def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a uniform estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W = get_matrix(W)
if not isinstance(W, np.ndarray):
W = W.toarray()
measuredQueries = []
mult_weight = inference.MultiplicativeWeights(updateRounds = self.update_rounds)
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds+1):
eps_round = eps / float(self.rounds)
# SW
worst_approx = pselection.WorstApprox(W,
measuredQueries,
x_hat,
eps_round * self.ratio,
'EXPONENTIAL')
M = worst_approx.select(x, prng)
measuredQueries.append(M.mwem_index)
if not isinstance(M, np.ndarray):
M = M.toarray()
# LM
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
M_history = np.vstack([M_history, M])
y_history.extend(y)
# MW
if self.use_history:
x_hat = mult_weight.infer(M_history, y_history, x_hat)
else:
x_hat = mult_weight.infer(M, y, x_hat)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
W = get_matrix(W)
W_partial = sparse.csr_matrix(W.shape)
mult_weight = inference.MultiplicativeWeights(updateRounds = self.update_rounds)
M_history = np.empty((0, domain_size))
y_history = []
for i in range(1, self.rounds+1):
eps_round = eps / float(self.rounds)
# SW
worst_approx = pselection.WorstApprox(sparse.csr_matrix(W),
W_partial,
x_hat,
eps_round * self.ratio,
'EXPONENTIAL')
W_next = worst_approx.select(x, prng)
M = support.extract_M(W_next)
W_partial += W_next
# LM
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
M_history = sparse.vstack([M_history, M])
y_history.extend(y)
# MW
if self.use_history:
x_hat = mult_weight.infer(M_history, y_history, x_hat)
else:
x_hat = mult_weight.infer(M, y, x_hat)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
domain_size = np.prod(self.domain_shape)
# Start with a unifrom estimation of x
x_hat = np.array([self.data_scale / float(domain_size)] * domain_size)
measuredQueries = []
mult_weight = inference.MultiplicativeWeights(updateRounds = self.update_rounds)
M_history = []
y_history = []
for i in range(1, self.rounds+1):
eps_round = eps / float(self.rounds)
# SW + SH2
worst_approx = pselection.WorstApprox(W,
measuredQueries,
x_hat,
eps_round * self.ratio)
W_next = worst_approx.select(x, prng)
measuredQueries.append(W_next.mwem_index)
M = selection.AddEquiWidthIntervals(W_next, i).select()
# LM
laplace = measurement.Laplace(M, eps_round * (1-self.ratio))
y = laplace.measure(x, prng)
M_history.append(M)
y_history.append(y)
# MW
if self.use_history:
x_hat = mult_weight.infer(M_history, y_history, x_hat)
else:
x_hat = mult_weight.infer(M, y, x_hat)
return x_hat
def Run(self, W, x, eps, seed):
prng = np.random.RandomState(seed)
x_hat = prng.rand(*x.shape)
W_partial = sparse.csr_matrix(W.get_matrix().shape)
mult_weight = inference.MultiplicativeWeights()
measured_queries = []
for i in range(1, self.rounds + 1):
eps_round = eps / float(self.rounds)
# SW + SH2
worst_approx = pselection.WorstApprox(
sparse.csr_matrix(W.get_matrix()), W_partial, x_hat,
eps_round * self.ratio)
W_next = worst_approx.select(x, prng)
M = selection.AddEquiWidthIntervals(W_next, i).select()
W_partial += W_next
laplace = measurement.Laplace(M, eps * (1 - self.ratio))
y = laplace.measure(x, prng)
x_hat = mult_weight.infer(M, y, x_hat)
return x_hat