conf['n'] = n
conf['k_max'] = k_max
conf['eps'] = eps
conf['rep'] = rep
conf['seed'] = seed
conf['t'] = t
modes = ['ind', 'iden', 'uni', 'fsum', 'buc_con', 'buc_qsd']

W_name = []
W_lst = []
domain = np.full(d, p, dtype=np.int)
for i in range(d):
    for j in range(i):
        key = 2**i + 2**j
        W_name.append(key)
        W_lst.append(workload.Marginal(domain, key))
A_lst = strategy_comp(W_lst, n, rep)

results = []
names = []
ks = []
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()
conf['d'] = d
conf['p'] = p
conf['n'] = n
conf['k_max'] = k_max
conf['eps'] = eps
conf['rep'] = rep
conf['seed'] = seed
conf['t'] = t
modes = ['ind', 'iden', 'uni', 'fsum', 'buc_con', 'buc_qsd']

W_name = []
W_lst = []
domain = np.full(d, p, dtype=np.int)
for i in range(d + 1):
    W_name.append(2**i)
    W_lst.append(workload.Marginal(domain, 2**i))
A_lst = strategy_comp(W_lst, n, rep)

results = []
names = []
ks = []
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()