def validate_select_model_with_ER(N):
p_set = np.linspace(0.0001, 0.05, 10)
dat = dict()
dat['p_set'] = p_set
dat['ret'] = []
for p in p_set:
normal_sigs = gen_sigs('ER', 100, N, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_ER-N-%s.pk' % (N))
def validate_select_model_with_ER(N):
p_set = np.linspace(0.0001, 0.05, 10)
dat = dict()
dat['p_set'] = p_set
dat['ret'] = []
for p in p_set:
normal_sigs = gen_sigs('ER', 100, N, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs,
50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_ER-N-%s.pk' % (N))
def validate_select_model_with_BA(m):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('BA', 100, N, m)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_BA-m-%i.pk' % (m))
def validate_select_model_with_BA(m):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('BA', 100, N, m)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs,
50, 50, True)
dat['ret'].append(debug_ret)
dump(dat, 'validate_select_model_with_BA-m-%i.pk' % (m))
def validate_select_model_with_power_law(p):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('powerlaw_cluster_graph', 100, N, 2, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs, 50, 50, True)
dat['ret'].append(debug_ret)
# import ipdb;ipdb.set_trace()
dump(dat, 'validate_select_model_with_power_law-p-%s.pk' % (p))
def validate_select_model_with_power_law(p):
N_set = [50, 150, 250, 350]
dat = dict()
dat['N'] = N_set
dat['ret'] = []
for N in N_set:
normal_sigs = gen_sigs('powerlaw_cluster_graph', 100, N, 2, p)
normal_nodes = range(N)
model, para, debug_ret = select_model(len(normal_nodes), normal_sigs,
50, 50, True)
dat['ret'].append(debug_ret)
# import ipdb;ipdb.set_trace()
dump(dat, 'validate_select_model_with_power_law-p-%s.pk' % (p))
