def Run(self, W, x, eps, seed):
prng = np.random.RandomState(seed)
M = selection.H2(x.shape).select()
y = measurement.Laplace(M, eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
return x_hat
def test_H2(self):
op_H2_1D = selection.H2(self.domain_shape_1D)
queries = op_H2_1D.select()
if sparse.issparse(queries):
queries = queries.todense()
self.assertEqual(queries.shape[0], 31)
self.assertEqual(queries.shape[1], 16)
def Run(self, W, x, eps, seed):
x = x.flatten()
prng = np.random.RandomState(seed)
M = selection.H2(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
def test_H2(self):
op_H2_1D = selection.H2(self.domain_shape_1D)
queries = op_H2_1D.select()
self.assertEqual(queries.shape[0], 31)
self.assertEqual(queries.shape[1], 16)