conf['seed'] = seed
conf['t'] = t
modes = [
'ind', 'iden', 'uni', 'fdiff', 'fmax', 'fsum', 'buc_eq', 'buc_con',
'buc_qeq', 'buc_qsd'
]
W_name = np.array([
'adult', 'age1', 'age2', 'age3', 'Total', 'Total', 'Identity', 'Prefix',
'Prefix'
])
W_lst = np.array([
census.__adult(),
census.__age1(),
census.__age2(),
census.__age3(),
workload.Total(n),
workload.Total(n),
workload.Identity(n),
workload.Prefix(n),
workload.Prefix(n)
])
A_lst = strategy_comp(W_lst, n, rep)
results = []
names = []
total_errors = pd.DataFrame()
mean_ratio_errors = pd.DataFrame()
max_ratio_errors = pd.DataFrame()
min_ratio_errors = pd.DataFrame()
max_distances = pd.DataFrame()
min_distances = pd.DataFrame()
gini_coefficients = pd.DataFrame()
seed = args.seed
t = args.t
if seed is not None:
np.random.seed(seed)
print(experiment_name)
print('n={}, k={}, eps={}, rep={}, seed={}, t={}'.format(n,k,eps,rep,seed,t))
conf = OrderedDict()
conf['n']=n
conf['k']=k
conf['eps'] = eps
conf['rep']=rep
conf['seed'] =seed
conf['t'] = t
modes = ['ind', 'iden', 'uni', 'fdiff', 'fmax', 'fsum', 'buc_eq', 'buc_con', 'buc_qeq', 'buc_qsd']
W_name = np.array(['race1', 'race2', 'white', 'Total', 'Identity'])
W_lst = np.array([census.__race1(), census.__race2(), census.__white(), workload.Total(n), workload.Identity(n)])
A_lst = strategy_comp(W_lst, n, rep)
results = []
names = []
total_errors = pd.DataFrame()
mean_ratio_errors = pd.DataFrame()
max_ratio_errors = pd.DataFrame()
min_ratio_errors = pd.DataFrame()
max_distances = pd.DataFrame()
min_distances = pd.DataFrame()
gini_coefficients = pd.DataFrame()
mean_idenratio_errors = pd.DataFrame()
max_idenratio_errors = pd.DataFrame()
min_idenratio_errors = pd.DataFrame()
iden_gini_coefficients = pd.DataFrame()
for i in range(t):
conf['n'] = n
conf['k'] = k
conf['eps'] = eps
conf['rep'] = rep
conf['seed'] = seed
outs = []
modes = [
'ind', 'uni', 'fdiff', 'fmax', 'fsum', 'buc_eq', 'buc_con', 'buc_qeq',
'buc_qsd'
]
W1 = np.zeros(n)
W1[1] = 1
W1 = matrix.EkteloMatrix(W1.reshape(1, n))
W2 = workload.Total(n)
Ws = [W1]
for i in range(1, k):
Ws.append(W2)
Wr = Ws[:2]
outs = []
index = []
res = error_calc(Ws, Wr, n, eps, modes, rep)
res_noW1 = error_calc(Ws[1:], Wr[1:], n, eps * (k - 1) / k, modes, rep)
for mode in modes[1:]:
print(mode)
outs.append(crossmode_analysis(res['ind'], res[mode]))
outs.append(interference_analysis(res_noW1[mode], res[mode][1:]))
index.extend([mode + '_ind', mode + '_inter'])
seed = args.seed
t = args.t
if seed is not None:
np.random.seed(seed)
print(experiment_name)
print('n={}, k={}, eps={}, rep={}, seed={}, t={}'.format(n,k,eps,rep,seed,t))
conf = OrderedDict()
conf['n']=n
conf['k']=k
conf['eps'] = eps
conf['rep']=rep
conf['seed'] =seed
conf['t'] = t
modes = ['ind', 'iden', 'uni', 'fdiff', 'fmax', 'fsum', 'buc_eq', 'buc_con', 'buc_qeq', 'buc_qsd']
W_name = np.array(['adult', 'age1', 'age2', 'age3', 'Total', 'Identity'])
W_lst = np.array([census.__adult(), census.__age1(), census.__age2(), census.__age3(), workload.Total(n), workload.Identity(n)])
A_lst = strategy_comp(W_lst, n, rep)
results = []
names = []
total_errors = pd.DataFrame()
mean_ratio_errors = pd.DataFrame()
max_ratio_errors = pd.DataFrame()
min_ratio_errors = pd.DataFrame()
max_distances = pd.DataFrame()
min_distances = pd.DataFrame()
gini_coefficients = pd.DataFrame()
mean_idenratio_errors = pd.DataFrame()
max_idenratio_errors = pd.DataFrame()
min_idenratio_errors = pd.DataFrame()
iden_gini_coefficients = pd.DataFrame()
for i in range(t):
