def test_get_subdomain_grid(self):
mapping1 = np.array([
[1,1,2,2],
[1,1,2,2],
[1,1,2,2]])
d1 = support.get_subdomain_grid(mapping1, mapping1.shape)
self.assertDictEqual(d1, {1: (3, 2), 2: (3, 2)})
mapping2 = np.array([
[1,1,2,2],
[1,1,2,2],
[3,3,0,0]])
d2 = support.get_subdomain_grid(mapping2, mapping2.shape)
self.assertDictEqual(d2, {1: (2, 2), 2: (2, 2), 3: (1,2), 0: (1,2)})
mapping3 = np.array([
[1,1,2,1]])
d3 = support.get_subdomain_grid(mapping3, mapping3.shape)
self.assertEqual(d3, None)
mapping4 = np.array([
[1,1,2,3],
[1,1,1,4]])
d4 = support.get_subdomain_grid(mapping4, mapping4.shape)
self.assertEqual(d4, None)
def Run(self, W, x, eps, seed):
assert len(x.shape) == 2, "Adaptive Grid only works for 2D domain"
shape_2d = x.shape
x = x.flatten()
prng = np.random.RandomState(seed)
Ms = []
ys = []
M = selection.UniformGrid(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).select()
y = measurement.Laplace(M, self.alpha*eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
Ms.append(M)
ys.append(y)
# Prepare parition object for later SplitByParition.
# This Partition selection operator is missing from Figure 2, plan 12 in the paper.
uniform_mapping = mapper.UGridPartition(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).mapping()
x_sub_list = meta.SplitByPartition(uniform_mapping).transform(x)
sub_domains = support.get_subdomain_grid(uniform_mapping, shape_2d)
ll, hi =[], []
for i in sorted(set(uniform_mapping)):
x_i = x_sub_list[i]
P_i = support.projection_matrix(uniform_mapping, i)
x_hat_i = P_i * x_hat
sub_domain_shape = sub_domains[i]
M_i = selection.AdaptiveGrid(sub_domain_shape,
x_hat_i,
(1-self.alpha)*eps,
c2=self.c2).select()
y_i = measurement.Laplace(M_i, (1-self.alpha)*eps).measure(x_i, prng)
offset = np.unravel_index(P_i.matrix.nonzero()[1][0], shape_2d)
ll.extend(M_i._lower + np.array(offset))
hi.extend(M_i._higher + np.array(offset))
ys.append(y_i)
Ms.append(workload.RangeQueries(shape_2d, np.array(ll), np.array(hi)))
x_hat = inference.LeastSquares().infer(Ms, ys)
return x_hat
def Run(self, W, x, eps, seed):
assert len(x.shape) == 2, "Adaptive Grid only works for 2D domain"
shape_2d = x.shape
x = x.flatten()
prng = np.random.RandomState(seed)
Ms = []
ys = []
M = selection.UniformGrid(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).select()
if not isinstance(M, np.ndarray):
M = M.toarray()
y = measurement.Laplace(M, self.alpha*eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
Ms.append(M)
ys.append(y)
# Prepare parition object for later SplitByParition.
# This Partition selection operator is missing from Figure 2, plan 12 in the paper.
uniform_mapping = mapper.UGridPartition(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c).mapping()
x_sub_list = meta.SplitByPartition(uniform_mapping).transform(x)
sub_domains = support.get_subdomain_grid(uniform_mapping, shape_2d)
for i in sorted(set(uniform_mapping)):
x_i = x_sub_list[i]
P_i = support.projection_matrix(uniform_mapping, i)
x_hat_i = P_i * x_hat
sub_domain_shape = sub_domains[i]
M_i = selection.AdaptiveGrid(sub_domain_shape,
x_hat_i,
(1-self.alpha)*eps,
c2=self.c2).select()
if not isinstance(M, np.ndarray):
M_i = M_i.toarray()
y_i = measurement.Laplace(M_i, (1-self.alpha)*eps).measure(x_i, prng)
M_i_o = M_i * P_i
Ms.append(M_i_o)
ys.append(y_i)
x_hat = inference.LeastSquares().infer(Ms, ys, [1.0]*len(ys))
return x_hat
def Run(self, W, x, eps):
assert len(self.domain_shape
) == 2, "Adaptive Grid only works for 2D domain_shape"
shape_2d = self.domain_shape
Ms = []
ys = []
M = ugrid_select(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c)
y = x.laplace(M, self.alpha * eps)
x_hat = least_squares(M, y)
Ms.append(M)
ys.append(y)
# Prepare parition object for later SplitByParition.
# This Partition selection operator is missing from Figure 2, plan 12 in the paper.
uniform_mapping = ugrid_mapper(shape_2d,
self.data_scale,
eps,
ag_flag=True,
c=self.c)
x_sub_list = x.split_by_partition(uniform_mapping)
sub_domains = support.get_subdomain_grid(uniform_mapping, shape_2d)
for i in sorted(set(uniform_mapping)):
x_i = x_sub_list[i]
P_i = support.projection_matrix(uniform_mapping, i)
x_hat_i = P_i * x_hat
sub_domain_shape = sub_domains[i]
M_i = agrid_select(sub_domain_shape,
x_hat_i, (1 - self.alpha) * eps,
c2=self.c2)
y_i = x_i.laplace(M_i, (1 - self.alpha) * eps)
M_i_o = M_i * P_i
Ms.append(M_i_o)
ys.append(y_i)
x_hat2 = least_squares(Ms, ys, [1.0] * len(ys))
return x_hat2
