def test_QuadTree(self):
op_quad_tree = selection.QuadTree(self.domain_shape_2D)
queries = op_quad_tree.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)
shape_2d = (x.shape[0]//2,2)
M = selection.QuadTree(shape_2d).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):
assert len(x.shape) == 2, "QuadTree only works for 2D domain"
prng = np.random.RandomState(seed)
shape_2d = x.shape
x = x.flatten()
M = selection.QuadTree(shape_2d).select()
y = measurement.Laplace(M, eps).measure(x, prng)
x_hat = inference.LeastSquares().infer(M, y)
return x_hat
def test_QuadTree(self):
op_quad_tree = selection.QuadTree(self.domain_shape_2D)
queries = op_quad_tree.select()
self.assertEqual(len(queries.shape), 2)
self.assertEqual(queries.shape[1], 256)