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()
mean_idenratio_errors = pd.DataFrame()
max_idenratio_errors = pd.DataFrame()
x_mean = np.sum(x_data[:,0]*x_data[:,1])/np.sum(x_data[:,1])
mid = np.sum(x_data[:,1])/2
prefix = np.cumsum(x_data[:,1])
x_median = x_data[np.searchsorted(prefix, mid),0]
x_mode = x_data[np.argmax(x_data[:,1]),0]
x_per = []
for percentile in [0.1, 0.25, 0.75, 0.9]:
loc = np.sum(x_data[:,1])*percentile
x_per.append(x_data[np.searchsorted(prefix, loc),0])
meanW = np.vstack((x_data[:,0], np.ones(n)))
meanW = matrix.EkteloMatrix(meanW)
W_name = np.array(['Mean', 'Median', 'Mode', 'Per_10', 'Per_25', 'Per_75', 'Per_90'])
W_lst = np.array([meanW, workload.Prefix(n), workload.Identity(n),workload.Prefix(n), workload.Prefix(n),workload.Prefix(n), workload.Prefix(n)])
A_lst = strategy_comp(W_lst, n, rep)
q_lst = W_name
ans_lst = np.array([x_mean, x_median, x_mode] + x_per)
print(ans_lst)
results = []
names = []
ks = []
total_errors = pd.DataFrame()
max_ratio_errors = pd.DataFrame()
inters = pd.DataFrame()
for i in range(t):
print(i, flush=True)
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):
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):
