learn.py 4.16 KB
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# Copyright (c) 2015 Michele Volpato
#
# 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.

import random

random.seed(100)

import os, inspect, sys
# Include project dir in path
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append(currentdir)

import socket
import logging

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import itertools

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from tictacteacher import TicTacToeTeacher
from tictacoracle import TicTacToeOracle
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from tictacpurpose import TicTacToeInputPurpose, TicTacToeOutputPurpose
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from learning.learning import LearningAlgorithm
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from testing.randomtesting import RandomTester
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from completetesting import CompleteTicTacToeTester
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from systems.implementations import SuspensionAutomaton

import helpers.bisimulation as bi

import csv
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)

HOST = 'localhost'
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PORT = 29000 # Arbitrary non-privileged port
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outputExpert = TicTacToeOutputPurpose()
inputExpert = TicTacToeInputPurpose()

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inputs = set(['0','1','2','3','4','5','6','7','8'])
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# Use a placeholder for outputs
outputs = outputExpert.allOutputs()
#outputs = set(itertools.product('XO_', repeat=9))
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quiescence = 'delta'
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T1 = TicTacToeTeacher(HOST, PORT)
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O1 = TicTacToeOracle(inputs, quiescence)
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#tester = RandomTester(T1, 50000, 100)
tester = CompleteTicTacToeTester(T1)
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currentdir = os.path.dirname(os.path.abspath(
                inspect.getfile(inspect.currentframe())))

path = os.path.join(currentdir, "dotFiles")

print("Starting learning...")

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#print(T1.oneOutput(('1')))

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L = LearningAlgorithm(T1, O1, printPath=path, maxLoops=4,
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    tablePreciseness=100000, logger=logger, tester=tester, outputPurpose=outputExpert,
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    inputPurpose=inputExpert)
minus, plus = L.run()

print("Models learned.")
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print("Number of inputs sent to the SUL: " + str(T1.getInputCounter()))
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T1.close()

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#######################################################################
# If there is a model learned by LearnLib, load it and run a bisimulation
# check with minus
# The file must be converted with learnlib_dot2jtorx_aut.py
with open("/home/mic/repo/learnLTS/examples/tictactoe/learnLib/TicTacToe.aut", 'r') as csvfile:
    first = True
    reader = csv.reader(csvfile, delimiter=';',
                            quoting=csv.QUOTE_MINIMAL)

    for row in reader:
        if first:
            first = False
            tup = row[0][3:] # remove 'des' from the first line
            tuple_row = eval(tup)
            learnLibmodel = SuspensionAutomaton(tuple_row[2],
                                        inputs.copy(),
                                        outputs.copy(),
                                        quiescence,
                                        False)
        else:
            tuple_row = eval(row[0])
            #try:
            #    label = eval(tuple_row[1])
            #except NameError:
            label = tuple_row[1]
            learnLibmodel.addTransition(tuple_row[0], label,
                              tuple_row[2])

print("Models learned. Check language equivalence...")

print("minus bisimilar to LearnLib model: " + str(bi.bisimilar(learnLibmodel,minus,startState1=0, startState2=0, noDelta=True)))

########################################################################