Python Statistics.Statistics類代碼示例

 def getVehicleTypeAmount(self):
     from Statistics import Statistics
     player = BigWorld.player()
     vehicles = player.arena.vehicles
     if player.playerVehicleID not in vehicles:
         return
     curVeh = vehicles[player.playerVehicleID]
     Statistics.getInfos(curVeh['accountDBID'])
     vehicles[player.playerVehicleID]['team']
     amounts = {VEHICLE_CLASS_NAME.HEAVY_TANK:{'ally':0,'enemy':0},
             VEHICLE_CLASS_NAME.MEDIUM_TANK:{'ally':0,'enemy':0},
             VEHICLE_CLASS_NAME.LIGHT_TANK:{'ally':0,'enemy':0},
             VEHICLE_CLASS_NAME.AT_SPG:{'ally':0,'enemy':0},
             VEHICLE_CLASS_NAME.SPG:{'ally':0,'enemy':0}}
     tiers = []
     for accountDBID,entityObj in Statistics.getEmo().getAll().iteritems():
         vID = g_sessionProvider.getCtx().getVehIDByAccDBID(accountDBID)
         if vID in vehicles:
             v_info = vehicles[vID]
             tiers.append(v_info['vehicleType'].level)
             if not BattleUtils.isMyTeam(v_info['team']):
                 tag = 'enemy'
             else:
                 tag = 'ally'
             for vehicleType in VEHICLE_TYPES_ORDER:
                 if vehicleType in v_info['vehicleType'].type.tags:
                     amounts[vehicleType][tag] += 1 
     currentTier = curVeh['vehicleType'].level       
     return (amounts,tiers,currentTier)

 def LoadDataForVisualisation(self):
     print 'Loading data for visualisation...'
     Reader.read(self.filePath2)
     self.data = Reader.load
     self.dataStr = Reader.load2 #data i czas
     self.data = Operations.CalculateToMetrics(self.data)
     stat = Statistics(self.data,self.dataStr)
     stat.makeStats()
     self.UpdateStatsGUI(stat)
     print 'All done.'

   def __init__(self, genome):
      """ The GPopulation Class creator """

      if isinstance(genome, GPopulation):
         self.oneSelfGenome  = genome.oneSelfGenome
         self.internalPop    = []
         self.internalPopRaw = []
         self.popSize       = genome.popSize
         self.sortType      = genome.sortType
         self.sorted        = False
         self.minimax       = genome.minimax
         self.scaleMethod   = genome.scaleMethod
         self.allSlots      = [self.scaleMethod]

         self.internalParams = genome.internalParams
         self.multiProcessing = genome.multiProcessing
         try:
            logging.debug("do I have a comm?")
            self.mpi_comm = genome.mpi_comm
            self.mpi_myeval = genome.mpi_myeval
            self.mpi_full_copy = genome.mpi_full_copy
            logging.debug("I do")
         except:
            logging.debug("I do not")
            pass

         self.statted = False
         self.stats   = Statistics()
         return

      logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
      self.oneSelfGenome  = genome
      self.internalPop    = []
      self.internalPopRaw = []
      self.popSize       = 0
      self.sortType      = Consts.CDefPopSortType
      self.sorted        = False
      self.minimax       = Consts.CDefPopMinimax
      self.scaleMethod   = FunctionSlot("Scale Method")
      self.scaleMethod.set(Consts.CDefPopScale)
      self.allSlots      = [self.scaleMethod]

      self.internalParams = {}
      self.multiProcessing = (False, False)

      # Statistics
      self.statted = False
      self.stats   = Statistics()

   def __init__(self, genome):
      """ The GPopulation Class creator """

      if isinstance(genome, GPopulation):
          #Cloning a population?
         self.oneSelfGenome  = genome.oneSelfGenome
         self.internalPop    = []
         self.internalPopRaw = []
         self.popSize       = genome.popSize
         self.sortType      = genome.sortType
         self.sorted        = False
         self.minimax       = genome.minimax
         self.scaleMethod   = genome.scaleMethod
         self.allSlots      = [self.scaleMethod]

         self.internalParams = genome.internalParams
         self.multiProcessing = genome.multiProcessing

         self.statted = False
         self.stats   = Statistics()
         self.proc_pool = genome.proc_pool
         return

      logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
      self.oneSelfGenome  = genome
      self.internalPop    = []
      self.internalPopRaw = []
      self.popSize       = 0
      self.proc_pool = None
      self.sortType      = Consts.CDefPopSortType
      self.sorted        = False
      self.minimax       = Consts.CDefPopMinimax
      self.scaleMethod   = FunctionSlot("Scale Method")
      self.scaleMethod.set(Consts.CDefPopScale)
      self.allSlots      = [self.scaleMethod]

      self.internalParams = {}
      self.multiProcessing = (False, False)

      # Statistics
      self.statted = False
      self.stats   = Statistics()

    def generateChart(self):
        self.graphView.setScrollView(self.scrollView)

        stat = Statistics(self.startDate, self.endDate)
        
        if self.reportType == "tasks":
            self.graphView.setData(stat.countTasks(), self.reportType) 
                
        elif self.reportType == "projects":
            self.graphView.setData(stat.countProjects(), self.reportType) 
                
        elif self.reportType == "slacking":
            self.graphView.setData(stat.countSlacking(), self.reportType) 
    
        self.graphView.setScale(stat.maxValue)
        self.lblWorkTotal.setStringValue_(secToTimeStr(stat.totalWork))
        self.lblAvgWork.setStringValue_(secToTimeStr(stat.avgWork))
        self.lblSlackTotal.setStringValue_(secToTimeStr(stat.totalSlacking))
        self.lblAvgSlack.setStringValue_(secToTimeStr(stat.avgSlacking))
        self.graphView.setNeedsDisplay_(True)

   def __init__(self, genome):
      """ The GPopulation Class creator """

      logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
      self.oneSelfGenome = genome
      self.internalPop   = []
      self.popSize       = 0
      self.sortType      = Consts.CDefPopSortType
      self.sorted        = False
      self.minimax       = Consts.CDefPopMinimax
      self.scaleMethod   = FunctionSlot("Scale Method")
      self.scaleMethod.set(Consts.CDefPopScale)
      self.allSlots      = [self.scaleMethod]

      # Statistics
      self.statted = False
      self.stats   = Statistics()

 def __init__(self):
     self.dataset = None
     self.Graph = nx.DiGraph()
     self.K =2
     self.items = dict()
     self.stats = Statistics()

class GraphDrawer():

    def __init__(self):
        self.dataset = None
        self.Graph = nx.DiGraph()
        self.K =2
        self.items = dict()
        self.stats = Statistics()


    def build_graph(self, dataset, keys):
        self.dataset = dataset
        for sessionID in keys:
            try:
                session_purchases = self.dataset[sessionID]
            except:
                print sessionID
            session_states = self.extract_states(session_purchases)
            self.insert_states(session_states)
        # self.print_edges_data()
        self.mark_popular_item()
        self.stats.num_of_edges = self.Graph.number_of_edges()
        self.stats.num_of_nodes = self.Graph.number_of_nodes()

    def mark_popular_item(self):
        max = 0
        for item in self.items.keys():
            if self.items[item] > max:
                max = self.items[item]
                self.most_popular_item = item

    def print_all_nodes(self):
        for n in self.Graph.nodes(): print str(n)

    def print_all_edges(self):
        for e in self.Graph.edges(): print e

    def print_successors(self,source_state):
        succ_list = self.Graph.successors(source_state)
        for succ in succ_list: print succ

    def print_edges_data(self):
        for edge in self.Graph.edges():
            print '{0} -->  {1}    {2}'.format(edge[0], edge[1],self.Graph.get_edge_data(edge[0], edge[1], None))

    def draw(self):
        # nx.draw(self.Graph)
        # nx.draw_networkx(self.Graph)
        pos = nx.shell_layout(self.Graph)
        nx.draw(self.Graph, pos)

        # show graph
        plt.show()
        raw_input('press any key to continue')

    def fit(self):
        """
        this method goes over all nodes and counts the number of out edges,
        then, it normalize the weight of each edge according to:
            for each node t in successor(s) do:
                sum += weight(s,t)
            for each node t in successors(s) do:
                normalized_weight(s,t)  =   weight(s,t)/sum
        :return: void
        """
        nodes = self.Graph.nodes()
        print "---- number of nodes in the graph = {0}".format(len(nodes))
        for curr_node in nodes:
            node_successors = self.Graph.successors(curr_node)
            sum = 0
            for s in node_successors:
                edge_data =self.Graph.get_edge_data(curr_node, s, None)
                # print edge_data
                count = edge_data[Config.COUNT]
                sum += count

            # print sum
            # print 'curr:  ' + str(curr_node)
            for s in node_successors:
                count = self.Graph[curr_node][s][Config.COUNT]
                self.Graph[curr_node][s][Config.WEIGHT] = float(count) / float(sum)
                # print 'succ:   ' + str(s) + ' ' + str(self.Graph[curr_node][s])

    def predict(self,testset):
        """
        :param testset: a map - 'itemIP' --> list<Purchase>

        the main idea is:
            1. for each session
                1.1 hide the last item purchased
                1.2 predict the last item using the model
                1.3 save the
        """
        y_true = []
        y_pred = []
        y_score = []

        for key in testset.keys():
            sequence = testset[key]
            seq_length = len(sequence)
#.........這裏部分代碼省略.........

CONFIG = Configuration()


#Load Hashcodes from File
DATABASE = set()
try:
    DATABASE_FILE = open(CONFIG.DATABASE_FILE_PATH, 'r')
except:
    DATABASE_FILE = open(CONFIG.DATABASE_FILE_PATH, 'w+')
    logging.info("Created New Database %s", CONFIG.DATABASE_FILE_PATH)

print "Loading Database...."
for line in DATABASE_FILE:
    DATABASE.add(line.replace('\n', ''))
logging.info("Loaded %i mail hash values from database", len(DATABASE))
STATS = Statistics(len(DATABASE))
DATABASE_FILE.close()
#Open Database-File for appending new HashCodes
DATABASE_FILE = open(CONFIG.DATABASE_FILE_PATH, 'a')

print "Connecting to Server..."
#Init Mail Connection
MAIL_CONNECTION = imaplib.IMAP4_SSL(CONFIG.MAIL_SERVER, CONFIG.MAIL_PORT) if CONFIG.MAIL_PORT else imaplib.IMAP4_SSL(CONFIG.MAIL_SERVER)
try:
    MAIL_CONNECTION.login(CONFIG.MAIL_USER, CONFIG.MAIL_PASSWORD)
    logging.info("Successfully connected to %[email protected]%s", CONFIG.MAIL_USER, CONFIG.MAIL_SERVER)
except imaplib.IMAP4.error as e:
    print "Failed to connect"
    logging.error("Could not connect to %[email protected]%s", CONFIG.MAIL_USER, CONFIG.MAIL_SERVER)
    logging.error("Reason: %s", e)
    exit(1)

        myShaper = Shaper({
                'capacity': capacity,        
                'full_watermark': full_watermark,
                'hot_watermark': hot_watermark,
                'write_seq_threshold': write_seq_threshold
            })
        myShaper.run('./output/gen.csv','./output/optimize.csv')
        myShaper.print_io_count()

        ##############################################################
        #400GB SSD with optimizer
        myStat = Statistics({
            'file'          : './output/optimize.csv',
            'ssd_size'      : 200*1024*1024,        
            'ssd_seq_write' : 400*1024*1024,
            'ssd_seq_read'  : 500*1024*1024,
            'ssd_ran_write' : 80*1000,
            'ssd_ran_read'  : 100*1000,
            'ssd_read_hit'   : 0.91,                
            'hdd_seq_write' : 400*1024*1024,
            'hdd_seq_read'  : 500*1024*1024,
            'hdd_ran_write' : 1400,
            'hdd_ran_read'  : 1400
        })
        myStat.run()
        text_file.write("%d,%d,%f\n" % (genRange,bufSize,myStat.total_time))

text_file.close()
exit(1)

def test_statistics(clean=False, language='en'):
    lib = Library(cleaning=clean)
    lib.load_library(language=language)
    st = Statistics(books=lib.get_books(),authors=lib.get_authors())
    st.collect_statistics()

class GPopulation:
   """ GPopulation Class - The container for the population

   **Examples**
      Get the population from the :class:`GSimpleGA.GSimpleGA` (GA Engine) instance
         >>> pop = ga_engine.getPopulation()

      Get the best fitness individual
         >>> bestIndividual = pop.bestFitness()

      Get the best raw individual
         >>> bestIndividual = pop.bestRaw()

      Get the statistics from the :class:`Statistics.Statistics` instance
         >>> stats = pop.getStatistics()
         >>> print stats["rawMax"]
         10.4

      Iterate, get/set individuals
         >>> for ind in pop:
         >>>   print ind
         (...)
         
         >>> for i in xrange(len(pop)):
         >>>    print pop[i]
         (...)

         >>> pop[10] = newGenome
         >>> pop[10].fitness
         12.5

   :param genome: the :term:`Sample genome`

   """

   def __init__(self, genome):
      """ The GPopulation Class creator """

      logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
      self.oneSelfGenome = genome
      self.internalPop   = []
      self.popSize       = 0
      self.sortType      = Consts.CDefPopSortType
      self.sorted        = False
      self.minimax       = Consts.CDefPopMinimax
      self.scaleMethod   = FunctionSlot("Scale Method")
      self.scaleMethod.set(Consts.CDefPopScale)
      self.allSlots      = [self.scaleMethod]

      # Statistics
      self.statted = False
      self.stats   = Statistics()

   def setMinimax(minimax):
      """ Sets the population minimax

      Example:
         >>> pop.setMinimax(Consts.minimaxType["maximize"])
   
      :param minimax: the minimax type

      """
      self.minimax = minimax

   def __repr__(self):
      """ Returns the string representation of the population """
      ret =  "- GPopulation\n"
      ret += "\tPopulation Size:\t %d\n" % (self.popSize,)
      ret += "\tSort Type:\t\t %s\n" % (Consts.sortType.keys()[Consts.sortType.values().index(self.sortType)].capitalize(),)
      ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)].capitalize(),)
      for slot in self.allSlots:
         ret+= "\t" + slot.__repr__()
      ret+="\n"
      ret+= self.stats.__repr__()
      return ret

   def __len__(self):
      """ Return the length of population """
      return len(self.internalPop)
      
   def __getitem__(self, key):
      """ Returns the specified individual from population """
      return self.internalPop[key]

   def __iter__(self):
      """ Returns the iterator of the population """
      return iter(self.internalPop)

   def __setitem__(self, key, value):
      """ Set an individual of population """
      self.internalPop[key] = value
      self.__clear_flags()

   def __clear_flags(self):
      self.sorted = False
      self.statted = False

   def getStatistics(self):
      """ Return a Statistics class for statistics

#.........這裏部分代碼省略.........

    def __init__(self):

        # all variables here
        self.TOTAL_TIME = 15000
        self.NUM_OF_CARS = 200000
        self.DELTA_TIME = 0.05
        self.PERCENT_OF_CARS = [0.3,0.7]

        # Units: m/delta_t
        self.AVG_SPEED_OF_CARS = {'s': 30, 'm': 25, 'b': 20}
        self.STD_DEV_OF_CARS = {'s': 10, 'm':10, 'b': 10}
        
        # Units: m
        self.LENGTHS_OF_CARS = {'s': 5, 'm': 7, 'b': 10}
        self.VISIBLE_DISTANCE_OF_CARS = {'s': 70, 'm': 80, 'b': 90}

        # Units: m/delta_t
        self.MIN_SPEED = 15
        self.MAX_SPEED = 60

        # Units: m
        self.ROAD_LENGTH = 5000
        self.SAFE_DISTANCE_BETWEEN_CARS = 20
        ''' this affects accident rate a lot'''
        self.INITIAL_DISTANCE_BETWEEN_CARS = 80

        self._recycledIndexes = set()
        
        self.all_cars = []

        self.current_num_of_cars = 0

        self.stat = Statistics()
        
        self.generateCar(self.generateIndex())
        for time in range(self.TOTAL_TIME):
            if self.needToGenerateCars():
                self.generateCar(self.generateIndex())
##                print("New Car generated")
##                print(self._recycledIndexes)
##                print()
                
            num_of_cars_on_r = 0
            num_of_cars_on_l = 0
            total_speed_on_r = 0
            total_speed_on_l = 0
            for i in range(len(self.all_cars)):
                each = self.all_cars[i]
                if each == None:
                    self._recycledIndexes.add(i)
                    continue
                if each.position > self.ROAD_LENGTH:
                    self.all_cars[i] = None
                    self._recycledIndexes.add(i)
##                    print(each.index, "th car reaches the end of the road!")
##                    print(self._recycledIndexes)
                    continue
##                print(each.index, "th car: ", each.position, " lane: ", each.lane)
##                print()
                each.move(self.all_cars)

###################################################Statistics################################
                if each.lane == 'r':
                    num_of_cars_on_r += 1
                    total_speed_on_r += each.Vcurrent
                elif each.lane == 'l':
                    num_of_cars_on_l += 1
                    total_speed_on_l += each.Vcurrent

            self.stat.listIncrease("density_of_r", time, num_of_cars_on_r / self.ROAD_LENGTH)
            self.stat.listIncrease("density_of_l", time, num_of_cars_on_l / self.ROAD_LENGTH)

            if num_of_cars_on_r == 0:
                    self.stat.listIncrease("avg_speed_on_r", time, 0)
            else:
                self.stat.listIncrease("avg_speed_on_r", time, total_speed_on_r / num_of_cars_on_r)
                    
            if num_of_cars_on_l == 0:
                    self.stat.listIncrease("avg_speed_on_l", time, 0)
            else:
                self.stat.listIncrease("avg_speed_on_l", time, total_speed_on_l / num_of_cars_on_l)
            
            time += self.DELTA_TIME


        print("accident rate: ", self.stat.getRate("num_of_accidents", "num_of_cars_generated"))
        print("succussful passing rate: ", self.stat.getRate("successful_passings", "passing_intents"))

        writeToFile(self.stat.getList("density_of_r"), "density_of_r.txt")
        writeToFile(self.stat.getList("density_of_l"), "density_of_l.txt")
        writeToFile(self.stat.getList("avg_speed_on_r"), "avg_speed_on_r.txt")
        writeToFile(self.stat.getList("avg_speed_on_l"), "avg_speed_on_l.txt")

class GPopulation:
   """ GPopulation Class - The container for the population

   **Examples**
      Get the population from the :class:`GSimpleGA.GSimpleGA` (GA Engine) instance
         >>> pop = ga_engine.getPopulation()

      Get the best fitness individual
         >>> bestIndividual = pop.bestFitness()

      Get the best raw individual
         >>> bestIndividual = pop.bestRaw()

      Get the statistics from the :class:`Statistics.Statistics` instance
         >>> stats = pop.getStatistics()
         >>> print stats["rawMax"]
         10.4

      Iterate, get/set individuals
         >>> for ind in pop:
         >>>   print ind
         (...)
         
         >>> for i in xrange(len(pop)):
         >>>    print pop[i]
         (...)

         >>> pop[10] = newGenome
         >>> pop[10].fitness
         12.5

   :param genome: the :term:`Sample genome`, or a GPopulation object, when cloning.

   """

   def __init__(self, genome):
      """ The GPopulation Class creator """

      if isinstance(genome, GPopulation):
          #Cloning a population?
         self.oneSelfGenome  = genome.oneSelfGenome
         self.internalPop    = []
         self.internalPopRaw = []
         self.popSize       = genome.popSize
         self.sortType      = genome.sortType
         self.sorted        = False
         self.minimax       = genome.minimax
         self.scaleMethod   = genome.scaleMethod
         self.allSlots      = [self.scaleMethod]

         self.internalParams = genome.internalParams
         self.multiProcessing = genome.multiProcessing

         self.statted = False
         self.stats   = Statistics()
         self.proc_pool = genome.proc_pool
         return

      logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
      self.oneSelfGenome  = genome
      self.internalPop    = []
      self.internalPopRaw = []
      self.popSize       = 0
      self.proc_pool = None
      self.sortType      = Consts.CDefPopSortType
      self.sorted        = False
      self.minimax       = Consts.CDefPopMinimax
      self.scaleMethod   = FunctionSlot("Scale Method")
      self.scaleMethod.set(Consts.CDefPopScale)
      self.allSlots      = [self.scaleMethod]

      self.internalParams = {}
      self.multiProcessing = (False, False)

      # Statistics
      self.statted = False
      self.stats   = Statistics()

   #---------------------------------------------------------------------------------
   def setMultiProcessing(self, flag=True, full_copy=False, number_of_processes=None):
        """ Sets the flag to enable/disable the use of python multiprocessing module.
        Use this option when you have more than one core on your CPU and when your
        evaluation function is very slow.
        The parameter "full_copy" defines where the individual data should be copied back
        after the evaluation or not. This parameter is useful when you change the
        individual in the evaluation function.

        :param flag: True (default) or False
        :param full_copy: True or False (default)
        :param number_of_processes: None = use the default, or specify the number

        .. warning:: Use this option only when your evaluation function is slow, se you
                   will get a good tradeoff between the process communication speed and the
                   parallel evaluation.
        """
        #Save the parameters
        old_settings = self.multiProcessing
        self.multiProcessing = (flag, full_copy, number_of_processes)
        #Re-initialize if anything changed.
        if (old_settings != self.multiProcessing):
#.........這裏部分代碼省略.........

def setup(env, num_gates, wait_time, t_inter):
    airport = Airport(env, num_gates, wait_time)
    for i in range(1):
        env.process(plane(env, 'Plane %d' % i, airport))
        stats.addArrivals()

    usage_res = env.now
    while True:
        yield env.timeout(random.randint(t_inter-2, t_inter+2))
        i += 1
        env.process(plane(env, 'Plane %d' % i, airport))
        stats.addArrivals()

        #print(airport.num_gates.count)
        if not airport.open:
            stats.addBusyTime(env.now - usage_res)
            usage_res = env.now

stats = Statistics(SIM_TIME)

stats.setNumRecursos(NUM_GATES)

random.seed(RANDOM_SEED)

env = simpy.Environment()
env.process(setup(env, NUM_GATES, WAIT_TIME, T_INTER))

env.run(until=SIM_TIME)

stats.printStats("Airport")