def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
if self.workload_based:
mapping = mapper.WorkloadBased(W).mapping()
reducer = transformation.ReduceByPartition(mapping)
x = reducer.transform(x)
# Reduce workload
# W = support.reduce_queries(mapping, W)
W = W * support.expansion_matrix(mapping)
# Orange AHPparition(PA) operator in paper can be expressed
# as the following sequence of simpler opeartors
M = selection.Identity(x.shape).select()
y = measurement.Laplace(M, self.ratio * eps).measure(x, prng)
xest = inference.AHPThresholding(self.eta, self.ratio).infer(M, y, eps)
mapping = mapper.AHPCluster(xest, (1 - self.ratio) * eps).mapping()
# TR
reducer = transformation.ReduceByPartition(mapping)
x_bar = reducer.transform(x)
# SI LM LS
M_bar = selection.Identity(x_bar.shape).select()
y_bar = measurement.Laplace(M_bar, eps * (1 - self.ratio)).measure(
x_bar, prng)
x_bar_hat = inference.LeastSquares().infer(M_bar, y_bar)
x_hat = support.expansion_matrix(mapping) * x_bar_hat
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
if self.workload_based:
W = get_matrix(W)
mapping = mapper.WorkloadBased(W).mapping()
reducer = transformation.ReduceByPartition(mapping)
x = reducer.transform(x)
# Reduce workload
# W = support.reduce_queries(mapping, W)
W = W * support.expansion_matrix(mapping)
self.domain_shape = x.shape
if len(self.domain_shape) == 2:
# apply hilbert transform to convert 2d domain into 1d
hilbert_mapping = mapper.HilbertTransform(self.domain_shape).mapping()
domain_reducer = transformation.ReduceByPartition(hilbert_mapping)
x = domain_reducer.transform(x)
W = get_matrix(W)
W = W * support.expansion_matrix(hilbert_mapping)
dawa = pmapper.Dawa(eps, self.ratio, self.approx)
mapping = dawa.mapping(x, prng)
elif len(self.domain_shape) == 1:
W = get_matrix(W)
dawa = pmapper.Dawa(eps, self.ratio, self.approx)
mapping = dawa.mapping(x, prng)
reducer = transformation.ReduceByPartition(mapping)
x_bar = reducer.transform(x)
W_bar = W * support.expansion_matrix(mapping)
M_bar = selection.GreedyH(x_bar.shape, W_bar).select()
if not isinstance(M_bar, np.ndarray):
M_bar = M_bar.toarray()
y = measurement.Laplace(M_bar, eps*(1-self.ratio)).measure(x_bar, prng)
x_bar_hat = inference.LeastSquares().infer(M_bar, y)
x_bar_hat_exp = support.expansion_matrix(mapping) * x_bar_hat
if len(self.domain_shape) == 1:
return x_bar_hat_exp
elif len(self.domain_shape) == 2:
return support.expansion_matrix(hilbert_mapping) * x_bar_hat_exp
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
if self.workload_based:
mapping = mapper.WorkloadBased(W).mapping()
reducer = transformation.ReduceByPartition(mapping)
x = reducer.transform(x)
# Reduce workload
# W = support.reduce_queries(mapping, W)
W = W * support.expansion_matrix(mapping)
M = selection.Identity(x.shape).select()
y = measurement.Laplace(M, eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
return x_hat
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
if self.workload_based:
W = get_matrix(W)
mapping = mapper.WorkloadBased(W).mapping()
reducer = transformation.ReduceByPartition(mapping)
x = reducer.transform(x)
# Reduce workload
# W = support.reduce_queries(mapping, W)
W = W * support.expansion_matrix(mapping)
self.domain_shape = x.shape
M = selection.HB(self.domain_shape).select()
if not isinstance(M, np.ndarray):
M = M.toarray()
y = measurement.Laplace(M, eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
return x_hat