Python Simulator.run方法代码示例

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
def simulationLoop(server, capacity, period, scaled, ex_time, int_time):
        name = server + ".csv"
        s = Simulator(stats = name)

        # Set the server
        if server == 'polling':
            s.server = PollingServer(capacity, period)
        elif server == 'deferrable':
            s.server = DeferrableServer(capacity, period)
        else:
            s.server = BackgroundServer()


        # Load the taskset
        for t in scaled:
            s.tasks.append(t)

        # Create the aperiodic task
        ap = AperiodicTask("Soft", ex_time, int_time)
        s.tasks.append(ap)


        # RUUUUUUUUN !!!
        s.init(until)
        s.run()


        return computeAverage(name)

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
def oneRoundDelay(step = 10000):
    injectRate = 0.9
    factory = GridNetworkFactory(makeSimpleNode(),Queues)
    factory.constructNetwork(6,6)\
        .setFlow(Flow((0,0),(0,5),injectRate))\
        .setFlow(Flow((5,0),(5,5),injectRate))\
        .setFlow(Flow((2,0),(3,5),injectRate))

    network = factory.getNetwork()

    packetFactory = PacketFactory()
    simulator = \
        Simulator(network,step,ConstLinkRateGenerator(1),packetFactory)
    simulator.run()
    #simulator.printNetwork()
    stat = simulator.getStaticsInfo()
    print stat['aveDelay']
    packetPool = sorted(stat['packetPool'],key=lambda p: p.getID)


    
    py.subplot(211)
    py.vlines([p.getCreateTime() for p in packetPool],[1],[p.getDelay() for p in packetPool],'r')
    py.xlabel('Packet create time(bp with $\lambda$ = 0.9)')
    py.ylabel('delay')
    py.grid(True)




    injectRate = 0.9
    factory = GridNetworkFactory(makeMNode(2),Queues)
    factory.constructNetwork(6,6)\
        .setFlow(Flow((0,0),(0,5),injectRate))\
        .setFlow(Flow((5,0),(5,5),injectRate))\
        .setFlow(Flow((2,0),(3,5),injectRate))

    network = factory.getNetwork()

    packetFactory = PacketFactory()
    simulator = \
        Simulator(network,step,ConstLinkRateGenerator(1),packetFactory)
    simulator.run()
    #simulator.printNetwork()

    stat = simulator.getStaticsInfo()
    print stat['aveDelay']
    packetPool = sorted(stat['packetPool'],key=lambda p: p.getID)

    py.subplot(212)
    py.vlines([p.getCreateTime() for p in packetPool],[1],[p.getDelay() for p in packetPool],'b')
    py.xlabel('Packet create time (m=2 with $\lambda$ = 0.9)')
    py.ylabel('delay')
    py.grid(True)
    py.savefig('packetDelayInOneRound_09')
    py.show()

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
def main():

	NUM_INITIAL_MOBILES = 150
	SIMULATION_ITERATIONS = 3600

	print("callAttempts, blkSig, blkCap, dropSig, HOfail, HO, complSuc, talkTime, GOS, HOFR, intensity")

	for iteration in range(10):
		simulator = Simulator(NUM_INITIAL_MOBILES, 1.0/3600)
		simulator.run(SIMULATION_ITERATIONS, iteration)

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
def main():

	NUM_INITIAL_MOBILES = 150
	SIMULATION_ITERATIONS = 3600

	print("callAttempts, blkSig, blkCap, dropSig, HOfail, HO, complSuc, talkTime, GOS, HOFR, intensity")

	for iteration in range(10):
		simulator = Simulator(NUM_INITIAL_MOBILES, 1.0/3600)
#		simulator.basestations[0].ANTENNA_GAIN = 10
#		simulator.basestations[1].ANTENNA_GAIN = 10
		simulator.basestations[0].NUM_CHANNELS = 20
		simulator.basestations[1].NUM_CHANNELS = 20
		simulator.run(SIMULATION_ITERATIONS, iteration)

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
def CounterTest(step = 10000):
    injectRate = 1
    factory = GridNetworkFactory(makeCNode(0),Queues)
    factory.constructNetwork(8,8)\
        .setFlow(Flow((0,0),(0,7),injectRate))\
        .setFlow(Flow((7,0),(7,7),injectRate))
                                
    network = factory.getNetwork()

    packetFactory = PacketFactory()
    simulator = \
    Simulator(network,step,ConstLinkRateGenerator(1),packetFactory)
    simulator.run()
    #simulator.printNetwork()
    stat = simulator.getStaticsInfo()
    print stat

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
def OrdTest():
    injectRate = 0.5
    factory = GridNetworkFactory(makeMNode(1),ShadowQueues)
    factory.constructNetwork(8,8)\
        .setFlow(Flow((2,0),(2,7),injectRate),Flow((4,0),(4,7),injectRate),\
                     Flow((0,2),(7,2),injectRate),Flow((0,4),(7,4),injectRate),\
                     Flow((1,1),(5,1),injectRate),Flow((6,1),(6,6),injectRate),\
                     Flow((5,6),(1,6),injectRate),Flow((1,5),(1,2),injectRate))
    network = factory.getNetwork()

    packetFactory = PacketFactory()
    simulator = \
    Simulator(network,gobalMaxStep,ConstLinkRateGenerator(1),packetFactory)
    simulator.run()
    stat = simulator.getStaticsInfo()
    print stat

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
	def _run_simulation(self, num_investments):
		'''Run a single simulation with a given number of investments

		For each position, the number of investments is the number of rows.
		For each investment, we run num_trial trials, where each trial is
		a column in that row.

		We multiply all the trials for each investment by the value of that
		investment, which is simply the intial budget divided by the number
		of investments.

		For each trial (column), we sum up the returns from all the investments
		(rows), producing a 1xnum_trials array with the cumulative returns from 
		each day.'''

		position_value = self.initial_budget / num_investments

		# Create the simulator object
		simulator = Simulator({(0, .51): 1.0, (.51, 1): -1.0}, 
								nrows=num_investments, 
								ncols=self.num_trials) 

		# Run the simulator, and multiply each of the results by the value
		# of a share for this position
		trial_returns = simulator.run() * position_value

		# Collapse all investments on each day into cumulative return
		cumu_ret = self.initial_budget + trial_returns.sum(axis=0)

		# The daily rate of return, which is the final result
		daily_ret = (cumu_ret / float(self.initial_budget)) - 1.

		return daily_ret

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
class DifferentInjectRateTest:
    def __init__(self,linkRate=1,maxStep=gobalMaxStep,injectRate=0.5,\
                     factory=gobalGridNetworkFactory):
        factory.constructNetwork(8,8)\
            .setFlow(Flow((2,0),(2,7),injectRate),Flow((4,0),(4,7),injectRate),
                     Flow((0,2),(7,2),injectRate),Flow((0,4),(7,4),injectRate),
                     Flow((1,1),(5,1),injectRate),Flow((6,1),(6,6),injectRate)
                     ,Flow((5,6),(1,6),injectRate),Flow((1,5),(1,2),injectRate))
        network = factory.getNetwork()

        self.packetFactory = PacketFactory()
        self.simulator = \
            Simulator(network,maxStep,ConstLinkRateGenerator(linkRate),self.packetFactory)

    def run(self):
        self.simulator.run(flag = False)

    def getStaticsInfo(self):
        return self.simulator.getStaticsInfo()

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
MCISoS = SoS(CSs, MCIMap)

MCIEvents = []
numOfPatients = 20
for i in range(numOfPatients):
    MCIEvents.append(Event(PatientOccurrence('patient +1', MCIMap), ConstantTimeBound(0)))
MCIScenario = Scenario(MCIEvents)

'''Simulation'''
simulationTime = 15
MCISim = Simulator(simulationTime, MCISoS, MCIScenario)

repeatSim = 2000
MCILogs = []
for i in range(repeatSim):
    MCILog = MCISim.run()
    MCILogs.append(MCILog)

'''
print('log print (only last log)')
for log in MCILogs:
    print(log[-1], sum(log[-1][0]), sum(log[-1][1]))
'''
'''
print()
for CS in CSs:
    print(CS.name, 'rescued:', CS.rescued)
print('final map:', MCIMap)
print()
'''

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
from  Simulator import Simulator
from States import S_Start, S_DeadCancer, S_DeadAge, S_DeadChemo
from numpy import average
import ConundrumModel as Model

s= Simulator(iters=10000, age=60, ca125=True, model=Model)
s.run()
print average([len(p.states) for p in s.patients])


s.clear_survival_curves()

s.add_survival_curve(start_states=[S_Start], 
                        end_states=[S_DeadCancer])

s.add_survival_curve(start_states=[S_Start], 
                        end_states=[S_DeadChemo])

s.add_survival_curve(start_states=[S_Start], 
                        end_states=[S_DeadAge])

s.draw_survival_curves(name="conundrum.png")

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]

	lb = Simulator(initial_slots=64, simulations=2000, min_tags=100, max_tags=1000,
							step=100, estimator=lower_bound)

	lee = Simulator(initial_slots=64, simulations=2000, min_tags=100, max_tags=1000,
							step=100, estimator=eom_lee)

	chen = Simulator(initial_slots=64, simulations=2000, min_tags=100, max_tags=1000,
							step=100, estimator=chen)


	# Use this to generate graphics
	start_time = time.time()

	lb_result = lb.run()
	print("Tempo para execucao do lower bound: " + str(time.time() - start_time))

	start_time = time.time()

	lee_result = lee.run()
	print("Tempo de execucao do eom lee: " + str(time.time() - start_time))

	start_time = time.time()

	chen_result = chen.run()
	print("Tempo de execucao do chen: " + str(time.time() - start_time))

	print(lb_result)
	print(lee_result)
	print(chen_result)

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
age = 60

smonths_with = {}
smonths_without = {}
icer = []

CostEvaluator.bProbabilistic = True

iters = 100
avg = []
for k in range(100):
    CostEvaluator.cache = {}
    for i in range(1):
        s2 = Simulator(iters=iters, age=age, ca125=False, model=Model)
        s2.run()
    
        s1 = Simulator(iters=iters, age=age, ca125=True, model=Model)
        s1.run()
        c1 = sum([CostEvaluator.EvaluatePatient(p)['cost'] for p in s1.patients]) * 1./iters
        q1 = sum([CostEvaluator.EvaluatePatient(p)['quality'] for p in s1.patients]) * 1./iters
        c2 = sum([CostEvaluator.EvaluatePatient(p)['cost'] for p in s2.patients]) * 1./iters
        q2 = sum([CostEvaluator.EvaluatePatient(p)['quality'] for p in s2.patients]) * 1./iters
        
        print np.average([len(x.states) for x in s1.patients])
        print np.average([len(x.states) for x in s2.patients])
        
    avg.append((q1-q2, c1-c2))
    print avg[-1]

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
from Simulator import Simulator
from Estimators import *
import matplotlib.pyplot as plt

if __name__ == '__main__':

	simulator = Simulator(initial_slots=64, simulations=1, min_tags=100, max_tags=1000,
							step=100, estimator=eom_lee)


	# Use this to generate graphics
	result = simulator.run()

	print(result)
	
	# Generating Collisions vs Tags
	plt.figure(1)
	plt.title('Collisions vs Tags')
	plt.plot([x['tags'] for x in result], [x['collisions'] for x in result], 'r--')
	plt.ylabel('Collisions')
	plt.xlabel('Tags')
	
	# Generating Empty vs Tags
	plt.figure(2)
	plt.title('Empty vs Tags')
	plt.plot([x['tags'] for x in result], [x['empty'] for x in result], 'r--')
	plt.ylabel('Empty')
	plt.xlabel('Tags')

	# Generating Slots vs Tags
	plt.figure(3)

# 需要导入模块: from Simulator import Simulator [as 别名]
# 或者: from Simulator.Simulator import run [as 别名]
from States import *
import MultiExponentialModel as Model

Model.bProbabilistic = False
age = 60
cost_bins = np.arange(0,50000,1000)
quality_bins = np.arange(0,30,1./12)


for x in np.arange(.001, 1, .01):
    Model.vProbabilistic[Model.pFirstRecurrence][2] = x
    idstr = ".pFirstRecurrence.%04f"%x
#    print idstr
    
    s1 = Simulator(iters=5000, age=age, ca125=True, model=Model)
    s1.run()
    
    c = np.array([CostEvaluator.EvaluatePatient(p)['cost'] for p in s1.patients])
    print x, "\t",np.average(c)

    


print "Quality: ", np.average([x[0] for x in icer])
print "Cost: ", np.average([x[1] for x in icer])

f = py.figure(0)
f.clear()
ax = f.add_subplot(2,1,1)
n, bins, patches = ax.hist([x[0] for x in icer], normed=1, alpha=.75, bins=75)
ax.set_xlabel("$\Delta$Quality")