def test_HB(self):
op_HB_1D = selection.HB(self.domain_shape_1D)
queries = op_HB_1D.select()
self.assertEqual(len(queries.shape), 2)
self.assertEqual(queries.shape[1], 16)
op_HB_2D = selection.HB(self.domain_shape_2D)
queries = op_HB_2D.select()
self.assertEqual(len(queries.shape), 2)
self.assertEqual(queries.shape[1], 256)
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
striped_mapping = mapper.Striped(self.domain,
self.stripe_dim).mapping()
x_sub_list = meta.SplitByPartition(striped_mapping).transform(x)
Ms = []
ys = []
scale_factors = []
group_idx = sorted(set(striped_mapping))
for i in group_idx:
x_i = x_sub_list[group_idx.index(i)]
P_i = support.projection_matrix(striped_mapping, i)
M_bar = selection.HB(x_i.shape).select()
y_i = measurement.Laplace(M_bar, eps).measure(x_i, prng)
noise_scale_factor = laplace_scale_factor(M_bar, eps)
M_i = M_bar * P_i
Ms.append(M_i)
ys.append(y_i)
scale_factors.append(noise_scale_factor)
x_hat = inference.LeastSquares().infer(Ms, ys, scale_factors)
return x_hat
def test_HB(self):
op_HB_1D = selection.HB(self.domain_shape_1D)
queries = op_HB_1D.select()
if sparse.issparse(queries):
queries = queries.todense()
self.assertEqual(len(queries.shape), 2)
self.assertEqual(queries.shape[1], 16)
op_HB_2D = selection.HB(self.domain_shape_2D)
queries = op_HB_2D.select()
if sparse.issparse(queries):
queries = queries.todense()
self.assertEqual(len(queries.shape), 2)
self.assertEqual(queries.shape[1], 256)
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
M = selection.HB(self.domain_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:
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()
y = measurement.Laplace(M, eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
return x_hat
