) initial_output = mdp.initial_state.output sul = AutomatonSUL(mdp) inputs = mdp.get_input_alphabet() data = [] for _ in range(100000): str_len = randint(5, 12) # add the initial output sul.pre() seq = [initial_output] for _ in range(str_len): i = choice(inputs) o = sul.step(i) seq.append((i, o)) sul.post() data.append(seq) # run alergia with the data and automaton_type set to 'mdp' to True to learn a MDP model = run_Alergia(data, automaton_type='mdp', eps=0.05, print_info=True) model.visualize() def alergia_smm_example(): from aalpy.SULs import AutomatonSUL from random import randint, choice from aalpy.learning_algs import run_Alergia from aalpy.utils import generate_random_smm smm = generate_random_smm(5, 2, 2) sul = AutomatonSUL(smm) inputs = smm.get_input_alphabet() data = [] for _ in range(100000): str_len = randint(5, 15) sul.pre() seq = [] for _ in range(str_len): i = choice(inputs) o = sul.step(i) seq.append((i, o)) sul.post() data.append(seq) # run alergia with the data and automaton_type set to 'mdp' to True to learn a MDP model = run_Alergia(data, automaton_type='smm', eps=0.05, print_info=True) model.visualize() return model def alergia_mc_example_with_loaded_data(): from os import remove from aalpy.SULs import AutomatonSUL from random import randint from aalpy.learning_algs import run_Alergia from aalpy.utils import generate_random_markov_chain from aalpy.utils import CharacterTokenizer mc = generate_random_markov_chain(10) initial_output = mc.initial_state.output mc.visualize('Original') sul = AutomatonSUL(mc) # note that this example shows writing to file just to show how tokenizer is used... # this step can ofc be skipped and lists passed to alergia data = [] for _ in range(20000): sul.pre() str_len = randint(4, 12) seq = [f'{initial_output}'] for _ in range(str_len): o = sul.step() seq.append(f'{o}') sul.post() data.append(''.join(seq)) with open('mcData.txt', 'w') as file: for seq in data: file.write(f'{seq}\n') file.close() # create tokenizer tokenizer = CharacterTokenizer() # parse data data = tokenizer.tokenize_data('mcData.txt') # run alergia with the data and automaton_type set to 'mc' to learn a Markov Chain model = run_Alergia(data, automaton_type='mc', eps=0.05, print_info=True) # print(model) model.visualize() remove('mcData.txt') return model def jAlergiaExample(): from aalpy.learning_algs import run_JAlergia # if you need more heap space check model = run_JAlergia(path_to_data_file='jAlergia/exampleMdpData.txt', automaton_type='mdp', eps=0.05, path_to_jAlergia_jar='jAlergia/alergia.jar') # # alternatively pass the data in following format # mc_data = [[1,2,3,4,5], [1,2,3,4,2,1], [1,3,5,2,3]] # mdp_data = [[1,2,3,1,2], [1,3,6,4,2]] # model = run_JAlergia(path_to_data_file=mc_data, automaton_type='mdp', eps=0.05, # path_to_jAlergia_jar='jAlergia/alergia.jar', optimize_for='memory') model.visualize() return model def active_alergia_example(example='first_grid'): from random import choice, randint from aalpy.SULs import AutomatonSUL from aalpy.utils import load_automaton_from_file from aalpy.learning_algs import run_active_Alergia from aalpy.learning_algs.stochastic_passive.ActiveAleriga import RandomWordSampler mdp = load_automaton_from_file(f'./DotModels/MDPs/{example}.dot', automaton_type='mdp') input_alphabet = mdp.get_input_alphabet() sul = AutomatonSUL(mdp) data = [] for _ in range(50000): input_query = tuple(choice(input_alphabet) for _ in range(randint(6, 14))) outputs = sul.query(input_query) # format data in [O, (I, O), (I, O)...] formatted_io = [outputs.pop(0)] for i, o in zip(input_query, outputs): formatted_io.append((i, o)) data.append(formatted_io) sampler = RandomWordSampler(num_walks=1000, min_walk_len=8, max_walk_len=20) model = run_active_Alergia(data, sul, sampler, n_iter=10) print(model) def rpni_example(): data = [(('a', 'a', 'a'), True), (('a', 'a', 'b', 'a'), True), (('b', 'b', 'a'), True), (('b', 'b', 'a', 'b', 'a'), True), (('a',), False), (('b', 'b'), False), (('a', 'a', 'b'), False), (('a', 'b', 'a'), False)] from aalpy.learning_algs import run_RPNI model = run_RPNI(data, automaton_type='dfa') model.visualize() def rpni_check_model_example(): from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_RPNI from aalpy.oracles import StatePrefixEqOracle from aalpy.utils import generate_random_moore_machine from aalpy.utils import generate_input_output_data_from_automata, convert_i_o_traces_for_RPNI # model = generate_random_dfa(num_states=5, alphabet=[1, 2, 3], num_accepting_states=2) model = generate_random_moore_machine(num_states=5, input_alphabet=[1, 2, 3], output_alphabet=['a', 'b']) input_al = model.get_input_alphabet() data = generate_input_output_data_from_automata(model, num_sequences=2000, min_seq_len=1, max_seq_len=12) data = convert_i_o_traces_for_RPNI(data) rpni_model = run_RPNI(data, automaton_type='moore', print_info=True) rpni_model.make_input_complete('sink_state') sul = AutomatonSUL(model) eq_oracle_2 = StatePrefixEqOracle(input_al, sul, walks_per_state=100) cex = eq_oracle_2.find_cex(rpni_model) # or simply do # if rpni_model != model if cex is None: print("Could not find a counterexample between the RPNI-model and the original model.") else: print('Counterexample found. Either RPNI data was incomplete (or there is a bug in RPNI algorithm :o )') def rpni_mealy_example(): import random from aalpy.learning_algs import run_RPNI from aalpy.utils import generate_random_deterministic_automata from aalpy.utils.HelperFunctions import all_prefixes # make reproducible random.seed(1) model = generate_random_deterministic_automata(automaton_type='mealy', num_states=5, input_alphabet_size=3, output_alphabet_size=4) # model = load_automaton_from_file('DotModels/Bluetooth/bluetooth_model.dot', automaton_type='mealy') input_al = model.get_input_alphabet() num_sequences = 1000 data = [] for _ in range(num_sequences): seq_len = random.randint(1, 10) random_seq = random.choices(input_al, k=seq_len) # make sure that all prefixes all included in the dataset for prefix in all_prefixes(random_seq): output = model.compute_output_seq(model.initial_state, prefix)[-1] data.append((prefix, output)) rpni_model = run_RPNI(data, automaton_type='mealy', print_info=True) return rpni_model def random_active_rpni_example(): import random from aalpy.learning_algs import run_active_RPNI from aalpy.learning_algs.deterministic_passive.active_RPNI import RandomWordSampler from aalpy.utils import generate_random_deterministic_automata from aalpy.utils.HelperFunctions import all_prefixes from aalpy.SULs import AutomatonSUL model = generate_random_deterministic_automata('mealy', num_states=50, input_alphabet_size=3, output_alphabet_size=5) input_al = model.get_input_alphabet() num_sequences = 100 data = [] for _ in range(num_sequences): seq_len = random.randint(1, 20) random_seq = random.choices(input_al, k=seq_len) # make sure that all prefixes all included in the dataset for prefix in all_prefixes(random_seq): output = model.compute_output_seq(model.initial_state, prefix)[-1] data.append((prefix, output)) sampler = RandomWordSampler(500, 5, 25) sul = AutomatonSUL(model) active_rpni_model = run_active_RPNI(data, sul, sampler=sampler, n_iter=5, automaton_type='mealy', print_info=True) return active_rpni_model def compare_stochastic_and_non_deterministic_learning(example='first_grid'): import aalpy.paths aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "Benchmarking/prism_eval_props/" from aalpy.SULs import AutomatonSUL from aalpy.automata import StochasticMealyMachine from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion from aalpy.learning_algs import run_stochastic_Lstar, run_non_det_Lstar from aalpy.utils import load_automaton_from_file, model_check_experiment, get_properties_file, \ get_correct_prop_values from aalpy.oracles import RandomWordEqOracle mdp = load_automaton_from_file(f'./DotModels/MDPs/first_grid.dot', automaton_type='mdp') input_alphabet = mdp.get_input_alphabet() # Stochastic Learning print("Stochastic Learning") sul = AutomatonSUL(mdp) eq_oracle = RandomWordEqOracle(input_alphabet, sul, num_walks=100, min_walk_len=5, max_walk_len=15, reset_after_cex=True) stochastic_learned_model = run_stochastic_Lstar(input_alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul, automaton_type='smm', target_unambiguity=0.99) # Non Deterministic Learning print("Non-deterministic Learning") sul = AutomatonSUL(mdp) eq_oracle = RandomWordEqOracle(input_alphabet, sul, num_walks=100, min_walk_len=5, max_walk_len=15, reset_after_cex=True) non_det_model = run_non_det_Lstar(alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul, n_sampling=5, stochastic=True, print_level=2) for model_type, model in [('Stochastic Learning', stochastic_learned_model), ('Non-deterministic learning', non_det_model)]: if isinstance(model, StochasticMealyMachine): mdp = smm_to_mdp_conversion(model) else: mdp = model values, diff = model_check_experiment(get_properties_file(example), get_correct_prop_values(example), mdp) print(model_type) print('Error for each property:', [round(d * 100, 2) for d in diff.values()]) def learning_context_free_grammar_example(): from aalpy.automata import SevpaAlphabet from aalpy.learning_algs import run_KV from aalpy.oracles import RandomWordEqOracle from aalpy.utils.BenchmarkSULs import get_balanced_string_sul call_return_map = {'(': ')', '[': ']'} sevpa_alphabet = SevpaAlphabet([], list(call_return_map.keys()), list(call_return_map.values())) # bounded deterministic approximation balanced_string_sul = get_balanced_string_sul(call_return_map, allow_empty_string=False) eq_oracle = RandomWordEqOracle(sevpa_alphabet.get_merged_alphabet(), balanced_string_sul, num_walks=1000, min_walk_len=5, max_walk_len=30) learned_deterministic_approximation = run_KV(sevpa_alphabet.get_merged_alphabet(), balanced_string_sul, eq_oracle, automaton_type='dfa', max_learning_rounds=20) balanced_string_sul = get_balanced_string_sul(call_return_map, allow_empty_string=False) eq_oracle = RandomWordEqOracle(sevpa_alphabet.get_merged_alphabet(), balanced_string_sul, num_walks=1000, min_walk_len=5, max_walk_len=30) learned_model = run_KV(sevpa_alphabet, balanced_string_sul, eq_oracle, automaton_type='vpa') learned_model.visualize() def arithmetic_expression_sevpa_learning(): from aalpy.base import SUL from aalpy.automata import SevpaAlphabet from aalpy.oracles import RandomWordEqOracle from aalpy.learning_algs import run_KV import warnings import ast warnings.filterwarnings("ignore") class ArithmeticSUL(SUL): def __init__(self): super().__init__() self.string_under_test = '' def pre(self): self.string_under_test = '' def post(self): pass def step(self, letter): if letter: self.string_under_test += ' ' + letter if len(self.string_under_test) > 0 else letter try: # Parse the expression using ast parsed_expr = ast.parse(self.string_under_test, mode='eval') # Check if the parsed expression is a valid arithmetic expression is_valid = all(isinstance(node, (ast.Expression, ast.BinOp, ast.UnaryOp, ast.Num, ast.Name, ast.Load)) or isinstance(node, ast.operator) or isinstance(node, ast.expr_context) or (isinstance(node, ast.BinOp) and isinstance(node.op, ast.operator)) for node in ast.walk(parsed_expr)) return is_valid except SyntaxError: return False sul = ArithmeticSUL() alphabet = SevpaAlphabet(internal_alphabet=['1', '+'], call_alphabet=['('], return_alphabet=[')']) eq_oracle = RandomWordEqOracle(alphabet.get_merged_alphabet(), sul, min_walk_len=2, max_walk_len=10, num_walks=2000) learned_model = run_KV(alphabet, sul, eq_oracle, automaton_type='vpa') learned_model.visualize() def benchmark_sevpa_learning(): from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWordEqOracle from aalpy.learning_algs import run_KV from aalpy.utils.BenchmarkSevpaModels import sevpa_for_L1, sevpa_for_L2, sevpa_for_L11, sevpa_for_L12, sevpa_for_L14 models = [sevpa_for_L1(), sevpa_for_L2(), sevpa_for_L11(), sevpa_for_L12(), sevpa_for_L14()] for inx, model in enumerate(models): alphabet = model.get_input_alphabet() sul = AutomatonSUL(model) if inx == 4: alphabet.exclusive_call_return_pairs = {'(': ')', '[': ']'} eq_oracle = RandomWordEqOracle(alphabet=alphabet.get_merged_alphabet(), sul=sul, num_walks=10000, min_walk_len=10, max_walk_len=30) learned_model = run_KV(alphabet=alphabet, sul=sul, eq_oracle=eq_oracle, automaton_type='vpa', print_level=2, cex_processing='rs') print(learned_model.get_random_accepting_word()) def random_sevpa_learning(): from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWordEqOracle from aalpy.learning_algs import run_KV from aalpy.utils import generate_random_sevpa random_svepa = generate_random_sevpa(num_states=50, internal_alphabet_size=3, call_alphabet_size=3, return_alphabet_size=3, acceptance_prob=0.4, return_transition_prob=0.5) # from aalpy.utils.BenchmarkVpaModels import vpa_for_L11 # balanced_parentheses = vpa_for_L11() alphabet = random_svepa.input_alphabet sul = AutomatonSUL(random_svepa) eq_oracle = RandomWordEqOracle(alphabet=alphabet.get_merged_alphabet(), sul=sul, num_walks=10000, min_walk_len=10, max_walk_len=30) model = run_KV(alphabet=alphabet, sul=sul, eq_oracle=eq_oracle, automaton_type='vpa', print_level=2, cex_processing='rs') def passive_vpa_learning_on_lists(): from aalpy.learning_algs import run_PAPNI from aalpy.automata import VpaAlphabet vpa_alphabet = VpaAlphabet(internal_alphabet=['1'], call_alphabet=['(', '['], return_alphabet=[')', ']']) list_data = [ (tuple(), False), (('[', '[', ']', ']'), True), (('(', '(', ')', ')'), True), (('[', '[', ']', ')'), False), (('[', '[', ')', ']'), False), (('[', ')'), False), (('(', ']'), False), (('[', '[', ']', '1', ']'), True), (('[', '1', '[', ']', '1', ']'), True), (('(', '1', '[', ']', '1', ')'), True), (('(', ')'), True), (('[', ']'), True), (('[', '1', '(', ']', '1', ')'), False), ] papni = run_PAPNI(list_data, vpa_alphabet, algorithm='gsm', print_info=True) papni.visualize() def passive_vpa_learning_arithmetics(): from aalpy.learning_algs import run_PAPNI from aalpy.utils.BenchmarkVpaModels import gen_arithmetic_data arithmetic_data, vpa_alphabet = gen_arithmetic_data(num_sequences=4000, min_seq_len=2, max_seq_len=10) print(f"Alphabet: {vpa_alphabet}") print('Data:') for i in range(10): print(arithmetic_data[i]) learned_model = run_PAPNI(arithmetic_data, vpa_alphabet) learned_model.visualize() def passive_vpa_learning_on_all_benchmark_models(): from aalpy.learning_algs import run_PAPNI from aalpy.utils.BenchmarkVpaModels import vpa_L1, vpa_L12, vpa_for_odd_parentheses from aalpy.utils import generate_input_output_data_from_vpa, convert_i_o_traces_for_RPNI for gt in [vpa_L1(), vpa_L12(), vpa_for_odd_parentheses()]: vpa_alphabet = gt.input_alphabet data = generate_input_output_data_from_vpa(gt, num_sequences=2000, max_seq_len=16) papni = run_PAPNI(data, vpa_alphabet, algorithm='gsm', print_info=True) for seq, o in data: papni.reset_to_initial() learned_output = papni.execute_sequence(papni.initial_state, seq)[-1] if o != learned_output: print(seq, o, learned_output) assert False, 'Papni Learned Model not consistent with data.' print('PAPNI model conforms to data.') def gsm_rpni(): from aalpy import load_automaton_from_file from aalpy.utils.Sampling import get_data_from_input_sequence, sample_with_length_limits from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM automaton = load_automaton_from_file("DotModels/car_alarm.dot", "moore") input_traces = sample_with_length_limits(automaton.get_input_alphabet(), 100, 20, 30) traces = get_data_from_input_sequence(automaton, input_traces, "io_traces") learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="deterministic") learned_model.visualize() def gsm_edsm(): from typing import Dict from aalpy import load_automaton_from_file from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import ScoreCalculation from aalpy.learning_algs.general_passive.GsmNode import GsmNode automaton = load_automaton_from_file("DotModels/car_alarm.dot", "moore") input_traces = sample_with_length_limits(automaton.get_input_alphabet(), 100, 20, 30) traces = get_io_traces(automaton, input_traces) def EDSM_score(part: Dict[GsmNode, GsmNode]): nr_partitions = len(set(part.values())) nr_merged = len(part) return nr_merged - nr_partitions score = ScoreCalculation(score_function=EDSM_score) learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="deterministic", score_calc=score) learned_model.visualize() def gsm_likelihood_ratio(): from typing import Dict from scipy.stats import chi2 from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import ScoreFunction, differential_info, ScoreCalculation from aalpy.learning_algs.general_passive.GsmNode import GsmNode from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits from aalpy import load_automaton_from_file automaton = load_automaton_from_file("DotModels/MDPs/faulty_car_alarm.dot", "mdp") input_traces = sample_with_length_limits(automaton.get_input_alphabet(), 2000, 20, 30) traces = get_io_traces(automaton, input_traces) def likelihood_ratio_score(alpha=0.05) -> ScoreFunction: if not 0 < alpha <= 1: raise ValueError(f"Confidence {alpha} not between 0 and 1") def score_fun(part: Dict[GsmNode, GsmNode]): llh_diff, param_diff = differential_info(part) if param_diff == 0: # This should cover the corner case when the partition merges only states with no outgoing transitions. return -1 # Let them be very bad merges. score = 1 - chi2.cdf(2 * llh_diff, param_diff) if score < alpha: return False return score return score_fun score = ScoreCalculation(score_function=likelihood_ratio_score()) learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="stochastic", score_calc=score) learned_model.visualize() def example_Alergia_extension(): from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import hoeffding_compatibility, ScoreCalculation from aalpy.learning_algs.general_passive.GsmNode import GsmNode from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits from aalpy import load_automaton_from_file automaton = load_automaton_from_file("DotModels/MDPs/faulty_car_alarm.dot", "mdp") input_traces = sample_with_length_limits(automaton.get_input_alphabet(), 2000, 20, 30) traces = get_io_traces(automaton, input_traces) # NOTE THAT This example is equivalent to a call to a function run_Alergia_EDSM class IOAlergiaWithEDSM(ScoreCalculation): def __init__(self, epsilon): super().__init__() self.ioa_compatibility = hoeffding_compatibility(epsilon) self.evidence = 0 def reset(self): self.evidence = 0 def local_compatibility(self, a: GsmNode, b: GsmNode): self.evidence += 1 return self.ioa_compatibility(a, b) def score_function(self, part: dict[GsmNode, GsmNode]): return self.evidence epsilon = 0.05 scores = { "IOA": ScoreCalculation(hoeffding_compatibility(epsilon)), "IOA+EDSM": IOAlergiaWithEDSM(epsilon), } for name, score in scores.items(): learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="stochastic", score_calc=score, compatibility_on_pta=True, compatibility_on_futures=True) learned_model.visualize(name) def gsm_IOAlergia_domain_knowldege(): from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import hoeffding_compatibility, ScoreCalculation from aalpy.learning_algs.general_passive.GsmNode import GsmNode from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits from aalpy import load_automaton_from_file automaton = load_automaton_from_file("DotModels/MDPs/faulty_car_alarm.dot", "mdp") input_traces = sample_with_length_limits(automaton.get_input_alphabet(), 2000, 20, 30) traces = get_io_traces(automaton, input_traces) ioa_compat = hoeffding_compatibility(0.05) def get_parity(node: GsmNode): pref = node.get_prefix() return [sum(in_s == key for in_s, out_s in pref) % 2 for key in ["l", "d"]] # The car has 4 physical states arising from the combination of locked/unlocked and open/closed. # Each input toggles a transition between these four states. While the car alarm system has richer behavior than that, # it still needs to discern the physical states. Thus, in every sane implementation of a car alarm system, every state # is associated with exactly one physical state. This additional assumption can be enforced by checking the parity of # all input symbols during merging. def ioa_compat_domain_knowledge(a: GsmNode, b: GsmNode): parity = get_parity(a) == get_parity(b) ioa = ioa_compat(a, b) return parity and ioa scores = { "IOA": ScoreCalculation(ioa_compat), "IOA+DK": ScoreCalculation(ioa_compat_domain_knowledge), } for name, score in scores.items(): learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="stochastic", score_calc=score, compatibility_on_pta=True, compatibility_on_futures=True) learned_model.visualize(name) def k_tails_example(): from aalpy.learning_algs import run_k_tails from aalpy.utils import generate_random_deterministic_automata, generate_input_output_data_from_automata model = generate_random_deterministic_automata('moore', num_states=5, input_alphabet_size=3, output_alphabet_size=3) data = generate_input_output_data_from_automata(model, num_sequences=2000, min_seq_len=1, max_seq_len=12, sequance_type='io_traces') # k-trails works with prefix-closed input output traces, not labeled sequences like RPNI # data is a list of sequences in this format [(i1, o1), (i2, o1), (i1, o3)] # run k_tails with two different k's k_trails_1 = run_k_tails(data, k=3, automaton_type='moore', print_info=True) k_tails_2 = run_k_tails(data, k=8, automaton_type='mealy', print_info=True) ================================================ FILE: LICENCE.txt ================================================ MIT License Copyright (c) 2026 Edi Muskardin TU Graz - SAL Dependable Embedded Systems Lab (DES Lab), Silicon Austria Labs Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================
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AALpy is a light-weight [automata learning](https://medium.com/p/82f7ec21d50d) library written in Python. You can start learning models of black-box systems with a few lines of code.
| **Automata Type** | **Supported Formalisms** | **Active Algorithms** | **Passive Algorithms** | |-------------------|:-----------------------------------------------------------------:|---------------------------------------------------------|-------------------------------| | Deterministic | DFAs
Mealy Machines
Moore Machines | L*
KV
L# | RPNI
EDSM | | Non-Deterministic | ONFSM
Non-deterministic Moore Machines
Abstracted ONFSM | L*ONFSM | k-tails | | Stochastic | Markov Decision Processes
Stochastic Mealy Machines
Markov Chains | L*MDP
L*SMM | Alergia
IoAlergia
AlergiaEDSM | | Pushdown | VPA/SEVPA | KVVPA | PAPNI |
## Installation Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the latest release of AALpy: ```bash pip install aalpy ``` To install current version of the master branch (it might contain bugfixes and added functionalities between releases): ```bash pip install https://github.com/DES-Lab/AALpy/archive/master.zip ``` The minimum required version of Python is 3.6. Ensure that you have [Graphviz](https://graphviz.org/) installed and added to your path if you want to visualize models. For manual installation, clone the repo and install `pydot` (the only dependency). ## Documentation and Wiki High level introduction to automata learning can be found at the following [Medium post](https://medium.com/p/82f7ec21d50d). If you are interested in automata learning or would like to understand the automata learning process in more detail, please check out our **Wiki**. On Wiki, you will find more detailed examples on how to use AALpy. - ***[Examples.py](https://github.com/DES-Lab/AALpy/blob/master/Examples.py)*** contains examples covering almost the whole of AALpy's functionality and its a great starting point. ### Usage All active automata learning procedures follow this high-level approach: - [Define the input alphabet and system under learning (SUL)](https://github.com/DES-Lab/AALpy/wiki/SUL-Interface,-or-How-to-Learn-Your-Systems) - [Choose the equivalence oracle](https://github.com/DES-Lab/AALpy/wiki/Equivalence-Oracles) - [Run the learning algorithm](https://github.com/DES-Lab/AALpy/wiki/Setting-Up-Learning) Passive learning algorithm simply require you to provide data in the appropriate format (check Wiki and Examples) and run the learning function.
Code snipped demonstrating some of AALpy's functionalities The following snippet demonstrates a short example in which an automaton is either [loaded](https://github.com/DES-Lab/AALpy/wiki/Loading,Saving,-Syntax-and-Visualization-of-Automata) or [randomly generated](https://github.com/DES-Lab/AALpy/wiki/Generation-of-Random-Automata) and then [learned](https://github.com/DES-Lab/AALpy/wiki/Setting-Up-Learning). ```python from aalpy.utils import load_automaton_from_file, generate_random_deterministic_automata from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWalkEqOracle from aalpy.learning_algs import run_Lstar, run_KV # load an automaton # automaton = load_automaton_from_file('path_to_the_file.dot', automaton_type='dfa') # or randomly generate one random_dfa = generate_random_deterministic_automata(automaton_type='dfa', num_states=8, input_alphabet_size=5, output_alphabet_size=2) # get input alphabet of the automaton alphabet = random_dfa.get_input_alphabet() # loaded or randomly generated automata are considered as BLACK-BOX that is queried # learning algorithm has no knowledge about its structure # create a SUL instance for the automaton/system under learning sul = AutomatonSUL(random_dfa) # define the equivalence oracle eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=5000, reset_prob=0.09) # start learning # run_KV is for the most part reacquires much fewer interactions with the system under learning learned_dfa = run_KV(alphabet, sul, eq_oracle, automaton_type='dfa') # or run L* # learned_dfa_lstar = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa') # save automaton to file and visualize it # save_automaton_to_file(learned_dfa, path='Learned_Automaton', file_type='dot') # or learned_dfa.save() # visualize automaton # visualize_automaton(learned_dfa) learned_dfa.visualize() # or just print its DOT representation print(learned_dfa) ```
To make experiments reproducible, define a random seed at the beginning of your program. ```Python from random import seed seed(2) # all experiments will be reproducible ``` ## Selected Applications AALpy has been used to: - [Learn Models of Bluetooth Low Energy](https://github.com/apferscher/ble-learning) - [Find bugs in VIM text editor](https://github.com/DES-Lab/AALpy/discussions/13) - [Learn Input-Output Behavior of RNNs](https://github.com/DES-Lab/Extracting-FSM-From-RNNs) - [Learn Models of GIT](https://github.com/taburg/git-learning) - [Solve RL Problems](https://github.com/DES-Lab/Learning-Environment-Models-with-Continuous-Stochastic-Dynamics) ## Cite AALpy and Research Contact If you use AALpy in your research, please cite us with of the following: - [Extended version (preferred)](https://www.researchgate.net/publication/359517046_AALpy_an_active_automata_learning_library/citation/download) - [Tool paper](https://dblp.org/rec/conf/atva/MuskardinAPPT21.html?view=bibtex) If you have research suggestions or you need specific help concerning your research, feel free to start a [discussion](https://github.com/DES-Lab/AALpy/discussions). We are happy to help you and consult you in developing automata learning algorithms and in applying automata learning in various domains. ## Contributing Pull requests are welcome. For significant changes, please open an issue first to discuss what you would like to change. In case of any questions or possible bugs, please open issues. ================================================ FILE: aalpy/SULs/AutomataSUL.py ================================================ from aalpy.base import Automaton from aalpy.base import SUL class AutomatonSUL(SUL): def __init__(self, automaton: Automaton): super().__init__() self.automaton: Automaton = automaton def pre(self): self.automaton.reset_to_initial() def step(self, letter=None): return self.automaton.step(letter) def post(self): pass MealySUL = OnfsmSUL = StochasticMealySUL = DfaSUL = MooreSUL = MdpSUL = McSUL = SevpaSUL = AutomatonSUL ================================================ FILE: aalpy/SULs/PyMethodSUL.py ================================================ from aalpy.base import SUL class FunctionDecorator: """ Decorator of methods found in the SUL class. """ def __init__(self, function, args=None): """ Args: function: function of the class to be learned args: arguments to be passed to the function. Either a single argument, or a list of arguments if function has more than one parameter. """ self.function = function self.args = None if args: self.args = [args] if not isinstance(args, (list, tuple)) else args def __repr__(self): if self.args: return f'{self.function.__name__}{self.args}' return self.function.__name__ class PyClassSUL(SUL): """ System under learning for inferring python classes. """ def __init__(self, python_class): """ Args: python_class: class to be learned """ super().__init__() self._class = python_class self.sul: object = None def pre(self): """ Do the reset by initializing the class again or call reset method of the class """ self.sul = self._class() def post(self): pass def step(self, letter): """ Executes the function(with arguments) found in letter against the SUL Args: letter: single input of type FunctionDecorator Returns: output of the function """ if letter.args: return getattr(self.sul, letter.function.__name__, letter)(*letter.args) return getattr(self.sul, letter.function.__name__, letter)() ================================================ FILE: aalpy/SULs/RegexSUL.py ================================================ from aalpy.base import SUL import re class RegexSUL(SUL): """ An example implementation of a system under learning that can be used to learn any regex expression. Note that the $ is added to the expression as in this SUL only exact matches are learned. """ def __init__(self, regex: str): super().__init__() self.regex = regex if regex[-1] == '$' else regex + '$' self.string = "" def pre(self): self.string = "" pass def post(self): self.string = "" pass def step(self, letter): """ Args: letter: single element of the input alphabet Returns: Whether the current string (previous string + letter) is accepted """ if letter is not None: self.string += str(letter) return True if re.match(self.regex, self.string) else False ================================================ FILE: aalpy/SULs/TomitaSUL.py ================================================ import re from aalpy.base import SUL class TomitaSUL(SUL): """ Tomita grammars are often used as a benchmark for automata-related challenges. Simple SUL that implements all 7 Tomita grammars and enables their learning. """ def __init__(self, tomita_level_fun): super().__init__() num_fun_map = {1: tomita_1, 2: tomita_2, 3: tomita_3, 4: tomita_4, 5: tomita_5, 6: tomita_6, 7: tomita_7, -3: not_tomita_3} assert tomita_level_fun in num_fun_map.keys() self.string = "" self.tomita_level = num_fun_map[tomita_level_fun] def pre(self): self.string = "" pass def post(self): self.string = "" pass def step(self, letter): if input: self.string += str(letter) return self.tomita_level(self.string) _not_tomita_3 = re.compile("((0|1)*0)*1(11)*(0(0|1)*1)*0(00)*(1(0|1)*)*$") def tomita_1(word): return "0" not in word def tomita_2(word): return word == "10" * (int(len(word) / 2)) def tomita_3(word): if not _not_tomita_3.match(word): return True return False def not_tomita_3(word): return not tomita_3(word) def tomita_4(word): return "000" not in word def tomita_5(word): return (word.count("0") % 2 == 0) and (word.count("1") % 2 == 0) def tomita_6(word): return ((word.count("0") - word.count("1")) % 3) == 0 def tomita_7(word): return word.count("10") <= 1 ================================================ FILE: aalpy/SULs/__init__.py ================================================ from .AutomataSUL import * from .PyMethodSUL import FunctionDecorator, PyClassSUL from .RegexSUL import RegexSUL from .TomitaSUL import TomitaSUL ================================================ FILE: aalpy/__init__.py ================================================ from .automata import ( Dfa, DfaState, MarkovChain, McState, Mdp, MdpState, MealyMachine, MealyState, MooreMachine, MooreState, NDMooreMachine, NDMooreState, Onfsm, OnfsmState, Sevpa, SevpaAlphabet, SevpaState, SevpaTransition, StochasticMealyMachine, StochasticMealyState, ) from .base import ( SUL, Automaton, AutomatonState, CacheTree, DeterministicAutomaton, Oracle, ) from .learning_algs import ( run_abstracted_ONFSM_Lstar, run_active_Alergia, run_active_RPNI, run_Alergia, run_JAlergia, run_KV, run_Lstar, run_non_det_Lstar, run_RPNI, run_stochastic_Lstar, run_GSM, run_PAPNI ) from .oracles import ( BreadthFirstExplorationEqOracle, CacheBasedEqOracle, KWayStateCoverageEqOracle, KWayTransitionCoverageEqOracle, PacOracle, PerfectKnowledgeEqOracle, ProvidedSequencesOracleWrapper, RandomWalkEqOracle, RandomWMethodEqOracle, RandomWordEqOracle, StatePrefixEqOracle, TransitionFocusOracle, UserInputEqOracle, WMethodEqOracle, KWayStateCoverageEqOracle, KWayTransitionCoverageEqOracle, ) from .SULs import ( AutomatonSUL, FunctionDecorator, PyClassSUL, RegexSUL, TomitaSUL ) from .utils import ( CharacterTokenizer, DataHandler, DelimiterTokenizer, IODelimiterTokenizer, bisimilar, compare_automata, convert_i_o_traces_for_RPNI, generate_random_deterministic_automata, generate_random_dfa, generate_random_markov_chain, generate_random_mdp, generate_random_mealy_machine, generate_random_moore_machine, generate_random_ONFSM, generate_random_sevpa, generate_random_smm, generate_test_cases, get_correct_prop_values, get_properties_file, load_automaton_from_file, make_input_complete, mdp_2_prism_format, model_check_experiment, model_check_properties, save_automaton_to_file, statistical_model_checking, visualize_automaton, ) ================================================ FILE: aalpy/automata/Dfa.py ================================================ from typing import Generic, Dict from aalpy.base import AutomatonState, DeterministicAutomaton from aalpy.base.Automaton import InputType class DfaState(AutomatonState, Generic[InputType]): """ Single state of a deterministic finite automaton. """ def __init__(self, state_id, is_accepting=False): super().__init__(state_id) self.transitions : Dict[InputType, DfaState] = dict() self.is_accepting = is_accepting @property def output(self): return self.is_accepting class Dfa(DeterministicAutomaton[DfaState[InputType]]): """ Deterministic finite automaton. """ def __init__(self, initial_state: DfaState, states): super().__init__(initial_state, states) def step(self, letter): """ Args: letter: single input that is looked up in the transition table of the DfaState Returns: True if the reached state is an accepting state, False otherwise """ if letter is not None: self.current_state = self.current_state.transitions[letter] return self.current_state.is_accepting def compute_characterization_set(self, char_set_init=None, online_suffix_closure=True, split_all_blocks=True, return_same_states=False, raise_warning=True): return super(Dfa, self).compute_characterization_set(char_set_init if char_set_init else [()], online_suffix_closure, split_all_blocks, return_same_states, raise_warning) def compute_output_seq(self, state, sequence): if not sequence: return [state.is_accepting] return super(Dfa, self).compute_output_seq(state, sequence) def execute_sequence(self, origin_state, seq): if not seq: self.current_state = origin_state return self.current_state.output return super(Dfa, self).execute_sequence(origin_state, seq) def to_state_setup(self): state_setup_dict = {} # ensure prefixes are computed self.compute_prefixes() sorted_states = sorted(self.states, key=lambda x: len(x.prefix) if x.prefix is not None else len(self.states)) for s in sorted_states: state_setup_dict[s.state_id] = (s.is_accepting, {k: v.state_id for k, v in s.transitions.items()}) return state_setup_dict @staticmethod def from_state_setup(state_setup : dict, **kwargs): """ First state in the state setup is the initial state. Example state setup: state_setup = { "a": (True, {"x": "b1", "y": "a"}), "b1": (False, {"x": "b2", "y": "a"}), "b2": (True, {"x": "b3", "y": "a"}), "b3": (False, {"x": "b4", "y": "a"}), "b4": (False, {"x": "c", "y": "a"}), "c": (True, {"x": "a", "y": "a"}), } Args: state_setup: map from state_id to tuple(output and transitions_dict) Returns: DFA """ # state_setup should map from state_id to tuple(is_accepting and transitions_dict) # build states with state_id and output states = {key: DfaState(key, val[0]) for key, val in state_setup.items()} # add transitions to states for state_id, state in states.items(): for _input, target_state_id in state_setup[state_id][1].items(): state.transitions[_input] = states[target_state_id] # states to list states = [state for state in states.values()] # build moore machine with first state as starting state dfa = Dfa(states[0], states) for state in states: state.prefix = dfa.get_shortest_path(dfa.initial_state, state) return dfa ================================================ FILE: aalpy/automata/MarkovChain.py ================================================ import random from typing import Generic, Tuple, List from aalpy.base import Automaton, AutomatonState from aalpy.base.Automaton import OutputType class McState(AutomatonState, Generic[OutputType]): def __init__(self, state_id, output): super().__init__(state_id) self.output: OutputType = output # transitions is a list of tuples (Node(output), probability) self.transitions: List[Tuple[McState, float]] = list() class MarkovChain(Automaton[McState[OutputType]]): """Markov Decision Process.""" def __init__(self, initial_state, states: list): super().__init__(initial_state, states) def reset_to_initial(self): self.current_state = self.initial_state def step(self, letter=None): """Next step is determined based on transition probabilities of the current state. Args: letter: input Returns: output of the current state """ if not self.current_state.transitions: return self.current_state.output probability_distributions = [i[1] for i in self.current_state.transitions] states = [i[0] for i in self.current_state.transitions] new_state = random.choices(states, probability_distributions, k=1)[0] self.current_state = new_state return self.current_state.output def step_to(self, input): """Performs a step on the automaton based on the input `inp` and output `out`. Args: input: input Returns: output of the reached state, None otherwise """ for s in self.current_state.transitions: if s[0].output == input: self.current_state = s[0] return self.current_state.output return None @staticmethod def from_state_setup(state_setup: dict, **kwargs): raise NotImplementedError() # TODO implement def to_state_setup(self): raise NotImplementedError() # TODO implement ================================================ FILE: aalpy/automata/Mdp.py ================================================ import random from collections import defaultdict from typing import Dict, Generic, List, Tuple from aalpy.base import Automaton, AutomatonState from aalpy.base.Automaton import OutputType, InputType class MdpState(AutomatonState, Generic[InputType, OutputType]): """ For transitions, each transition is a tuple (Node(output), probability) """ def __init__(self, state_id, output=None): super().__init__(state_id) self.output: OutputType = output # each transition is a tuple (Node(output), probability) self.transitions: Dict[InputType, List[Tuple[MdpState, float]]] = defaultdict(list) class Mdp(Automaton[MdpState[InputType, OutputType]]): """Markov Decision Process.""" def __init__(self, initial_state: MdpState, states: list): super().__init__(initial_state, states) def reset_to_initial(self): self.current_state = self.initial_state def step(self, letter): """Next step is determined based on transition probabilities of the current state. Args: letter: input Returns: output of the current state """ if letter is None: return self.current_state.output probability_distributions = [i[1] for i in self.current_state.transitions[letter]] states = [i[0] for i in self.current_state.transitions[letter]] new_state = random.choices(states, probability_distributions, k=1)[0] self.current_state = new_state return self.current_state.output def step_to(self, inp, out): """Performs a step on the automaton based on the input `inp` and output `out`. Args: inp: input out: output Returns: output of the reached state, None otherwise """ for new_state in self.current_state.transitions[inp]: if new_state[0].output == out: self.current_state = new_state[0] return out return None def to_state_setup(self): state_setup_dict = {} # ensure initial state is first in the list if self.states[0] != self.initial_state: self.states.remove(self.initial_state) self.states.insert(0, self.initial_state) for s in self.states: state_setup_dict[s.state_id] = (s.output, {k: [(node.state_id, prob) for node, prob in v] for k, v in s.transitions.items()}) return state_setup_dict @staticmethod def from_state_setup(state_setup: dict, **kwargs): states_map = {key: MdpState(key, output=value[0]) for key, value in state_setup.items()} for key, values in state_setup.items(): source = states_map[key] for i, transitions in values[1].items(): for node, prob in transitions: source.transitions[i].append((states_map[node], prob)) initial_state = states_map[list(state_setup.keys())[0]] return Mdp(initial_state, list(states_map.values())) ================================================ FILE: aalpy/automata/MealyMachine.py ================================================ from typing import Generic, Dict from aalpy.base import AutomatonState, DeterministicAutomaton from aalpy.base.Automaton import OutputType, InputType class MealyState(AutomatonState, Generic[InputType, OutputType]): """ Single state of a Mealy machine. Each state has an output_fun dictionary that maps inputs to outputs. """ def __init__(self, state_id): super().__init__(state_id) self.transitions : Dict[InputType, MealyState] = dict() self.output_fun : Dict[InputType, OutputType] = dict() class MealyMachine(DeterministicAutomaton[MealyState[InputType, OutputType]]): def __init__(self, initial_state: MealyState, states): super().__init__(initial_state, states) def step(self, letter): """ In Mealy machines, outputs depend on the input and the current state. Args: letter: single input that is looked up in the transition and output functions Returns: output corresponding to the input from the current state """ output = self.current_state.output_fun[letter] self.current_state = self.current_state.transitions[letter] return output def to_state_setup(self): state_setup_dict = {} # ensure prefixes are computed self.compute_prefixes() sorted_states = sorted(self.states, key=lambda x: len(x.prefix) if x.prefix is not None else len(self.states)) for s in sorted_states: state_setup_dict[s.state_id] = {k: (s.output_fun[k], v.state_id) for k, v in s.transitions.items()} return state_setup_dict @staticmethod def from_state_setup(state_setup : dict, **kwargs): """ First state in the state setup is the initial state. state_setup = { "a": {"x": ("o1", "b1"), "y": ("o2", "a")}, "b1": {"x": ("o3", "b2"), "y": ("o1", "a")}, "b2": {"x": ("o1", "b3"), "y": ("o2", "a")}, "b3": {"x": ("o3", "b4"), "y": ("o1", "a")}, "b4": {"x": ("o1", "c"), "y": ("o4", "a")}, "c": {"x": ("o3", "a"), "y": ("o5", "a")}, } Args: state_setup: state_setup should map from state_id to tuple(transitions_dict). Returns: Mealy Machine """ # state_setup should map from state_id to tuple(transitions_dict). # Each entry in transition dict is : # build states with state_id and output states = {key: MealyState(key) for key, _ in state_setup.items()} # add transitions to states for state_id, state in states.items(): for _input, (output, new_state) in state_setup[state_id].items(): state.transitions[_input] = states[new_state] state.output_fun[_input] = output # states to list states = [state for state in states.values()] # build moore machine with first state as starting state mm = MealyMachine(states[0], states) for state in states: state.prefix = mm.get_shortest_path(mm.initial_state, state) return mm ================================================ FILE: aalpy/automata/MooreMachine.py ================================================ from typing import Generic, Dict from aalpy.base import AutomatonState, DeterministicAutomaton from aalpy.base.Automaton import InputType, OutputType class MooreState(AutomatonState, Generic[InputType,OutputType]): """ Single state of a Moore machine. Each state has an output value. """ def __init__(self, state_id, output=None): super().__init__(state_id) self.output : OutputType = output self.transitions : Dict[InputType, MooreState] = dict() class MooreMachine(DeterministicAutomaton[MooreState[InputType, OutputType]]): def __init__(self, initial_state: AutomatonState, states: list): super().__init__(initial_state, states) def step(self, letter): """ In Moore machines outputs depend on the current state. Args: letter: single input that is looked up in the transition function leading to a new state Returns: the output of the reached state """ if letter is not None: self.current_state = self.current_state.transitions[letter] return self.current_state.output def compute_characterization_set(self, char_set_init=None, online_suffix_closure=True, split_all_blocks=True, return_same_states=False, raise_warning=True): return super(MooreMachine, self).compute_characterization_set(char_set_init if char_set_init else [()], online_suffix_closure, split_all_blocks, return_same_states, raise_warning) def compute_output_seq(self, state, sequence): if not sequence: return [state.output] return super(MooreMachine, self).compute_output_seq(state, sequence) def execute_sequence(self, origin_state, seq): if not seq: self.current_state = origin_state return self.current_state.output return super(MooreMachine, self).execute_sequence(origin_state, seq) def to_state_setup(self): state_setup_dict = {} # ensure prefixes are computed self.compute_prefixes() sorted_states = sorted(self.states, key=lambda x: len(x.prefix) if x.prefix is not None else len(self.states)) for s in sorted_states: state_setup_dict[s.state_id] = (s.output, {k: v.state_id for k, v in s.transitions.items()}) return state_setup_dict @staticmethod def from_state_setup(state_setup : dict, **kwargs): """ First state in the state setup is the initial state. Example state setup: state_setup = { "a": ("a", {"x": "b1", "y": "a"}), "b1": ("b", {"x": "b2", "y": "a"}), "b2": ("b", {"x": "b3", "y": "a"}), "b3": ("b", {"x": "b4", "y": "a"}), "b4": ("b", {"x": "c", "y": "a"}), "c": ("c", {"x": "a", "y": "a"}), } Args: state_setup: map from state_id to tuple(output and transitions_dict) Returns: Moore machine """ # build states with state_id and output states = {key: MooreState(key, val[0]) for key, val in state_setup.items()} # add transitions to states for state_id, state in states.items(): for _input, target_state_id in state_setup[state_id][1].items(): state.transitions[_input] = states[target_state_id] # states to list states = [state for state in states.values()] # build moore machine with first state as starting state mm = MooreMachine(states[0], states) for state in states: state.prefix = mm.get_shortest_path(mm.initial_state, state) return mm ================================================ FILE: aalpy/automata/NonDeterministicMooreMachine.py ================================================ import random from collections import defaultdict from typing import List, Dict, Generic from aalpy.base import AutomatonState, Automaton from aalpy.base.Automaton import OutputType, InputType class NDMooreState(AutomatonState, Generic[InputType, OutputType]): """ Single state of a non-deterministic Moore machine. Each state has an output value. """ def __init__(self, state_id, output=None): super().__init__(state_id) self.transitions: Dict[InputType, List['NDMooreState']] = defaultdict(lambda: list()) self.output: OutputType = output class NDMooreMachine(Automaton[NDMooreState[InputType, OutputType]]): def to_state_setup(self): state_setup = dict() def set_dict_entry(state: NDMooreState): state_setup[state.state_id] = (state.output, {in_sym: [target.state_id for target in trans] for in_sym, trans in state.transitions.items()}) set_dict_entry(self.initial_state) for state in self.states: if state is self.initial_state: continue set_dict_entry(state) @staticmethod def from_state_setup(state_setup: dict, **kwargs) -> 'NDMooreMachine': states_map = {key: NDMooreState(key, output=value[0]) for key, value in state_setup.items()} for key, values in state_setup.items(): source = states_map[key] for i, transitions in values[1].items(): for node in transitions: source.transitions[i].append(states_map[node]) initial_state = states_map[list(state_setup.keys())[0]] return NDMooreMachine(initial_state, list(states_map.values())) def __init__(self, initial_state: AutomatonState, states: list): super().__init__(initial_state, states) def step(self, letter): """ In Moore machines outputs depend on the current state. Args: letter: single input that is looked up in the transition function leading to a new state Returns: the output of the reached state """ options = self.current_state.transitions[letter] self.current_state = random.choice(options) return self.current_state.output ================================================ FILE: aalpy/automata/Onfsm.py ================================================ from collections import defaultdict from random import choice from typing import Generic, Tuple, Dict, List from aalpy.base import Automaton, AutomatonState from aalpy.base.Automaton import OutputType, InputType class OnfsmState(AutomatonState, Generic[InputType, OutputType]): """ """ def __init__(self, state_id): super().__init__(state_id) # TODO this order is inconsistent with probabilistic models # key/input maps to the list of tuples of possible output/new state [(output1, state1), (output2, state2)] self.transitions : Dict[InputType, List[Tuple[OutputType, OnfsmState]]] = defaultdict(list) def add_transition(self, inp, out, new_state) : """ Args: inp: out: new_state: Returns: """ self.transitions[inp].append((out, new_state)) def get_transition(self, input, output=None): """ Args: input: output: (Default value = None) Returns: """ possible_transitions = self.transitions[input] if output: return next((t for t in possible_transitions if t[0] == output), None) else: return possible_transitions class Onfsm(Automaton[OnfsmState[InputType, OutputType]]): """ Observable non-deterministic finite state automaton. """ def __init__(self, initial_state: OnfsmState, states: list): super().__init__(initial_state, states) def step(self, letter): """Next step is determined based on a uniform distribution over all transitions with the input 'letter'. Args: letter: input Returns: output of the probabilistically chosen transition """ transition = choice(self.current_state.transitions[letter]) output = transition[0] self.current_state = transition[1] return output def outputs_on_input(self, letter): """All possible observable outputs after executing the current input 'letter'. Args: letter: input Returns: list of observable outputs """ return [trans[0] for trans in self.current_state.transitions[letter]] def step_to(self, inp, out): """Performs a step on the automaton based on the input `inp` and output `out`. Args: inp: input out: output Returns: output of the reached state, None otherwise """ for new_state in self.current_state.transitions[inp]: if new_state[0] == out: self.current_state = new_state[1] return out return None @staticmethod def from_state_setup(state_setup : dict, **kwargs): raise NotImplementedError() # TODO implement def to_state_setup(self): raise NotImplementedError # TODO implement ================================================ FILE: aalpy/automata/Sevpa.py ================================================ import random from collections import defaultdict, deque from typing import Union, List, Dict from aalpy.base import Automaton, AutomatonState class SevpaAlphabet: """ The Alphabet of a 1-SEVPA. Attributes: internal_alphabet (List[str]): Letters for internal transitions. call_alphabet (List[str]): Letters for push transitions. return_alphabet (List[str]): Letters for pop transitions. exclusive_call_return_pairs (Dict[str, str]): A dictionary representing exclusive pairs of call and return symbols. """ def __init__(self, internal_alphabet: List[str], call_alphabet: List[str], return_alphabet: List[str], exclusive_call_return_pairs: Dict[str, str] = None): self.internal_alphabet = internal_alphabet self.call_alphabet = call_alphabet self.return_alphabet = return_alphabet self.exclusive_call_return_pairs = exclusive_call_return_pairs def get_merged_alphabet(self) -> List[str]: """ Get the merged alphabet, including internal, call, and return symbols. Returns: List[str]: A list of all symbols in the alphabet. """ alphabet = list() alphabet.extend(self.internal_alphabet) alphabet.extend(self.call_alphabet) alphabet.extend(self.return_alphabet) return alphabet def __str__(self) -> str: """ Returns: str: A string representation of the alphabet. """ return f'Internal: {self.internal_alphabet} Call: {self.call_alphabet} Return: {self.return_alphabet}' class SevpaState(AutomatonState): """ Single state of a 1-SEVPA. """ def __init__(self, state_id, is_accepting=False): super().__init__(state_id) # list of SevpaTransition self.transitions = defaultdict(list) self.is_accepting = is_accepting class SevpaTransition: """ Represents a transition in a 1-SEVPA. Attributes: target (SevpaState): The target state of the transition. letter: The symbol associated with the transition. action: The action performed during the transition (pop | None). stack_guard: Pair of (automaton_state_id, call_letter) """ def __init__(self, target: SevpaState, letter, action, stack_guard=None): self.target_state = target self.letter = letter self.action = action self.stack_guard = stack_guard def __str__(self): """ Returns: str: A string representation of the transition. """ return f'{self.letter} --> {self.target_state.state_id}' + \ f' | {self.action}: {self.stack_guard}' if self.stack_guard else '' class Sevpa(Automaton): """ 1-Module Single Entry Visibly Pushdown Automaton. """ empty = "_" def __init__(self, initial_state: SevpaState, states: List[SevpaState]): super().__init__(initial_state, states) self.initial_state = initial_state self.states = states self.input_alphabet = self.get_input_alphabet() self.current_state = None self.stack = [] self.error_state_reached = False # alphabet sets for faster inclusion checks (as in SevpaAlphabet we have lists, for reproducibility) self.internal_set = set(self.input_alphabet.internal_alphabet) self.call_set = set(self.input_alphabet.call_alphabet) self.return_set = set(self.input_alphabet.return_alphabet) def reset_to_initial(self): super().reset_to_initial() self.current_state = self.initial_state self.stack = [self.empty] self.error_state_reached = False return self.current_state.is_accepting and self.stack[-1] == self.empty def step(self, letter): """ Perform a single step on the 1-SEVPA by transitioning with the given input letter. Args: letter: A single input that is looked up in the transition table of the SevpaState. Returns: bool: True if the reached state is an accepting state and the stack is empty, False otherwise. """ if self.error_state_reached: return False if letter is None: return self.current_state.is_accepting and self.stack[-1] == self.empty if letter in self.call_set: self.stack.append((self.current_state.state_id, letter)) self.current_state = self.initial_state return self.current_state.is_accepting and self.stack[-1] == self.empty # get possible transitions transitions = self.current_state.transitions[letter] taken_transition = None for t in transitions: if t.letter in self.return_set: if t.stack_guard == self.stack[-1]: taken_transition = t break elif t.letter in self.internal_set: taken_transition = t break else: assert False # No transition is possible if not taken_transition: self.error_state_reached = True return False self.current_state = taken_transition.target_state if taken_transition.action == 'pop': # empty stack elem should always be on the stack if len(self.stack) <= 1: self.error_state_reached = True return False self.stack.pop() return self.current_state.is_accepting and self.stack[-1] == self.empty def get_state_by_id(self, state_id) -> Union[SevpaState, None]: for state in self.states: if state.state_id == state_id: return state return None def is_input_complete(self) -> bool: pass def execute_sequence(self, origin_state, seq): if origin_state.prefix != self.initial_state.prefix: assert False, 'execute_sequence for Sevpa only is only supported from the initial state.' self.reset_to_initial() self.current_state = origin_state return [self.step(s) for s in seq] def to_state_setup(self): state_setup_dict = {} sorted_states = sorted(self.states, key=lambda x: x.state_id) for state in sorted_states: transitions_for_symbol = {} for symbol, transition_list in state.transitions.items(): trans_list_for_setup = [] for transition in transition_list: trans_list_for_setup.append( (transition.target_state.state_id, transition.action, transition.stack_guard)) if trans_list_for_setup: transitions_for_symbol[symbol] = trans_list_for_setup state_setup_dict[state.state_id] = (state.is_accepting, transitions_for_symbol) return state_setup_dict @staticmethod def from_state_setup(state_setup: dict, **kwargs): init_state_id = kwargs['init_state_id'] # build states with state_id and output states = {key: SevpaState(key, val[0]) for key, val in state_setup.items()} # states[Sevpa.error_state.state_id] = Sevpa.error_state # PdaState(Pda.error_state,False) # add transitions to states for state_id, state in states.items(): for _input, trans_spec in state_setup[state_id][1].items(): for (target_state_id, action, stack_guard) in trans_spec: if action == 'pop': stack_guard = (stack_guard[0], stack_guard[1]) trans = SevpaTransition(target=states[target_state_id], letter=_input, action=action, stack_guard=stack_guard) elif action is None: trans = SevpaTransition(target=states[target_state_id], letter=_input, action=None, stack_guard=None) else: assert False, 'Action must either be "pop" or None, note that there are no push actions ' \ 'definitions in SEVPA' state.transitions[_input].append(trans) init_state = states[init_state_id] return Sevpa(init_state, [state for state in states.values()]) def transform_access_string(self, state=None, stack_content=None) -> List[str]: """ Transform the access string by omitting redundant call and return letters, as well as internal letters. This function creates the following word: For every element in the stack (except the first element '_'): - Append the state prefix from where the stack element was pushed - Append the call letter Append the state prefix from the state where you are calling this function from. Args: state: The state from which the transformation is initiated (default: initial state). stack_content: The content of the stack for transformation (default: Current Stack content). Returns: List[str]: The transformed access string. """ word = [] calling_state = self.initial_state if not state else state stack = self.stack if not stack_content else stack_content for index, stack_elem in enumerate(stack): # Skip the first element because it's the start of the stack '_' if index == 0: continue from_state_id = stack_elem[0] # The corresponding state where the stack element was pushed from call_letter = stack_elem[1] # The call letter that was pushed on the stack from_state = self.get_state_by_id(from_state_id) if from_state.prefix != (): word.extend(from_state.prefix) word.append(call_letter) word.extend(calling_state.prefix) return word @staticmethod def create_daisy_hypothesis(initial_state, alphabet): """ Create a Daisy Hypothesis 1-SEVPA using the given initial state and alphabet. This function creates self-loop transitions for the internal state on every internal letter. Additionally, it creates self-loop transitions with a pop action for every call letter. Args: initial_state (SevpaState): The initial state of the 1-SEVPA. alphabet (SevpaAlphabet): The alphabet for the 1-SEVPA. Returns: Sevpa: The created 1-SEVPA with the specified initial state and alphabet. """ for i in alphabet.internal_alphabet: trans = SevpaTransition(target=initial_state, letter=i, action=None) initial_state.transitions[i].append(trans) for c in alphabet.call_alphabet: for r in alphabet.return_alphabet: trans = SevpaTransition(target=initial_state, letter=r, action='pop', stack_guard=(initial_state.state_id, c)) initial_state.transitions[r].append(trans) return Sevpa(initial_state, [initial_state]) def get_input_alphabet(self): int_alphabet, ret_alphabet, call_alphabet = [], [], [] for state in self.states: for transition_list in state.transitions.values(): for transition in transition_list: if transition.action == 'pop': if transition.letter not in ret_alphabet: ret_alphabet.append(transition.letter) if transition.stack_guard[1] not in call_alphabet: call_alphabet.append(transition.stack_guard[1]) else: if transition.letter not in int_alphabet: int_alphabet.append(transition.letter) return SevpaAlphabet(int_alphabet, call_alphabet, ret_alphabet) def get_error_state(self): """ A state is an error state iff: - if all transitions self loop to itself - if the pop transitions from the corresponding stack symbol lead to the same state - for example: - all q2 transitions lead to q2 - the pop transitions from the initial state which pop the q2+call-symbol from the stack lead to q2 as well - Not an error state if it is the initial state or an accepting state """ for state in self.states: is_error_state = True if state.is_accepting or state == self.initial_state: continue state_target = None # check internal and return transitions ret_int_al = [] ret_int_al.extend(self.input_alphabet.internal_alphabet) ret_int_al.extend(self.input_alphabet.return_alphabet) for letter in ret_int_al: for transition in state.transitions[letter]: if state_target is None: state_target = transition.target_state else: if state_target != transition.target_state: is_error_state = False break if not is_error_state: break # check return transitions from the initial state if is_error_state: for return_letter in self.input_alphabet.return_alphabet: for transition in self.initial_state.transitions[return_letter]: if transition.stack_guard[0] == state_target.state_id: if transition.target_state != state_target: is_error_state = False break if not is_error_state: break else: continue if is_error_state: return state return None def delete_state(self, state_to_remove): if state_to_remove is not None: self.states.remove(state_to_remove) else: return for state in self.states: ret_int_al = [] ret_int_al.extend(self.input_alphabet.internal_alphabet) ret_int_al.extend(self.input_alphabet.return_alphabet) for letter in ret_int_al: cleaned_transitions = [] for transition in state.transitions[letter]: if transition.stack_guard is not None: if transition.stack_guard[0] == state_to_remove.state_id: continue if transition.target_state.state_id == state_to_remove.state_id: continue cleaned_transitions.append(transition) del state.transitions[letter] state.transitions[letter] = cleaned_transitions def get_allowed_call_transitions(self): """ Returns a dict of states that are allowed to push a call letters on the stack. For all states that are connected via internal transitions from the initial state on, the state_id and call_letter of the stack_guard from every return transition is used. States are not allowed to push something somthing on the stack if there is no possibility to pop the stack guard, where their state_id is used, from the stack, which would lead into a dead-end otherwise. Returns: - dict: A dictionary where keys are the call_letters and values are sets of the allowed states. """ # get all states that are connected via internal transitions by using BFS connected_states = set() queue = deque([self.initial_state]) while queue: current_state = queue.popleft() connected_states.add(current_state) for internal_letter in self.input_alphabet.internal_alphabet: for internal_trans in current_state.transitions[internal_letter]: target_state = internal_trans.target_state if target_state not in connected_states: queue.append(target_state) allowed_call_transitions = defaultdict(set) for state in connected_states: for return_letter in self.input_alphabet.return_alphabet: for trans in state.transitions[return_letter]: allowed_call_transitions[trans.stack_guard[1]].add(trans.stack_guard[0]) return allowed_call_transitions def get_accepting_words_bfs(self, min_word_length: int = 0, num_words: int = 1) -> list: """ Generate a list of random words that are accepted by the automaton using the breadth-first search approach. Args: - min_word_length (int): Minimum length of the generated words. - amount_words (int): Number of words to generate. Returns: - set: A set of randomly generated words that are accepted by the automaton. """ allowed_call_trans = self.get_allowed_call_transitions() self.reset_to_initial() queue = deque() shuffled_alphabet = self.input_alphabet.get_merged_alphabet() random.shuffle(shuffled_alphabet) for letter in shuffled_alphabet: queue.append([letter]) found_words = set() while queue: word = queue.popleft() self.reset_to_initial() self.execute_sequence(self.initial_state, word) # skipping words that lead into the error state will also shorten growth of the queue if self.error_state_reached: continue if self.current_state.is_accepting and self.stack[-1] == self.empty and len(word) >= min_word_length: found_words.add(tuple(word)) if len(found_words) >= num_words: found_words = list(found_words) found_words.sort(key=len) return found_words shuffled_alphabet = self.input_alphabet.get_merged_alphabet() for letter in shuffled_alphabet: if letter in allowed_call_trans: # skip words where it's not possible to pop the stack_guard if self.current_state.state_id not in allowed_call_trans[letter]: continue new_word = word + [letter] queue.append(new_word) def get_random_accepting_word(self, return_letter_prob: float = 0.5, min_len: int = 2) -> list: """ Generate a random word that is accepted by the automaton. Only internal letters and return letters will be chosen. If a return letter is randomly chosen a random stack guard will be selected. Then the stack needed stack configuration will be searched by using BFS Args: - return_letter_prob (float): Probability for selecting a letter from the return alphabet. - min_len (int): Minimum length of the generated word. Returns: - list: A randomly generated word that gets accepted by the automaton. """ assert return_letter_prob <= 1.0 word = [] internal_letter_prob = 0.0 if len(self.input_alphabet.internal_alphabet) != 0: internal_letter_prob = 1.0 - return_letter_prob else: return_letter_prob = 1.0 assert (return_letter_prob + internal_letter_prob) == 1.0 return_letter_boarder = return_letter_prob internal_letter_boarder = return_letter_boarder + internal_letter_prob allowed_call_trans = self.get_allowed_call_transitions() self.reset_to_initial() while True: letter_type = random.uniform(0.0, 1.0) is_return_letter = False if letter_type <= return_letter_boarder: possible_letters = self.input_alphabet.return_alphabet is_return_letter = True elif return_letter_boarder < letter_type <= internal_letter_boarder: possible_letters = self.input_alphabet.internal_alphabet else: assert False assert len(possible_letters) > 0 random_trans_letter_index = random.randint(0, len(possible_letters) - 1) letter_for_word = possible_letters[random_trans_letter_index] # find the sub-word for the needed stack guard beginning from the initial state # the new word will be: letter_prefix + word + letter if is_return_letter: # randomly select one of the return transitions with the respective return symbol if len(self.current_state.transitions[letter_for_word]) == 0: continue elif len(self.current_state.transitions[letter_for_word]) == 1: random_stack_guard = self.current_state.transitions[letter_for_word][0].stack_guard else: random_stack_guard_index = random.randint(0, len(self.current_state.transitions[letter_for_word]) - 1) random_stack_guard = self.current_state.transitions[letter_for_word][ random_stack_guard_index].stack_guard # start from the initial state self.reset_to_initial() letter_prefix = [] needed_stack = self.stack.copy() needed_stack.append(random_stack_guard) queue = deque() for letter in self.input_alphabet.get_merged_alphabet(): queue.append([letter]) while queue: letter_prefix = queue.popleft() self.reset_to_initial() self.execute_sequence(self.initial_state, letter_prefix) if self.error_state_reached: continue if self.stack == needed_stack: break for letter in self.input_alphabet.get_merged_alphabet(): if letter in allowed_call_trans: # skip words where it's not possible to pop the stack_guard if self.current_state.state_id not in allowed_call_trans[letter]: continue new_word = letter_prefix + [letter] queue.append(new_word) for letter in word: self.step(letter) self.step(letter_for_word) if not self.error_state_reached: word = letter_prefix + word word.append(letter_for_word) else: self.execute_sequence(self.initial_state, word) else: self.step(letter_for_word) if not self.error_state_reached: word.append(letter_for_word) else: self.execute_sequence(self.initial_state, word) if self.current_state.is_accepting and self.stack[-1] == self.empty and len(word) >= min_len \ and random.random() < 0.2: break self.reset_to_initial() return word ================================================ FILE: aalpy/automata/StochasticMealyMachine.py ================================================ import random from collections import defaultdict from typing import Generic, Tuple, List, Dict from aalpy.automata import MdpState, Mdp from aalpy.base import Automaton, AutomatonState from aalpy.base.Automaton import OutputType, InputType class StochasticMealyState(AutomatonState, Generic[InputType, OutputType]): def __init__(self, state_id): super().__init__(state_id) # Each transition is a tuple (newNode, output, probability) self.transitions: Dict[InputType, List[Tuple[StochasticMealyState, OutputType, float]]] = defaultdict(list) class StochasticMealyMachine(Automaton[StochasticMealyState[InputType, OutputType]]): def __init__(self, initial_state: StochasticMealyState, states: list): super().__init__(initial_state, states) def reset_to_initial(self): self.current_state = self.initial_state def step(self, letter): """ Next step is determined based on transition probabilities of the current state. Args: letter: input Returns: output of the current state """ prob = random.random() probability_distributions = [i[2] for i in self.current_state.transitions[letter]] index = 0 for i, p in enumerate(probability_distributions): prob -= p if prob <= 0: index = i break transition = self.current_state.transitions[letter][index] self.current_state = transition[0] return transition[1] def step_to(self, inp, out): """Performs a step on the automaton based on the input `inp` and output `out`. Args: inp: input out: output Returns: output of the reached state, None otherwise """ for (new_state, output, prob) in self.current_state.transitions[inp]: if output == out: self.current_state = new_state return out return None def to_mdp(self): return smm_to_mdp_conversion(self) def to_state_setup(self): state_setup_dict = {} # ensure initial state is first in the list if self.states[0] != self.initial_state: self.states.remove(self.initial_state) self.states.insert(0, self.initial_state) for s in self.states: state_setup_dict[s.state_id] = {k: [(node.state_id, output, prob) for node, output, prob in v] for k, v in s.transitions.items()} return state_setup_dict @staticmethod def from_state_setup(state_setup : dict, **kwargs): states_map = {key: StochasticMealyState(key) for key in state_setup.keys()} for key, values in state_setup.items(): source = states_map[key] for i, transitions in values.items(): for node, output, prob in transitions: source.transitions[i].append((states_map[node], output, prob)) initial_state = states_map[list(state_setup.keys())[0]] return StochasticMealyMachine(initial_state, list(states_map.values())) def smm_to_mdp_conversion(smm: StochasticMealyMachine): """ Convert SMM to MDP. Args: smm: StochasticMealyMachine: SMM to convert Returns: equivalent MDP """ inputs = smm.get_input_alphabet() mdp_states = [] smm_state_to_mdp_state = dict() init_state = MdpState("0", "___start___") mdp_states.append(init_state) for s in smm.states: incoming_edges = defaultdict(list) incoming_outputs = set() for pre_s in smm.states: for i in inputs: incoming_edges[i] += filter(lambda t: t[0] == s, pre_s.transitions[i]) incoming_outputs.update(map(lambda t: t[1], incoming_edges[i])) state_id = 0 for o in incoming_outputs: new_state_id = s.state_id + str(state_id) state_id += 1 new_state = MdpState(new_state_id, o) mdp_states.append(new_state) smm_state_to_mdp_state[(s.state_id, o)] = new_state for s in smm.states: mdp_states_for_s = {mdp_state for (s_id, o), mdp_state in smm_state_to_mdp_state.items() if s_id == s.state_id} for i in inputs: for outgoing_t in s.transitions[i]: target_smm_state = outgoing_t[0] output = outgoing_t[1] prob = outgoing_t[2] target_mdp_state = smm_state_to_mdp_state[(target_smm_state.state_id, output)] for mdp_state in mdp_states_for_s: mdp_state.transitions[i].append((target_mdp_state, prob)) if s == smm.initial_state: init_state.transitions[i].append((target_mdp_state, prob)) return Mdp(init_state, mdp_states) ================================================ FILE: aalpy/automata/Vpa.py ================================================ import random from collections import defaultdict from typing import List, Dict from aalpy.base import Automaton, AutomatonState class VpaAlphabet: """ The Alphabet of a VPA. Attributes: internal_alphabet (List[str]): Letters for internal transitions. call_alphabet (List[str]): Letters for push transitions. return_alphabet (List[str]): Letters for pop transitions. exclusive_call_return_pairs (Dict[str, str]): A dictionary representing exclusive pairs of call and return symbols. """ def __init__(self, internal_alphabet: List[str], call_alphabet: List[str], return_alphabet: List[str], exclusive_call_return_pairs: Dict[str, str] = None): self.internal_alphabet = internal_alphabet self.call_alphabet = call_alphabet self.return_alphabet = return_alphabet self.exclusive_call_return_pairs = exclusive_call_return_pairs def get_merged_alphabet(self) -> List[str]: """ Get the merged alphabet, including internal, call, and return symbols. Returns: List[str]: A list of all symbols in the alphabet. """ alphabet = list() alphabet.extend(self.internal_alphabet) alphabet.extend(self.call_alphabet) alphabet.extend(self.return_alphabet) return alphabet def __str__(self) -> str: """ Returns: str: A string representation of the alphabet. """ return f'Internal: {self.internal_alphabet} Call: {self.call_alphabet} Return: {self.return_alphabet}' class VpaState(AutomatonState): """ Single state of a VPA. """ def __init__(self, state_id, is_accepting=False): super().__init__(state_id) self.transitions = defaultdict(list) self.is_accepting = is_accepting class VpaTransition: """ Represents a transition in a VPA. Attributes: start (VpaState): The starting state of the transition. target (VpaState): The target state of the transition. symbol: The symbol associated with the transition. action: The action performed during the transition (push | pop | None). stack_guard: The stack symbol to be pushed/popped. """ def __init__(self, start: VpaState, target: VpaState, symbol, action, stack_guard=None): self.start = start self.target_state = target self.letter = symbol self.action = action self.stack_guard = stack_guard def __str__(self): return f"{self.letter}: {self.start.state_id} --> {self.target_state.state_id} | {self.action}: {self.stack_guard}" class Vpa(Automaton): """ Visibly Pushdown Automaton. """ error_state = VpaState("ErrorSinkState", False) def __init__(self, initial_state: VpaState, states): super().__init__(initial_state, states) self.initial_state = initial_state self.states = states self.input_alphabet = self.get_input_alphabet() self.current_state = None self.stack = [] # alphabet sets for faster inclusion checks (as in VpaAlphabet we have lists, for reproducibility) self.internal_set = set(self.input_alphabet.internal_alphabet) self.call_set = set(self.input_alphabet.call_alphabet) self.return_set = set(self.input_alphabet.return_alphabet) def reset_to_initial(self): self.current_state = self.initial_state self.stack = [] def top(self): return self.stack[-1] if self.stack else [] def step(self, letter): """ Perform a single step on the VPA by transitioning with the given input letter. Args: letter: A single input that is looked up in the transition table of the VpaState. Returns: bool: True if the reached state is an accepting state and the stack is empty, False otherwise. """ if self.current_state == Vpa.error_state: return False if letter is None: return self.current_state.is_accepting and self.stack == [] transitions = self.current_state.transitions[letter] taken_transition = None for t in transitions: if t.action == 'push' or t.action is None: taken_transition = t break else: if t.stack_guard == self.top(): taken_transition = t break if taken_transition is None: self.current_state = Vpa.error_state return False self.current_state = taken_transition.target_state if taken_transition.action == 'push': self.stack.append(taken_transition.stack_guard) elif taken_transition.action == 'pop': # empty stack elem should always be there if not self.stack: self.current_state = Vpa.error_state return False self.stack.pop() return self.current_state.is_accepting and self.stack == [] def to_state_setup(self): state_setup_dict = {} # ensure prefixes are computed # self.compute_prefixes() sorted_states = sorted(self.states, key=lambda x: len(x.prefix) if x.prefix is not None else len(self.states)) for s in sorted_states: state_setup_dict[s.state_id] = ( s.is_accepting, {k: (v.target_state.state_id, v.action) for k, v in s.transitions.items()}) return state_setup_dict def get_input_alphabet(self) -> VpaAlphabet: int_alphabet, ret_alphabet, call_alphabet = [], [], [] for state in self.states: for transition_list in state.transitions.values(): for transition in transition_list: if transition.action == 'pop': if transition.letter not in ret_alphabet: ret_alphabet.append(transition.letter) elif transition.action == 'push': if transition.letter not in call_alphabet: call_alphabet.append(transition.letter) elif transition.letter not in int_alphabet: int_alphabet.append(transition.letter) return VpaAlphabet(int_alphabet, call_alphabet, ret_alphabet) def is_input_complete(self) -> bool: """ Check whether all states have defined transition for all inputs :return: true if automaton is input complete Returns: True if input complete, False otherwise """ alphabet = set(self.get_input_alphabet().get_merged_alphabet()) for state in self.states: if set(state.transitions.keys()) != alphabet: return False return True @staticmethod def from_state_setup(state_setup: dict, **kwargs): """ Create a VPA from a state setup. Example state setup: state_setup = { "q0": (False, {"(": [("q1", 'push', "(")], "[": [("q1", 'push', "[")], # exclude empty seq }), "q1": (False, {"(": [("q1", 'push', "(")], "[": [("q1", 'push', "[")], ")": [("q2", 'pop', "(")], "]": [("q2", 'pop', "[")]}), "q2": (True, { ")": [("q2", 'pop', "(")], "]": [("q2", 'pop', "[")] }), Args: state_setup (dict): A dictionary mapping from state IDs to tuples containing (is_accepting: bool, transitions_dict: dict), where transitions_dict maps input symbols to lists of tuples (target_state_id, action, stack_guard). init_state_id (str): The state ID for the initial state of the VPA. input_alphabet (VpaAlphabet): The alphabet for the VPA. Returns: Vpa: The constructed Variable Pushdown Automaton. """ # state_setup should map from state_id to tuple(is_accepting and transitions_dict) init_state_id = kwargs['init_state_id'] # build states with state_id and output states = {key: VpaState(key, val[0]) for key, val in state_setup.items()} states[Vpa.error_state.state_id] = Vpa.error_state # PdaState(Pda.error_state,False) # add transitions to states for state_id, state in states.items(): if state_id == Vpa.error_state.state_id: continue for _input, trans_spec in state_setup[state_id][1].items(): for (target_state_id, action, stack_guard) in trans_spec: trans = VpaTransition(start=state, target=states[target_state_id], symbol=_input, action=action, stack_guard=stack_guard) state.transitions[_input].append(trans) init_state = states[init_state_id] # states to list states = [state for state in states.values()] vpa = Vpa(init_state, states) return vpa def is_balanced(self, seq): from aalpy.utils import is_balanced return is_balanced(seq, self.input_alphabet) def generate_random_accepting_word(self, min_steps=4, max_steps=20): """ Generate a random valid sequence for a given VPDA. Args: min_steps : Minimum number of steps max_steps : Maximum number of steps before the process terminates Returns: list: A list of input symbols (the generated sequence) leading to an accepting state. """ sequence = [] self.reset_to_initial() for step_count in range(max_steps): current_state = self.current_state # If we have met the min_steps requirement and are in an accepting state with an empty stack, stop if step_count >= min_steps and current_state.is_accepting and not self.stack: return sequence # Get all possible transitions from the current state possible_transitions = [] for letter, transitions in current_state.transitions.items(): for t in transitions: if t.action == 'pop' and self.stack and t.stack_guard == self.top(): possible_transitions.append(t) elif t.action == 'push' or t.action is None: possible_transitions.append(t) # If no valid transitions exist, return an incomplete sequence or error if not possible_transitions: break # Randomly choose a valid transition chosen_transition = random.choice(possible_transitions) # Perform the transition self.step(chosen_transition.letter) # Add the chosen letter to the sequence sequence.append(chosen_transition.letter) # None indicates that a sequance was not successfully generated return None def vpa_from_dfa_representation(dfa_repr, vpa_alphabet): vpa_states = dict() for dfa_state in dfa_repr.states: vpa_state = VpaState(state_id=dfa_state.state_id, is_accepting=dfa_state.is_accepting) vpa_states[dfa_state.state_id] = vpa_state for dfa_state in dfa_repr.states: for input_symbol, reached_dfa_state in dfa_state.transitions.items(): origin_state = vpa_states[dfa_state.state_id] reached_state = vpa_states[reached_dfa_state.state_id] top_of_stack = None if isinstance(input_symbol, tuple): input_symbol, top_of_stack = input_symbol[0], input_symbol[1] if input_symbol in vpa_alphabet.return_alphabet: transition = VpaTransition(origin_state, reached_state, input_symbol, 'pop', top_of_stack) else: action = 'push' if input_symbol in vpa_alphabet.call_alphabet else None stack_guard = input_symbol if action == 'push' else None transition = VpaTransition(origin_state, reached_state, input_symbol, action, stack_guard) origin_state.transitions[input_symbol].append(transition) initial_state = vpa_states[dfa_repr.initial_state.state_id] learned_model = Vpa(initial_state, list(vpa_states.values())) return learned_model ================================================ FILE: aalpy/automata/__init__.py ================================================ from .Dfa import Dfa, DfaState from .Mdp import Mdp, MdpState from .MealyMachine import MealyMachine, MealyState from .MooreMachine import MooreMachine, MooreState from .Onfsm import Onfsm, OnfsmState from .StochasticMealyMachine import StochasticMealyMachine, StochasticMealyState from .MarkovChain import MarkovChain, McState from .NonDeterministicMooreMachine import NDMooreMachine, NDMooreState from .Sevpa import Sevpa, SevpaState, SevpaAlphabet, SevpaTransition from .Vpa import Vpa, VpaAlphabet, VpaState, VpaTransition ================================================ FILE: aalpy/base/Automaton.py ================================================ import copy import warnings from abc import ABC, abstractmethod from collections import defaultdict from typing import Union, TypeVar, Generic, List class AutomatonState(ABC): def __init__(self, state_id): """ Single state of an automaton. Each state consists of a state id, a dictionary of transitions, where the keys are inputs and the values are the corresponding target states, and a prefix that leads to the state from the initial state. Args: state_id(Any): used for graphical representation of the state. A good practice is to keep it unique. """ self.state_id = state_id self.transitions = None self.prefix = None def get_diff_state_transitions(self) -> list: """ Returns a list of transitions that lead to new states, not same-state transitions. """ transitions = [] for trans, state in self.transitions.items(): if state != self: transitions.append(trans) return transitions def get_same_state_transitions(self) -> list: """ Get all transitions that lead to the same state (self loops). """ dst = self.get_diff_state_transitions() all_trans = set(self.transitions.keys()) return [t for t in all_trans if t not in dst] AutomatonStateType = TypeVar("AutomatonStateType", bound=AutomatonState) OutputType = TypeVar("OutputType") InputType = TypeVar("InputType") class Automaton(ABC, Generic[AutomatonStateType]): """ Abstract class representing an automaton. """ def __init__(self, initial_state: AutomatonStateType, states: List[AutomatonStateType]): """ Args: initial_state (AutomatonState): initial state of the automaton states (list) : list containing all states of the automaton """ self.initial_state: AutomatonStateType = initial_state self.states: List[AutomatonStateType] = states self.characterization_set: list = [] self.current_state: AutomatonStateType = initial_state @property def size(self): return len(self.states) def reset_to_initial(self): """ Resets the current state of the automaton to the initial state """ self.current_state = self.initial_state @abstractmethod def step(self, letter): """ Performs a single step on the automaton changing its current state. Args: letter: element of the input alphabet to be executed on the system under learning Returns: Output produced when executing the input letter from the current state """ pass def is_input_complete(self) -> bool: """ Check whether all states have defined transition for all inputs :return: true if automaton is input complete Returns: True if input complete, False otherwise """ alphabet = set(self.get_input_alphabet()) for state in self.states: if set(state.transitions.keys()) != alphabet: return False return True # returns a list which is input alphabet, or a sevpa alphabet in case of VPAs def get_input_alphabet(self): """ Returns the input alphabet """ alphabet = list() for s in self.states: for i in s.transitions.keys(): if i not in alphabet: alphabet.append(i) return list(alphabet) def get_state_by_id(self, state_id) -> Union[AutomatonStateType, None]: for state in self.states: if state.state_id == state_id: return state return None def __str__(self): """ :return: A string representation of the automaton """ from aalpy.utils import save_automaton_to_file return save_automaton_to_file(self, path='learnedModel', file_type='string', round_floats=2) def make_input_complete(self, missing_transition_go_to='self_loop'): """ For more details check the implementation of this method in utils.HelperFunctions missing_transition_go_to: either 'self_loop' or 'sink_state'. """ from aalpy.utils.HelperFunctions import make_input_complete make_input_complete(self, missing_transition_go_to) def execute_sequence(self, origin_state, seq): """ Note that execute sequance changes the state! """ self.current_state = origin_state return [self.step(s) for s in seq] def save(self, file_path='LearnedModel', file_type='dot'): from aalpy.utils import save_automaton_to_file save_automaton_to_file(self, path=file_path, file_type=file_type) def visualize(self, path='LearnedModel', file_type='pdf', display_same_state_transitions=True): from aalpy.utils import visualize_automaton visualize_automaton(self, path, file_type, display_same_state_transitions) @staticmethod @abstractmethod def from_state_setup(state_setup: dict, **kwargs) -> 'Automaton': pass @abstractmethod def to_state_setup(self): pass def copy(self) -> 'Automaton': return self.from_state_setup(self.to_state_setup()) def __reduce__(self): return self.from_state_setup, (self.to_state_setup(),) class DeterministicAutomaton(Automaton[AutomatonStateType]): @abstractmethod def step(self, letter): pass def get_shortest_path(self, origin_state: AutomatonStateType, target_state: AutomatonStateType) -> Union[ tuple, None]: """ Breath First Search over the automaton to find the shortest path Args: origin_state (AutomatonState): state from which the BFS will start target_state (AutomatonState): state that will be reached with the return value Returns: sequence of inputs that lead from origin_state to target state, or None if target state is not reachable from origin state """ if origin_state not in self.states or target_state not in self.states: warnings.warn('Origin or target state not in automaton. Returning None.') return None explored = [] queue = [[origin_state]] if origin_state == target_state: return () while queue: path = queue.pop(0) node = path[-1] if node not in explored: neighbours = node.transitions.values() for neighbour in neighbours: new_path = list(path) new_path.append(neighbour) queue.append(new_path) # return path if neighbour is goal if neighbour == target_state: acc_seq = new_path[:-1] inputs = [] for ind, state in enumerate(acc_seq): inputs.append(next(key for key, value in state.transitions.items() if value == new_path[ind + 1])) return tuple(inputs) # mark node as explored explored.append(node) return None def is_strongly_connected(self) -> bool: """ Check whether the automaton is strongly connected, meaning that every state can be reached from every other state. Returns: True if strongly connected, False otherwise """ import itertools state_comb_list = itertools.permutations(self.states, 2) for state_comb in state_comb_list: if self.get_shortest_path(state_comb[0], state_comb[1]) is None: return False return True def output_step(self, state, letter): """ Given an input letter, compute the output response from a given state. Args: state: state from which the output response shall be computed letter: an input letter from the alphabet Returns: the single-step output response """ state_save = self.current_state self.current_state = state output = self.step(letter) self.current_state = state_save return output def find_distinguishing_seq(self, state1, state2, alphabet): """ A BFS to determine an input sequence that distinguishes two states in the automaton, i.e., a sequence such that the output response from the given states is different. In a minimal automaton, this function always returns a sequence different from None Args: state1: first state state2: second state to distinguish alphabet: input alphabet of the automaton Returns: an input sequence distinguishing two states, or None if the states are equivalent """ visited = set() to_explore = [(state1, state2, [])] while to_explore: (curr_s1, curr_s2, prefix) = to_explore.pop(0) visited.add((curr_s1, curr_s2)) for i in alphabet: o1 = self.output_step(curr_s1, i) o2 = self.output_step(curr_s2, i) new_prefix = prefix + [i] if o1 != o2: return new_prefix else: next_s1 = curr_s1.transitions[i] next_s2 = curr_s2.transitions[i] if (next_s1, next_s2) not in visited: to_explore.append((next_s1, next_s2, new_prefix)) return None def compute_output_seq(self, state, sequence): """ Given an input sequence, compute the output response from a given state. Args: state: state from which the output response shall be computed sequence: an input sequence over the alphabet Returns: the output response """ state_save = self.current_state output = self.execute_sequence(state, sequence) self.current_state = state_save return output def is_minimal(self): if not self.is_input_complete(): warnings.warn('Minimization of non input complete automata is not yet supported. Returning False.') return False return self.compute_characterization_set(raise_warning=False) is not None def compute_characterization_set(self, char_set_init=None, online_suffix_closure=True, split_all_blocks=True, return_same_states=False, raise_warning=True): """ Computation of a characterization set, that is, a set of sequences that can distinguish all states in the automation. The implementation follows the approach for finding multiple preset diagnosing experiments described by Arthur Gill in "Introduction to the Theory of Finite State Machines". Some optional parameterized adaptations, e.g., for computing suffix-closed sets target the application in L*-based learning and conformance testing. The function only works for minimal automata. Args: char_set_init: a list of sequence that will be included in the characterization set, e.g., the input alphabet. A empty sequance is added to this list when using automata with state labels (DFA and Moore) online_suffix_closure: if true, ensures suffix closedness of the characterization set at every computation step split_all_blocks: if false, the computation follows the original tree-based strategy, where newly computed sequences are only checked on a subset of the states to be distinguished if true, sequences are used to distinguish all states, yielding a potentially smaller set, which is useful for conformance testing and learning return_same_states: if True, a single distinguishable pair of states will be returned, or None None if there are no non-distinguishable states raise_warning: prints warning message if characterization set cannot be computed Returns: a characterization set or None if a non-minimal automaton is passed to the function """ blocks = list() blocks.append(copy.copy(self.states)) char_set = [] if not char_set_init else char_set_init if char_set_init: for seq in char_set_init: blocks = self._split_blocks(blocks, seq) alphabet = self.get_input_alphabet() while True: # Given a partition (of states), this function returns a block with at least two elements. try: block_to_split = next(filter(lambda b: len(b) > 1, blocks)) except StopIteration: block_to_split = None if not block_to_split: break split_state1 = block_to_split[0] split_state2 = block_to_split[1] dist_seq = self.find_distinguishing_seq(split_state1, split_state2, alphabet) if dist_seq is None: if return_same_states: return split_state1, split_state2 if raise_warning: warnings.warn("Automaton is non-canonical: could not compute characterization set." "Returning None.") return None # in L*-based learning, we use suffix-closed column labels, so it makes sense to use a suffix-closed # char set in this context if online_suffix_closure: dist_seq_closure = [tuple(dist_seq[len(dist_seq) - i - 1:]) for i in range(len(dist_seq))] else: dist_seq_closure = [tuple(dist_seq)] # the standard approach described by Gill, computes a sequence that splits one block and really only splits # one block, that is, it is only applied to the states in said block # in L*-based learning we combine every prefix with every, therefore it makes sense to apply the sequence # on all blocks and split all if split_all_blocks: for seq in dist_seq_closure: # seq may be in char_set if we do the closure on the fly if seq in char_set: continue char_set.append(seq) blocks = self._split_blocks(blocks, seq) else: blocks.remove(block_to_split) new_blocks = [block_to_split] for seq in dist_seq_closure: char_set.append(seq) new_blocks = self._split_blocks(new_blocks, seq) for new_block in new_blocks: blocks.append(new_block) unique_char_set = [] for seq in char_set: if seq not in unique_char_set: unique_char_set.append(seq) char_set = unique_char_set if return_same_states: return None, None return char_set def _split_blocks(self, blocks, seq): """ Refines a partition of states (blocks) using the output response to a given input sequence seq. Args: blocks: a partition of states seq: an input sequence Returns: a refined partition of states """ new_blocks = [] for block in blocks: block_after_split = defaultdict(list) for state in block: output_seq = tuple(self.compute_output_seq(state, seq)) block_after_split[output_seq].append(state) for new_block in block_after_split.values(): new_blocks.append(new_block) return new_blocks def compute_prefixes(self): for s in self.states: if not s.prefix: s.prefix = self.get_shortest_path(self.initial_state, s) def minimize(self): if not self.is_input_complete(): warnings.warn('Minimization of non input complete automata is not yet supported.\n Model not minimized.') return s1, s2 = self.compute_characterization_set(return_same_states=True) while s1 and s2: for s in self.states: for i, new_state in s.transitions.items(): if new_state == s2: s.transitions[i] = s1 self.states.remove(s2) s1, s2 = self.compute_characterization_set(return_same_states=True) self.compute_prefixes() def __eq__(self, other): from aalpy.utils import bisimilar return bisimilar(self, other) ================================================ FILE: aalpy/base/CacheTree.py ================================================ class Node(object): __slots__ = ['value', 'children'] def __init__(self, value=None): self.value = value self.children = {} class CacheTree: """ Tree in which all membership queries and corresponding outputs/values are stored. Membership queries update the tree and while updating, check if determinism is maintained. Root node corresponds to the initial state, and from that point on, for every new input/output pair, a new child is created where the output is the value of the child, and the input is the transition leading from the parent to the child. """ def __init__(self): self.root_node = Node() self.curr_node = None self.inputs = () self.outputs = () def reset(self): self.curr_node = self.root_node self.inputs = () self.outputs = () def step_in_cache(self, inp, out): """ Preform a step in the cache. If output exist for the current state, and is not the same as `out`, throw the non-determinism violation error and abort learning. Args: inp: input out: output """ self.inputs += (inp,) self.outputs += (out,) if inp is None: self.root_node.value = out return if inp not in self.curr_node.children.keys(): node = Node(out) self.curr_node.children[inp] = node else: node = self.curr_node.children[inp] if node.value != out: expected_seq = self.outputs[:-1] expected_seq += (node.value,) msg = f'Non-determinism detected.\n' \ f'Error inserting: {self.inputs}\n' \ f'Conflict detected: {node.value} vs {out}\n' \ f'Expected Output: {expected_seq}\n' \ f'Received output: {self.outputs}' raise SystemExit(msg) self.curr_node = node def in_cache(self, input_seq: tuple): """ Check if the result of the membership query for input_seq is cached is in the tree. If it is, return the corresponding output sequence. Args: input_seq: corresponds to the membership query Returns: outputs associated with inputs if it is in the query, None otherwise """ curr_node = self.root_node output_seq = () for letter in input_seq: if letter in curr_node.children.keys(): curr_node = curr_node.children[letter] output_seq += (curr_node.value,) else: return None return output_seq def add_to_cache(self, input_sequence, output_sequence): """ Add input-output sequence to cache """ self.reset() for i, o in zip(input_sequence, output_sequence): self.step_in_cache(i, o) class CacheDict: """ Dictionary in which all membership queries and corresponding outputs/values are stored. Membership queries update the tree and while updating, check if determinism is maintained. Root node corresponds to the initial state, and from that point on, for every new input/output pair, a new child is created where the output is the value of the child, and the input is the transition leading from the parent to the child. """ def __init__(self): self.cache_dict = dict() self.inputs = () def reset(self): self.inputs = () pass def step_in_cache(self, inp, out): """ Preform a step in the cache. If output exist for the current state, and is not the same as `out`, throw the non-determinism violation error and abort learning. Args: inp: input out: output """ if inp is None: return self.cache_dict[()] self.inputs += (inp,) if self.inputs not in self.cache_dict.keys(): self.cache_dict[self.inputs] = out else: cache_output = self.cache_dict[self.inputs] if cache_output != out: expected_seq = self.get_output_sequence(self.inputs) received_seq = expected_seq[:-1] + (out,) msg = f'Non-determinism detected.\n' \ f'Error inserting: {self.inputs}\n' \ f'Conflict detected: {cache_output} vs {out}\n' \ f'Expected Output: {expected_seq}\n' \ f'Received output: {received_seq}' raise SystemExit(msg) def in_cache(self, input_seq: tuple): """ Check if the result of the membership query for input_seq is cached is in the tree. If it is, return the corresponding output sequence. Args: input_seq: corresponds to the membership query Returns: outputs associated with inputs if it is in the query, None otherwise """ if input_seq in self.cache_dict.keys(): return self.get_output_sequence(input_seq) return None def add_to_cache(self, input_sequence, output_sequence): """ Add input-output sequence to cache """ for i in range(1, len(input_sequence) + 1): self.cache_dict[input_sequence[:i]] = output_sequence[i-1] def get_output_sequence(self, input_seq): return tuple(self.cache_dict[input_seq[:i]] for i in range(1, len(input_seq) + 1)) ================================================ FILE: aalpy/base/Oracle.py ================================================ from abc import ABC, abstractmethod from aalpy.base import SUL class Oracle(ABC): """Abstract class implemented by all equivalence oracles.""" def __init__(self, alphabet: list, sul: SUL): """ Default constructor for all equivalence oracles. Args: alphabet: input alphabet sul: system under learning """ self.alphabet = alphabet self.sul = sul self.num_queries = 0 self.num_steps = 0 @abstractmethod def find_cex(self, hypothesis): """ Return a counterexample (inputs) that displays different behavior on system under learning and current hypothesis. Args: hypothesis: current hypothesis Returns: tuple or list containing counterexample inputs, None if no counterexample is found """ pass def reset_hyp_and_sul(self, hypothesis): """ Reset SUL and hypothesis to initial state. Args: hypothesis: current hypothesis """ hypothesis.reset_to_initial() self.sul.pre() self.num_queries += 1 ================================================ FILE: aalpy/base/SUL.py ================================================ from abc import ABC, abstractmethod from aalpy.base.CacheTree import CacheTree, CacheDict class SUL(ABC): """ System Under Learning (SUL) abstract class. Defines the interaction between the learning algorithm and the system under learning. All systems under learning have to implement this class, as it is passed to the learning algorithm and the equivalence oracle. """ def __init__(self): self.num_queries = 0 self.num_steps = 0 self.num_cached_queries = 0 def query(self, word: tuple) -> list: """ Performs a membership query on the SUL. Before the query, pre() method is called and after the query post() method is called. Each letter in the word (input in the input sequence) is executed using the step method. Args: word: membership query (word consisting of letters/inputs) Returns: list of outputs, where the i-th output corresponds to the output of the system after the i-th input """ self.pre() # Empty string for DFA if len(word) == 0: out = [self.step(None)] else: out = [self.step(letter) for letter in word] self.post() self.num_queries += 1 self.num_steps += len(word) return out def io_query(self, word : tuple): return list(zip(word, self.query(word))) def adaptive_query(self, word, ads): """ Performs an adaptive output query on the SUL. Before the query, pre() method is called and after the query post() method is called. The ADS is a tree like object, the next input depends on the previous input-output pairs. Each input is executed using the step method. Currently only implemented for Mealy machines Args: word: membership query (word consisting of letters/inputs) ads: adaptive distinguishing suffix Returns: list of outputs, where the i-th output corresponds to the output of the system after the i-th input """ self.pre() outputs_received = [] last_output = None for inp in word: output = self.step(inp) outputs_received.append(output) self.num_steps += len(word) while True: next_input = ads.next_input(last_output) if next_input is None: break if next_input is tuple(): # Relevant for DFA/Moore if outputs_received: last_output = outputs_received[-1] else: last_output = self.step(None) else: word.append(next_input) output = self.step(next_input) outputs_received.append(output) last_output = output self.num_steps += 1 self.num_queries += 1 self.post() return word, outputs_received @abstractmethod def pre(self): """ Resets the system. Called after post method in the equivalence query. """ pass @abstractmethod def post(self): """ Performs additional cleanup on the system in necessary. Called before pre method in the equivalence query. """ pass @abstractmethod def step(self, letter): """ Executes an action on the system under learning and returns its result. Args: letter: Single input that is executed on the SUL. Returns: Output received after executing the input. """ pass class CacheSUL(SUL): """ System under learning that keeps a multiset of all queries in memory. This multiset/cache is encoded as a tree. """ def __init__(self, sul: SUL, cache_type='tree'): super().__init__() self.sul = sul self.cache = CacheTree() if cache_type == 'tree' else CacheDict() def query(self, word): """ Performs a membership query on the SUL if and only if `word` is not a prefix of any trace in the cache. Before the query, pre() method is called and after the query post() method is called. Each letter in the word (input in the input sequence) is executed using the step method. Args: word: membership query (word consisting of letters/inputs) Returns: list of outputs, where the i-th output corresponds to the output of the system after the i-th input """ cached_query = self.cache.in_cache(word) if cached_query: self.num_cached_queries += 1 return cached_query # get outputs using default query method out = self.sul.query(word) # add input/outputs to tree self.cache.reset() for i, o in zip(word, out): self.cache.step_in_cache(i, o) self.num_queries += 1 self.num_steps += len(word) return out def pre(self): """ Reset the system under learning and current node in the cache tree. """ self.cache.reset() self.sul.pre() def post(self): self.sul.post() def step(self, letter): """ Executes an action on the system under learning, adds it to the cache and returns its result. Args: letter: Single input that is executed on the SUL. Returns: Output received after executing the input. """ out = self.sul.step(letter) self.cache.step_in_cache(letter, out) return out ================================================ FILE: aalpy/base/__init__.py ================================================ from .Automaton import Automaton, AutomatonState, DeterministicAutomaton from .Oracle import Oracle from .SUL import SUL ================================================ FILE: aalpy/learning_algs/__init__.py ================================================ # public API for running automata learning algorithms from .deterministic.LStar import run_Lstar from .deterministic.KV import run_KV from .deterministic.LSharp import run_Lsharp from .adaptive.AdaptiveLSharp import run_adaptive_Lsharp from .non_deterministic.OnfsmLstar import run_non_det_Lstar from .non_deterministic.AbstractedOnfsmLstar import run_abstracted_ONFSM_Lstar from .stochastic.StochasticLStar import run_stochastic_Lstar from .stochastic_passive.Alergia import run_Alergia, run_JAlergia from .stochastic_passive.ActiveAleriga import run_active_Alergia from .deterministic_passive.RPNI import run_RPNI from .deterministic_passive.PAPNI import run_PAPNI from .deterministic_passive.active_RPNI import run_active_RPNI from .general_passive.GeneralizedStateMerging import run_GSM from .general_passive.GsmAlgorithms import run_EDSM, run_Alergia_EDSM, run_k_tails ================================================ FILE: aalpy/learning_algs/adaptive/AdaptiveLSharp.py ================================================ import time from aalpy.base import Oracle, SUL from aalpy.utils.HelperFunctions import print_learning_info from .AdaptiveObservationTree import AdaptiveObservationTree from ...base.SUL import CacheSUL def run_adaptive_Lsharp(alphabet: list, sul: SUL, references: list, eq_oracle: Oracle, automaton_type, extension_rule=None, separation_rule="SepSeq", rebuilding=True, state_matching="Approximate", samples=None, max_learning_rounds=None, cache_and_non_det_check=True, return_data=False, print_level=2): """ Based on ''State Matching and Multiple References in Adaptive Active Automata Learning'' from Kruger, Junges and Rot. The algorithm learns a Mealy machine using a set of references. These references are used by two procedures 1) Rebuilding which kickstarts the learning process using the references and 2) State Matching which matches the basis states and references states to find new basis states faster. Args: alphabet: input alphabet sul: system under learning references: a list of references eq_oracle: equivalence oracle automaton_type: type of automaton to be learned. Either 'dfa', 'mealy' or 'moore' extension_rule: strategy used during the extension rule. Options: "Nothing" (default), "SepSeq" and "ADS". separation_rule: strategy used during the extension rule. Options: "SepSeq" (default) and "ADS". rebuilding: procedure that poses output queries to rebuild the observation tree based on prefixes and separating sequences from the reference(s). Only executes at the start of adaptive L#. default value: True. state_matching: if not None, the learner maintains a matching relation between basis states (in the observation tree) and reference model states. This matching relation is used in three rules added on top of L# to either identify a frontier state faster or isolate it when the matching indicates that the frontier state corresponds to a reference model state not yet present in the basis. default value: "Approximate". - Two states match according to "total matching" if all output over the defined and shared alphabet are exactly the same. - Two states match according to "approximate matching" if they have the highest ratio of equivalent outputs to defined outputs over the shared alphabet. - None can be used if only the rebuilding procedure is needed. samples: input output traces provided to the learning algorithm. They are added to cache and could reduce total interaction with the system. Syntax: list of [(input_sequence, output_sequence)] or None max_learning_rounds: number of learning rounds after which learning will terminate (Default value = None) cache_and_non_det_check: Use caching and non-determinism checks (Default value = True) return_data: if True, a map containing all information(runtime/#queries/#steps) will be returned (Default value = False) print_level: 0 - None, 1 - just results, 2 - current round and hypothesis size, 3 - educational/debug (Default value = 2) Returns: automaton of type automaton_type (dict containing all information about learning if 'return_data' is True) """ assert extension_rule in {None, "SepSeq", "ADS"} assert separation_rule in {"SepSeq", "ADS"} assert state_matching in {None, "Total", "Approximate"} assert references is not None, 'List of reference models is empty. Use L*, KV, L#, or provide models.' if not rebuilding and not state_matching: raise Exception(f"Use L# instead of Adaptive L# if rebuilding is set to False and state matching to None.") if cache_and_non_det_check or samples is not None: # Wrap the sul in the CacheSUL, so that all steps/queries are cached sul = CacheSUL(sul) eq_oracle.sul = sul if samples: for input_seq, output_seq in samples: sul.cache.add_to_cache(input_seq, output_seq) ob_tree = AdaptiveObservationTree(alphabet, sul, references, automaton_type, extension_rule, separation_rule, rebuilding, state_matching) start_time = time.time() eq_query_time = 0 learning_rounds = 0 hypothesis = None while True: learning_rounds += 1 if max_learning_rounds and learning_rounds == max_learning_rounds: break # Building the hypothesis hypothesis = ob_tree.build_hypothesis() if print_level > 1: print(f'Hypothesis {learning_rounds}: {hypothesis.size} states.') if print_level == 3: print(hypothesis) if ob_tree.state_matching: ob_tree.state_matcher.print_match_table(ob_tree) # Pose Equivalence Query eq_query_start = time.time() cex = eq_oracle.find_cex(hypothesis) eq_query_time += time.time() - eq_query_start if print_level > 2: print(f'Counterexample: {cex}') if cex is None: break # Process the counterexample and start a new learning round cex_outputs = sul.query(cex) ob_tree.process_counter_example(hypothesis, cex, cex_outputs) total_time = round(time.time() - start_time, 2) eq_query_time = round(eq_query_time, 2) learning_time = round(total_time - eq_query_time, 2) info = { 'learning_rounds': learning_rounds, 'automaton_size': hypothesis.size, 'queries_learning': sul.num_queries, 'steps_learning': sul.num_steps, 'queries_eq_oracle': eq_oracle.num_queries, 'steps_eq_oracle': eq_oracle.num_steps, 'learning_time': learning_time, 'eq_oracle_time': eq_query_time, 'total_time': total_time, 'cache_saved': sul.num_cached_queries, } if rebuilding: info['rebuild_states'] = ob_tree.rebuild_states if state_matching != "None": info['matching_type'] = state_matching info['matching_states'] = ob_tree.matching_states if print_level > 0: print_learning_info(info) if return_data: return hypothesis, info return hypothesis ================================================ FILE: aalpy/learning_algs/adaptive/AdaptiveObservationTree.py ================================================ from aalpy.automata import MealyMachine, MealyState from aalpy.learning_algs.adaptive.StateMatching import TotalStateMatching, ApproximateStateMatching from aalpy.learning_algs.deterministic.Apartness import Apartness from aalpy.learning_algs.deterministic.ObservationTree import ObservationTree from aalpy.oracles.WpMethodEqOracle import state_characterization_set from aalpy.base import Automaton, SUL from aalpy.automata import Dfa, DfaState, MealyState, MealyMachine, MooreMachine, MooreState class AdaptiveObservationTree(ObservationTree): def __init__(self, alphabet, sul, references, automaton_type, extension_rule, separation_rule, rebuilding=True, state_matching="Approximate"): """ Initialize the tree with a root node and the alphabet A temporary new basis is needed for the prioritized promotion rule The rebuild states counter counts the number of states found with rebuilding excluding the root The matching states counter counts the number of states found with match refinement and match separation (NOT prioritized separation) """ super().__init__(alphabet, sul, automaton_type, extension_rule, separation_rule) self.references = references self.rebuild_states = 0 self.matching_states = 0 if not references: self.state_matching = None print(f"Warning: no references given, normal L# is executed.") return self.rebuilding = rebuilding self.state_matching = state_matching self.prefixes_map = {} self.characterization_map = {} self.combined_model = self.get_combined_model() if not self.combined_model: self.state_matching = None return # We keep track of a new basis to ensure maximal overlap between prefixes in the references and the new model self.new_basis = [self.root] self.initial_OQs = [] if self.rebuilding: self.rebuild_obs_tree() if self.state_matching == "Total": self.state_matcher = TotalStateMatching(self.alphabet, self.combined_model) elif self.state_matching == "Approximate": self.state_matcher = ApproximateStateMatching(self.alphabet, self.combined_model) if self.state_matching: self.state_matcher.initialize_matching(self) def build_hypothesis(self): """ Builds the hypothesis which will be sent to the SUL and checks consistency This is either done with or without matching rules """ while True: if self.state_matching: self.make_observation_tree_adequate_matching() else: super().make_observation_tree_adequate() hypothesis = self.construct_hypothesis() counter_example = Apartness.compute_witness_in_tree_and_hypothesis_states(self, self.root, hypothesis.initial_state) if not counter_example: return hypothesis cex_outputs = self.get_observation(counter_example) self.process_counter_example(hypothesis, counter_example, cex_outputs) def make_observation_tree_adequate_matching(self): """ Updates the frontier and basis based on several rules Terminates when the observation tree is adequate and no progress has been made in one round The separation rule is only used when prioritized separation did not make progress The matching rules are only used when the observation tree is already adequate """ self.update_frontier_and_basis() ob_tree_size = self.get_size() while not self.is_observation_tree_adequate() or ob_tree_size != self.get_size(): self.make_basis_complete() ob_tree_size = self.get_size() self.make_frontiers_identified_with_matching() if ob_tree_size == self.get_size(): self.make_frontiers_identified() self.promote_frontier_state() self.update_frontier_and_basis() if self.is_observation_tree_adequate(): old_basis = len(self.basis) self.match_refinement() self.match_separation() if old_basis < len(self.basis): self.matching_states += len(self.basis) - old_basis def make_frontiers_identified_with_matching(self): """ Loop over all frontier states to identify them using prioritized identification, Only enabled when L# is running with the SepSeq separation rule """ if self.separation_rule == "SepSeq": for frontier_state in self.frontier_to_basis_dict: self.identify_frontier_with_matching(frontier_state) def identify_frontier_with_matching(self, frontier_state): """ Determines the reference state which matches the frontier state (by looking at the basis parent) Then finds the state identifiers for the matched reference state Tries to identify the frontier state using the state identifiers of the matched state """ if frontier_state not in self.frontier_to_basis_dict: raise Exception( f"Warning: {frontier_state} not found in frontier_to_basis_dict.") self.update_basis_candidates(frontier_state) parent_basis = frontier_state.parent inp = frontier_state.input_to_parent match = self.state_matcher.best_match[parent_basis] if not match: return if inp in match[0].transitions: frontier_match = match[0].transitions[inp] identifiers = self.characterization_map[frontier_match] self.identify_frontier_with_identifiers(frontier_state, identifiers) def identify_frontier_with_identifiers(self, frontier_state, identifiers): """ Loops through all candidates states and checks whether they can be separated using one of the state identifiers of the state matched with the frontier state """ basis_candidates = self.frontier_to_basis_dict.get(frontier_state) for i in range(0, len(basis_candidates)): for j in range(i+1, len(basis_candidates)): newest_basis_candidates = self.frontier_to_basis_dict.get(frontier_state) basis_one = basis_candidates[i] basis_two = basis_candidates[j] if basis_one not in newest_basis_candidates: break if basis_two not in newest_basis_candidates: continue witness = self.get_or_compute_witness(basis_one, basis_two) if tuple(witness) in identifiers: inputs = self.get_transfer_sequence( self.root, frontier_state) inputs.extend(witness) outputs = self.sul.query(inputs) self.insert_observation(inputs, outputs) self.update_basis_candidates(frontier_state) if len(self.frontier_to_basis_dict.get(frontier_state)) < 2: return def match_refinement(self): # Loops over the basis states to refine the match for each basis state old_basis = list(self.basis) for basis_state in old_basis: matches = self.state_matcher.best_match[basis_state] if len(matches) > 1: self.refine_matches_basis(basis_state, matches) self.update_frontier_and_basis() def find_distinguishing_seq_partial(self, model, state1, state2, alphabet): """ A BFS to determine an input sequence that distinguishes two states in the automaton Can handle partial models """ visited = set() to_explore = [(state1, state2, [])] while to_explore: (curr_s1, curr_s2, prefix) = to_explore.pop(0) visited.add((curr_s1, curr_s2)) for i in alphabet: if i in curr_s1.transitions and i in curr_s2.transitions: o1 = model.output_step(curr_s1, i) o2 = model.output_step(curr_s2, i) new_prefix = prefix + [i] if o1 != o2: return new_prefix else: next_s1 = curr_s1.transitions[i] next_s2 = curr_s2.transitions[i] if (next_s1, next_s2) not in visited: to_explore.append((next_s1, next_s2, new_prefix)) return None def refine_matches_basis(self, basis_state, matches): """ Loops over the matched reference states and separates them using a separating sequence Returns when only one matching reference state remains, or some states are not distinguishable """ for i in range(0, len(matches)): for j in range(i+1, len(matches)): ref_state_one = matches[i] ref_state_two = matches[j] current_matches = self.state_matcher.best_match[basis_state] if ref_state_one not in current_matches: break if ref_state_two not in current_matches: continue witness = self.find_distinguishing_seq_partial(self.combined_model, ref_state_one, ref_state_two, self.alphabet) if witness is None: continue inputs = self.get_transfer_sequence(self.root, basis_state) inputs.extend(witness) outputs = self.sul.query(inputs) self.insert_observation(inputs, outputs) current_matches = self.state_matcher.best_match[basis_state] if len(current_matches) < 2: return def match_separation(self): """ Loops over frontier states and calls the match separation with as goal isolation of the frontier state """ matched_states = [] all_best_matches = [] for basis_state in self.basis: matches = self.state_matcher.best_match[basis_state] if len(matches) == 1: all_best_matches.append(matches) for matches in all_best_matches: for ref_state in matches: if ref_state not in matched_states: matched_states.append(ref_state) frontier_states = list(self.frontier_to_basis_dict.keys()) for frontier_state in frontier_states: if frontier_state in self.frontier_to_basis_dict.keys(): basis_candidates = self.frontier_to_basis_dict[frontier_state] self.match_separation_frontier( matched_states, frontier_state, basis_candidates) self.update_frontier_and_basis() def match_separation_frontier(self, matched_states, frontier_state, basis_candidates): """ Tries to isolate the frontier state if it matches a reference state that currently is not matched to any basis state """ parent_basis = frontier_state.parent inp = frontier_state.input_to_parent for match in self.state_matcher.best_match[parent_basis]: if (inp in match.transitions and match.transitions[inp] in matched_states) or (inp not in match.transitions): continue frontier_match = match.transitions[inp] for basis_state in basis_candidates: if basis_state not in self.frontier_to_basis_dict[frontier_state]: continue if Apartness.compute_witness_in_tree_and_hypothesis_states( self, frontier_state, frontier_match): continue witness = Apartness.compute_witness_in_tree_and_hypothesis_states( self, basis_state, frontier_match) if witness is None: continue inputs = self.get_transfer_sequence(self.root, frontier_state) inputs.extend(witness) outputs = self.sul.query(inputs) self.insert_observation(inputs, outputs) self.update_basis_candidates(frontier_state) def promote_frontier_state(self): """ Searches for an isolated frontier state and adds it to the basis states if it is not associated with another basis state """ for iso_frontier_state, basis_list in self.frontier_to_basis_dict.items(): if not basis_list: new_basis = iso_frontier_state self.basis.append(new_basis) self.frontier_to_basis_dict.pop(new_basis) if self.state_matching: self.state_matcher.update_matching_basis(new_basis, self) for frontier_state, new_basis_list in self.frontier_to_basis_dict.items(): if not Apartness.states_are_apart(new_basis, frontier_state, self): new_basis_list.append(new_basis) break def insert_observation(self, inputs, outputs): """ Insert an observation into the tree using sequences of inputs and outputs If state matching is enabled, ensure that the matching is updated """ if len(inputs) != len(outputs): raise ValueError("Inputs and outputs must have the same length.") if self.state_matching: self.extend_node_and_update_matching(inputs, outputs) else: current_node = self.root for input_val, output_val in zip(inputs, outputs): current_node = current_node.extend_and_get( input_val, output_val) def extend_node_and_update_matching(self, inputs, outputs): """ Extends the observation tree with new inputs Splits the input sequence in "already defined" part and the "new inputs" part If the inputs are not already present in the tree, we update the matching """ to_recalc = [] split = None current_node = self.root for i in range(0, len(inputs)): input_val = inputs[i] output_val = outputs[i] if current_node in self.basis: to_recalc.append(current_node) if input_val not in current_node.successors and split is None: split = (inputs[:i], inputs[i:]) current_node = current_node.extend_and_get(input_val, output_val) if split: self.state_matcher.update_matching(to_recalc, split, self) # Functions related to rebuilding the observation tree def rebuild_obs_tree(self): """ Rebuilds the observation tree by finding pairs of frontier and basis states that occur in the same reference model Then posing output queries to try to distinguish them in the SUL Try to apply the prioritized promotion rule """ tup = self.find_frontier_new_basis() while tup: (basis_state_access, frontier_state_access, sep_seq) = tup query1 = frontier_state_access + sep_seq self.insert_observation_rebuilding(query1, self.sul.query(query1)) query2 = basis_state_access + sep_seq self.insert_observation_rebuilding(query2, self.sul.query(query2)) self.prioritized_promotion() tup = self.find_frontier_new_basis() self.basis = self.new_basis self.update_frontier_and_basis() def prioritized_promotion(self): """ Promotes an isolated frontier state with an access sequence in the prefix set of one of the references """ for reference_id in range(0, len(self.references)): for reference_prefix in self.prefixes_map[reference_id]: ob_tree_state = self.get_successor(reference_prefix) if not ob_tree_state: continue basis_parent = ob_tree_state.parent if (basis_parent in self.new_basis) and (ob_tree_state not in self.new_basis) and self.apart_from_all(ob_tree_state): self.new_basis.append(ob_tree_state) self.rebuild_states += 1 def find_frontier_new_basis(self): """ This function find a frontier and basis state pair which both occur in one of the reference models Because they occur in the same reference model, we have a separating sequence to distinguish them """ for basis_state_one in self.new_basis: for inp in self.alphabet: frontier_state_access = self.get_access_sequence( basis_state_one) + (inp,) frontier_state = basis_state_one.get_successor(inp) if self.get_successor(frontier_state_access) in self.new_basis: continue if self.find_basis_frontier_pair(frontier_state, frontier_state_access): return self.find_basis_frontier_pair(frontier_state, frontier_state_access) return None def find_basis_frontier_pair(self, frontier_state, frontier_state_access): """ Find a basis state and reference model such that the prefixes of the basis state and frontier state are in the reference model prefix set Find a separating sequence that separates the frontier and basis state """ for basis_state in self.new_basis: basis_state_access = self.get_access_sequence(basis_state) for reference_id in range(0, len(self.references)): reference = self.references[reference_id] if (basis_state_access not in self.prefixes_map[reference_id]) or (frontier_state_access not in self.prefixes_map[reference_id]): continue if frontier_state and Apartness.compute_witness(basis_state, frontier_state, self) is not None: continue reference.execute_sequence(reference.initial_state, frontier_state_access) state_one = reference.current_state reference.execute_sequence(reference.initial_state, basis_state_access) state_two = reference.current_state sep_seq = self.find_distinguishing_seq_partial(reference, state_one, state_two, self.alphabet) if sep_seq and (self.get_successor(frontier_state_access + tuple(sep_seq)) is None or self.get_successor(basis_state_access + tuple(sep_seq)) is None): return basis_state_access, frontier_state_access, tuple(sep_seq) return None def insert_observation_rebuilding(self, inputs, outputs): """ Insert an observation into the tree using sequences of inputs and outputs """ if len(inputs) != len(outputs): raise ValueError("Inputs and outputs must have the same length.") split = None current_node = self.root for i in range(0, len(inputs)): input_val = inputs[i] output_val = outputs[i] if input_val not in current_node.successors and split is None: split = (inputs[:i], inputs[i:]) current_node = current_node.extend_and_get(input_val, output_val) if split: self.initial_OQs.append(split) def apart_from_all(self, frontier_state): """ Checks if a frontier state is apart from all new basis states """ for basis_state in self.new_basis: if not Apartness.states_are_apart(basis_state, frontier_state, self): return False return True # Functions related to finding the combined model def add_ref_transitions_to_states(self, reference, reference_id): """ Makes a copy of the states of a reference with a unique state id and only transitions with the new input alphabet """ automaton_state = {'dfa': DfaState, 'mealy': MealyState, 'moore': MooreState} states = [automaton_state[self.automaton_type](f"s({reference_id},{ref_state})") for ref_state in range(0, len(reference.states))] for state_id in range(0, len(reference.states)): if self.automaton_type == 'mealy': states[state_id].output_fun = reference.states[state_id].output_fun elif self.automaton_type == 'dfa': states[state_id].is_accepting = reference.states[state_id].is_accepting else: states[state_id].output = reference.states[state_id].output for inp in self.alphabet: if inp in reference.get_input_alphabet(): old_index = reference.states.index( reference.states[state_id].transitions[inp]) states[state_id].transitions[inp] = states[old_index] return states def compute_prefix_map(self, reference, reference_id): """ Computes the prefixes of a reference model and stores them in a prefix map """ for state in reference.states: state.prefix = reference.get_shortest_path( reference.initial_state, state) self.prefixes_map[reference_id] = [state.prefix for state in reference.states if state.prefix is not None] def compute_characterization_map(self, reference, states): """ Computes the separating sequences of a reference model and stores them in a characterization map """ for state, ref_state in zip(states, reference.states): all_sepseqs = state_characterization_set(reference, reference.get_input_alphabet(), ref_state) unique_sepseqs = list(dict.fromkeys(all_sepseqs)) self.characterization_map[state] = unique_sepseqs def get_combined_model(self): """ Builds a combined model from the reference models Compute the prefix and characterization maps used during construction of the combined model The resulting mealy machine may be partial """ automaton_class = {'dfa': Dfa, 'mealy': MealyMachine, 'moore': MooreMachine} all_states = [] for reference_id in range(0, len(self.references)): reference = self.references[reference_id] overlap = [inp for inp in self.alphabet if inp in reference.get_input_alphabet()] if not overlap: print( f"Warning: reference model {reference_id} has no common inputs and will not be used as a reference.") self.references.remove(reference) reference_id -= 1 continue states = self.add_ref_transitions_to_states(reference, reference_id) all_states += states self.compute_prefix_map(automaton_class[self.automaton_type](states[0], states), reference_id) self.compute_characterization_map(reference, states) if all_states == []: print(f"Warning: the references did not lead to any usable states, this could be due to empty models or no common inputs.") return None else: return automaton_class[self.automaton_type](all_states[0], all_states) ================================================ FILE: aalpy/learning_algs/adaptive/StateMatching.py ================================================ from abc import abstractmethod class StateMatching: def __init__(self, alphabet, combined_model): """ Initializes the super class for state matching """ self.alphabet = alphabet self.combined_model = combined_model self.matchings = {} # map from basis states to reference states self.best_match = {} self.best_score = {} @abstractmethod def add_entry_basis(self, basis_state): pass @abstractmethod def update_best_score(self, basis_state): pass @abstractmethod def update_score(self, ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part): pass @abstractmethod def update_best_match(self, basis_state, score): pass def initialize_matching(self, ob_tree): """ Initializes the matching by adding an entry for every basis state found during rebuilding Updates the matching for the output queries posed during rebuilding """ for basis_state in ob_tree.basis: self.add_entry_basis(basis_state, ob_tree.automaton_type) for defined_part, new_part in ob_tree.initial_OQs: to_recalc = [] for basis_state in ob_tree.basis: if self.is_prefix_of(tuple(ob_tree.get_access_sequence(basis_state)), (defined_part + new_part)): to_recalc.append(basis_state) self.update_matching(to_recalc, (defined_part, new_part), ob_tree) def update_matching(self, to_recalc, split, ob_tree): """ Updates the matching for the to be recalculated basis states The split contains the already defined part and the new part of an output query For every basis state, we determine the defined part and new part AFTER accessing the basis state Then for every reference model check if the defined part uses only inputs valid in the reference model If these are all valid, we calculate the score of the new part """ defined_part, orig_new_part = split for basis_state in to_recalc: basis_state_access = ob_tree.get_access_sequence(basis_state) defined_after_access = () new_part = orig_new_part if len(defined_part) > len(basis_state_access): defined_after_access = defined_part[len(basis_state_access):] else: new_part = (defined_part + orig_new_part)[len(basis_state_access):] for reference_state in self.combined_model.states: # defined_after_access contains invalid input for reference model, score remains 0 if self.validate_reference_input(defined_after_access, reference_state): self.update_score(ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part) score = self.update_best_score(basis_state) self.update_best_match(basis_state, score) def update_matching_basis(self, basis_state, ob_tree): """ Initializes and updates the matching for a newly added basis state """ self.add_entry_basis(basis_state, ob_tree.automaton_type) longest_words = list(self.find_longest_words(basis_state, ob_tree, [])) basis_state_access = ob_tree.get_access_sequence(basis_state) for i in range(0, len(longest_words)): split = basis_state_access, longest_words[i][len(basis_state_access):] if i > 0: split = self.find_longest_common_part(longest_words[i-1], longest_words[i]) self.update_matching([basis_state], split, ob_tree) def find_longest_words(self, current_state, ob_tree, all_seqs): """ Finds prefix closed words in the observation tree starting from the current state DFS-like procedure """ leaf = True for inp in self.alphabet: if inp in current_state.successors: new_seqs = self.find_longest_words( current_state.get_successor(inp), ob_tree, all_seqs) for new_seq in new_seqs: if new_seq not in all_seqs: all_seqs.add(new_seq) leaf = False if leaf: all_seqs.append(ob_tree.get_access_sequence(current_state)) return all_seqs def validate_reference_input(self, inputs, reference_state): """ Check if all inputs are valid (part of the alphabet of the reference model) """ for input_val in inputs: if input_val not in reference_state.transitions: return False return True def is_prefix_of(self, str1, str2): """ Checks if input sequence str1 is a prefix of str2 """ if len(str1) > len(str2): return False for i in range(0, len(str1)): if str2[i] != str1[i]: return False return True def find_longest_common_part(self, str1, str2): """ Finds the longest common prefix of input sequences str1 and str2 """ so_far = [] for i in range(0, len(str2)): if str2[i] == str1[i]: so_far.append(str2[i]) else: return tuple(so_far), tuple(str2[i:]) return tuple(so_far), tuple() def print_match_table(self, ob_tree): """ Prints the match table Code based on https://stackoverflow.com/questions/13214809/pretty-print-2d-list """ print(f"Mapping of basis state ids to access sequences") for basis_state in ob_tree.basis: print(f"{basis_state.id}: {ob_tree.get_access_sequence(basis_state)}") first_row = ["state", "|", "match", "|", "score", "|"] + \ [ref_state.state_id for ref_state in self.combined_model.states] data = [first_row] for basis_state in ob_tree.basis: row = [basis_state.id, "|", [ st.state_id for st in self.best_match[basis_state]], "|", self.best_score[basis_state], "|"] row += [sc for sc in self.matchings[basis_state].values()] data.append(row) s = [[str(e) for e in row] for row in data] lens = [max(map(len, col)) for col in zip(*s)] fmt = '\t'.join('{{:{}}}'.format(x) for x in lens) table = [fmt.format(*row) for row in s] print('\n'.join(table)) class TotalStateMatching(StateMatching): """ Total State Matching is an instance of State Matching A basis and reference state match if all inputs sequences (over the shared alphabet) defined from the basis state give EXACTLY the same outputs """ def __init__(self, alphabet, combined_model): super().__init__(alphabet, combined_model) def add_entry_basis(self, basis_state, aut_type): """ Initializes a new matching row with 1 or empty word evaluation""" if aut_type == 'mealy': self.matchings[basis_state] = {ref_state: 1 for ref_state in self.combined_model.states} else: self.matchings[basis_state] = dict() basis_out = basis_state.output for ref_state in self.combined_model.states: if aut_type == 'dfa': ref_out = ref_state.is_accepting elif aut_type == 'moore': ref_out = ref_state.output if ref_out == basis_out: self.matchings[basis_state][ref_state] = 1 else: self.matchings[basis_state][ref_state] = 0 def update_best_score(self, basis_state): """ Updates the best score for a basis state """ score = max([self.matchings[basis_state][ref_state] for ref_state in self.combined_model.states]) self.best_score[basis_state] = score return score def update_best_match(self, basis_state, score): """ Updates the best match for a basis state """ if score == 0: self.best_match[basis_state] = [] else: matches = [] for ref_state in self.combined_model.states: if self.matchings[basis_state][ref_state] == score: matches.append(ref_state) self.best_match[basis_state] = matches def update_score(self, ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part): # calls update score for either mealy or moore/dfa if ob_tree.automaton_type == 'mealy': return self.update_score_mealy(ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part) else: return self.update_score_moore(ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part) def update_score_mealy(self, ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part): """ Updates the matching score for a basis and reference state based on the: basis access, already defined part after access and the new part For every input in the new part, we take a step in the ob_tree and in the combined model If the combined model has no transition for some input (because it is not in the reference alphabet), we return If the outputs differ, we set the matching to 0 """ if self.matchings[basis_state][reference_state] == 0: return current_ob_state = ob_tree.get_successor( tuple(basis_state_access) + tuple(defined_after_access)) self.combined_model.execute_sequence( reference_state, defined_after_access) for inp in new_part: ob_out = current_ob_state.get_output(inp) if inp not in reference_state.output_fun: return ref_out = self.combined_model.step(inp) if ob_out != ref_out: self.matchings[basis_state][reference_state] = 0 return current_ob_state = current_ob_state.get_successor(inp) def update_score_moore(self, ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part): """ Updates the matching score for a basis and reference state based on the: basis access, already defined part after access and the new part For every input in the new part, we take a step in the ob_tree and in the combined model If the outputs differ, we set the matching to 0 """ orig_ref = reference_state if self.matchings[basis_state][reference_state] == 0: return current_ob_state = ob_tree.get_successor( tuple(basis_state_access) + tuple(defined_after_access)) self.combined_model.execute_sequence(reference_state, defined_after_access) reference_state = self.combined_model.current_state # need to test the empty word if defined_after_access == (): if reference_state.is_accepting != current_ob_state.output: self.matchings[basis_state][orig_ref] = 0 return for inp in new_part: if inp not in reference_state.transitions: return ref_out = self.combined_model.step(inp) reference_state = self.combined_model.current_state current_ob_state = current_ob_state.get_successor(inp) ob_out = current_ob_state.output if ob_out != ref_out: self.matchings[basis_state][orig_ref] = 0 return class ApproximateStateMatching(StateMatching): """ Approximate State Matching is an instance of State Matching A basis matches a reference state if the reference state has the highest ratio of matching outputs over all inputs sequences (over the shared alphabet) defined from the basis state compared to the other reference states """ def __init__(self, alphabet, combined_model): super().__init__(alphabet, combined_model) self.unmatched = set() def add_entry_basis(self, basis_state, aut_type): """ Initializes a new matching row with [0,0] or empty word evaluation """ if aut_type == 'mealy': self.matchings[basis_state] = {ref_state: [0, 0] for ref_state in self.combined_model.states} else: self.matchings[basis_state] = dict() basis_out = basis_state.output for ref_state in self.combined_model.states: if aut_type == 'dfa': ref_out = ref_state.is_accepting elif aut_type == 'moore': ref_out = ref_state.output if ref_out == basis_out: self.matchings[basis_state][ref_state] = [1,1] else: self.matchings[basis_state][ref_state] = [0,1] def get_score(self, basis_state, ref_state): """ Gets the score for a basis and reference states """ if self.matchings[basis_state][ref_state][1] == 0: return 0 return self.matchings[basis_state][ref_state][0]/self.matchings[basis_state][ref_state][1] def update_best_score(self, basis_state): """ Updates the best score for a basis state """ score = max([self.get_score(basis_state, ref_state) for ref_state in self.combined_model.states]) self.best_score[basis_state] = round(score, 2) return score def update_best_match(self, basis_state, score): """ Updates the best match for a basis state """ if score == 0: self.best_match[basis_state] = [] else: matches = [] for ref_state in self.combined_model.states: if self.get_score(basis_state, ref_state) == score: matches.append(ref_state) self.best_match[basis_state] = matches def update_score(self, ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part): # calls update score for either mealy or moore/dfa if ob_tree.automaton_type == 'mealy': return self.update_score_mealy(ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part) else: return self.update_score_moore(ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part) def update_score_mealy(self, ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part): """ Updates the matching score for a basis and reference state based on the: basis access, already defined part after access and the new part For every input in the new part, we take a step in the ob_tree and in the combined model If the combined model has no transition for some input (because it is not in the reference alphabet), we return If the outputs are equivalent, we add [1,1] If the outputs differ, we add [0,1] """ current_ob_state = ob_tree.get_successor(tuple(basis_state_access) + tuple(defined_after_access)) self.combined_model.execute_sequence(reference_state, defined_after_access) for inp in new_part: ob_out = current_ob_state.get_output(inp) if inp not in self.combined_model.current_state.output_fun: break ref_out = self.combined_model.step(inp) self.matchings[basis_state][reference_state][1] += 1 if ob_out == ref_out: self.matchings[basis_state][reference_state][0] += 1 current_ob_state = current_ob_state.get_successor(inp) def update_score_moore(self, ob_tree, basis_state, reference_state, basis_state_access, defined_after_access, new_part): """ Updates the matching score for a basis and reference state based on the: basis access, already defined part after access and the new part For every input in the new part, we take a step in the ob_tree and in the combined model If the combined model has no transition for some input (because it is not in the reference alphabet), we return If the outputs are equivalent, we add [1,1] If the outputs differ, we add [0,1] """ orig_ref = reference_state current_ob_state = ob_tree.get_successor(tuple(basis_state_access) + tuple(defined_after_access)) self.combined_model.execute_sequence(reference_state, defined_after_access) reference_state = self.combined_model.current_state for inp in new_part: if inp not in reference_state.transitions: return ref_out = self.combined_model.step(inp) reference_state = self.combined_model.current_state current_ob_state = current_ob_state.get_successor(inp) ob_out = current_ob_state.output self.matchings[basis_state][orig_ref][1] += 1 if ob_out == ref_out: self.matchings[basis_state][orig_ref][0] += 1 ================================================ FILE: aalpy/learning_algs/adaptive/__init__.py ================================================ ================================================ FILE: aalpy/learning_algs/deterministic/ADS.py ================================================ from collections import defaultdict class AdsNode: __slots__ = ['input', 'children', 'score'] def __init__(self, input_val=None, children=None, score=0): self.input = input_val self.children = children if children else {} self.score = score @staticmethod def create_leaf(): return AdsNode() def get_input(self): return self.input def get_child_node(self, output): if output in self.children: return self.children[output] return None def get_score(self): return self.score class Ads: def __init__(self, ob_tree, current_block): self.initial_node = self.construct_ads(ob_tree, current_block) self.current_node = self.initial_node def get_score(self): return self.initial_node.get_score() def construct_ads(self, ob_tree, current_block): # Builds the ADS tree recursively by selecting optimal inputs for splitting states # For DFA/Moore we have to consider the output for the empty word if ob_tree.automaton_type == 'mealy': return self.construct_ads_rec(ob_tree, current_block) else: if len(current_block) == 1: return AdsNode.create_leaf() # if none of the nodes in the current block have a successor, we cannot decide a next input if not any([True for node in current_block if node.successors is not None]): raise RuntimeError("No input available during ADS computation") input = tuple() empty_part = self.partition_on_output_empty(current_block, ob_tree.automaton_type) u_i = sum(len(part) for part in empty_part.values()) score = 0 children = {} for output, partition in empty_part.items(): output_score, subtree = self.compute_output_subtree(ob_tree, partition, u_i) score += output_score children[output] = subtree return AdsNode(input, children, score) def construct_ads_rec(self, ob_tree, current_block): # Builds the ADS tree recursively by selecting optimal inputs for splitting states if len(current_block) == 1: return AdsNode.create_leaf() # If none of the nodes in the current block have a successor, we cannot decide a next input if not any([True for node in current_block if node.successors is not None]): raise RuntimeError("No input available during ADS computation") best_input, best_score = self.maximal_base_input(ob_tree.alphabet, current_block, ob_tree.automaton_type) best_children = None # The maximal apartness pairs is len(current block) - 1, for any current block, immediately return if best_score == len(current_block) - 1: return AdsNode(best_input, best_children, best_score) for input_val in ob_tree.alphabet: input_partitions = self.partition_on_output(current_block, input_val, ob_tree.automaton_type) u_i = sum(len(part) for part in input_partitions.values()) input_score = 0 children = {} for output, partition in input_partitions.items(): output_score, subtree = self.compute_output_subtree(ob_tree, partition, u_i) input_score += output_score children[output] = subtree if input_score > best_score: best_score = input_score best_input = input_val best_children = children if best_score == len(current_block) - 1: return AdsNode(best_input, best_children, best_score) if best_input is None: raise RuntimeError("Error during ADS construction") return AdsNode(best_input, best_children, best_score) # def make_subtree(self, ob_tree, sub_trees, partition): # # Constructs a subtree for a partition and calculates its score # partition_size = len(partition) # child_score = self.construct_ads_rec(ob_tree, partition).get_score() # return self.compute_reg_score(partition_size, sub_trees, child_score) def compute_output_subtree(self, ob_tree, partition, u_i): # Computes and scores a subtree for a specific output partition output_subtree = self.construct_ads_rec(ob_tree, partition) output_score = self.compute_score(len(partition), u_i, output_subtree.get_score()) return output_score, output_subtree def compute_score(self, u_io, u_i, child_score): # Calculates a score based on partition size and subtree characteristics return (u_io * (u_i - u_io + child_score)) / u_i def partition_on_output_empty(self, block, automaton_type): # Partitions states in the block based on their output for the empty word # Only use during the initial call partition = defaultdict(list) for node in block: output = node.output partition[output].append(node) return partition def partition_on_output(self, block, input_val, automaton_type): # Partitions states in the block based on their output for a given input partition = defaultdict(list) for node in block: if automaton_type == 'mealy': output = node.get_output(input_val) if output is not None: successor = node.get_successor(input_val) if successor is not None: partition[output].append(successor) else: successor = node.get_successor(input_val) if successor is not None: output = successor.output if output is not None: partition[output].append(successor) return partition def next_input(self, prev_output): # Returns the next input based on the previous output and updates the current node if prev_output is not None: child = self.current_node.get_child_node(prev_output) if child is None: return None self.current_node = child return self.current_node.get_input() def maximal_base_input(self, alphabet, block, automaton_type): # Identifies the input with the highest ability to split the state block based on apartness # Does not use the recursive part of the formula best_input = alphabet[0] best_score = 0 for input_val in alphabet: partition = self.partition_on_output(block, input_val, automaton_type) u_i = sum(len(part) for part in partition.values()) score = 0 for part in partition.values(): u_io = len(part) output_score = (u_io * (u_i - u_io)) / u_i score += output_score if score > best_score: best_score = score best_input = input_val return best_input, best_score def reset_to_root(self): # Resets the current ADS node to the initial root node self.current_node = self.initial_node ================================================ FILE: aalpy/learning_algs/deterministic/Apartness.py ================================================ from collections import deque class Apartness: @staticmethod def compute_witness(state1, state2, ob_tree): # Finds a distinguishing sequence between two states if they are apart based on the observation tree if ob_tree.automaton_type == 'mealy': state1_destination = Apartness._show_states_are_apart_mealy( state1, state2, ob_tree.alphabet) else: state1_destination = Apartness._show_states_are_apart_moore( state1, state2, ob_tree.alphabet) if not state1_destination: return return ob_tree.get_transfer_sequence(state1, state1_destination) @staticmethod def states_are_apart(state1, state2, ob_tree): # Checks if two states are apart by checking any output difference in the observation tree if ob_tree.automaton_type == 'mealy': return Apartness._show_states_are_apart_mealy(state1, state2, ob_tree.alphabet) is not None else: return Apartness._show_states_are_apart_moore(state1, state2, ob_tree.alphabet) is not None @staticmethod def _show_states_are_apart_mealy(first, second, alphabet): # Identifies if two states can be distinguished by any input-output pair in the provided alphabet pairs = deque([(first, second)]) while pairs: first_node, second_node = pairs.popleft() for input_val in alphabet: first_output = first_node.get_output(input_val) second_output = second_node.get_output(input_val) if first_output is not None and second_output is not None: if first_output != second_output: return first_node.get_successor(input_val) pairs.append((first_node.get_successor( input_val), second_node.get_successor(input_val))) return None @staticmethod def _show_states_are_apart_moore(first, second, alphabet): # Identifies if two states can be distinguished by any input-output pair in the provided alphabet pairs = deque([(first, second)]) while pairs: first_node, second_node = pairs.popleft() if first_node is not None and second_node is not None: first_output = first_node.output second_output = second_node.output if first_output != second_output: return first_node for input_val in alphabet: pairs.append((first_node.get_successor( input_val), second_node.get_successor(input_val))) return None @staticmethod def compute_witness_in_tree_and_hypothesis_states(ob_tree, ob_tree_state, hyp_state): """ Determines if the observation tree and the hypothesis are distinguishable based on their state outputs """ if ob_tree.automaton_type == 'mealy': return Apartness.compute_witness_in_tree_and_hypothesis_states_mealy(ob_tree, ob_tree_state, hyp_state) else: return Apartness.compute_witness_in_tree_and_hypothesis_states_moore(ob_tree, ob_tree_state, hyp_state) @staticmethod def compute_witness_in_tree_and_hypothesis_states_mealy(ob_tree, ob_tree_state, hyp_state): """ Determines if the observation tree and the hypothesis are distinguishable based on their state outputs """ pairs = deque([(ob_tree_state, hyp_state)]) while pairs: tree_state, hyp_state = pairs.popleft() for input_val in ob_tree.alphabet: tree_output = tree_state.get_output(input_val) if tree_output is not None and input_val in hyp_state.output_fun: hyp_output = hyp_state.output_fun[input_val] if tree_output != hyp_output: tree_dest = tree_state.get_successor(input_val) return ob_tree.get_transfer_sequence(ob_tree_state, tree_dest) pairs.append((tree_state.get_successor( input_val), hyp_state.transitions[input_val])) return None @staticmethod def compute_witness_in_tree_and_hypothesis_states_moore(ob_tree, ob_tree_state, hyp_state): """ Determines if the observation tree and the hypothesis are distinguishable based on their state outputs """ pairs = deque([(ob_tree_state, hyp_state)]) while pairs: tree_state, hyp_state = pairs.popleft() if (tree_state is not None) and (hyp_state is not None): tree_output = tree_state.output if ob_tree.automaton_type == 'dfa': hyp_output = hyp_state.is_accepting else: hyp_output = hyp_state.output if tree_output != hyp_output: return ob_tree.get_transfer_sequence(ob_tree_state, tree_state) for input_val in ob_tree.alphabet: if input_val in hyp_state.transitions: pairs.append((tree_state.get_successor( input_val), hyp_state.transitions[input_val])) return None ================================================ FILE: aalpy/learning_algs/deterministic/ClassificationTree.py ================================================ from collections import defaultdict from itertools import product from typing import Union from aalpy.automata import DfaState, Dfa, MealyState, MealyMachine, MooreState, MooreMachine, \ SevpaAlphabet, SevpaState, SevpaTransition, Sevpa from aalpy.base import SUL from aalpy.learning_algs.deterministic.CounterExampleProcessing import rs_cex_processing, linear_cex_processing, \ exponential_cex_processing automaton_class = {'dfa': Dfa, 'mealy': MealyMachine, 'moore': MooreMachine} class CTNode: __slots__ = ['parent', 'path_to_node'] def __init__(self, parent, path_to_node): self.parent = parent self.path_to_node = path_to_node def is_leaf(self): pass class CTInternalNode(CTNode): __slots__ = ['distinguishing_string', 'children'] def __init__(self, distinguishing_string: tuple, parent, path_to_node): super().__init__(parent, path_to_node) self.distinguishing_string = distinguishing_string self.children = defaultdict(None) # {True: None, False: None} def is_leaf(self): return False class CTLeafNode(CTNode): __slots__ = ['access_string'] def __init__(self, access_string: tuple, parent, path_to_node): super().__init__(parent, path_to_node) self.access_string = access_string def __repr__(self): return f"{self.__class__.__name__} '{self.access_string}'" @property def output(self): c, p = self, self.parent while p.parent: c = p p = p.parent for output, child in p.children.items(): if child == c: return output assert False def is_leaf(self): return True class ClassificationTree: def __init__(self, alphabet: Union[list, SevpaAlphabet], sul: SUL, automaton_type: str, cex: tuple): self.sul = sul self.alphabet = alphabet self.automaton_type = automaton_type self.leaf_nodes = {} self.initial_state = None self.hypothesis_states = {} # prefix of identified error state in VPDA learning self.error_state_prefix = None if self.automaton_type != 'mealy': initial_output = sul.query(())[-1] cex_output = sul.query(cex)[-1] root_distinguishing_string = () if automaton_type != 'vpa' else ([(), ()]) self.root = CTInternalNode(distinguishing_string=root_distinguishing_string, parent=None, path_to_node=None) initial_output_node = CTLeafNode(access_string=tuple(), parent=self.root, path_to_node=initial_output) cex_output_node = CTLeafNode(access_string=cex, parent=self.root, path_to_node=cex_output) self.root.children[initial_output] = initial_output_node self.root.children[cex_output] = cex_output_node self.leaf_nodes[tuple()] = initial_output_node self.leaf_nodes[cex] = cex_output_node else: self.root = CTInternalNode(distinguishing_string=(cex[-1],), parent=None, path_to_node=None) hypothesis_output = sul.query((cex[-1],))[-1] cex_output = sul.query(cex)[-1] hypothesis_output_node = CTLeafNode(access_string=tuple(), parent=self.root, path_to_node=hypothesis_output) cex_output_node = CTLeafNode(access_string=cex[:-1], parent=self.root, path_to_node=cex_output) self.root.children[hypothesis_output] = hypothesis_output_node self.root.children[cex_output] = cex_output_node self.leaf_nodes[tuple()] = self.root.children[hypothesis_output] self.leaf_nodes[cex[:-1]] = self.root.children[cex_output] self.new_states = list(self.leaf_nodes.values()) self.transitions_to_update = [] def _sift(self, word): """ Sifting a word into the classification tree. Starting at the root, at every inner node (a CTInternalNode), we branch into the child, depending on the result of the membership query (word * node.distinguishing_string). Repeated until a leaf (a CTLeafNode) is reached, which is the result of the sifting. Args: word: the word to sift into the discrimination tree (a tuple of all letters) Returns: the CTLeafNode that is reached by the sifting operation. """ node = self.root while not node.is_leaf(): if self.automaton_type != 'vpa': query = word + node.distinguishing_string else: query = node.distinguishing_string[0] + word + node.distinguishing_string[1] mq_result = self.sul.query(query)[-1] if mq_result not in node.children.keys(): new_leaf = CTLeafNode(access_string=word, parent=node, path_to_node=mq_result) self.leaf_nodes[word] = new_leaf node.children[mq_result] = new_leaf node = node.children[mq_result] assert node.is_leaf() return node def update_hypothesis(self): # for each CTLeafNode of this CT, # create a state in the hypothesis that is labeled by that # node's access string. The start state is the empty word state_counter = len(self.hypothesis_states.values()) while self.new_states: node = self.new_states.pop(0) if self.automaton_type == 'dfa': new_state = DfaState(state_id=f's{state_counter}', is_accepting=node.output) elif self.automaton_type == 'moore': new_state = MooreState(state_id=f's{state_counter}', output=node.output) elif self.automaton_type == 'vpa': new_state = SevpaState(state_id=f'q{state_counter}', is_accepting=node.output) else: new_state = MealyState(state_id=f's{state_counter}') new_state.prefix = node.access_string if new_state.prefix == (): self.initial_state = new_state self.hypothesis_states[new_state.prefix] = new_state if self.automaton_type != 'vpa': self.transitions_to_update.extend(product([new_state], self.alphabet)) else: self.transitions_to_update.extend(product([new_state], self.alphabet.internal_alphabet)) self.transitions_to_update.extend(product([new_state], self.alphabet.call_alphabet)) state_counter += 1 assert self.initial_state is not None # For each access state s of the hypothesis and each letter b in the # alphabet, compute the b-transition out of state s by sifting s.state_id*b while self.transitions_to_update: state, input_element = self.transitions_to_update.pop(0) if self.automaton_type != 'vpa': transition_target_node = self._sift(state.prefix + (input_element,)) transition_target_access_string = transition_target_node.access_string if self.automaton_type != "dfa" and transition_target_access_string not in self.hypothesis_states: if self.automaton_type == 'mealy': new_state = MealyState(state_id=f's{state_counter}') else: output = self.sul.query(transition_target_access_string)[-1] new_state = MooreState(state_id=f's{state_counter}', output=output) new_state.prefix = transition_target_access_string self.hypothesis_states[new_state.prefix] = new_state self.transitions_to_update.extend(product([new_state], self.alphabet)) state_counter += 1 state.transitions[input_element] = self.hypothesis_states[transition_target_access_string] if self.automaton_type == "mealy": state.output_fun[input_element] = self.sul.query(state.prefix + (input_element,))[-1] else: # internal transitions if input_element in self.alphabet.internal_alphabet: transition_target_node = self._sift(state.prefix + (input_element,)) transition_target_access_string = transition_target_node.access_string assert transition_target_access_string in self.hypothesis_states trans = SevpaTransition(target=self.hypothesis_states[transition_target_access_string], letter=input_element, action=None) state.transitions[input_element].append(trans) # call transitions elif input_element in self.alphabet.call_alphabet: # Add return transitions for return_letter in self.alphabet.return_alphabet: # check if exclusive pairs of call and return letters are defined in an alphabets if self.alphabet.exclusive_call_return_pairs and \ self.alphabet.exclusive_call_return_pairs[input_element] != return_letter: continue for other_state in self.hypothesis_states.values(): # ignore other state if other state is error state if other_state.prefix == self.error_state_prefix: continue transition_target_node = self._sift( other_state.prefix + (input_element,) + state.prefix + (return_letter,)) transition_target_access_string = transition_target_node.access_string trans = SevpaTransition(target=self.hypothesis_states[transition_target_access_string], letter=return_letter, action='pop', stack_guard=(other_state.state_id, input_element)) state.transitions[return_letter].append(trans) if self.automaton_type == 'vpa': hypothesis = Sevpa(initial_state=self.initial_state, states=list(self.hypothesis_states.values())) if not self.error_state_prefix: error_state = hypothesis.get_error_state() if error_state: self.error_state_prefix = error_state.prefix return hypothesis return automaton_class[self.automaton_type](initial_state=self.initial_state, states=list(self.hypothesis_states.values())) def _least_common_ancestor(self, node_1_id, node_2_id): """ Find the distinguishing string of the least common ancestor of the leaf nodes node_1 and node_2. Both nodes have to exist. Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ Args: node_1_id: first leaf node's id node_2_id: second leaf node's id Returns: the distinguishing string of the lca """ def ancestor(parent, node): for child in parent.children.values(): if child.is_leaf(): if child.access_string == node: return True else: next_ancestor = ancestor(child, node) if next_ancestor: return True return False def findLCA(n1_id, n2_id): node = self.leaf_nodes[n1_id] parent = node.parent while parent: if ancestor(parent, n2_id): return parent if parent.parent: parent = parent.parent else: return parent return None return findLCA(node_1_id, node_2_id).distinguishing_string def update(self, cex: tuple, hypothesis): """ Updates the classification tree based on a counterexample. - For each prefix cex[:i] of the counterexample, get s_i = self.sift(cex[:i]) and s_star_i = id of the state with the access sequence cex[:i] in the hypothesis and let j be the least i such that s_i != s_star_i. - Replace the CTLeafNode labeled with the access string of the state that is reached by the sequence cex[:j-1] in the hypothesis with an CTInternalNode with two CTLeafNodes: one keeps the old access string, and one gets the new access string cex[:j-1]. The internal node is labeled with the distinguishing string (cex[j-1],*d), where d is the distinguishing string of the LCA of s_i and s_star_i. Args: cex: the counterexample used to update the tree hypothesis: the former (wrong) hypothesis """ j = d = None for i in range(1, len(cex) + 1): s_i = self._sift(cex[:i]).access_string hypothesis.execute_sequence(hypothesis.initial_state, cex[:i]) s_star_i = hypothesis.current_state.prefix if s_i != s_star_i: j = i d = self._least_common_ancestor(s_i, s_star_i) break if j is None and d is None: j = len(cex) d = [] assert j is not None and d is not None hypothesis.execute_sequence(hypothesis.initial_state, cex[:j - 1] or tuple()) self._insert_new_leaf(discriminator=(cex[j - 1], *d), old_leaf_access_string=hypothesis.current_state.prefix, new_leaf_access_string=tuple(cex[:j - 1]) or tuple(), new_leaf_position=self.sul.query((*cex[:j - 1], *(cex[j - 1], *d)))[-1]) def process_counterexample(self, cex: tuple, hypothesis, cex_processing_fun): """ Updates the classification tree based on a counterexample, using Rivest & Schapire counterexample processing - Replace the CTLeafNode labeled with the access string of the state that is reached by the sequence cex[:j-1] in the hypothesis with an CTInternalNode with two CTLeafNodes: one keeps the old access string, and one gets the new access string cex[:j-1]. The internal node is labeled with the distinguishing string (cex[j-1],*d), where d is the distinguishing string of the LCA of s_i and s_star_i. Args: cex: the counterexample used to update the tree hypothesis: the former (wrong) hypothesis cex_processing_fun: string choosing which cex_processing to use """ v = None if 'linear' in cex_processing_fun: direction = cex_processing_fun[-3:] v = linear_cex_processing(self.sul, cex, hypothesis, is_vpa=self.automaton_type == 'vpa', direction=direction, suffix_closedness=False)[0] elif 'exponential' in cex_processing_fun: direction = cex_processing_fun[-3:] v = exponential_cex_processing(self.sul, cex, hypothesis, is_vpa=self.automaton_type == 'vpa', direction=direction, suffix_closedness=False)[0] elif cex_processing_fun == 'rs': v = rs_cex_processing(self.sul, cex, hypothesis, is_vpa=self.automaton_type == 'vpa', suffix_closedness=False)[0] assert v a = cex[len(cex) - len(v) - 1] u = cex[:len(cex) - len(v) - 1] assert (*u, a, *v) == cex hypothesis.execute_sequence(hypothesis.initial_state, u) u_state = hypothesis.current_state top_of_stack = hypothesis.stack[-1] if self.automaton_type == 'vpa' else None # get state reached after executing last action => old leaf hypothesis.step(a) ua_state = hypothesis.current_state # get discriminator and new_leaf_access_string if self.automaton_type == 'vpa': discriminator = (tuple(hypothesis.transform_access_string()), tuple(v)) if a in self.alphabet.internal_alphabet: new_leaf_access_string = (*u_state.prefix, a) else: assert a in self.alphabet.return_alphabet l_prime, call = hypothesis.get_state_by_id(top_of_stack[0]), top_of_stack[1] new_leaf_access_string = l_prime.prefix + (call,) + u_state.prefix + (a,) else: discriminator = v new_leaf_access_string = (*u_state.prefix, a) if self.automaton_type == 'dfa' or self.automaton_type == 'vpa': new_leaf_position = not hypothesis.execute_sequence(hypothesis.initial_state, cex)[-1] else: new_leaf_position = self.sul.query(cex)[-1] self._insert_new_leaf(discriminator=discriminator, old_leaf_access_string=ua_state.prefix, new_leaf_access_string=new_leaf_access_string, new_leaf_position=new_leaf_position) def _insert_new_leaf(self, discriminator, old_leaf_access_string, new_leaf_access_string, new_leaf_position): """ Inserts a new leaf in the classification tree by: - moving the leaf node specified by down one level - inserting an internal node at the former position of the old node (i.e. as the parent of the old node) - adding a new leaf node with as child of the new internal node/sibling of the old node Could also be thought of as 'splitting' the old node into two (one of which keeps the old access string and one of which gets the new one) with as the distinguishing string between the two. where one of the resulting nodes keeps the old node's access string and the other gets new_leaf_access_string. Args: discriminator: The distinguishing string of the new internal node old_leaf_access_string: The access string specifying the leaf node to be 'split' (or rather moved down) new_leaf_access_string: The access string of the leaf node that will be created new_leaf_position: The path from the new internal node to the new leaf node Returns: """ if self.automaton_type == "dfa" or self.automaton_type == 'vpa': other_leaf_position = not new_leaf_position else: # check if this query is in the node cache other_leaf_position = self.sul.query((*old_leaf_access_string, *discriminator))[-1] old_leaf = self.leaf_nodes[old_leaf_access_string] # create an internal node at the same position as the old leaf node discriminator_node = CTInternalNode(distinguishing_string=discriminator, parent=old_leaf.parent, path_to_node=old_leaf.path_to_node) # create the new leaf node and add it as child of the internal node new_leaf = CTLeafNode(access_string=new_leaf_access_string, parent=discriminator_node, path_to_node=new_leaf_position) self.leaf_nodes[new_leaf_access_string] = new_leaf # redirect the old nodes former parent to the internal node old_leaf.parent.children[old_leaf.path_to_node] = discriminator_node # add the internal node as parent of the old leaf old_leaf.parent = discriminator_node old_leaf.path_to_node = other_leaf_position # set the two nodes as children of the internal node discriminator_node.children[new_leaf_position] = new_leaf discriminator_node.children[other_leaf_position] = old_leaf # sifting cache update self.new_states.append(new_leaf) if self.automaton_type != 'vpa': for state in self.hypothesis_states.values(): for inp, destination in state.transitions.items(): if old_leaf_access_string == destination.prefix: self.transitions_to_update.append((state, inp)) else: for state in self.hypothesis_states.values(): state.transitions.clear() self.transitions_to_update.extend(product([state], self.alphabet.internal_alphabet)) self.transitions_to_update.extend(product([state], self.alphabet.call_alphabet)) ================================================ FILE: aalpy/learning_algs/deterministic/CounterExampleProcessing.py ================================================ from aalpy.base import SUL from aalpy.utils.HelperFunctions import all_suffixes, all_prefixes def counterexample_successfully_processed(sul, cex, hypothesis): cex_outputs = sul.query(cex) hyp_outputs = hypothesis.execute_sequence(hypothesis.initial_state, cex) return cex_outputs[-1] == hyp_outputs[-1] def longest_prefix_cex_processing(s_union_s_dot_a: list, cex: tuple, closedness='suffix'): """ Suffix processing strategy found in Shahbaz-Groz paper 'Inferring Mealy Machines'. It splits the counterexample into prefix and suffix. The prefix is the longest element of the S union S.A that matches the beginning of the counterexample. By removing such prefixes from counterexample, no consistency check is needed. Args: s_union_s_dot_a: list of all prefixes found in observation table sorted from shortest to longest cex: counterexample closedness: either 'suffix' or 'prefix'. (Default value = 'suffix') s_union_s_dot_a: list: cex: tuple: counterexample Returns: suffixes to add to the E set """ prefixes = s_union_s_dot_a prefixes.reverse() trimmed_suffix = None for p in prefixes: if p == cex[:len(p)]: trimmed_suffix = cex[len(p):] break trimmed_suffix = trimmed_suffix if trimmed_suffix else cex suffixes = all_suffixes(trimmed_suffix) if closedness == 'suffix' else all_prefixes(trimmed_suffix) suffixes.reverse() return suffixes def rs_cex_processing(sul: SUL, cex: tuple, hypothesis, suffix_closedness=True, closedness='suffix', is_vpa=False, lower=None, upper=None): """ Rivest-Schapire counter example processing. Args: sul: system under learning cex: found counterexample hypothesis: hypothesis on which counterexample was found suffix_closedness: If true all suffixes will be added, else just one (Default value = True) closedness: either 'suffix' or 'prefix'. (Default value = 'suffix') sul: SUL: system under learning cex: tuple: counterexample is_vpa: system under learning behaves as a context free language upper: upper boarder for cex (from preprocessing), None will set it to 1 lower: lower boarder for cex (from preprocessing), None will set it to len(cex_input) - 2 Returns: suffixes to be added to the E set """ cex_out = sul.query(cex) cex_input = list(cex) lower = 1 if lower is None else lower upper = len(cex_input) - 2 if upper is None else upper while True: hypothesis.reset_to_initial() mid = (lower + upper) // 2 # arr[:n] -> first n values # arr[n:] -> last n values for s_p in cex_input[:mid]: hypothesis.step(s_p) if not is_vpa: s_bracket = hypothesis.current_state.prefix else: s_bracket = tuple(hypothesis.transform_access_string(hypothesis.current_state)) d = tuple(cex_input[mid:]) mq = sul.query(s_bracket + d) if mq[-1] == cex_out[-1]: # only check if the last element is the same as the cex lower = mid + 1 if upper < lower: suffix = d[1:] break else: upper = mid - 1 if upper < lower: suffix = d break if suffix_closedness: suffixes = all_suffixes(suffix) if closedness == 'suffix' else all_prefixes(suffix) suffixes.reverse() suffix_to_query = suffixes else: suffix_to_query = [suffix] return suffix_to_query def linear_cex_processing(sul: SUL, cex: tuple, hypothesis, suffix_closedness=True, closedness='suffix', direction='fwd', is_vpa=False): assert direction in {'fwd', 'bwd'} direction = 'fwd' distinguishing_suffix = None previous_output = None for i in range(0, len(cex)): bp = i if direction == 'fwd' else -i - 1 prefix = cex[:bp] suffix = cex[bp:] assert cex == prefix + suffix hypothesis.reset_to_initial() hypothesis.execute_sequence(hypothesis.initial_state, prefix) if not is_vpa: s_bracket = hypothesis.current_state.prefix else: s_bracket = tuple(hypothesis.transform_access_string(hypothesis.current_state)) sul_out = sul.query(s_bracket + suffix)[-1] if previous_output is None: previous_output = sul_out continue if sul_out != previous_output: distinguishing_suffix = suffix if direction == 'fwd' else cex[bp + 1:] break previous_output = sul_out assert distinguishing_suffix if suffix_closedness: suffixes = all_suffixes(distinguishing_suffix) if closedness == 'suffix' else all_prefixes( distinguishing_suffix) suffixes.reverse() suffix_to_query = suffixes else: suffix_to_query = [distinguishing_suffix] return suffix_to_query def exponential_cex_processing(sul: SUL, cex: tuple, hypothesis, suffix_closedness=True, closedness='suffix', direction='fwd', is_vpa=False): assert direction in {'fwd', 'bwd'} cex_out = sul.query(cex) bwd_subtrahend = 1 if direction == 'fwd': bp_recent = 0 bp = 1 else: bp_recent = len(cex) bp = len(cex)-1 suffix = None while True: if direction == 'fwd': if bp >= len(cex): bp = len(cex) break else: if bp <= 1: bp = 1 break prefix = cex[:bp] suffix = cex[bp:] assert cex == prefix + suffix hypothesis.reset_to_initial() hypothesis.execute_sequence(hypothesis.initial_state, prefix) if not is_vpa: s_bracket = hypothesis.current_state.prefix else: s_bracket = tuple(hypothesis.transform_access_string(hypothesis.current_state)) sul_out = sul.query(s_bracket + suffix) if sul_out[-1] != cex_out[-1] and direction == 'fwd': break elif sul_out[-1] == cex_out[-1] and direction == 'bwd': break bp_recent = bp if direction == 'fwd': bp *= 2 else: bp -= bwd_subtrahend bwd_subtrahend *= 2 if (bp - bp_recent) == 1: return [suffix] else: if direction == 'fwd': return rs_cex_processing(sul, cex, hypothesis, suffix_closedness, closedness, is_vpa, lower=bp_recent) else: return rs_cex_processing(sul, cex, hypothesis, suffix_closedness, closedness, is_vpa, upper=bp_recent) ================================================ FILE: aalpy/learning_algs/deterministic/KV.py ================================================ import time from typing import Union from aalpy.automata import Dfa, DfaState, MealyState, MealyMachine, MooreState, MooreMachine, \ Sevpa, SevpaState, SevpaAlphabet from aalpy.base import Oracle, SUL from aalpy.utils.HelperFunctions import print_learning_info, visualize_classification_tree from .ClassificationTree import ClassificationTree from .CounterExampleProcessing import counterexample_successfully_processed from ...base.SUL import CacheSUL print_options = [0, 1, 2, 3] counterexample_processing_strategy = ['rs', 'linear_fwd', 'linear_bwd', 'exponential_fwd', 'exponential_bwd'] automaton_class = {'dfa': Dfa, 'mealy': MealyMachine, 'moore': MooreMachine, 'vpa': Sevpa} def run_KV(alphabet: Union[list, SevpaAlphabet], sul: SUL, eq_oracle: Oracle, automaton_type, cex_processing='rs', max_learning_rounds=None, cache_and_non_det_check=True, return_data=False, print_level=2): """ Executes the KV algorithm. Args: alphabet: input alphabet sul: system under learning eq_oracle: equivalence oracle automaton_type: type of automaton to be learned. One of 'dfa', 'mealy', 'moore', 'vpa' cex_processing: Counterexample processing strategy. Either 'rs' (Rivest-Schapire), 'longest_prefix'. (Default value = 'rs'), 'longest_prefix', 'linear_fwd', 'linear_bwd', 'exponential_fwd', 'exponential_bwd' max_learning_rounds: number of learning rounds after which learning will terminate (Default value = None) cache_and_non_det_check: Use caching and non-determinism checks (Default value = True) return_data: if True, a map containing all information(runtime/#queries/#steps) will be returned (Default value = False) print_level: 0 - None, 1 - just results, 2 - current round and hypothesis size, 3 - educational/debug (Default value = 2) Returns: automaton of type automaton_type (dict containing all information about learning if 'return_data' is True) """ assert print_level in print_options assert cex_processing in counterexample_processing_strategy assert automaton_type in [*automaton_class] assert automaton_type != 'vpa' and isinstance(alphabet, list) or isinstance(alphabet, SevpaAlphabet) start_time = time.time() eq_query_time = 0 learning_rounds = 0 if cache_and_non_det_check: # Wrap the sul in the CacheSUL, so that all steps/queries are cached sul = CacheSUL(sul) eq_oracle.sul = sul if automaton_type != 'mealy': # Do a membership query on the empty string to determine whether # the start state of the SUL is accepting or rejecting empty_string_mq = sul.query(tuple())[-1] # Construct a hypothesis automaton that consists simply of this # single (accepting or rejecting) state with self-loops for # all transitions. if automaton_type == 'dfa': initial_state = DfaState(state_id='q0', is_accepting=empty_string_mq) elif automaton_type == 'moore': initial_state = MooreState(state_id='q0', output=empty_string_mq) else: initial_state = SevpaState(state_id='q0', is_accepting=empty_string_mq) else: initial_state = MealyState(state_id='q0') initial_state.prefix = tuple() if automaton_type != 'vpa': for a in alphabet: initial_state.transitions[a] = initial_state if automaton_type == 'mealy': initial_state.output_fun[a] = sul.query((a,))[-1] if automaton_type != 'vpa': hypothesis = automaton_class[automaton_type](initial_state, [initial_state]) else: hypothesis = Sevpa.create_daisy_hypothesis(initial_state, alphabet) # Perform an equivalence query on this automaton eq_query_start = time.time() cex = eq_oracle.find_cex(hypothesis) eq_query_time += time.time() - eq_query_start classification_tree = None if cex is not None: cex = tuple(cex) # initialise the classification tree to have a root # labeled with the empty word as the distinguishing string # and two leaves labeled with access strings cex and empty word classification_tree = ClassificationTree(alphabet=alphabet, sul=sul, automaton_type=automaton_type, cex=cex) while True: learning_rounds += 1 if max_learning_rounds and learning_rounds - 1 == max_learning_rounds: break hypothesis = classification_tree.update_hypothesis() if print_level == 2: print(f'\rHypothesis {learning_rounds}: {hypothesis.size} states.', end="") if print_level == 3: # would be nice to have an option to print classification tree print(f'Hypothesis {learning_rounds}: {hypothesis.size} states.') if counterexample_successfully_processed(sul, cex, hypothesis): # Perform an equivalence query on this automaton eq_query_start = time.time() cex = eq_oracle.find_cex(hypothesis) eq_query_time += time.time() - eq_query_start if cex is None: break else: cex = tuple(cex) if print_level == 3: print('Counterexample', cex) classification_tree.process_counterexample(cex, hypothesis, cex_processing) if automaton_type == 'vpa': hypothesis.delete_state(hypothesis.get_error_state()) total_time = round(time.time() - start_time, 2) eq_query_time = round(eq_query_time, 2) learning_time = round(total_time - eq_query_time, 2) info = { 'learning_rounds': learning_rounds, 'automaton_size': hypothesis.size, 'queries_learning': sul.num_queries, 'steps_learning': sul.num_steps, 'queries_eq_oracle': eq_oracle.num_queries, 'steps_eq_oracle': eq_oracle.num_steps, 'learning_time': learning_time, 'eq_oracle_time': eq_query_time, 'total_time': total_time, 'cache_saved': sul.num_cached_queries, } if print_level > 0: if print_level == 2: print("") print_learning_info(info) if print_level == 3 and classification_tree: print('Visualization of classification tree saved to classification_tree.pdf') visualize_classification_tree(classification_tree.root) if return_data: return hypothesis, info return hypothesis ================================================ FILE: aalpy/learning_algs/deterministic/LSharp.py ================================================ import time from aalpy.base import Oracle, SUL from aalpy.utils.HelperFunctions import print_learning_info from .ObservationTree import ObservationTree from ...base.SUL import CacheSUL def run_Lsharp(alphabet: list, sul: SUL, eq_oracle: Oracle, automaton_type, extension_rule='SepSeq', separation_rule="ADS", samples=None, max_learning_rounds=None, cache_and_non_det_check=True, return_data=False, print_level=2): """ Based on ''A New Approach for Active Automata Learning Based on Apartness'' from Vaandrager, Garhewal, Rot and Wissmann. and ''L# for DFAs'' from Vaandrager, Sanders. The algorithm learns a DFA/Moore machine/Mealy machine using apartness and an observation tree. Args: alphabet: input alphabet sul: system under learning eq_oracle: equivalence oracle automaton_type: type of automaton to be learned. Either 'dfa', 'mealy' or 'moore' extension_rule: strategy used during the extension rule. Options: None, "SepSeq" (default) and "ADS". separation_rule: strategy used during the extension rule. Options: "SepSeq" (default) and "ADS". samples: input output traces provided to the learning algorithm. They are added to cache and could reduce total interaction with the system. Syntax: list of [(input_sequence, output_sequence)] or None max_learning_rounds: number of learning rounds after which learning will terminate (Default value = None) cache_and_non_det_check: Use caching and non-determinism checks (Default value = True) return_data: if True, a map containing all information(runtime/#queries/#steps) will be returned (Default value = False) print_level: 0 - None, 1 - just results, 2 - current round and hypothesis size, 3 - educational/debug (Default value = 2) Returns: automaton of type automaton_type (dict containing all information about learning if 'return_data' is True) """ assert extension_rule in {None, "SepSeq", "ADS"} assert separation_rule in {"SepSeq", "ADS"} if cache_and_non_det_check or samples is not None: # Wrap the sul in the CacheSUL, so that all steps/queries are cached sul = CacheSUL(sul) eq_oracle.sul = sul if samples: for input_seq, output_seq in samples: sul.cache.add_to_cache(input_seq, output_seq) ob_tree = ObservationTree(alphabet, sul, automaton_type, extension_rule, separation_rule) start_time = time.time() eq_query_time = 0 learning_rounds = 0 hypothesis = None while True: learning_rounds += 1 if max_learning_rounds and learning_rounds == max_learning_rounds: break # Building the hypothesis hypothesis = ob_tree.build_hypothesis() if print_level > 1: print(f'Hypothesis {learning_rounds}: {hypothesis.size} states.') if print_level == 3: print(hypothesis) # Pose Equivalence Query eq_query_start = time.time() cex = eq_oracle.find_cex(hypothesis) eq_query_time += time.time() - eq_query_start if print_level > 2: print(f'Counterexample: {cex}') if cex is None: break # Process the counterexample and start a new learning round cex_outputs = sul.query(cex) ob_tree.process_counter_example(hypothesis, cex, cex_outputs) total_time = round(time.time() - start_time, 2) eq_query_time = round(eq_query_time, 2) learning_time = round(total_time - eq_query_time, 2) info = { 'learning_rounds': learning_rounds, 'automaton_size': hypothesis.size, 'queries_learning': sul.num_queries, 'steps_learning': sul.num_steps, 'queries_eq_oracle': eq_oracle.num_queries, 'steps_eq_oracle': eq_oracle.num_steps, 'learning_time': learning_time, 'eq_oracle_time': eq_query_time, 'total_time': total_time, 'cache_saved': sul.num_cached_queries, } if print_level > 0: print_learning_info(info) if return_data: return hypothesis, info return hypothesis ================================================ FILE: aalpy/learning_algs/deterministic/LStar.py ================================================ import time from aalpy.base import Oracle, SUL from aalpy.utils.HelperFunctions import extend_set, print_learning_info, print_observation_table, all_prefixes from .CounterExampleProcessing import longest_prefix_cex_processing, rs_cex_processing, \ counterexample_successfully_processed, linear_cex_processing, exponential_cex_processing from .ObservationTable import ObservationTable from ...base.SUL import CacheSUL counterexample_processing_strategy = [None, 'rs', 'longest_prefix', 'linear_fwd', 'linear_bwd', 'exponential_fwd', 'exponential_bwd'] closedness_options = ['suffix_all', 'suffix_single'] print_options = [0, 1, 2, 3] def run_Lstar(alphabet: list, sul: SUL, eq_oracle: Oracle, automaton_type, samples=None, closing_strategy='shortest_first', cex_processing='rs', e_set_suffix_closed=False, all_prefixes_in_obs_table=True, max_learning_rounds=None, cache_and_non_det_check=True, return_data=False, print_level=2): """ Executes L* algorithm. Args: alphabet: input alphabet sul: system under learning eq_oracle: equivalence oracle automaton_type: type of automaton to be learned. Either 'dfa', 'mealy' or 'moore'. samples: input output traces provided to the learning algorithm. They are added to cache and could reduce total interaction with the system. Syntax: list of [(input_sequence, output_sequence)] or None closing_strategy: closing strategy used in the close method. Either 'longest_first', 'shortest_first' or 'single' (Default value = 'shortest_first') cex_processing: Counterexample processing strategy. Either None, 'rs' (Rivest-Schapire), 'longest_prefix'. (Default value = 'rs'), 'longest_prefix', 'linear_fwd', 'linear_bwd', 'exponential_fwd', 'exponential_bwd' e_set_suffix_closed: True option ensures that E set is suffix closed, False adds just a single suffix per counterexample. all_prefixes_in_obs_table: if True, entries of observation table will contain the whole output of the whole suffix, otherwise just the last output meaning that all prefixes of the suffix will be added. If False, just a single suffix will be added. max_learning_rounds: number of learning rounds after which learning will terminate (Default value = None) cache_and_non_det_check: Use caching and non-determinism checks (Default value = True) return_data: if True, a map containing all information(runtime/#queries/#steps) will be returned (Default value = False) print_level: 0 - None, 1 - just results, 2 - current round and hypothesis size, 3 - educational/debug (Default value = 2) Returns: automaton of type automaton_type (dict containing all information about learning if 'return_data' is True) """ assert cex_processing in counterexample_processing_strategy assert print_level in print_options if cache_and_non_det_check or samples is not None: # Wrap the sul in the CacheSUL, so that all steps/queries are cached sul = CacheSUL(sul) eq_oracle.sul = sul if samples: for input_seq, output_seq in samples: sul.cache.add_to_cache(input_seq, output_seq) start_time = time.time() eq_query_time = 0 learning_rounds = 0 hypothesis = None observation_table = ObservationTable(alphabet, sul, automaton_type, all_prefixes_in_obs_table) # Initial update of observation table, for empty row observation_table.update_obs_table() cex = None while True: if max_learning_rounds and learning_rounds == max_learning_rounds: break # Make observation table consistent (iff there is no counterexample processing) if not cex_processing: inconsistent_rows = observation_table.get_causes_of_inconsistency() while inconsistent_rows is not None: added_suffix = extend_set(observation_table.E, inconsistent_rows) observation_table.update_obs_table(e_set=added_suffix) inconsistent_rows = observation_table.get_causes_of_inconsistency() # Close observation table rows_to_close = observation_table.get_rows_to_close(closing_strategy) while rows_to_close is not None: rows_to_query = [] for row in rows_to_close: observation_table.S.append(row) rows_to_query.extend([row + (a,) for a in alphabet]) observation_table.update_obs_table(s_set=rows_to_query) rows_to_close = observation_table.get_rows_to_close(closing_strategy) # Generate hypothesis hypothesis = observation_table.gen_hypothesis(no_cex_processing_used=cex_processing is None) # Find counterexample if none has previously been found (first round) and cex is successfully processed # (not a counterexample in the current hypothesis) if cex is None or counterexample_successfully_processed(sul, cex, hypothesis): learning_rounds += 1 if print_level > 1: print(f'Hypothesis {learning_rounds}: {len(hypothesis.states)} states.') if print_level == 3: print_observation_table(observation_table, 'det') eq_query_start = time.time() cex = eq_oracle.find_cex(hypothesis) eq_query_time += time.time() - eq_query_start # If no counterexample is found, return the hypothesis if cex is None: break # make sure counterexample is a tuple in case oracle returns a list cex = tuple(cex) if print_level == 3: print('Counterexample', cex) # Process counterexample and ask membership queries if not cex_processing: s_to_update = [] added_rows = extend_set(observation_table.S, all_prefixes(cex)) s_to_update.extend(added_rows) for p in added_rows: s_to_update.extend([p + (a,) for a in alphabet]) observation_table.update_obs_table(s_set=s_to_update) continue elif cex_processing == 'longest_prefix': cex_suffixes = longest_prefix_cex_processing(observation_table.S + list(observation_table.s_dot_a()), cex, closedness='suffix') elif cex_processing == 'rs': cex_suffixes = rs_cex_processing(sul, cex, hypothesis, e_set_suffix_closed, closedness='suffix') else: direction = cex_processing[-3:] if 'linear' in cex_processing: cex_suffixes = linear_cex_processing(sul, cex, hypothesis, e_set_suffix_closed, direction=direction, closedness='suffix') else: cex_suffixes = exponential_cex_processing(sul, cex, hypothesis, e_set_suffix_closed, direction=direction, closedness='suffix') added_suffixes = extend_set(observation_table.E, cex_suffixes) observation_table.update_obs_table(e_set=added_suffixes) total_time = round(time.time() - start_time, 2) eq_query_time = round(eq_query_time, 2) learning_time = round(total_time - eq_query_time, 2) info = { 'learning_rounds': learning_rounds, 'automaton_size': hypothesis.size, 'queries_learning': sul.num_queries, 'steps_learning': sul.num_steps, 'queries_eq_oracle': eq_oracle.num_queries, 'steps_eq_oracle': eq_oracle.num_steps, 'learning_time': learning_time, 'eq_oracle_time': eq_query_time, 'total_time': total_time, 'characterization_set': observation_table.E } if cache_and_non_det_check: info['cache_saved'] = sul.num_cached_queries if print_level > 0: print_learning_info(info) if return_data: return hypothesis, info return hypothesis ================================================ FILE: aalpy/learning_algs/deterministic/ObservationTable.py ================================================ from collections import defaultdict from aalpy.base import Automaton, SUL from aalpy.automata import Dfa, DfaState, MealyState, MealyMachine, MooreMachine, MooreState aut_type = ['dfa', 'mealy', 'moore'] closing_options = ['shortest_first', 'longest_first', 'single', 'single_longest'] class ObservationTable: def __init__(self, alphabet: list, sul: SUL, automaton_type, prefixes_in_cell=False): """ Constructor of the observation table. Initial queries are asked in the constructor. Args: alphabet: input alphabet sul: system under learning automaton_type: automaton type, one of ['dfa', 'mealy', 'moore'] Returns: """ assert automaton_type in aut_type assert alphabet is not None and sul is not None self.automaton_type = automaton_type # If True add prefixes of each element of E set to a cell, else only add the output self.prefixes_in_cell = prefixes_in_cell self.A = [tuple([a]) for a in alphabet] self.S = list() # prefixes of S # DFA's can also take whole alphabet in E, this convention follows Angluin's paper self.E = [] if self.automaton_type != 'mealy' else [tuple([a]) for a in alphabet] # For performance reasons, the T function maps S to a tuple where element at index i is the element of the E # set of index i. Therefore it is important to keep E set ordered and ask membership queries only when needed # and in correct order. It would make more sense to implement it as a defaultdict(dict) where you can access # elements via self.T[s][e], but it causes significant performance hit. self.T = defaultdict(tuple) self.sul = sul empty_word = tuple() self.S.append(empty_word) # DFAs and Moore machines use empty word for identification of accepting states/state outputs if self.automaton_type == 'dfa' or self.automaton_type == 'moore': self.E.insert(0, empty_word) def get_rows_to_close(self, closing_strategy='longest_first'): """ Get rows for that need to be closed. Row selection is done according to closing_strategy. The length of the row is defined by the length of the prefix corresponding to the row in the S set. longest_first -> get all rows that need to be closed and ask membership queries for the longest row first shortest_first -> get all rows that need to be closed and ask membership queries for the shortest row first single -> find and ask membership query for the single row single_longest -> returns single longest row to close Args: closing_strategy: one of ['shortest_first', 'longest_first', 'single'] (Default value = 'longest_first') Returns: list if non-closed exist, None otherwise: rows that will be moved to S set and closed """ assert closing_strategy in closing_options rows_to_close = [] row_values = set() s_rows = {self.T[s] for s in self.S} for t in self.s_dot_a(): row_t = self.T[t] if row_t not in s_rows and row_t not in row_values: rows_to_close.append(t) row_values.add(row_t) if closing_strategy == 'single': return rows_to_close if not rows_to_close: return None if 'longest' in closing_strategy: rows_to_close.sort(key=len, reverse=True) if closing_strategy == 'longest_first': return rows_to_close if closing_strategy == 'single_longest': return [rows_to_close[0]] return rows_to_close def get_causes_of_inconsistency(self): """ If the two rows in the S set are the same, but their one letter extensions are not, this method founds the cause of inconsistency and returns it. :return: Returns: a+e values that are the causes of inconsistency """ for i, s1 in enumerate(self.S): for s2 in self.S[i + 1:]: if self.T[s1] == self.T[s2]: for a in self.A: if self.T[s1 + a] != self.T[s2 + a]: for index, e in enumerate(self.E): if self.T[s1 + a][index] != self.T[s2 + a][index]: return [(a + e)] return None def s_dot_a(self): """ Helper generator function that returns extended S, or S.A set. """ s_set = set(self.S) for s in self.S: for a in self.A: if s + a not in s_set: yield s + a def update_obs_table(self, s_set: list = None, e_set: list = None): """ Perform the membership queries. Args: s_set: Prefixes of S set on which to preform membership queries. If None, then whole S set will be used. e_set: Suffixes of E set on which to perform membership queries. If None, then whole E set will be used. Returns: """ update_S = s_set if s_set else list(self.S) + list(self.s_dot_a()) update_E = e_set if e_set else self.E # This could save few queries update_S.reverse() for s in update_S: for e in update_E: if len(self.T[s]) != len(self.E): output = tuple(self.sul.query(s + e)) if self.prefixes_in_cell and len(e) > 1: obs_table_entry = tuple([output[-len(e):]],) else: obs_table_entry = (output[-1],) self.T[s] += obs_table_entry def gen_hypothesis(self, no_cex_processing_used=False) -> Automaton: """ Generate automaton based on the values found in the observation table. :return: Args: check_for_duplicate_rows: (Default value = False) Returns: Automaton of type `automaton_type` """ state_distinguish = dict() states_dict = dict() initial_state = None automaton_class = {'dfa': Dfa, 'mealy': MealyMachine, 'moore': MooreMachine} s_set = self.S # Added check for the algorithm without counterexample processing if no_cex_processing_used: s_set = self._get_row_representatives() # create states based on S set stateCounter = 0 for prefix in s_set: state_id = f's{stateCounter}' if self.automaton_type == 'dfa': states_dict[prefix] = DfaState(state_id) states_dict[prefix].is_accepting = self.T[prefix][0] elif self.automaton_type == 'moore': states_dict[prefix] = MooreState(state_id, output=self.T[prefix][0]) else: states_dict[prefix] = MealyState(state_id) states_dict[prefix].prefix = prefix state_distinguish[tuple(self.T[prefix])] = states_dict[prefix] if not prefix: initial_state = states_dict[prefix] stateCounter += 1 # add transitions based on extended S set for prefix in s_set: for a in self.A: state_in_S = state_distinguish[self.T[prefix + a]] states_dict[prefix].transitions[a[0]] = state_in_S if self.automaton_type == 'mealy': states_dict[prefix].output_fun[a[0]] = self.T[prefix][self.E.index(a)] automaton = automaton_class[self.automaton_type](initial_state, list(states_dict.values())) automaton.characterization_set = self.E return automaton def _get_row_representatives(self): self.S.sort(key=len) representatives = defaultdict(list) for prefix in self.S: representatives[self.T[prefix]].append(prefix) return [r[0] for r in representatives.values()] ================================================ FILE: aalpy/learning_algs/deterministic/ObservationTree.py ================================================ from .ADS import Ads from .Apartness import Apartness from ... import Dfa, DfaState, MealyState, MealyMachine, MooreMachine, MooreState aut_type = ['dfa', 'mealy', 'moore'] class MooreNode: _id_counter = 0 __slots__ = ['id', 'output', 'successors', 'parent', 'input_to_parent'] def __init__(self, parent=None): MooreNode._id_counter += 1 self.id = MooreNode._id_counter self.output = None self.successors = {} self.parent = parent self.input_to_parent = None def __hash__(self): return hash(self.id) def add_successor(self, input_val, output_val, successor_node): """ Adds a successor node to the current node based on input """ self.successors[input_val] = successor_node self.successors[input_val].output = output_val def get_successor(self, input_val): """ Returns the successor node for the given input """ if input_val in self.successors: return self.successors[input_val] return None def extend_and_get(self, inp, output): """ Extend the node with a new successor and return the successor node """ if inp in self.successors: return self.successors[inp] successor_node = MooreNode(parent=self) self.add_successor(inp, output, successor_node) successor_node.input_to_parent = inp return successor_node @property def id_counter(self): return self._id_counter class MealyNode: _id_counter = 0 __slots__ = ['id', 'successors', 'parent', 'input_to_parent'] def __init__(self, parent=None): MealyNode._id_counter += 1 self.id = MealyNode._id_counter self.successors = {} self.parent = parent self.input_to_parent = None def __hash__(self): return hash(self.id) def add_successor(self, input_val, output_val, successor_node): """ Adds a successor node to the current node based on input """ self.successors[input_val] = (output_val, successor_node) def get_successor(self, input_val): """ Returns the successor node for the given input """ if input_val in self.successors: return self.successors[input_val][1] return None def get_output(self, input_val): """ Returns the output for the given input """ if input_val in self.successors: return self.successors[input_val][0] return None def extend_and_get(self, inp, output): """ Extend the node with a new successor and return the successor node """ if inp in self.successors: out = self.successors[inp][0] if out != output: raise Exception( f"observation not consistent with tree with output from tree: {out} and output from call: {output}") return self.successors[inp][1] successor_node = MealyNode(parent=self) self.add_successor(inp, output, successor_node) successor_node.input_to_parent = inp return successor_node @property def id_counter(self): return self._id_counter class ObservationTree: def __init__(self, alphabet, sul, automaton_type, extension_rule, separation_rule): """ Initialize the tree with a root node and the alphabet """ assert automaton_type in aut_type assert alphabet is not None and sul is not None self.automaton_type = automaton_type self.alphabet = alphabet self.sul = sul self.extension_rule = extension_rule self.separation_rule = separation_rule if self.automaton_type == 'mealy': self.root = MealyNode() else: self.root = MooreNode() # initialize MooreNode with empty word output self.root.output = self.sul.query([])[0] self.basis = [] self.basis.append(self.root) self.frontier_to_basis_dict = {} # Caches the separating sequences between basis states self.witness_cache = {} # Maps the basis states to hypothesis states self.states_dict = dict() def insert_observation(self, inputs, outputs): # Insert an observation into the tree using sequences of inputs and outputs if len(inputs) != len(outputs): raise ValueError("Inputs and outputs must have the same length.") current_node = self.root for input_val, output_val in zip(inputs, outputs): current_node = current_node.extend_and_get(input_val, output_val) def get_observation(self, inputs): # Retrieve the list of outputs based on a given input sequence current_node = self.root observation = [] for input_val in inputs: if self.automaton_type == 'mealy': output = current_node.get_output(input_val) current_node = current_node.get_successor(input_val) else: current_node = current_node.get_successor(input_val) output = current_node.output if output is None: return None observation.append(output) return observation def get_outputs(self, basis_state, inputs): # Retrieve the list of outputs based on a basis state and a given input sequence prefix = self.get_transfer_sequence(self.root, basis_state) current_node = self.get_successor(prefix) observation = [] for input_val in inputs: if self.automaton_type == 'mealy': output = current_node.get_output(input_val) else: output = current_node.output if output is None: return None observation.append(output) current_node = current_node.get_successor(input_val) return observation def get_successor(self, inputs): # Retrieve the node (subtree) corresponding to the given input sequence current_node = self.root for input_val in inputs: successor_node = current_node.get_successor(input_val) if successor_node is None: return None current_node = successor_node return current_node def get_transfer_sequence(self, from_node, to_node): # Get the transfer sequence (inputs) that moves from one node to another transfer_sequence = [] current_node = to_node while current_node != from_node: if current_node.parent is None: return None transfer_sequence.append(current_node.input_to_parent) current_node = current_node.parent transfer_sequence.reverse() return transfer_sequence def get_access_sequence(self, to_node): # Get the transfer sequence (inputs) that moves from one node to another transfer_sequence = [] current_node = to_node while current_node != self.root: if current_node.parent is None: return None transfer_sequence.append(current_node.input_to_parent) current_node = current_node.parent transfer_sequence.reverse() return tuple(transfer_sequence) def get_size(self): return self.root.id_counter # Functions related to finding new basis and frontier states def update_frontier_and_basis(self): # Updates the frontier to basis map, promotes a frontier state and checks for consistency self.update_frontier_to_basis_dict() self.promote_frontier_state() self.check_frontier_consistency() self.update_frontier_to_basis_dict() def update_basis_candidates(self, frontier_state): """ Updates the basis candidates for the specified frontier state. Removes basis states that are deemed apart from the frontier state. """ if frontier_state not in self.frontier_to_basis_dict: print( f"Warning: {frontier_state} not found in frontier_to_basis_dict.") return basis_list = self.frontier_to_basis_dict[frontier_state] self.frontier_to_basis_dict[frontier_state] = [basis_state for basis_state in basis_list if not Apartness.states_are_apart(frontier_state, basis_state, self)] def update_frontier_to_basis_dict(self): """ Checks for basis candidates (basis states with the same behavior) for each frontier state. If a frontier state and a basis state are "apart", the basis state is removed from the basis list. """ for frontier_state, basis_list in self.frontier_to_basis_dict.items(): self.frontier_to_basis_dict[frontier_state] = [ basis_state for basis_state in basis_list if not Apartness.states_are_apart(frontier_state, basis_state, self)] def promote_frontier_state(self): """ Searches for an isolated frontier state and adds it to the basis states if it is not associated with another basis state """ for iso_frontier_state, basis_list in self.frontier_to_basis_dict.items(): if not basis_list: new_basis = iso_frontier_state self.basis.append(new_basis) self.frontier_to_basis_dict.pop(new_basis) for frontier_state, new_basis_list in self.frontier_to_basis_dict.items(): if not Apartness.states_are_apart(new_basis, frontier_state, self): new_basis_list.append(new_basis) break def check_frontier_consistency(self): """ Checks if all the states are correctly defined and creates new frontier states when possible """ for basis_state in self.basis: for i in self.alphabet: maybe_frontier = basis_state.get_successor(i) if maybe_frontier is None or maybe_frontier in self.basis or maybe_frontier in self.frontier_to_basis_dict: continue self.frontier_to_basis_dict[maybe_frontier] = [ new_basis_state for new_basis_state in self.basis if not Apartness.states_are_apart(new_basis_state, maybe_frontier, self) ] def is_observation_tree_adequate(self): # Check if the frontier state have only 1 basis candidate, and if all basis # states have some output for every input. self.check_frontier_consistency() for _, basis_list in self.frontier_to_basis_dict.items(): if len(basis_list) != 1: return False for basis_state in self.basis: for inp in self.alphabet: if self.automaton_type == 'mealy': if basis_state.get_output(inp) is None: return False else: if basis_state.get_successor(inp) is None: return False return True def make_basis_complete(self): # Explore new frontier states and adding them to the frontier to basis map for basis_state in self.basis: for inp in self.alphabet: if basis_state.get_successor(inp) is None: self.explore_frontier(basis_state, inp) new_frontier = basis_state.get_successor(inp) basis_candidates = self.find_basis_candidates(new_frontier) self.frontier_to_basis_dict[new_frontier] = basis_candidates def find_basis_candidates(self, new_frontier): return { new_basis_state for new_basis_state in self.basis if not Apartness.states_are_apart(new_basis_state, new_frontier, self) } def explore_frontier(self, basis_state, inp): # Explores a specific frontier state (basis state + input) by passing a query to the SUL if self.extension_rule is None or (self.extension_rule == "SepSeq" and len(self.basis) == 1): inputs = self.get_transfer_sequence(self.root, basis_state) inputs.append(inp) outputs = self.sul.query(inputs) self.insert_observation(inputs, outputs) elif self.extension_rule == "ADS": ads = Ads(self, self.basis) ads_in, ads_out = self.adaptive_output_query( self.get_transfer_sequence(self.root, basis_state), inp, ads) self.insert_observation(ads_in, ads_out) elif self.extension_rule == "SepSeq": iterator = iter(self.basis) basis_two = next(iterator) if basis_two == basis_state: basis_two = next(iterator) witness = self.get_or_compute_witness(basis_state, basis_two) inputs = self.get_transfer_sequence(self.root, basis_state) inputs.append(inp) inputs.extend(witness) outputs = self.sul.query(inputs) self.insert_observation(inputs, outputs) def adaptive_output_query(self, prefix, infix, ads): # Adds input to the prefix and calls the base function prefix.append(infix) return self.adaptive_output_query_base(prefix, ads) def adaptive_output_query_base(self, prefix, ads): # Query the tree for a result, if unsuccessful query the SUL and update the tree from_node = self.get_successor(prefix) if from_node: tree_in, tree_out = self._answer_ads_from_tree(ads, from_node) ads.reset_to_root() if tree_out: inputs = prefix outputs = self.get_observation(prefix) inputs.extend(tree_in) outputs.extend(tree_out) return inputs, outputs sul_in, sul_out = self.sul.adaptive_query(prefix, ads) if sul_out: outputs = sul_out self.insert_observation(sul_in, outputs) return sul_in, outputs def _answer_ads_from_tree(self, ads, from_node): # searches the tree based on the inputs returning the inputs/outputs when all ads inputs are used prev_output = None inputs_sent = [] outputs_received = [] current_node = from_node while True: next_input = ads.next_input(prev_output) if next_input is None: break inputs_sent.append(next_input) if self.automaton_type == 'mealy': output_from_node = current_node.get_output(next_input) successor_from_node = current_node.get_successor(next_input) if successor_from_node is None: return None, None else: successor_from_node = current_node.get_successor(next_input) if successor_from_node is None: return None, None output_from_node = successor_from_node.output prev_output = output_from_node outputs_received.append(output_from_node) current_node = successor_from_node ads.reset_to_root() return inputs_sent, outputs_received def get_or_compute_witness(self, state_one, state_two): """ Get witness by checking cache and computing it otherwise. Only add pairs (a,b) with a < b. """ if state_one.id < state_two.id: pair = (state_one.id, state_two.id) else: pair = (state_two.id, state_one.id) if pair in self.witness_cache: return self.witness_cache.get(pair) witness = Apartness.compute_witness(state_one, state_two, self) self.witness_cache[pair] = witness return witness def make_frontiers_identified(self): # Loop over all frontier states to identify them for frontier_state in self.frontier_to_basis_dict: self.identify_frontier(frontier_state) def identify_frontier(self, frontier_state): # Identify a specific frontier state if frontier_state not in self.frontier_to_basis_dict: raise Exception( f"Warning: {frontier_state} not found in frontier_to_basis_dict.") self.update_basis_candidates(frontier_state) old_candidate_size = len( self.frontier_to_basis_dict.get(frontier_state)) if old_candidate_size < 2: return if self.separation_rule == "SepSeq" or old_candidate_size == 2: inputs, outputs = self._identify_frontier_sepseq(frontier_state) else: inputs, outputs = self._identify_frontier_ads(frontier_state) self.insert_observation(inputs, outputs) self.update_basis_candidates(frontier_state) if len(self.frontier_to_basis_dict.get(frontier_state)) == old_candidate_size: raise RuntimeError("Identification did not increase the norm") def _identify_frontier_sepseq(self, frontier_state): # Specifically identify frontier states using separating sequences basis_candidates = self.frontier_to_basis_dict.get(frontier_state) basis_one = basis_candidates[0] basis_two = basis_candidates[1] witness = self.get_or_compute_witness(basis_one, basis_two) inputs = self.get_transfer_sequence(self.root, frontier_state) inputs.extend(witness) outputs = self.sul.query(inputs) return inputs, outputs def _identify_frontier_ads(self, frontier_state): # Specifically identify frontier states using ADS basis_candidates = self.frontier_to_basis_dict.get(frontier_state) ads = Ads(self, basis_candidates) ads.reset_to_root() return self.adaptive_output_query_base(self.get_transfer_sequence(self.root, frontier_state), ads) def construct_hypothesis_states(self): # Construct the hypothesis states from the basis self.states_dict = dict() state_counter = 0 for basis_state in self.basis: state_id = f's{state_counter}' if self.automaton_type == 'dfa': self.states_dict[basis_state] = DfaState(state_id) self.states_dict[basis_state].is_accepting = basis_state.output elif self.automaton_type == 'moore': self.states_dict[basis_state] = MooreState( state_id, output=basis_state.output) else: self.states_dict[basis_state] = MealyState(state_id) state_counter += 1 def construct_hypothesis_transitions(self): # Construct the hypothesis transitions from the basis, frontier and basis to frontier mapping for basis_state in self.basis: for input_val in self.alphabet: # set transition successor = basis_state.get_successor(input_val) if successor in self.frontier_to_basis_dict: # set successor for frontier state candidates = self.frontier_to_basis_dict[successor] if len(candidates) > 1: raise RuntimeError( "Multiple basis candidates for a single frontier state.") successor = next(iter(candidates)) if successor not in self.states_dict: raise RuntimeError( "Successor is not in the basisToStateMap.") destination = self.states_dict[successor] self.states_dict[basis_state].transitions[input_val] = destination if self.automaton_type == 'mealy': self.states_dict[basis_state].output_fun[input_val] = basis_state.get_output( input_val) def construct_hypothesis(self): # Construct a hypothesis (Mealy Machine) based on the observation tree self.construct_hypothesis_states() self.construct_hypothesis_transitions() automaton_class = {'dfa': Dfa, 'mealy': MealyMachine, 'moore': MooreMachine} hypothesis = automaton_class[self.automaton_type]( self.states_dict[self.root], list(self.states_dict.values())) hypothesis.compute_prefixes() hypothesis.characterization_set = hypothesis.compute_characterization_set(raise_warning=False) return hypothesis def build_hypothesis(self): # Builds the hypothesis which will be sent to the SUL and checks consistency while True: self.make_observation_tree_adequate() hypothesis = self.construct_hypothesis() counter_example = Apartness.compute_witness_in_tree_and_hypothesis_states(self, self.root, hypothesis.initial_state) if not counter_example: return hypothesis cex_outputs = self.get_observation(counter_example) self.process_counter_example(hypothesis, counter_example, cex_outputs) def make_observation_tree_adequate(self): # Updates the frontier and basis based on extension and separation rule self.update_frontier_and_basis() while not self.is_observation_tree_adequate(): self.make_basis_complete() self.make_frontiers_identified() self.promote_frontier_state() # Counterexample Processing def process_counter_example(self, hypothesis, cex_inputs, cex_outputs): """ Inserts the counter example into the observation tree and searches for the input-output sequence which is different """ self.insert_observation(cex_inputs, cex_outputs) hyp_outputs = hypothesis.compute_output_seq( hypothesis.initial_state, cex_inputs) prefix_index = self._get_counter_example_prefix_index( cex_outputs, hyp_outputs) self._process_binary_search( hypothesis, cex_inputs[:prefix_index], cex_outputs[:prefix_index]) def _get_counter_example_prefix_index(self, cex_outputs, hyp_outputs): """ Checks at which index the output functions differ """ for index in range(len(cex_outputs)): if cex_outputs[index] != hyp_outputs[index]: return index raise RuntimeError("counterexample and hypothesis outputs are equal") def _process_binary_search(self, hypothesis, cex_inputs, cex_outputs): """ use binary search on the counter example to compute a witness between the real system and the hypothesis """ tree_node = self.get_successor(cex_inputs) self.update_frontier_and_basis() if tree_node in self.frontier_to_basis_dict or tree_node in self.basis: return hyp_state = self._get_automaton_successor( hypothesis, hypothesis.initial_state, cex_inputs) hyp_node = list(self.states_dict.keys())[list( self.states_dict.values()).index(hyp_state)] prefix = [] current_state = self.root for input in cex_inputs: if current_state in self.frontier_to_basis_dict: break current_state = current_state.get_successor(input) prefix.append(input) h = (len(prefix) + len(cex_inputs)) // 2 sigma1 = list(cex_inputs[:h]) sigma2 = list(cex_inputs[h:]) hyp_state_p = self._get_automaton_successor( hypothesis, hypothesis.initial_state, sigma1) hyp_node_p = list(self.states_dict.keys())[list( self.states_dict.values()).index(hyp_state_p)] hyp_p_access = self.get_transfer_sequence(self.root, hyp_node_p) witness = Apartness.compute_witness(tree_node, hyp_node, self) if witness is None: raise RuntimeError("Binary search: There should be a witness") query_inputs = hyp_p_access + sigma2 + witness query_outputs = self.sul.query(query_inputs) self.insert_observation(query_inputs, query_outputs) tree_node_p = self.get_successor(sigma1) witness_p = Apartness.compute_witness(tree_node_p, hyp_node_p, self) if witness_p is not None: self._process_binary_search(hypothesis, sigma1, cex_outputs[:h]) else: new_inputs = list(hyp_p_access) + sigma2 self._process_binary_search( hypothesis, new_inputs, query_outputs[:len(new_inputs)]) def _get_automaton_successor(self, automaton, from_state, inputs): automaton.current_state = from_state for inp in inputs: automaton.current_state = automaton.current_state.transitions[inp] return automaton.current_state ================================================ FILE: aalpy/learning_algs/deterministic/__init__.py ================================================ ================================================ FILE: aalpy/learning_algs/deterministic_passive/ClassicRPNI.py ================================================ import time from bisect import insort from aalpy.learning_algs.deterministic_passive.rpni_helper_functions import to_automaton, createPTA, \ check_sequence, extract_unique_sequences class ClassicRPNI: def __init__(self, data, automaton_type, print_info=True): self.data = data self.automaton_type = automaton_type self.print_info = print_info pta_construction_start = time.time() self.root_node = createPTA(data, automaton_type) self.test_data = extract_unique_sequences(self.root_node, automaton_type) if self.print_info: print(f'PTA Construction Time: {round(time.time() - pta_construction_start, 2)}') def run_rpni(self): start_time = time.time() red = [self.root_node] blue = list(red[0].children.values()) while blue: lex_min_blue = min(list(blue)) merged = False for red_state in red: if not red_state.compatible_outputs(lex_min_blue): continue merge_candidate = self._merge(red_state, lex_min_blue, copy_nodes=True) if self._compatible(merge_candidate): self._merge(red_state, lex_min_blue) merged = True break if not merged: insort(red, lex_min_blue) if self.print_info: print(f'\rCurrent automaton size: {len(red)}', end="") blue.clear() for r in red: for c in r.children.values(): if c not in red: blue.append(c) if self.print_info: print(f'\nRPNI Learning Time: {round(time.time() - start_time, 2)}') print(f'RPNI Learned {len(red)} state automaton.') assert sorted(red, key=lambda x: len(x.prefix)) == red return to_automaton(red, self.automaton_type) def _compatible(self, root_node): """ Check if current model is compatible with the data. """ for sequence in self.test_data: if not check_sequence(root_node, sequence, automaton_type=self.automaton_type): return False return True def _merge(self, red_node, lex_min_blue, copy_nodes=False): """ Merge two states and return the root node of resulting model. """ root_node = self.root_node.copy() if copy_nodes else self.root_node lex_min_blue = lex_min_blue.copy() if copy_nodes else lex_min_blue red_node_in_tree = root_node for p in red_node.prefix: red_node_in_tree = red_node_in_tree.children[p] to_update = root_node for p in lex_min_blue.prefix[:-1]: to_update = to_update.children[p] to_update.children[lex_min_blue.prefix[-1]] = red_node_in_tree if self.automaton_type != 'mealy': self._fold(red_node_in_tree, lex_min_blue) else: self._fold_mealy(red_node_in_tree, lex_min_blue) return root_node def _fold(self, red_node, blue_node): # Change the output of red only to concrete output, ignore None red_node.output = blue_node.output if blue_node.output is not None else red_node.output for i in blue_node.children.keys(): if i in red_node.children.keys(): self._fold(red_node.children[i], blue_node.children[i]) else: red_node.children[i] = blue_node.children[i] def _fold_mealy(self, red_node, blue_node): for i, o in blue_node.output.items(): red_node.output[i] = o for i in blue_node.children.keys(): if i in red_node.children.keys(): self._fold_mealy(red_node.children[i], blue_node.children[i]) else: red_node.children[i] = blue_node.children[i] ================================================ FILE: aalpy/learning_algs/deterministic_passive/GsmRPNI.py ================================================ import time from collections import deque from aalpy.learning_algs.deterministic_passive.rpni_helper_functions import to_automaton, RpniNode, createPTA class GsmRPNI: def __init__(self, data, automaton_type, print_info=True): self.data = data self.final_automaton_type = automaton_type self.automaton_type = automaton_type if automaton_type != 'dfa' else 'moore' self.print_info = print_info pta_construction_start = time.time() self.root_node = createPTA(data, self.automaton_type) self.log = [] if self.print_info: print(f'PTA Construction Time: {round(time.time() - pta_construction_start, 2)}') def run_rpni(self): start_time = time.time() # sorted list of states already considered red_states = [self.root_node] # used to get the minimal non-red state blue_states = list(red_states[0].children.values()) while blue_states: blue_state = min(list(blue_states)) partition = None red_state = None for red_state in red_states: partition = self._partition_from_merge(red_state, blue_state) if partition is not None: break if partition is None: self.log.append(["promote", (blue_state.prefix,)]) red_states.append(blue_state) if self.print_info: print(f'\rCurrent automaton size: {len(red_states)}', end="") else: self.log.append(["merge", (red_state.prefix, blue_state.prefix)]) # use the partition for merging for node in partition.keys(): block = partition[node] # assert RpniNode.compatible(node, block) node.output = block.output node.children = block.children node = self.root_node.get_child_by_prefix(blue_state.prefix[:-1]) node.children[blue_state.prefix[-1]] = red_state blue_states.clear() for r in red_states: for c in r.children.values(): if c not in red_states: blue_states.append(c) if self.print_info: print(f'\nRPNI-GSM Learning Time: {round(time.time() - start_time, 2)}') print(f'RPNI-GSM Learned {len(red_states)} state automaton.') return to_automaton(red_states, self.final_automaton_type) def _partition_from_merge(self, red: RpniNode, blue: RpniNode): """ Compatibility check based on partitions """ partitions = dict() q = deque() q.append((red, blue)) while len(q) != 0: red, blue = q.popleft() def get_partition(node: RpniNode): if node not in partitions: p = node.shallow_copy() partitions[node] = p else: p = partitions[node] return p partition = get_partition(red) if not RpniNode.compatible_outputs(partition, blue): return None if self.automaton_type == 'moore' and partition.output is None: partition.output = blue.output if self.automaton_type == 'mealy': for key in filter(lambda k: k not in partition.output or partition.output[k] is None, blue.output): partition.output[key] = blue.output[key] partitions[blue] = partition for symbol, blue_child in blue.children.items(): if symbol in partition.children.keys(): q.append((partition.children[symbol], blue_child)) else: # blue_child is blue after merging if there is a red state in the partition partition.children[symbol] = blue_child return partitions ================================================ FILE: aalpy/learning_algs/deterministic_passive/PAPNI.py ================================================ from aalpy.utils import is_balanced from aalpy.automata.Vpa import vpa_from_dfa_representation def run_PAPNI(data, vpa_alphabet, algorithm='edsm', print_info=True): """ Run PAPNI, a deterministic passive model learning algorithm of deterministic pushdown automata. Resulting model conforms to the provided data. Args: data: sequence of input sequences and corresponding label. Eg. [[(i1,i2,i3, ...), label], ...] vpa_alphabet: grouping of alphabet elements to call symbols, return symbols, and internal symbols. Call symbols push to stack, return symbols pop from stack, and internal symbols do not affect the stack. algorithm: either 'gsm' for classic RPNI or 'edsm' for evidence driven state merging variant of RPNI print_info: print learning progress and runtime information Returns: VPA conforming to the data, or None if data is non-deterministic. """ from aalpy.learning_algs import run_EDSM, run_RPNI assert algorithm in {'gsm', 'classic', 'edsm'} # preprocess input sequences to keep track of stack papni_data = [] for input_seq, label in data: # if input sequance is not balanced we do not consider it (it would lead to error state anyway) if not is_balanced(input_seq, vpa_alphabet): continue # for each sequance keep track of the stack, and when pop/return element is observed encode it along with the # current top of stack. This keeps track of stack during execution processed_sequance = [] stack = [] for input_symbol in input_seq: input_element = input_symbol # if call/push symbol push to stack if input_symbol in vpa_alphabet.call_alphabet: stack.append(input_symbol) # if return/pop symbol pop from stack and add it to the input data if input_symbol in vpa_alphabet.return_alphabet: top_of_stack = stack.pop() input_element = (input_symbol, top_of_stack) processed_sequance.append(input_element) papni_data.append((processed_sequance, label)) # instantiate and run PAPNI as base RPNI with stack-aware data if algorithm != 'edsm': learned_model = run_RPNI(papni_data, automaton_type='dfa', algorithm=algorithm, print_info=print_info) else: learned_model = run_EDSM(papni_data, automaton_type='dfa', print_info=print_info) # convert intermediate DFA representation to VPA learned_model = vpa_from_dfa_representation(learned_model, vpa_alphabet) return learned_model ================================================ FILE: aalpy/learning_algs/deterministic_passive/RPNI.py ================================================ from typing import Union from aalpy.base import DeterministicAutomaton from aalpy.learning_algs.deterministic_passive.ClassicRPNI import ClassicRPNI from aalpy.learning_algs.deterministic_passive.GsmRPNI import GsmRPNI def run_RPNI(data, automaton_type, algorithm='gsm', input_completeness=None, print_info=True) -> Union[DeterministicAutomaton, None]: """ Run RPNI, a deterministic passive model learning algorithm. Resulting model conforms to the provided data. For more information on RPNI, check out AALpy' Wiki: https://github.com/DES-Lab/AALpy/wiki/RPNI---Passive-Deterministic-Automata-Learning Args: data: sequence of input sequences and corresponding label. Eg. [[(i1,i2,i3, ...), label], ...] automaton_type: either 'dfa', 'mealy', 'moore'. Note that for 'mealy' machine learning, data has to be prefix-closed. algorithm: either 'gsm' (generalized state merging) or 'classic' for base RPNI implementation. GSM is much faster and less resource intensive. input_completeness: either None, 'sink_state', or 'self_loop'. If None, learned model could be input incomplete, sink_state will lead all undefined inputs form some state to the sink state, whereas self_loop will simply create a self loop. In case of Mealy learning output of the added transition will be 'epsilon'. print_info: print learning progress and runtime information Returns: Model conforming to the data, or None if data is non-deterministic. """ assert algorithm in {'gsm', 'classic'} assert automaton_type in {'dfa', 'mealy', 'moore'} assert input_completeness in {None, 'self_loop', 'sink_state'} if algorithm == 'classic': rpni = ClassicRPNI(data, automaton_type, print_info) else: rpni = GsmRPNI(data, automaton_type, print_info) if rpni.root_node is None: print('Data provided to RPNI is not deterministic. Ensure that the data is deterministic, ' 'or consider using Alergia.') return None learned_model = rpni.run_rpni() if not learned_model.is_input_complete(): if not input_completeness: if print_info: print('Warning: Learned Model is not input complete (inputs not defined for all states). ' 'Consider calling .make_input_complete()') else: if print_info: print(f'Learned model was not input complete. Adapting it with {input_completeness} transitions.') learned_model.make_input_complete(input_completeness) return learned_model ================================================ FILE: aalpy/learning_algs/deterministic_passive/__init__.py ================================================ ================================================ FILE: aalpy/learning_algs/deterministic_passive/active_RPNI.py ================================================ from abc import ABC, abstractmethod from random import randint, choice from aalpy.learning_algs import run_RPNI from aalpy.utils import convert_i_o_traces_for_RPNI class RpniActiveSampler(ABC): """ Abstract class whose implementations are used to provide samples for active passive learning. """ @abstractmethod def sample(self, sul, model): """ Abstract method implementing sampling strategy. Args: sul: system under learning model: current learned model Returns: Data to be added to the data set for the passive RPNI learning in its data-format. """ pass class RandomWordSampler(RpniActiveSampler): def __init__(self, num_walks, min_walk_len, max_walk_len): self.num_walks = num_walks self.min_walk_len = min_walk_len self.max_walk_len = max_walk_len def sample(self, sul, model): input_al = list({el for s in model.states for el in s.transitions.keys()}) samples = [] for _ in range(self.num_walks): walk_len = randint(self.min_walk_len, self.max_walk_len) random_walk = tuple(choice(input_al) for _ in range(walk_len)) outputs = sul.query(random_walk) samples.append(list(zip(random_walk, outputs))) samples = convert_i_o_traces_for_RPNI(samples) return samples def run_active_RPNI(data, sul, sampler, n_iter, automaton_type, print_info=True): model = None for i in range(n_iter): if print_info: print(f'-------------Active RPNI Iteration: {i}-------------') model = run_RPNI(data, automaton_type=automaton_type, print_info=print_info) new_samples = sampler.sample(sul, model) data.extend(new_samples) return model ================================================ FILE: aalpy/learning_algs/deterministic_passive/rpni_helper_functions.py ================================================ import pickle from functools import total_ordering @total_ordering class RpniNode: __slots__ = ['output', 'children', 'prefix', "type"] def __init__(self, output=None, children=None, automaton_type='moore'): if output is None and automaton_type == 'mealy': output = dict() if children is None: children = dict() self.output = output self.children = children self.prefix = () self.type = automaton_type def shallow_copy(self): output = self.output if self.type != 'mealy' else dict(self.output) return RpniNode(output, dict(self.children), self.type) def copy(self): return pickle.loads(pickle.dumps(self, -1)) def __lt__(self, other): return len(self.prefix) < len(other.prefix) # return (len(self.prefix), self.prefix) < (len(other.prefix), other.prefix) def __eq__(self, other): return self.prefix == other.prefix def __hash__(self): return id(self) # TODO This is a hack def compatible_outputs(self, other): """ Only allow merging of states that have same output(s). """ # None is compatible with everything if self.type != 'mealy': return self.output == other.output or self.output is None or other.output is None else: red_io = {i: o for i, o in self.output.items()} blue_io = {i: o for i, o in other.output.items()} for common_i in set(red_io.keys()).intersection(blue_io.keys()): if red_io[common_i] != blue_io[common_i]: return False return True def get_child_by_prefix(self, prefix): node = self for symbol in prefix: node = node.children[symbol] return node def check_sequence(root_node, seq, automaton_type): """ Checks whether each sequence in the dataset is valid in the current automaton. """ curr_node = root_node # Check initial output for Moore machines and the like if automaton_type != 'mealy' and len(seq) != 0: if seq[0] is not None and seq[0] != root_node.output: return False seq = seq[1:] for i, o in seq: if automaton_type == 'mealy': if i not in curr_node.output or o is not None and curr_node.output[i] != o: return False curr_node = curr_node.children[i] else: # For dfa and moore, check if outputs are the same, iff output in test data is concrete (not None) curr_node = curr_node.children[i] if o is not None and curr_node.output != o: return False return True def createPTA(data, automaton_type): data.sort(key=lambda x: len(x[0])) root_node = RpniNode(automaton_type=automaton_type) for seq, label in data: curr_node = root_node for idx, symbol in enumerate(seq): if symbol not in curr_node.children.keys(): node = RpniNode(automaton_type=automaton_type) node.prefix = curr_node.prefix + (symbol,) curr_node.children[symbol] = node if automaton_type == 'mealy' and idx == len(seq) - 1: if symbol not in curr_node.output: curr_node.output[symbol] = label if curr_node.output[symbol] != label: return None curr_node = curr_node.children[symbol] if automaton_type == 'moore' or automaton_type == 'dfa': if curr_node.output is None: curr_node.output = label if curr_node.output != label: return None return root_node def extract_unique_sequences(root_node, automaton_type): def get_leaf_nodes(root): leaves = [] def _get_leaf_nodes(node): if node is not None: if len(node.children.keys()) == 0: leaves.append(node) for n in node.children.values(): _get_leaf_nodes(n) _get_leaf_nodes(root) return leaves leaf_nodes = get_leaf_nodes(root_node) paths = [] for node in leaf_nodes: seq = [] if automaton_type == 'mealy' else [root_node.output] curr_node = root_node for i in node.prefix: curr_node = curr_node.children[i] if automaton_type == 'mealy': seq.append((i, curr_node.output.get(i))) else: seq.append((i, curr_node.output)) paths.append(seq) return paths def to_automaton(red, automaton_type): from aalpy.automata import DfaState, Dfa, MooreMachine, MooreState, MealyMachine, MealyState if automaton_type == 'dfa': state, automaton = DfaState, Dfa elif automaton_type == 'moore': state, automaton = MooreState, MooreMachine else: state, automaton = MealyState, MealyMachine initial_state = None prefix_state_map = {} for i, r in enumerate(red): if automaton_type == 'moore' or automaton_type == 'dfa': # make sure all None states (incomplete information to deduce output) are set to False for DFAs if automaton_type == 'dfa' and r.output is None: r.output = False prefix_state_map[r.prefix] = state(f's{i}', r.output) else: prefix_state_map[r.prefix] = state(f's{i}') if i == 0: initial_state = prefix_state_map[r.prefix] for r in red: for i, c in r.children.items(): if automaton_type == 'moore' or automaton_type == 'dfa': prefix_state_map[r.prefix].transitions[i] = prefix_state_map[c.prefix] else: prefix_state_map[r.prefix].transitions[i] = prefix_state_map[c.prefix] prefix_state_map[r.prefix].output_fun[i] = r.output[i] if i in r.output else None return automaton(initial_state, list(prefix_state_map.values())) def visualize_pta(root_node, path='pta.pdf'): from pydot import Dot, Node, Edge graph = Dot('fpta', graph_type='digraph') graph.add_node(Node(str(root_node.prefix), label=f'{root_node.output}')) queue = [root_node] visited = set() visited.add(root_node.prefix) while queue: curr = queue.pop(0) for i, c in curr.children.items(): if c.prefix not in visited: graph.add_node(Node(str(c.prefix), label=f'{c.output}')) graph.add_edge(Edge(str(curr.prefix), str(c.prefix), label=f'{i}')) if c.prefix not in visited: queue.append(c) visited.add(c.prefix) graph.add_node(Node('__start0', shape='none', label='')) graph.add_edge(Edge('__start0', str(root_node.prefix), label='')) graph.write(path=path, format='pdf') ================================================ FILE: aalpy/learning_algs/general_passive/GeneralizedStateMerging.py ================================================ import functools from collections import deque from typing import Dict, Tuple, Callable, List, Optional from aalpy.learning_algs.general_passive.GsmNode import GsmNode, OutputBehavior, TransitionBehavior, TransitionInfo, \ OutputBehaviorRange, TransitionBehaviorRange, intersection_iterator, unknown_output, detect_data_format from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import ScoreCalculation, hoeffding_compatibility # TODO add option for making checking of futures and partition non mutual exclusive? # Easiest done by adding a new method / field to ScoreCalculation class Partitioning: def __init__(self, red: GsmNode, blue: GsmNode): self.red: GsmNode = red self.blue: GsmNode = blue self.score = False self.red_mapping: Dict[GsmNode, GsmNode] = dict() self.full_mapping: Dict[GsmNode, GsmNode] = dict() class Instrumentation: def __init__(self): pass def reset(self, gsm: 'GeneralizedStateMerging'): pass def pta_construction_done(self, root: GsmNode): pass def log_promote(self, node: GsmNode): pass def log_merge(self, part: Partitioning): pass def learning_done(self, root: GsmNode): pass class GeneralizedStateMerging: def __init__(self, *, output_behavior: OutputBehavior = "moore", transition_behavior: TransitionBehavior = "deterministic", score_calc: ScoreCalculation = None, pta_preprocessing: Callable[[GsmNode], GsmNode] = None, postprocessing: Callable[[GsmNode], GsmNode] = None, compatibility_on_pta: bool = False, compatibility_on_futures: bool = False, node_order: Callable[[GsmNode, GsmNode], bool] = None, consider_only_min_blue=False, depth_first=False): if output_behavior not in OutputBehaviorRange: raise ValueError(f"invalid output behavior {output_behavior}. should be in {OutputBehaviorRange}") self.output_behavior: OutputBehavior = output_behavior if transition_behavior not in TransitionBehaviorRange: raise ValueError(f"invalid transition behavior {transition_behavior}. should be in {TransitionBehaviorRange}") self.transition_behavior: TransitionBehavior = transition_behavior if score_calc is None: if transition_behavior == "deterministic": score_calc = ScoreCalculation() elif transition_behavior == "nondeterministic" : raise ValueError("Missing score_calc for nondeterministic transition behavior. No default available.") elif transition_behavior == "stochastic" : score_calc = ScoreCalculation(hoeffding_compatibility(0.005, compatibility_on_pta)) self.score_calc: ScoreCalculation = score_calc if node_order is None: self.node_order = GsmNode.default_order else: self.node_order = functools.cmp_to_key(lambda a, b: -1 if node_order(a, b) else 1) self.pta_preprocessing = pta_preprocessing or (lambda x: x) self.postprocessing = postprocessing or (lambda x: x) self.compatibility_on_pta = compatibility_on_pta self.compatibility_on_futures = compatibility_on_futures self.consider_only_min_blue = consider_only_min_blue self.depth_first = depth_first def compute_local_compatibility(self, a: GsmNode, b: GsmNode): if self.output_behavior == "moore" and not GsmNode.moore_compatible(a, b): return False if self.transition_behavior == "deterministic" and not GsmNode.deterministic_compatible(a, b): return False return self.score_calc.local_compatibility(a, b) # TODO: make more generic by adding the option to use a different algorithm than red blue # for selecting potential merge candidates. Maybe using inheritance with abstract `run`. def run(self, data, convert=True, instrumentation: Instrumentation=None, data_format=None): if instrumentation is None: instrumentation = Instrumentation() instrumentation.reset(self) if data_format is None: data_format = detect_data_format(data) if data_format == "labeled_sequences" and self.transition_behavior != "deterministic": raise ValueError("learning from labeled_sequences is not possible for nondeterministic systems") if data_format == "traces" and self.transition_behavior == "deterministic": print("learning deterministic systems from (output) traces only. this rarely makes sense. is `data_format` set correctly?") root = GsmNode.createPTA(data, self.output_behavior, data_format) root = self.pta_preprocessing(root) instrumentation.pta_construction_done(root) instrumentation.log_promote(root) if self.transition_behavior == "deterministic": if not root.is_deterministic(): raise ValueError("required deterministic automaton but input data is nondeterministic") # sorted list of states already considered red_states = [root] partition_candidates: Dict[Tuple[GsmNode, GsmNode], Partitioning] = dict() while True: # sort states. states are always sorted using default order on original prefix if self.node_order is not GsmNode.default_order: red_states.sort(key=self.node_order) # get blue states blue_states = [] for r in red_states: for _, _, t in r.transition_iterator(): c = t.target if c in red_states: continue blue_states.append(c) if self.consider_only_min_blue and self.node_order is GsmNode.default_order: break # no blue states left -> done if len(blue_states) == 0: break if self.consider_only_min_blue: # does it make sense to check the score function here? blue_states = [min(blue_states, key=self.node_order)] if self.node_order is not GsmNode.default_order: blue_states.sort(key=self.node_order) # loop over blue states promotion = False for blue_state in blue_states: # FUTURE: Parallelize # FUTURE: Save partitions? # calculate partitions resulting from merges with red states if necessary current_candidates: Dict[GsmNode, Partitioning] = dict() perfect_partitioning = None red_state = None for red_state in red_states: partition = partition_candidates.get((red_state, blue_state)) if partition is None: partition = self._partition_from_merge(red_state, blue_state) if partition.score is True: perfect_partitioning = partition break current_candidates[red_state] = partition assert red_state is not None # partition with perfect score found: don't consider anything else if perfect_partitioning: partition_candidates = {(red_state, blue_state): perfect_partitioning} break # no merge candidates for this blue state -> promote if all(part.score is False for part in current_candidates.values()): red_states.append(blue_state) instrumentation.log_promote(blue_state) promotion = True break # update tracking dict with new candidates new_candidates = (((red, blue_state), part) for red, part in current_candidates.items() if part.score is not False) partition_candidates.update(new_candidates) # a state was promoted -> don't clear candidates if promotion: continue # find best partitioning and clear candidates best_candidate = max(partition_candidates.values(), key=lambda part: part.score) for real_node, partition_node in best_candidate.red_mapping.items(): real_node.transitions = partition_node.transitions real_node.predecessor = partition_node.predecessor real_node.prefix_access_pair = partition_node.prefix_access_pair instrumentation.log_merge(best_candidate) # FUTURE: optimizations for compatibility tests where merges can be orthogonal # FUTURE: caching for aggregating compatibility tests partition_candidates.clear() instrumentation.learning_done(root) root = self.postprocessing(root) if convert: root = root.to_automaton(self.output_behavior, self.transition_behavior) return root def _check_futures(self, red: GsmNode, blue: GsmNode) -> bool: q: deque[Tuple[GsmNode, GsmNode]] = deque([(red, blue)]) pop = q.pop if self.depth_first else q.popleft while len(q) != 0: red, blue = pop() if self.compute_local_compatibility(red, blue) is False: return False for in_sym, red_trans, blue_trans in intersection_iterator(red.transitions, blue.transitions): for out_sym, red_child, blue_child in intersection_iterator(red_trans, blue_trans): if self.compatibility_on_pta: if blue_child.original_count == 0 or red_child.original_count == 0: continue q.append((red_child.original_target, blue_child.original_target)) else: q.append((red_child.target, blue_child.target)) return True def _partition_from_merge(self, red: GsmNode, blue: GsmNode) -> Partitioning: # Compatibility check based on partitions. # assumes that blue is a tree and red is not reachable from blue partitioning = Partitioning(red, blue) self.score_calc.reset() if self.compatibility_on_futures: if self._check_futures(red, blue) is False: return partitioning # when compatibility is determined only by future and scores are disabled, we need not create partitions. if self.compatibility_on_futures and not self.score_calc.has_score_function(): def update_partition(red_node: GsmNode, blue_node: Optional[GsmNode]) -> GsmNode: return red_node else: def update_partition(red_node: GsmNode, blue_node: Optional[GsmNode]) -> GsmNode: p = partitioning.full_mapping.get(red_node) # could check smaller .red_mapping? if p is None: p = red_node.shallow_copy() partitioning.full_mapping[red_node] = p partitioning.red_mapping[red_node] = p if blue_node is not None: partitioning.full_mapping[blue_node] = p return p # rewire the blue node's parent blue_parent = update_partition(blue.predecessor, None) blue_in_sym, blue_out_sym = blue.prefix_access_pair blue_parent.transitions[blue_in_sym][blue_out_sym].target = red partition = update_partition(red, None) if self.output_behavior == "moore": partition.resolve_unknown_prefix_output(blue_out_sym) # loop over implied merges q: deque[Tuple[GsmNode, GsmNode]] = deque([(red, blue)]) pop = q.pop if self.depth_first else q.popleft while len(q) != 0: red, blue = pop() partition = update_partition(red, blue) if not self.compatibility_on_futures: if self.compute_local_compatibility(partition, blue) is False: return partitioning # create implied merges for all common successors for in_sym, blue_transitions in blue.transitions.items(): partition_transitions = partition.transitions[in_sym] for out_sym, blue_transition in blue_transitions.items(): partition_transition = partition_transitions.get(out_sym) # handle unknown output if partition_transition is None and len(partition_transitions) != 0: if out_sym is unknown_output: assert len(partition_transitions) == 1 partition_transition = list(partition_transitions.values())[0] if unknown_output in partition_transitions: assert len(partition_transitions) == 1 partition_transition = partition_transitions.pop(unknown_output) partition_transitions[out_sym] = partition_transition # re-hook access pair succ_part = update_partition(partition_transition.target, None) if self.output_behavior == "moore" or succ_part.predecessor is red: succ_part.resolve_unknown_prefix_output(out_sym) # add pairs if partition_transition is not None: q.append((partition_transition.target, blue_transition.target)) partition_transition.count += blue_transition.count else: # blue child is blue after merging if there is a red state in blue's partition partition_transition = TransitionInfo(blue_transition.target, blue_transition.count, None, 0) partition_transitions[out_sym] = partition_transition # update predecessor of blue child blue_target_partition = update_partition(blue_transition.target, None) blue_target_partition.predecessor = red partitioning.score = self.score_calc.score_function(partitioning.full_mapping) return partitioning def run_GSM(data: list, *, output_behavior: OutputBehavior = "moore", transition_behavior: TransitionBehavior = "deterministic", score_calc: ScoreCalculation = None, pta_preprocessing: Callable[[GsmNode], GsmNode] = None, postprocessing: Callable[[GsmNode], GsmNode] = None, compatibility_on_pta: bool = False, compatibility_on_futures: bool = False, node_order: Callable[[GsmNode, GsmNode], bool] = None, consider_only_min_blue=False, depth_first=False, instrumentation=None, convert=True, data_format=None, ): """ Performs a state merging algorithm in the red-blue framework on provided data. Args: data: Data used for learning. Recorded behavior of the system. output_behavior: Specifies whether outputs are emitted by states ("moore") or transitions ("mealy"). transition_behavior: Either "deterministic", "nondeterministic" or "stochastic". score_calc: A ScoreCalculation object which determines how compatibility and merge scores are calculated. pta_preprocessing: A pre-processing function applied to the PTA. postprocessing: A postprocessing function applied to the learned automaton. compatibility_on_pta: Whether compatibility is evaluated on the PTA or the current hypothesis. compatibility_on_futures: Whether compatibility is evaluated using the futures of both states or all partition information. node_order: Order in which merge candidates are considered. Defaults to short-lex. consider_only_min_blue: Whether to consider merge candidates from all blue nodes or just a single. depth_first: Whether compatibility is checked depth- or breadth-first. instrumentation: Instrumentation object for reporting progress or debugging. convert: Whether to return a normal AALpy automaton type or a `GsmNode` object (internal representation). data_format: Whether the input is given in the form of input-output traces or labeled input traces. Returns: The learned automaton. """ # instantiate gsm gsm = GeneralizedStateMerging( output_behavior=output_behavior, transition_behavior=transition_behavior, score_calc=score_calc, pta_preprocessing=pta_preprocessing, postprocessing=postprocessing, compatibility_on_pta=compatibility_on_pta, compatibility_on_futures=compatibility_on_futures, node_order=node_order, consider_only_min_blue=consider_only_min_blue, depth_first=depth_first, ) # run the algorithm return gsm.run(data=data, instrumentation=instrumentation, convert=convert, data_format=data_format) ================================================ FILE: aalpy/learning_algs/general_passive/GsmAlgorithms.py ================================================ from typing import Dict, Union from aalpy import DeterministicAutomaton, Onfsm, NDMooreMachine from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM from aalpy.learning_algs.general_passive.Instrumentation import ProgressReport from aalpy.learning_algs.general_passive.GsmNode import GsmNode from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import ScoreCalculation, hoeffding_compatibility, \ ScoreWithKTail from aalpy.utils.HelperFunctions import dfa_from_moore def run_EDSM(data, automaton_type, input_completeness=None, print_info=True) -> Union[DeterministicAutomaton, None]: """ Run Evidence Driven State Merging. Args: data: sequence of input sequences and corresponding label. Eg. [[(i1,i2,i3, ...), label], ...] automaton_type: either 'dfa', 'mealy', 'moore'. Note that for 'mealy' machine learning, data has to be prefix-closed. input_completeness: either None, 'sink_state', or 'self_loop'. If None, learned model could be input incomplete, sink_state will lead all undefined inputs form some state to the sink state, whereas self_loop will simply create a self loop. In case of Mealy learning output of the added transition will be 'epsilon'. print_info: print learning progress and runtime information Returns: Model conforming to the data, or None if data is non-deterministic. """ assert automaton_type in {'dfa', 'mealy', 'moore'} assert input_completeness in {None, 'self_loop', 'sink_state'} print_level = ProgressReport(1) if print_info else None def EDSM_score(part: Dict[GsmNode, GsmNode]): nr_partitions = len(set(part.values())) nr_merged = len(part) return nr_merged - nr_partitions score = ScoreCalculation(score_function=EDSM_score) internal_automaton_type = 'moore' if automaton_type != 'mealy' else automaton_type learned_model = run_GSM(data, output_behavior=internal_automaton_type, transition_behavior="deterministic", score_calc=score, data_format='labeled_sequences', instrumentation=print_level) if automaton_type == 'dfa': learned_model = dfa_from_moore(learned_model) if not learned_model.is_input_complete(): if not input_completeness: if print_info: print('Warning: Learned Model is not input complete (inputs not defined for all states). ' 'Consider calling .make_input_complete()') else: if print_info: print(f'Learned model was not input complete. Adapting it with {input_completeness} transitions.') learned_model.make_input_complete(input_completeness) return learned_model def run_k_tails(data, automaton_type, k, input_completeness=None, print_info=True) -> Union[ Onfsm, NDMooreMachine, None]: """ Runs k-tails. Args: data: sequence of input-output traces automaton_type: either 'mealy' or 'moore'. Note that the data has to be prefix-closed, and the resulting model could be non-deterministic. k: depth until which to check node compatibility input_completeness: either None, 'sink_state', or 'self_loop'. If None, learned model could be input incomplete, sink_state will lead all undefined inputs form some state to the sink state, whereas self_loop will simply create a self loop. In case of Mealy learning output of the added transition will be 'epsilon'. print_info: print learning progress and runtime information Returns: Model conforming to the data such that future compatibility is checked only until the depth of k. """ assert automaton_type in {'mealy', 'moore'} assert input_completeness in {None, 'self_loop', 'sink_state'} print_level = ProgressReport(1) if print_info else None internal_automaton_type = 'moore' if automaton_type != 'mealy' else automaton_type score = ScoreWithKTail(ScoreCalculation(GsmNode.deterministic_compatible), k) learned_model = run_GSM(data, output_behavior=internal_automaton_type, transition_behavior="nondeterministic", score_calc=score, data_format='io_traces', instrumentation=print_level) if not learned_model.is_input_complete(): if not input_completeness: if print_info: print('Warning: Learned Model is not input complete (inputs not defined for all states). ' 'Consider calling .make_input_complete()') else: if print_info: print(f'Learned model was not input complete. Adapting it with {input_completeness} transitions.') learned_model.make_input_complete(input_completeness) return learned_model def run_Alergia_EDSM(data, automaton_type, eps=0.05, print_info=False): """ Run IoAlergia with EDSM on provided data. Args: data: [[O,(I,O),(I,O)...], [O,(I,O), (I, O)_,...],..,] if learning MDPs, or [[I,O,I,O...], [I,O_,...],..,] if learning SMMs (I represent input, O output), or [[O, O, O], ...] if learning Markov chains. Note that when learning MDPs and MCs the first symbol of each entry should be the same (Initial output). eps: epsilon value if you are using default HoeffdingCompatibility. automaton_type: either 'mdp' if you wish to learn an MDP, or 'smm' if you want to learn stochastic Mealy machine print_info: default False Returns: A Mc, Mdp or SMM """ from aalpy.utils.HelperFunctions import mc_format_to_mdp, mc_from_mdp assert automaton_type in {'mc', 'mdp', 'smm',} print_level = ProgressReport(1) if print_info else None class IOAlergiaWithEDSM(ScoreCalculation): def __init__(self, epsilon): super().__init__() self.ioa_compatibility = hoeffding_compatibility(epsilon) self.evidence = 0 def reset(self): self.evidence = 0 def local_compatibility(self, a: GsmNode, b: GsmNode): self.evidence += 1 return self.ioa_compatibility(a, b) def score_function(self, part: dict[GsmNode, GsmNode]): return self.evidence output_behaviour = 'moore' if automaton_type != 'smm' else 'mealy' learning_data = data if automaton_type != 'mc' else mc_format_to_mdp(data) learned_model = run_GSM(learning_data, output_behavior=output_behaviour, transition_behavior="stochastic", score_calc=IOAlergiaWithEDSM(eps), compatibility_on_pta=True, compatibility_on_futures=True, instrumentation=print_level, data_format='io_traces') if automaton_type == 'mc': learned_model = mc_from_mdp(learned_model) return learned_model ================================================ FILE: aalpy/learning_algs/general_passive/GsmNode.py ================================================ import functools import math import pathlib from collections import defaultdict from functools import total_ordering from typing import Dict, Any, List, Tuple, Iterable, Callable, Union, TypeVar, Iterator, Optional, Sequence import pydot from copy import copy from aalpy.automata import StochasticMealyMachine, StochasticMealyState, MooreState, MooreMachine, NDMooreState, \ NDMooreMachine, Mdp, MdpState, MealyMachine, MealyState, Onfsm, OnfsmState from aalpy.base import Automaton Key = TypeVar("Key") Val = TypeVar("Val") OutputBehavior = str OutputBehaviorRange = ["moore", "mealy"] TransitionBehavior = str TransitionBehaviorRange = ["deterministic", "nondeterministic", "stochastic"] DataFormat = str DataFormatRange = ["io_traces", "labeled_sequences", "traces", "tree"] IOPair = Tuple[Any, Any] IOTrace = Sequence[IOPair] IOExample = Tuple[Sequence[Any], Any] StateFunction = Callable[['GsmNode'], str] TransitionFunction = Callable[['GsmNode', Any, Any], str] unknown_output = None # can be set to a special value if required def intersection_iterator(a: Dict[Key, Val], b: Dict[Key, Val]) -> Iterator[Tuple[Key, Val, Val]]: missing = object() for key, a_val in a.items(): b_val = b.get(key, missing) if b_val is missing: continue yield key, a_val, b_val def union_iterator(a: Dict[Key, Val], b: Dict[Key, Val], default: Val = None) -> Iterator[Tuple[Key, Val, Val]]: for key, a_val in a.items(): b_val = b.get(key, default) yield key, a_val, b_val for key, b_val in b.items(): if key in a: continue a_val = a.get(key, default) yield key, a_val, b_val # TODO reuse in RPNI def detect_data_format(data, check_consistency=False, guess=False): # The different data formats are # - "tree": a tree-shaped automaton provided as a GsmNode # - "io_traces": either # - Moore traces [[o, (i,o), (i,o), ...], ...] # - Mealy traces [[(i,o), (i,o), ...], ...] # - "labeled_sequences": [([i, i, ...], o), ...] # - "traces": [[o, o, ...], ...] if isinstance(data, GsmNode): return "tree" accepted_types = (Tuple, List) # mapping data formats to compatibility criteria check_dict = dict( io_traces=lambda obj: len(obj) <= 1 or all(isinstance(o, accepted_types) and len(o) == 2 for o in obj[1:]), labeled_sequences=lambda obj: len(obj) == 2 and isinstance(obj[0], accepted_types), ) accept_dict = {k: True for k in check_dict} if not isinstance(data, accepted_types): raise ValueError("wrong input format. expected tuple or list.") if len(data) == 0: return "io_traces" accepted_formats = list(accept_dict.keys()) for data_point in data: if not isinstance(data_point, accepted_types): raise ValueError("wrong input format. expected tuple or list.") for k, check in check_dict.items(): accept_dict[k] &= check(data_point) accepted_formats = [k for k, v in accept_dict.items() if v] if len(accepted_formats) == 1 and not check_consistency: return accepted_formats[0] if len(accepted_formats) == 0: return "traces" # default to traces #raise ValueError("invalid or inconsistent data. no options left") if len(accepted_formats) != 1 and not guess: raise ValueError("ambiguous data format. data format needs to be specified explicitly.") return accepted_formats[0] # TODO maybe split this for maintainability (and perfomance?) class TransitionInfo: __slots__ = ["target", "count", "original_target", "original_count"] def __init__(self, target, count, original_target, original_count): self.target: 'GsmNode' = target self.count: int = count self.original_target: 'GsmNode' = original_target self.original_count: int = original_count # TODO add custom pickling code that flattens the Node structure in order to circumvent running into recursion issues for large models @total_ordering class GsmNode: """ Generic class for observably deterministic automata. The prefix is given as (minimal) list of IO pairs leading to that state. We assume an initial transition to the initial state, which has to be reflected in the prefix. This way, the output of the initial state for Moore machines can be encoded in its prefix. Transition count is preferred over state count as it allows to easily count transitions for non-tree-shaped automata """ __slots__ = ['transitions', 'predecessor', 'prefix_access_pair'] def __init__(self, prefix_access_pair, predecessor: 'GsmNode' = None): # TODO try single dict self.transitions: defaultdict[Any, Dict[Any, TransitionInfo]] = defaultdict(dict) self.predecessor: GsmNode = predecessor self.prefix_access_pair = prefix_access_pair def __lt__(self, other, compare_length_only=False): own_l, other_l = self.get_prefix_length(), other.get_prefix_length() if own_l != other_l: return own_l < other_l if compare_length_only: return False own_p = self.get_prefix() other_p = other.get_prefix() try: return own_p < other_p except TypeError: return [str(x) for x in own_p] < [str(x) for x in other_p] # TODO implicit prefixes as currently implemented require O(length) time for prefix calculations (e.g. to determine the minimal blue node) # other options would be to have more efficient explicit prefixes such as shared list representations def get_prefix_length(self): node = self length = 0 while node.predecessor: node = node.predecessor length += 1 return length def get_prefix_output(self): return self.prefix_access_pair[1] def get_prefix_input(self): return self.prefix_access_pair[0] def resolve_unknown_prefix_output(self, value): if self.get_prefix_output() is unknown_output: self.prefix_access_pair = (self.get_prefix_input(), value) def get_prefix(self, include_output=True): node = self prefix = [] while node.predecessor: symbol = node.prefix_access_pair if not include_output: symbol = symbol[0] prefix.append(symbol) node = node.predecessor prefix.reverse() return prefix def get_root(self): current = self while current.predecessor: current = current.predecessor return current def get_or_create_transitions(self, in_sym) -> Dict[Any, TransitionInfo]: t = self.transitions.get(in_sym) if t is None: t = dict() self.transitions[in_sym] = t return t def transition_iterator(self) -> Iterable[Tuple[Any, Any, TransitionInfo]]: for in_sym, transitions in self.transitions.items(): for out_sym, node in transitions.items(): yield in_sym, out_sym, node def shallow_copy(self) -> 'GsmNode': node = GsmNode(self.prefix_access_pair, self.predecessor) for in_sym, t in self.transitions.items(): d = dict() # appears to be faster than dict comprehension for out_sym, ti in t.items(): d[out_sym] = TransitionInfo(ti.target, ti.count, ti.original_target, ti.original_count) node.transitions[in_sym] = d return node def get_by_prefix(self, seq: IOTrace) -> Optional['GsmNode']: node: GsmNode = self for in_sym, out_sym in seq: if in_sym is None: # ignore initial transition of Node.get_prefix() continue trans = node.transitions.get(in_sym) if trans is None: return None t_info = trans.get(out_sym) if t_info is None: return None node = t_info.target return node def get_all_nodes(self) -> List['GsmNode']: result = [self] backing_set = {self} for state in result: for _, _, transition in state.transition_iterator(): child = transition.target if child not in backing_set: backing_set.add(child) result.append(child) return result def is_tree(self): q: List['GsmNode'] = [self] backing_set = {self} while len(q) != 0: current = q.pop(0) for _, _, transition in current.transition_iterator(): child = transition.target if child in backing_set: return False q.append(child) backing_set.add(child) return True def to_automaton(self, output_behavior: OutputBehavior, transition_behavior: TransitionBehavior, check_behavior=True, set_prefix=False) -> Automaton: nodes = self.get_all_nodes() if check_behavior: if output_behavior == "moore" and not self.is_moore(): raise ValueError("Tried to obtain Moore machine from non-Moore structure") if transition_behavior == "deterministic" and not self.is_deterministic(): raise ValueError("Tried to obtain deterministic automaton from non-deterministic structure") type_dict = { ("moore", "deterministic"): (MooreMachine, MooreState), ("moore", "nondeterministic"): (NDMooreMachine, NDMooreState), ("moore", "stochastic"): (Mdp, MdpState), ("mealy", "deterministic"): (MealyMachine, MealyState), ("mealy", "nondeterministic"): (Onfsm, OnfsmState), ("mealy", "stochastic"): (StochasticMealyMachine, StochasticMealyState), } AutomatonClass, StateClass = type_dict[(output_behavior, transition_behavior)] # create states state_map = dict() for i, node in enumerate(nodes): state_id = f's{i}' if output_behavior == "mealy": state = StateClass(state_id) elif output_behavior == "moore": state = StateClass(state_id, node.get_prefix_output()) state_map[node] = state if set_prefix: if transition_behavior == "deterministic": state.prefix = tuple(p[0] for p in node.get_prefix()) else: state.prefix = tuple(node.get_prefix()) else: state.prefix = None initial_state = state_map[self] # add transitions for node in nodes: state = state_map[node] for in_sym, transitions in node.transitions.items(): total = sum(t.count for t in transitions.values()) for out_sym, target_node in transitions.items(): target_state = state_map[target_node.target] count = target_node.count if AutomatonClass is MooreMachine: state.transitions[in_sym] = target_state elif AutomatonClass is MealyMachine: state.transitions[in_sym] = target_state state.output_fun[in_sym] = out_sym elif AutomatonClass is NDMooreMachine: state.transitions[in_sym].append(target_state) elif AutomatonClass is Onfsm: state.transitions[in_sym].append((out_sym, target_state)) elif AutomatonClass is Mdp: state.transitions[in_sym].append((target_state, count / total)) elif AutomatonClass is StochasticMealyMachine: state.transitions[in_sym].append((target_state, out_sym, count / total)) return AutomatonClass(initial_state, list(state_map.values())) def visualize(self, path: Union[str, pathlib.Path], output_behavior: OutputBehavior = "mealy", format: str = "dot", engine="dot", *, state_label: StateFunction = None, state_color: StateFunction = None, trans_label: TransitionFunction = None, trans_color: TransitionFunction = None, state_props: Dict[str, StateFunction] = None, trans_props: Dict[str, TransitionFunction] = None, node_naming: StateFunction = None): # handle default parameters if output_behavior not in ["moore", "mealy", None]: raise ValueError(f"Invalid OutputBehavior {output_behavior}") if state_props is None: state_props = dict() if trans_props is None: trans_props = dict() if state_label is None: if output_behavior == "moore": def state_label(node: GsmNode): return f'{node.get_prefix_output()} {node.count()}' else: def state_label(node: GsmNode): return f'{sum(t.count for _, _, t in node.transition_iterator())}' if trans_label is None and "label" not in trans_props: if output_behavior == "moore": def trans_label(node: GsmNode, in_sym, out_sym): return f'{in_sym} [{node.transitions[in_sym][out_sym].count}]' else: def trans_label(node: GsmNode, in_sym, out_sym): return f'{in_sym} / {out_sym} [{node.transitions[in_sym][out_sym].count}]' if state_color is None: def state_color(x): return "black" if trans_color is None: def trans_color(x, y, z): return "black" if node_naming is None: node_dict = dict() def node_naming(node: GsmNode): if node not in node_dict: node_dict[node] = f"s{len(node_dict)}" return node_dict[node] state_props = {"label": state_label, "color": state_color, "fontcolor": state_color, **state_props} trans_props = {"label": trans_label, "color": trans_color, "fontcolor": trans_color, **trans_props} # create new graph graph = pydot.Dot('automaton', graph_type='digraph') # graph.add_node(pydot.Node(str(self.prefix), label=state_label(self))) nodes = self.get_all_nodes() # add nodes for node in nodes: arg_dict = {key: fun(node) for key, fun in state_props.items()} graph.add_node(pydot.Node(node_naming(node), **arg_dict)) # add transitions for node in nodes: for in_sym, options in node.transitions.items(): for out_sym, c in options.items(): arg_dict = {key: fun(node, in_sym, out_sym) for key, fun in trans_props.items()} graph.add_edge(pydot.Edge(node_naming(node), node_naming(c.target), **arg_dict)) # add initial state # TODO maybe add option to parameterize this graph.add_node(pydot.Node('__start0', shape='none', label='')) graph.add_edge(pydot.Edge('__start0', node_naming(self), label='')) file_ext = format if format == 'dot': format = 'raw' if format == 'raw': file_ext = 'dot' graph.write(path=str(path) + "." + file_ext, prog=engine, format=format) def make_input_complete(self) -> List[Tuple['GsmNode', Any, Any]]: all_nodes = self.get_all_nodes() inputs = {in_sym for node in all_nodes for in_sym in node.transitions} missing_trans = [] for node in all_nodes: for in_sym in inputs: transitions = node.transitions[in_sym] if len(transitions) == 0: out_sym = node.prefix_access_pair[1] missing_trans.append((node, in_sym, out_sym)) t_info = TransitionInfo(node, 1, None, None) transitions[out_sym] = t_info return missing_trans def add_trace(self, trace: IOTrace): curr_node: GsmNode = self for in_sym, out_sym in trace: transitions = curr_node.transitions[in_sym] info = transitions.get(out_sym) if info is None: node = GsmNode((in_sym, out_sym), curr_node) transitions[out_sym] = TransitionInfo(node, 1, node, 1) else: info.count += 1 info.original_count += 1 node = info.target curr_node = node def add_labeled_sequence(self, example: IOExample): inputs, output = example curr_node: GsmNode = self in_sym = None # step through inputs and add transitions for in_sym in inputs: transitions = curr_node.transitions[in_sym] t_infos = list(transitions.values()) if len(t_infos) == 0: node = GsmNode((in_sym, unknown_output), curr_node) t_info = TransitionInfo(node, 1, node, 1) transitions[unknown_output] = t_info elif len(t_infos) == 1: t_info = t_infos[0] t_info.count += 1 t_info.original_count += 1 node = t_info.target else: # This should never happen raise ValueError("Nondeterminism encountered for GSM with labeled_sequences. not supported") curr_node = node # set last output curr_node.resolve_unknown_prefix_output(output) pred = curr_node.predecessor if pred: transitions = pred.transitions[in_sym] if unknown_output in transitions: transitions[output] = transitions.pop(unknown_output) if output not in transitions: raise ValueError("nondeterminism encountered for GSM with labeled_sequences. not supported") @staticmethod def createPTA(data, output_behavior, data_format=None) -> 'GsmNode': if data_format is None: data_format = detect_data_format(data) if data_format not in DataFormatRange: raise ValueError(f"invalid data format {data_format}. should be in {DataFormatRange}") if data_format == "tree": if not data.is_tree(): raise ValueError("provided automaton is not a tree") return data root_node = GsmNode((None, unknown_output), None) if data_format == "labeled_sequences": for example in data: root_node.add_labeled_sequence(example) if data_format == "io_traces" or data_format == "traces": if output_behavior == "moore": initial_output = data[0][0] root_node.prefix_access_pair = (None, initial_output) data = (d[1:] for d in data) for trace in data: if data_format == "traces": trace = (("step", t) for t in trace) root_node.add_trace(trace) return root_node def is_locally_deterministic(self): return all(len(item) == 1 for item in self.transitions.values()) def is_deterministic(self): return all(node.is_locally_deterministic() for node in self.get_all_nodes()) def deterministic_compatible(self, other: 'GsmNode'): for _, trans_self, trans_other in intersection_iterator(self.transitions, other.transitions): if unknown_output in trans_self or unknown_output in trans_other: continue if trans_self.keys() != trans_other.keys(): return False return True def is_moore(self): for node in self.get_all_nodes(): for in_sym, out_sym, transition in node.transition_iterator(): child_output = transition.target.get_prefix_output() if out_sym is not unknown_output and child_output != out_sym: return False return True def moore_compatible(self, other: 'GsmNode'): so = self.get_prefix_output() oo = other.get_prefix_output() return so == oo or so is unknown_output or oo is unknown_output def local_log_likelihood_contribution(self): llc = 0 for in_sym, trans in self.transitions.items(): total_count = 0 for out_sym, info in trans.items(): total_count += info.count llc += info.count * math.log(info.count) if total_count != 0: llc -= total_count * math.log(total_count) return llc def count(self): return sum(trans.count for _, _, trans in self.transition_iterator()) default_order = functools.cmp_to_key(lambda a, b: -1 if a < b else 1) ================================================ FILE: aalpy/learning_algs/general_passive/Instrumentation.py ================================================ from time import perf_counter from typing import Dict, Optional from aalpy.learning_algs.general_passive.GeneralizedStateMerging import Instrumentation, Partitioning, \ GeneralizedStateMerging from aalpy.learning_algs.general_passive.GsmNode import GsmNode class ProgressReport(Instrumentation): def __init__(self, lvl): super().__init__() self.lvl = lvl if lvl < 1: return self.gsm: Optional[GeneralizedStateMerging] = None self.log = [] self.pta_size = None self.nr_merged_states_total = 0 self.nr_merged_states = 0 self.nr_red_states = 0 self.stats = dict() self.previous_time = None def reset(self, gsm: GeneralizedStateMerging): self.gsm = gsm self.log = [] self.pta_size = None self.nr_merged_states_total = 0 self.nr_merged_states = 0 self.nr_red_states = 0 self.stats = dict() self.previous_time = perf_counter() def pta_construction_done(self, root): pta_const_time = perf_counter() - self.previous_time self.stats["pta creation time"] = pta_const_time print(f'PTA Construction Time: {round(pta_const_time, 2)} s') if 0 < self.lvl: states = root.get_all_nodes() leafs = [state for state in states if len(state.transitions.keys()) == 0] depth = [state.get_prefix_length() for state in leafs] self.pta_size = len(states) if 1 < self.lvl: print(f'PTA has {len(states)} states leading to {len(leafs)} leafs') print(f'min / avg / max depth : {min(depth)} / {sum(depth) / len(depth)} / {max(depth)}') self.previous_time = perf_counter() def print_status(self): reset_char = "\33[2K\r" print_str = reset_char + f'Current automaton size: {self.nr_red_states}' if 0 < self.lvl and not self.gsm.compatibility_on_futures: print_str += f' Merged: {self.nr_merged_states_total} Remaining: {self.pta_size - self.nr_red_states - self.nr_merged_states_total}' print(print_str, end="") def log_promote(self, node: GsmNode): self.log.append(["promote", (node.get_prefix(),)]) self.nr_red_states += 1 self.print_status() def log_merge(self, part: Partitioning): self.log.append(["merge", (part.red.get_prefix(), part.blue.get_prefix())]) self.nr_merged_states_total += len(part.full_mapping) - len(part.red_mapping) self.nr_merged_states += 1 self.print_status() def learning_done(self, root: GsmNode): learning_time = perf_counter() - self.previous_time self.stats["learning time"] = learning_time self.stats["total time"] = learning_time + self.stats["pta creation time"] print(f'\nLearning Time: {round(learning_time, 2)} s') print(f'Learned {self.nr_red_states} state automaton via {self.nr_merged_states} merges.') if 2 < self.lvl: root.visualize("model", self.gsm.output_behavior) class MergeViolationDebugger(Instrumentation): def __init__(self, ground_truth: GsmNode): super().__init__() self.root = ground_truth self.map: Dict[GsmNode, GsmNode] = dict() self.log = [] self.gsm: Optional[GeneralizedStateMerging] = None def reset(self, gsm: GeneralizedStateMerging): self.gsm = gsm self.map = dict() self.log = [] def log_promote(self, new_red: GsmNode): new_red_prefix = new_red.get_prefix() node = self.root.get_by_prefix(new_red_prefix) old_red = self.map.get(node) if old_red is None: self.map[node] = new_red self.log.append(("promote", new_red_prefix)) elif old_red is not new_red: print(f"Erroneous promotion detected:") print(f" Ground truth: {node.get_prefix()}") print(f" Representative (old): {old_red.get_prefix()}") print(f" Representative (new): {new_red_prefix}") self.log.append(("wrong promote", new_red_prefix)) def log_merge(self, part: Partitioning): red_prefix = part.red.get_prefix() blue_prefix = part.blue.get_prefix() red_node = self.root.get_by_prefix(red_prefix) blue_node = self.root.get_by_prefix(blue_prefix) if red_node is None or blue_node is None: self.log.append(("broken merge", red_prefix, blue_prefix)) elif red_node is blue_node: self.log.append(("merge", red_prefix, blue_prefix)) else: print(f"Erroneous merge detected:") print(f" PTA red: {red_prefix}") print(f" PTA blue: {blue_prefix}") print(f" real red: {red_node.get_prefix()}") print(f" real blue: {blue_node.get_prefix()}") self.log.append(("wrong merge", red_prefix, blue_prefix)) ================================================ FILE: aalpy/learning_algs/general_passive/ScoreFunctionsGSM.py ================================================ from math import sqrt, log from typing import Callable, Dict, List, Iterable, Any from aalpy.learning_algs.general_passive.GsmNode import GsmNode, intersection_iterator, union_iterator, TransitionInfo LocalCompatibilityFunction = Callable[[GsmNode, GsmNode], bool] ScoreFunction = Callable[[Dict[GsmNode, GsmNode]], Any] AggregationFunction = Callable[[Iterable], Any] class ScoreCalculation: def __init__(self, local_compatibility: LocalCompatibilityFunction = None, score_function: ScoreFunction = None): # This is a hack that gives a simple implementation where we can easily - determine whether the default is # overridden (for optimization) - override behavior in a functional way by providing the functions as # arguments (no extra class) - override behavior in a stateful way by implementing a new class that provides # `local_compatibility` and / or `score_function` methods if not hasattr(self, "local_compatibility"): self.local_compatibility: LocalCompatibilityFunction = local_compatibility or self.default_local_compatibility if not hasattr(self, "score_function"): self.score_function: ScoreFunction = score_function or self.default_score_function def reset(self): pass @staticmethod def default_local_compatibility(a: GsmNode, b: GsmNode): return True @staticmethod def default_score_function(part: Dict[GsmNode, GsmNode]): return True def has_score_function(self): return self.score_function is not self.default_score_function def has_local_compatibility(self): return self.local_compatibility is not self.default_local_compatibility def hoeffding_compatibility(eps, compare_original=True) -> LocalCompatibilityFunction: eps_fact = sqrt(0.5 * log(2 / eps)) count_name = "original_count" if compare_original else "count" transition_dummy = TransitionInfo(None, 0, None, 0) def similar(a: GsmNode, b: GsmNode): # iterate over inputs that are common to both states for in_sym, a_trans, b_trans in intersection_iterator(a.transitions, b.transitions): # could create appropriate dict here a_total, b_total = (sum(getattr(x, count_name) for x in trans.values()) for trans in (a_trans, b_trans)) if a_total == 0 or b_total == 0: continue # parameter combinations require this check threshold = eps_fact * (sqrt(1 / a_total) + sqrt(1 / b_total)) # iterate over outputs that appear in either distribution for out_sym, a_info, b_info in union_iterator(a_trans, b_trans, transition_dummy): ac, bc = (getattr(x, count_name) for x in (a_info, b_info)) if abs(ac / a_total - bc / b_total) > threshold: return False return True return similar class ScoreWithKTail(ScoreCalculation): """Applies k-Tails to a compatibility function: Compatibility is only evaluated up to a certain depth k.""" def __init__(self, other_score: ScoreCalculation, k: int): super().__init__(None, other_score.score_function) self.other_score = other_score self.k = k self.depth_offset = None def reset(self): self.other_score.reset() self.depth_offset = None def local_compatibility(self, a: GsmNode, b: GsmNode): # assuming b is tree shaped. if self.depth_offset is None: self.depth_offset = b.get_prefix_length() depth = b.get_prefix_length() - self.depth_offset if self.k <= depth: return True return self.other_score.local_compatibility(a, b) class ScoreWithSinks(ScoreCalculation): """This class allows rejecting merge candidates based on additional criteria for the initial merge""" def __init__(self, other_score: ScoreCalculation, sink_cond: Callable[[GsmNode], bool], allow_sink_merge=True): super().__init__(None, other_score.score_function) self.other_score = other_score self.sink_cond = sink_cond self.allow_sink_merge = allow_sink_merge self.is_first = True def reset(self): self.other_score.reset() self.is_first = True def local_compatibility(self, a: GsmNode, b: GsmNode): if self.is_first: self.is_first = False a_sink, b_sink = self.sink_cond(a), self.sink_cond(b) if a_sink != b_sink: return False if a_sink and b_sink and not self.allow_sink_merge: return False return self.other_score.local_compatibility(a, b) class ScoreCombinator(ScoreCalculation): """ This class is used to combine several scoring / compatibility mechanisms by aggregating the results of the individual methods in a user defined manner. It uses generator expressions to allow for short circuit evaluation. """ def __init__(self, scores: List[ScoreCalculation], aggregate_compatibility: AggregationFunction = None, aggregate_score: AggregationFunction = None): super().__init__() self.scores = scores self.aggregate_compatibility = aggregate_compatibility or self.default_aggregate_compatibility self.aggregate_score = aggregate_score or self.default_aggregate_score def reset(self): for score in self.scores: score.reset() def local_compatibility(self, a: GsmNode, b: GsmNode): return self.aggregate_compatibility(score.local_compatibility(a, b) for score in self.scores) def score_function(self, part: Dict[GsmNode, GsmNode]): return self.aggregate_score(score.score_function(part) for score in self.scores) @staticmethod def default_aggregate_compatibility(compatibility_iterable): """Commits to the first value that is not inconclusive (== None). Accepts if in doubt.""" for compat in compatibility_iterable: if compat is None: continue return compat return True @staticmethod def default_aggregate_score(score_iterable): return list(score_iterable) def local_to_global_compatibility(local_fun: LocalCompatibilityFunction) -> ScoreFunction: """ Converts a local compatibility function to a global score function by evaluating the local compatibility for each of the new partitions with all nodes that make up that partition. One use case for this is to evaluate a local score function after the partitions are complete. The order of arguments for the local compatibility function is partition, original. """ def fun(part: Dict[GsmNode, GsmNode]): for old_node, new_node in part.items(): if local_fun(new_node, old_node) is False: # Follows local_fun(red, blue) return False return True return fun def differential_info(part: Dict[GsmNode, GsmNode]): relevant_nodes_old = list(part.keys()) relevant_nodes_new = set(part.values()) partial_llh_old = sum(node.local_log_likelihood_contribution() for node in relevant_nodes_old) partial_llh_new = sum(node.local_log_likelihood_contribution() for node in relevant_nodes_new) num_params_old = sum(1 for node in relevant_nodes_old for _ in node.transition_iterator()) num_params_new = sum(1 for node in relevant_nodes_new for _ in node.transition_iterator()) return partial_llh_old - partial_llh_new, num_params_old - num_params_new def transform_score(score, transform: Callable): if isinstance(score, Callable): return lambda *args: transform(score(*args)) if isinstance(score, ScoreCalculation): score.score_function = lambda *args: transform(score.score_function(*args)) return score return transform(score) def make_greedy(score): return transform_score(score, lambda x: x is not False) def lower_threshold(score, thresh): return transform_score(score, lambda x: x if thresh < x else False) def AIC_score(alpha=0) -> ScoreFunction: def score(part: Dict[GsmNode, GsmNode]): llh_diff, param_diff = differential_info(part) return lower_threshold(param_diff - llh_diff, alpha) return score def EDSM_frequency_score(min_evidence=-1) -> ScoreFunction: def score(part: Dict[GsmNode, GsmNode]): total_evidence = 0 for old_node, new_node in part.items(): for in_sym, trans_old, trans_new in intersection_iterator(old_node.transitions, new_node.transitions): for out_sym, t_info_old, t_info_new in intersection_iterator(trans_old, trans_new): if t_info_old.count != t_info_new.count: total_evidence += t_info_old.count return lower_threshold(total_evidence, min_evidence) return score def EDSM_score(min_evidence=-1) -> ScoreFunction: def score(part: Dict[GsmNode, GsmNode]): nr_partitions = len(set(part.values())) nr_merged = len(part) return lower_threshold(nr_merged - nr_partitions, min_evidence) return score ================================================ FILE: aalpy/learning_algs/general_passive/__init__.py ================================================ ================================================ FILE: aalpy/learning_algs/non_deterministic/AbstractedOnfsmLstar.py ================================================ import time from aalpy.base import SUL, Oracle from aalpy.learning_algs.non_deterministic.AbstractedOnfsmObservationTable import AbstractedNonDetObservationTable from aalpy.learning_algs.non_deterministic.NonDeterministicSULWrapper import NonDeterministicSULWrapper from aalpy.utils.HelperFunctions import print_learning_info, print_observation_table print_options = [0, 1, 2, 3] def run_abstracted_ONFSM_Lstar(alphabet: list, sul: SUL, eq_oracle: Oracle, abstraction_mapping: dict, n_sampling=100, max_learning_rounds=None, return_data=False, print_level=2): """ Based on ''Learning Abstracted Non-deterministic Finite State Machines'' from Pferscher and Aichernig. The algorithm learns an abstracted onfsm of a non-deterministic system. For the additional abstraction, equivalence classes for outputs are used. Learning ONFSM relies on all-weather assumption. If this assumption is not satisfied by sampling, learning might not converge to the minimal model and runtime could increase substantially. Note that this is the inherent flaw of the all-weather assumption. (All outputs will be seen) AALpy v.2.0 will try to solve that problem with a novel approach. Args: alphabet: input alphabet sul: system under learning eq_oracle: equivalence oracle abstraction_mapping: dictionary containing mappings from abstracted to concrete values (equivalence classes) n_sampling: number of times that membership/input queries will be asked for each cell in the observation (Default value = 100) max_learning_rounds: if max_learning_rounds is reached, learning will stop (Default value = None) return_data: if True, map containing all information like number of queries... will be returned (Default value = False) print_level: 0 - None, 1 - just results, 2 - current round and hypothesis size, 3 - educational/debug (Default value = 2) Returns: learned abstracted ONFSM """ start_time = time.time() eq_query_time = 0 learning_rounds = 0 hypothesis = None sul = NonDeterministicSULWrapper(sul) eq_oracle.sul = sul abstracted_observation_table = AbstractedNonDetObservationTable(alphabet, sul, abstraction_mapping, n_sampling) # We fist query the initial row. Then based on output in its cells, we generate new rows in S.A, # and then we perform membership/input queries for them. abstracted_observation_table.update_obs_table() new_rows = abstracted_observation_table.update_extended_S() abstracted_observation_table.update_obs_table(s_set=new_rows) while True: learning_rounds += 1 if max_learning_rounds and learning_rounds - 1 == max_learning_rounds: break closed_complete_consistent = False while not closed_complete_consistent: closed_complete_consistent = True row_to_close = abstracted_observation_table.get_row_to_close() while row_to_close is not None: # First we add new rows to the S.A. They are added based on the values in the cells of the # rows that is to be closed. Once those rows are created, they are populated and closedness is checked # once again. closed_complete_consistent = False extended_rows = abstracted_observation_table.update_extended_S(row_to_close) abstracted_observation_table.update_obs_table(s_set=extended_rows) row_to_close = abstracted_observation_table.get_row_to_close() row_to_complete = abstracted_observation_table.get_row_to_complete() while row_to_complete is not None: closed_complete_consistent = False abstracted_observation_table.extend_S_dot_A([row_to_complete]) abstracted_observation_table.update_obs_table(s_set=[row_to_complete]) row_to_complete = abstracted_observation_table.get_row_to_complete() e_column_for_consistency = abstracted_observation_table.get_row_to_make_consistent() while e_column_for_consistency is not None: closed_complete_consistent = False extended_col = abstracted_observation_table.update_E(e_column_for_consistency) abstracted_observation_table.update_obs_table(e_set=extended_col) e_column_for_consistency = abstracted_observation_table.get_row_to_make_consistent() abstracted_observation_table.clean_tables() hypothesis = abstracted_observation_table.gen_hypothesis() if print_level == 3: print('Observation Table') print_observation_table(abstracted_observation_table.observation_table, 'non-det') print() print('Abstracted Observation Table') # CHANGED, but not important to alg print_observation_table(abstracted_observation_table, 'abstracted-non-det') if print_level > 1: print(f'Hypothesis {learning_rounds} has {len(hypothesis.states)} states.') # Find counterexample eq_query_start = time.time() cex = eq_oracle.find_cex(hypothesis) eq_query_time += time.time() - eq_query_start if cex is None: break if print_level >= 2: print('Counterexample', cex) # Process counterexample -> add cex to S.A or E abstracted_observation_table.cex_processing(cex, hypothesis) total_time = round(time.time() - start_time, 2) eq_query_time = round(eq_query_time, 2) learning_time = round(total_time - eq_query_time, 2) info = { 'learning_rounds': learning_rounds, 'automaton_size': len(hypothesis.states), 'queries_learning': sul.num_queries, 'steps_learning': sul.num_steps, 'queries_eq_oracle': eq_oracle.num_queries, 'steps_eq_oracle': eq_oracle.num_steps, 'learning_time': learning_time, 'eq_oracle_time': eq_query_time, 'total_time': total_time } if print_level > 0: print_learning_info(info) if return_data: return hypothesis, info return hypothesis ================================================ FILE: aalpy/learning_algs/non_deterministic/AbstractedOnfsmObservationTable.py ================================================ from collections import defaultdict from aalpy.automata import Onfsm, OnfsmState from aalpy.learning_algs.non_deterministic.OnfsmObservationTable import NonDetObservationTable from aalpy.learning_algs.non_deterministic.NonDeterministicSULWrapper import NonDeterministicSULWrapper from aalpy.utils.HelperFunctions import all_suffixes, extend_set class AbstractedNonDetObservationTable: def __init__(self, alphabet: list, sul: NonDeterministicSULWrapper, abstraction_mapping: dict, n_sampling=100): """ Construction of the abstracted non-deterministic observation table. Args: alphabet: input alphabet sul: system under learning abstraction_mapping: map that translates outputs to abstracted outputs n_sampling: number of samples to be performed for each cell """ assert alphabet is not None and sul is not None self.observation_table = NonDetObservationTable(alphabet, sul, n_sampling) self.S = list() self.S_dot_A = [] self.E = [] self.T = defaultdict(dict) self.A = [tuple([a]) for a in alphabet] self.abstraction_mapping = abstraction_mapping self.sul = sul empty_word = tuple() self.S.append((empty_word, empty_word)) def update_obs_table(self, s_set=None, e_set: list = None): """ Perform the membership queries and abstraction on observation table With the all-weather assumption, each output query is tried a number of times on the system, and the driver reports the set of all possible outputs. Args: s_set: Prefixes of S set on which to preform membership queries (Default value = None) e_set: Suffixes of E set on which to perform membership queries """ self.observation_table.query_missing_observations(s_set, e_set) self.abstract_obs_table() self.clean_obs_table() def abstract_obs_table(self): """ Creation of abstracted observation table. The provided abstraction mapping is used to replace outputs by abstracted outputs. """ self.S = self.observation_table.S self.S_dot_A = list(set(self.observation_table.get_extended_S()).union(set(self.S_dot_A) - set(self.S))) self.E = self.observation_table.E update_S = self.S + self.S_dot_A update_E = self.E for s in update_S: for e in update_E: for o_tup in self.get_all_outputs(s, e): abstracted_outputs = [] o_tup = tuple([o_tup]) for outputs in o_tup: for o in outputs: abstract_output = self.get_abstraction(o) abstracted_outputs.append(abstract_output) self.add_to_T(s, e, tuple(abstracted_outputs)) def add_to_T(self, s, e, value): """ Add values to the cell at T[s][e]. Args: s: prefix e: element of S value: value to be added to the cell """ if e not in self.T[s]: self.T[s][e] = set() self.T[s][e].add(value) # CHANGED # helper function def get_all_outputs(self, s, e): cell_outputs = set() cell_outputs.update(self.sul.cache.get_all_traces(s, e)) return cell_outputs def update_extended_S(self, row_prefix=None): """ Helper generator function that returns extended S, or S.A set. For all values in the cell, create a new row where inputs is parent input plus element of alphabet, and output is parent output plus value in cell. Returns: New rows of extended S set. """ return self.observation_table.get_extended_S(row_prefix=row_prefix) def get_row_to_close(self): """ Get row for that needs to be closed. Returns: row that will be moved to S set and closed """ s_rows = set() for s in self.S: s_rows.add(self.row_to_hashable(s)) for t in self.S_dot_A: row_t = self.row_to_hashable(t) if row_t not in s_rows: self.S.append(t) self.S_dot_A.remove(t) return t return None def get_row_to_complete(self): """ Get row for that needs to be completed. Returns: row that will be added to S.A """ s_rows = set() for s in self.S: s_rows.add(tuple((s, self.row_to_hashable(s)))) for s_row in s_rows: similar_s_dot_a_rows = [] for t in self.S_dot_A: row_t = self.row_to_hashable(t) if row_t == s_row[1]: similar_s_dot_a_rows.append(t) similar_s_dot_a_rows.sort(key=lambda row: len(row[0])) for a in self.A: complete_outputs = self.get_all_outputs(s_row[0], a) for similar_s_dot_a_row in similar_s_dot_a_rows: t_row_outputs = self.get_all_outputs(similar_s_dot_a_row, a) output_difference = t_row_outputs.difference(complete_outputs) if len(output_difference) > 0: for o in output_difference: extension = (similar_s_dot_a_row[0] + a, similar_s_dot_a_row[1] + tuple([o[0]])) if extension not in self.S and extension not in self.S_dot_A: return extension else: complete_outputs = complete_outputs.union(output_difference) return None def get_row_to_make_consistent(self): """ Get row that violates consistency. """ unified_S = self.S + self.S_dot_A s_rows = set() for s in self.S: s_rows.add(tuple((s, self.row_to_hashable(s)))) for s_row in s_rows: similar_s_dot_a_rows = [] for t in self.S_dot_A: row_t = self.row_to_hashable(t) if row_t == s_row[1]: similar_s_dot_a_rows.append(t) similar_s_dot_a_rows.sort(key=lambda row: len(row[0])) for a in self.A: # CHANGED # outputs = self.observation_table.T[s_row[0]][a] outputs = self.get_all_outputs(s_row[0], a) for o in outputs: extended_s_sequence = (s_row[0][0] + a, s_row[0][1] + tuple([o])) if extended_s_sequence in unified_S: extended_s_sequence_row = self.row_to_hashable(extended_s_sequence) for similar_s_dot_a_row in similar_s_dot_a_rows: extended_s_dot_a_sequence = ( similar_s_dot_a_row[0] + a, similar_s_dot_a_row[1] + tuple([o])) if extended_s_dot_a_sequence in unified_S: extended_s_dot_a_sequence_row = self.row_to_hashable(extended_s_dot_a_sequence) if extended_s_sequence_row is not extended_s_dot_a_sequence_row: return self.get_distinctive_input_sequence(extended_s_sequence, extended_s_dot_a_sequence, a) return None def get_distinctive_input_sequence(self, first_row, second_row, inp): """ get input sequence that leads to a different output sequence for two given input/output sequences Args: first_row: row to be compared second_row: row to be compared inp: appended input to first_row and second_row that leads to different state Returns: input sequence that leads to different outputs """ for e in self.E: if len(self.T[first_row][e].difference(self.T[second_row][e])) > 0: return tuple([inp]) + e return None def update_E(self, seq): if seq not in self.E: self.E.append(seq) def clean_obs_table(self): """ Moves duplicates from S to S_dot_A. The entries in S_dot_A which are based on the moved row get deleted. The table will be smaller and more efficient. """ # just for testing without cleaning # return False tmp_S = self.S.copy() tmp_both_S = self.S + self.S_dot_A hashed_rows_from_s = set() tmp_S.sort(key=lambda t: len(t[0])) for s in tmp_S: hashed_s_row = self.row_to_hashable(s) if hashed_s_row in hashed_rows_from_s: if s in self.S: self.S.remove(s) self.observation_table.S.remove(s) size = len(s[0]) for row_prefix in tmp_both_S: s_both_row = (row_prefix[0][:size], row_prefix[1][:size]) if s != row_prefix and s == s_both_row: if row_prefix in self.S: self.S.remove(row_prefix) self.observation_table.S.remove(s) else: hashed_rows_from_s.add(hashed_s_row) def row_to_hashable(self, row_prefix): """ Creates the hashable representation of the row. Frozenset is used as the order of element in each cell does not matter Args: row_prefix: prefix of the row in the observation table Returns: hashable representation of the row """ row_repr = tuple() for e in self.E: # if e in self.T[row_prefix].keys(): row_repr += (frozenset(self.T[row_prefix][e]),) return row_repr def gen_hypothesis(self) -> Onfsm: """ Generate automaton based on the values found in the abstracted observation table. Returns: Current abstracted hypothesis """ state_distinguish = dict() states_dict = dict() initial = None unified_S = self.S + self.S_dot_A stateCounter = 0 for prefix in self.S: state_id = f's{stateCounter}' states_dict[prefix] = OnfsmState(state_id) states_dict[prefix].prefix = prefix state_distinguish[self.row_to_hashable(prefix)] = states_dict[prefix] if prefix == self.S[0]: initial = states_dict[prefix] stateCounter += 1 for prefix in self.S: similar_rows = [] for row in unified_S: if self.row_to_hashable(row) == self.row_to_hashable(prefix): similar_rows.append(row) for row in similar_rows: for a in self.A: for t in self.get_all_outputs(row, a): s_entry = (row[0] + a, row[1] + t) if s_entry in unified_S: state_in_S = state_distinguish[self.row_to_hashable(s_entry)] if (t[0], state_in_S) not in states_dict[prefix].transitions[a[0]]: states_dict[prefix].transitions[a[0]].append((t[0], state_in_S)) assert initial automaton = Onfsm(initial, [s for s in states_dict.values()]) automaton.characterization_set = self.E return automaton def extend_S_dot_A(self, cex_prefixes: list): """ Extends S.A based on counterexample prefixes. Args: cex_prefixes: input/output sequences that are added to S.A Returns: input/output sequences that have been added to the S.A """ prefixes = self.S + self.S_dot_A prefixes_to_extend = [] for cex_prefix in cex_prefixes: if cex_prefix not in prefixes: prefixes_to_extend.append(cex_prefix) self.S_dot_A.append(cex_prefix) return prefixes_to_extend def get_abstraction(self, out): """ Get an abstraction for a concrete output. If such abstraction is not defined, return output. Args: out: output to be abstracted if possible Returns: abstracted output or output itself """ return self.abstraction_mapping[out] if out in self.abstraction_mapping.keys() else out def cex_processing(self, cex: tuple, hypothesis: Onfsm): """ Add counterexample to the observation table. If the counterexample leads to a state where an output of the same equivalence class already exists, the prefixes of the counterexample are added to S.A. Otherwise, the postfixes of counterexample are added to E. Args: cex: counterexample that should be added to the observation table hypothesis: onfsm that implements the counterexample """ cex_len = len(cex[0]) hypothesis.reset_to_initial() for step in range(0, cex_len - 1): hypothesis.step_to(cex[0][step], cex[1][step]) possible_outputs = hypothesis.outputs_on_input(cex[0][cex_len - 1]) equivalent_output = False for out in possible_outputs: abstracted_out = self.get_abstraction(out) abstracted_out_cex = self.get_abstraction(cex[1][cex_len - 1]) if abstracted_out_cex == abstracted_out: equivalent_output = True break if equivalent_output: # add prefixes of cex to S_dot_A cex_prefixes = [(tuple(cex[0][0:i + 1]), tuple(cex[1][0:i + 1])) for i in range(0, len(cex[0]))] prefixes_to_extend = self.extend_S_dot_A(cex_prefixes) # CHANGED: REMOVED # self.observation_table.S_dot_A.extend(prefixes_to_extend) self.update_obs_table(s_set=prefixes_to_extend) else: # add distinguishing suffixes of cex to E # CHANGED CEX PROX # TODO: this will now not work as cex processing was changed # cex_suffixes = non_det_longest_prefix_cex_processing(self.observation_table, cex) cex_suffixes = all_suffixes(cex[0]) added_suffixes = extend_set(self.observation_table.E, cex_suffixes) self.update_obs_table(e_set=added_suffixes) def clean_tables(self): self.observation_table.clean_obs_table() self.abstract_obs_table() update_S = self.S.copy() whole_S = self.S + self.S_dot_A update_S.sort() update_S.sort(key=lambda t: len(t[0])) s_rows = set() for s in update_S: hashed_s_row = self.row_to_hashable(s) if hashed_s_row not in s_rows: s_rows.add(hashed_s_row) else: size = len(s[0]) for row in whole_S: cmp_row = (row[0][:size], row[1][:size]) if s == cmp_row: if row in self.S_dot_A: self.S_dot_A.remove(row) elif row in self.S: self.S.remove(row) self.S_dot_A.append(s) self.S.remove(s) ================================================ FILE: aalpy/learning_algs/non_deterministic/NonDeterministicSULWrapper.py ================================================ from aalpy.base import SUL from aalpy.learning_algs.non_deterministic.TraceTree import TraceTree class NonDeterministicSULWrapper(SUL): """ Wrapper for non-deterministic SUL. After every step, input/output pair is added to the tree containing all traces. """ def __init__(self, sul: SUL): super().__init__() self.sul = sul self.cache = TraceTree() def pre(self): self.cache.reset() self.sul.pre() def post(self): self.sul.post() def step(self, letter): out = self.sul.step(letter) self.cache.add_to_tree(letter, out) return out ================================================ FILE: aalpy/learning_algs/non_deterministic/OnfsmLstar.py ================================================ import time from aalpy.base import SUL, Oracle from aalpy.learning_algs.non_deterministic.NonDeterministicSULWrapper import NonDeterministicSULWrapper from aalpy.learning_algs.non_deterministic.OnfsmObservationTable import NonDetObservationTable from aalpy.utils.HelperFunctions import print_learning_info, print_observation_table, \ get_available_oracles_and_err_msg, all_suffixes print_options = [0, 1, 2, 3] available_oracles, available_oracles_error_msg = get_available_oracles_and_err_msg() def run_non_det_Lstar(alphabet: list, sul: SUL, eq_oracle: Oracle, n_sampling=5, samples=None, stochastic=False, max_learning_rounds=None, return_data=False, print_level=2): """ A ONFSM learning algorithm that does not rely on all weather assumption (once an input is queried, all possible outputs are observed). Args: alphabet: input alphabet sul: system under learning eq_oracle: equivalence oracle n_sampling: number of times that each cell has to be updated. If this number is to low, all-weather condition will not hold and learning will not converge to the correct model. (Default value = 50) samples: input output sequences provided to learning algorithm. List of ((input sequence), (output sequence)). stochastic: if True, non deterministic learning will be performed but probabilities will be added to the returned model, making it a stochastic Mealy machine max_learning_rounds: if max_learning_rounds is reached, learning will stop (Default value = None) return_data: if True, map containing all information like number of queries... will be returned (Default value = False) print_level: 0 - None, 1 - just results, 2 - current round and hypothesis size, 3 - educational/debug (Default value = 2) Returns: learned ONFSM """ start_time = time.time() eq_query_time = 0 learning_rounds = 0 sul = NonDeterministicSULWrapper(sul) if samples: for inputs, outputs in samples: sul.cache.add_trace(inputs, outputs) eq_oracle.sul = sul ot = NonDetObservationTable(alphabet, sul, n_sampling) # Keep track of last counterexample and last hypothesis size # With this data we can check if the extension of the E set lead to state increase last_cex = None hypothesis = None while True: if max_learning_rounds and learning_rounds - 1 == max_learning_rounds: break ot.S = list() ot.S.append((tuple(), tuple())) ot.query_missing_observations() row_to_close = ot.get_row_to_close() while row_to_close is not None: ot.query_missing_observations() row_to_close = ot.get_row_to_close() ot.clean_obs_table() hypothesis = ot.gen_hypothesis() if counterexample_not_valid(hypothesis, last_cex): cex = sul.cache.find_cex_in_cache(hypothesis) if cex is None: learning_rounds += 1 # Find counterexample if print_level > 1: print(f'Hypothesis {learning_rounds}: {len(hypothesis.states)} states.') if print_level == 3: print_observation_table(ot, 'non-det') eq_query_start = time.time() cex = eq_oracle.find_cex(hypothesis) eq_query_time += time.time() - eq_query_start last_cex = cex else: cex = last_cex if cex is None: break else: cex_suffixes = all_suffixes(cex[0]) for suffix in cex_suffixes: if suffix not in ot.E: ot.E.append(suffix) break if stochastic: hypothesis = ot.gen_hypothesis(stochastic=True) total_time = round(time.time() - start_time, 2) eq_query_time = round(eq_query_time, 2) learning_time = round(total_time - eq_query_time, 2) info = { 'learning_rounds': learning_rounds, 'automaton_size': len(hypothesis.states), 'queries_learning': sul.num_queries, 'steps_learning': sul.num_steps, 'queries_eq_oracle': eq_oracle.num_queries, 'steps_eq_oracle': eq_oracle.num_steps, 'learning_time': learning_time, 'eq_oracle_time': eq_query_time, 'total_time': total_time } if print_level > 0: print_learning_info(info) if return_data: return hypothesis, info return hypothesis def counterexample_not_valid(hypothesis, cex): if cex is None: return True hypothesis.reset_to_initial() for i, o in zip(cex[0], cex[1]): out = hypothesis.step_to(i, o) if out is None: return False return True ================================================ FILE: aalpy/learning_algs/non_deterministic/OnfsmObservationTable.py ================================================ from collections import Counter from aalpy.automata import Onfsm, OnfsmState, StochasticMealyState, StochasticMealyMachine from aalpy.learning_algs.non_deterministic.NonDeterministicSULWrapper import NonDeterministicSULWrapper class NonDetObservationTable: def __init__(self, alphabet: list, sul: NonDeterministicSULWrapper, n_sampling): """ Construction of the non-deterministic observation table. Args: alphabet: input alphabet sul: system under learning n_sampling: number of samples to be performed for each cell """ assert alphabet is not None and sul is not None self.alphabet = alphabet self.A = [tuple([a]) for a in alphabet] self.S = list() # prefixes of S self.E = [tuple([a]) for a in alphabet] self.n_samples = n_sampling self.closing_counter = 0 self.sul = sul self.sampling_counter = Counter() empty_word = tuple() # Elements of S are in form that is presented in 'Learning Finite State Models of Observable Nondeterministic # Systems in a Testing Context'. Each element of S is a (inputs, outputs) tuple, where first element of the # tuple are inputs and second element of the tuple are outputs associated with inputs. self.S.append((empty_word, empty_word)) self.pruned_nodes = set() def get_row_to_close(self): """ Get row for that need to be closed. Returns: row that will be moved to S set and closed """ s_rows = set() update_S_dot_A = self.get_extended_S() for s in self.S.copy(): s_rows.add(self.row_to_hashable(s)) for t in update_S_dot_A: row_t = self.row_to_hashable(t) if row_t not in s_rows: self.closing_counter += 1 self.S.append(t) return t self.closing_counter = 0 return None def get_extended_S(self, row_prefix=None): """ Helper generator function that returns extended S, or S.A set. For all values in the cell, create a new row where inputs is parent input plus element of alphabet, and output is parent output plus value in cell. Returns: extended S set. """ rows = self.S if row_prefix is None else [row_prefix] S_dot_A = [] for row in rows: for a in self.A: trace = self.sul.cache.get_all_traces(row, a) for t in trace: new_row = (row[0] + a, row[1] + (t[-1],)) if new_row not in self.S: S_dot_A.append(new_row) return S_dot_A def query_missing_observations(self, s=None, e=None): s_set = s if s is not None else self.S + self.get_extended_S() e_set = e if e is not None else self.E for s in s_set: for e in e_set: while self.sul.cache.get_s_e_sampling_frequency(s, e) < self.n_samples: self.sul.query(s[0] + e) def row_to_hashable(self, row_prefix): """ Creates the hashable representation of the row. Frozenset is used as the order of element in each cell does not matter Args: row_prefix: prefix of the row in the observation table Returns: hashable representation of the row """ row_repr = tuple() for e in self.E: cell = self.sul.cache.get_all_traces(row_prefix, e) while cell is None: self.query_missing_observations([row_prefix], [e]) cell = self.sul.cache.get_all_traces(row_prefix, e) row_repr += (frozenset(cell),) return row_repr def clean_obs_table(self): """ Moves duplicates from S to S_dot_A. The entries in S_dot_A which are based on the moved row get deleted. The table will be smaller and more efficient. """ tmp_S = self.S.copy() tmp_both_S = self.S + self.get_extended_S() hashed_rows_from_s = set() tmp_S.sort(key=lambda t: len(t[0])) for s in tmp_S: hashed_s_row = self.row_to_hashable(s) if hashed_s_row in hashed_rows_from_s: if s in self.S: self.S.remove(s) size = len(s[0]) for row_prefix in tmp_both_S: s_both_row = (row_prefix[0][:size], row_prefix[1][:size]) if s != row_prefix and s == s_both_row: if row_prefix in self.S: self.S.remove(row_prefix) else: hashed_rows_from_s.add(hashed_s_row) def gen_hypothesis(self, stochastic=False): """ Generate automaton based on the values found in the observation table. If stochastic is set to True, returns a Stochastic Mealy Machine. Returns: Current hypothesis """ state_distinguish = dict() states_dict = dict() initial = None stateCounter = 0 state_class = OnfsmState if not stochastic else StochasticMealyState model_class = Onfsm if not stochastic else StochasticMealyMachine for prefix in self.S: state_id = f's{stateCounter}' states_dict[prefix] = state_class(state_id) states_dict[prefix].prefix = prefix state_distinguish[self.row_to_hashable(prefix)] = states_dict[prefix] if prefix == self.S[0]: initial = states_dict[prefix] stateCounter += 1 for prefix in self.S: for a in self.A: observations_in_cell = self.sul.cache.get_all_traces(prefix, a) probability_distribution = None if stochastic: probability_distribution = self.sul.cache.get_sampling_distributions(prefix, a[0]) for obs in observations_in_cell: reached_row = (prefix[0] + a, prefix[1] + (obs[-1],)) destination = state_distinguish[self.row_to_hashable(reached_row)] assert destination if not stochastic: states_dict[prefix].transitions[a[0]].append((obs[-1], destination)) else: states_dict[prefix].transitions[a[0]].append((destination, obs[-1], probability_distribution[obs[-1]])) assert initial automaton = model_class(initial, [s for s in states_dict.values()]) automaton.characterization_set = self.E return automaton ================================================ FILE: aalpy/learning_algs/non_deterministic/TraceTree.py ================================================ from collections import defaultdict class Node: __slots__ = ['output', 'children', 'parent', 'frequency_counter'] def __init__(self, output): self.output = output self.children = defaultdict(list) self.parent = None # frq counter self.frequency_counter = 0 def get_child(self, inp, out): """ Args: inp: out: Returns: """ return next((child for child in self.children[inp] if child.output == out), None) def get_prefix(self): prefix = () curr_node = self while curr_node.parent is not None: prefix = (curr_node.output,) + prefix curr_node = curr_node.parent return prefix class TraceTree: """ Tree used for keeping track of seen observations. """ def __init__(self): self.root_node = Node(None) self.curr_node = None def reset(self): self.curr_node = self.root_node def add_to_tree(self, inp, out): """ Adds new element to tree and makes it the current node Args: inp: Input out: Output """ if inp not in self.curr_node.children.keys() or \ out not in {child.output for child in self.curr_node.children[inp]}: node = Node(out) self.curr_node.children[inp].append(node) node.parent = self.curr_node self.curr_node = self.curr_node.get_child(inp, out) self.curr_node.frequency_counter += 1 def add_trace(self, inputs, outputs): self.reset() for i, o in zip(inputs, outputs): self.add_to_tree(i, o) def get_to_node(self, inputs, outputs): """ Follows the path described by inp and out and returns the node which is reached Args: inputs: Inputs outputs: Outputs Returns: Node that is reached when following the given input and output through the tree """ curr_node = self.root_node for i, o in zip(inputs, outputs): node = curr_node.get_child(i, o) if node is None: return None curr_node = node return curr_node def get_all_traces(self, prefix, e=None): """ Args: prefix: prefix e: List of inputs Returns: Traces of outputs corresponding to the input-sequence given by e """ if not prefix or not e: return [] curr_node = self.root_node for i, o in zip(prefix[0], prefix[1]): curr_node = curr_node.get_child(i, o) if curr_node is None: return [] queue = [(curr_node, 0)] reached_nodes = [] while queue: node, depth = queue.pop(0) if depth == len(e): reached_nodes.append(node) else: children_with_same_input = node.children[e[depth]] for c in children_with_same_input: queue.append((c, depth + 1)) cell = [node.get_prefix()[-len(e):] for node in reached_nodes] return cell def get_table(self, s, e): """ Generates a table from the tree Args: s: rows from S, S_dot_A, or both which should be presented in the table. e: E Returns: a table in a format that can be used for printing. """ result = {} for prefix in s: result[prefix] = {} for inp in e: result[prefix][inp] = self.get_all_traces(prefix, inp) return result def find_cex_in_cache(self, hypothesis): queue = [(self.root_node, tuple())] while queue: curr_node, path = queue.pop(0) if path: hypothesis.reset_to_initial() inputs, outputs = [], [] for i, o in zip(path[0::2], path[1::2]): inputs.append(i) outputs.append(o) out = hypothesis.step_to(i, o) if out is None: return inputs, outputs for inp in curr_node.children.keys(): children = curr_node.children[inp] for child in children: # if curr_node.frequency_counter[(inp, child_out)] >= threshold: queue.append((child, path + (inp, child.output))) return None def get_s_e_sampling_frequency(self, prefix, suffix): sampling_frequency = 0 curr_node = self.root_node for i, o in zip(prefix[0], prefix[1]): curr_node = curr_node.get_child(i, o) if curr_node is None: return 0 queue = [(curr_node, 0)] while queue: node, depth = queue.pop(0) children_with_same_input = node.children[suffix[depth]] if depth == len(suffix) - 1: for c in children_with_same_input: sampling_frequency += c.frequency_counter else: for c in children_with_same_input: queue.append((c, depth + 1)) return sampling_frequency def get_sampling_distributions(self, prefix, input_from_alphabet): sampling_distribution = {} curr_node = self.root_node for i, o in zip(prefix[0], prefix[1]): curr_node = curr_node.get_child(i, o) children = curr_node.children[input_from_alphabet] sampling_sum = sum(c.frequency_counter for c in children) for c in children: sampling_distribution[c.output] = c.frequency_counter / sampling_sum return sampling_distribution ================================================ FILE: aalpy/learning_algs/non_deterministic/__init__.py ================================================ ================================================ FILE: aalpy/learning_algs/stochastic/DifferenceChecker.py ================================================ from abc import ABC, abstractmethod from math import sqrt, log chi2_table = dict() chi2_table[0.95] = \ dict([(1, 3.841458820694124), (2, 5.991464547107979), (3, 7.814727903251179), (4, 9.487729036781154), (5, 11.070497693516351), (6, 12.591587243743977), (7, 14.067140449340169), (8, 15.50731305586545), (9, 16.918977604620448), (10, 18.307038053275146), (11, 19.67513757268249), (12, 21.02606981748307), (13, 22.362032494826934), (14, 23.684791304840576), (15, 24.995790139728616), (16, 26.29622760486423), (17, 27.58711163827534), (18, 28.869299430392623), (19, 30.14352720564616), (20, 31.410432844230918)]) chi2_table[0.99] = \ dict([(1, 6.6348966010212145), (2, 9.21034037197618), (3, 11.344866730144373), (4, 13.276704135987622), (5, 15.08627246938899), (6, 16.811893829770927), (7, 18.475306906582357), (8, 20.090235029663233), (9, 21.665994333461924), (10, 23.209251158954356), (11, 24.724970311318277), (12, 26.216967305535853), (13, 27.68824961045705), (14, 29.141237740672796), (15, 30.57791416689249), (16, 31.999926908815176), (17, 33.40866360500461), (18, 34.805305734705065), (19, 36.19086912927004), (20, 37.56623478662507)]) chi2_table[0.999] = \ dict([(1, 10.827566170662733), (2, 13.815510557964274), (3, 16.26623619623813), (4, 18.46682695290317), (5, 20.515005652432873), (6, 22.457744484825323), (7, 24.321886347856854), (8, 26.12448155837614), (9, 27.877164871256568), (10, 29.58829844507442), (11, 31.264133620239985), (12, 32.90949040736021), (13, 34.52817897487089), (14, 36.12327368039813), (15, 37.69729821835383), (16, 39.252354790768464), (17, 40.79021670690253), (18, 42.31239633167996), (19, 43.82019596451753), (20, 45.31474661812586)]) class DifferenceChecker(ABC): @abstractmethod def are_cells_different(self, c1: dict, c2: dict, **kwargs) -> bool: pass def difference_value(self, c1: dict, c2: dict): return None def use_diff_value(self): return False class HoeffdingChecker(DifferenceChecker): def __init__(self, alpha=0.05): self.alpha = alpha def are_cells_different(self, c1: dict, c2: dict, **kwargs) -> bool: if c1.keys() != c2.keys(): return True n1 = sum(c1.values()) n2 = sum(c2.values()) if n1 > 0 and n2 > 0: for o in c1.keys(): if abs(c1[o] / n1 - c2[o] / n2) > \ ((sqrt(1 / n1) + sqrt(1 / n2)) * sqrt(0.5 * log(2 / self.alpha))): return True return False def compute_epsilon(alpha1, n1): epsilon1 = sqrt((1. / (2 * n1)) * log(2. / alpha1)) return epsilon1 class AdvancedHoeffdingChecker(DifferenceChecker): def __init__(self, alpha=0.05, use_diff=False): self.alpha = alpha self.use_diff = use_diff def are_cells_different(self, c1: dict, c2: dict, **kwargs) -> bool: n1 = sum(c1.values()) n2 = sum(c2.values()) if n1 > 0 and n2 > 0: for o in set(c1.keys()).union(c2.keys()): c1o = c1[o] if o in c1.keys() else 0 c2o = c2[o] if o in c2.keys() else 0 alpha1 = self.alpha alpha2 = self.alpha epsilon1 = compute_epsilon(alpha1, n1) epsilon2 = compute_epsilon(alpha2, n2) if abs(c1o / n1 - c2o / n2) > epsilon1 + epsilon2: return True return False def use_diff_value(self): return self.use_diff def difference_value(self, c1_out_freq: dict, c2_out_freq: dict): n1 = 0 if not c1_out_freq else sum(c1_out_freq.values()) n2 = 0 if not c2_out_freq else sum(c2_out_freq.values()) if n1 > 0 and n2 > 0: dist = 0 for o in set(c1_out_freq.keys()).union(c2_out_freq.keys()): c1o = c1_out_freq[o] if o in c1_out_freq.keys() else 0 c2o = c2_out_freq[o] if o in c2_out_freq.keys() else 0 dist += abs(c1o / n1 - c2o / n2) return dist elif n1 > 0 or n2 > 0: alpha1 = self.alpha alpha2 = self.alpha epsilon1 = compute_epsilon(alpha1, max(n1, n2)) epsilon2 = compute_epsilon(alpha2, max(n1, n2)) return epsilon1 + epsilon2 else: return 0 class ChiSquareChecker(DifferenceChecker): def __init__(self, alpha=0.001, use_diff_value=False): self.alpha = alpha self.chi2_cache = dict() if 1 - self.alpha not in chi2_table.keys(): raise ValueError("alpha must be in [0.01,0.001,0.05]") self.chi2_values = chi2_table[1 - self.alpha] self.use_diff = use_diff_value def are_cells_different(self, c1_out_freq: dict, c2_out_freq: dict, **kwargs) -> bool: # chi square test for homogeneity (see, for instance: https://online.stat.psu.edu/stat415/lesson/17/17.1) if not c1_out_freq or not c2_out_freq: return False keys = list(set(c1_out_freq.keys()).union(c2_out_freq.keys())) dof = len(keys) - 1 if dof == 0: return False shared_keys = set(c1_out_freq.keys()).intersection(c2_out_freq.keys()) if len(shared_keys) == 0: # if the supports of the tested frequencies are completely then chi2 makes no sense, use the Hoeffding test # to determine if there are enough observations for a difference hoeffding_checker = AdvancedHoeffdingChecker() return hoeffding_checker.are_cells_different(c1_out_freq, c2_out_freq) Q = self.compute_Q(c1_out_freq, c2_out_freq, keys) if dof not in self.chi2_values.keys(): raise ValueError("Too many possible outputs, chi2 table needs to be extended.") else: chi2_val = self.chi2_values[dof] return Q >= chi2_val def use_diff_value(self): return self.use_diff def difference_value(self, c1_out_freq: dict, c2_out_freq: dict): if not c1_out_freq or not c2_out_freq: # return a value on the threshold if we don't have information c1_outs = set(c1_out_freq.keys()) if c1_out_freq else set() c2_outs = set(c2_out_freq.keys()) if c2_out_freq else set() nr_outs = len(c1_outs.union(c2_outs)) return self.chi2_values[max(1, nr_outs)] keys = list(set(c1_out_freq.keys()).union(c2_out_freq.keys())) shared_keys = set(c1_out_freq.keys()).intersection(c2_out_freq.keys()) dof = len(keys) - 1 if dof == 0: return 0 Q = self.compute_Q(c1_out_freq, c2_out_freq, keys) return Q def compute_Q(self, c1_out_freq, c2_out_freq, keys): n_1 = sum(c1_out_freq.values()) n_2 = sum(c2_out_freq.values()) Q = 0 default_val = 0 yates_correction = -0.5 if len(keys) == 2 and \ any(c1_out_freq.get(k, 0) < 5 or c2_out_freq.get(k, 0) < 5 for k in keys) else 0 for k in keys: p_hat_k = float(c1_out_freq.get(k, default_val) + c2_out_freq.get(k, default_val)) / (n_1 + n_2) q_1_k = float(((abs(c1_out_freq.get(k, default_val) - n_1 * p_hat_k)) + yates_correction) ** 2) / ( n_1 * p_hat_k) q_2_k = float(((abs(c2_out_freq.get(k, default_val) - n_2 * p_hat_k)) + yates_correction) ** 2) / ( n_2 * p_hat_k) Q = Q + q_1_k + q_2_k return Q ================================================ FILE: aalpy/learning_algs/stochastic/SamplingBasedObservationTable.py ================================================ from collections import defaultdict from aalpy.automata import Mdp, MdpState, StochasticMealyState, StochasticMealyMachine from .DifferenceChecker import DifferenceChecker from .StochasticTeacher import StochasticTeacher, Node from ...utils.HelperFunctions import is_suffix_of class SamplingBasedObservationTable: def __init__(self, input_alphabet: list, automaton_type, teacher: StochasticTeacher, compatibility_checker: DifferenceChecker, alpha=0.05, strategy='normal', cex_processing=None): """Constructor of the observation table. Initial queries are asked in the constructor. Args: input_alphabet: input alphabet teacher: stochastic teacher alpha: constant used in Hoeffding bound """ self.compatibility_checker = compatibility_checker assert input_alphabet is not None and teacher is not None self.automaton_type = automaton_type self.input_alphabet = [tuple([a]) for a in input_alphabet] self.S = list() # prefixes of S self.E = [tuple([a]) for a in input_alphabet] self.T = defaultdict(dict) self.teacher = teacher self.empty_word = tuple() self.alpha = alpha self.strategy = strategy self.cex_processing = cex_processing # initial output if automaton_type == 'mdp': self.initial_output = tuple(teacher.initial_value) self.S.append(self.initial_output) else: self.S.append(tuple()) # Cache self.compatibility_classes_representatives = None self.compatibility_class = dict() self.freq_query_cache = dict() self.unambiguity_values = [] def refine_not_completed_cells(self, n_resample, uniform=False): """ Firstly a prefix-tree acceptor is constructed for all non-completed cells and then that tree is used for online testing/sampling. Args: uniform: if true, all cells will be uniformly sampled (Default value = False) n_resample: Number of resamples Returns: False if no cells are to be refined, True if refining happened """ if self.automaton_type == 'mdp': pta_root = Node(self.initial_output[0]) else: pta_root = Node(None) dynamic = 0 if self.strategy == 'classic': to_refine = [] for s in self.S + list(self.get_extended_s()): for e in self.E: if not self.teacher.complete_query(s, e): to_refine.append(s + e) if not to_refine: return False to_refine.sort(key=len, reverse=True) for trace in to_refine: self.add_to_PTA(pta_root, trace) else: for s in self.S + list(self.get_extended_s()): if uniform: for e in self.E: self.add_to_PTA(pta_root, s + e, 1) else: for e in self.E: longest_row_trace_prefix = (s + e)[:-1] while longest_row_trace_prefix not in self.T.keys(): longest_row_trace_prefix = longest_row_trace_prefix[:-1] row_repr = 0 for r in self.compatibility_classes_representatives: if self.are_rows_compatible(longest_row_trace_prefix, r): row_repr += 1 # row_repr can be zero for non-closed # (int(row_repr - 1 * 2)) uncertainty_value = max((row_repr - 1) * 2, 1) dynamic += uncertainty_value self.add_to_PTA(pta_root, s + e, uncertainty_value) resample_value = n_resample if self.strategy == 'classic' else max(dynamic // 2, 500) for i in range(resample_value): self.teacher.tree_query(pta_root) return True def update_obs_table_with_freq_obs(self, element_of_s=None): """ Updates cells in the observation table with frequency data. If the row in S has no extension yet, it is generated and its cells populated. Args: element_of_s: if not None, selected row and its extensions will be updated (Default value = None) Returns: """ if element_of_s: s_set = element_of_s + list(self.get_extended_s(element_of_s=element_of_s)) else: s_set = self.S + list(self.get_extended_s()) # s_set = element_of_s if else self.S + list(self.get_extended_s()) for s in s_set: for e in self.E: self.T[s][e] = self.teacher.frequency_query(s, e) self.freq_query_cache[s + e] = self.T[s][e] def get_extended_s(self, element_of_s=None): """Generator returning all elements of the extended S set. Args: element_of_s: (Default value = None) Returns: """ s_set = element_of_s if element_of_s else self.S for s in s_set: for i in self.input_alphabet: if s + i in self.freq_query_cache.keys(): freq_dict = self.freq_query_cache[s + i] else: freq_dict = self.teacher.frequency_query(s, i) for out, freq in freq_dict.items(): new_pref = s + i + tuple([out]) if freq > 0 and new_pref not in self.S: yield new_pref def make_closed_and_consistent(self): """ Observation table is updated until it is closed and consistent. Note that due the updated notion of row equivalence no sampling is needed. """ self.update_compatibility_classes() while True: closed, consistent = False, False row_to_close = self.get_row_to_close() if not row_to_close: closed = True if row_to_close: self.S.append(row_to_close) self.update_obs_table_with_freq_obs(element_of_s=[row_to_close]) self.update_compatibility_classes() consistency_violation = self.get_consistency_violation() if not consistency_violation: consistent = True if consistency_violation: if consistency_violation not in self.E: self.E.append(consistency_violation) self.update_obs_table_with_freq_obs() self.update_compatibility_classes() if closed and consistent: break def get_row_to_close(self): """ Returns a row that is not closed. Returns: row that needs to be closed """ for lt in self.get_extended_s(): row_is_closed = False for r in self.compatibility_classes_representatives: if self.are_rows_compatible(r, lt): row_is_closed = True break if not row_is_closed: return lt return None def get_consistency_violation(self, ignore=None): """Find and return cause of consistency violation. Only computed on the compatibility class representatives. :return: element of input + element of output + element of e that lead to the inconsistency Args: ignore: (Default value = None) Returns: i + o + e that violate consistency """ if self.cex_processing is not None: return None for ind, s1 in enumerate(self.S): for s2 in self.S[ind + 1:]: if self.are_rows_compatible(s1, s2, ignore): i_o_pairs = [(i, tuple([o])) for i in self.input_alphabet for o in self.T[s1][i].keys()] for i, o in i_o_pairs: s1_keys = self.T[s1 + i + o].keys() s2_keys = self.T[s2 + i + o].keys() if not s1_keys or not s2_keys: continue for e in self.E: if e == ignore: continue if self.are_cells_incompatible(s1 + i + o, s2 + i + o, e): return i + o + e return None def get_representative(self, target): """ Args: target: row in the observation table Returns: a representative compatible with the target """ if self.compatibility_checker.use_diff_value(): smallest_diff_value = 2 ** 32 best_rep = None if target in self.compatibility_classes_representatives: return target for r in self.compatibility_classes_representatives: if self.automaton_type == "mdp" and r[-1] != target[-1]: continue if not self.are_rows_compatible(r, target): continue diff_value = 0 row_target = self.T[target] row_r = self.T[r] for e in self.E: diff_value += self.compatibility_checker.difference_value(row_r.get(e, None), row_target.get(e, None)) if diff_value < smallest_diff_value: # if smallest_diff_value != 2**32: # print("Found a better rep") smallest_diff_value = diff_value best_rep = r return best_rep else: if target in self.S: for r in self.compatibility_classes_representatives: if target == r or target in self.compatibility_class[r]: return r else: for r in self.compatibility_classes_representatives: if self.are_rows_compatible(r, target): return r assert False def trim_columns(self): """ """ reverse_sorted_E = list(self.E) reverse_sorted_E.sort(key=len, reverse=True) to_remove = [] to_keep = [] self.update_obs_table_with_freq_obs() self.make_closed_and_consistent() # need a closed observation table for e in reverse_sorted_E: if e in self.input_alphabet: continue contains_dependent = False for other_e in to_keep: if is_suffix_of(e, other_e): contains_dependent = True if contains_dependent: to_keep.append(e) elif self.get_consistency_violation(e): to_keep.append(e) else: self.E.remove(e) # need to remove here for get_consistency_violation to work to_remove.append(e) for e in to_remove: for s in self.T.keys(): if e in self.T[s]: self.T[s].pop(e) def trim(self, hypothesis): """ Removes unnecessary rows from the observation table. Args: hypothesis: """ prefix_to_state_dict = {state.prefix: state for state in hypothesis.states} to_remove = [] for s in self.S: if s in self.compatibility_classes_representatives or s in to_remove: continue rep = self.get_representative(s) if self.automaton_type == 'mdp': if 'chaos' in {t[0].output for transitions in prefix_to_state_dict[rep].transitions.values() for t in transitions}: continue else: if 'chaos' in {t[1] for transitions in prefix_to_state_dict[rep].transitions.values() for t in transitions}: continue num_compatible_repr = 0 row_is_prefix = False for r in self.compatibility_classes_representatives: if self.are_rows_compatible(r, s): num_compatible_repr += 1 if len(s) < len(r) and s == r[:len(s)]: row_is_prefix = True continue if num_compatible_repr != 1 or row_is_prefix: continue to_remove.append(s) for otherS in self.S: if s == otherS[:len(s)] and otherS not in to_remove: to_remove.append(otherS) for s in to_remove: self.S.remove(s) self.T.pop(s, None) for i in self.input_alphabet: for o in self.T[s + i]: self.T.pop(s + i + o, None) if not self.cex_processing: self.trim_columns() else: self.update_obs_table_with_freq_obs() def stop(self, learning_round, chaos_cex_present, cex, stopping_range_dict, min_rounds=10, max_rounds=None, target_unambiguity=0.99, print_unambiguity=False): """ Decide if learning should terminate. Args: learning_round: current learning round chaos_cex_present: is chaos counterexample present in the hypothesis cex: counterexample found by the eq oracle stopping_range_dict: dictionary where keys are number of last unambiguity values and value is maximum differance allowed between them min_rounds: minimum number of learning rounds (Default value = 5) max_rounds: maximum number of learning rounds (Default value = None) target_unambiguity: percentage of rows with unambiguous representatives (Default value = 0.99) print_unambiguity: if true, current unambiguity rate will be printed (Default value = False) Returns: True if stopping condition satisfied, false otherwise """ if max_rounds: assert min_rounds <= max_rounds if max_rounds and learning_round == max_rounds: return True if chaos_cex_present or cex is not None: return False extended_s = list(self.get_extended_s()) self.update_compatibility_classes() numerator = 0 for row in self.S + extended_s: row_repr = 0 for r in self.compatibility_classes_representatives: if self.are_rows_compatible(row, r): row_repr += 1 numerator += 1 if row_repr == 1 else 0 unambiguous_rows_percentage = numerator / len(self.S + extended_s) self.unambiguity_values.append(unambiguous_rows_percentage) if self.strategy != 'classic' and learning_round >= min_rounds: # keys are number of last unambiguity values and value is maximum differance allowed between them for num_last, diff in stopping_range_dict.items(): if len(self.unambiguity_values) < num_last: continue last_n_unamb = self.unambiguity_values[-num_last:] if abs(max(last_n_unamb) - min(last_n_unamb) <= diff): return True if print_unambiguity and learning_round % 5 == 0: print(f'Unambiguous rows: {round(unambiguous_rows_percentage * 100, 2)}%;' f' {numerator} out of {len(self.S + extended_s)}') if learning_round >= min_rounds and unambiguous_rows_percentage >= target_unambiguity: return True return False def get_unamb_percentage(self): extended_s = list(self.get_extended_s()) self.update_compatibility_classes() numerator = 0 for row in self.S + extended_s: row_repr = 0 for r in self.compatibility_classes_representatives: if self.are_rows_compatible(row, r): row_repr += 1 numerator += 1 if row_repr == 1 else 0 unambiguous_rows_percentage = numerator / len(self.S + extended_s) return round(unambiguous_rows_percentage * 100, 2) def are_cells_incompatible(self, s1, s2, e): """ Checks if 2 cells are considered different. Args: s1: prefix of row s1 s2: prefix of row s2 e: element of E Returns: True if cells are different, false otherwise """ if self.strategy == 'classic': if self.teacher.complete_query(s1, e) and self.teacher.complete_query(s2, e): return self.compatibility_checker.are_cells_different(self.T[s1][e], self.T[s2][e]) elif self.strategy == 'normal' or self.strategy == 'chi2': if e in self.T[s1] and e in self.T[s2]: return self.compatibility_checker.are_cells_different(self.T[s1][e], self.T[s2][e]) else: if e in self.T[s1] and e in self.T[s2]: return self.compatibility_checker.are_cells_different(self.T[s1][e], self.T[s2][e], s1=s1, s2=s2, e=e) return False def are_rows_compatible(self, s1, s2, e_ignore=None): """ Check if the rows are compatible. Rows are compatible if all cells are compatible(not different) and their prefixes end in the same output element. Args: s1: prefix of row s1 s2: prefix of row s2 e_ignore: e not considered for the computation of row compatibility (Default value = None) Returns: True if rows are compatible, False otherwise """ if self.automaton_type == 'mdp' and s1[-1] != s2[-1]: return False for e in self.E: if e == e_ignore: continue if self.are_cells_incompatible(s1, s2, e): return False return True def update_compatibility_classes(self): """Updates the compatibility classes and stores their representatives.""" self.compatibility_class.clear() class_rank_pair = [] for s in self.S: rank = sum([sum(self.T[s][i].values()) for i in self.input_alphabet]) class_rank_pair.append((s, rank)) # sort according to frequency class_rank_pair.sort(key=lambda x: x[1], reverse=True) # # sort according to prefix length, and elements of same length sort by value # class_rank_pair = [(s, -rank) for (s, rank) in class_rank_pair] # class_rank_pair.sort(key=lambda x: (len(x[0]), x[1])) # class_rank_pair = [(s, -rank) for (s, rank) in class_rank_pair] compatibility_classes = [c[0] for c in class_rank_pair] tmp_classes = list(compatibility_classes) not_partitioned = list(self.S) representatives = [] while not_partitioned: r = tmp_classes.pop(0) not_partitioned.remove(r) cg_r = [s for s in not_partitioned if self.are_rows_compatible(r, s)] self.compatibility_class[r] = cg_r representatives.append(r) for sp in cg_r: not_partitioned.remove(sp) tmp_classes.remove(sp) self.compatibility_classes_representatives = representatives def chaos_counterexample(self, hypothesis): """ Check whether the chaos state is reachable. Args: hypothesis: current hypothesis Returns: True if chaos state is reachable, False otherwise """ for state in hypothesis.states: if self.automaton_type == "mdp" and state.output == "chaos" \ or self.automaton_type == "smm" and state.state_id == "chaos": # we are not interested in chaos state, but in prefix to chaos continue for i in self.input_alphabet: output_states = state.transitions[i[0]] if self.automaton_type == 'mdp': for (s, _) in output_states: if s.output == 'chaos': return True # return state.prefix + i else: for (_, o, _) in output_states: if o == 'chaos': return True # return state.prefix return False # return None def add_to_PTA(self, pta_root, trace, uncertainty_value=None): """Adds a trace to the PTA. PTA is later used for online sampling. The uncertainty value is added to inputs as frequencies, which specify how often a particular input should be sampled. Args: pta_root: root of the prefix tree acceptor trace: trace to add to the PTA uncertainty_value: uncertainty value (Default value = None) Returns: """ curr_node = pta_root start = 1 if self.automaton_type == 'mdp' else 0 for index in range(start, len(trace), 2): inp = trace[index] if uncertainty_value: # use frequencies for uncertainties curr_node.input_frequencies[inp] += uncertainty_value # need to add a dummy output in the leaves output = trace[index + 1] if index + 1 < len(trace) else "dummy" child = curr_node.get_child(inp, output) if child: curr_node = child else: new_node = Node(output) curr_node.children[inp][output] = new_node curr_node = new_node def generate_hypothesis(self): """Generates the hypothesis from the observation table. :return: current hypothesis Args: Returns: """ r_state_map = dict() state_counter = 0 for r in self.compatibility_classes_representatives: if self.automaton_type == 'mdp': r_state_map[r] = MdpState(state_id=f's{state_counter}', output=r[-1]) else: r_state_map[r] = StochasticMealyState(state_id=f's{state_counter}') r_state_map[r].prefix = r state_counter += 1 if self.automaton_type == 'mdp': r_state_map['chaos'] = MdpState(state_id=f's{state_counter}', output='chaos') for i in self.input_alphabet: r_state_map['chaos'].transitions[i[0]].append((r_state_map['chaos'], 1.)) else: r_state_map['chaos'] = StochasticMealyState(state_id=f'chaos') for i in self.input_alphabet: r_state_map['chaos'].transitions[i[0]].append((r_state_map['chaos'], 'chaos', 1.)) for s in self.compatibility_classes_representatives: for i in self.input_alphabet: freq_dict = self.T[s][i] total_sum = sum(freq_dict.values()) origin_state = s if self.strategy == 'classic' and not self.teacher.complete_query(s, i) \ or self.strategy != 'classic' and i not in self.T[s]: if self.automaton_type == 'mdp': r_state_map[origin_state].transitions[i[0]].append((r_state_map['chaos'], 1.)) else: r_state_map[origin_state].transitions[i[0]].append((r_state_map['chaos'], 'chaos', 1.)) else: if len(freq_dict.items()) == 0: if self.automaton_type == 'mdp': r_state_map[origin_state].transitions[i[0]].append((r_state_map['chaos'], 1.)) else: r_state_map[origin_state].transitions[i[0]].append((r_state_map['chaos'], 'chaos', 1.)) else: for output, frequency in freq_dict.items(): new_state = self.get_representative(s + i + tuple([output])) if self.automaton_type == 'mdp': r_state_map[origin_state].transitions[i[0]].append( (r_state_map[new_state], frequency / total_sum)) else: r_state_map[origin_state].transitions[i[0]].append( (r_state_map[new_state], output, frequency / total_sum)) if self.automaton_type == 'mdp': return Mdp(r_state_map[self.get_representative(self.initial_output)], list(r_state_map.values())) else: return StochasticMealyMachine(r_state_map[tuple()], list(r_state_map.values())) ================================================ FILE: aalpy/learning_algs/stochastic/StochasticCexProcessing.py ================================================ from aalpy.automata import Mdp from aalpy.base import SUL def stochastic_longest_prefix(cex, prefixes): """ Counterexample processing based on Shabaz-Groz cex processing. Args: cex: counterexample prefixes: all prefixes in the observation table Returns: Single suffix. """ prefixes = list(prefixes) prefixes.sort(key=len, reverse=True) trimmed_cex = None trimmed = False for p in prefixes: if p[1::2] == cex[:len(p)][1::2]: trimmed_cex = cex[len(p):] trimmed = True break trimmed_cex = trimmed_cex if trimmed else cex trimmed_cex = list(trimmed_cex) if not trimmed_cex: return () # get all suffixes and return suffixes = [tuple(trimmed_cex[len(trimmed_cex) - i - 1:]) for i in range(0, len(trimmed_cex), 2)] # prefixes # need to pop 0 for MDP, for SMM remove the line # trimmed_cex.pop(0) # prefixes = [tuple(trimmed_cex[:i + 1]) for i in range(0, len(trimmed_cex), 2)] return suffixes def stochastic_rs(sul: SUL, cex: tuple, hypothesis): """Rivest-Schapire counter example processing. Args: sul: system under learning cex: found counterexample hypothesis: hypothesis on which counterexample was found Returns: suffixes to be added to the E set """ # cex_out = self.sul.query(tuple(cex)) if isinstance(hypothesis, Mdp): cex = cex[1:] inputs = tuple(cex[::2]) outputs = tuple(cex[1::2]) # cex_out = self.teacher.sul.query(cex) lower = 1 upper = len(inputs) - 2 while True: hypothesis.reset_to_initial() mid = (lower + upper) // 2 # arr[:n] -> first n values # arr[n:] -> last n values for i, o in zip(inputs[:mid], outputs[:mid]): hypothesis.step_to(i, o) s_bracket = hypothesis.current_state.prefix # prefix in hyp is reached prefix_inputs = s_bracket[1::2] if isinstance(hypothesis, Mdp) else s_bracket[::2] # prefix_outputs = s_bracket[0::2] if isinstance(hypothesis, Mdp) else s_bracket[1::2] not_same = False prefix_reached = False while not prefix_reached: hypothesis.reset_to_initial() sul.post() sul.pre() repeat = False for inp in prefix_inputs: o_sul = sul.step(inp) o_hyp = hypothesis.step_to(inp, o_sul) if o_hyp is None: repeat = True break prefix_reached = not repeat for inp in inputs[mid:]: o_sul = sul.step(inp) o_hyp = hypothesis.step_to(inp, o_sul) if o_hyp is None: not_same = True break if not not_same: lower = mid + 1 if upper < lower: suffix = cex[(mid + 1) * 2:] break else: upper = mid - 1 if upper < lower: suffix = cex[mid * 2:] break suffixes = [tuple(suffix[len(suffix) - i - 1:]) for i in range(0, len(suffix), 2)] # suffixes = [suffixes[-1]] # print(len(cex), len(suffixes[-1])) return suffixes ================================================ FILE: aalpy/learning_algs/stochastic/StochasticLStar.py ================================================ import time from aalpy.base import SUL, Oracle from aalpy.learning_algs.stochastic.DifferenceChecker import AdvancedHoeffdingChecker, HoeffdingChecker, \ ChiSquareChecker, DifferenceChecker from aalpy.learning_algs.stochastic.SamplingBasedObservationTable import SamplingBasedObservationTable from aalpy.learning_algs.stochastic.StochasticCexProcessing import stochastic_longest_prefix, stochastic_rs from aalpy.learning_algs.stochastic.StochasticTeacher import StochasticTeacher from aalpy.utils.HelperFunctions import print_learning_info, print_observation_table, get_cex_prefixes, \ get_available_oracles_and_err_msg from aalpy.utils.ModelChecking import stop_based_on_confidence strategies = ['classic', 'normal', 'chi2'] cex_sampling_options = [None, 'bfs'] cex_processing_options = [None, 'longest_prefix', 'rs'] print_options = [0, 1, 2, 3] diff_checker_options = {'classic': HoeffdingChecker(), 'chi2': ChiSquareChecker(), 'normal': AdvancedHoeffdingChecker()} available_oracles, available_oracles_error_msg = get_available_oracles_and_err_msg() def run_stochastic_Lstar(input_alphabet, sul: SUL, eq_oracle: Oracle, target_unambiguity=0.99, min_rounds=10, max_rounds=200, automaton_type='mdp', strategy='normal', cex_processing=None, samples_cex_strategy=None, stopping_range_dict='strict', custom_oracle=False, return_data=False, property_based_stopping=None, n_c=20, n_resample=100, print_level=2): """ Learning of Markov Decision Processes and Stochastic Mealy machines based on 'L*-Based Learning of Markov Decision Processes' and 'Active Model Learning of Stochastic Reactive Systems' by Tappler et al. Args: input_alphabet: input alphabet sul: system under learning eq_oracle: equivalence oracle target_unambiguity: target unambiguity value (default 0.99) min_rounds: minimum number of learning rounds (Default value = 10) max_rounds: if learning_rounds >= max_rounds, learning will stop (Default value = 200) automaton_type: either 'mdp' or 'smm' (Default value = 'mdp') strategy: either one of ['classic', 'normal', 'chi2'] or a object implementing DifferenceChecker class, default value is 'normal'. Classic strategy is the one presented in the seed paper, 'normal' is the updated version and chi2 is based on chi squared. cex_processing: cex processing strategy, None , 'longest_prefix' or 'rs' (rs is experimental) samples_cex_strategy: strategy for finding counterexamples in the trace tree. None, 'bfs' or "random:<#traces to check:int>:" eg. random:200:0.2 stopping_range_dict: Values in form of a dictionary, or 'strict', 'relaxed' to use predefined stopping criteria. Custom values: Dictionary where keys encode the last n unambiguity values which need to be in range of its value in order to perform early stopping. Eg. {5: 0.001, 10: 0.01} would stop if last 5 hypothesis had unambiguity values when max(last_5_vals) - (last_5_vals) <= 0.001. property_based_stopping: A tuple containing (path to the properties file, correct values of each property, allowed error for each property. Recommended one is 0.02 (2%)). custom_oracle: if True, warning about oracle type will be removed and custom oracle can be used return_data: if True, map containing all information like number of queries... will be returned (Default value = False) n_c: cutoff for a cell to be considered complete (Default value = 20), only used with 'classic' strategy n_resample: resampling size (Default value = 100), only used with 'classic' strategy print_level: 0 - None, 1 - just results, 2 - current round and hypothesis size, 3 - educational/debug (Default value = 2) Returns: learned MDP/SMM """ assert samples_cex_strategy in cex_sampling_options or samples_cex_strategy.startswith('random') assert cex_processing in cex_processing_options assert automaton_type in {'mdp', 'smm'} if not isinstance(stopping_range_dict, dict): assert stopping_range_dict in {'strict', 'relaxed'} if property_based_stopping: assert len(property_based_stopping) == 3 if strategy in diff_checker_options: compatibility_checker = diff_checker_options[strategy] else: assert isinstance(strategy, DifferenceChecker) compatibility_checker = strategy if not custom_oracle and type(eq_oracle) not in available_oracles: raise SystemExit(available_oracles_error_msg) if stopping_range_dict == 'strict': stopping_range_dict = {12: 0.001, 18: 0.002, 25: 0.005, 30: 0.01, 35: 0.02} elif stopping_range_dict == 'relaxed': stopping_range_dict = {7: 0.001, 12: 0.003, 17: 0.005, 22: 0.01, 28: 0.02} stochastic_teacher = StochasticTeacher(sul, n_c, eq_oracle, automaton_type, compatibility_checker, samples_cex_strategy=samples_cex_strategy) # This way all steps from eq. oracle will be added to the tree eq_oracle.sul = stochastic_teacher.sul observation_table = SamplingBasedObservationTable(input_alphabet, automaton_type, stochastic_teacher, compatibility_checker=compatibility_checker, strategy=strategy, cex_processing=cex_processing) start_time = time.time() eq_query_time = 0 # Ask queries for non-completed cells and update the observation table observation_table.refine_not_completed_cells(n_resample, uniform=True) observation_table.update_obs_table_with_freq_obs() learning_rounds = 0 while True: learning_rounds += 1 observation_table.make_closed_and_consistent() hypothesis = observation_table.generate_hypothesis() observation_table.trim(hypothesis) # If there is no chaos state is not reachable, remove it from state set chaos_cex_present = observation_table.chaos_counterexample(hypothesis) if not chaos_cex_present: if automaton_type == 'mdp': hypothesis.states.remove(next(state for state in hypothesis.states if state.output == 'chaos')) else: hypothesis.states.remove(next(state for state in hypothesis.states if state.state_id == 'chaos')) if print_level > 1: print(f'Hypothesis: {learning_rounds}: {len(hypothesis.states)} states.') if print_level == 3: print_observation_table(observation_table, 'stochastic') cex = None if not chaos_cex_present: eq_query_start = time.time() cex = stochastic_teacher.equivalence_query(hypothesis) eq_query_time += time.time() - eq_query_start if cex: if print_level == 3: print('Counterexample', cex) # get all prefixes and add them to the S set if cex_processing is None: for pre in get_cex_prefixes(cex, automaton_type): if pre not in observation_table.S: observation_table.S.append(pre) else: suffixes = None if cex_processing == 'longest_prefix': prefixes = observation_table.S + list(observation_table.get_extended_s()) suffixes = [stochastic_longest_prefix(cex, prefixes)[-1]] elif cex_processing == 'rs': suffixes = stochastic_rs(sul, cex, hypothesis) for suf in suffixes: if suf not in observation_table.E: observation_table.E.append(suf) break # Ask queries for non-completed cells and update the observation table refined = observation_table.refine_not_completed_cells(n_resample) observation_table.update_obs_table_with_freq_obs() if property_based_stopping and learning_rounds >= min_rounds: # stop based on maximum allowed error if stop_based_on_confidence(hypothesis, property_based_stopping, print_level): break else: # stop based on number of unambiguous rows stop_based_on_unambiguity = observation_table.stop(learning_rounds, chaos_cex_present, cex, stopping_range_dict, target_unambiguity=target_unambiguity, min_rounds=min_rounds, max_rounds=max_rounds, print_unambiguity=print_level > 1) if stop_based_on_unambiguity: break if not refined: break total_time = round(time.time() - start_time, 2) eq_query_time = round(eq_query_time, 2) learning_time = round(total_time - eq_query_time, 2) info = { 'learning_rounds': learning_rounds, 'automaton_size': len(hypothesis.states), 'queries_learning': stochastic_teacher.sul.num_queries - eq_oracle.num_queries, 'steps_learning': stochastic_teacher.sul.num_steps - eq_oracle.num_queries, 'queries_eq_oracle': eq_oracle.num_queries, 'steps_eq_oracle': eq_oracle.num_steps, 'learning_time': learning_time, 'eq_oracle_time': eq_query_time, 'total_time': total_time } if print_level > 0: print_learning_info(info) if return_data: return hypothesis, info return hypothesis ================================================ FILE: aalpy/learning_algs/stochastic/StochasticTeacher.py ================================================ from collections import defaultdict from random import choice, random from aalpy.base import SUL from aalpy.learning_algs.stochastic.DifferenceChecker import DifferenceChecker class StochasticSUL(SUL): def __init__(self, sul, teacher): super().__init__() self.sul = sul self.teacher = teacher def pre(self): self.num_queries += 1 self.teacher.back_to_root() return self.sul.pre() def post(self): self.sul.post() def step(self, letter): self.num_steps += 1 out = self.sul.step(letter) self.teacher.add(letter, out) return out class Node: """ Node of the cache/multiset of all traces. """ def __init__(self, output): self.output = output self.frequency = 0 self.children = defaultdict(dict) self.input_frequencies = defaultdict(int) def get_child(self, inp, out): """ Args: inp: input out: output Returns: Child with output that equals to `out` reached when performing `inp`. If such child does not exist, return None. """ if inp not in self.children.keys() or out not in self.children[inp].keys(): return None return self.children[inp][out] def get_frequency_sum(self, input_letter): """ Returns: number of times input was observed in current state """ return self.input_frequencies[input_letter] def get_output_frequencies(self, input_letter): """ Args: input_letter: input Returns: observed outputs and their frequencies for given `input_letter` in the current state """ if input_letter not in self.children.keys(): return dict() return {child.output: child.frequency for child in self.children[input_letter].values()} class StochasticTeacher: """ The sampling-based teacher maintains a multiset of traces S for the estimation of output distributions. Whenever new traces are sampled in the course of learning, they are added to S. """ def __init__(self, sul: SUL, n_c, eq_oracle, automaton_type, compatibility_checker: DifferenceChecker, samples_cex_strategy=None): self.automaton_type = automaton_type if automaton_type == 'mdp': self.initial_value = sul.query(tuple()) self.root_node = Node(self.initial_value[-1]) else: self.root_node = Node(None) self.sul = StochasticSUL(sul=sul, teacher=self) self.eq_oracle = eq_oracle self.n_c = n_c self.curr_node = None # cache self.complete_query_cache = set() self.compatibility_checker = compatibility_checker self.samples_cex_strategy = samples_cex_strategy # eq query cache self.last_cex = None self.last_tree_cex = None def back_to_root(self): self.curr_node = self.root_node def add(self, inp, out): """ Adds a input/output to the tree. Args: inp: input out: output """ self.curr_node.input_frequencies[inp] += 1 if inp not in self.curr_node.children.keys() or out not in self.curr_node.children[inp].keys(): node = Node(out) self.curr_node.children[inp][out] = node self.curr_node = self.curr_node.children[inp][out] self.curr_node.frequency += 1 def frequency_query(self, s: tuple, e: tuple): """Output frequencies observed after trace s + e. Args: s: sequence from S set e: sequence from E set Returns: sum of output frequencies """ if self.automaton_type == 'mdp': s = s[1:] input_seq = list(s[0::2] + e[0::2]) output_seq = list(s[1::2] + e[1::2]) last_input = input_seq.pop() curr_node = self.root_node for i, o in zip(input_seq, output_seq): curr_node = curr_node.get_child(i, o) if not curr_node: return dict() output_freq = curr_node.get_output_frequencies(last_input) if sum(output_freq.values()) >= self.n_c: self.complete_query_cache.add(s + e) return output_freq def complete_query(self, s: tuple, e: tuple): """ Given a test sequences returns true if sufficient information is available to estimate an output distribution from frequency queries; returns false otherwise. Args: s: sequence from S set e: sequence from E set Returns: True if cell is completed, false otherwise """ # extract inputs and outputs if s + e in self.complete_query_cache: return True if self.automaton_type == 'mdp': s = s[1:] input_seq = list(s[0::2] + e[0::2]) output_seq = list(s[1::2] + e[1::2]) # get last input last_input = input_seq.pop() curr_node = self.root_node for i, o in zip(input_seq, output_seq): new_node = curr_node.get_child(i, o) if not new_node: curr_node_complete = curr_node.get_frequency_sum(i) >= self.n_c # if curr_node_complete: # self.complete_query_cache.add(s + e) return curr_node_complete else: curr_node = new_node sum_freq = curr_node.get_frequency_sum(last_input) if sum_freq >= self.n_c: self.complete_query_cache.add(s + e) return sum_freq >= self.n_c def tree_query(self, pta_root): """ Execute a refine query based on input/output trace. If at some point real outputs differ from expected outputs, trace to that point is added to the tree, otherwise whole trace is executed. Args: pta_root: root of the PTA Returns: number of steps taken """ self.sul.pre() curr_node = pta_root inputs = [] outputs = [] while True: if curr_node.children: frequency_sum = sum(curr_node.input_frequencies.values()) if frequency_sum == 0: # uniform sampling in case we have no information inp = choice(list(curr_node.children.keys())) else: # use float random rather than integers to be able to work with non-integer frequency information selection_value = random() * frequency_sum inp = None for i in curr_node.input_frequencies.keys(): inp = i selection_value -= curr_node.input_frequencies[i] if selection_value <= 0: break # curr_node.input_frequencies[inp] -= 1 inputs.append(inp) out = self.sul.step(inp) new_node = curr_node.get_child(inp, out) if new_node: outputs.append(out) curr_node = new_node else: self.sul.post() return else: curr_node = pta_root for i, o in zip(inputs, outputs): self.curr_node.input_frequencies[i] -= 1 curr_node = curr_node.get_child(i, o) self.sul.post() return def single_dfs_for_cex(self, stop_prob, hypothesis): curr_node = self.root_node curr_state = hypothesis.initial_state if self.automaton_type == "mdp": trace = tuple(self.initial_value) else: trace = () while True: rep_trace = curr_state.prefix if trace != rep_trace: for i in curr_node.children.keys(): freq_in_tree = self.frequency_query(trace, (i,)) freq_in_hyp = self.frequency_query(rep_trace, (i,)) if self.compatibility_checker.are_cells_different(freq_in_tree, freq_in_hyp): return trace + (i,) # choose next node randomly and return None if there is no next node if not curr_node.children: return None i = choice(list(curr_node.children.keys())) if not curr_node.children[i]: return None c = choice(list(curr_node.children[i].values())) o = c.output if self.automaton_type == 'mdp': next_state = next( (out_state[0] for out_state in curr_state.transitions[i] if out_state[0].output == o), None) else: next_state = next((out_state[0] for out_state in curr_state.transitions[i] if out_state[1] == o), None) if not next_state: return trace + (i,) if random() <= stop_prob: return None else: curr_node = c curr_state = next_state trace = trace + (i,) + (o,) def dfs_for_cex_in_tree(self, hypothesis, nr_traces, stop_prob): for i in range(nr_traces): cex = self.single_dfs_for_cex(stop_prob, hypothesis) if cex: return cex return None def bfs_for_cex_in_tree(self, hypothesis): # BFS for cex if self.automaton_type == "mdp": to_check = [(self.root_node, hypothesis.initial_state, tuple(self.initial_value))] else: to_check = [(self.root_node, hypothesis.initial_state, ())] while to_check: (curr_node, curr_state, trace) = to_check.pop(0) rep_trace = curr_state.prefix if trace != rep_trace: for i in curr_node.children.keys(): freq_in_tree = self.frequency_query(trace, (i,)) freq_in_hyp = self.frequency_query(rep_trace, (i,)) if self.compatibility_checker.are_cells_different(freq_in_tree, freq_in_hyp): return trace + (i,) for i in curr_node.children.keys(): for c in curr_node.children[i].values(): o = c.output if self.automaton_type == 'mdp': next_state = next((out_state[0] for out_state in curr_state.transitions[i] if out_state[0].output == o), None) else: next_state = next((out_state[0] for out_state in curr_state.transitions[i] if out_state[1] == o), None) if not next_state: return trace + (i,) new_trace = trace + (i,) + (o,) to_check.append((c, next_state, new_trace)) return None def equivalence_query(self, hypothesis): """ Finds and returns a counterexample Args: hypothesis: current hypothesis Returns: counterexample """ if self.last_cex and not self.is_cex_processed(hypothesis, self.last_cex): return self.last_cex if self.samples_cex_strategy: cex = None if self.samples_cex_strategy == 'bfs': cex = self.bfs_for_cex_in_tree(hypothesis) elif self.samples_cex_strategy.startswith('random'): split_strategy = self.samples_cex_strategy.split(":") try: nr_traces = int(split_strategy[1]) stop_prob = float(split_strategy[2]) cex = self.dfs_for_cex_in_tree(hypothesis, nr_traces, stop_prob) except Exception as e: print("Problem in random DFS for cex in samples:", e) if cex: self.last_cex = cex self.eq_oracle.reset_counter() return cex cex = self.eq_oracle.find_cex(hypothesis) if cex: # remove last output cex = cex[:-1] self.last_cex = cex return cex def is_cex_processed(self, hypothesis, cex): if self.automaton_type == 'mdp': cex = cex[1:] last_inp = cex[-1] hypothesis.reset_to_initial() for i in range(0, len(cex) - 1, 2): o = hypothesis.step_to(cex[i], cex[i + 1]) if o is None: return False o = hypothesis.step(last_inp) return o is not None ================================================ FILE: aalpy/learning_algs/stochastic/__init__.py ================================================ ================================================ FILE: aalpy/learning_algs/stochastic_passive/ActiveAleriga.py ================================================ from abc import ABC, abstractmethod from random import randint, choice from aalpy.learning_algs import run_Alergia class Sampler(ABC): """ Abstract class whose implementations are used to provide samples for active passive learning. """ @abstractmethod def sample(self, sul, model): """ Abstract method implementing sampling strategy. Args: sul: system under learning model: current learned model Returns: Data to be added to the data set for the passive learnign. """ pass class RandomWordSampler(Sampler): def __init__(self, num_walks, min_walk_len, max_walk_len): self.num_walks = num_walks self.min_walk_len = min_walk_len self.max_walk_len = max_walk_len def sample(self, sul, model): input_al = list({el for s in model.states for el in s.transitions.keys()}) samples = [] for _ in range(self.num_walks): walk_len = randint(self.min_walk_len, self.max_walk_len) random_walk = tuple(choice(input_al) for _ in range(walk_len)) outputs = sul.query(random_walk) sample = [outputs.pop(0)] for i in range(len(outputs)): sample.append((random_walk[i], outputs[i])) samples.append(sample) return samples def run_active_Alergia(data, sul, sampler, n_iter, eps=0.05, compatibility_checker=None, automaton_type='mdp', print_info=True): """ Active version of IOAlergia algorithm. Based on intermediate hypothesis sampling on the system is performed. Sampled data is added to the learning data and more accurate model is learned. Proposed in "Aichernig and Tappler, Probabilistic Black-Box Reachability Checking" Args: data: initial learning data, in form [[O, (I,O), (I,O)...] ,...] where O is outputs and I input. sul: system under learning which is basis for sampling sampler: instance of Sampler class n_iter: number of iterations of active learning eps: epsilon value if the default checker is used. Look in run_Alergia for description compatibility_checker: passed to run_Alergia, check there for description automaton_type: either 'mdp' or 'smm' (Markov decision process or Stochastic Mealy Machine) print_info: print current learning iteration Returns: learned MDP """ model = None for i in range(n_iter): if print_info: print(f'Active Alergia Iteration: {i}') model = run_Alergia(data, automaton_type='mdp', eps=eps, compatibility_checker=compatibility_checker) new_samples = sampler.sample(sul, model) data.extend(new_samples) return model ================================================ FILE: aalpy/learning_algs/stochastic_passive/Alergia.py ================================================ import time from bisect import insort from aalpy.automata import MarkovChain, MdpState, Mdp, McState, StochasticMealyState, \ StochasticMealyMachine from aalpy.learning_algs.stochastic_passive.CompatibilityChecker import HoeffdingCompatibility from aalpy.learning_algs.stochastic_passive.FPTA import create_fpta state_automaton_map = {'mc': (McState, MarkovChain), 'mdp': (MdpState, Mdp), 'smm': (StochasticMealyState, StochasticMealyMachine)} class Alergia: def __init__(self, data, automaton_type, eps=0.05, compatibility_checker=None, print_info=False): assert eps == 'auto' or 0 < eps <= 2 self.automaton_type = automaton_type self.print_info = print_info if eps == 'auto': eps = 10 / sum(len(d) - 1 for d in data) # len - 1 to ignore initial output self.diff_checker = HoeffdingCompatibility(eps) if not compatibility_checker else compatibility_checker pta_start = time.time() self.fpta = create_fpta(data, automaton_type) pta_time = round(time.time() - pta_start, 2) if self.print_info: print(f'PTA Construction Time: {pta_time}') def compatibility_test(self, a, b): # for MDPs and MC output of the state needs to be the same if self.automaton_type != 'smm' and a.output != b.output: return False # leaf nodes are merged if not a.original_children.keys() or not b.original_children.keys(): return True # if states are statistically different, do not merge if self.diff_checker.are_states_different(a, b): return False # check future for compatibility for el in set(a.original_children.keys()).intersection(b.original_children.keys()): if not self.compatibility_test(a.original_children[el], b.original_children[el]): return False return True def merge(self, red_state, blue_state): b_prefix = blue_state.prefix to_update = self.fpta for p in b_prefix[:-1]: to_update = to_update.children[p] to_update.children[b_prefix[-1]] = red_state self.fold(red_state, blue_state) def fold(self, red, blue): for i, blue_child in blue.children.items(): if i in red.children: red.input_frequency[i] += blue.input_frequency[i] self.fold(red.children[i], blue_child) else: red.children[i] = blue.children[i] red.input_frequency[i] = blue.input_frequency[i] def run(self): start_time = time.time() # representative nodes that will be included in the final output model red = [self.fpta] # intermediate successors scheduled for testing blue = self.fpta.successors() while blue: # get lexicographically minimal blue node (one with the shortest prefix) lex_min_blue = min(list(blue)) merged = False for red_state in red: if self.compatibility_test(red_state, lex_min_blue): self.merge(red_state, lex_min_blue) merged = True break if not merged: insort(red, lex_min_blue) blue.clear() for r in red: for s in r.successors(): if s not in red: blue.append(s) assert sorted(red, key=lambda x: len(x.prefix)) == red self.normalize(red) for i, r in enumerate(red): r.state_id = f'q{i}' if self.print_info: print(f'Alergia Learning Time: {round(time.time() - start_time, 2)}') print(f'Alergia Learned {len(red)} state automaton.') return self.to_automaton(red) def normalize(self, red): red_sorted = sorted(list(red), key=lambda x: len(x.prefix)) for r in red_sorted: # Initializing in here saves many unnecessary initializations r.children_prob = dict() if self.automaton_type == 'mc': total_output = sum(r.input_frequency.values()) for i in r.input_frequency.keys(): r.children_prob[i] = r.input_frequency[i] / total_output else: for i, o in r.input_frequency.keys(): r.children_prob[(i, o)] = r.input_frequency[(i, o)] / r.get_input_frequency(i) def to_automaton(self, red): s_c = state_automaton_map[self.automaton_type][0] a_c = state_automaton_map[self.automaton_type][1] states = [] initial_state = None red_mdp_map = dict() for s in red: if self.automaton_type != 'smm': automaton_state = s_c(s.state_id, output=s.output) else: automaton_state = s_c(s.state_id) automaton_state.prefix = s.prefix states.append(automaton_state) red_mdp_map[tuple(s.prefix)] = automaton_state red_mdp_map[automaton_state.state_id] = s if not s.prefix: initial_state = automaton_state for s in states: red_eq = red_mdp_map[s.state_id] for io, c in red_eq.children.items(): destination = red_mdp_map[tuple(c.prefix)] i = io if self.automaton_type == 'mc' else io[0] if self.automaton_type == 'mdp': s.transitions[i].append((destination, red_eq.children_prob[io])) elif self.automaton_type == 'mc': s.transitions.append((destination, red_eq.children_prob[i])) elif self.automaton_type == 'smm': s.transitions[i].append((destination, io[1], red_eq.children_prob[io])) else: s.transitions[i] = destination return a_c(initial_state, states) def run_Alergia(data, automaton_type, eps=0.05, compatibility_checker=None, print_info=False): """ Run Alergia or IOAlergia on provided data. Args: data: data either in a form [[I,I,I],[I,I,I],...] if learning Markov Chains or [[O,(I,O),(I,O)...], [O,(I,O), (I, O)_,...],..,] if learning MDPs, or [[I,O,I,O...], [I,O_,...],..,] if learning SMMs (I represents input, O output). Note that in whole data first symbol of each entry should be the same (Initial output of the MDP/MC). eps: epsilon value if you are using default HoeffdingCompatibility. If it is set to 'auto' it will be computed as 10/(all steps in the data) automaton_type: either 'mdp' if you wish to learn an MDP, 'mc' if you want to learn Markov Chain, or 'smm' if you want to learn stochastic Mealy machine compatibility_checker: impl. of class CompatibilityChecker, HoeffdingCompatibility with eps value by default (note: not interchangeable, depends on data) print_info: Returns: mdp, smm, or markov chain """ assert automaton_type in {'mdp', 'mc', 'smm'} alergia = Alergia(data, eps=eps, automaton_type=automaton_type, compatibility_checker=compatibility_checker, print_info=print_info) model = alergia.run() del alergia.fpta, alergia return model def run_JAlergia(path_to_data_file, automaton_type, path_to_jAlergia_jar, eps=0.05, heap_memory='-Xmx2048M'): """ Run Alergia or IOAlergia on provided data. Args: path_to_data_file: either a data in a list of lists or a path to file containing data. Form [[I,I,I],[I,I,I],...] if learning Markov Chains or [[O,I,O,I,O...], [O,I,O_,...],..,] if learning MDPs (I represents input, O output), or [[I,O,I,O...], [I,O_,...],..,] if learning SMMs. Note that in whole data first symbol of each entry should be the same (Initial output of the MDP/MC). eps: epsilon value heap_memory: java heap memory flag, increase if heap is full automaton_type: either 'mdp' if you wish to learn an MDP, 'mc' if you want to learn Markov Chain, or 'smm' if you want to learn stochastic Mealy machine Returns: learnedModel """ assert automaton_type in {'mdp', 'smm', 'mc'} import os import subprocess from aalpy.utils.FileHandler import load_automaton_from_file save_file = "jAlergiaModel.dot" delete_tmp_file = False if os.path.exists(save_file): os.remove(save_file) if os.path.exists(path_to_jAlergia_jar): path_to_jAlergia_jar = os.path.abspath(path_to_jAlergia_jar) else: print(f'JAlergia jar not found at {path_to_jAlergia_jar}.') return if isinstance(path_to_data_file, str): if os.path.exists(path_to_data_file): abs_path = os.path.abspath(path_to_data_file) else: print('Input file not found.') return else: if not isinstance(path_to_data_file, (list, tuple)): print('Data should be either a list of sequences or a path to the data file.') with open('jAlergiaInputs.txt', 'w') as f: for seq in path_to_data_file: f.write(','.join([str(s) for s in seq]) + '\n') delete_tmp_file = True abs_path = os.path.abspath('jAlergiaInputs.txt') subprocess.call(['java', heap_memory, '-jar', path_to_jAlergia_jar, '-input', abs_path, '-eps', str(eps), '-type', automaton_type]) if not os.path.exists(save_file): print("JAlergia error occurred.") return model = load_automaton_from_file(save_file, automaton_type=automaton_type) os.remove(save_file) if delete_tmp_file: os.remove('jAlergiaInputs.txt') return model ================================================ FILE: aalpy/learning_algs/stochastic_passive/CompatibilityChecker.py ================================================ from abc import ABC, abstractmethod from math import sqrt, log from aalpy.learning_algs.stochastic_passive.FPTA import AlergiaPtaNode class CompatibilityChecker(ABC): @abstractmethod def are_states_different(self, a: AlergiaPtaNode, b: AlergiaPtaNode, **kwargs) -> bool: pass class HoeffdingCompatibility(CompatibilityChecker): def __init__(self, eps): self.eps = eps self.log_term = sqrt(0.5 * log(2 / self.eps)) def hoeffding_bound(self, a: dict, b: dict): n1 = sum(a.values()) n2 = sum(b.values()) if n1 * n2 == 0: return False bound = (sqrt(1 / n1) + sqrt(1 / n2)) * self.log_term for o in set(a.keys()).union(b.keys()): a_freq = a[o] if o in a else 0 b_freq = b[o] if o in b else 0 if abs(a_freq / n1 - b_freq / n2) > bound: return True return False def are_states_different(self, a: AlergiaPtaNode, b: AlergiaPtaNode, **kwargs): # no data available for any node if len(a.original_input_frequency) * len(b.original_children) == 0: return False # assuming tuples are used for IOAlergia and not as Alergia outputs if not isinstance(list(a.original_input_frequency.keys())[0], tuple): return self.hoeffding_bound(a.original_input_frequency, b.original_input_frequency) # IOAlergia: check hoeffding bound conditioned on inputs for i in a.get_immutable_inputs().intersection(b.get_immutable_inputs()): if self.hoeffding_bound(a.get_original_output_frequencies(i), b.get_original_output_frequencies(i)): return True return False ================================================ FILE: aalpy/learning_algs/stochastic_passive/FPTA.py ================================================ from functools import total_ordering @total_ordering class AlergiaPtaNode: __slots__ = ['prefix', 'output', 'input_frequency', 'children', 'original_input_frequency', 'original_children', 'state_id', 'children_prob'] def __init__(self, output, prefix): self.prefix = prefix self.output = output # mutable values self.input_frequency = dict() self.children = dict() # immutable values used for statistical computability check self.original_input_frequency = dict() self.original_children = dict() # # for visualization self.state_id = None self.children_prob = None def successors(self): return list(self.children.values()) def get_inputs(self): return {i for i, _ in self.input_frequency.keys()} def get_input_frequency(self, target_input): return sum(freq for (i, _), freq in self.input_frequency.items() if i == target_input) def get_output_frequencies(self, target_input): return {o: freq for (i, o), freq in self.input_frequency.items() if i == target_input} def get_immutable_inputs(self): return {i for i, _ in self.original_children.keys()} def get_immutable_input_frequency(self, target_input): return sum(freq for (i, _), freq in self.original_input_frequency.items() if i == target_input) def get_original_output_frequencies(self, target_input): return {o: freq for (i, o), freq in self.original_input_frequency.items() if i == target_input} def __lt__(self, other): return (len(self.prefix), self.prefix) < (len(other.prefix), other.prefix) def __le__(self, other): return self < other or self == other def __eq__(self, other): return self.prefix == other.prefix def create_fpta(data, automaton_type): # in case of SMM, there is no initial input seq_iter_index = 0 if automaton_type == 'smm' else 1 initial_output = None if automaton_type == 'smm' else data[0][0] root_node = AlergiaPtaNode(initial_output, ()) for seq in data: if automaton_type != 'smm' and seq[0] != root_node.output: print('All sequences passed to Alergia should have the same initial output!') assert False curr_node = root_node for el in seq[seq_iter_index:]: if el not in curr_node.children: out = None if automaton_type == 'mc': out = el elif automaton_type == 'mdp': out = el[1] reached_node = AlergiaPtaNode(out, curr_node.prefix + (el,)) curr_node.children[el] = reached_node curr_node.original_children[el] = reached_node curr_node.input_frequency[el] = 0 curr_node.original_input_frequency[el] = 0 curr_node.input_frequency[el] += 1 curr_node.original_input_frequency[el] += 1 curr_node = curr_node.children[el] return root_node ================================================ FILE: aalpy/learning_algs/stochastic_passive/__init__.py ================================================ ================================================ FILE: aalpy/oracles/BreadthFirstExplorationEqOracle.py ================================================ from aalpy.base.Oracle import Oracle from aalpy.base.SUL import SUL from itertools import product from random import shuffle class BreadthFirstExplorationEqOracle(Oracle): """ Breadth-First Exploration of all possible input combinations up to a certain depth. Extremely inefficient equivalence oracle and should only be used for demonstrations. """ def __init__(self, alphabet, sul: SUL, depth=5): """ Args: alphabet: input alphabet sul: system under learning depth: depth of the tree """ super().__init__(alphabet, sul) self.depth = depth self.queue = [] # generate all test-cases for seq in product(self.alphabet, repeat=self.depth): self.queue.append(seq) shuffle(self.queue) def find_cex(self, hypothesis): while self.queue: test_case = self.queue.pop() self.reset_hyp_and_sul(hypothesis) for ind, letter in enumerate(test_case): out_hyp = hypothesis.step(letter) out_sul = self.sul.step(letter) self.num_steps += 1 if out_hyp != out_sul: self.sul.post() return test_case[:ind + 1] # cleanup after the test case self.sul.post() return None ================================================ FILE: aalpy/oracles/CacheBasedEqOracle.py ================================================ from aalpy.base import Oracle, SUL from aalpy.base.SUL import CacheSUL from random import choice class CacheBasedEqOracle(Oracle): """ Equivalence oracle where test case selection is based on the multiset of all traces observed during learning and conformance checking. Firstly all leaves of the tree are gathered and then random leaves are extended with a suffix of length (max_tree_depth + 'depth_increase') - len(prefix), where prefix is a path to the leaf. """ def __init__(self, alphabet: list, sul: SUL, num_walks=100, depth_increase=5, reset_after_cex=True): """ Args: alphabet: input alphabet sul: system under learning num_walks: number of random walks to perform depth_increase: length of random walk that exceeds the maximum depth of the tree reset_after_cex: if False, total number of queries will equal num_walks, if True, in each execution of find_cex method at most num_walks will be executed """ super().__init__(alphabet, sul) self.cache_tree = None self.num_walks = num_walks self.depth_increase = depth_increase self.reset_after_cex = reset_after_cex self.num_walks_done = 0 def find_cex(self, hypothesis): assert isinstance(self.sul, CacheSUL) self.cache_tree = self.sul.cache paths_to_leaves = self.get_paths(self.cache_tree.root_node) max_tree_depth = len(max(paths_to_leaves, key=len)) while self.num_walks_done < self.num_walks: self.num_walks_done += 1 self.reset_hyp_and_sul(hypothesis) prefix = choice(paths_to_leaves) walk_len = (max_tree_depth + self.depth_increase) - len(prefix) inputs = [] inputs.extend(prefix) for p in prefix: hypothesis.step(p) self.sul.step(p) self.num_steps += 1 for _ in range(walk_len): inputs.append(choice(self.alphabet)) out_sul = self.sul.step(inputs[-1]) out_hyp = hypothesis.step(inputs[-1]) self.num_steps += 1 if out_sul != out_hyp: if self.reset_after_cex: self.num_walks_done = 0 self.sul.post() return inputs # cleanup after the test case self.sul.post() return None def get_paths(self, t, paths=None, current_path=None): """ Args: t: paths: (Default value = None) current_path: (Default value = None) Returns: """ if paths is None: paths = [] if current_path is None: current_path = [] if len(t.children) == 0: paths.append(current_path) else: for inp, child in t.children.items(): current_path.append(inp) self.get_paths(child, paths, list(current_path)) return paths ================================================ FILE: aalpy/oracles/KWayStateCoverageEqOracle.py ================================================ from random import choices, shuffle from aalpy.base import Oracle, SUL from itertools import combinations, permutations class KWayStateCoverageEqOracle(Oracle): """ A test case will be computed for every k-combination or k-permutation of states with additional random walk at the end. """ def __init__(self, alphabet: list, sul: SUL, k=2, random_walk_len=20, method='permutations', num_test_lower_bound=None, num_test_upper_bound=None): """ Args: alphabet: input alphabet sul: system under learning k: k value used for k-wise combinations/permutations of states random_walk_len: length of random walk performed at the end of each combination/permutation method: either 'combinations' or 'permutations' num_test_lower_bound= either None or number a minimum number of test-cases to be performed in each testing round num_test_upper_bound= either None or number a maximum number of test-cases to be performed in each testing round """ super().__init__(alphabet, sul) assert k > 1 and method in ['combinations', 'permutations'] self.k = k self.cache = set() self.fun = combinations if method == 'combinations' else permutations self.random_walk_len = random_walk_len self.num_test_lower_bound = num_test_lower_bound self.num_test_upper_bound = num_test_upper_bound def find_cex(self, hypothesis): shuffle(hypothesis.states) test_cases = [] for comb in self.fun(hypothesis.states, self.k): prefixes = frozenset([c.prefix for c in comb]) if prefixes in self.cache: continue self.cache.add(prefixes) index = 0 path = comb[0].prefix possible_test_case = True while index < len(comb) - 1: path_between_states = hypothesis.get_shortest_path(comb[index], comb[index + 1]) index += 1 if not path_between_states: possible_test_case = False break path += path_between_states if possible_test_case is None: continue path += tuple(choices(self.alphabet, k=self.random_walk_len)) test_cases.append(path) # lower bound (also accounts for single state hypothesis when a lower bound is not defined) lower_bound = self.num_test_lower_bound if len(hypothesis.states) == 1 and lower_bound is None: lower_bound = 50 while lower_bound is not None and len(test_cases) < lower_bound: path = tuple(choices(self.alphabet, k=self.random_walk_len)) test_cases.append(path) # upper bound if self.num_test_upper_bound is not None: test_cases = test_cases[:self.num_test_upper_bound] for path in test_cases: self.reset_hyp_and_sul(hypothesis) for i, p in enumerate(path): out_sul = self.sul.step(p) out_hyp = hypothesis.step(p) self.num_steps += 1 if out_sul != out_hyp: self.sul.post() return path[:i + 1] # cleanup after the test case self.sul.post() return None ================================================ FILE: aalpy/oracles/KWayTransitionCoverageEqOracle.py ================================================ from collections import namedtuple from itertools import product from random import choices, randint, random from aalpy.base import SUL, Automaton, Oracle KWayTransition = namedtuple("KWayTransition", "start_state end_state steps") Path = namedtuple("Path", "start_state end_state steps kWayTransitions, transitions_log") class KWayTransitionCoverageEqOracle(Oracle): """ This Equivalence oracle selects test cases based on k-way transitions coverage. It does that by generating random queries and finding the smallest subset with the highest coverage. In other words, this oracle finds counter examples by running random paths that cover all pairwise / k-way transitions. """ def __init__(self, alphabet: list, sul: SUL, k: int = 2, method='random', num_generate_paths: int = 1000, max_path_len: int = 50, max_number_of_steps: int = 0, optimize: str = 'steps', random_walk_len=10, num_test_lower_bound=None, num_test_upper_bound=None, ): """ Args: alphabet: input alphabet sul: system under learning k: k value used for K-Way transitions, i.e the number of steps between the start and the end of a transition method: defines how the queries are generated 'random' or 'prefix' num_generate_paths: number of random queries used to find the optimal subset max_path_len: the maximum step size of a generated path max_number_of_steps: maximum number of steps that will be executed on the SUL (0 = no limit) optimize: minimize either the number of 'steps' or 'queries' that are executed random_walk_len: the number of steps that are added by 'prefix' generated paths num_test_lower_bound= either None or number a minimum number of test-cases to be performed in each testing round num_test_upper_bound= either None or number a maximum number of test-cases to be performed in each testing round """ super().__init__(alphabet, sul) assert k >= 2 assert method in ['random', 'prefix'] assert optimize in ['steps', 'queries'] self.k = k self.method = method self.num_generate_paths = num_generate_paths self.max_path_len = max_path_len self.max_number_of_steps = max_number_of_steps self.optimize = optimize self.random_walk_len = random_walk_len # to account for cases where number of test-cases generated by the oracle is either too big or too small # num_test_lower_bound does not affect k-way transition coverage, but limiting the upper bound might as not all # tests will be executed self.num_test_lower_bound = num_test_lower_bound self.num_test_upper_bound = num_test_upper_bound self.cached_paths = list() def find_cex(self, hypothesis: Automaton): if self.method == 'random': paths = self.generate_random_paths(hypothesis) + self.cached_paths self.cached_paths = self.greedy_set_cover(hypothesis, paths) # to account for lower bound additional_test_cases = [] num_executed_tcs = 0 if self.num_test_lower_bound is not None: while len(self.cached_paths) + len(additional_test_cases) < self.num_test_lower_bound: random_length = randint(self.k, self.max_path_len) steps = tuple(choices(self.alphabet, k=random_length)) additional_test_cases.append(self.create_path(hypothesis, steps)) for path in list(self.cached_paths + additional_test_cases): if self.num_test_upper_bound is not None and num_executed_tcs >= self.num_test_upper_bound: break counter_example = self.check_path(hypothesis, path.steps) num_executed_tcs += 1 if counter_example is not None: return counter_example elif self.method == 'prefix': generated_paths = [] # generate tcs with prefix coverage for steps in self.generate_prefix_steps(hypothesis): generated_paths.append(self.create_path(hypothesis, steps)) if self.num_test_lower_bound is not None and len(generated_paths) >= self.num_test_lower_bound: break # add random paths if needed while self.num_test_lower_bound is not None and len(generated_paths) < self.num_test_lower_bound: random_length = randint(self.k, self.max_path_len) steps = tuple(choices(self.alphabet, k=random_length)) generated_paths.append(self.create_path(hypothesis, steps)) # limit upper bound if self.num_test_upper_bound is not None: generated_paths = generated_paths[:self.num_test_upper_bound] for path in generated_paths: counter_example = self.check_path(hypothesis, path.steps) if counter_example is not None: return counter_example return None def greedy_set_cover(self, hypothesis: Automaton, paths: list): result = list() covered = set() step_count = 0 size_of_universe = len(hypothesis.states) * pow(len(self.alphabet), self.k) while size_of_universe > len(covered): path = self.select_optimal_path(covered, paths) if path is not None: covered = set.union(covered, path.kWayTransitions) paths.remove(path) result.append(path) step_count += len(path.steps) if path is None or not paths: paths = [self.create_path(hypothesis, steps) for steps in self.generate_prefix_steps(hypothesis)] if self.max_number_of_steps != 0 and step_count > self.max_number_of_steps: break return result def select_optimal_path(self, covered: set, paths: list) -> Path: result = None if self.optimize == 'steps': result = max(paths, key=lambda p: len( p.kWayTransitions - covered) / len(p.steps)) if self.optimize == 'queries': result = max(paths, key=lambda p: len(p.kWayTransitions - covered)) return result if len(result.kWayTransitions - covered) != 0 else None def generate_random_paths(self, hypothesis: Automaton) -> list: result = list() for _ in range(self.num_generate_paths): random_length = randint(self.k, self.max_path_len) steps = tuple(choices(self.alphabet, k=random_length)) path = self.create_path(hypothesis, steps) result.append(path) return result def generate_prefix_steps(self, hypothesis: Automaton): for state in reversed(hypothesis.states): prefix = state.prefix for steps in sorted(product(self.alphabet, repeat=self.k), key=lambda k: random()): yield prefix + steps + tuple(choices(self.alphabet, k=self.random_walk_len)) def create_path(self, hypothesis: Automaton, steps: tuple) -> Path: transitions = set() transitions_log = list() prev_states = list() end_states = list() hypothesis.reset_to_initial() for i, s in enumerate(steps): prev_states.append(hypothesis.current_state) hypothesis.step(s) end_states.append(hypothesis.current_state) for i in range(len(steps) - self.k + 1): prev_state = prev_states[i] end_state = end_states[i + self.k - 1] chunk = tuple(steps[i:i + self.k]) transition = KWayTransition(prev_state.state_id, end_state.state_id, chunk) transitions_log.append(transition) transitions.add(transition) return Path(hypothesis.initial_state, end_states[-1], steps, transitions, transitions_log) def check_path(self, hypothesis: Automaton, steps: tuple): self.reset_hyp_and_sul(hypothesis) for i, s in enumerate(steps): out_sul = self.sul.step(s) out_hyp = hypothesis.step(s) self.num_steps += 1 if out_sul != out_hyp: self.sul.post() return steps[:i + 1] # cleanup after the test case self.sul.post() return None ================================================ FILE: aalpy/oracles/PacOracle.py ================================================ from math import ceil, log from random import choice, randint from aalpy.base import Oracle, SUL class PacOracle(Oracle): """ Probably approximately correct oracle. Number of queries is defined by the following equation: 1 / self.epsilon * (log(1 / self.delta) + self.round * log(2)), where epsilon is the generalization error and delta the confidence. Thus, returned hypothesis is the epsilon-approximation of the correct hypothesis with the probability 1 - delta (Mohri, M et al.: Foundations of Machine Learning). Queries are of random length in a predefined range. """ def __init__(self, alphabet: list, sul: SUL, epsilon=0.01, delta=0.01, min_walk_len=10, max_walk_len=25): super().__init__(alphabet, sul) self.min_walk_len = min_walk_len self.max_walk_len = max_walk_len self.epsilon = epsilon self.delta = delta self.round = 0 def find_cex(self, hypothesis): self.round += 1 num_test_cases = 1 / self.epsilon * (log(1 / self.delta) + self.round * log(2)) for i in range(ceil(num_test_cases)): inputs = [] self.reset_hyp_and_sul(hypothesis) num_steps = randint(self.min_walk_len, self.max_walk_len) for _ in range(num_steps): inputs.append(choice(self.alphabet)) out_sul = self.sul.step(inputs[-1]) out_hyp = hypothesis.step(inputs[-1]) self.num_steps += 1 if out_sul != out_hyp: self.sul.post() return inputs # cleanup after the test case self.sul.post() return None ================================================ FILE: aalpy/oracles/PerfectKnowledgeEqOracle.py ================================================ from aalpy.base import Oracle, SUL, DeterministicAutomaton from aalpy.utils import bisimilar class PerfectKnowledgeEqOracle(Oracle): """ Oracle that can be used when developing and testing deterministic learning algorithms, so that the focus is put off equivalence query. """ def __init__(self, alphabet: list, sul: SUL, model_under_learning: DeterministicAutomaton): super().__init__(alphabet, sul, ) self.model_under_learning = model_under_learning def find_cex(self, hypothesis): return bisimilar(hypothesis, self.model_under_learning, return_cex=True) ================================================ FILE: aalpy/oracles/ProvidedSequencesOracleWrapper.py ================================================ from aalpy.base import Oracle, SUL class ProvidedSequencesOracleWrapper(Oracle): def __init__(self, alphabet: list, sul: SUL, oracle: Oracle, provided_counterexamples: list): """ Oracle wrapper which first executes provided sequences (possible counterexamples) and then switches to another oracle instance. Args: alphabet: input alphabet sul: system under learning oracle: oracle which will be used once all provided counterexamples are used provided_counterexamples: list of input sequance lists. eg [[1,2,3], [2,3,1], ...] where 1,2,3 are elements of input alphabet """ super().__init__(alphabet, sul) self.provided_counterexamples = provided_counterexamples self.oracle = oracle def find_cex(self, hypothesis): for provided_cex in self.provided_counterexamples.copy(): inputs = [] self.reset_hyp_and_sul(hypothesis) for i in provided_cex: inputs.append(i) out_sul = self.sul.step(i) out_hyp = hypothesis.step(i) self.num_steps += 1 if out_sul != out_hyp: self.sul.post() return tuple(inputs) # cleanup after the test case self.sul.post() self.provided_counterexamples.remove(provided_cex) cex = self.oracle.find_cex(hypothesis) # to account for steps statistics from actual oracle if cex is None: self.num_queries += self.oracle.num_queries self.num_steps += self.oracle.num_steps return cex ================================================ FILE: aalpy/oracles/RandomWalkEqOracle.py ================================================ import random from aalpy.automata import Onfsm, Mdp, StochasticMealyMachine from aalpy.base import Oracle, SUL automaton_dict = {Onfsm: 'onfsm', Mdp: 'mdp', StochasticMealyMachine: 'smm'} class RandomWalkEqOracle(Oracle): """ Equivalence oracle where queries contain random inputs. After every step, 'reset_prob' determines the probability that the system will reset and a new query asked. """ def __init__(self, alphabet: list, sul: SUL, num_steps=5000, reset_after_cex=True, reset_prob=0.09): """ Args: alphabet: input alphabet sul: system under learning num_steps: number of steps to be preformed reset_after_cex: if true, num_steps will be preformed after every counter example, else the total number or steps will equal to num_steps reset_prob: probability that the new query will be asked """ super().__init__(alphabet, sul) self.step_limit = num_steps self.reset_after_cex = reset_after_cex self.reset_prob = reset_prob self.random_steps_done = 0 self.automata_type = None def find_cex(self, hypothesis): if not self.automata_type: self.automata_type = automaton_dict.get(type(hypothesis), 'det') inputs = [] outputs = [] self.reset_hyp_and_sul(hypothesis) while self.random_steps_done < self.step_limit: self.num_steps += 1 self.random_steps_done += 1 if random.random() <= self.reset_prob: self.sul.post() self.reset_hyp_and_sul(hypothesis) inputs.clear() outputs.clear() inputs.append(random.choice(self.alphabet)) out_sul = self.sul.step(inputs[-1]) outputs.append(out_sul) if self.automata_type == 'det': out_hyp = hypothesis.step(inputs[-1]) else: out_hyp = hypothesis.step_to(inputs[-1], out_sul) if self.automata_type == 'det' and out_sul != out_hyp: if self.reset_after_cex: self.random_steps_done = 0 self.sul.post() return inputs elif out_hyp is None and self.automata_type != 'det': if self.reset_after_cex: self.random_steps_done = 0 self.sul.post() if self.automata_type == 'onfsm': return inputs, outputs else: # hypothesis is MDP or SMM cex = [hypothesis.initial_state.output] if self.automata_type == 'mdp' else [] for i, o in zip(inputs, outputs): cex.extend([i, o]) return cex return None def reset_counter(self): if self.reset_after_cex: self.random_steps_done = 0 ================================================ FILE: aalpy/oracles/RandomWordEqOracle.py ================================================ from statistics import mean from aalpy.automata import Onfsm, Mdp, StochasticMealyMachine from aalpy.base import Oracle, SUL from random import randint, choice automaton_dict = {Onfsm: 'onfsm', Mdp: 'mdp', StochasticMealyMachine: 'smm'} class RandomWordEqOracle(Oracle): """ Equivalence oracle where queries are of random length in a predefined range. """ def __init__(self, alphabet: list, sul: SUL, num_walks=500, min_walk_len=10, max_walk_len=30, reset_after_cex=True): """ Args: alphabet: input alphabet sul: system under learning num_walks: number of walks to perform during search for cex min_walk_len: minimum length of each walk max_walk_len: maximum length of each walk reset_after_cex: if True, num_walks will be preformed after every counter example, else the total number or walks will equal to num_walks """ super().__init__(alphabet, sul) self.num_walks = num_walks self.min_walk_len = min_walk_len self.max_walk_len = max_walk_len self.reset_after_cex = reset_after_cex self.num_walks_done = 0 self.automata_type = None self.walk_lengths = [randint(min_walk_len, max_walk_len) for _ in range(num_walks)] def find_cex(self, hypothesis): if not self.automata_type: self.automata_type = automaton_dict.get(type(hypothesis), 'det') while self.num_walks_done < self.num_walks: inputs = [] outputs = [] self.reset_hyp_and_sul(hypothesis) self.num_walks_done += 1 num_steps = self.walk_lengths.pop(0) for _ in range(num_steps): inputs.append(choice(self.alphabet)) out_sul = self.sul.step(inputs[-1]) if self.automata_type == 'det': out_hyp = hypothesis.step(inputs[-1]) else: out_hyp = hypothesis.step_to(inputs[-1], out_sul) outputs.append(out_sul) self.num_steps += 1 if self.automata_type == 'det' and out_sul != out_hyp: self.sul.post() if self.reset_after_cex: self.walk_lengths = [randint(self.min_walk_len, self.max_walk_len) for _ in range(self.num_walks)] self.num_walks_done = 0 return inputs elif out_hyp is None and self.automata_type != 'det': self.sul.post() if self.reset_after_cex: self.walk_lengths = [randint(self.min_walk_len, self.max_walk_len) for _ in range(self.num_walks)] self.num_walks_done = 0 if self.automata_type == 'onfsm': return inputs, outputs else: # hypothesis is MDP or SMM cex = [hypothesis.initial_state.output] if self.automata_type == 'mdp' else [] for i, o in zip(inputs, outputs): cex.extend([i, o]) return cex # cleanup after the test case self.sul.post() return None def reset_counter(self): if self.reset_after_cex: self.num_walks_done = 0 ================================================ FILE: aalpy/oracles/StatePrefixEqOracle.py ================================================ import random from aalpy.base.Oracle import Oracle from aalpy.base.SUL import SUL class StatePrefixEqOracle(Oracle): """ Equivalence oracle that achieves guided exploration by starting random walks from each state a walk_per_state times. Starting the random walk ensures that all states are reached at least walk_per_state times and that their surrounding is randomly explored. Note that each state serves as a root of random exploration of maximum length rand_walk_len exactly walk_per_state times during learning. Therefore excessive testing of initial states is avoided. """ def __init__(self, alphabet: list, sul: SUL, walks_per_state=25, walk_len=12, max_tests=None, depth_first=True): """ Args: alphabet: input alphabet sul: system under learning walks_per_state:individual walks per state of the automaton over the whole learning process walk_len:length of random walk max_tests:number of maximum tests. If set to None, this parameter will be ignored. depth_first:first explore the newest states """ super().__init__(alphabet, sul) self.walks_per_state = walks_per_state self.steps_per_walk = walk_len self.depth_first = depth_first self.max_tests = max_tests self.freq_dict = dict() def find_cex(self, hypothesis): states_to_cover = [] for state in hypothesis.states: if state.prefix is None: state.prefix = hypothesis.get_shortest_path(hypothesis.initial_state, state) if state.prefix not in self.freq_dict.keys(): self.freq_dict[state.prefix] = 0 states_to_cover.extend([state] * (self.walks_per_state - self.freq_dict[state.prefix])) if self.depth_first: # reverse sort the states by length of their access sequences # first do the random walk on the state with longest access sequence states_to_cover.sort(key=lambda x: len(x.prefix), reverse=True) else: random.shuffle(states_to_cover) for state in states_to_cover: self.freq_dict[state.prefix] = self.freq_dict[state.prefix] + 1 self.reset_hyp_and_sul(hypothesis) if self.max_tests and self.num_queries == self.max_tests: return None prefix = state.prefix for p in prefix: hypothesis.step(p) self.sul.step(p) self.num_steps += 1 suffix = () for _ in range(self.steps_per_walk): suffix += (random.choice(self.alphabet),) out_sul = self.sul.step(suffix[-1]) out_hyp = hypothesis.step(suffix[-1]) self.num_steps += 1 if out_sul != out_hyp: self.sul.post() return prefix + suffix # cleanup after the test case self.sul.post() return None ================================================ FILE: aalpy/oracles/TransitionFocusOracle.py ================================================ import random from aalpy.base.Oracle import Oracle from aalpy.base.SUL import SUL class TransitionFocusOracle(Oracle): """ This equivalence oracle focuses either on the same state transitions or transitions that lead to the different states. This equivalence oracle should be used on grammars like balanced parentheses. In such grammars, all interesting behavior occurs on the transitions between states and potential bugs can be found only by focusing on transitions. """ def __init__(self, alphabet, sul: SUL, num_random_walks=500, walk_len=20, same_state_prob=0.2): """ Args: alphabet: input alphabet sul: system under learning num_random_walks: number of walks walk_len: length of each walk same_state_prob: probability that the next input will lead to same state transition """ super().__init__(alphabet, sul) self.num_walks = num_random_walks self.steps_per_walk = walk_len self.same_state_prob = same_state_prob def find_cex(self, hypothesis): for _ in range(self.num_walks): self.reset_hyp_and_sul(hypothesis) curr_state = hypothesis.current_state inputs = [] for _ in range(self.steps_per_walk): if random.random() <= self.same_state_prob: possible_inputs = curr_state.get_same_state_transitions() else: possible_inputs = curr_state.get_diff_state_transitions() act = random.choice(possible_inputs) if possible_inputs else random.choice(self.alphabet) inputs.append(act) out_sul = self.sul.step(inputs[-1]) out_hyp = hypothesis.step(inputs[-1]) self.num_steps += 1 curr_state = hypothesis.current_state if out_sul != out_hyp: self.sul.post() return inputs # cleanup after the test case self.sul.post() return None ================================================ FILE: aalpy/oracles/UserInputEqOracle.py ================================================ from aalpy.base import Oracle, SUL from aalpy.utils.FileHandler import visualize_automaton class UserInputEqOracle(Oracle): """ Interactive equivalence oracle. For every counterexample, the current hypothesis will be visualized and the user can enter the counterexample step by step. The user provides elements of the input alphabet or commands. When the element of the input alphabet is entered, the step will be performed in the current hypothesis and output will be printed. Commands offered to the users are: print alphabet - prints the input alphabet current inputs - inputs entered so far cex - returns inputs entered so far as the counterexample end - no counterexample exists reset - resets the current state of the hypothesis and clears inputs """ def __init__(self, alphabet: list, sul: SUL): super().__init__(alphabet, sul) self.curr_hypothesis = 0 def find_cex(self, hypothesis): self.reset_hyp_and_sul(hypothesis) self.curr_hypothesis += 1 inputs = [] visualize_automaton(hypothesis, path=f'Hypothesis_{self.curr_hypothesis}') while True: inp = input('Please provide an input: ') if inp == 'help': print('Use one of following commands [print alphabet, current inputs, cex, end, reset] ' 'or provide an input') continue if inp == 'print alphabet': print(self.alphabet) continue if inp == 'current inputs': print(inputs) continue if inp == 'cex': if inputs: self.sul.post() return inputs if inp == 'end': self.sul.post() return None if inp == 'reset': inputs.clear() self.sul.post() self.reset_hyp_and_sul(hypothesis) print('You are back in the initial state. Please provide an input: ') continue if inp not in self.alphabet: print("Provided input is not in the input alphabet.") continue inputs.append(inp) self.num_steps += 1 out_hyp = hypothesis.step(inp) out_sul = self.sul.step(inp) print('Hypothesis Output :', out_hyp) print('SUL Output :', out_sul) if out_hyp != out_sul: print('Counterexample found.\nIf you want to return it, type \'cex\'.') ================================================ FILE: aalpy/oracles/WMethodEqOracle.py ================================================ from random import shuffle, choice, randint from aalpy.base.Oracle import Oracle from aalpy.base.SUL import SUL from itertools import product class WMethodEqOracle(Oracle): """ Equivalence oracle based on characterization set/ W-set. From 'Tsun S. Chow. Testing software design modeled by finite-state machines'. """ def __init__(self, alphabet: list, sul: SUL, max_number_of_states): """ Args: alphabet: input alphabet sul: system under learning max_number_of_states: maximum number of states in the automaton """ super().__init__(alphabet, sul) self.m = max_number_of_states self.cache = set() def test_suite(self, cover, depth, char_set): """ Construct the test suite for the W Method using the provided state cover and characterization set, exploring up to a given depth. Args: cover: list of states to cover depth: maximum length of middle part char_set: characterization set """ # fix the length of the middle part per loop # to avoid generating large sequences early on char_set = char_set or [()] for d in range(depth): middle = product(self.alphabet, repeat=d) for m in middle: for (s, c) in product(cover, char_set): yield s + m + c def find_cex(self, hypothesis): if not hypothesis.characterization_set: hypothesis.characterization_set = hypothesis.compute_characterization_set() # covers every transition of the specification at least once. transition_cover = [ state.prefix + (letter,) for state in hypothesis.states for letter in self.alphabet ] depth = self.m + 1 - len(hypothesis.states) for seq in self.test_suite(transition_cover, depth, hypothesis.characterization_set): if seq not in self.cache: self.reset_hyp_and_sul(hypothesis) outputs = [] for ind, letter in enumerate(seq): out_hyp = hypothesis.step(letter) out_sul = self.sul.step(letter) self.num_steps += 1 outputs.append(out_sul) if out_hyp != out_sul: self.sul.post() return seq[:ind + 1] self.cache.add(seq) self.sul.post() return None class RandomWMethodEqOracle(Oracle): """ Randomized version of the W-Method equivalence oracle. Random walks stem from fixed prefix (path to the state). At the end of the random walk an element from the characterization set is added to the test case. """ def __init__(self, alphabet: list, sul: SUL, walks_per_state=25, walk_len=12): """ Args: alphabet: input alphabet sul: system under learning walks_per_state: number of random walks that should start from each state walk_len: length of random walk """ super().__init__(alphabet, sul) self.walks_per_state = walks_per_state self.random_walk_len = walk_len self.freq_dict = dict() def find_cex(self, hypothesis): if not hypothesis.characterization_set: hypothesis.characterization_set = hypothesis.compute_characterization_set() # fix for non-minimal intermediate hypothesis that can occur in KV if not hypothesis.characterization_set: hypothesis.characterization_set = [(a,) for a in hypothesis.get_input_alphabet()] states_to_cover = [] for state in hypothesis.states: if state.prefix is None: state.prefix = hypothesis.get_shortest_path(hypothesis.initial_state, state) if state.prefix not in self.freq_dict.keys(): self.freq_dict[state.prefix] = 0 states_to_cover.extend([state] * (self.walks_per_state - self.freq_dict[state.prefix])) shuffle(states_to_cover) for state in states_to_cover: self.freq_dict[state.prefix] = self.freq_dict[state.prefix] + 1 self.reset_hyp_and_sul(hypothesis) prefix = state.prefix random_walk = tuple(choice(self.alphabet) for _ in range(randint(1, self.random_walk_len))) test_case = prefix + random_walk + choice(hypothesis.characterization_set) for ind, i in enumerate(test_case): output_hyp = hypothesis.step(i) output_sul = self.sul.step(i) self.num_steps += 1 if output_sul != output_hyp: self.sul.post() return test_case[:ind + 1] self.sul.post() return None ================================================ FILE: aalpy/oracles/WpMethodEqOracle.py ================================================ import random from aalpy.base.Oracle import Oracle from aalpy.base.SUL import SUL from itertools import chain, product def state_characterization_set(hypothesis, alphabet, state): """ Return a list of sequences that distinguish the given state from all other states in the hypothesis. Args: hypothesis: hypothesis automaton alphabet: input alphabet state: state for which to find distinguishing sequences """ result = [] for i in range(len(hypothesis.states)): if hypothesis.states[i] == state: continue seq = hypothesis.find_distinguishing_seq(state, hypothesis.states[i], alphabet) if seq: result.append(tuple(seq)) return result def first_phase_it(alphabet, state_cover, depth, char_set): """ Return an iterator that generates all possible sequences for the first phase of the Wp-method. Args: alphabet: input alphabet state_cover: list of states to cover depth: maximum length of middle part char_set: characterization set """ char_set = char_set or [()] for d in range(depth): middle = product(alphabet, repeat=d) for m in middle: for s in state_cover: for c in char_set: yield s + m + c def second_phase_it(hyp, alphabet, difference, depth): """ Return an iterator that generates all possible sequences for the second phase of the Wp-method. Args: hyp: hypothesis automaton alphabet: input alphabet difference: set of sequences that are in the transition cover but not in the state cover depth: maximum length of middle part """ state_mapping = {} for d in range(depth): middle = product(alphabet, repeat=d) for mid in middle: for t in difference: _ = hyp.execute_sequence(hyp.initial_state, t + mid) state = hyp.current_state if state not in state_mapping: state_mapping[state] = state_characterization_set(hyp, alphabet, state) for sm in state_mapping[state]: yield t + mid + sm class WpMethodEqOracle(Oracle): """ Implements the Wp-method equivalence oracle. """ def __init__(self, alphabet: list, sul: SUL, max_number_of_states=4): super().__init__(alphabet, sul) self.m = max_number_of_states self.cache = set() def find_cex(self, hypothesis): if not hypothesis.characterization_set: hypothesis.characterization_set = hypothesis.compute_characterization_set() transition_cover = set( state.prefix + (letter,) for state in hypothesis.states for letter in self.alphabet ) state_cover = set(state.prefix for state in hypothesis.states) difference = transition_cover.difference(state_cover) depth = self.m + 1 - len(hypothesis.states) # first phase State Cover * Middle * Characterization Set first_phase = first_phase_it(self.alphabet, state_cover, depth, hypothesis.characterization_set) # second phase (Transition Cover - State Cover) * Middle * Characterization Set # of the state that the prefix leads to second_phase = second_phase_it(hypothesis, self.alphabet, difference, depth) test_suite = chain(first_phase, second_phase) for seq in test_suite: if seq not in self.cache: self.reset_hyp_and_sul(hypothesis) for ind, letter in enumerate(seq): out_hyp = hypothesis.step(letter) out_sul = self.sul.step(letter) self.num_steps += 1 if out_hyp != out_sul: self.sul.post() return seq[: ind + 1] self.cache.add(seq) self.sul.post() return None class RandomWpMethodEqOracle(Oracle): """ Implements the Random Wp-Method as described in "Complementing Model Learning with Mutation-Based Fuzzing" by Rick Smetsers, Joshua Moerman, Mark Janssen, Sicco Verwer. 1) sample uniformly from the states for a prefix 2) sample geometrically a random word 3) sample a word from the set of suffixes / state identifiers """ def __init__( self, alphabet: list, sul: SUL, min_length=1, expected_length=10, num_tests=1000,): super().__init__(alphabet, sul) self.min_length = min_length self.expected_length = expected_length self.bound = num_tests def find_cex(self, hypothesis): # fix for non-minimal intermediate hypothesis that can occur in KV hypothesis.characterization_set = hypothesis.compute_characterization_set() if not hypothesis.characterization_set: hypothesis.characterization_set = [(a,) for a in hypothesis.get_input_alphabet()] state_mapping = {s : state_characterization_set(hypothesis, self.alphabet, s) for s in hypothesis.states} for _ in range(self.bound): state = random.choice(hypothesis.states) input = state.prefix limit = self.min_length while limit > 0 or random.random() > 1 / (self.expected_length + 1): letter = random.choice(self.alphabet) input += (letter,) limit -= 1 if random.random() > 0.5: # global suffix with characterization_set input += random.choice(hypothesis.characterization_set) else: # local suffix _ = hypothesis.execute_sequence(hypothesis.initial_state, input) if state_mapping[hypothesis.current_state]: input += random.choice(state_mapping[hypothesis.current_state]) else: continue self.reset_hyp_and_sul(hypothesis) for ind, letter in enumerate(input): out_hyp = hypothesis.step(letter) out_sul = self.sul.step(letter) self.num_steps += 1 if out_hyp != out_sul: self.sul.post() return input[: ind + 1] self.sul.post() return None ================================================ FILE: aalpy/oracles/__init__.py ================================================ from .BreadthFirstExplorationEqOracle import BreadthFirstExplorationEqOracle from .CacheBasedEqOracle import CacheBasedEqOracle from .KWayStateCoverageEqOracle import KWayStateCoverageEqOracle from .KWayTransitionCoverageEqOracle import KWayTransitionCoverageEqOracle from .RandomWalkEqOracle import RandomWalkEqOracle from .RandomWordEqOracle import RandomWordEqOracle from .StatePrefixEqOracle import StatePrefixEqOracle from .TransitionFocusOracle import TransitionFocusOracle from .UserInputEqOracle import UserInputEqOracle from .WMethodEqOracle import RandomWMethodEqOracle, WMethodEqOracle from .WpMethodEqOracle import RandomWpMethodEqOracle, WpMethodEqOracle from .PacOracle import PacOracle from .ProvidedSequencesOracleWrapper import ProvidedSequencesOracleWrapper from .PerfectKnowledgeEqOracle import PerfectKnowledgeEqOracle ================================================ FILE: aalpy/paths.py ================================================ """ File in which necessary paths for model checking are defined. path_to_prism is the absolute or relative path to the prism executable. Note that it has to include the executable file, not just the folder. Eg. /usr/edi/prism/prism.bat and NOT /usr/edi/prism/ If you learn one of the provided examples path to properties should be relative or absolute path to 'Benchmarking\prism_eval_props'. """ path_to_prism = None path_to_properties = None ================================================ FILE: aalpy/utils/AutomatonGenerators.py ================================================ import random import warnings from aalpy.automata import Dfa, DfaState, MdpState, Mdp, MealyMachine, MealyState, \ MooreMachine, MooreState, OnfsmState, Onfsm, MarkovChain, McState, StochasticMealyState, StochasticMealyMachine, \ Sevpa, SevpaState, SevpaAlphabet, SevpaTransition def generate_random_deterministic_automata(automaton_type, num_states, input_alphabet_size, output_alphabet_size=None, ensure_minimality=True, **kwargs ): """ Generates a random deterministic automata of 'automaton_type'. Args: automaton_type: type of automaton, either 'dfa', 'mealy', or 'moore' num_states: number of states input_alphabet_size: size of input alphabet output_alphabet_size: size of output alphabet. (ignored for DFAs) ensure_minimality: ensure that the automaton is minimal **kwargs: : 'num_accepting_states' number of accepting states for DFA generation. If not defined, half of states will be accepting Returns: Random deterministic automaton of user defined type, size. If ensure_minimality is set to False returned automaton is not necessarily minimal. If minimality is reacquired and random automaton cannot be produced in multiple interactions, non-minimal automaton will be returned and a warning message printed. """ assert automaton_type in {'dfa', 'mealy', 'moore'} if output_alphabet_size and output_alphabet_size < 2 or output_alphabet_size is None: output_alphabet_size = 2 state_class_map = {'dfa': DfaState, 'mealy': MealyState, 'moore': MooreState} automaton_class_map = {'dfa': Dfa, 'mealy': MealyMachine, 'moore': MooreMachine} input_alphabet = [f'i{i + 1}' for i in range(input_alphabet_size)] output_alphabet = [f'o{i + 1}' for i in range(output_alphabet_size)] if automaton_type != 'dfa' else [True, False] # For backwards comparability or if uses passes custom input output functions if 'custom_input_alphabet' in kwargs: input_alphabet = kwargs.get('custom_input_alphabet') if len(input_alphabet) != input_alphabet_size: assert False, 'Lenght of input_alphabet_size and custom input alphabet should be equal.' if 'custom_output_alphabet' in kwargs: output_alphabet = kwargs.get('custom_output_alphabet') if len(output_alphabet) != output_alphabet_size: assert False, 'Lenght of output_alphabet_size and custom output alphabet should be equal.' num_accepting_states = None if 'num_accepting_states' in kwargs: num_accepting_states = kwargs.get('num_accepting_states') if num_accepting_states is None: num_accepting_states = num_states // 2 num_random_outputs = num_states if automaton_type != 'mealy' else num_states * input_alphabet_size output_list = [] output_al_copy = output_alphabet.copy() if automaton_type != 'dfa': for _ in range(num_random_outputs): if output_al_copy: o = random.choice(output_al_copy) output_al_copy.remove(o) else: o = random.choice(output_alphabet) output_list.append(o) else: output_list = [True] * num_accepting_states + [False] * (num_states - num_accepting_states) random.shuffle(output_list) states = [state_class_map[automaton_type](state_id=f's{i + 1}') for i in range(num_states)] # define an output function for state_index, state in enumerate(states): if automaton_type == 'dfa': state.is_accepting = output_list[state_index] if automaton_type == 'moore': state.output = output_list[state_index] if automaton_type == 'mealy': for i in input_alphabet: state.output_fun[i] = output_list.pop(0) state_buffer = [] state_buffer.extend(states) while len(state_buffer) < num_states * input_alphabet_size: state_buffer.append(random.choice(states)) random_automaton = None # keep changing transitions until all states are reachable (update in future ) all_states_reachable = False while not all_states_reachable: random.shuffle(state_buffer) transition_index = 0 for state in states: for i in input_alphabet: state.transitions[i] = state_buffer[transition_index] transition_index += 1 random_automaton = automaton_class_map[automaton_type](states[0], states) unreachable_state_exits = False for state in random_automaton.states: state.prefix = random_automaton.get_shortest_path(random_automaton.initial_state, state) if state != random_automaton.initial_state and state.prefix is None: unreachable_state_exits = True break all_states_reachable = not unreachable_state_exits if ensure_minimality: minimality_iterations = 1 while not random_automaton.is_minimal() or random_automaton.size != num_states: # to avoid infinite loops if minimality_iterations == 100: warnings.warn(f'Non-minimal automaton ({automaton_type}, num_states : {num_states}) returned.') break custom_args = {} if 'custom_input_alphabet' in kwargs: custom_args['custom_input_alphabet'] = kwargs.get('custom_input_alphabet') if 'custom_output_alphabet' in kwargs: custom_args['custom_output_alphabet'] = kwargs.get('custom_output_alphabet') if 'num_accepting_states' in kwargs: custom_args['num_accepting_states'] = kwargs.get('num_accepting_states') random_automaton = generate_random_deterministic_automata(automaton_type, num_states, input_alphabet_size, output_alphabet_size, False, # ensure minimality **custom_args) return random_automaton def generate_random_mealy_machine(num_states, input_alphabet, output_alphabet, compute_prefixes=False, ensure_minimality=True) -> MealyMachine: """ Generates a random Mealy machine. Kept for backwards compatibility. Args: num_states: number of states input_alphabet: input alphabet output_alphabet: output alphabet compute_prefixes: if true, shortest path to reach each state will be computed (Default value = False) ensure_minimality: returned automaton will be minimal Returns: Mealy machine with num_states states """ random_mealy_machine = generate_random_deterministic_automata('mealy', num_states, input_alphabet_size=len(input_alphabet), output_alphabet_size=len(output_alphabet), ensure_minimality=ensure_minimality, compute_prefixes=compute_prefixes, custom_input_alphabet=input_alphabet, custom_output_alphabet=output_alphabet) return random_mealy_machine def generate_random_moore_machine(num_states, input_alphabet, output_alphabet, compute_prefixes=False, ensure_minimality=True) -> MooreMachine: """ Generates a random Moore machine. Args: num_states: number of states input_alphabet: input alphabet output_alphabet: output alphabet compute_prefixes: if true, shortest path to reach each state will be computed (Default value = False) ensure_minimality: returned automaton will be minimal Returns: Random Moore machine with num_states states """ random_moore_machine = generate_random_deterministic_automata('moore', num_states, input_alphabet_size=len(input_alphabet), output_alphabet_size=len(output_alphabet), ensure_minimality=ensure_minimality, compute_prefixes=compute_prefixes, custom_input_alphabet=input_alphabet, custom_output_alphabet=output_alphabet) return random_moore_machine def generate_random_dfa(num_states, alphabet, num_accepting_states=1, compute_prefixes=False, ensure_minimality=True) -> Dfa: """ Generates a random DFA. Args: num_states: number of states alphabet: input alphabet num_accepting_states: number of accepting states (Default value = 1) compute_prefixes: if true, shortest path to reach each state will be computed (Default value = False) ensure_minimality: returned automaton will be minimal Returns: Randomly generated DFA """ if num_states <= num_accepting_states: num_accepting_states = num_states // 2 random_dfa = generate_random_deterministic_automata('dfa', num_states, input_alphabet_size=len(alphabet), output_alphabet_size=2, ensure_minimality=ensure_minimality, compute_prefixes=compute_prefixes, custom_input_alphabet=alphabet, num_accepting_states=num_accepting_states) return random_dfa def generate_random_mdp(num_states, input_size, output_size, possible_probabilities=None): """ Generates random MDP. Args: num_states: number of states input_size: number of inputs output_size: user predefined outputs possible_probabilities: list of possible probability pairs to choose from Returns: random MDP """ deterministic_model = generate_random_deterministic_automata('moore', num_states, input_size, output_size) if not possible_probabilities: possible_probabilities = [(1.,), (1.,), (1.,), (0.9, 0.1), (0.8, 0.2), (0.7, 0.3), (0.8, 0.1, 0.1), (0.7, 0.2, 0.1), (0.6, 0.2, 0.1, 0.1)] # ensure that there are no infinite loops max_prob_num = min(num_states, output_size) possible_probabilities = [p for p in possible_probabilities if len(p) <= max_prob_num] mdp_states = [] state_id_state_map = {} for state in deterministic_model.states: mdp_state = MdpState(state.state_id, state.output) state_id_state_map[state.state_id] = mdp_state mdp_states.append(mdp_state) input_al = deterministic_model.get_input_alphabet() for deterministic_state in deterministic_model.states: for i in input_al: state_from_det_model = state_id_state_map[deterministic_state.transitions[i].state_id] prob = random.choice(possible_probabilities) reached_states = [state_from_det_model] for _ in range(len(prob) - 1): while True: new_state = random.choice(mdp_states) # ensure determinism if new_state.output not in {s.output for s in reached_states}: break reached_states.append(new_state) for prob, reached_state in zip(prob, reached_states): mdp_origin_state = state_id_state_map[deterministic_state.state_id] mdp_origin_state.transitions[i].append((reached_state, prob)) # deterministically labeled check for state in mdp_states: for _, transition_values in state.transitions.items(): reached_outputs = [s.output for s, _ in transition_values] assert len(reached_outputs) == len(set(reached_outputs)) return Mdp(mdp_states[0], mdp_states) def generate_random_smm(num_states, input_size, output_size, possible_probabilities=None): """ Generates random SMM. Args: num_states: number of states input_size: number of inputs output_size: number of outputs possible_probabilities: list of possible probability pairs to choose from Returns: random SMM """ deterministic_model = generate_random_deterministic_automata('mealy', num_states, input_size, output_size) input_al = deterministic_model.get_input_alphabet() output_al = list(set([o for state in deterministic_model.states for o in state.output_fun.values()])) output_al.sort() if not possible_probabilities: possible_probabilities = [(1.,), (1.,), (1.,), (0.9, 0.1), (0.8, 0.2), (0.7, 0.3), (0.8, 0.1, 0.1), (0.7, 0.2, 0.1), (0.6, 0.2, 0.1, 0.1)] # ensure that there are no infinite loops max_prob_num = min(num_states, output_size) possible_probabilities = [p for p in possible_probabilities if len(p) <= max_prob_num] smm_states = [] state_id_state_map = {} for state in deterministic_model.states: smm_state = StochasticMealyState(state.state_id) state_id_state_map[state.state_id] = smm_state smm_states.append(smm_state) for deterministic_state in deterministic_model.states: for i in input_al: state_from_det_model = state_id_state_map[deterministic_state.transitions[i].state_id] output_from_det_model = deterministic_state.output_fun[i] prob = random.choice(possible_probabilities) state_id_state_map[deterministic_state.state_id].transitions[i].append( (state_from_det_model, output_from_det_model, prob[0])) observed_outputs = [output_from_det_model] for prob_index in range(1, len(prob)): while True: new_state = random.choice(smm_states) new_output = random.choice(output_al) # ensure determinism if new_output not in observed_outputs: state_id_state_map[deterministic_state.state_id].transitions[i].append( (new_state, new_output, prob[prob_index])) break return StochasticMealyMachine(smm_states[0], smm_states) def generate_random_ONFSM(num_states, num_inputs, num_outputs, multiple_out_prob=0.33): """ Randomly generate an observable non-deterministic finite-state machine. Args: num_states: number of states num_inputs: number of inputs num_outputs: number of outputs multiple_out_prob: probability that state will have multiple outputs (Default value = 0.5) Returns: randomly generated ONFSM """ inputs = [f'i{i + 1}' for i in range(num_inputs)] outputs = [f'o{i + 1}' for i in range(num_outputs)] states = [] for i in range(num_states): state = OnfsmState(f's{i}') states.append(state) state_buffer = states.copy() for state in states: for i in inputs: state_outputs = 1 if random.random() <= multiple_out_prob and num_outputs >= 2: state_outputs = random.randint(2, num_outputs) random_out = random.sample(outputs, state_outputs) for index in range(state_outputs): if state_buffer: new_state = random.choice(state_buffer) state_buffer.remove(new_state) else: new_state = random.choice(states) state.transitions[i].append((random_out[index], new_state)) return Onfsm(states[0], states) def generate_random_markov_chain(num_states): assert num_states >= 3 possible_probabilities = [1.0, 1.0, 0.8, 0.5, 0.9] states = [] for i in range(num_states): states.append(McState(f'q{i}', i)) for index, state in enumerate(states[:-1]): prob = random.choice(possible_probabilities) if prob == 1.: new_state = states[index + 1] state.transitions.append((new_state, prob)) else: next_state = states[index + 1] up_states = list(states) up_states.remove(next_state) rand_state = random.choice(up_states) state.transitions.append((next_state, prob)) state.transitions.append((rand_state, round(1 - prob, 2))) return MarkovChain(states[0], states) def _has_transition(state: SevpaState, transition_letter, stack_guard) -> bool: transitions = state.transitions[transition_letter] if transitions is not None: if stack_guard is None: # internal transition for transition in transitions: if transition.letter == transition_letter: return True else: # return transition for transition in transitions: if transition.stack_guard == stack_guard and transition.letter == transition_letter: return True return False def generate_random_sevpa(num_states, internal_alphabet_size, call_alphabet_size, return_alphabet_size , acceptance_prob, return_transition_prob): """ Generate a random Single Entry Visibly Pushdown Automaton (SEVPA). Args: num_states (int): The number of states in the SEVPA. internal_alphabet_size (int): The size of the internal alphabet. call_alphabet_size (int): The size of the call alphabet. return_alphabet_size (int): The size of the return alphabet. acceptance_prob (float): The probability of a state being an accepting state. return_transition_prob (float): The probability of generating a return transition. Returns: Sevpa: A randomly generated SEVPA. """ internal_alphabet = [f'i{i}' for i in range(internal_alphabet_size)] call_alphabet = [f'c{i}' for i in range(call_alphabet_size)] return_alphabet = [f'r{i}' for i in range(return_alphabet_size)] sevpa_alphabet = SevpaAlphabet(internal_alphabet, call_alphabet, return_alphabet) states = [SevpaState(f'q{i}', random.random() < acceptance_prob) for i in range(num_states)] state_buffer = states.copy() for state in states: if not internal_alphabet or random.uniform(0.0, 1.0) < return_transition_prob: # add return transition while True: return_letter = random.choice(return_alphabet) stack_state = random.choice(states) if not state_buffer else random.choice(state_buffer) if stack_state in state_buffer: state_buffer.remove(stack_state) call_letter = random.choice(call_alphabet) stack_guard = (stack_state.state_id, call_letter) if not _has_transition(state, return_letter, stack_guard): break target_state = random.choice(states) state.transitions[return_letter].append( SevpaTransition(target_state, return_letter, 'pop', stack_guard)) else: # add an internal transition while True: internal_letter = random.choice(internal_alphabet) if not _has_transition(state, internal_letter, None): break target_state = random.choice(states) if not state_buffer else random.choice(state_buffer) if target_state in state_buffer: state_buffer.remove(target_state) state.transitions[internal_letter].append( SevpaTransition(target_state, internal_letter, None, None)) assert len(states) == num_states initial_state = random.choice(states) for state in states: for internal_letter in internal_alphabet: if state.transitions[internal_letter] is None: target_state = random.choice(states) state.transitions[internal_letter].append( SevpaTransition(target_state, internal_letter, None, None)) for call_letter in call_alphabet: for stack_state in states: stack_guard = (stack_state.state_id, call_letter) for return_letter in return_alphabet: if not _has_transition(state, return_letter, stack_guard): target_state = states[random.randint(0, len(states) - 1)] state.transitions[return_letter].append( SevpaTransition(target_state, return_letter, 'pop', stack_guard)) return Sevpa(initial_state, states) ================================================ FILE: aalpy/utils/BenchmarkSULs.py ================================================ def get_Angluin_dfa(): from aalpy.automata import Dfa angluin_dfa = { 'q0': (True, {'a': 'q1', 'b': 'q2'}), 'q1': (False, {'a': 'q0', 'b': 'q3'}), 'q2': (False, {'a': 'q3', 'b': 'q0'}), 'q3': (False, {'a': 'q2', 'b': 'q1'}) } return Dfa.from_state_setup(angluin_dfa) def get_benchmark_ONFSM(): """ Returns ONFSM presented in 'Learning Finite State Models of Observable Nondeterministic Systems in a Testing Context'. """ from aalpy.automata import Onfsm, OnfsmState a = OnfsmState('q0') b = OnfsmState('q1') c = OnfsmState('g2') d = OnfsmState('q3') a.transitions['a'].append((0, b)) a.transitions['b'].append((2, a)) a.transitions['b'].append((0, c)) b.transitions['a'].append((2, a)) b.transitions['b'].append((3, b)) c.transitions['a'].append((2, d)) c.transitions['b'].append((0, c)) c.transitions['b'].append((3, c)) d.transitions['a'].append((2, b)) d.transitions['b'].append((3, d)) return Onfsm(a, [a, b, c, d]) def get_ONFSM(): """ Returns example of an ONFSM. """ from aalpy.automata import Onfsm, OnfsmState q0 = OnfsmState('q0') q1 = OnfsmState('q1') q2 = OnfsmState('q2') q3 = OnfsmState('q3') q4 = OnfsmState('q4') q5 = OnfsmState('q5') q6 = OnfsmState('q6') q7 = OnfsmState('q7') q8 = OnfsmState('q8') q0.transitions['a'].append((2, q1)) q0.transitions['b'].append((0, q0)) q1.transitions['a'].append((2, q0)) q1.transitions['b'].append((0, q2)) q2.transitions['a'].append((1, q2)) q2.transitions['b'].append((0, q3)) q3.transitions['a'].append((2, q8)) q3.transitions['b'].append((0, q4)) q4.transitions['a'].append((1, q4)) q4.transitions['b'].append((0, q5)) q5.transitions['a'].append((2, q6)) q5.transitions['b'].append((0, q7)) q6.transitions['a'].append((2, q5)) q6.transitions['b'].append((0, q6)) q7.transitions['a'].append((1, q7)) q7.transitions['b'].append(('O', q0)) q8.transitions['a'].append((2, q3)) q8.transitions['b'].append((0, q8)) return Onfsm(q0, [q0, q1, q2, q3, q4, q5, q6, q7, q8]) def get_faulty_coffee_machine_MDP(): from aalpy.automata import Mdp, MdpState q0 = MdpState("q0", "init") q1 = MdpState("q1", "beep") q2 = MdpState("q2", "coffee") q0.transitions['but'].append((q0, 1)) q0.transitions['coin'].append((q1, 1)) q1.transitions['but'].append((q0, 0.1)) q1.transitions['but'].append((q2, 0.9)) q1.transitions['coin'].append((q1, 1)) q2.transitions['but'].append((q0, 1)) q2.transitions['coin'].append((q1, 1)) mdp = Mdp(q0, [q0, q1, q2]) return mdp def get_weird_coffee_machine_MDP(): from aalpy.automata import Mdp, MdpState q0 = MdpState("q0", "init") q1 = MdpState("q1", "beep") q2 = MdpState("q2", "coffee") q3 = MdpState("q3", "beep") q4 = MdpState("q4", "coffee") q5 = MdpState("q5", "init") q6 = MdpState("q6", "crash") q0.transitions['but'].append((q0, 1)) q0.transitions['coin'].append((q1, 1)) q0.transitions['koin'].append((q3, 1)) q1.transitions['but'].append((q0, 0.1)) q1.transitions['but'].append((q2, 0.9)) q3.transitions['but'].append((q0, 0.1)) q3.transitions['but'].append((q4, 0.9)) q1.transitions['coin'].append((q1, 1)) q3.transitions['koin'].append((q3, 1)) q1.transitions['koin'].append((q3, 1)) q3.transitions['coin'].append((q1, 1)) q2.transitions['but'].append((q0, 1)) q2.transitions['coin'].append((q1, 1)) q2.transitions['koin'].append((q3, 1)) q4.transitions['coin'].append((q1, 1)) q4.transitions['koin'].append((q3, 1)) q4.transitions['but'].append((q5, 1)) q5.transitions['but'].append((q6, 1)) q5.transitions['coin'].append((q6, 1)) q5.transitions['koin'].append((q6, 1)) q6.transitions['but'].append((q6, 1)) q6.transitions['coin'].append((q6, 1)) q6.transitions['koin'].append((q6, 1)) mdp = Mdp(q0, [q0, q1, q2, q3, q4, q5, q6]) return mdp def get_faulty_coffee_machine_SMM(): from aalpy.automata import StochasticMealyMachine, StochasticMealyState s0 = StochasticMealyState('q0') s1 = StochasticMealyState('q1') s2 = StochasticMealyState('q2') s0.transitions['but'].append((s0, 'init', 1.)) s0.transitions['coin'].append((s1, 'beep', 1.)) s1.transitions['but'].append((s0, 'init', 0.1)) s1.transitions['but'].append((s2, 'coffee', 0.9)) s1.transitions['coin'].append((s1, 'beep', 1.)) s2.transitions['but'].append((s0, 'init', 1.)) s2.transitions['coin'].append((s1, 'beep', 1.)) smm = StochasticMealyMachine(s0, [s0, s1, s2]) return smm def get_minimal_faulty_coffee_machine_SMM(): from aalpy.automata import StochasticMealyMachine, StochasticMealyState s0 = StochasticMealyState('q0') s1 = StochasticMealyState('q1') s0.transitions['but'].append((s0, 'init', 1.)) s0.transitions['coin'].append((s1, 'beep', 1.)) s1.transitions['but'].append((s0, 'init', 0.1)) s1.transitions['but'].append((s0, 'coffee', 0.9)) s1.transitions['coin'].append((s1, 'beep', 1.)) smm = StochasticMealyMachine(s0, [s0, s1]) return smm def get_faulty_mqtt_SMM(): from aalpy.automata import StochasticMealyMachine, StochasticMealyState s0 = StochasticMealyState('q0') s1 = StochasticMealyState('q1') s2 = StochasticMealyState('q2') s0.transitions['connect'].append((s1, 'CONNACK', 1.)) s0.transitions['disconnect'].append((s0, 'CONCLOSED', 1.)) s0.transitions['publish'].append((s0, 'CONCLOSED', 1.)) s0.transitions['subscribe'].append((s0, 'CONCLOSED', 1.)) s0.transitions['unsubscribe'].append((s0, 'CONCLOSED', 1.)) s1.transitions['connect'].append((s0, 'CONCLOSED', 1.)) s1.transitions['disconnect'].append((s0, 'CONCLOSED', 1.)) s1.transitions['publish'].append((s1, 'PUBACK', 0.9)) s1.transitions['publish'].append((s0, 'CONCLOSED', 0.1)) s1.transitions['subscribe'].append((s2, 'SUBACK', 1.)) s1.transitions['unsubscribe'].append((s1, 'UNSUBACK', 1.)) s2.transitions['connect'].append((s0, 'CONCLOSED', 1.)) s2.transitions['disconnect'].append((s0, 'CONCLOSED', 1.)) s2.transitions['publish'].append((s2, 'PUBLISH_PUBACK', 1.)) s2.transitions['subscribe'].append((s2, 'SUBACK', 1.)) s2.transitions['unsubscribe'].append((s1, 'UNSUBACK', 0.8)) s2.transitions['unsubscribe'].append((s2, 'SUBACK', 0.2)) smm = StochasticMealyMachine(s0, [s0, s1, s2]) return smm def get_small_gridworld(): from aalpy.automata import StochasticMealyMachine, StochasticMealyState s0 = StochasticMealyState('q0') s1 = StochasticMealyState('q1') s2 = StochasticMealyState('q2') s3 = StochasticMealyState('q3') p_g = 0.8 p_m = 0.6 # gridworld of the form # W W W W with a start in the top left # W G M W states like s0 s1 # W M G W s2 s3 # W W W W s0.transitions['north'].append((s0, 'wall', 1.)) s0.transitions['west'].append((s0, 'wall', 1.)) s0.transitions['east'].append((s1, 'mud', p_m)) s0.transitions['east'].append((s3, 'grass', 1 - p_m)) s0.transitions['south'].append((s2, 'mud', p_m)) s0.transitions['south'].append((s3, 'grass', 1 - p_m)) s1.transitions['north'].append((s1, 'wall', 1.)) s1.transitions['east'].append((s1, 'wall', 1.)) s1.transitions['west'].append((s0, 'grass', p_g)) s1.transitions['west'].append((s2, 'mud', 1 - p_g)) s1.transitions['south'].append((s3, 'grass', p_g)) s1.transitions['south'].append((s2, 'mud', 1 - p_g)) s2.transitions['south'].append((s2, 'wall', 1.)) s2.transitions['west'].append((s2, 'wall', 1.)) s2.transitions['east'].append((s3, 'grass', p_g)) s2.transitions['east'].append((s1, 'mud', 1 - p_g)) s2.transitions['north'].append((s0, 'grass', p_g)) s2.transitions['south'].append((s1, 'mud', 1 - p_g)) s3.transitions['south'].append((s3, 'wall', 1.)) s3.transitions['east'].append((s3, 'wall', 1.)) s3.transitions['west'].append((s2, 'mud', p_m)) s3.transitions['west'].append((s0, 'grass', 1 - p_m)) s3.transitions['north'].append((s1, 'mud', p_m)) s3.transitions['north'].append((s0, 'grass', 1 - p_m)) smm = StochasticMealyMachine(s0, [s0, s1, s2, s3]) return smm class MockMqttExample: def __init__(self): self.state = 'CONCLOSED' self.topics = set() def subscribe(self, topic: str): if '\n' in topic or '\u0000' in topic: self.state = 'CONCLOSED' self.topics.clear() elif self.state != 'CONCLOSED': self.topics.add(topic) self.state = 'SUBACK' return self.state def unsubscribe(self, topic): if '\n' in topic or '\u0000' in topic: self.state = 'CONCLOSED' self.topics.clear() elif self.state != 'CONCLOSED': if topic in self.topics: self.topics.remove(topic) self.state = 'UNSUBACK' return self.state def connect(self): if self.state == 'CONCLOSED': self.state = 'CONNACK' else: self.topics.clear() self.state = 'CONCLOSED' return self.state def disconnect(self): self.state = 'CONCLOSED' self.topics.clear() return self.state def publish(self, topic): if '\n' in topic or '\u0000' in topic: self.state = 'CONCLOSED' self.topics.clear() if self.state != 'CONCLOSED': if topic not in self.topics: self.state = 'PUBACK' else: self.state = 'PUBACK_PUBACK' return self.state class DateValidator: """ Class mimicking Date Validator API. It does not account for the leap years. The format of the dates is %d/%m/%Y' """ def is_date_accepted(self, date_string: str): values = date_string.split('/') if len(values) != 3: return False try: day = int(values[0]) month = int(values[1]) year = int(values[2]) except ValueError: return False if not (0 <= year <= 9999): return False if month == 2 and not (1 <= day <= 28): return False if month in [1, 3, 5, 7, 8, 10, 12] and not (1 <= day <= 31): return False elif not (1 <= day <= 31): return False return True def get_small_pomdp(): from aalpy.automata import Mdp, MdpState q0 = MdpState("q0", "init") q1 = MdpState("q1", "beep") q2 = MdpState("q2", "beep") q3 = MdpState("q3", "coffee") q4 = MdpState("q4", "tea") q0.transitions['but'].append((q0, 1)) q0.transitions['coin'].append((q1, 0.8)) q0.transitions['coin'].append((q2, 0.2)) q1.transitions['coin'].append((q1, 1)) q1.transitions['but'].append((q3, 1)) q2.transitions['coin'].append((q2, 0.3)) q2.transitions['coin'].append((q1, 0.7)) q2.transitions['but'].append((q4, 1)) q3.transitions['coin'].append((q3, 1)) q3.transitions['but'].append((q3, 1)) q4.transitions['coin'].append((q4, 1)) q4.transitions['but'].append((q4, 1)) return Mdp(q0, [q0, q1, q2, q3, q4]) def is_balanced(test_string, call_return_map, allow_empty_string): stack = [] # Create a set of open and close characters for faster lookup open_chars = set(call_return_map.keys()) close_chars = set(call_return_map.values()) for char in test_string: if char in open_chars: stack.append(char) elif char in close_chars: # Stack should exist if not stack: return False last_open = stack.pop() # Mismatched open and close character if call_return_map[last_open] != char: return False return not stack if allow_empty_string else not stack and len(test_string) > 0 def get_balanced_string_sul(call_return_map, allow_empty_string): from aalpy.base import SUL class BalancedStringSUL(SUL): def __init__(self, call_return_map, allow_empty_string): super(BalancedStringSUL, self).__init__() self.call_return_map = call_return_map self.allow_empty_string = allow_empty_string self.sting_under_test = [] def pre(self): self.sting_under_test = [] def post(self): pass def step(self, letter): if letter: self.sting_under_test += letter return is_balanced(self.sting_under_test, self.call_return_map, self.allow_empty_string) return BalancedStringSUL(call_return_map, allow_empty_string) ================================================ FILE: aalpy/utils/BenchmarkSevpaModels.py ================================================ from aalpy.automata.Sevpa import Sevpa from aalpy.utils import load_automaton_from_file def sevpa_for_L1(): state_setup = { 'q0': (False, {'b': [('q1', 'pop', ('q0', 'a'))] }), 'q1': (True, {'b': [('q1', 'pop', ('q0', 'a'))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L2(): state_setup = { 'q0': (False, {'d': [('q1', 'pop', ('q0', 'a')), ('q1', 'pop', ('q0', 'b'))], 'c': [('q1', 'pop', ('q0', 'a')), ('q1', 'pop', ('q0', 'b'))] }), 'q1': (True, {'d': [('q1', 'pop', ('q0', 'a')), ('q1', 'pop', ('q0', 'b'))], 'c': [('q1', 'pop', ('q0', 'a')), ('q1', 'pop', ('q0', 'b'))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L3(): state_setup = { 'q0': (False, {'g': [('q6', 'pop', ('q0', 'd')), ('q4', 'pop', ('q0', 'b'))], 'e': [('q5', 'pop', ('q0', 'd')), ('q2', 'pop', ('q0', 'b'))] }), 'q1': (True, {'g': [('q6', 'pop', ('q0', 'd')), ('q4', 'pop', ('q0', 'b'))], 'e': [('q5', 'pop', ('q0', 'd')), ('q2', 'pop', ('q0', 'b'))] }), 'q2': (False, {'f': [('q1', 'pop', ('q0', 'a'))] }), 'q4': (False, {'h': [('q1', 'pop', ('q0', 'a'))] }), 'q5': (False, {'f': [('q1', 'pop', ('q0', 'c'))] }), 'q6': (False, {'h': [('q1', 'pop', ('q0', 'c'))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L4(): state_setup = { 'q0': (False, {'c': [('q2', 'pop', ('q0', 'b'))] }), 'q1': (True, {'c': [('q2', 'pop', ('q0', 'b'))] }), 'q2': (False, {'d': [('q1', 'pop', ('q0', 'a'))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L5(): state_setup = { 'q0': (False, {'d': [('q2', 'pop', ('q0', 'c'))] }), 'q1': (True, {'d': [('q2', 'pop', ('q0', 'c'))] }), 'q2': (False, {'e': [('q3', 'pop', ('q0', 'b'))] }), 'q3': (False, {'f': [('q1', 'pop', ('q0', 'a'))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L7(): state_setup = { 'q0': (False, {')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))], ']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))] }), 'q1': (True, {')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))], ']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L8(): state_setup = { 'q0': (False, {')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))], '}': [('q1', 'pop', ('q0', '{')), ('q1', 'pop', ('q1', '{'))], ']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))] }), 'q1': (True, {')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))], '}': [('q1', 'pop', ('q0', '{')), ('q1', 'pop', ('q1', '{'))], ']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L9(): state_setup = { 'q0': (False, {']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))], '}': [('q1', 'pop', ('q0', '{')), ('q1', 'pop', ('q1', '{'))], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))], '>': [('q1', 'pop', ('q0', '<')), ('q1', 'pop', ('q1', '<'))] }), 'q1': (True, {']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))], '}': [('q1', 'pop', ('q0', '{')), ('q1', 'pop', ('q1', '{'))], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))], '>': [('q1', 'pop', ('q0', '<')), ('q1', 'pop', ('q1', '<'))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L10(): state_setup = { "q0": (False, {"b": [("qb", None, None)], }), "qb": (False, {"c": [("qc", None, None)], }), "qc": (False, {"d": [("qd", None, None)], }), "qd": (False, {"e": [("q1", None, None)], }), "q1": (False, {"v": [("qv", 'pop', ('q0', 'a')), ("qv", 'pop', ('q1', 'a')), ("qv", 'pop', ('q2', 'a'))] }), "qv": (False, {"w": [("qw", None, None)] }), "qw": (False, {"x": [("qx", None, None)] }), "qx": (False, {"y": [("qy", None, None)] }), "qy": (False, {"z": [("q2", None, None)] }), "q2": (True, {"v": [("qv", 'pop', ('q0', 'a')), ("qv", 'pop', ('q1', 'a')), ("qv", 'pop', ('q2', 'a'))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L11(): state_setup = { 'q0': (False, {'i1': [('q2', None, None)], 'r1': [('q3', 'pop', ('q0', 'c2')), ('q3', 'pop', ('q1', 'c2')), ('q5', 'pop', ('q2', 'c2'))], 'r2': [('q1', 'pop', ('q0', 'c2')), ('q1', 'pop', ('q1', 'c2')), ('q2', 'pop', ('q2', 'c2'))] }), 'q1': (True, {'r1': [('q3', 'pop', ('q0', 'c2')), ('q3', 'pop', ('q1', 'c2')), ('q5', 'pop', ('q2', 'c2'))], 'r2': [('q1', 'pop', ('q0', 'c2')), ('q1', 'pop', ('q1', 'c2')), ('q2', 'pop', ('q2', 'c2'))] }), 'q2': (False, {'r1': [('q3', 'pop', ('q0', 'c1')), ('q3', 'pop', ('q1', 'c1')), ('q5', 'pop', ('q2', 'c1'))], 'r2': [('q1', 'pop', ('q0', 'c1')), ('q1', 'pop', ('q1', 'c1')), ('q2', 'pop', ('q2', 'c1'))] }), 'q3': (False, {'i2': [('q1', None, None)] }), 'q5': (False, {'i2': [('q2', None, None)] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L12(): state_setup = { 'q0': (False, {']': [('q1', 'pop', ('q0', '['))], ')': [('q1', 'pop', ('q0', '('))] }), 'q1': (True, {']': [('q1', 'pop', ('q0', '['))], ')': [('q1', 'pop', ('q0', '('))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0", ) def sevpa_for_L13(): state_setup = { 'q0': (False, {'c': [('q1', None, None)], 'b': [('q1', None, None)], 'a': [('q1', None, None)], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))] }), 'q1': (True, {'c': [('q1', None, None)], 'b': [('q1', None, None)], 'a': [('q1', None, None)], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L14(): state_setup = { 'q0': (False, {'a': [('q1', None, None)], 'b': [('q1', None, None)], 'c': [('q1', None, None)], ']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))] }), 'q1': (True, {'a': [('q1', None, None)], 'b': [('q1', None, None)], 'c': [('q1', None, None)], ']': [('q1', 'pop', ('q0', '[')), ('q1', 'pop', ('q1', '['))], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") def sevpa_for_L15(): # Dyck order 1 state_setup = { 'q0': (False, {'d': [('q1', None, None)], 'a': [('q2', None, None)], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))] }), 'q1': (True, {'d': [('q1', None, None)], 'a': [('q2', None, None)], ')': [('q1', 'pop', ('q0', '(')), ('q1', 'pop', ('q1', '('))] }), 'q2': (False, {'b': [('q3', None, None)] }), 'q3': (False, {'c': [('q1', None, None)] }) } return Sevpa.from_state_setup(state_setup, init_state_id="q0") if __name__ == '__main__': e = sevpa_for_L13() print(e) print(e.get_input_alphabet()) e.save('test') m = load_automaton_from_file('test.dot', automaton_type='vpa') print('Loaded') print(m) ================================================ FILE: aalpy/utils/BenchmarkVpaModels.py ================================================ import random from aalpy.automata.Vpa import Vpa, VpaAlphabet def vpa_L1(): # we always ensure that n >= 1 call_set = ['a'] return_set = ['b'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"a": [("q1", 'push', "a")], }), "q1": (False, {"a": [("q1", 'push', "a")], "b": [("q2", 'pop', "a")]}), "q2": (True, {"b": [("q2", 'pop', "a")]}), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L2(): call_set = ['a', 'b'] return_set = ['c', 'd'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"a": [("q1", 'push', "a")], "b": [("q1", 'push', "b")], }), "q1": (False, {"a": [("q1", 'push', "a")], "b": [("q1", 'push', "b")], "c": [("q2", 'pop', "a"), ("q2", 'pop', "b")], "d": [("q2", 'pop', "a"), ("q2", 'pop', "b")]}), "q2": (True, {"c": [("q2", 'pop', "a"), ("q2", 'pop', "b")], "d": [("q2", 'pop', "a"), ("q2", 'pop', "b")]}), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_for_L3(): call_set = ['a', 'c', 'b', 'd'] return_set = ['e', 'g', 'f', 'h'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"a": [("q0a", 'push', "a")], "c": [("q0c", 'push', "c")], }), "q0a": (False, {"b": [("q1", 'push', "b")]}), "q0c": (False, {"d": [("q1", 'push', "d")]}), "q1": (False, {"a": [("q1a", 'push', "a")], "c": [("q1c", 'push', "c")], "e": [("q2e", 'pop', "b"), ("q2e", 'pop', "d")], "g": [("q2g", 'pop', "b"), ("q2g", 'pop', "d")], # stack should actually be redundant }), "q1a": (False, {"b": [("q1", 'push', "b")]}), "q1c": (False, {"d": [("q1", 'push', "d")]}), "q2e": (False, {"f": [("q2", 'pop', "a"), ("q2", 'pop', "c")]}), "q2g": (False, {"h": [("q2", 'pop', "a"), ("q2", 'pop', "c")]}), "q2": (True, {"e": [("q2e", 'pop', "b"), ("q2e", 'pop', "d")], "g": [("q2g", 'pop', "b"), ("q2g", 'pop', "d")]}) } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L3(): call_set = ['a', 'b'] return_set = ['c', 'd'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"a": [("q01", 'push', "a")], }), "q01": (False, {"b": [("q1", 'push', "b")], }), "q1": (False, {"a": [("q11", 'push', "a")], "c": [("q21", 'pop', "b")]}), "q11": (False, {"b": [("q1", 'push', "b")], }), "q21": (False, {"d": [("q2", 'pop', "a")]}), "q2": (True, {"c": [("q21", 'pop', "b")]}), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L4(): # Dyck order 2 call_set = ['(', '['] return_set = [')', ']'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"(": [("q1", 'push', '(')], "[": [("q1", 'push', '[')], # exclude empty seq }), "q1": (True, {"(": [("q1", 'push', '(')], "[": [("q1", 'push', '[')], ")": [("q1", 'pop', "(")], "]": [("q1", 'pop', "[")] }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L6(): # Dyck order 3 call_set = ['(', '[', '{'] return_set = [')', ']', '}'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"(": [("q1", 'push', '(')], "[": [("q1", 'push', '[')], "{": [("q1", 'push', '{')], }), "q1": (True, {"(": [("q1", 'push', '(')], "[": [("q1", 'push', '[')], "{": [("q1", 'push', '{')], ")": [("q1", 'pop', "(")], "]": [("q1", 'pop', "[")], "}": [("q1", 'pop', "{")], }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L8(): # Dyck order 4 call_set = ['(', '[', '{', '<'] return_set = [')', ']', '}', '>'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"(": [("q1", 'push', '(')], "[": [("q1", 'push', '[')], "{": [("q1", 'push', '{')], "<": [("q1", 'push', '<')], }), "q1": (True, {"(": [("q1", 'push', '(')], "[": [("q1", 'push', '[')], "{": [("q1", 'push', '{')], "<": [("q1", 'push', '<')], ")": [("q1", 'pop', "(")], "]": [("q1", 'pop', "[")], "}": [("q1", 'pop', "{")], ">": [("q1", 'pop', "<")], }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L9(): # Dyck order 2 (single-nested) call_set = ['(', '['] return_set = [')', ']'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"(": [("q1", 'push', "(")], "[": [("q1", 'push', "[")], # exclude empty seq }), "q1": (False, {"(": [("q1", 'push', "(")], "[": [("q1", 'push', "[")], ")": [("q2", 'pop', "(")], "]": [("q2", 'pop', "[")]}), "q2": (True, { ")": [("q2", 'pop', "(")], "]": [("q2", 'pop', "[")] }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L10(): # Dyck order 1 call_set = ['('] return_set = [')'] internal_set = ['a', 'b', 'c'] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"(": [("q1", 'push', "(")], "a": [("q1", None, None)], "b": [("q1", None, None)], "c": [("q1", None, None)], # exclude empty seq }), "q1": (True, {"(": [("q1", 'push', "(")], ")": [("q1", 'pop', "(")], "a": [("q1", None, None)], "b": [("q1", None, None)], "c": [("q1", None, None)] }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L11(): # Dyck order 2 call_set = ['(', '['] return_set = [')', ']'] internal_set = ['a', 'b', 'c'] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"(": [("q1", 'push', "(")], "[": [("q1", 'push', "[")], "a": [("q1", None, None)], "b": [("q1", None, None)], "c": [("q1", None, None)], # exclude empty seq }), "q1": (True, {"(": [("q1", 'push', "(")], "[": [("q1", 'push', "[")], ")": [("q1", 'pop', "(")], "]": [("q1", 'pop', "[")], "a": [("q1", None, None)], "b": [("q1", None, None)], "c": [("q1", None, None)] }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_L12(): # Dyck order 1 call_set = ['('] return_set = [')'] internal_set = ['a', 'b', 'c', 'd'] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"(": [("q1", 'push', "(")], "a": [("qa", None, None)], "d": [("q1", None, None)], # exclude empty seq }), "q1": (True, {"(": [("q1", 'push', "(")], ")": [("q1", 'pop', "(")], "a": [("qa", None, None)], "d": [("q1", None, None)], }), "qa": (False, {"b": [("qb", None, None)], }), "qb": (False, {"c": [("q1", None, None)], }) } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_for_L16(): # just a testing language call_set = ['a'] return_set = ['b'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, {"a": [("q1", 'push', "$")]}), "q1": (False, {"a": [("q1", 'push', "x")], "b": [("q1", 'pop', "x"), ("q2", 'pop', "$")], }), "q2": (True, {}) } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_for_odd_parentheses(): # VPA for accepting only odd number of fully balanced parentheses # It accepts patterns like () and ((()))), but rejects odd pairs or multiple groups call_set = ['('] return_set = [')'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, { "(": [("q1", 'push', "(")] }), "q1": (False, { "(": [("q0", 'push', "(")], ")": [("q2", 'pop', "(")] }), "q2": (True, { ")": [("q2", 'pop', "(")] }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def vpa_for_even_parentheses(): # VPA for accepting only even number of fully balanced parentheses call_set = ['('] return_set = [')'] internal_set = [] input_alphabet = VpaAlphabet(internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set) state_setup = { "q0": (False, { "(": [("q1", 'push', "(")] }), "q1": (False, { "(": [("q2", 'push', "(")], }), "q2": (True, { ")": [("q2", 'pop', "(")], "(": [("q1", 'push', "(")] }), } vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def gen_arithmetic_data(num_sequences=3000, min_seq_len=2, max_seq_len=8): import ast from aalpy.base import SUL from aalpy.utils import convert_i_o_traces_for_RPNI class ArithmeticSUL(SUL): def __init__(self): super().__init__() self.string_under_test = '' def pre(self): self.string_under_test = '' def post(self): pass def step(self, letter): if letter: self.string_under_test += ' ' + letter if len(self.string_under_test) > 0 else letter try: # Parse the expression using ast parsed_expr = ast.parse(self.string_under_test, mode='eval') # Check if the parsed expression is a valid arithmetic expression is_valid = all(isinstance(node, (ast.Expression, ast.BinOp, ast.UnaryOp, ast.Num, ast.Name, ast.Load)) or isinstance(node, ast.operator) or isinstance(node, ast.expr_context) or (isinstance(node, ast.BinOp) and isinstance(node.op, ast.operator)) for node in ast.walk(parsed_expr)) return is_valid except SyntaxError: return False sul = ArithmeticSUL() alphabet = VpaAlphabet(internal_alphabet=['1', '+', ], call_alphabet=['(', ], return_alphabet=[')', ]) merged_alphabet = alphabet.get_merged_alphabet() data = [] while len(data) < num_sequences: seq = [] for _ in range(random.randint(min_seq_len, max_seq_len)): seq.append(random.choice(merged_alphabet)) outputs = sul.query(tuple(seq)) data.append(list(zip(seq, outputs))) rpni_format = convert_i_o_traces_for_RPNI(data) return rpni_format, alphabet def vpa_json(): # Define call, return, and internal symbols for JSON call_set = ['{', '['] return_set = ['}', ']'] internal_set = [':', ',', 'key', 'val'] # Keys, values, and separators as internal alphabet symbols # Define the input alphabet input_alphabet = VpaAlphabet( internal_alphabet=internal_set, call_alphabet=call_set, return_alphabet=return_set ) # Define states and transitions state_setup = { "q0": (False, { "{": [("q1", 'push', '{')], "[": [("q2", 'push', '[')], "}": [("q0", 'pop', '{')], "]": [("q0", 'pop', '[')], "key": [("q3", None, None)], # Expect a key in an object }), # Array parsing state "q2": (False, { "val": [("q5", None, None)], # Allow multiple values in an array "[": [("q2", 'push', '[')], # Nested arrays "]": [("q0", 'pop', '[')], # End of array }), # After parsing a key "q3": (False, { ":": [("q4", None, None)], # Key-value separator }), # After parsing a key-value separator "q4": (False, { "val": [("q5", None, None)], # Expecting a value "{": [("q0", 'push', '{')], # Nested object "[": [("q2", 'push', '[')], # Nested array }), # After parsing a value in an object "q5": (False, { ",": [("q4", None, None)], # Another key-value pair "}": [("q0", 'pop', '{')], # End of object "]": [("q0", 'pop', '[')], # End of object }), } # Construct and return the VPA vpa = Vpa.from_state_setup(state_setup, init_state_id="q0", input_alphabet=input_alphabet) return vpa def get_all_VPAs(): from aalpy import load_automaton_from_file arithmetics_vpa = load_automaton_from_file('../DotModels/arithmetics.dot', 'vpa') return [vpa_L1(), vpa_L2(), vpa_L3(), vpa_L4(), vpa_L6(), vpa_L8(), vpa_L9(), vpa_L10(), vpa_L11(), vpa_L12(), vpa_for_odd_parentheses(), vpa_for_even_parentheses(), arithmetics_vpa] ================================================ FILE: aalpy/utils/DataHandler.py ================================================ from abc import ABC, abstractmethod class DataHandler(ABC): """ Abstract class used for data loading for Alergia algorithm. Usage of class is not needed, but recommended for consistency. """ @abstractmethod def tokenize_data(self, path): pass class CharacterTokenizer(DataHandler): """ Used for Markov Chain data parsing. Processes data where each input is a single character. Each input sequence is in the separate line. """ def tokenize_data(self, path): data = [] lines = open(path).read().splitlines() for l in lines: data.append(list(l)) return data class DelimiterTokenizer(DataHandler): """ Used for Markov Chain data parsing. Processes data where each input is separated by the delimiter. Each input sequence is in the separate line. """ def tokenize_data(self, path, delimiter=','): data = [] lines = open(path).read().splitlines() for l in lines: data.append(l.split(delimiter)) return data class IODelimiterTokenizer(DataHandler): """ Used for Markov Decision Process data parsing. Processes data where each input/output is separated by the io_delimiter, and i/o pairs are separated by word delimiter. Each [output, tuple(input,output)*] sequence is in the separate line. """ def tokenize_data(self, path, io_delimiter='/', word_delimiter=','): data = [] lines = open(path).read().splitlines() for l in lines: words = l.split(word_delimiter) seq = [words[0]] for w in words[1:]: i_o = w.split(io_delimiter) if len(i_o) != 2: print('Data formatting error. io_delimiter should split words into ' 'where is values of param \"io_delimiter\'"') exit(-1) seq.append(tuple([try_int(i_o[0]), try_int(i_o[1])])) data.append(seq) return data def try_int(x): if str.isdigit(x): return int(x) return x ================================================ FILE: aalpy/utils/FileHandler.py ================================================ import re import sys import traceback from pathlib import Path from pydot import Dot, Node, Edge from aalpy.automata import Dfa, MooreMachine, Mdp, Onfsm, MealyState, DfaState, MooreState, MealyMachine, \ MdpState, StochasticMealyMachine, StochasticMealyState, OnfsmState, MarkovChain, McState, Sevpa, SevpaState, \ SevpaTransition, Vpa, VpaState, VpaTransition, NDMooreMachine, NDMooreState file_types = ['dot', 'png', 'svg', 'pdf', 'string'] automaton_types = {Dfa: 'dfa', MealyMachine: 'mealy', MooreMachine: 'moore', Mdp: 'mdp', StochasticMealyMachine: 'smm', Onfsm: 'onfsm', NDMooreMachine: 'ndmoore', MarkovChain: 'mc', Sevpa: 'sevpa', Vpa: 'vpa'} def _wrap_label(label): """ Adds a " " around a label if not already present on both ends. """ if label[0] == '\"' and label[-1] == '\"': return label return f'\"{label}\"' def _get_node(state, automaton_type): if automaton_type == 'dfa': if state.is_accepting: return Node(state.state_id, label=_wrap_label(state.state_id), shape='doublecircle') return Node(state.state_id, label=_wrap_label(state.state_id)) if automaton_type == 'mealy': return Node(state.state_id, label=_wrap_label(state.state_id)) if automaton_type in ['moore', 'ndmoore']: return Node(state.state_id, label=_wrap_label(f'{state.state_id}|{state.output}'), shape='record', style='rounded') if automaton_type == 'onfsm': return Node(state.state_id, label=_wrap_label(state.state_id)) if automaton_type == 'mc': return Node(state.state_id, label=_wrap_label(f'{state.output}')) if automaton_type == 'mdp': return Node(state.state_id, label=_wrap_label(f'{state.output}')) if automaton_type == 'smm': return Node(state.state_id, label=_wrap_label(state.state_id)) if automaton_type == 'sevpa' or automaton_type == 'vpa': if state.is_accepting: return Node(state.state_id, label=_wrap_label(state.state_id), shape='doublecircle') return Node(state.state_id, label=_wrap_label(state.state_id)) def _add_transition_to_graph(graph, state, automaton_type, display_same_state_trans, round_floats): if automaton_type == 'dfa' or automaton_type == 'moore': for i in state.transitions.keys(): new_state = state.transitions[i] if not display_same_state_trans and new_state.state_id == state.state_id: continue graph.add_edge(Edge(state.state_id, new_state.state_id, label=_wrap_label(f'{i}'))) if automaton_type == 'mealy': for i in state.transitions.keys(): new_state = state.transitions[i] if not display_same_state_trans and new_state.state_id == state.state_id: continue graph.add_edge(Edge(state.state_id, new_state.state_id, label=_wrap_label(f'{i}/{state.output_fun[i]}'))) if automaton_type == 'onfsm': for i in state.transitions.keys(): new_state = state.transitions[i] for s in new_state: if not display_same_state_trans and state.state_id == s[1].state_id: continue graph.add_edge(Edge(state.state_id, s[1].state_id, label=_wrap_label(f'{i}/{s[0]}'))) if automaton_type == 'ndmoore': for i in state.transitions.keys(): new_states = state.transitions[i] for new_state in new_states: if not display_same_state_trans and state.state_id == new_state.state_id: continue graph.add_edge(Edge(state.state_id, new_state.state_id, label=_wrap_label(f'{i}'))) if automaton_type == 'mc': for new_state, prob in state.transitions: prob = round(prob, round_floats) if round_floats else prob graph.add_edge(Edge(state.state_id, new_state.state_id, label=f'{prob}')) if automaton_type == 'mdp': for i in state.transitions.keys(): new_state = state.transitions[i] for s in new_state: if not display_same_state_trans and s[0].state_id == state.state_id: continue prob = round(s[1], round_floats) if round_floats else s[1] graph.add_edge(Edge(state.state_id, s[0].state_id, label=_wrap_label(f'{i}:{prob}'))) if automaton_type == 'smm': for i in state.transitions.keys(): new_state = state.transitions[i] for s in new_state: if not display_same_state_trans and s[0].state_id == state.state_id: continue prob = round(s[2], round_floats) if round_floats else s[2] graph.add_edge(Edge(state.state_id, s[0].state_id, label=_wrap_label(f'{i}/{s[1]}:{prob}'))) if automaton_type == 'sevpa': for i in state.transitions.keys(): transitions_list = state.transitions[i] for transition in transitions_list: if transition.action == 'pop': edge = Edge(state.state_id, transition.target_state.state_id, label=_wrap_label(f'{transition.letter} / {transition.stack_guard}')) elif transition.action is None: edge = Edge(state.state_id, transition.target_state.state_id, label=_wrap_label(f'{transition.letter}')) else: assert False graph.add_edge(edge) if automaton_type == 'vpa': for i in state.transitions.keys(): transitions_list = state.transitions[i] for transition in transitions_list: if transition.action == 'pop': edge = Edge(state.state_id, transition.target_state.state_id, label=_wrap_label(f'{transition.letter} / pop({transition.stack_guard})')) elif transition.action == 'push': edge = Edge(state.state_id, transition.target_state.state_id, label=_wrap_label(f'{transition.letter} / push({transition.stack_guard})')) elif transition.action is None: edge = Edge(state.state_id, transition.target_state.state_id, label=_wrap_label(f'{transition.letter}')) else: assert False graph.add_edge(edge) def visualize_automaton(automaton, path="LearnedModel", file_type="pdf", display_same_state_trans=True): """ Create a graphical representation of the automaton. Function is round in the separate thread in the background. If possible, it will be opened by systems default program. Args: automaton: automaton to be visualized path: pathlike or str, file in which visualization will be saved (Default value = "LearnedModel.pdf") file_type: type of file/visualization. Can be ['png', 'svg', 'pdf'] (Default value = "pdf") display_same_state_trans: if True, same state transitions will be displayed (Default value = True) """ print('Visualization started in the background thread.') if len(automaton.states) >= 25: print(f'Visualizing {len(automaton.states)} state automaton could take some time.') import threading visualization_thread = threading.Thread(target=save_automaton_to_file, name="Visualization", args=(automaton, path, file_type, display_same_state_trans, True, 2)) visualization_thread.start() def save_automaton_to_file(automaton, path="LearnedModel", file_type="dot", display_same_state_trans=True, visualize=False, round_floats=None): """ The Standard of the automata strictly follows the syntax found at: https://automata.cs.ru.nl/Syntax/Overview. For non-deterministic and stochastic systems syntax can be found on AALpy's Wiki. Args: automaton: automaton to be saved to file path: pathlike or str, file in which visualization will be saved (Default value = "LearnedModel") file_type: type of file/visualization. Can be ['dot', 'png', 'svg', 'pdf'] (Default value = "dot) display_same_state_trans: True, should not be set to false except from the visualization method (Default value = True) visualize: visualize the automaton round_floats: for stochastic automata, round the floating point numbers to defined number of decimal places Returns: """ path = Path(path) file_type = file_type.lower() assert file_type in file_types, f"Filetype {file_type} not in allowed filetypes" path = path.with_suffix(f".{file_type}") if file_type == 'dot' and not display_same_state_trans: print("When saving to file all transitions will be saved") display_same_state_trans = True automaton_type = automaton_types[automaton.__class__] graph = Dot(path.stem, graph_type='digraph') for state in automaton.states: if automaton_type in {'vpa', 'sevpa'} and state.state_id == 'ErrorSinkState': continue graph.add_node(_get_node(state, automaton_type)) for state in automaton.states: _add_transition_to_graph(graph, state, automaton_type, display_same_state_trans, round_floats) # add initial node graph.add_node(Node('__start0', shape='none', label='')) graph.add_edge(Edge('__start0', automaton.initial_state.state_id, label='')) if file_type == 'string': return graph.to_string() else: try: graph.write(path=path, format=file_type if file_type != 'dot' else 'raw') print(f'Model saved to {path.as_posix()}.') if visualize and file_type in {'pdf', 'png', 'svg'}: try: import webbrowser webbrowser.open(path.resolve().as_uri()) except OSError: traceback.print_exc() print(f'Could not open the file {path.as_posix()}.', file=sys.stderr) except OSError: traceback.print_exc() print(f'Could not write to the file {path.as_posix()}.', file=sys.stderr) sevpa_transition_pattern = r"(\S+)\s*/\s*\(\s*'(\S+)'\s*,\s*'(\S+)'\s*\)" vpa_push_pattern = r"(\S+)\s*/\s*push\(\s*(.*?)\s*\)" vpa_pop_pattern = r"(\S+)\s*/\s*pop\(\s*(.*?)\s*\)" def _process_label(label, source, destination, automaton_type): if automaton_type == 'dfa' or automaton_type == 'moore': source.transitions[int(label) if label.isdigit() else label] = destination if automaton_type == 'mealy': inp, out = label.split('/', maxsplit=1) inp = int(inp) if inp.isdigit() else inp out = int(out) if out.isdigit() else out source.transitions[inp] = destination source.output_fun[inp] = out if automaton_type == 'onfsm': inp, out = label.split('/', maxsplit=1) inp = int(inp) if inp.isdigit() else inp out = int(out) if out.isdigit() else out source.transitions[inp].append((out, destination)) if automaton_type == 'ndmoore': source.transitions[int(label) if label.isdigit() else label].append(destination) if automaton_type == 'mc': prob = label source.transitions.append((destination, float(prob))) if automaton_type == 'mdp': inp, prob = label.split(':', maxsplit=1) inp = int(inp) if inp.isdigit() else inp prob = float(prob) source.transitions[inp].append((destination, prob)) if automaton_type == 'smm': inp, out_prob = label.split('/', maxsplit=1) out, prob = out_prob.split(':', maxsplit=1) inp = int(inp) if inp.isdigit() else inp out = int(out) if out.isdigit() else out source.transitions[inp].append((destination, out, float(prob))) if automaton_type == 'sevpa': match = re.match(sevpa_transition_pattern, label) # cast to integer label = int(label) if label.isdigit() else label if match: ret, stack_guard, top_of_stack = match.groups() return_transition = SevpaTransition(destination, ret, 'pop', (stack_guard, top_of_stack)) source.transitions[label].append(return_transition) else: internal_transition = SevpaTransition(destination, label, None, None) source.transitions[label].append(internal_transition) pass if automaton_type == 'vpa': input_symbol, stack_symbol, action = None, None, None if 'push' in label: push_elements = re.match(vpa_push_pattern, label) input_symbol, stack_symbol = push_elements.group(1), push_elements.group(2) action = 'push' elif 'pop' in label: pop_elements = re.match(vpa_pop_pattern, label) input_symbol, stack_symbol = pop_elements.group(1), pop_elements.group(2) action = 'pop' else: input_symbol = label input_symbol = int(input_symbol) if input_symbol.isdigit() else input_symbol if stack_symbol: stack_symbol = int(stack_symbol) if stack_symbol.isdigit() else stack_symbol transition = VpaTransition(source, destination, input_symbol, action, stack_symbol) source.transitions[input_symbol].append(transition) def _process_node_label(node, label, node_label_dict, node_type, automaton_type): node_name = node.get_name() if automaton_type == 'mdp' or automaton_type == 'mc': node_label_dict[node_name] = node_type(node_name, label) else: if automaton_type == 'moore' and label != "": label_output = _strip_label(label) label, output = label_output.split("|", maxsplit=1) output = output.strip() if not output.isdigit() else int(output) node_label_dict[node_name] = node_type(label, output) else: node_label_dict[node_name] = node_type(label) if automaton_type == 'dfa' or automaton_type == 'sevpa': if 'shape' in node.get_attributes().keys() and 'doublecircle' in node.get_attributes()['shape']: node_label_dict[node_name].is_accepting = True def _strip_label(label: str) -> str: label = label.strip() if label[0] == '\"' and label[-1] == label[0]: label = label[1:-1] if label[0] == '{' and label[-1] == '}': label = label[1:-1] # label = label.replace(" ", "") return label def _process_node_label_prime(node_name, label, line, node_label_dict, node_type, automaton_type): if automaton_type == 'mdp' or automaton_type == 'mc': node_label_dict[node_name] = node_type(node_name, label) else: if automaton_type in {'moore', 'ndmoore'} and label != "": label_output = _strip_label(label) if "|" in label_output: label, output = label_output.split("|", maxsplit=1) else: label = node_name output = label_output output = output.strip() if not output.isdigit() else int(output) node_label_dict[node_name] = node_type(label, output) else: node_label_dict[node_name] = node_type(label) if automaton_type in {'dfa', 'vpa', 'sevpa'}: if 'doublecircle' in line: node_label_dict[node_name].is_accepting = True # TODO: robust patterns (break eg. if state label contains "-") label_pattern = r'label=("[^"]*"|[^\s\],]*)' starting_state_pattern = r'__start0\s*->\s*(\w+)\s*(?:\[label=""\])?;?' transition_pattern = r'(\w+)\s*->\s*(\w+)\s*(.*)?;?' def load_automaton_from_file(path, automaton_type, compute_prefixes=False): """ Loads the automaton from the file. Standard of the automatas strictly follows syntax found at: https://automata.cs.ru.nl/Syntax/Overview. For non-deterministic and stochastic systems syntax can be found on AALpy's Wiki. Args: path: pathlike or str to the file automaton_type: type of the automaton, one of ['dfa', 'mealy', 'moore', 'mdp', 'smm', 'onfsm', 'ndmoore', 'mc', 'sevpa', 'vpa'] compute_prefixes: it True, shortest path to reach every state will be computed and saved in the prefix of the state. Useful when loading the model to use them as a equivalence oracle. (Default value = False) Returns: loaded automaton """ assert automaton_type in automaton_types.values() id_node_aut_map = {'dfa': (DfaState, Dfa), 'mealy': (MealyState, MealyMachine), 'moore': (MooreState, MooreMachine), 'onfsm': (OnfsmState, Onfsm), 'mdp': (MdpState, Mdp), 'mc': (McState, MarkovChain), 'smm': (StochasticMealyState, StochasticMealyMachine), 'sevpa': (SevpaState, Sevpa), 'vpa': (VpaState, Vpa), 'ndmoore': (NDMooreState, NDMooreMachine)} nodeType, aut_type = id_node_aut_map[automaton_type] initial_state = None transition_data = [] node_label_dict = dict() with open(Path(path)) as f: for line in f.readlines(): line = line.strip() if '__start0 ->' in line: match = re.search(starting_state_pattern, line) initial_state = match.group(1).strip() # State definitions elif '__start0' not in line and 'label' in line and '->' not in line: state_id = line.split('[')[0].strip() match = re.search(label_pattern, line) label = _strip_label(match.group(1)) _process_node_label_prime(state_id, label, line, node_label_dict, nodeType, automaton_type) # transitions elif '->' in line: match = re.match(transition_pattern, line) # source, destination, label label = re.search(label_pattern, match.group(3)).group(1) transition_data.append((match.group(1).strip(), match.group(2).strip(), label)) # ensure initial state is defined and it is defined states assert initial_state is not None and initial_state in node_label_dict.keys() initial_state = node_label_dict[initial_state] for source, destination, transition_label in transition_data: label = _strip_label(transition_label) _process_label(label, node_label_dict[source], node_label_dict[destination], automaton_type) automaton = aut_type(initial_state, list(node_label_dict.values())) if automaton_type not in {'mc', 'sevpa', 'vpa'} and not automaton.is_input_complete(): print('Warning: Loaded automaton is not input complete.') if compute_prefixes and not automaton_type not in {'mc', 'sevpa', 'vpa'}: for state in automaton.states: state.prefix = automaton.get_shortest_path(automaton.initial_state, state) return automaton ================================================ FILE: aalpy/utils/HelperFunctions.py ================================================ import random import string from itertools import product from collections import defaultdict from aalpy import Mdp, MarkovChain, McState, MooreMachine, Dfa, DfaState def extend_set(list_to_extend: list, new_elements: list) -> list: """ Helper function to extend a list while maintaining set property. They are stored as lists, so with this function set property is maintained. :return Returns: list of elements that were added to the set """ set_repr = set(list_to_extend) added_elements = [s for s in new_elements if s not in set_repr] list_to_extend.extend(added_elements) return added_elements def all_prefixes(li): """ Returns all prefixes of a list. Args: li: list from which to compute all prefixes Returns: list of all prefixes """ return [tuple(li[:i + 1]) for i in range(len(li))] def all_suffixes(li): """ Returns all suffixes of a list. Args: li: list from which to compute all suffixes Returns: list of all suffixes """ return [tuple(li[len(li) - i - 1:]) for i in range(len(li))] def profile_function(function: callable, sort_key='cumtime'): """ Args: function: callable: sort_key: (Default value = 'cumtime') Returns: prints the profiling results """ import cProfile pr = cProfile.Profile() pr.enable() function() pr.disable() pr.print_stats(sort=sort_key) def random_string_generator(size=10, chars=string.ascii_lowercase + string.digits): """ Args: size: (Default value = 10) chars: (Default value = string.ascii_lowercase + string.digits) Returns: a random string of length size """ import random return ''.join(random.choice(chars) for _ in range(size)) def print_learning_info(info: dict): """ Print learning statistics. """ print('-----------------------------------') print('Learning Finished.') print('Learning Rounds: {}'.format(info['learning_rounds'])) print('Number of states: {}'.format(info['automaton_size'])) if 'rebuild_states' in info.keys(): print(' # Found by Rebuilding: {}'.format(info['rebuild_states'])) if 'matching_states' in info.keys(): print(' # Found by {} State Matching: {}'.format(info['matching_type'], info['matching_states'])) print('Time (in seconds)') print(' Total : {}'.format(info['total_time'])) print(' Learning algorithm : {}'.format(info['learning_time'])) print(' Conformance checking : {}'.format(info['eq_oracle_time'])) print('Learning Algorithm') print(' # Membership Queries : {}'.format(info['queries_learning'])) if 'cache_saved' in info.keys(): print(' # MQ Saved by Caching : {}'.format(info['cache_saved'])) print(' # Steps : {}'.format(info['steps_learning'])) print('Equivalence Query') print(' # Membership Queries : {}'.format(info['queries_eq_oracle'])) print(' # Steps : {}'.format(info['steps_eq_oracle'])) print('-----------------------------------') def print_observation_table(ot, table_type): """ Prints the whole observation table. Args: ot: observation table table_type: 'det', 'non-det', or 'stoc' """ if table_type == 'det': s_set, extended_s, e_set, table = ot.S, ot.s_dot_a(), ot.E, ot.T elif table_type == 'non-det': s_set, extended_s, e_set = ot.S, ot.get_extended_S(), ot.E table = ot.sul.cache.get_table(s_set + extended_s, e_set) elif table_type == 'abstracted-non-det': s_set, extended_s, e_set, table = ot.S, ot.S_dot_A, ot.E, ot.T else: s_set, extended_s, e_set, table = ot.S, ot.get_extended_s(), ot.E, ot.T headers = [str(e) for e in e_set] s_rows = [] extended_rows = [] headers.insert(0, 'Prefixes / E set') for s in s_set: row = [str(s)] if table_type == 'det': row.extend(str(e) for e in table[s]) else: row.extend(str(table[s][e]) for e in e_set) s_rows.append(row) for s in extended_s: row = [str(s)] if table_type == 'det': row.extend(str(e) for e in table[s]) else: row.extend(str(table[s][e]) for e in e_set) extended_rows.append(row) table = [headers] + s_rows columns = defaultdict(int) for i in table + extended_rows: for index, el in enumerate(i): columns[index] = max(columns[index], len(el)) row_len = 0 for row in table: row = "|".join(element.ljust(columns[ind] + 1) for ind, element in enumerate(row)) print("-" * len(row)) row_len = len(row) print(row) print('=' * row_len) for row in extended_rows: row = "|".join(element.ljust(columns[ind] + 1) for ind, element in enumerate(row)) print("-" * len(row)) print(row) print('-' * row_len) def is_suffix_of(suffix, trace) -> bool: """ Args: suffix: target suffix trace: trace in question Returns: True if suffix is the suffix of trace. """ if len(trace) < len(suffix): return False else: return trace[-len(suffix):] == suffix def get_cex_prefixes(cex, automaton_type): """ Returns all prefixes of the stochastic automaton. Args: cex: counterexample automaton_type: `mdp` or `smm` Returns: all prefixes of the counterexample based on the `automaton_type` """ if automaton_type == 'mdp': return [tuple(cex[:i + 1]) for i in range(0, len(cex), 2)] return [tuple(cex[:i]) for i in range(0, len(cex) + 1, 2)] def get_available_oracles_and_err_msg(): from aalpy.oracles import RandomWalkEqOracle from aalpy.oracles import RandomWordEqOracle available_oracles = {RandomWalkEqOracle, RandomWordEqOracle} available_oracles_msg = 'Warning! Only Random Walk and Random Word oracles are supported for non-deterministic and ' \ 'stochastic learning. If you have implemented the custom oracle, set the custom_oracle ' \ 'flag to True. ' return available_oracles, available_oracles_msg def make_input_complete(automaton, missing_transition_go_to='self_loop'): """ Makes the automaton input complete/enabled. If a input is not defined in a state, it will lead to the self loop. In case of Mealy Machines, Stochastic Mealy machines and ONFSM 'epsilon' is used as output. Self loop simply loops the transition back to state which was not input complete, whereas 'sink_state' leads all transitions to a newly added sink state. If transitions have output (Mealy machines and their derivatives), 'epsilon' is used as an output value. If a state has an output value, it is either False (in case of DFA) or 'sink_state' in case of Moore machines and its derivatives. Args: automaton: automaton that is potentially not input complete missing_transition_go_to: either 'self_loop' or 'sink_state'. Returns: an input complete automaton """ from aalpy.base import DeterministicAutomaton from aalpy.automata import Dfa, MooreState, MealyMachine, Mdp, StochasticMealyMachine, Onfsm, \ DfaState, MealyState, MooreMachine, OnfsmState, MdpState, StochasticMealyState, NDMooreMachine, NDMooreState assert missing_transition_go_to in {'self_loop', 'sink_state'} input_al = automaton.get_input_alphabet() if automaton.is_input_complete(): return automaton sink_state = None sink_state_type_dict = {Dfa: DfaState(state_id='sink', is_accepting=False), MooreMachine: MooreState(state_id='sink', output='sink_state'), MealyMachine: MealyState(state_id='sink'), Onfsm: OnfsmState(state_id='sink'), NDMooreMachine: NDMooreState(state_id='sink'), Mdp: MdpState(state_id='sink', output='sink_state'), StochasticMealyMachine: StochasticMealyState(state_id='sink')} if missing_transition_go_to == 'sink_state': sink_state = sink_state_type_dict[automaton.__class__] automaton.states.append(sink_state) for state in automaton.states: for i in input_al: if i not in state.transitions.keys(): target_state = state if missing_transition_go_to == 'self_loop' else sink_state if isinstance(automaton, (DeterministicAutomaton, Dfa, MooreMachine, MealyMachine)): state.transitions[i] = target_state if isinstance(automaton, MealyMachine): state.output_fun[i] = 'epsilon' elif isinstance(automaton, (Onfsm, NDMooreMachine)): state.transitions[i].append(('epsilon', target_state) if isinstance(automaton, Onfsm) else target_state) elif isinstance(automaton, Mdp): state.transitions[i].append((target_state, 1.)) elif isinstance(automaton, StochasticMealyMachine): state.transitions[i].append((target_state, 'epsilon', 1.)) return automaton def convert_i_o_traces_for_RPNI(sequences, automaton_type="mealy"): """ Converts a list of input-output sequences to RPNI format. Eg. [[(1,'a'), (2,'b'), (3,'c')], [(6,'7'), (4,'e'), (3,'c')]] to [((1,), 'a'), ((1, 2), 'b'), ((1, 2, 3), 'c'), ((6,), '7'), ((6, 4), 'e'), ((6, 4, 3), 'c')] """ rpni_sequences = [] seen = set() if automaton_type not in ["mealy", "moore", "dfa"]: raise ValueError() for s in sequences: if automaton_type in ["moore", "dfa"]: key = (tuple(), s[0]) if key not in seen: seen.add(key) rpni_sequences.append(key) s = s[1:] for i in range(len(s)): inputs = tuple([io[0] for io in s[:i + 1]]) key = (inputs, s[i][1]) if key not in seen: seen.add(key) rpni_sequences.append(key) return rpni_sequences def visualize_classification_tree(root_node): from pydot import Dot, Node, Edge graph = Dot('classification_tree', graph_type='digraph') root_node_dot = Node(id(root_node), shape='box', label=f'Distinguishing String:\n{root_node.distinguishing_string}') graph.add_node(root_node_dot) queue = [(root_node, root_node_dot)] while queue: origin_node, origin_node_dot = queue.pop(0) for key, child in origin_node.children.items(): if child.is_leaf(): destination_dot = Node(id(child), label=f'Access String:\n{child.access_string}') else: destination_dot = Node(id(child), shape='box', label=f'Distinguishing String:\n{child.distinguishing_string}') queue.append((child, destination_dot)) graph.add_node(destination_dot) graph.add_edge(Edge(origin_node_dot, destination_dot, label=key)) # print(graph.to_string()) graph.write(path='classification_tree.pdf', format='pdf') def is_balanced(input_seq, vpa_alphabet): counter = 0 for i in input_seq: if i in vpa_alphabet.call_alphabet: counter += 1 if i in vpa_alphabet.return_alphabet: counter -= 1 if counter < 0: return False return counter == 0 def generate_input_output_data_from_automata(model, num_sequences=4000, min_seq_len=1, max_seq_len=16, sequance_type='io_traces'): assert sequance_type in {'io_traces', 'labeled_sequences'} alphabet = model.get_input_alphabet() dataset = [] while len(dataset) < num_sequences: sequance = [] for _ in range(random.randint(min_seq_len, max_seq_len)): sequance.append(random.choice(alphabet)) model.reset_to_initial() outputs = model.execute_sequence(model.initial_state, sequance) if sequance_type == 'io_traces': dataset.append(list(zip(sequance, outputs))) else: dataset.append((sequance, outputs[-1])) return dataset def generate_input_output_data_from_vpa(vpa, num_sequences=1000, max_seq_len=16, max_attempts=None): alphabet = vpa.input_alphabet.get_merged_alphabet() data_set, in_set = [], set() num_nominal_tries = num_sequences // 2 num_generation_attempts = 0 max_attempts = max_attempts if max_attempts is not None else num_sequences * 50 while len(data_set) < num_sequences: num_generation_attempts += 1 # it can happen that it is not possible to generate num_sequences balanced words of lenght <= max_seq_len if num_generation_attempts == max_attempts: break sequance = () vpa.reset_to_initial() for _ in range(max_seq_len): if num_generation_attempts <= num_nominal_tries: possible_transitions = list(vpa.current_state.transitions.keys()) if not possible_transitions: break chosen_input = random.choice(possible_transitions) else: chosen_input = random.choice(alphabet) sequance += (chosen_input,) output = vpa.step(chosen_input) # not elegant, but preserves order data_point = (sequance, output) if data_point not in in_set: data_set.append(data_point) in_set.add(data_point) return data_set def product_with_possible_empty_iterable(*iterables, repeat=1): """ Words like regular product, but if one of the iterables is empty it will just ignore it, instead of returning []. """ non_empty_iterables = [it for it in iterables if it] return product(*non_empty_iterables, repeat=repeat) def dfa_from_moore(moore_model: MooreMachine) -> Dfa: dfa_state_map = dict() # define states for moore_state in moore_model.states: if moore_state.output not in {True, False, None}: raise ValueError('Cannot convert Moore model with unrestricted output domain to DFA. ' f'Output domain should be {True, False, None}. Problematic output: {moore_state.output}' ) is_accepting = moore_state.output if moore_state.output is not None else False dfa_state_map[moore_state.state_id] = DfaState(moore_state.state_id, is_accepting) # define transitions for moore_state in moore_model.states: for i, reached_state in moore_state.transitions.items(): dfa_state_map[moore_state.state_id].transitions[i] = dfa_state_map[reached_state.state_id] initial_state = dfa_state_map[moore_model.initial_state.state_id] return Dfa(initial_state, list(dfa_state_map.values())) def mc_from_mdp(mdp: Mdp, input_symbol=None) -> MarkovChain: alphabet = mdp.get_input_alphabet() if len(alphabet) != 1 and input_symbol is None: raise ValueError('Cannot convert MDP with several inputs to Markov chain.') input_symbol = input_symbol or alphabet[0] state_map = {state.state_id: McState(state.state_id, state.output) for state in mdp.states} for state in mdp.states: mdp_transitions = state.transitions.get(input_symbol) if mdp_transitions is None: continue mc_transitions = [(state_map[mdp_target.state_id], prob) for mdp_target, prob in mdp_transitions] state_map[state.state_id].transitions = mc_transitions initial_state = state_map[mdp.initial_state.state_id] return MarkovChain(initial_state, list(state_map.values())) def mc_format_to_mdp(data): # a hack to learn MC's with GSM -> treat them as MDP with a single input augmented_data = [] for sequence in data: new_sequence = [sequence[0]] for item in sequence[1:]: new_sequence.append(('Input', item)) augmented_data.append(new_sequence) return augmented_data ================================================ FILE: aalpy/utils/ModelChecking.py ================================================ import itertools as it import os import re from collections import defaultdict from queue import Queue from random import choices from typing import Tuple, Union import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.automata import Mdp, StochasticMealyMachine, MealyMachine, Dfa, MooreMachine, MooreState, MealyState, \ DfaState from aalpy.base import DeterministicAutomaton, SUL, AutomatonState prism_prob_output_regex = re.compile("Result: (\d+\.\d+)") def get_properties_file(exp_name): property_files = { 'first_grid': aalpy.paths.path_to_properties + 'first_eval.props', 'second_grid': aalpy.paths.path_to_properties + 'second_eval.props', 'shared_coin': aalpy.paths.path_to_properties + 'shared_coin_eval.props', 'slot_machine': aalpy.paths.path_to_properties + 'slot_machine_eval.props', 'mqtt': aalpy.paths.path_to_properties + 'emqtt_two_client.props', 'tcp': aalpy.paths.path_to_properties + 'tcp_eval.props', 'bluetooth': aalpy.paths.path_to_properties + 'bluetooth.props', } return property_files[exp_name] def get_correct_prop_values(exp_name): correct_model_properties = { 'first_grid': {'prob1': 0.96217534, 'prob2': 0.6499274956800001, 'prob3': 0.6911765746880001}, 'second_grid': {'prob1': 0.93480795088125, 'prob2': 0.6711947700000002, 'prob3': 0.9742903305241055, 'prob4': 0.14244219329051103}, 'shared_coin': {'prob1': 0.10694382182657244, 'prob2': 0.5555528623795738, 'prob3': 0.3333324384052837, 'prob4': 0.42857002816478273, 'prob5': 0.001708984375, 'prob6': 0.266845703125, 'prob7': 0.244384765625, 'prob8': 0.263427734375}, 'slot_machine': {'prob1': 0.36380049887344645, 'prob2': 0.6445910164135946, 'prob3': 1.0, 'prob4': 0.159, 'prob5': 0.28567, 'prob6': 0.2500000000000001, 'prob7': 0.025445087448668406}, 'mqtt': {'prob1': 0.9612, 'prob2': 0.34390000000000004, 'prob3': 0.6513215599000001, 'prob4': 0.814697981114816, 'prob5': 0.7290000000000001}, 'tcp': {'prob1': 0.19, 'prob2': 0.5695327900000001, 'prob3': 0.7712320754503901, 'prob4': 0.8784233454094308}, 'bluetooth': {'prop1': 0.16800000000000004, 'prop2': 0.3926480000000001, 'prop3': 0.5572338000000001, 'prop4': 0.6772233874640001, 'prop5': 0.7646958490393682, 'prop6': 0.8284632739463244, 'prop7': 0.36000000000000004, 'prop8': 0.5904, 'prop9': 0.7902848, 'prop10': 0.8926258176000001, 'prop11': 0.9450244186112, 'prop12': 0.9718525023289344, 'prop13': 0.9855884811924145} } return list(correct_model_properties[exp_name].values()) def _target_string(target, orig_id_to_int_id): target_state = target[0] target_prob = target[1] target_id = orig_id_to_int_id[target_state.state_id] return f"{target_prob} : (loc'={target_id})" def _sanitize_for_prism(symbol): if symbol in ["mdp", "init", "module", "endmodule", "label"]: return "___" + symbol + "___" else: return symbol def mdp_2_prism_format(mdp: Mdp, name: str, output_path=None): """ Translates MDP to Prims modelling language. Args: mdp: markov decision process name: name of the mdp/experiment output_path: output file (Default value = None) """ module_string = "mdp" module_string += os.linesep module_string += f"module {name}" module_string += os.linesep nr_states = len(mdp.states) orig_id_to_int_id = dict() for i, s in enumerate(mdp.states): orig_id_to_int_id[s.state_id] = i module_string += "loc : [0..{}] init {};".format(nr_states, orig_id_to_int_id[mdp.initial_state.state_id]) module_string += os.linesep # print transitions for source in mdp.states: source_id = orig_id_to_int_id[source.state_id] for inp in source.transitions.keys(): if source.transitions[inp]: target_strings = \ map(lambda target: _target_string(target, orig_id_to_int_id), source.transitions[inp]) target_joined = " + ".join(target_strings) module_string += f"[{_sanitize_for_prism(inp)}] loc={source_id} -> {os.linesep} {target_joined};" module_string += os.linesep module_string += "endmodule" module_string += os.linesep # labelling function output_to_state_id = defaultdict(list) for s in mdp.states: joined_output = s.output outputs = joined_output.split("__") for o in outputs: if o: output_to_state_id[o].append(orig_id_to_int_id[s.state_id]) for output, states in output_to_state_id.items(): state_propositions = map(lambda s_id: "loc={}".format(s_id), states) state_disjunction = "|".join(state_propositions) output_string = _sanitize_for_prism(output) module_string += f"label \"{output_string}\" = {state_disjunction};" module_string += os.linesep if output_path: with open(output_path, "w") as text_file: text_file.write(module_string) return module_string def evaluate_all_properties(prism_file_name, properties_file_name): import subprocess import io from os import path prism_file = aalpy.paths.path_to_prism.split('/')[-1] path_to_prism_file = aalpy.paths.path_to_prism[:-len(prism_file)] file_abs_path = path.abspath(prism_file_name) properties_als_path = path.abspath(properties_file_name) results = {} proc = subprocess.Popen( [aalpy.paths.path_to_prism, file_abs_path, properties_als_path], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, cwd=path_to_prism_file) for line in io.TextIOWrapper(proc.stdout, encoding="utf-8"): if not line: break else: match = prism_prob_output_regex.match(line) if match: results[f'prop{len(results) + 1}'] = float(match.group(1)) proc.kill() return results def model_check_properties(model: Mdp, properties: str): """ Args: model: Markov Decision Process that serves as a basis for model checking. properties: Properties file. It should point to a file under the path_to_properties folder. Returns: results of model checking """ from os import remove from aalpy.utils import mdp_2_prism_format mdp_2_prism_format(mdp=model, name='mc_exp', output_path=f'mc_exp.prism') prism_model_path = f'mc_exp.prism' data = evaluate_all_properties(prism_model_path, properties) remove(prism_model_path) return data def model_check_experiment(path_to_properties, correct_prop_values, mdp, precision=4): """ For our stochastic experiments you can use this function. For example, check learn_stochastic_system_and_do_model_checking in Examples.py Args: path_to_properties: path to the properties file correct_prop_values: correct values of all properties. In list, where property at index i corresponds to the i-th element of the list. mdp: MDP precision: precision to which round up results Returns: results of model checking and absolute differance to the correct results """ model_checking_results = model_check_properties(mdp, path_to_properties) diff_2_correct = dict() for ind, val in enumerate(model_checking_results.values()): diff_2_correct[f'prop{ind + 1}'] = round(abs(correct_prop_values[ind] - val), precision) results = {key: round(val, precision) for key, val in model_checking_results.items()} return results, diff_2_correct def stop_based_on_confidence(hypothesis, property_based_stopping, print_level=2): """ Args: hypothesis: Markov decision process property_based_stopping: a tuple (path to properties file, list of correct property values, max allowed error) print_level: 2 or 3 if output of model checking is to be printed during learning Returns: True if absolute error for all properties is smaller then property_based_stopping[2] """ from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion path_2_prop = property_based_stopping[0] correct_values = property_based_stopping[1] error_bound = property_based_stopping[2] model = hypothesis if isinstance(hypothesis, StochasticMealyMachine): model = smm_to_mdp_conversion(hypothesis) res, diff = model_check_experiment(path_2_prop, correct_values, model) if print_level >= 2: print('Error for each property:', [round(d * 100, 2) for d in diff.values()]) if not diff: return False for d in diff.values(): if d > error_bound: return False return True def bisimilar(a1: DeterministicAutomaton, a2: DeterministicAutomaton, return_cex=False) -> Union[bool, None, tuple]: """ Checks whether the provided automata are bisimilar. If return_cex the function returns a counter example or None, otherwise a Boolean is returned. Returns: object: true or false if return_cex is set to False, otherwise None (no counterexample) or a counterexample """ # TODO allow states as inputs instead of automata if a1.__class__ != a2.__class__: raise ValueError("tried to check bisimilarity of different automaton types") # if you use this function with the same object (does not make sense anyway) if a1 is a2: a2 = a1.copy() supported_automaton_types = (Dfa, MooreMachine, MealyMachine) if not isinstance(a1, supported_automaton_types): raise NotImplementedError( f"bisimilarity is not implemented for {a1.__class__.__name__}. Supported: {', '.join(t.__name__ for t in supported_automaton_types)}") to_check: Queue[Tuple[AutomatonState, AutomatonState]] = Queue() to_check.put((a1.initial_state, a2.initial_state)) requirements = dict() requirements[(a1.initial_state, a2.initial_state)] = () while not to_check.empty(): s1, s2 = to_check.get() # check output equivalence for Dfa / Moore if isinstance(s1, (DfaState, MooreState)) and s1.output != s2.output: return requirements[(s1, s2)] if return_cex else False # check whether the same inputs are enabled + output equivalence for Mealy t1, t2 = s1.transitions, s2.transitions for t in it.chain(t1.keys(), filter(lambda x : x not in t1.keys(), t2.keys())): common = t in t1.keys() and t in t2.keys() if (not common) or (isinstance(s1, MealyState) and s1.output_fun[t] != s2.output_fun[t]) : return requirements[(s1, s2)] + (t,) if return_cex else False for t in t1.keys(): c1, c2 = t1[t], t2[t] if (c1, c2) not in requirements: requirements[(c1, c2)] = requirements[(s1, s2)] + (t,) to_check.put((c1, c2)) return None if return_cex else True def compare_automata(aut_1: DeterministicAutomaton, aut_2: DeterministicAutomaton, num_cex=10): """ Finds cases of non-conformance between first and second automaton. This is done by performing RandomW equivalence check. It is possible that number of found counterexamples is smaller than num_cex, as no counterexample will be a suffix of a previously found counterexample. Args: aut_1: first automaton aut_2: second automaton num_cex: max. number of searches for counterexamples Returns: A list of input sequences that revel different behaviour on both automata. Counterexamples are sorted by length. """ # from aalpy.oracles import RandomWMethodEqOracle # if you use this function with the same object (does not make sense anyway) if aut_1 is aut_2: aut_2 = aut_1.copy() if set(aut_1.get_input_alphabet()) != set(aut_2.get_input_alphabet()): assert False, "Cannot compare automata with different input alphabets" input_al = aut_1.get_input_alphabet() # larger automaton is used as hypothesis, as then test-cases will contain prefixes leading to states # not in smaller automaton base_automaton, test_automaton = (aut_1, aut_2) if aut_1.size < aut_2.size else (aut_2, aut_1) base_sul = AutomatonSUL(base_automaton) # compute prefixes for all states of the test automaton (needed for advanced eq. oracle) for state in test_automaton.states: if not state.prefix: state.prefix = test_automaton.get_shortest_path(test_automaton.initial_state, state) # setup the eq oracle eq_oracle = RandomWMethodEqOracle(input_al, base_sul, walks_per_state=min(100, len(input_al) * 10), walk_len=10) found_cex = [] # to avoid near "infinite" loops due to while loop and set requirement # that is, if you can only find 1 cex and all other cexs are suffixes of that cex, first while condition will never # be reached failsafe_counter = 0 failsafe_stopping = num_cex * 100 while len(found_cex) < num_cex and failsafe_counter < failsafe_stopping: cex = eq_oracle.find_cex(test_automaton) # if no counterexample can be found terminate the loop if cex is None: break if cex not in found_cex: found_cex.append(cex) failsafe_counter += 1 found_cex.sort(key=len) return found_cex class TestCaseWrapperSUL(SUL): def __init__(self, sul): super().__init__() self.sul = sul self.test_cases = [] self.test_case_inputs = None self.test_case_outputs = None def pre(self): self.test_case_inputs = [] self.test_case_outputs = [] return self.sul.pre() def post(self): if self.test_case_inputs and self.test_case_outputs: self.test_cases.append((tuple(self.test_case_inputs), tuple(self.test_case_outputs))) return self.sul.post() def step(self, letter): output = self.sul.step(letter) self.test_case_inputs.append(letter) self.test_case_outputs.append(output) return output def generate_test_cases(automaton: DeterministicAutomaton, oracle): """ Uses parametrized eq. oracle to construct test cases on the automaton. If automaton are big (200+ states), increase recursion depth if necessary (eg. sys.setrecursionlimit(10000)). Args: automaton: deterministic automaton that serves as a basis for test case generation oracle: oracle that will construct test-cases and record inputs and outputs Returns: List of test cases, where each testcase is a tuple containing two elements, and input and an output sequance. """ from copy import deepcopy automaton_copy = deepcopy(automaton) base_sul = AutomatonSUL(automaton_copy) wrapped_sul = TestCaseWrapperSUL(base_sul) oracle.sul = wrapped_sul # no counterexamples can be found cex = oracle.find_cex(automaton) assert cex is None return wrapped_sul.test_cases def statistical_model_checking(model, goals, max_num_steps, num_tests=105967): """ Args: model: model on which model checking is performed goals: set of goal outputs max_num_steps: bounded length of tests num_tests: num of tests that will be performed Returns: num of tests containing element of goals set / num_tests """ def compute_output_sequence(model, seq): model.reset_to_initial() observed_outputs = {model.step(i) for i in seq} return observed_outputs goal_reached = 0 inputs = model.get_input_alphabet() for _ in range(num_tests): test_sequence = choices(inputs, k=max_num_steps) outputs = compute_output_sequence(model, test_sequence) if goals & outputs: goal_reached += 1 return goal_reached / num_tests ================================================ FILE: aalpy/utils/Sampling.py ================================================ from functools import wraps from random import randint, choices, random from aalpy import MooreMachine, Dfa, NDMooreMachine, Mdp, MarkovChain from aalpy.base import Automaton, DeterministicAutomaton def get_io_traces(automaton: Automaton, input_traces: list) -> list: moore_automata = (MooreMachine, Dfa, NDMooreMachine, Mdp, MarkovChain) is_moore = isinstance(automaton, moore_automata) traces = [] for input_trace in input_traces: output_trace = automaton.execute_sequence(automaton.initial_state, input_trace) trace = list(zip(input_trace, output_trace)) if is_moore: trace = [automaton.initial_state.output] + trace traces.append(trace) return traces def get_labeled_sequences(automaton: Automaton, input_traces: list) -> list: moore_automata = (MooreMachine, Dfa, NDMooreMachine, Mdp, MarkovChain) is_moore = isinstance(automaton, moore_automata) data = [] for input_trace in input_traces: if len(input_trace) == 0: if not is_moore: raise ValueError("tried to get label of empty sequence for Mealy automaton.") output = automaton.initial_state.output else: output = automaton.execute_sequence(automaton.initial_state, input_trace)[-1] data.append((input_trace, output)) return data def get_data_from_input_sequence(automaton: Automaton, input_sequence: list, data_format: str = "io_sequences"): if data_format == "io_sequences": return get_io_traces(automaton, input_sequence) elif data_format == "labeled_sequences": return get_labeled_sequences(automaton, input_sequence) else: raise ValueError(f"invalid data_format {data_format}. must be 'io_sequences' or 'labeled_sequences'") def support_automaton_arg(require_transform): def decorator(f): @wraps(f) def inner(alphabet, *args, include_outputs=False, **kwargs): automaton = None if isinstance(alphabet, Automaton): automaton = alphabet if require_transform: alphabet = alphabet.get_input_alphabet() traces = f(alphabet, *args, **kwargs) if include_outputs: if automaton is None: raise ValueError("automaton must be provided") traces = get_io_traces(automaton, traces) return traces return inner return decorator @support_automaton_arg(True) def sample_with_length_limits(alphabet, nr_samples, min_len, max_len): return [choices(alphabet, k = randint(min_len, max_len)) for _ in range(nr_samples)] @support_automaton_arg(True) def sample_with_term_prob(alphabet, nr_samples, term_prob): ret = [] for _ in range(nr_samples): k = 0 while term_prob < random(): k += 1 ret.append(choices(alphabet, k=k)) return ret @support_automaton_arg(False) def get_complete_sample(automaton: DeterministicAutomaton): alphabet = automaton.get_input_alphabet() automaton.compute_prefixes() char_set = automaton.compute_characterization_set() infixes = [(x,) for x in alphabet] + [tuple()] return [state.prefix + infix + suffix for state in automaton.states for suffix in char_set for infix in infixes] ================================================ FILE: aalpy/utils/__init__.py ================================================ from .AutomatonGenerators import ( generate_random_dfa, generate_random_mealy_machine, generate_random_smm, generate_random_moore_machine, generate_random_markov_chain, generate_random_deterministic_automata, ) from .AutomatonGenerators import ( generate_random_mdp, generate_random_ONFSM, generate_random_sevpa, ) from .BenchmarkSULs import * from .DataHandler import ( DataHandler, CharacterTokenizer, DelimiterTokenizer, IODelimiterTokenizer, ) from .FileHandler import ( save_automaton_to_file, load_automaton_from_file, visualize_automaton, ) from .ModelChecking import ( model_check_experiment, mdp_2_prism_format, model_check_properties, get_properties_file, get_correct_prop_values, compare_automata, generate_test_cases, statistical_model_checking, bisimilar, ) from .HelperFunctions import ( make_input_complete, convert_i_o_traces_for_RPNI, is_balanced, generate_input_output_data_from_automata, generate_input_output_data_from_vpa, ) ================================================ FILE: docs/README.md ================================================ AALpy is a light-weight automata learning library written in Python. You can start learning models of black-box systems with a few lines of code. AALpy supports both **active** and **passive** automata learning algorithms that can be used to learn a variety of modeling formalisms, including **deterministic**, **non-deterministic**, and **stochastic automata**, as well as **deterministic context-free grammars/pushdown automata**. ## Installation Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the latest release of AALpy: ```bash pip install aalpy ``` To install current version of the master branch (it might contain bugfixes and added functionalities between releases): ```bash pip install https://github.com/DES-Lab/AALpy/archive/master.zip ``` The minimum required version of Python is 3.6. Ensure that you have [Graphviz](https://graphviz.org/) installed and added to your path if you want to visualize models. For manual installation, clone the repo and install `pydot` (the only dependency). ## Documentation and Wiki If you are interested in automata learning or would like to understand the automata learning process in more detail, please check out our **Wiki**. On Wiki, you will find more detailed examples on how to use AALpy. - ***[Examples.py](https://github.com/DES-Lab/AALpy/blob/master/Examples.py)*** contains examples covering almost the whole of AALpy's functionality and its a great starting point. ### Usage All active automata learning procedures follow this high-level approach: - [Define the input alphabet and system under learning (SUL)](https://github.com/DES-Lab/AALpy/wiki/SUL-Interface,-or-How-to-Learn-Your-Systems) - [Choose the equivalence oracle](https://github.com/DES-Lab/AALpy/wiki/Equivalence-Oracles) - [Run the learning algorithm](https://github.com/DES-Lab/AALpy/wiki/Setting-Up-Learning) Passive learning algorithm simply require you to provide data in the appropriate format (check Wiki and Examples) and run the learning function. ## Selected Applications AALpy has been used to: - [Learn Models of Bluetooth Low-Energy](https://github.com/apferscher/ble-learning) - [Find bugs in VIM text editor](https://github.com/DES-Lab/AALpy/discussions/13) - [Learn Input-Output Behavior of RNNs](https://github.com/DES-Lab/Extracting-FSM-From-RNNs) - [Learn Models of GIT](https://github.com/taburg/git-learning) - [Solve RL Problems](https://github.com/DES-Lab/Learning-Environment-Models-with-Continuous-Stochastic-Dynamics) ## Cite AALpy and Research Contact If you use AALpy in your research, please cite us with of the following: - [Extended version (preferred)](https://www.researchgate.net/publication/359517046_AALpy_an_active_automata_learning_library/citation/download) - [Tool paper](https://dblp.org/rec/conf/atva/MuskardinAPPT21.html?view=bibtex) If you have research suggestions or you need specific help concerning your research, feel free to start a [discussion](https://github.com/DES-Lab/AALpy/discussions) or contact [edi.muskardin@silicon-austria.com](mailto:edi.muskardin@silicon-austria.com). We are happy to help you and consult you in applying automata learning in various domains. ## Contributing Pull requests are welcome. For significant changes, please open an issue first to discuss what you would like to change. In case of any questions or possible bugs, please open issues. ================================================ FILE: docs/_config.yml ================================================ title : AALpy author: Edi Muskardin description: An Active Automata Learning Library rss-description: An Active Automata Learning Library theme: jekyll-theme-slate google-site-verification: google306875680a34d740.html ================================================ FILE: docs/google306875680a34d740.html ================================================ google-site-verification: google306875680a34d740.html ================================================ FILE: docs/instructions.txt ================================================ 1. For commenting use Google style with one additional space for between Args: and arguments, and one space between Returns and return values. 2. 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C,1,A,0,D,1,C,1,D,1,C,0,C,1,D,1,C,0,C,1,D,0,C,1,D C,1,A,1,C,0,B,1,C,1,D,1,C,0,C,0,C,1,D,0,C,1,D,1,C C,0,B,0,A,0,D,1,C,0,C,0,D,0,C,1,D,0,C,0,C C,0,B,1,C,0,C,0,C,1,D,1,C C,1,A,1,C,0,B,0,A,0,D,1,C,0,C,0,C C,0,B,1,C,1,D,0,C,1,D,1,C,1,D C,1,A,0,D,0,A,0,D,0,A,0,D,0,C,0,C,0,C,0,C,0,C C,1,A,0,D,0,A,0,D,0,A,0,D,1,C,1,D,1,C,1,D C,1,A,1,C,1,A,0,D,1,C,0,C,0,C,0,C,1,D,1,C,0,C,1,D C,0,B,1,C,1,D,1,C,1,D,1,C C,1,A,0,D,0,C,1,D,0,C C,1,A,1,C,0,B,0,A,0,D,1,C,1,D,0,C,1,D,1,C C,1,A,1,C,1,A,0,D,0,C,1,D,0,A,1,C,1,A C,0,B,1,C,1,D,1,C,0,C,0,C,1,D,1,C,1,D,0,C C,0,B,0,A,0,D,1,C,1,D,0,C,0,C,1,D,1,C,1,D C,0,B,1,C,0,D,0,C,1,D,0,C,1,D,0,A,0,D,0,A,0,D,0,C C,0,B,1,C,1,D,0,A,1,C,0,B,1,C,0,C,0,C,0,C,1,D,1,C C,0,B,0,A,0,D,0,C,1,D,1,C C,0,B,1,C,0,C,0,C,0,C,0,C,1,D,1,C,1,D,1,C,1,D C,0,B,1,C,0,C,0,D,1,C,0,C,0,C,1,D,0,A C,1,A,0,D,0,A,0,D,0,A,1,C,0,B,1,C,0,C,0,D,0,C C,1,A,1,C,0,B,0,A,0,D,0,A,0,D,0,A C,1,A,1,C,0,B,1,C,1,D,0,C,1,D C,1,A,0,D,1,C,1,D,0,A,0,D,1,C,0,C,1,D,1,C C,1,A,1,C,1,A,0,D,1,C,0,C,1,D,0,C,1,D,0,C C,1,A,0,D,1,C,0,C,1,D,0,C C,0,B,1,C,1,D,0,C,0,C C,1,A,0,D,0,A,1,C,1,A,0,D,1,C,0,C,0,C,1,D,1,C C,0,B,0,A,1,C,1,A,1,C,1,A,1,C,1,A,1,C,1,A,1,C,1,A C,1,A,1,C,0,B,0,A,0,D,1,C,0,D,1,C,0,C,0,C,1,D,1,C C,0,B,0,A,0,D,0,A,0,D,0,A,1,C,1,A C,0,B,1,C,0,C,0,C,0,C C,1,A,1,C,1,A,1,C,0,B C,1,A,0,D,1,C,1,D,1,C,1,D,0,A,1,C,0,B,0,A C,1,A,0,D,1,C,0,C,1,D,1,C,1,D,1,C,1,D,0,A,1,C C,1,A,0,D,0,C,1,D,1,C,1,D,0,A,1,C,1,A C,0,B,0,A,1,C,1,A,0,D,0,C,0,D,1,C,1,D,0,A,1,C,0,B C,1,A,1,C,0,B,1,C,1,D C,0,B,0,A,0,D,0,C,0,C,0,D,0,A C,0,B,1,C,1,D,0,A,1,C C,0,B,1,C,1,D,1,C,0,C,1,D,0,C,1,D,1,C C,0,B,1,C,0,C,1,D,1,C,0,C,1,D,0,A,1,C,1,A,1,C,0,B C,1,A,1,C,1,A,1,C,1,A,0,D,0,A,0,D,0,A C,0,B,1,C,0,C,1,D,0,A,1,C,1,A,0,D,0,C,1,D,1,C,0,D C,1,A,0,D,0,A,1,C,1,A,1,C,0,B,1,C,0,D,0,A,1,C,1,A C,1,A,1,C,0,B,0,A,1,C,1,A C,1,A,1,C,0,B,0,A,1,C,0,B,1,C C,0,B,0,A,1,C,0,B,0,A,1,C,1,A,0,D,0,A C,0,B,0,A,0,D,1,C,0,C C,1,A,1,C,0,B,0,A,0,D,1,C,1,D C,1,A,0,D,1,C,1,D,1,C,0,C C,0,B,0,A,1,C,1,A,0,D,0,C,0,C C,1,A,1,C,1,A,0,D,1,C,0,C,1,D,0,C,0,C,0,C,1,D,0,C 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C,0,B,0,A,0,D,0,C,1,D,0,C,0,C,1,D,1,C C,1,A,0,D,1,C,1,D,1,C,1,D,1,C,0,C,0,D,1,C,0,C C,0,B,1,C,0,C,1,D,1,C C,0,B,0,A,0,D,1,C,1,D,1,C,0,C,0,C,0,C,1,D,1,C,0,D C,0,B,1,C,0,D,1,C,0,C,1,D C,0,B,0,A,1,C,1,A,1,C,0,B,1,C,0,C,1,D,1,C C,1,A,0,D,1,C,0,C,0,C,0,C,1,D,1,C,0,C C,0,B,1,C,0,C,1,D,0,A C,1,A,1,C,0,B,1,C,0,D,0,C,1,D C,1,A,0,D,1,C,1,D,0,C,1,D,0,A,0,D,1,C,0,C,0,C,1,D C,1,A,1,C,1,A,1,C,1,A,0,D,0,A C,0,B,1,C,0,C,0,C,1,D C,1,A,1,C,0,B,0,A,1,C,1,A,1,C,0,B,1,C C,1,A,0,D,0,C,1,D,0,A,0,D C,0,B,0,A,0,D,1,C,1,D,0,C,1,D,0,C,0,C,1,D,0,C,0,C C,0,B,0,A,0,D,1,C,1,D,0,A,1,C,0,B,0,A,0,D C,0,B,1,C,0,C,0,C,1,D,1,C C,0,B,1,C,0,C,0,D,0,C,0,C,1,D,0,C C,0,B,1,C,0,C,1,D,1,C,0,C,1,D,1,C,1,D C,0,B,0,A,1,C,1,A,0,D,0,C,1,D,1,C C,0,B,1,C,0,C,0,C,0,C,0,C,0,C,1,D,0,C,0,D,1,C C,1,A,1,C,0,B,1,C,1,D,0,A,0,D,1,C,1,D,1,C,1,D,1,C C,1,A,1,C,1,A,0,D,1,C,1,D,1,C,0,C,1,D C,1,A,1,C,1,A,1,C,1,A,1,C,0,B C,0,B,0,A,0,D,0,A,1,C,0,B,0,A C,1,A,1,C,1,A,0,D,1,C,1,D,0,C C,0,B,1,C,0,D,0,C,1,D,1,C,0,D,1,C,0,C,1,D,1,C,1,D 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C,0,B,1,C,1,D,1,C,0,C,1,D C,1,A,1,C,0,B,0,A,1,C,1,A C,0,B,1,C,0,C,0,C,1,D,1,C,0,C,0,C,0,C C,1,A,1,C,1,A,0,D,1,C,1,D,1,C C,0,B,1,C,1,D,0,C,0,D,0,A,1,C C,0,B,1,C,1,D,0,C,1,D,1,C,0,C,1,D,1,C C,1,A,1,C,1,A,1,C,1,A C,0,B,0,A,0,D,1,C,0,C C,1,A,1,C,0,B,1,C,1,D,0,C,1,D,0,A,1,C,0,B,1,C,0,C C,1,A,1,C,1,A,1,C,0,B C,0,B,1,C,0,C,1,D,1,C,0,C C,0,B,1,C,1,D,0,C,1,D,0,C,1,D,0,A,1,C,0,B,1,C C,0,B,0,A,0,D,1,C,0,D,1,C,1,D,0,A,1,C,0,B,0,A C,0,B,0,A,0,D,0,A,1,C,1,A,1,C,1,A,1,C,1,A,0,D,1,C C,0,B,1,C,1,D,1,C,0,C,1,D C,0,B,1,C,0,D,1,C,1,D,1,C,1,D,0,A,1,C,1,A,1,C,0,B C,1,A,1,C,0,B,1,C,1,D C,1,A,0,D,1,C,0,C,0,D,0,A C,1,A,0,D,1,C,0,C,1,D,0,A,0,D,1,C C,1,A,0,D,0,A,1,C,1,A,1,C,1,A C,0,B,1,C,1,D,1,C,1,D,1,C,0,C,0,D,1,C,1,D,1,C C,0,B,1,C,1,D,1,C,1,D,0,A,0,D C,0,B,1,C,0,C,0,C,0,C,1,D,0,A,1,C,1,A,1,C,1,A,0,D C,1,A,1,C,0,B,1,C,1,D,0,C,0,C,1,D,0,C,0,C,1,D C,1,A,1,C,1,A,0,D,0,C,0,C C,1,A,1,C,0,B,1,C,0,C,1,D,1,C,1,D,1,C,0,C,1,D C,0,B,1,C,0,C,1,D,1,C,1,D,1,C,1,D,0,C,1,D C,0,B,0,A,0,D,0,A,1,C 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C,1,A,0,D,1,C,0,C,1,D,1,C,1,D,0,C,0,C,0,C,0,C,0,C C,1,A,0,D,0,A,0,D,1,C C,0,B,1,C,0,C,0,D,0,A,0,D,0,C,1,D,0,A,1,C,1,A,1,C C,0,B,1,C,0,C,1,D,0,C,1,D,0,C,0,C,0,C,1,D C,0,B,1,C,1,D,0,C,1,D,1,C,0,C,1,D,0,A,0,D C,0,B,1,C,1,D,1,C,1,D,0,A,1,C,0,B,1,C,0,C,0,C,0,D C,0,B,1,C,0,C,0,D,1,C,1,D,1,C C,0,B,1,C,1,D,0,C,1,D,1,C C,0,B,1,C,1,D,1,C,1,D,0,A,1,C,0,B C,0,B,0,A,0,D,0,A,1,C,1,A,1,C,0,B,1,C,0,D C,0,B,0,A,1,C,0,B,1,C C,0,B,1,C,1,D,1,C,1,D,1,C,0,C,0,D,1,C,1,D,1,C C,1,A,0,D,0,A,1,C,0,B,1,C,1,D,1,C,1,D,0,A,0,D C,0,B,1,C,0,D,1,C,1,D C,1,A,0,D,1,C,0,D,0,C,1,D,1,C,0,C,0,D,0,A,0,D,1,C C,0,B,1,C,0,C,0,C,0,C,1,D,1,C,1,D,1,C,1,D C,1,A,1,C,0,B,1,C,0,C,0,C C,0,B,1,C,1,D,0,C,0,C C,0,B,0,A,0,D,0,A,0,D,0,C,1,D,1,C,0,D,0,C,1,D C,0,B,0,A,1,C,0,B,0,A,1,C C,1,A,0,D,0,C,1,D,1,C,1,D,0,C,1,D,0,A,1,C,0,B C,1,A,1,C,1,A,0,D,0,A C,1,A,1,C,1,A,0,D,0,A,0,D,0,C,0,C,0,C C,0,B,0,A,0,D,0,C,1,D,0,C,0,C,0,C C,0,B,1,C,0,C,1,D,1,C,1,D,1,C,0,C,0,C,0,D,0,C C,0,B,0,A,0,D,0,A,0,D C,0,B,1,C,0,C,0,C,1,D,1,C,1,D,1,C,0,C 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C,1,A,1,C,1,A,0,D,0,C,1,D,1,C,0,D,1,C C,1,A,1,C,1,A,0,D,0,C,0,C C,0,B,0,A,0,D,0,A,1,C,1,A,1,C,1,A C,1,A,0,D,0,C,1,D,1,C,1,D,0,C,0,D,1,C,0,C,0,D C,1,A,1,C,1,A,1,C,0,B C,0,B,1,C,0,C,0,D,1,C,0,C C,1,A,0,D,1,C,0,C,1,D,1,C,0,C,0,C,0,D C,0,B,0,A,0,D,0,C,1,D,1,C,1,D,0,A,0,D,1,C,1,D C,1,A,1,C,0,B,1,C,1,D,1,C,0,C C,0,B,0,A,1,C,1,A,1,C,0,B,0,A,0,D,0,C,0,C,0,C,1,D C,0,B,1,C,0,C,0,C,0,C,0,C,1,D,1,C,1,D C,1,A,0,D,1,C,0,D,1,C,1,D,1,C,0,C,0,C,0,C,1,D,1,C C,0,B,0,A,1,C,1,A,0,D,1,C,0,C,0,C,1,D,0,C,1,D,0,A C,1,A,0,D,0,A,1,C,1,A,0,D,0,A C,1,A,1,C,1,A,1,C,1,A,1,C,0,B,0,A,1,C C,0,B,1,C,0,C,0,C,0,C,1,D,1,C,0,C,0,C,1,D C,0,B,0,A,1,C,1,A,1,C,0,B C,1,A,1,C,1,A,1,C,0,B,1,C,0,D C,0,B,0,A,1,C,0,B,1,C,1,D,1,C,0,C,1,D,0,A,1,C C,0,B,1,C,1,D,1,C,1,D,0,C,0,C,0,C,0,C,1,D,0,A C,0,B,0,A,0,D,0,A,0,D,1,C,0,C,0,D C,1,A,0,D,0,C,1,D,0,C,1,D,1,C,1,D,1,C C,1,A,1,C,1,A,0,D,1,C,1,D,1,C,0,D,0,A,0,D,1,C,0,C C,1,A,1,C,1,A,1,C,0,B,1,C,1,D,0,A C,0,B,1,C,0,C,1,D,0,C,0,C,0,C,1,D,1,C,1,D,0,C C,0,B,0,A,0,D,1,C,0,C,1,D,1,C,1,D 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C,0,B,1,C,0,C,1,D,0,C,0,C,1,D,1,C,0,C,1,D,1,C,0,C C,0,B,0,A,1,C,0,B,1,C,1,D,1,C,0,D,0,A C,0,B,1,C,1,D,0,C,0,C,0,C,0,C,1,D,0,A,1,C C,0,B,0,A,1,C,1,A,0,D,0,C C,0,B,1,C,1,D,1,C,0,C,1,D,0,A,1,C C,0,B,1,C,0,C,1,D,0,A,0,D,0,A,1,C,1,A,1,C,1,A C,0,B,0,A,0,D,1,C,1,D,0,C,1,D,1,C,1,D C,1,A,1,C,0,B,0,A,1,C,0,B,0,A C,0,B,0,A,1,C,0,B,1,C,0,C,0,C,1,D C,1,A,0,D,1,C,1,D,0,C,0,C,0,C C,0,B,0,A,0,D,1,C,1,D,1,C C,1,A,1,C,0,B,1,C,0,C,0,C,0,C,0,C C,1,A,1,C,0,B,0,A,0,D,1,C,1,D,0,A,1,C C,0,B,1,C,1,D,1,C,0,C,1,D C,1,A,1,C,0,B,0,A,0,D C,0,B,1,C,1,D,1,C,0,C,0,C,0,C,0,C,0,C,1,D C,0,B,1,C,1,D,1,C,0,C,1,D,0,C,1,D,1,C C,1,A,1,C,0,B,0,A,0,D,0,A,1,C,1,A,1,C,0,B,0,A C,1,A,0,D,0,C,1,D,1,C,0,C,0,C C,0,B,1,C,0,C,0,C,1,D,0,C,0,C,0,C C,0,B,0,A,1,C,0,B,0,A,1,C,0,B,0,A,1,C,0,B,0,A C,0,B,1,C,0,C,1,D,1,C,0,C,0,D,0,A,1,C,1,A,1,C C,1,A,1,C,0,B,0,A,1,C,1,A C,0,B,0,A,0,D,1,C,0,C,1,D,0,A,1,C,1,A,1,C C,1,A,0,D,1,C,0,C,1,D,0,C,0,C,0,D,1,C C,0,B,0,A,0,D,0,C,0,C,1,D,1,C,0,C C,0,B,1,C,1,D,1,C,0,C,0,C C,0,B,1,C,1,D,0,A,1,C,0,B C,0,B,0,A,1,C,1,A,0,D,1,C,1,D,1,C C,1,A,0,D,0,C,1,D,1,C ================================================ FILE: notebooks/Abstracted_Non-Det_FSM.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "cell_type": "code", "source": "#Example from paper P'Learning Abstracted Non-deterministic Finite State Machines'.\n# https://link.springer.com/chapter/10.1007/978-3-030-64881-7_4", "execution_count": 1, "outputs": [], "id": "dc193bc9e266e83c" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": [ "# Import statemets\n", "\n", "import random\n", "\n", "from aalpy.base import SUL\n", "from aalpy.oracles import RandomWalkEqOracle\n", "from aalpy.learning_algs import run_abstracted_ONFSM_Lstar\n", "from aalpy.SULs import MealySUL\n", "from aalpy.utils import load_automaton_from_file" ], "execution_count": 2, "outputs": [], "id": "b1c805ab64f95d6c" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "# Mapper/SUL used for abstracted learning \n\nclass Multi_Client_MQTT_Mapper(SUL):\n def __init__(self):\n super().__init__()\n\n five_clients_mqtt_mealy = load_automaton_from_file('../DotModels/five_clients_mqtt_abstracted_onfsm.dot',\n automaton_type='mealy')\n self.five_client_mqtt = MealySUL(five_clients_mqtt_mealy)\n self.connected_clients = set()\n self.subscribed_clients = set()\n\n self.clients = ('c0', 'c1', 'c2', 'c3', 'c4')\n\n def get_input_alphabet(self):\n return ['connect', 'disconnect', 'subscribe', 'unsubscribe', 'publish']\n\n def pre(self):\n self.five_client_mqtt.pre()\n\n def post(self):\n self.five_client_mqtt.post()\n self.connected_clients = set()\n self.subscribed_clients = set()\n\n def step(self, letter):\n client = random.choice(self.clients)\n inp = client + '_' + letter\n concrete_output = self.five_client_mqtt.step(inp)\n all_out = ''\n\n if letter == 'connect':\n if client not in self.connected_clients:\n self.connected_clients.add(client)\n elif client in self.connected_clients:\n self.connected_clients.remove(client)\n if client in self.subscribed_clients:\n self.subscribed_clients.remove(client)\n if len(self.subscribed_clients) == 0:\n all_out = '_UNSUB_ALL'\n\n elif letter == 'subscribe' and client in self.connected_clients:\n self.subscribed_clients.add(client)\n elif letter == 'disconnect' and client in self.connected_clients:\n self.connected_clients.remove(client)\n if client in self.subscribed_clients:\n self.subscribed_clients.remove(client)\n if len(self.subscribed_clients) == 0:\n all_out = '_UNSUB_ALL'\n elif letter == 'unsubscribe' and client in self.connected_clients:\n if client in self.subscribed_clients:\n self.subscribed_clients.remove(client)\n if len(self.subscribed_clients) == 0:\n all_out = '_ALL'\n\n concrete_outputs = concrete_output.split('__')\n abstract_outputs = set([e[3:] for e in concrete_outputs])\n if 'Empty' in abstract_outputs:\n abstract_outputs.remove('Empty')\n if abstract_outputs == {'CONCLOSED'}:\n if len(self.connected_clients) == 0:\n all_out = '_ALL'\n return 'CONCLOSED' + all_out\n else:\n if 'CONCLOSED' in abstract_outputs:\n abstract_outputs.remove('CONCLOSED')\n abstract_outputs = sorted(list(abstract_outputs))\n output = '_'.join(abstract_outputs)\n return '_'.join(set(output.split('_'))) + all_out", "execution_count": 5, "outputs": [], "id": "52438a260e21eb62" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "# instantiate SUL and input alphabet\nsul = Multi_Client_MQTT_Mapper()\nalphabet = sul.get_input_alphabet()\n\n# create eq. oracle\neq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=5000, reset_prob=0.09, reset_after_cex=True)\n\n# define mapping from concrete outputs to abstract outputs\nabstraction_mapping = {\n 'CONCLOSED': 'CONCLOSED',\n 'CONCLOSED_UNSUB_ALL': 'CONCLOSED',\n 'CONCLOSED_ALL': 'CONCLOSED',\n 'UNSUBACK' : 'UNSUBACK',\n 'UNSUBACK_ALL': 'UNSUBACK'\n}", "execution_count": 6, "outputs": [], "id": "e315f28e5a2d8180" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": [ "# Start learning process\n", "\n", "learned_abstracter_onfsm = run_abstracted_ONFSM_Lstar(alphabet, sul, eq_oracle, abstraction_mapping=abstraction_mapping,\n", " n_sampling=200, print_level=2)" ], "execution_count": 8, "outputs": [ { "output_type": "stream", "text": "Hypothesis 1 has 3 states.\n-----------------------------------\nLearning Finished.\nLearning Rounds: 1\nNumber of states: 3\nTime (in seconds)\n Total : 13.62\n Learning algorithm : 13.62\n Conformance checking : 0.0\nLearning Algorithm\n # Membership Queries : 232995\n # Steps : 985895\nEquivalence Query\n # Membership Queries : 418\n # Steps : 5000\n-----------------------------------\n", "name": "stdout" } ], "id": "a5fac43c5a52fb" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": [ "# Print the automaton (note that the original, non-abstracted automation has 273 states)\n", "print(learned_abstracter_onfsm)" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": "digraph learnedModel {\ns0 [label=s0];\ns1 [label=s1];\ns2 [label=s2];\ns0 -> s1 [label=\"connect/CONNACK\"];\ns0 -> s0 [label=\"disconnect/CONCLOSED_ALL\"];\ns0 -> s0 [label=\"subscribe/CONCLOSED_ALL\"];\ns0 -> s0 [label=\"unsubscribe/CONCLOSED_ALL\"];\ns0 -> s0 [label=\"publish/CONCLOSED_ALL\"];\ns1 -> s0 [label=\"connect/CONCLOSED_ALL\"];\ns1 -> s1 [label=\"connect/CONNACK\"];\ns1 -> s1 [label=\"connect/CONCLOSED_UNSUB_ALL\"];\ns1 -> s0 [label=\"disconnect/CONCLOSED_ALL\"];\ns1 -> s1 [label=\"disconnect/CONCLOSED\"];\ns1 -> s1 [label=\"disconnect/CONCLOSED_UNSUB_ALL\"];\ns1 -> s2 [label=\"subscribe/SUBACK\"];\ns1 -> s1 [label=\"subscribe/CONCLOSED\"];\ns1 -> s1 [label=\"unsubscribe/UNSUBACK_ALL\"];\ns1 -> s1 [label=\"unsubscribe/CONCLOSED\"];\ns1 -> s1 [label=\"publish/PUBACK\"];\ns1 -> s1 [label=\"publish/CONCLOSED\"];\ns2 -> s0 [label=\"connect/CONCLOSED_ALL\"];\ns2 -> s2 [label=\"connect/CONNACK\"];\ns2 -> s2 [label=\"connect/CONCLOSED\"];\ns2 -> s1 [label=\"connect/CONCLOSED_UNSUB_ALL\"];\ns2 -> s0 [label=\"disconnect/CONCLOSED_ALL\"];\ns2 -> s2 [label=\"disconnect/CONCLOSED\"];\ns2 -> s1 [label=\"disconnect/CONCLOSED_UNSUB_ALL\"];\ns2 -> s2 [label=\"subscribe/SUBACK\"];\ns2 -> s2 [label=\"subscribe/CONCLOSED\"];\ns2 -> s1 [label=\"unsubscribe/UNSUBACK_ALL\"];\ns2 -> s2 [label=\"unsubscribe/CONCLOSED\"];\ns2 -> s2 [label=\"unsubscribe/UNSUBACK\"];\ns2 -> s2 [label=\"publish/CONCLOSED\"];\ns2 -> s2 [label=\"publish/PUBACK_PUBLISH\"];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ], "id": "ad8a0651b263c35e" } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/AngluinExample.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "id": "ordinary-diving", "cell_type": "code", "source": "from aalpy.utils import get_Angluin_dfa\n\n# Import the DFA presented in Angluin's seminal paper\ndfa = get_Angluin_dfa()", "execution_count": 1, "outputs": [] }, { "metadata": { "trusted": true }, "id": "divine-ceramic", "cell_type": "code", "source": "# Get its input alphabet\nalphabet = dfa.get_input_alphabet()", "execution_count": 2, "outputs": [] }, { "metadata": { "trusted": true }, "id": "upper-ladder", "cell_type": "code", "source": "# Create a SUL instance weapping the Anguin's automaton\nfrom aalpy.SULs import DfaSUL\nsul = DfaSUL(dfa)", "execution_count": 3, "outputs": [] }, { "metadata": { "trusted": true }, "id": "alive-living", "cell_type": "code", "source": "# create a random walk equivelance oracle that will perform up to 500 steps every learning round\nfrom aalpy.oracles import RandomWalkEqOracle\n\neq_oracle = RandomWalkEqOracle(alphabet, sul, 500, reset_after_cex=True)\n\n", "execution_count": 4, "outputs": [] }, { "metadata": { "trusted": true }, "id": "several-cursor", "cell_type": "code", "source": "from aalpy.learning_algs import run_Lstar\n\n# start the L* and print the whole process in detail\nlearned_dfa = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa',\n cache_and_non_det_check=True, cex_processing=None, print_level=3)", "execution_count": 5, "outputs": [ { "output_type": "stream", "text": "Hypothesis 1: 2 states.\n------------------------\nPrefixes / E set |() \n------------------------\n() |True \n------------------------\n('a',) |False \n========================\n------------------------\n('b',) |False \n------------------------\n('a', 'a') |True \n------------------------\n('a', 'b') |False \n------------------------\nCounterexample ['a', 'a', 'b', 'b']\nHypothesis 2: 4 states.\n--------------------------------------------\nPrefixes / E set |() |('a',) |('b',) \n--------------------------------------------\n() |True |False |False \n--------------------------------------------\n('a',) |False |True |False \n--------------------------------------------\n('a', 'a', 'b') |False |False |True \n--------------------------------------------\n('a', 'b') |False |False |False \n============================================\n--------------------------------------------\n('b',) |False |False |True \n--------------------------------------------\n('a', 'a') |True |False |False \n--------------------------------------------\n('a', 'a', 'b', 'a') |False |False |False \n--------------------------------------------\n('a', 'a', 'b', 'b') |True |False |False \n--------------------------------------------\n('a', 'b', 'a') |False |False |True \n--------------------------------------------\n('a', 'b', 'b') |False |True |False \n--------------------------------------------\n-----------------------------------\nLearning Finished.\nLearning Rounds: 2\nNumber of states: 4\nTime (in seconds)\n Total : 0.01\n Learning algorithm : 0.01\n Conformance checking : 0.0\nLearning Algorithm\n # Membership Queries : 24\n # MQ Saved by Caching : 14\n # Steps : 91\nEquivalence Query\n # Membership Queries : 57\n # Steps : 504\n-----------------------------------\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "id": "preceding-wednesday", "cell_type": "code", "source": [ "# print the DOT representation of the final automaton\n", "print(learned_dfa)\n", "learned_dfa.save('learned_dfa.dot')" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": "digraph learnedModel {\ns0 [label=s0, shape=doublecircle];\ns1 [label=s1];\ns2 [label=s2];\ns3 [label=s3];\ns0 -> s1 [label=a];\ns0 -> s2 [label=b];\ns1 -> s0 [label=a];\ns1 -> s3 [label=b];\ns2 -> s3 [label=a];\ns2 -> s0 [label=b];\ns3 -> s2 [label=a];\ns3 -> s1 [label=b];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ] } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/MDP_Example.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "id": "higher-alexander", "cell_type": "code", "source": "from aalpy.SULs import MdpSUL\nfrom aalpy.learning_algs import run_stochastic_Lstar\nfrom aalpy.oracles import RandomWalkEqOracle\nfrom aalpy.utils import get_faulty_coffee_machine_MDP\n\n# get faulty coffe machine found in Chapter 5 of Martin's Tappler PhD thesis\nmdp = get_faulty_coffee_machine_MDP()\n\n# get its input alphabet\ninput_alphabet = mdp.get_input_alphabet()\n\n# wrap it in the SUL\nsul = MdpSUL(mdp)", "execution_count": 4, "outputs": [] }, { "metadata": { "trusted": true }, "id": "demanding-minister", "cell_type": "code", "source": "# Define the eq. oracle\neq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=1000, reset_prob=0.11,\n reset_after_cex=True)\n\n# start learning with verbose output\nlearned_mdp = run_stochastic_Lstar(input_alphabet, sul, eq_oracle, n_c=20, n_resample=200, strategy='classic', min_rounds=10,\n max_rounds=50, print_level=3)", "execution_count": 8, "outputs": [ { "output_type": "stream", "text": "Hypothesis: 1: 3 states.\n------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n------------------------------------------------------\n('init',) |{'init': 106} |{'beep': 94} \n------------------------------------------------------\n('init', 'coin', 'beep') |{} |{} \n======================================================\n------------------------------------------------------\n('init', 'but', 'init') |{} |{} \n------------------------------------------------------\nHypothesis: 2: 4 states.\n------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n------------------------------------------------------------------------------------\n('init',) |{'init': 210} |{'beep': 190} \n------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 37, 'init': 3} |{'beep': 56} \n------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{} |{} \n====================================================================================\n------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 60} |{'beep': 44} \n------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{} |{} \n------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{} |{} \n------------------------------------------------------------------------------------\nHypothesis: 3: 3 states.\n------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n------------------------------------------------------------------------------------------------------\n('init',) |{'init': 210} |{'beep': 390} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 118, 'init': 15} |{'beep': 163} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 40} |{'beep': 41} \n======================================================================================================\n------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 60} |{'beep': 44} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 8} |{'beep': 4} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 46, 'init': 6} |{'beep': 55} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{} |{} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{} |{} \n------------------------------------------------------------------------------------------------------\nHypothesis: 4: 3 states.\n------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n------------------------------------------------------------------------------------------------------\n('init',) |{'init': 262} |{'beep': 646} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 322, 'init': 39} |{'beep': 184} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 136} |{'beep': 146} \n======================================================================================================\n------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 82} |{'beep': 72} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 18} |{'beep': 18} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 54, 'init': 6} |{'beep': 65} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 61} |{'beep': 34} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 34, 'init': 3} |{'beep': 65} \n------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'coin', 'beep', 'but', 'init', 'coin', 'beep', 'but')\nHypothesis: 5: 3 states.\n------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n------------------------------------------------------------------------------------------------------\n('init',) |{'init': 262} |{'beep': 846} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 510, 'init': 51} |{'beep': 184} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 136} |{'beep': 146} \n======================================================================================================\n------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 82} |{'beep': 72} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 26} |{'beep': 22} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 54, 'init': 6} |{'beep': 65} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 61} |{'beep': 34} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 34, 'init': 3} |{'beep': 65} \n------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'coin', 'beep', 'but', 'init', 'coin', 'beep', 'but')\nUnambiguous rows: 100.0%; 8 out of 8\n-----------------------------------\nLearning Finished.\nLearning Rounds: 5\nNumber of states: 3\nTime (in seconds)\n Total : 0.05\n Learning algorithm : 0.03\n Conformance checking : 0.02\nLearning Algorithm\n # Membership Queries : 1000\n # Steps : 3285\nEquivalence Query\n # Membership Queries : 108\n # Steps : 1000\n-----------------------------------\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "id": "dangerous-catholic", "cell_type": "code", "source": "# print the learned MDP\nprint(learned_mdp)", "execution_count": 9, "outputs": [ { "output_type": "stream", "text": "digraph learnedModel {\ns0 [label=init];\ns1 [label=beep];\ns2 [label=coffee];\ns0 -> s0 [label=\"but : 1.0\"];\ns0 -> s1 [label=\"coin : 1.0\"];\ns1 -> s2 [label=\"but : 0.91\"];\ns1 -> s0 [label=\"but : 0.09\"];\ns1 -> s1 [label=\"coin : 1.0\"];\ns2 -> s0 [label=\"but : 1.0\"];\ns2 -> s1 [label=\"coin : 1.0\"];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ] } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/MDP_and_SMM_Example.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "id": "higher-alexander", "cell_type": "code", "source": "from aalpy.SULs import MdpSUL\nfrom aalpy.learning_algs import run_stochastic_Lstar\nfrom aalpy.oracles import RandomWalkEqOracle\nfrom aalpy.utils import get_faulty_coffee_machine_MDP\n\n# get faulty coffe machine found in Chapter 5 of Martin's Tappler PhD thesis\nmdp = get_faulty_coffee_machine_MDP()\n\n# get its input alphabet\ninput_alphabet = mdp.get_input_alphabet()\n\n# wrap it in the SUL\nsul = MdpSUL(mdp)", "execution_count": 1, "outputs": [ ] }, { "metadata": { "trusted": true }, "id": "demanding-minister", "cell_type": "code", "source": "# Define the eq. oracle\neq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=1000, reset_prob=0.11,\n reset_after_cex=True)\n\n# start learning with verbose output\nlearned_mdp = run_stochastic_Lstar(input_alphabet, sul, eq_oracle, n_c=20, n_resample=500, strategy='classic', min_rounds=10,\n max_rounds=50, cex_processing=None, print_level=3)", "execution_count": 19, "outputs": [ { "output_type": "stream", "text": "Hypothesis: 1: 3 states.\n-------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-------------------------------------------------------\n('init',) |{'init': 256} |{'beep': 244} \n-------------------------------------------------------\n('init', 'coin', 'beep') |{} |{} \n=======================================================\n-------------------------------------------------------\n('init', 'but', 'init') |{} |{} \n-------------------------------------------------------\nHypothesis: 2: 4 states.\n--------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n--------------------------------------------------------------------------------------\n('init',) |{'init': 508} |{'beep': 492} \n--------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 107, 'init': 15} |{'beep': 126} \n--------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{} |{} \n======================================================================================\n--------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 122} |{'beep': 130} \n--------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{} |{} \n--------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{} |{} \n--------------------------------------------------------------------------------------\nHypothesis: 3: 3 states.\n------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n------------------------------------------------------------------------------------------------------\n('init',) |{'init': 508} |{'beep': 992} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 336, 'init': 40} |{'beep': 372} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 118} |{'beep': 111} \n======================================================================================================\n------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 122} |{'beep': 130} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 14} |{'beep': 11} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 131, 'init': 7} |{'beep': 108} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{} |{} \n------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{} |{} \n------------------------------------------------------------------------------------------------------\nHypothesis: 4: 3 states.\n-------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-------------------------------------------------------------------------------------------------------\n('init',) |{'init': 562} |{'beep': 1549} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 813, 'init': 88} |{'beep': 399} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 368} |{'beep': 334} \n=======================================================================================================\n-------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 142} |{'beep': 158} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 39} |{'beep': 34} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 140, 'init': 9} |{'beep': 121} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 122} |{'beep': 128} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 106, 'init': 10} |{'beep': 106} \n-------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'but', 'init', 'coin', 'beep', 'but')\nHypothesis: 5: 3 states.\n-------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-------------------------------------------------------------------------------------------------------\n('init',) |{'init': 1062} |{'beep': 1549} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 813, 'init': 88} |{'beep': 399} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 368} |{'beep': 334} \n=======================================================================================================\n-------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 369} |{'beep': 431} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 39} |{'beep': 34} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 140, 'init': 9} |{'beep': 121} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 122} |{'beep': 128} \n-------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 106, 'init': 10} |{'beep': 106} \n-------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'coin', 'beep', 'but', 'init', 'coin', 'beep', 'but')\nUnambiguous rows: 100.0%; 12 out of 12\nHypothesis: 6: 3 states.\n---------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------------------------------\n('init',) |{'init': 1062} |{'beep': 2049} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 1255, 'init': 146} |{'beep': 399} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 368} |{'beep': 334} \n=========================================================================================================\n---------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 369} |{'beep': 431} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 67} |{'beep': 64} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 140, 'init': 9} |{'beep': 121} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 122} |{'beep': 128} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 106, 'init': 10} |{'beep': 106} \n---------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'coin', 'beep', 'but', 'init', 'coin', 'beep', 'but')\nHypothesis: 7: 3 states.\n---------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------------------------------\n('init',) |{'init': 1062} |{'beep': 2549} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 1705, 'init': 196} |{'beep': 399} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 368} |{'beep': 334} \n=========================================================================================================\n---------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 369} |{'beep': 431} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 89} |{'beep': 92} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 140, 'init': 9} |{'beep': 121} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 122} |{'beep': 128} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 106, 'init': 10} |{'beep': 106} \n---------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'coin', 'beep', 'coin', 'beep', 'coin', 'beep', 'coin', 'beep', 'but')\nHypothesis: 8: 3 states.\n---------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------------------------------\n('init',) |{'init': 1062} |{'beep': 3049} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 1705, 'init': 196} |{'beep': 899} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 368} |{'beep': 334} \n=========================================================================================================\n---------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 369} |{'beep': 431} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 89} |{'beep': 92} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 358, 'init': 27} |{'beep': 385} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 122} |{'beep': 128} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 106, 'init': 10} |{'beep': 106} \n---------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init', 'coin', 'beep', 'but')\nHypothesis: 9: 3 states.\n---------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------------------------------\n('init',) |{'init': 1062} |{'beep': 3549} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 2141, 'init': 260} |{'beep': 899} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 804} |{'beep': 334} \n=========================================================================================================\n---------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 369} |{'beep': 431} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 89} |{'beep': 92} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 358, 'init': 27} |{'beep': 385} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 330} |{'beep': 356} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 106, 'init': 10} |{'beep': 106} \n---------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep', 'coin', 'beep', 'but')\nHypothesis: 10: 3 states.\n---------------------------------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------------------------------\n('init',) |{'init': 1062} |{'beep': 4049} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep') |{'coffee': 2586, 'init': 315} |{'beep': 899} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee') |{'init': 804} |{'beep': 779} \n=========================================================================================================\n---------------------------------------------------------------------------------------------------------\n('init', 'but', 'init') |{'init': 369} |{'beep': 431} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'init') |{'init': 89} |{'beep': 92} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'coin', 'beep') |{'coffee': 358, 'init': 27} |{'beep': 385} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'but', 'init') |{'init': 330} |{'beep': 356} \n---------------------------------------------------------------------------------------------------------\n('init', 'coin', 'beep', 'but', 'coffee', 'coin', 'beep') |{'coffee': 300, 'init': 30} |{'beep': 337} \n---------------------------------------------------------------------------------------------------------\nCounterexample ('init', 'but', 'init', 'but', 'init', 'but', 'init', 'coin', 'beep', 'but')\nUnambiguous rows: 100.0%; 17 out of 17\n-----------------------------------\nLearning Finished.\nLearning Rounds: 10\nNumber of states: 3\nTime (in seconds)\n Total : 0.2\n Learning algorithm : 0.19\n Conformance checking : 0.01\nLearning Algorithm\n # Membership Queries : 5500\n # Steps : 19566\nEquivalence Query\n # Membership Queries : 111\n # Steps : 1000\n-----------------------------------\n", "name": "stdout" } ] }, { "metadata": { "scrolled": true, "trusted": true }, "id": "dangerous-catholic", "cell_type": "code", "source": "# print the learned MDP\nprint(learned_mdp)", "execution_count": 20, "outputs": [ { "output_type": "stream", "text": "digraph learnedModel {\ns0 [label=init];\ns1 [label=beep];\ns2 [label=coffee];\ns0 -> s0 [label=\"but : 1.0\"];\ns0 -> s1 [label=\"coin : 1.0\"];\ns1 -> s2 [label=\"but : 0.89\"];\ns1 -> s0 [label=\"but : 0.11\"];\ns1 -> s1 [label=\"coin : 1.0\"];\ns2 -> s0 [label=\"but : 1.0\"];\ns2 -> s1 [label=\"coin : 1.0\"];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "id": "seven-toolbox", "cell_type": "code", "source": "# let us repeat the procedure, but this time learn Stochastic Mealy Machine\n\n# Define the eq. oracle\neq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=1000, reset_prob=0.11,\n reset_after_cex=True)\n\n# start learning with verbose output\nlearned_smm = run_stochastic_Lstar(input_alphabet, sul, eq_oracle, automaton_type='smm', strategy='normal', min_rounds=10,\n max_rounds=50, cex_processing=None, print_level=3)", "execution_count": 10, "outputs": [ { "output_type": "stream", "text": "Hypothesis: 1: 1 states.\n-----------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-----------------------------------------------\n() |{'init': 266} |{'beep': 234} \n===============================================\n-----------------------------------------------\n('but', 'init') |{} |{} \n-----------------------------------------------\n('coin', 'beep') |{} |{} \n-----------------------------------------------\nCounterexample ['coin', 'beep', 'coin', 'beep', 'coin', 'beep', 'but']\nHypothesis: 2: 2 states.\n-----------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-----------------------------------------------------------------------------\n() |{'init': 379} |{'beep': 622} \n-----------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 38, 'init': 5} |{'beep': 345} \n=============================================================================\n-----------------------------------------------------------------------------\n('but', 'init') |{'init': 51} |{'beep': 62} \n-----------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{} |{} \n-----------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{} |{} \n-----------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 55, 'init': 14} |{'beep': 276} \n-----------------------------------------------------------------------------\nHypothesis: 3: 2 states.\n-------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-------------------------------------------------------------------------------\n() |{'init': 515} |{'beep': 1084} \n-------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 338, 'init': 36} |{'beep': 469} \n===============================================================================\n-------------------------------------------------------------------------------\n('but', 'init') |{'init': 116} |{'beep': 129} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 142} |{'beep': 156} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 17} |{'beep': 14} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 107, 'init': 19} |{'beep': 340} \n-------------------------------------------------------------------------------\nHypothesis: 4: 2 states.\n-------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-------------------------------------------------------------------------------\n() |{'init': 639} |{'beep': 1460} \n-------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 538, 'init': 61} |{'beep': 620} \n===============================================================================\n-------------------------------------------------------------------------------\n('but', 'init') |{'init': 177} |{'beep': 192} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 250} |{'beep': 248} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 31} |{'beep': 25} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 175, 'init': 24} |{'beep': 418} \n-------------------------------------------------------------------------------\nHypothesis: 5: 2 states.\n-------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n-------------------------------------------------------------------------------\n() |{'init': 767} |{'beep': 1832} \n-------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 737, 'init': 92} |{'beep': 762} \n===============================================================================\n-------------------------------------------------------------------------------\n('but', 'init') |{'init': 242} |{'beep': 255} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 351} |{'beep': 346} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 45} |{'beep': 42} \n-------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 250, 'init': 33} |{'beep': 476} \n-------------------------------------------------------------------------------\nUnambiguous rows: 100.0%; 6 out of 6\nHypothesis: 6: 2 states.\n--------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n--------------------------------------------------------------------------------\n() |{'init': 898} |{'beep': 2201} \n--------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 933, 'init': 117} |{'beep': 910} \n================================================================================\n--------------------------------------------------------------------------------\n('but', 'init') |{'init': 315} |{'beep': 313} \n--------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 456} |{'beep': 437} \n--------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 59} |{'beep': 53} \n--------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 320, 'init': 44} |{'beep': 543} \n--------------------------------------------------------------------------------\nHypothesis: 7: 2 states.\n---------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------\n() |{'init': 1015} |{'beep': 2584} \n---------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 1140, 'init': 144} |{'beep': 1059} \n=================================================================================\n---------------------------------------------------------------------------------\n('but', 'init') |{'init': 372} |{'beep': 373} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 568} |{'beep': 532} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 72} |{'beep': 67} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 384, 'init': 48} |{'beep': 624} \n---------------------------------------------------------------------------------\nHypothesis: 8: 2 states.\n---------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------\n() |{'init': 1129} |{'beep': 2970} \n---------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 1363, 'init': 172} |{'beep': 1194} \n=================================================================================\n---------------------------------------------------------------------------------\n('but', 'init') |{'init': 433} |{'beep': 426} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 679} |{'beep': 644} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 90} |{'beep': 77} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 444, 'init': 56} |{'beep': 691} \n---------------------------------------------------------------------------------\nHypothesis: 9: 2 states.\n---------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------\n() |{'init': 1261} |{'beep': 3338} \n---------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 1578, 'init': 189} |{'beep': 1330} \n=================================================================================\n---------------------------------------------------------------------------------\n('but', 'init') |{'init': 497} |{'beep': 494} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 781} |{'beep': 757} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 93} |{'beep': 91} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 511, 'init': 69} |{'beep': 747} \n---------------------------------------------------------------------------------\nHypothesis: 10: 2 states.\n---------------------------------------------------------------------------------\nPrefixes / E set |('but',) |('coin',) \n---------------------------------------------------------------------------------\n() |{'init': 1379} |{'beep': 3720} \n---------------------------------------------------------------------------------\n('coin', 'beep') |{'coffee': 1810, 'init': 210} |{'beep': 1459} \n=================================================================================\n---------------------------------------------------------------------------------\n('but', 'init') |{'init': 557} |{'beep': 552} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'coffee') |{'init': 894} |{'beep': 876} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'but', 'init') |{'init': 99} |{'beep': 106} \n---------------------------------------------------------------------------------\n('coin', 'beep', 'coin', 'beep') |{'coffee': 565, 'init': 73} |{'beep': 818} \n---------------------------------------------------------------------------------\n", "name": "stdout" }, { "output_type": "stream", "text": "Unambiguous rows: 100.0%; 6 out of 6\n-----------------------------------\nLearning Finished.\nLearning Rounds: 10\nNumber of states: 2\nTime (in seconds)\n Total : 0.22\n Learning algorithm : 0.11\n Conformance checking : 0.11\nLearning Algorithm\n # Membership Queries : 5500\n # Steps : 15611\nEquivalence Query\n # Membership Queries : 107\n # Steps : 1004\n-----------------------------------\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "id": "spoken-velvet", "cell_type": "code", "source": "print(learned_smm)", "execution_count": 11, "outputs": [ { "output_type": "stream", "text": "digraph learnedModel {\ns0 [label=s0];\ns1 [label=s1];\ns0 -> s0 [label=\"but/init:1.0\"];\ns0 -> s1 [label=\"coin/beep:1.0\"];\ns1 -> s0 [label=\"but/coffee:0.9\"];\ns1 -> s0 [label=\"but/init:0.1\"];\ns1 -> s1 [label=\"coin/beep:1.0\"];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "", "execution_count": null, "outputs": [ ] } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/ONFSM_Example.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "id": "strange-memory", "cell_type": "code", "source": "#Learning a ONFSM presented in 'Learning Finite State Models of Observable Nondeterministic Systems in a Testing Context'", "execution_count": 13, "outputs": [] }, { "metadata": { "trusted": true }, "id": "prompt-sample", "cell_type": "code", "source": "from aalpy.utils import get_benchmark_ONFSM\n\n# get the automaton \n\nonfsm = get_benchmark_ONFSM()\n\n# get its alphabet\n\nalphabet = onfsm.get_input_alphabet()", "execution_count": 18, "outputs": [] }, { "metadata": { "trusted": true }, "id": "threaded-mongolia", "cell_type": "code", "source": "from aalpy.SULs import OnfsmSUL\n\n# warp the benchmark in the SUL\n\nsul = OnfsmSUL(onfsm)", "execution_count": 19, "outputs": [] }, { "metadata": { "trusted": true }, "id": "brown-generic", "cell_type": "code", "source": "from aalpy.oracles import RandomWalkEqOracle\n\n# define the eq. oracle\n\neq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=5000, reset_prob=0.09, reset_after_cex=True)", "execution_count": 20, "outputs": [] }, { "metadata": { "trusted": true }, "id": "foster-background", "cell_type": "code", "source": [ "from aalpy.learning_algs import run_non_det_Lstar\n", "\n", "# start learning and print detailed output\n", "\n", "learned_onfsm = run_non_det_Lstar(alphabet, sul, eq_oracle, n_sampling=200, print_level=2)\n" ], "execution_count": 21, "outputs": [ { "output_type": "stream", "text": "Starting learning with an all-weather assumption.\nSee run_Lstar_ONFSM documentation for more details about possible non-convergence.\nHypothesis 1: 3 states.\nHypothesis 2: 4 states.\n-----------------------------------\nLearning Finished.\nLearning Rounds: 2\nNumber of states: 4\nTime (in seconds)\n Total : 0.56\n Learning algorithm : 0.52\n Conformance checking : 0.04\nLearning Algorithm\n # Membership Queries : 22527\n # Steps : 114735\nEquivalence Query\n # Membership Queries : 432\n # Steps : 5027\n-----------------------------------\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "id": "after-vacation", "cell_type": "code", "source": "# print the result\n\nprint(learned_onfsm)", "execution_count": 22, "outputs": [ { "output_type": "stream", "text": "digraph learnedModel {\ns0 [label=s0];\ns1 [label=s1];\ns2 [label=s2];\ns3 [label=s3];\ns0 -> s1 [label=\"a/0\"];\ns0 -> s2 [label=\"b/0\"];\ns0 -> s0 [label=\"b/2\"];\ns1 -> s0 [label=\"a/2\"];\ns1 -> s1 [label=\"b/3\"];\ns2 -> s3 [label=\"a/2\"];\ns2 -> s2 [label=\"b/0\"];\ns2 -> s2 [label=\"b/3\"];\ns3 -> s1 [label=\"a/2\"];\ns3 -> s3 [label=\"b/3\"];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "", "execution_count": null, "outputs": [], "id": "8937c6a9ec70157f" } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/RandomMealyExample.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "id": "nonprofit-volume", "cell_type": "code", "source": [ "from random import seed\n", "\n", "from DotModels.Bluetooth.convert_to_stochastic import alphabet\n", "\n", "# By defining the radnom seed, experiment is repr\n", "\n", "seed(5)" ], "execution_count": 1, "outputs": [] }, { "metadata": { "trusted": true }, "id": "loose-channels", "cell_type": "code", "source": [ "from aalpy.utils import generate_random_deterministic_automata\n", "\n", "# define parameters and create random Mealy machine based on them\n", "\n", "random_mealy = generate_random_deterministic_automata('mealy', num_states=10, input_alphabet_size=3, output_alphabet_size=3)\n", "alphabet = random_mealy.get_input_alphabet()" ], "execution_count": 2, "outputs": [] }, { "metadata": { "trusted": true }, "id": "technological-massage", "cell_type": "code", "source": [ "from aalpy.SULs import MealySUL\n", "\n", "# wrap the randomly generated Mealy machine in SUL\n", "\n", "sul_mealy = MealySUL(random_mealy)" ], "execution_count": 3, "outputs": [] }, { "metadata": { "trusted": true }, "id": "colonial-dairy", "cell_type": "code", "source": [ "from aalpy.oracles import StatePrefixEqOracle\n", "\n", "# create the equivalence oracle\n", "\n", "state_origin_eq_oracle = StatePrefixEqOracle(alphabet, sul_mealy, walks_per_state=10, walk_len=15)" ], "execution_count": 4, "outputs": [] }, { "metadata": { "trusted": true }, "id": "central-attachment", "cell_type": "code", "source": "from aalpy.learning_algs import run_Lstar\n\n# start learning with Shabaz-Groz counter-example processing\n\nlearned_mealy = run_Lstar(alphabet, sul_mealy, state_origin_eq_oracle, automaton_type='mealy',\n cex_processing='longest_prefix')\n\n", "execution_count": 5, "outputs": [ { "output_type": "stream", "text": "Hypothesis 1: 6 states.\nHypothesis 2: 10 states.\n-----------------------------------\nLearning Finished.\nLearning Rounds: 2\nNumber of states: 10\nTime (in seconds)\n Total : 0.02\n Learning algorithm : 0.0\n Conformance checking : 0.02\nLearning Algorithm\n # Membership Queries : 57\n # MQ Saved by Caching : 27\n # Steps : 276\nEquivalence Query\n # Membership Queries : 100\n # Steps : 1699\n-----------------------------------\n", "name": "stdout" } ] }, { "metadata": { "trusted": true }, "id": "above-circuit", "cell_type": "code", "source": "# print the DOT represetnation of the learned Mealy machine\nprint(learned_mealy)", "execution_count": 6, "outputs": [ { "output_type": "stream", "text": "digraph learnedModel {\ns0 [label=s0];\ns1 [label=s1];\ns2 [label=s2];\ns3 [label=s3];\ns4 [label=s4];\ns5 [label=s5];\ns6 [label=s6];\ns7 [label=s7];\ns8 [label=s8];\ns9 [label=s9];\ns0 -> s2 [label=\"0/1\"];\ns0 -> s1 [label=\"1/2\"];\ns1 -> s4 [label=\"0/1\"];\ns1 -> s3 [label=\"1/0\"];\ns2 -> s4 [label=\"0/0\"];\ns2 -> s9 [label=\"1/1\"];\ns3 -> s5 [label=\"0/2\"];\ns3 -> s4 [label=\"1/0\"];\ns4 -> s8 [label=\"0/0\"];\ns4 -> s7 [label=\"1/2\"];\ns5 -> s6 [label=\"0/0\"];\ns5 -> s8 [label=\"1/0\"];\ns6 -> s9 [label=\"0/0\"];\ns6 -> s0 [label=\"1/0\"];\ns7 -> s7 [label=\"0/0\"];\ns7 -> s5 [label=\"1/0\"];\ns8 -> s2 [label=\"0/2\"];\ns8 -> s8 [label=\"1/0\"];\ns9 -> s0 [label=\"0/0\"];\ns9 -> s5 [label=\"1/1\"];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ] } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/RegexExample.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "id": "charming-absence", "cell_type": "code", "source": "from aalpy.base import SUL\nimport re\n\n# Create a system under learning that can learn any regular experssion\n\nclass RegexSUL(SUL):\n \"\"\"\n An example implementation of a system under learning that can be used to learn any regex expression.\n Note that the $ is added to the expression as in this SUL only exact matches are learned.\n \"\"\"\n def __init__(self, regex: str):\n super().__init__()\n self.regex = regex if regex[-1] == '$' else regex + '$'\n self.string = \"\"\n\n def pre(self):\n self.string = \"\"\n pass\n\n def post(self):\n self.string = \"\"\n pass\n\n def step(self, letter):\n \"\"\"\n\n Args:\n\n letter: single element of the input alphabet\n\n Returns:\n\n Whether the current string (previous string + letter) is accepted\n\n \"\"\"\n if letter is not None:\n self.string += str(letter)\n return True if re.match(self.regex, self.string) else False", "execution_count": 1, "outputs": [] }, { "metadata": { "trusted": true }, "id": "norman-bolivia", "cell_type": "code", "source": "# define a regex and its alphabet\nregex = '((0|1)*0)*1(11)*(0(0|1)*1)*0(00)*(1(0|1)*)*'\nalphabet = [0,1]", "execution_count": 2, "outputs": [] }, { "metadata": { "trusted": true }, "id": "industrial-sociology", "cell_type": "code", "source": "# pass the regex to the RegexSUL\n\nregex_sul = RegexSUL(regex)\n\nfrom aalpy.oracles import StatePrefixEqOracle\n\n# create the oracle\n\neq_oracle = StatePrefixEqOracle(alphabet, regex_sul, walks_per_state=15,\n walk_len=10)\n\nfrom aalpy.learning_algs import run_Lstar\n\n# start learning with no counterexample processing\n\nlearned_regex = run_Lstar(alphabet, regex_sul, eq_oracle, automaton_type='dfa', cex_processing=None)\n\nprint(learned_regex)", "execution_count": 3, "outputs": [ { "output_type": "stream", "text": "Hypothesis 1: 1 states.\nHypothesis 2: 4 states.\nHypothesis 3: 5 states.\n-----------------------------------\nLearning Finished.\nLearning Rounds: 3\nNumber of states: 5\nTime (in seconds)\n Total : 0.02\n Learning algorithm : 0.0\n Conformance checking : 0.02\nLearning Algorithm\n # Membership Queries : 37\n # MQ Saved by Caching : 37\n # Steps : 271\nEquivalence Query\n # Membership Queries : 75\n # Steps : 935\n-----------------------------------\ndigraph learnedModel {\ns0 [label=s0];\ns1 [label=s1];\ns2 [label=s2, shape=doublecircle];\ns3 [label=s3, shape=doublecircle];\ns4 [label=s4];\ns0 -> s0 [label=0];\ns0 -> s1 [label=1];\ns1 -> s2 [label=0];\ns1 -> s0 [label=1];\ns2 -> s4 [label=0];\ns2 -> s3 [label=1];\ns3 -> s3 [label=0];\ns3 -> s3 [label=1];\ns4 -> s2 [label=0];\ns4 -> s4 [label=1];\n__start0 [label=\"\", shape=none];\n__start0 -> s0 [label=\"\"];\n}\n\n", "name": "stdout" } ] } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/Stochstic_Examples.ipynb ================================================ { "cells": [ { "metadata": { "trusted": true }, "cell_type": "code", "source": "# This notebook contains example on how to run examples presented in 'Active Model Learning of Stochastic Reactive\nSystems'", "execution_count": null, "outputs": [], "id": "af65e2dc8986b294" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": [ "# Import Statements\n", "\n", "from aalpy.SULs import StochasticMealySUL\n", "from aalpy.oracles import RandomWalkEqOracle, RandomWordEqOracle\n", "from aalpy.learning_algs import run_stochastic_Lstar\n", "from aalpy.utils import load_automaton_from_file\n", "from aalpy.automata import StochasticMealyMachine\n", "from aalpy.utils import mdp_2_prism_format" ], "execution_count": 21, "outputs": [], "id": "55ca665dd831d315" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "# Simply change parameters here and see how it affect the leraning process\n# example and paremeters\nexample = 'first_grid' # can also be 'second_grid', 'slot_machine', 'mqtt', 'tcp'\nautomaton_type = 'smm' # to learn either mdp (Markov Decision Process) or smm (Stochastic Mealy Machine)\nmin_rounds = 20\nmax_rounds = 250\nstrategy = 'normal' # row bisimilarity metric\ncex_processing = 'longest_prefix' # counterexample processsing strategy\nsamples_cex_strategy = 'bfs' # search for counterexample in the PTA ", "execution_count": 22, "outputs": [], "id": "7017e66c307952e9" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": [ "# load the example that we want to learn as MDP\n", "mdp = load_automaton_from_file(f'../DotModels/MDPs/{example}.dot', automaton_type='mdp')\n", "# get its input alphabet\n", "input_alphabet = mdp.get_input_alphabet()\n", "\n", "# create a SUL for that system\n", "sul = StochasticMealySUL(mdp)\n", "\n", "# define an eq. oracle\n", "eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=200, reset_prob=0.25,\n", " reset_after_cex=True)" ], "execution_count": 23, "outputs": [], "id": "8debfa625810ab87" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "learned_stochastic_model = run_stochastic_Lstar(input_alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul,\n min_rounds=min_rounds, max_rounds=max_rounds,\n automaton_type=automaton_type, strategy=strategy, cex_processing=cex_processing,\n samples_cex_strategy=samples_cex_strategy, target_unambiguity=0.99)", "execution_count": 24, "outputs": [ { "output_type": "stream", "text": "Hypothesis: 1: 1 states.\nHypothesis: 2: 2 states.\nHypothesis: 3: 5 states.\nHypothesis: 4: 7 states.\nHypothesis: 5: 9 states.\nUnambiguous rows: 71.74%; 33 out of 46\nHypothesis: 6: 10 states.\nHypothesis: 7: 12 states.\nHypothesis: 8: 15 states.\nHypothesis: 9: 18 states.\nHypothesis: 10: 19 states.\nUnambiguous rows: 72.73%; 88 out of 121\nHypothesis: 11: 19 states.\nHypothesis: 12: 19 states.\nHypothesis: 13: 19 states.\nHypothesis: 14: 19 states.\nHypothesis: 15: 19 states.\nUnambiguous rows: 88.43%; 107 out of 121\nHypothesis: 16: 19 states.\nHypothesis: 17: 19 states.\nHypothesis: 18: 19 states.\nHypothesis: 19: 19 states.\nHypothesis: 20: 19 states.\nUnambiguous rows: 95.04%; 115 out of 121\nHypothesis: 21: 19 states.\nHypothesis: 22: 19 states.\nHypothesis: 23: 19 states.\nHypothesis: 24: 19 states.\nHypothesis: 25: 19 states.\nUnambiguous rows: 97.52%; 118 out of 121\nHypothesis: 26: 19 states.\nHypothesis: 27: 19 states.\n-----------------------------------\nLearning Finished.\nLearning Rounds: 27\nNumber of states: 19\nTime (in seconds)\n Total : 4.58\n Learning algorithm : 4.52\n Conformance checking : 0.06\nLearning Algorithm\n # Membership Queries : 22516\n # Steps : 131478\nEquivalence Query\n # Membership Queries : 1\n # Steps : 3\n-----------------------------------\n", "name": "stdout" } ], "id": "5835c30b95bc5ea2" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "# If we have learned a Stochastic Mealy Machine, transform it in the equivalent MDP\nif isinstance(learned_stochastic_model, StochasticMealyMachine):\n learned_stochastic_model = smm_to_mdp_conversion(learned_stochastic_model)", "execution_count": 25, "outputs": [], "id": "d41b04e3ac586ddf" }, { "metadata": { "trusted": true }, "cell_type": "code", "source": "# Translate MDP into PRISM format that can be used for model checking\nprism_repr = mdp_2_prism_format(learned_stochastic_model, f'learned_model_{example}')\n\nprint(prism_repr)", "execution_count": 26, "outputs": [ { "output_type": "stream", "text": "mdp\nmodule learned_model_first_grid\nloc : [0..36] init 0;\n[East] loc=0 -> \n 1.0 : (loc'=3);\n[North] loc=0 -> \n 1.0 : (loc'=2);\n[South] loc=0 -> \n 1.0 : (loc'=2);\n[West] loc=0 -> \n 1.0 : (loc'=2);\n[East] loc=1 -> \n 1.0 : (loc'=3);\n[North] loc=1 -> \n 1.0 : (loc'=2);\n[South] loc=1 -> \n 1.0 : (loc'=2);\n[West] loc=1 -> \n 1.0 : (loc'=2);\n[East] loc=2 -> \n 1.0 : (loc'=3);\n[North] loc=2 -> \n 1.0 : (loc'=2);\n[South] loc=2 -> \n 1.0 : (loc'=2);\n[West] loc=2 -> \n 1.0 : (loc'=2);\n[East] loc=3 -> \n 1.0 : (loc'=5);\n[North] loc=3 -> \n 1.0 : (loc'=4);\n[South] loc=3 -> \n 1.0 : (loc'=4);\n[West] loc=3 -> \n 1.0 : (loc'=1);\n[East] loc=4 -> \n 1.0 : (loc'=5);\n[North] loc=4 -> \n 1.0 : (loc'=4);\n[South] loc=4 -> \n 1.0 : (loc'=4);\n[West] loc=4 -> \n 1.0 : (loc'=1);\n[East] loc=5 -> \n 0.5995554636617388 : (loc'=7) + 0.40044453633826116 : (loc'=9);\n[North] loc=5 -> \n 1.0 : (loc'=6);\n[South] loc=5 -> \n 1.0 : (loc'=6);\n[West] loc=5 -> \n 1.0 : (loc'=3);\n[East] loc=6 -> \n 0.5995554636617388 : (loc'=7) + 0.40044453633826116 : (loc'=9);\n[North] loc=6 -> \n 1.0 : (loc'=6);\n[South] loc=6 -> \n 1.0 : (loc'=6);\n[West] loc=6 -> \n 1.0 : (loc'=3);\n[East] loc=7 -> \n 1.0 : (loc'=8);\n[North] loc=7 -> \n 1.0 : (loc'=8);\n[South] loc=7 -> \n 1.0 : (loc'=9);\n[West] loc=7 -> \n 1.0 : (loc'=5);\n[East] loc=8 -> \n 1.0 : (loc'=8);\n[North] loc=8 -> \n 1.0 : (loc'=8);\n[South] loc=8 -> \n 1.0 : (loc'=9);\n[West] loc=8 -> \n 1.0 : (loc'=5);\n[East] loc=9 -> \n 0.6279620853080569 : (loc'=32) + 0.37203791469194314 : (loc'=26);\n[North] loc=9 -> \n 0.5089285714285714 : (loc'=7) + 0.49107142857142855 : (loc'=5);\n[South] loc=9 -> \n 1.0 : (loc'=11);\n[West] loc=9 -> \n 1.0 : (loc'=10);\n[East] loc=10 -> \n 0.6279620853080569 : (loc'=32) + 0.37203791469194314 : (loc'=26);\n[North] loc=10 -> \n 0.5089285714285714 : (loc'=7) + 0.49107142857142855 : (loc'=5);\n[South] loc=10 -> \n 1.0 : (loc'=11);\n[West] loc=10 -> \n 1.0 : (loc'=10);\n[East] loc=11 -> \n 0.19294117647058823 : (loc'=32) + 0.8070588235294117 : (loc'=26);\n[North] loc=11 -> \n 1.0 : (loc'=9);\n[South] loc=11 -> \n 0.3713670613562971 : (loc'=19) + 0.628632938643703 : (loc'=22);\n[West] loc=11 -> \n 0.7993691957245488 : (loc'=12) + 0.2006308042754512 : (loc'=19);\n[East] loc=12 -> \n 1.0 : (loc'=11);\n[North] loc=12 -> \n 1.0 : (loc'=13);\n[South] loc=12 -> \n 1.0 : (loc'=19);\n[West] loc=12 -> \n 0.6083883129123469 : (loc'=14) + 0.39161168708765315 : (loc'=16);\n[East] loc=13 -> \n 1.0 : (loc'=11);\n[North] loc=13 -> \n 1.0 : (loc'=13);\n[South] loc=13 -> \n 1.0 : (loc'=19);\n[West] loc=13 -> \n 0.6083883129123469 : (loc'=14) + 0.39161168708765315 : (loc'=16);\n[East] loc=14 -> \n 0.8354430379746836 : (loc'=12) + 0.16455696202531644 : (loc'=19);\n[North] loc=14 -> \n 1.0 : (loc'=15);\n[South] loc=14 -> \n 0.821078431372549 : (loc'=16) + 0.06372549019607843 : (loc'=19) + 0.11519607843137254 : (loc'=24);\n[West] loc=14 -> \n 0.24579489962018447 : (loc'=24) + 0.7542051003798155 : (loc'=18);\n[East] loc=15 -> \n 0.8354430379746836 : (loc'=12) + 0.16455696202531644 : (loc'=19);\n[North] loc=15 -> \n 1.0 : (loc'=15);\n[South] loc=15 -> \n 0.821078431372549 : (loc'=16) + 0.06372549019607843 : (loc'=19) + 0.11519607843137254 : (loc'=24);\n[West] loc=15 -> \n 0.24579489962018447 : (loc'=24) + 0.7542051003798155 : (loc'=18);\n[East] loc=16 -> \n 1.0 : (loc'=19);\n[North] loc=16 -> \n 0.19704433497536947 : (loc'=18) + 0.541871921182266 : (loc'=14) + 0.26108374384236455 : (loc'=12);\n[South] loc=16 -> \n 0.7495840266222962 : (loc'=21) + 0.12562396006655574 : (loc'=35) + 0.12479201331114809 : (loc'=30);\n[West] loc=16 -> \n 0.6243386243386243 : (loc'=24) + 0.2328042328042328 : (loc'=18) + 0.14285714285714285 : (loc'=35);\n[East] loc=17 -> \n 0.5860655737704918 : (loc'=14) + 0.4139344262295082 : (loc'=16);\n[North] loc=17 -> \n 1.0 : (loc'=17);\n[South] loc=17 -> \n 0.6308724832214765 : (loc'=24) + 0.3691275167785235 : (loc'=16);\n[West] loc=17 -> \n 1.0 : (loc'=17);\n[East] loc=18 -> \n 0.5860655737704918 : (loc'=14) + 0.4139344262295082 : (loc'=16);\n[North] loc=18 -> \n 1.0 : (loc'=17);\n[South] loc=18 -> \n 0.6308724832214765 : (loc'=24) + 0.3691275167785235 : (loc'=16);\n[West] loc=18 -> \n 1.0 : (loc'=17);\n[East] loc=19 -> \n 0.20967741935483872 : (loc'=26) + 0.5913978494623656 : (loc'=22) + 0.1989247311827957 : (loc'=11);\n[North] loc=19 -> \n 0.8131487889273357 : (loc'=12) + 0.06920415224913495 : (loc'=14) + 0.11764705882352941 : (loc'=11);\n[South] loc=19 -> \n 0.6 : (loc'=30) + 0.19777777777777777 : (loc'=21) + 0.20222222222222222 : (loc'=28);\n[West] loc=19 -> \n 0.8009259259259259 : (loc'=16) + 0.09259259259259259 : (loc'=21) + 0.10648148148148148 : (loc'=14);\n[East] loc=20 -> \n 0.4230769230769231 : (loc'=19) + 0.5769230769230769 : (loc'=30);\n[North] loc=20 -> \n 0.8014184397163121 : (loc'=16) + 0.1182033096926714 : (loc'=19) + 0.08037825059101655 : (loc'=24);\n[South] loc=20 -> \n 1.0 : (loc'=20);\n[West] loc=20 -> \n 0.7368421052631579 : (loc'=35) + 0.2631578947368421 : (loc'=24);\n[East] loc=21 -> \n 0.4230769230769231 : (loc'=19) + 0.5769230769230769 : (loc'=30);\n[North] loc=21 -> \n 0.8014184397163121 : (loc'=16) + 0.1182033096926714 : (loc'=19) + 0.08037825059101655 : (loc'=24);\n[South] loc=21 -> \n 1.0 : (loc'=20);\n[West] loc=21 -> \n 0.7368421052631579 : (loc'=35) + 0.2631578947368421 : (loc'=24);\n[East] loc=22 -> \n 1.0 : (loc'=23);\n[North] loc=22 -> \n 1.0 : (loc'=11);\n[South] loc=22 -> \n 0.1893939393939394 : (loc'=30) + 0.8106060606060606 : (loc'=28);\n[West] loc=22 -> \n 1.0 : (loc'=19);\n[East] loc=23 -> \n 1.0 : (loc'=23);\n[North] loc=23 -> \n 1.0 : (loc'=11);\n[South] loc=23 -> \n 0.1893939393939394 : (loc'=30) + 0.8106060606060606 : (loc'=28);\n[West] loc=23 -> \n 1.0 : (loc'=19);\n[East] loc=24 -> \n 0.8302752293577982 : (loc'=16) + 0.0963302752293578 : (loc'=14) + 0.07339449541284404 : (loc'=21);\n[North] loc=24 -> \n 0.6976744186046512 : (loc'=18) + 0.3023255813953488 : (loc'=14);\n[South] loc=24 -> \n 0.6759259259259259 : (loc'=35) + 0.32407407407407407 : (loc'=21);\n[West] loc=24 -> \n 1.0 : (loc'=25);\n[East] loc=25 -> \n 0.8302752293577982 : (loc'=16) + 0.0963302752293578 : (loc'=14) + 0.07339449541284404 : (loc'=21);\n[North] loc=25 -> \n 0.6976744186046512 : (loc'=18) + 0.3023255813953488 : (loc'=14);\n[South] loc=25 -> \n 0.6759259259259259 : (loc'=35) + 0.32407407407407407 : (loc'=21);\n[West] loc=25 -> \n 1.0 : (loc'=25);\n[East] loc=26 -> \n 1.0 : (loc'=27);\n[North] loc=26 -> \n 0.36363636363636365 : (loc'=9) + 0.6363636363636364 : (loc'=32);\n[South] loc=26 -> \n 1.0 : (loc'=27);\n[West] loc=26 -> \n 1.0 : (loc'=11);\n[East] loc=27 -> \n 1.0 : (loc'=27);\n[North] loc=27 -> \n 0.36363636363636365 : (loc'=9) + 0.6363636363636364 : (loc'=32);\n[South] loc=27 -> \n 1.0 : (loc'=27);\n[West] loc=27 -> \n 1.0 : (loc'=11);\n[East] loc=28 -> \n 1.0 : (loc'=29);\n[North] loc=28 -> \n 0.5853658536585366 : (loc'=22) + 0.4146341463414634 : (loc'=19);\n[South] loc=28 -> \n 1.0 : (loc'=29);\n[West] loc=28 -> \n 0.36065573770491804 : (loc'=19) + 0.639344262295082 : (loc'=30);\n[East] loc=29 -> \n 1.0 : (loc'=29);\n[North] loc=29 -> \n 0.5853658536585366 : (loc'=22) + 0.4146341463414634 : (loc'=19);\n[South] loc=29 -> \n 1.0 : (loc'=29);\n[West] loc=29 -> \n 0.36065573770491804 : (loc'=19) + 0.639344262295082 : (loc'=30);\n[East] loc=30 -> \n 0.8211382113821138 : (loc'=26) + 0.17886178861788618 : (loc'=22);\n[North] loc=30 -> \n 1.0 : (loc'=19);\n[South] loc=30 -> \n 1.0 : (loc'=31);\n[West] loc=30 -> \n 0.7352941176470589 : (loc'=21) + 0.2647058823529412 : (loc'=16);\n[East] loc=31 -> \n 0.8211382113821138 : (loc'=26) + 0.17886178861788618 : (loc'=22);\n[North] loc=31 -> \n 1.0 : (loc'=19);\n[South] loc=31 -> \n 1.0 : (loc'=31);\n[West] loc=31 -> \n 0.7352941176470589 : (loc'=21) + 0.2647058823529412 : (loc'=16);\n[East] loc=32 -> \n 1.0 : (loc'=33);\n[North] loc=32 -> \n 1.0 : (loc'=33);\n[South] loc=32 -> \n 0.7950819672131147 : (loc'=26) + 0.20491803278688525 : (loc'=11);\n[West] loc=32 -> \n 1.0 : (loc'=9);\n[East] loc=33 -> \n 1.0 : (loc'=33);\n[North] loc=33 -> \n 1.0 : (loc'=33);\n[South] loc=33 -> \n 0.7950819672131147 : (loc'=26) + 0.20491803278688525 : (loc'=11);\n[West] loc=33 -> \n 1.0 : (loc'=9);\n[East] loc=34 -> \n 0.6730769230769231 : (loc'=21) + 0.3269230769230769 : (loc'=16);\n[North] loc=34 -> \n 0.34782608695652173 : (loc'=16) + 0.6521739130434783 : (loc'=24);\n[South] loc=34 -> \n 1.0 : (loc'=34);\n[West] loc=34 -> \n 1.0 : (loc'=34);\n[East] loc=35 -> \n 0.6730769230769231 : (loc'=21) + 0.3269230769230769 : (loc'=16);\n[North] loc=35 -> \n 0.34782608695652173 : (loc'=16) + 0.6521739130434783 : (loc'=24);\n[South] loc=35 -> \n 1.0 : (loc'=34);\n[West] loc=35 -> \n 1.0 : (loc'=34);\nendmodule\nlabel \"_start\" = loc=0;\nlabel \"_\" = loc=0;\nlabel \"concrete\" = loc=1|loc=3|loc=5|loc=9|loc=11|loc=19;\nlabel \"wall\" = loc=2|loc=4|loc=6|loc=8|loc=10|loc=13|loc=15|loc=17|loc=20|loc=23|loc=25|loc=27|loc=29|loc=31|loc=33|loc=34;\nlabel \"mud\" = loc=7|loc=14|loc=22|loc=24|loc=30|loc=32;\nlabel \"grass\" = loc=12|loc=16|loc=26|loc=28|loc=35;\nlabel \"sand\" = loc=18|loc=21;\nlabel \"goal\" = loc=34|loc=35;\n\n", "name": "stdout" } ], "id": "ec88d7c553737915" } ], "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "language_info": { "name": "python", "version": "3.7.10", "mimetype": "text/x-python", "codemirror_mode": { "name": "ipython", "version": 3 }, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: setup.py ================================================ from setuptools import setup from os import path this_directory = path.abspath(path.dirname(__file__)) with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f: long_description = f.read() setup( name='aalpy', version='1.5.3', packages=['aalpy', 'aalpy.base', 'aalpy.SULs', 'aalpy.utils', 'aalpy.oracles', 'aalpy.automata', 'aalpy.learning_algs', 'aalpy.learning_algs.stochastic', 'aalpy.learning_algs.deterministic', 'aalpy.learning_algs.non_deterministic', 'aalpy.learning_algs.general_passive', 'aalpy.learning_algs.adaptive', 'aalpy.learning_algs.stochastic_passive', 'aalpy.learning_algs.deterministic_passive'], url='https://github.com/DES-Lab/AALpy', license='MIT', license_files=('LICENSE.txt',), long_description=long_description, long_description_content_type='text/markdown', author='Edi Muskardin', author_email='edi.muskardin@silicon-austria.com', description='An active automata learning library', classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent" ], install_requires=['pydot'], python_requires=">=3.6", ) # python setup.py sdist # pip wheel . -w dist # twine upload dist/* # for test pypi # twine upload --repository testpypi dist/* ================================================ FILE: tests/oracles/test_baseOracle.py ================================================ import unittest from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_Lstar from aalpy.oracles import WMethodEqOracle from aalpy.utils import generate_random_dfa class BaseOracleTests(unittest.TestCase): """ Abstract class for testing oracles. """ def generate_dfa_suls(self, number_of_states=10, alphabet_size=10, num_accepting_states=5): """ Creates a random DFA and creates a learning and validation SUL, both SUL are identical. Args: number_of_states: number of states (Default value = 10) alphabet_size: size of alphabet (Default value = 10) num_accepting_states: number of accepting states (Default value = 5) Returns: learning_sul, validation_sul, alphabet """ alphabet = [*range(0, alphabet_size)] dfa = generate_random_dfa(number_of_states, alphabet, num_accepting_states) learning_sul = AutomatonSUL(dfa) validation_sul = AutomatonSUL(dfa) return learning_sul, validation_sul, alphabet def test_validate_eq_oracle(self, alphabet, eq_oracle, learning_sul, validation_sul): """ Validates the correctness of the given eq_oracle via WMethodEqOracle. Args: alphabet: input alphabet eq_oracle: oracle to be validated learning_sul:the SUL form the eq_oracle validation_sul: identical SUL that was not used to learn the oracle Returns: """ learned_model = run_Lstar( alphabet, learning_sul, eq_oracle, 'dfa', print_level=2) validation_eq_oracle = WMethodEqOracle( alphabet, validation_sul, max_number_of_states=len(learned_model.states) + 2) self.assertIsNone(validation_eq_oracle.find_cex( learned_model), "Counterexample found by WMethodEqOracle") ================================================ FILE: tests/oracles/test_kWayTransitionCoverageEqOracle.py ================================================ import unittest from aalpy.oracles import KWayTransitionCoverageEqOracle from tests.oracles.test_baseOracle import BaseOracleTests class KWayTransitionCoverageEqOracleTests(BaseOracleTests): def test_default(self): learning_sul, validation_sul, alphabet = self.generate_dfa_suls() eq_oracle = KWayTransitionCoverageEqOracle(alphabet, learning_sul) self.test_validate_eq_oracle(alphabet, eq_oracle, learning_sul, validation_sul) def test_k_4(self): learning_sul, validation_sul, alphabet = self.generate_dfa_suls(5, 5, 2) eq_oracle = KWayTransitionCoverageEqOracle( alphabet, learning_sul, k=4) self.test_validate_eq_oracle(alphabet, eq_oracle, learning_sul, validation_sul) def test_method_prefix(self): learning_sul, validation_sul, alphabet = self.generate_dfa_suls() eq_oracle = KWayTransitionCoverageEqOracle( alphabet, learning_sul, method='prefix') self.test_validate_eq_oracle(alphabet, eq_oracle, learning_sul, validation_sul) @unittest.expectedFailure def test_max_number_of_steps_10(self): learning_sul, validation_sul, alphabet = self.generate_dfa_suls(50, 4, 4) eq_oracle = KWayTransitionCoverageEqOracle( alphabet, learning_sul, max_number_of_steps=10, max_path_len=10) self.test_validate_eq_oracle(alphabet, eq_oracle, learning_sul, validation_sul) def test_default_large_dfa(self): learning_sul, validation_sul, alphabet = self.generate_dfa_suls(50, 10, 10) eq_oracle = KWayTransitionCoverageEqOracle(alphabet, learning_sul) self.test_validate_eq_oracle(alphabet, eq_oracle, learning_sul, validation_sul) ================================================ FILE: tests/test_charSet.py ================================================ import unittest from aalpy.utils import get_Angluin_dfa, load_automaton_from_file from aalpy.utils.HelperFunctions import all_suffixes class TestCharSet(unittest.TestCase): def get_test_automata(self): return {"angluin_dfa": get_Angluin_dfa(), "angluin_mealy": load_automaton_from_file('../DotModels/Angluin_Mealy.dot', automaton_type='mealy'), "angluin_moore": load_automaton_from_file('../DotModels/Angluin_Moore.dot', automaton_type='moore'), "mqtt": load_automaton_from_file('../DotModels/MQTT/emqtt__two_client_will_retain.dot', automaton_type='mealy'), "openssl": load_automaton_from_file('../DotModels/TLS/OpenSSL_1.0.2_server_regular.dot', automaton_type='mealy'), "tcp_server": load_automaton_from_file('../DotModels/TCP/TCP_Linux_Client.dot', automaton_type='mealy')} def test_can_differentiate(self): automata = self.get_test_automata() for init_with_alphabet in [True, False]: for (online_suffix_closure, split_all_blocks) in [(False, False), (False, True), (True, False), (True, True)]: for test_aut_name in automata: print(f"Testing with {test_aut_name}") test_aut = automata[test_aut_name] char_set_init = list(map(lambda input: tuple([input]), test_aut.get_input_alphabet())) \ if init_with_alphabet else None if "dfa" in test_aut_name or "moore" in test_aut_name: char_set_init = [] if char_set_init is None else char_set_init char_set_init.append(()) char_set = test_aut.compute_characterization_set(char_set_init=char_set_init, online_suffix_closure=online_suffix_closure, split_all_blocks=split_all_blocks) print(f"Char. set {char_set}") all_responses = set() for s in test_aut.states: responses_from_s = [] for c in char_set: responses_from_s.append(tuple(test_aut.compute_output_seq(s, c))) all_responses.add(tuple(responses_from_s)) # every state must have a unique response to the whole characterization set assert len(all_responses) == len(test_aut.states) def test_suffix_closed(self): automata = self.get_test_automata() for init_with_alphabet in [True, False]: online_suffix_closure = True for split_all_blocks in [True, False]: for test_aut_name in automata: print(f"Testing with {test_aut_name}") test_aut = automata[test_aut_name] char_set_init = list(map(lambda input: tuple([input]), test_aut.get_input_alphabet())) \ if init_with_alphabet else None if "dfa" in test_aut_name or "moore" in test_aut_name: char_set_init = [] if char_set_init is None else char_set_init char_set_init.append(()) char_set = test_aut.compute_characterization_set(char_set_init=char_set_init, online_suffix_closure=online_suffix_closure, split_all_blocks=split_all_blocks) print(f"Char. set {char_set}") for s in char_set: for suffix in all_suffixes(s): if suffix not in char_set: print(suffix) assert suffix in char_set ================================================ FILE: tests/test_deterministic.py ================================================ import unittest from aalpy.SULs import AutomatonSUL from aalpy.automata import Dfa, MealyMachine, MooreMachine from aalpy.learning_algs import run_Lstar from aalpy.oracles import WMethodEqOracle, WpMethodEqOracle, RandomWalkEqOracle, StatePrefixEqOracle, TransitionFocusOracle, \ RandomWMethodEqOracle, BreadthFirstExplorationEqOracle, RandomWordEqOracle, CacheBasedEqOracle, \ KWayStateCoverageEqOracle, RandomWpMethodEqOracle from aalpy.utils import get_Angluin_dfa, load_automaton_from_file from aalpy.utils.ModelChecking import bisimilar correct_automata = {Dfa: get_Angluin_dfa(), MealyMachine: load_automaton_from_file('../DotModels/Angluin_Mealy.dot', automaton_type='mealy'), MooreMachine: load_automaton_from_file('../DotModels/Angluin_Moore.dot', automaton_type='moore')} class DeterministicTest(unittest.TestCase): def prove_equivalence(self, learned_automaton): correct_automaton = correct_automata[learned_automaton.__class__] # only work if correct automaton is already minimal if len(learned_automaton.states) != len(correct_automaton.states): print(len(learned_automaton.states), len(correct_automaton.states)) return False return bisimilar(correct_automaton, learned_automaton) def test_closing_strategies(self): dfa = get_Angluin_dfa() alphabet = dfa.get_input_alphabet() closing_strategies = ['shortest_first', 'longest_first', 'single'] automata_type = ['dfa', 'mealy', 'moore'] for automata in automata_type: for closing in closing_strategies: sul = AutomatonSUL(dfa) eq_oracle = RandomWalkEqOracle(alphabet, sul, 1000) learned_dfa = run_Lstar(alphabet, sul, eq_oracle, automaton_type=automata, closing_strategy=closing, cache_and_non_det_check=True, cex_processing='rs', print_level=0) is_eq = self.prove_equivalence(learned_dfa) if not is_eq: assert False assert True def test_suffix_closedness(self): angluin_example = get_Angluin_dfa() alphabet = angluin_example.get_input_alphabet() suffix_closedness = [True, False] automata_type = ['dfa', 'mealy', 'moore'] for automata in automata_type: for s_closed in suffix_closedness: sul = AutomatonSUL(angluin_example) eq_oracle = RandomWalkEqOracle(alphabet, sul, 500) learned_dfa = run_Lstar(alphabet, sul, eq_oracle, automaton_type=automata, all_prefixes_in_obs_table=True, e_set_suffix_closed=s_closed, cache_and_non_det_check=True, cex_processing='rs', print_level=0) is_eq = self.prove_equivalence(learned_dfa) if not is_eq: assert False assert True def test_cex_processing(self): angluin_example = get_Angluin_dfa() alphabet = angluin_example.get_input_alphabet() cex_processing = [None, 'longest_prefix', 'rs'] automata_type = ['dfa', 'mealy', 'moore'] for automata in automata_type: for cex in cex_processing: sul = AutomatonSUL(angluin_example) eq_oracle = RandomWalkEqOracle(alphabet, sul, 500) learned_dfa = run_Lstar(alphabet, sul, eq_oracle, automaton_type=automata, cache_and_non_det_check=True, cex_processing=cex, print_level=0) is_eq = self.prove_equivalence(learned_dfa) if not is_eq: assert False assert True def test_eq_oracles(self): angluin_example = get_Angluin_dfa() alphabet = angluin_example.get_input_alphabet() automata_type = ['dfa', 'mealy', 'moore'] for automata in automata_type: sul = AutomatonSUL(angluin_example) random_walk_eq_oracle = RandomWalkEqOracle(alphabet, sul, 5000, reset_after_cex=True) state_origin_eq_oracle = StatePrefixEqOracle(alphabet, sul, walks_per_state=10, walk_len=50) tran_cov_eq_oracle = TransitionFocusOracle(alphabet, sul, num_random_walks=200, walk_len=30, same_state_prob=0.3) w_method_eq_oracle = WMethodEqOracle(alphabet, sul, max_number_of_states=len(angluin_example.states) + 1) wp_method_eq_oracle = WpMethodEqOracle(alphabet, sul, max_number_of_states=len(angluin_example.states) + 1) rwp_method_eq_oracle = RandomWpMethodEqOracle(alphabet, sul) random_W_method_eq_oracle = RandomWMethodEqOracle(alphabet, sul, walks_per_state=10, walk_len=50) bf_exploration_eq_oracle = BreadthFirstExplorationEqOracle(alphabet, sul, 4) random_word_eq_oracle = RandomWordEqOracle(alphabet, sul) cache_based_eq_oracle = CacheBasedEqOracle(alphabet, sul) kWayStateCoverageEqOracle = KWayStateCoverageEqOracle(alphabet, sul) oracles = [random_walk_eq_oracle, random_word_eq_oracle, random_W_method_eq_oracle, w_method_eq_oracle, wp_method_eq_oracle, rwp_method_eq_oracle, kWayStateCoverageEqOracle, cache_based_eq_oracle, bf_exploration_eq_oracle, tran_cov_eq_oracle, state_origin_eq_oracle] for oracle in oracles: sul = AutomatonSUL(angluin_example) oracle.sul = sul learned_model = run_Lstar(alphabet, sul, oracle, automaton_type=automata, cache_and_non_det_check=True, cex_processing=None, print_level=0) is_eq = self.prove_equivalence(learned_model) if not is_eq: print(learned_model) print(oracle, automata) assert False assert True def test_all_configuration_combinations(self): angluin_example = get_Angluin_dfa() alphabet = angluin_example.get_input_alphabet() automata_type = ['dfa', 'mealy', 'moore'] closing_strategies = ['shortest_first', 'longest_first', 'single'] cex_processing = [None, 'longest_prefix', 'rs'] suffix_closedness = [True, False] caching = [True, False] for automata in automata_type: for closing in closing_strategies: for cex in cex_processing: for suffix in suffix_closedness: for cache in caching: sul = AutomatonSUL(angluin_example) random_walk_eq_oracle = RandomWalkEqOracle(alphabet, sul, 5000, reset_after_cex=True) state_origin_eq_oracle = StatePrefixEqOracle(alphabet, sul, walks_per_state=10, walk_len=50) tran_cov_eq_oracle = TransitionFocusOracle(alphabet, sul, num_random_walks=200, walk_len=30, same_state_prob=0.3) w_method_eq_oracle = WMethodEqOracle(alphabet, sul, max_number_of_states=len(angluin_example.states)) wp_method_eq_oracle = WpMethodEqOracle(alphabet, sul, max_number_of_states=len(angluin_example.states)) rwp_method_eq_oracle = RandomWpMethodEqOracle(alphabet, sul) random_W_method_eq_oracle = RandomWMethodEqOracle(alphabet, sul, walks_per_state=10, walk_len=50) bf_exploration_eq_oracle = BreadthFirstExplorationEqOracle(alphabet, sul, 4) random_word_eq_oracle = RandomWordEqOracle(alphabet, sul) cache_based_eq_oracle = CacheBasedEqOracle(alphabet, sul) oracles = [random_walk_eq_oracle, random_word_eq_oracle, random_W_method_eq_oracle, rwp_method_eq_oracle, cache_based_eq_oracle, bf_exploration_eq_oracle, wp_method_eq_oracle, tran_cov_eq_oracle, w_method_eq_oracle, state_origin_eq_oracle] if not cache: oracles.remove(cache_based_eq_oracle) for oracle in oracles: sul = AutomatonSUL(angluin_example) oracle.sul = sul learned_model = run_Lstar(alphabet, sul, oracle, automaton_type=automata, closing_strategy=closing, cache_and_non_det_check=cache, cex_processing=cex, e_set_suffix_closed=suffix, print_level=0) is_eq = self.prove_equivalence(learned_model) if not is_eq: print(oracle, automata) assert False assert True ================================================ FILE: tests/test_deterministic_passive.py ================================================ import unittest from itertools import product import aalpy from aalpy.automata import Dfa, MooreMachine, MealyMachine from aalpy.learning_algs import run_RPNI from aalpy.utils import load_automaton_from_file # from aalpy.utils.ModelChecking import bisimilar from aalpy.utils.ModelChecking import compare_automata correct_automata = {Dfa: load_automaton_from_file('../DotModels/SimpleABC/simple_abc_dfa.dot', automaton_type='dfa'), MooreMachine: load_automaton_from_file('../DotModels/SimpleABC/simple_abc_moore.dot', automaton_type='moore'), MealyMachine: load_automaton_from_file('../DotModels/SimpleABC/simple_abc_mealy.dot', automaton_type='mealy')} class DeterministicPassiveTest(unittest.TestCase): def prove_equivalence(self, learned_automaton): correct_automaton = correct_automata[learned_automaton.__class__] # only work if correct automaton is already minimal if len(learned_automaton.states) != len(correct_automaton.states): print(len(learned_automaton.states), len(correct_automaton.states)) return False return correct_automaton == learned_automaton # bisimilar def generate_data(self, ground_truth, depth=5, step=1): data = [] if isinstance(ground_truth, aalpy.automata.Dfa) or isinstance(ground_truth, aalpy.automata.MooreMachine): data.append(((), ground_truth.initial_state.output)) alphabet = ground_truth.get_input_alphabet() for level in range(1, depth + 1, step): for seq in product(alphabet, repeat=level): ground_truth.reset_to_initial() outputs = ground_truth.execute_sequence(ground_truth.initial_state, seq) data.append((seq, outputs[-1])) return data def test_all_configuration_combinations(self): automata_type = {Dfa: 'dfa', MooreMachine: 'moore', MealyMachine: 'mealy'} algorithms = ['gsm', 'classic'] for automata in correct_automata: correct_automaton = correct_automata[automata] alphabet = correct_automaton.get_input_alphabet() data = self.generate_data(correct_automaton, depth=3) for algorithm in algorithms: learned_model = run_RPNI(data, automaton_type=automata_type[automata], algorithm=algorithm, print_info=False) is_eq = self.prove_equivalence(learned_model) if not is_eq: print("Learned:") print(learned_model) print(algorithm, automata_type[automata]) cex = compare_automata(learned_model, correct_automaton) print(cex) assert False assert True def test_all_configuration_combinations_input_incomplete_data(self): automata_type = {Dfa: 'dfa', MooreMachine: 'moore', MealyMachine: 'mealy'} algorithms = ['gsm', 'classic'] for automata in correct_automata: correct_automaton = correct_automata[automata] alphabet = correct_automaton.get_input_alphabet() data = self.generate_data(correct_automaton, depth=3, step=2) if automata_type[automata] == 'moore': data += [(('a', 'a', 'a', 'a'), 1), (('b', 'b', 'b', 'b'), 2), (('c', 'c', 'c', 'c'), 3)] for algorithm in algorithms: learned_model = run_RPNI(data, automaton_type=automata_type[automata], algorithm=algorithm, print_info=False) is_eq = self.prove_equivalence(learned_model) if not is_eq: print("Learned:") print(learned_model) print(algorithm, automata_type[automata]) cex = compare_automata(learned_model, correct_automaton) print(cex) assert False assert True ================================================ FILE: tests/test_file_operations.py ================================================ import unittest from aalpy.utils import generate_random_markov_chain, load_automaton_from_file from aalpy.utils.BenchmarkSULs import * class TestFileHandler(unittest.TestCase): def test_saving_loading(self): try: type_model_pairs = [ ("dfa", get_Angluin_dfa()), ("mealy", load_automaton_from_file('../DotModels/Angluin_Mealy.dot', automaton_type='mealy')), ("moore", load_automaton_from_file('../DotModels/Angluin_Moore.dot', automaton_type='moore')), ("onfsm", get_benchmark_ONFSM()), ("mdp", get_small_pomdp()), ("mdp", load_automaton_from_file('../DotModels/MDPs/first_grid.dot', automaton_type='mdp')), ("smm", get_faulty_coffee_machine_SMM()), ("mc", generate_random_markov_chain(num_states=10)), ] for type, model in type_model_pairs: model.save() print(model) loaded_model = load_automaton_from_file('LearnedModel.dot', type) loaded_model.save() loaded_model2 = load_automaton_from_file('LearnedModel.dot', type) if type != 'mc': assert set(model.get_input_alphabet()) == set(loaded_model.get_input_alphabet()) assert set(model.get_input_alphabet()) == set(loaded_model2.get_input_alphabet()) if type in {'dfa', 'moore', 'mealy'}: assert model.compute_characterization_set() == loaded_model2.compute_characterization_set() assert True except: assert False ================================================ FILE: tests/test_non_deterministic.py ================================================ import unittest class NonDeterministicTest(unittest.TestCase): def test_non_det(self): from aalpy.SULs import AutomatonSUL from aalpy.oracles import RandomWordEqOracle, RandomWalkEqOracle from aalpy.learning_algs import run_non_det_Lstar from aalpy.utils import get_benchmark_ONFSM onfsm = get_benchmark_ONFSM() alphabet = onfsm.get_input_alphabet() for _ in range(100): sul = AutomatonSUL(onfsm) oracle = RandomWordEqOracle(alphabet, sul, num_walks=500, min_walk_len=2, max_walk_len=5) learned_onfsm = run_non_det_Lstar(alphabet, sul, oracle, n_sampling=50, print_level=0) eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=10000, reset_prob=0.09, reset_after_cex=True) cex = eq_oracle.find_cex(learned_onfsm) if cex or len(learned_onfsm.states) != len(onfsm.states): assert False assert True ================================================ FILE: tests/test_rwpmethod_oracle.py ================================================ import unittest try: from aalpy.automata import MooreMachine, MooreState from aalpy.learning_algs import run_Lstar from aalpy.oracles.WpMethodEqOracle import RandomWpMethodEqOracle from aalpy.SULs import AutomatonSUL from aalpy.utils import visualize_automaton except ImportError: import sys from pathlib import Path # if you want to run the test directly from CLI # either from root or from tests folder p = Path(__file__).parent.resolve() sys.path.append(str(p)) sys.path.append(str(p.parent)) from aalpy.automata import MooreMachine, MooreState from aalpy.learning_algs import run_Lstar from aalpy.oracles.WpMethodEqOracle import RandomWpMethodEqOracle from aalpy.SULs import AutomatonSUL from aalpy.utils import visualize_automaton class TestRandomWpMethodOracle(unittest.TestCase): @staticmethod def gen_moore_from_state_setup(state_setup) -> MooreMachine: # state_setup shoud map from state_id to tuple(output and transitions_dict) # build states with state_id and output states = {key: MooreState(key, val[0]) for key, val in state_setup.items()} # add transitions to states for state_id, state in states.items(): for _input, target_state_id in state_setup[state_id][1].items(): state.transitions[_input] = states[target_state_id] # states to list states = [state for state in states.values()] # build moore machine with first state as starting state mm = MooreMachine(states[0], states) for state in states: state.prefix = mm.get_shortest_path(mm.initial_state, state) return mm def generate_real_automata(self) -> MooreMachine: state_setup = { "a": ("a", {"x": "b1", "y": "a"}), "b1": ("b", {"x": "b2", "y": "a"}), "b2": ("b", {"x": "b3", "y": "a"}), "b3": ("b", {"x": "b4", "y": "a"}), "b4": ("b", {"x": "c", "y": "a"}), "c": ("c", {"x": "a", "y": "a"}), } mm = self.gen_moore_from_state_setup(state_setup) mm.characterization_set = mm.compute_characterization_set() + [tuple()] return mm def generate_hypothesis(self) -> MooreMachine: state_setup = { "a": ("a", {"x": "b", "y": "a"}), "b": ("b", {"x": "b", "y": "a"}), } mm = self.gen_moore_from_state_setup(state_setup) # ! computer_characterization_set does not work for Moore machines in general! # mm.characterization_set = mm.compute_characterization_set() + [tuple()] mm.characterization_set = [tuple(), ("x",), ("y",)] return mm def test_rwpmethod_oracle(self): real = self.generate_real_automata() hyp = self.generate_hypothesis() # visualize_automaton(real) # visualize_automaton(hyp) assert set(real.get_input_alphabet()) == {"x", "y"} assert set(hyp.get_input_alphabet()) == {"x", "y"} assert len(real.states) == 6 assert len(hyp.states) == 2 alphabet = real.get_input_alphabet() oracle = RandomWpMethodEqOracle( alphabet, AutomatonSUL(real) ) cex = oracle.find_cex(hyp) assert cex is not None, "Expected a counterexample, but got None" def test_rwpmethod_oracle_with_lstar(self): real = self.generate_real_automata() hyp = self.generate_hypothesis() # visualize_automaton(real) # visualize_automaton(hyp) assert real.get_input_alphabet() == ["x", "y"] assert hyp.get_input_alphabet() == ["x", "y"] assert len(real.states) == 6 assert len(hyp.states) == 2 alphabet = real.get_input_alphabet() oracle = RandomWpMethodEqOracle( alphabet, AutomatonSUL(real) ) lstar_hyp = run_Lstar(alphabet, AutomatonSUL(real), oracle, "moore") # print(lstar_hyp) # visualize_automaton(lstar_hyp) assert ( len(lstar_hyp.states) == 6 ), f"Expected {6} states got {len(lstar_hyp.states)} in lstar hypothesis" if __name__ == "__main__": unittest.main() ================================================ FILE: tests/test_stochastic.py ================================================ import unittest import aalpy.paths from aalpy.SULs import AutomatonSUL from aalpy.learning_algs import run_stochastic_Lstar from aalpy.oracles import RandomWalkEqOracle from aalpy.utils import load_automaton_from_file class StochasticTest(unittest.TestCase): def test_learning_based_on_accuracy_based_stopping(self): example = 'first_grid' mdp = load_automaton_from_file(f'../DotModels/MDPs/{example}.dot', automaton_type='mdp') min_rounds = 10 max_rounds = 500 from aalpy.automata import StochasticMealyMachine from aalpy.utils import model_check_experiment, get_properties_file, \ get_correct_prop_values from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat" aalpy.paths.path_to_properties = "../Benchmarking/prism_eval_props/" stopping_based_on_prop = (get_properties_file(example), get_correct_prop_values(example), 0.02) input_alphabet = mdp.get_input_alphabet() automaton_type = ['mdp', 'smm'] similarity_strategy = ['classic', 'normal', 'chi2'] cex_processing = [None, 'longest_prefix'] samples_cex_strategy = [None, 'bfs', 'random:200:0.3'] for aut_type in automaton_type: for strategy in similarity_strategy: for cex in cex_processing: for sample_cex in samples_cex_strategy: sul = AutomatonSUL(mdp) eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=200, reset_prob=0.25, reset_after_cex=True) learned_model = run_stochastic_Lstar(input_alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul, n_c=20, n_resample=1000, min_rounds=min_rounds, max_rounds=max_rounds, automaton_type=aut_type, strategy=strategy, cex_processing=cex, samples_cex_strategy=sample_cex, target_unambiguity=0.99, property_based_stopping=stopping_based_on_prop, print_level=0) if isinstance(learned_model, StochasticMealyMachine): mdp = smm_to_mdp_conversion(learned_model) else: mdp = learned_model results, diff = model_check_experiment(get_properties_file(example), get_correct_prop_values(example), mdp) for d in diff.values(): if d > stopping_based_on_prop[2]: assert False assert True ================================================ FILE: tests/test_wmethod_oracle.py ================================================ import unittest try: from aalpy.automata import MooreMachine, MooreState from aalpy.learning_algs import run_Lstar from aalpy.oracles.WMethodEqOracle import WMethodEqOracle from aalpy.SULs import AutomatonSUL from aalpy.utils import visualize_automaton except ImportError: import sys from pathlib import Path # if you want to run the test directly from CLI # either from root or from tests folder p = Path(__file__).parent.resolve() sys.path.append(str(p)) sys.path.append(str(p.parent)) from aalpy.automata import MooreMachine, MooreState from aalpy.learning_algs import run_Lstar from aalpy.oracles.WMethodEqOracle import WMethodEqOracle from aalpy.SULs import AutomatonSUL from aalpy.utils import visualize_automaton class TestWMethodOracle(unittest.TestCase): @staticmethod def gen_moore_from_state_setup(state_setup) -> MooreMachine: # state_setup shoud map from state_id to tuple(output and transitions_dict) # build states with state_id and output states = {key: MooreState(key, val[0]) for key, val in state_setup.items()} # add transitions to states for state_id, state in states.items(): for _input, target_state_id in state_setup[state_id][1].items(): state.transitions[_input] = states[target_state_id] # states to list states = [state for state in states.values()] # build moore machine with first state as starting state mm = MooreMachine(states[0], states) for state in states: state.prefix = mm.get_shortest_path(mm.initial_state, state) return mm def generate_real_automata(self) -> MooreMachine: state_setup = { "a": ("a", {"x": "b1", "y": "a"}), "b1": ("b", {"x": "b2", "y": "a"}), "b2": ("b", {"x": "b3", "y": "a"}), "b3": ("b", {"x": "b4", "y": "a"}), "b4": ("b", {"x": "c", "y": "a"}), "c": ("c", {"x": "a", "y": "a"}), } mm = self.gen_moore_from_state_setup(state_setup) mm.characterization_set = mm.compute_characterization_set() + [tuple()] return mm def generate_hypothesis(self) -> MooreMachine: state_setup = { "a": ("a", {"x": "b", "y": "a"}), "b": ("b", {"x": "b", "y": "a"}), } mm = self.gen_moore_from_state_setup(state_setup) # ! computer_characterization_set does not work for Moore machines in general! # mm.characterization_set = mm.compute_characterization_set() + [tuple()] mm.characterization_set = [tuple(), ("x",), ("y",)] return mm def test_wmethod_oracle(self): real = self.generate_real_automata() hyp = self.generate_hypothesis() # visualize_automaton(real) # visualize_automaton(hyp) assert set(real.get_input_alphabet()) == {"x", "y"} assert set(hyp.get_input_alphabet()) == {"x", "y"} assert len(real.states) == 6 assert len(hyp.states) == 2 alphabet = real.get_input_alphabet() oracle = WMethodEqOracle(alphabet, AutomatonSUL(real), len(real.states) + 1) cex = oracle.find_cex(hyp) assert cex is not None, "Expected a counterexample, but got None" def test_wmethod_oracle_with_lstar(self): real = self.generate_real_automata() hyp = self.generate_hypothesis() # visualize_automaton(real) # visualize_automaton(hyp) assert real.get_input_alphabet() == ["x", "y"] assert hyp.get_input_alphabet() == ["x", "y"] assert len(real.states) == 6 assert len(hyp.states) == 2 alphabet = real.get_input_alphabet() oracle = WMethodEqOracle(alphabet, AutomatonSUL(real), len(real.states) + 1) lstar_hyp = run_Lstar(alphabet, AutomatonSUL(real), oracle, "moore") # print(lstar_hyp) # visualize_automaton(lstar_hyp) assert ( len(lstar_hyp.states) == 6 ), f"Expected {6} states got {len(lstar_hyp.states)} in lstar hypothesis" if __name__ == "__main__": unittest.main() ================================================ FILE: tests/test_wpmethod_oracle.py ================================================ import unittest try: from aalpy.automata import MooreMachine, MooreState from aalpy.learning_algs import run_Lstar from aalpy.oracles.WpMethodEqOracle import WpMethodEqOracle from aalpy.SULs import AutomatonSUL from aalpy.utils import visualize_automaton except ImportError: import sys from pathlib import Path # if you want to run the test directly from CLI # either from root or from tests folder p = Path(__file__).parent.resolve() sys.path.append(str(p)) sys.path.append(str(p.parent)) from aalpy.automata import MooreMachine, MooreState from aalpy.learning_algs import run_Lstar from aalpy.oracles.WpMethodEqOracle import WpMethodEqOracle from aalpy.SULs import AutomatonSUL from aalpy.utils import visualize_automaton class TestWpMethodOracle(unittest.TestCase): @staticmethod def gen_moore_from_state_setup(state_setup) -> MooreMachine: # state_setup shoud map from state_id to tuple(output and transitions_dict) # build states with state_id and output states = {key: MooreState(key, val[0]) for key, val in state_setup.items()} # add transitions to states for state_id, state in states.items(): for _input, target_state_id in state_setup[state_id][1].items(): state.transitions[_input] = states[target_state_id] # states to list states = [state for state in states.values()] # build moore machine with first state as starting state mm = MooreMachine(states[0], states) for state in states: state.prefix = mm.get_shortest_path(mm.initial_state, state) return mm def generate_real_automata(self) -> MooreMachine: state_setup = { "a": ("a", {"x": "b1", "y": "a"}), "b1": ("b", {"x": "b2", "y": "a"}), "b2": ("b", {"x": "b3", "y": "a"}), "b3": ("b", {"x": "b4", "y": "a"}), "b4": ("b", {"x": "c", "y": "a"}), "c": ("c", {"x": "a", "y": "a"}), } mm = self.gen_moore_from_state_setup(state_setup) mm.characterization_set = mm.compute_characterization_set() + [tuple()] return mm def generate_hypothesis(self) -> MooreMachine: state_setup = { "a": ("a", {"x": "b", "y": "a"}), "b": ("b", {"x": "b", "y": "a"}), } mm = self.gen_moore_from_state_setup(state_setup) # ! computer_characterization_set does not work for Moore machines in general! # mm.characterization_set = mm.compute_characterization_set() + [tuple()] mm.characterization_set = [tuple(), ("x",), ("y",)] return mm def test_wpmethod_oracle(self): real = self.generate_real_automata() hyp = self.generate_hypothesis() # visualize_automaton(real) # visualize_automaton(hyp) assert set(real.get_input_alphabet()) == {"x", "y"} assert set(hyp.get_input_alphabet()) == {"x", "y"} assert len(real.states) == 6 assert len(hyp.states) == 2 alphabet = real.get_input_alphabet() oracle = WpMethodEqOracle( alphabet, AutomatonSUL(real), len(real.states) + 1 ) cex = oracle.find_cex(hyp) assert cex is not None, "Expected a counterexample, but got None" def test_wpmethod_oracle_with_lstar(self): real = self.generate_real_automata() hyp = self.generate_hypothesis() # visualize_automaton(real) # visualize_automaton(hyp) assert real.get_input_alphabet() == ["x", "y"] assert hyp.get_input_alphabet() == ["x", "y"] assert len(real.states) == 6 assert len(hyp.states) == 2 alphabet = real.get_input_alphabet() oracle = WpMethodEqOracle( alphabet, AutomatonSUL(real), len(real.states) + 1 ) lstar_hyp = run_Lstar(alphabet, AutomatonSUL(real), oracle, "moore") # print(lstar_hyp) # visualize_automaton(lstar_hyp) assert ( len(lstar_hyp.states) == 6 ), f"Expected {6} states got {len(lstar_hyp.states)} in lstar hypothesis" if __name__ == "__main__": unittest.main() ================================================ FILE: tests/tests_imports.py ================================================ import unittest class ImportTest(unittest.TestCase): def test_imports(self): try: import aalpy.utils import aalpy.oracles import aalpy.utils import aalpy.SULs import aalpy.learning_algs import aalpy.base import aalpy.base.Automaton import aalpy.utils.HelperFunctions import aalpy.utils.DataHandler import aalpy.utils.AutomatonGenerators import aalpy.utils.ModelChecking import aalpy.utils.FileHandler except: assert False assert True