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):
print(i, flush=True)
k = np.random.randint(2, k_max + 1)
ks.append(k)
Ws, Wn, As = workload_selection(W_lst, W_name, A_lst, n, k, rep)
res = error_calc(Ws, Ws, n, eps, modes, rep, As=As, Ar=As)
results.append(res)
names.append(Wn)
total_error = OrderedDict()
mean_ratio_error = OrderedDict()
max_ratio_error = OrderedDict()
min_ratio_error = OrderedDict()
max_distance = OrderedDict()
min_distance = OrderedDict()
gini_coefficient = OrderedDict()
mean_idenratio_error = OrderedDict()
max_idenratio_error = OrderedDict()
min_idenratio_error = OrderedDict()
iden_gini_coefficient = OrderedDict()
for mode in modes:
results = []
names = []
ks = []
total_errors = pd.DataFrame()
max_ratio_errors = pd.DataFrame()
inters = pd.DataFrame()
for i in range(t):
print(i, flush=True)
k = np.random.randint(2, k_max + 1)
ks.append(k)
Ws, Wn, As = workload_selection(W_lst,
W_name,
A_lst,
n,
k,
rep,
types=types,
prob=0.8)
error_base, total_error, max_ratio_error, inter = interference_custom(
Ws, As, n, eps, modes, rep)
results.append(error_base)
names.append(Wn)
total_errors = pd.concat(
[total_errors, pd.DataFrame(total_error, index=[i])])
max_ratio_errors = pd.concat(
[max_ratio_errors,
pd.DataFrame(max_ratio_error, index=[i])])
inters = pd.concat([inters, pd.DataFrame(inter, index=[i])])
names = np.asarray(names)