def select_caida_backbone():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 4.33
dur_set = np.linspace(0.1, T*0.9, 20)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT + 'passive-2013-sigs-%f/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=200, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-caida-backbone.pk')
def select_caida_backbone():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 4.33
dur_set = np.linspace(0.1, T * 0.9, 20)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT + 'passive-2013-sigs-%f/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=200, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-caida-backbone.pk')
def select_simple_pkt():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 66095.977196
# msv = []
dur_set = np.linspace(10, T*0.9, 50)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT+'sigs1/loc6-%i/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=400, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-simple-pkt.pk')
def select_simple_pkt():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 66095.977196
# msv = []
dur_set = np.linspace(10, T * 0.9, 50)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT + 'sigs1/loc6-%i/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=400, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-simple-pkt.pk')
def plot_diff_sample_graph(sigs):
beta_hat_v = []
lk_er_v = []
n_set = xrange(10, 500, 20)
for n in n_set:
s_v = mg_sample(n=n, k=100, **sigs)
beta_hat, lk_er = mle(s_v, 'ER')
beta_hat_v.append(beta_hat)
lk_er_v.append(lk_er)
plt.subplot(211)
plt.plot(n_set, beta_hat_v)
plt.subplot(212)
plt.plot(n_set, lk_er_v)
plt.show()
res = {
'beta_hat_v': beta_hat_v,
'lk_er_v': lk_er_v,
'n_set': n_set,
}
dump(res, './ER_model_selection_with_num_sampled_graph.pk')
def plot_diff_sample_graph(sigs):
beta_hat_v = []
lk_er_v = []
n_set = xrange(10, 500, 20)
for n in n_set:
s_v = mg_sample(n=n, k=100, **sigs)
beta_hat, lk_er = mle(s_v, 'ER')
beta_hat_v.append(beta_hat)
lk_er_v.append(lk_er)
plt.subplot(211)
plt.plot(n_set, beta_hat_v)
plt.subplot(212)
plt.plot(n_set, lk_er_v)
plt.show()
res = {
'beta_hat_v': beta_hat_v,
'lk_er_v': lk_er_v,
'n_set': n_set,
}
dump(res, './ER_model_selection_with_num_sampled_graph.pk')
def plot_diff_one_graph(sigs):
beta_hat_v = []
lk_er_v = []
k_set = xrange(10, 500, 20)
for k in k_set:
s_v = mg_sample(n=100, k=k, **sigs)
beta_hat, lk_er = mle(s_v, "ER")
beta_hat_v.append(beta_hat)
lk_er_v.append(lk_er)
res = {
'beta_hat_v': beta_hat_v,
'lk_er_v': lk_er_v,
'k_set': k_set,
}
dump(res, './ER_model_selection_with_num_sampled_points_2.pk')
plt.subplot(211)
plt.plot(k_set, beta_hat_v)
plt.subplot(212)
plt.plot(k_set, lk_er_v)
plt.show()
def plot_diff_one_graph(sigs):
beta_hat_v = []
lk_er_v = []
k_set = xrange(10, 500, 20)
for k in k_set:
s_v = mg_sample(n=100, k=k, **sigs)
beta_hat, lk_er = mle(s_v, "ER")
beta_hat_v.append(beta_hat)
lk_er_v.append(lk_er)
res = {
'beta_hat_v': beta_hat_v,
'lk_er_v': lk_er_v,
'k_set': k_set,
}
dump(res, './ER_model_selection_with_num_sampled_points_2.pk')
plt.subplot(211)
plt.plot(k_set, beta_hat_v)
plt.subplot(212)
plt.plot(k_set, lk_er_v)
plt.show()
