def __init__(self, sim_param=SimParam(), no_seed=False):
"""
Initialize the Simulation object.
:param sim_param: is an optional SimParam object for parameter pre-configuration
:param no_seed: is an optional parameter. If it is set to True, the RNG should be initialized without a
a specific seed.
"""
self.sim_param = sim_param
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
# TODO Task 2.4.3: Uncomment the line below
self.counter_collection = CounterCollection(self)
# TODO Task 3.1.2: Uncomment the line below and replace the "None"
if no_seed:
#if the mean = 1.0, then 1/lambda_ = 1.0 -> lambda_ = 1
self.rng = RNG(ExponentialRNS(1.0),
ExponentialRNS(1. / float(self.sim_param.RHO)))
else:
self.rng = RNG(
ExponentialRNS(1.0, self.sim_param.SEED_IAT),
ExponentialRNS(1. / float(self.sim_param.RHO),
self.sim_param.SEED_ST))
def task_3_2_1():
"""
This function plots two histograms for verification of the random distributions.
One histogram is plotted for a uniform distribution, the other one for an exponential distribution.
"""
rns_exp = ExponentialRNS(1, the_seed=0)
rns_uni = UniformRNS(1, 100, the_seed=0)
n = 10000
exp_distr = []
uni_distr = []
weights = []
for _ in range(n):
exp_distr.append(rns_exp.next())
uni_distr.append(rns_uni.next())
weights.append(1. / float(n))
pyplot.subplot(121)
pyplot.hist(exp_distr, bins=30, weights=weights, edgecolor='black')
pyplot.xlabel("x")
pyplot.ylabel("distribution over n")
pyplot.title("Exponential distribution")
pyplot.subplot(122)
pyplot.hist(uni_distr, bins=30, weights=weights, edgecolor='black')
pyplot.xlabel("x")
pyplot.ylabel("distribution over n")
pyplot.title("Uniform distribution")
pyplot.show()
def task_3_2_1():
"""
This function plots two histograms for verification of the random distributions.
One histogram is plotted for a uniform distribution, the other one for an exponential distribution.
"""
# TODO Task 3.2.1: Your code goes here
no_of_runs = 1000
rns_exp = ExponentialRNS(5.0)
rns_uni = UniformRNS(1, 300)
exponential_values = []
uniform_values = []
weight = numpy.full(no_of_runs, 1.0 / float(no_of_runs))
while no_of_runs != 0:
exponential_values.append(rns_exp.next())
uniform_values.append(rns_uni.next())
no_of_runs -= 1
pyplot.subplot(121)
pyplot.title("Uniform Distribution")
pyplot.xlabel("x")
pyplot.ylabel("Distribution")
pyplot.hist(uniform_values, bins=25, weights=weight)
pyplot.subplot(122)
pyplot.title("Exponential distribution for Lambda = 5")
pyplot.xlabel("x")
pyplot.ylabel("Distribution")
pyplot.hist(exponential_values, bins=25, weights=weight)
pyplot.show()
def task_3_2_1():
"""
This function plots two histograms for verification of the random distributions.
One histogram is plotted for a uniform distribution, the other one for an exponential distribution.
"""
# TODO Task 3.2.1: Your code goes here
exp_rns = ExponentialRNS(mean=1, the_seed=0)
uni_rns = UniformRNS(low=2, high=3, the_seed=0)
exp_list = []
uni_list = []
for i in range(100000):
x = exp_rns.next()
y = uni_rns.next()
exp_list.append(x)
uni_list.append(y)
weights1 = numpy.full(len(exp_list), 1.0 / float(len(exp_list)))
weights2 = numpy.full(len(uni_list), 1.0 / float(len(uni_list)))
pyplot.hist(exp_list, bins=10, weights=weights1, histtype='bar')
pyplot.xlabel("Exponential")
pyplot.show()
pyplot.hist(uni_list, bins=10, weights=weights2, histtype='bar')
pyplot.xlabel("Uniform")
pyplot.show()
pass
def task_3_2_1():
"""
This function plots two histograms for verification of the random distributions.
One histogram is plotted for a uniform distribution, the other one for an exponential distribution.
"""
# TODO Task 3.2.1: Your code goes here
# For exponential dist. & uniform dist.
samples = 10000
exp = ExponentialRNS(1.)
uni = UniformRNS(0.,10.)
exp_dist = []
uni_dist = []
for k in range(samples):
exp_dist.append(exp.next())
uni_dist.append(uni.next())
#weights_ = numpy.full(len(uni_dist), 1.0 / float(len(uni_dist)))
ax0=plt.subplot(121)
plt.xlabel("x")
plt.ylabel("Number of instances")
ax0.set_title("Exponential Distribution")
plt.hist(exp_dist, density=True, bins=int(samples/100))
ax1=plt.subplot(122)
plt.xlabel("x")
plt.ylabel("Probability density function of bin")
ax1.set_title("Uniform Distribution")
plt.hist(uni_dist,density=True,bins=int(samples/100))
plt.show()
def reset(self, no_seed=False):
"""
Reset the Simulation object.
:param no_seed: is an optional parameter. If it is set to True, the RNG should be reset without a
a specific seed.
"""
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
# TODO Task 2.4.3: Uncomment the line below
self.counter_collection = CounterCollection(self)
# TODO Task 3.1.2: Uncomment the line below and replace the "None"
if no_seed:
self.rng = RNG(ExponentialRNS(1.0), ExponentialRNS(1./float(self.sim_param.RHO)))
else:
self.rng = RNG(ExponentialRNS(1.0, self.sim_param.SEED_IAT), ExponentialRNS(1./float(self.sim_param.RHO),self.sim_param.SEED_ST))
def reset(self, no_seed=False):
"""
Reset the Simulation object.
:param no_seed: is an optional parameter. If it is set to True, the RNG should be reset without a
a specific seed.
"""
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
self.counter_collection = CounterCollection(self)
if no_seed:
self.rng = RNG(ExponentialRNS(1),
ExponentialRNS(1. / float(self.sim_param.RHO)))
else:
self.rng = RNG(
ExponentialRNS(1, self.sim_param.SEED_IAT),
ExponentialRNS(1. / float(self.sim_param.RHO),
self.sim_param.SEED_ST))
def __init__(self, sim_param=SimParam(), no_seed=False):
"""
Initialize the Simulation object.
:param sim_param: is an optional SimParam object for parameter pre-configuration
:param no_seed: is an optional parameter. If it is set to True, the RNG should be initialized without a
a specific seed.
"""
self.sim_param = sim_param
self.sim_state = SimState()
self.system_state = SystemState(self)
self.event_chain = EventChain()
self.sim_result = SimResult(self)
self.counter_collection = CounterCollection(self)
if no_seed:
self.rng = RNG(ExponentialRNS(1),
ExponentialRNS(1. / float(self.sim_param.RHO)))
else:
self.rng = RNG(
ExponentialRNS(1, self.sim_param.SEED_IAT),
ExponentialRNS(1. / float(self.sim_param.RHO),
self.sim_param.SEED_ST))
def poisson_arrivals(self, slicesim):
"""
"""
iat = slicesim.slice_param.MEAN_IAT
self.rng = RNG(ExponentialRNS(1. / float(iat), self.seed_iat),
s_type='iat')
#iat = self.sim_param.MEAN_IAT
t_start = slicesim.sim_state.now
t_end = self.sim_param.T_FINAL
# Poisson
t_arr = []
iat_arr = []
t_temp = t_start + self.rng.get_iat()
while t_temp < t_end:
t_arr.append(t_temp)
t_temp += self.rng.get_iat()
if len(t_arr) > 2:
iat_arr.append(t_arr[-1] - t_arr[-2])
# # Fixed IAT
# t_arr = np.arange(t_start, t_end, iat)
# # Plot histogram of interarrivals for poisson dist
# count, bins, ignored = plt.hist(iat_arr, 30, normed=True)
# lmda = 1/iat
# plt.plot(bins, lmda * np.exp(- lmda*bins), linewidth=2, color='r')
# plt.show()
arrivals = []
for i in t_arr:
arrivals.append(PacketArrival(slicesim, i))
return arrivals
def task_3_2_1():
"""
This function plots two histograms for verification of the random distributions.
One histogram is plotted for a uniform distribution, the other one for an exponential distribution.
"""
# TODO Task 3.2.1: Your code goes here
sim_param = SimParam()
random.seed(sim_param.SEED)
sim_param.RHO = 0.01
sim = Simulation(sim_param)
rns_iat = ExponentialRNS(1.0)
rns_st = ExponentialRNS(1.0/sim.sim_param.RHO)
rns_uniform = UniformRNS((2,4))
hist1 = TimeIndependentHistogram(sim, "Line")
hist2 = TimeIndependentHistogram(sim, "Line")
hist3 = TimeIndependentHistogram(sim, "bp")
for i in range(1000000):
hist1.count(rns_iat.next())
hist2.count(rns_st.next())
hist3.count(rns_uniform.next())
hist1.report()
hist2.report()
hist3.report()