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)
