def __init__(self): GuidanceBase.__init__(self) self.setParameter('lookahead',15) self.setParameter('randomseed',time.time()) self.setParameter('rerouteafter',1) self._lastroute=[] self._steps_to_reroute=0
def __init__(self): GuidanceBase.__init__(self) self._NUM_TABU_ACTIONS = None self._NUM_TABU_STATES = None self._NUM_TABU_TRANSITIONS = None self._tabulist_action = None self._tabulist_state = None self._tabulist_transition = None
def __init__(self): GuidanceBase.__init__(self) self._NUM_TABU_ACTIONS = None self._NUM_TABU_STATES = None self._NUM_TABU_TRANSITIONS = None self._tabulist_action = None self._tabulist_state = None self._tabulist_transition = None
def __init__(self): GuidanceBase.__init__(self) self._guidances = [] self._guidanceOpts = [] self._covs = [] self._currIndex = -1 self._model = None self._filename = None
def __init__(self): GuidanceBase.__init__(self) self._guidances = [] self._guidanceOpts = [] self._covs = [] self._currIndex = -1 self._model = None self._filename = None
def __init__(self): GuidanceBase.__init__(self) # default parameters: self.setParameter("transitionweight", 0) self.setParameter("searchdepth", 10) self.setParameter("searchorder", "shortestfirst") self.setParameter("maxtransitions", 10000) self.setParameter("greedy", 0) self.setParameter("searchconstraint", "noloops")
def __init__(self): GuidanceBase.__init__(self) # default parameters: self.setParameter("transitionweight", 0) self.setParameter("searchdepth", 10) self.setParameter("searchorder", "shortestfirst") self.setParameter("maxtransitions", 10000) self.setParameter("greedy", 0) self.setParameter("searchconstraint", "noloops")
def __init__(self): GuidanceBase.__init__(self) self.setParameter('maxdepth',100) self.setParameter('mindepth',1) self.setParameter('randomseed',time.time()) self._lastroute=[] self._steps_to_reroute=0 # search front is a list of triplets: (score, path, coverage) # where score is a pair: (coverage_percentage, steps since # last change coverage_percentage). Thus, the bigger the # number of steps, the faster the final coverage_percentage is # achieved. self.search_front=[] self._thread_id=None self._front_shortened=0 # msg from markExecuted to thread
def __init__(self): GuidanceBase.__init__(self) self.setParameter('maxdepth', 100) self.setParameter('mindepth', 1) self.setParameter('randomseed', time.time()) self._lastroute = [] self._steps_to_reroute = 0 # search front is a list of triplets: (score, path, coverage) # where score is a pair: (coverage_percentage, steps since # last change coverage_percentage). Thus, the bigger the # number of steps, the faster the final coverage_percentage is # achieved. self.search_front = [] self._thread_id = None self._front_shortened = 0 # msg from markExecuted to thread
def __init__(self): GuidanceBase.__init__(self) self.began = 0 # initializes the random number generator random.seed() # a list of suggestions # seach method appends a suggestion every time it makes a decision # about next chosen transition, and suggestAction pops suggestions # from the beginning of it. self.suggestions = [] # tells how many keywords there are in the suggestions list self.keywordsInSuggestions = 0 # the lock is acquired before accessing the variables shared by # suggestAction and searcher threads self.lock = threading.Lock() # the suggestion event signals for a new suggestion added to the suggestions list self.suggestionEvent = threading.Event() # the state event signals for a new root state for search thread to start searching # next suggestions (search waits for this after a verification action) self.stateEvent = threading.Event() # is suggestAction waiting for suggestion? self.waitingForSuggestion = 0 # is searcher thread waiting for verification? self.waitingForVerification = 0 # The estimated delay after a keyword execution, is updated by suggestAction. self.keywordDelay = 0.1
def __init__(self): GuidanceBase.__init__(self) self.began = 0 # initializes the random number generator random.seed() # a list of suggestions # seach method appends a suggestion every time it makes a decision # about next chosen transition, and suggestAction pops suggestions # from the beginning of it. self.suggestions = [] # tells how many keywords there are in the suggestions list self.keywordsInSuggestions = 0 # the lock is acquired before accessing the variables shared by # suggestAction and searcher threads self.lock = threading.Lock() # the suggestion event signals for a new suggestion added to the suggestions list self.suggestionEvent = threading.Event() # the state event signals for a new root state for search thread to start searching # next suggestions (search waits for this after a verification action) self.stateEvent = threading.Event() # is suggestAction waiting for suggestion? self.waitingForSuggestion = 0 # is searcher thread waiting for verification? self.waitingForVerification = 0 # The estimated delay after a keyword execution, is updated by suggestAction. self.keywordDelay = 0.1
def __init__(self): GuidanceBase.__init__(self) self._port = None self._manager = None self._iAmTheManagerStarter = False self._sgParams = []
def __init__(self): GuidanceBase.__init__(self) self._port = None self._manager = None self._iAmTheManagerStarter = False self._sgParams = []
def __init__(self): GuidanceBase.__init__(self)
def __init__(self): GuidanceBase.__init__(self) self.setParameter('randomseed',time.time())
def __init__(self): GuidanceBase.__init__(self) self._stored_path = [] self._random_select = random.Random(time.time()).choice self._sleep_ts_re = re.compile(r"SLEEPts.*")
def __init__(self): GuidanceBase.__init__(self) self.setParameter('randomseed', time.time())
def __init__(self): GuidanceBase.__init__(self) self._stored_path=[] self._random_select=random.Random(time.time()).choice self._sleep_ts_re = re.compile(r"SLEEPts.*")
def __init__(self): GuidanceBase.__init__(self)