def task_1_7_3(queue_size):
"""
Execute bonus task 1.7.3.
"""
# TODO Bonus Task 1.7.3: Your code goes here (if necessary)
sim_param = SimParam()
random.seed(sim_param.SEED)
sim = Simulation(sim_param)
sim.sim_param.S=queue_size
result_set=[]
for i in range(sim.sim_param.NO_OF_RUNS):
sim.reset()
sim_result=sim.do_simulation()
result_set.append(sim_result.blocking_probability)
result_length=len(result_set)
n, bins, patches = pylab.hist(result_set)
nc=np.cumsum(n/result_length)
cdf=[0.0]
for i in nc:
cdf.append(i)
sim_param = SimParam()
random.seed(sim_param.SEED)
sim_param.SIM_TIME = 1000000
sim_param.MAX_DROPPED = 100
sim_param.NO_OF_RUNS = 100
sim = Simulation(sim_param)
sim.sim_param.S=queue_size
result_set=[]
for i in range(sim.sim_param.NO_OF_RUNS):
sim.reset()
sim_result=sim.do_simulation()
result_set.append(sim_result.blocking_probability)
result_length=len(result_set)
n1, bins1, patches1 = pylab.hist(result_set)
nc1=np.cumsum(n/result_length)
cdf1=[0.0]
for i in nc:
cdf1.append(i)
pylab.figure(1)
pylab.xlabel('blocking_probability')
pylab.ylabel('density')
pylab.title('Histogram of the probability density function')
pylab.hist(n, bins)
pylab.hist(n1, bins1)
pylab.figure(2)
pylab.xlim(0.0,1.0)
pylab.ylim(0.0,1.0)
pylab.xlabel('blocking_probability')
pylab.ylabel('CDF')
pylab.title('CDF function')
line1, = pylab.plot(bins,cdf, marker='o', label='100000 ms, 10 MAX_DROPPED, 1000 runs')
line2, = pylab.plot(bins1,cdf1, marker='o', label='1000000 ms, 100 MAX_DROPPED, 100 runs')
pylab.legend(handler_map={line1: HandlerLine2D(numpoints=4)})
pylab.show()
def task_1_7_2():
"""
Execute task 1.7.2 and perform a simulation study according to the task assignment.
:return: Minimum number of buffer spaces to meet requirements.
"""
sim_param = SimParam()
random.seed(sim_param.SEED)
sim_param.SIM_TIME = 1000000
sim_param.MAX_DROPPED = 100
sim_param.NO_OF_RUNS = 100
sim = Simulation(sim_param)
return do_simulation_study(sim)
