def test_stack(shape, axis): xx = sparse.random(shape, density=0.5) x = xx.todense() yy = sparse.random(shape, density=0.5) y = yy.todense() zz = sparse.random(shape, density=0.5) z = zz.todense() assert_eq(np.stack([x, y, z], axis=axis), sparse.stack([xx, yy, zz], axis=axis))
def test_large_concat_stack(): data = np.array([1], dtype=np.uint8) coords = np.array([[255]], dtype=np.uint8) xs = COO(coords, data, shape=(256, ), has_duplicates=False, sorted=True) x = xs.todense() assert_eq(np.stack([x, x]), sparse.stack([xs, xs])) assert_eq(np.concatenate((x, x)), sparse.concatenate((xs, xs)))
def test_stack(shape, axis): x = random_x(shape) xx = COO.from_numpy(x) y = random_x(shape) yy = COO.from_numpy(y) z = random_x(shape) zz = COO.from_numpy(z) assert_eq(np.stack([x, y, z], axis=axis), sparse.stack([xx, yy, zz], axis=axis))
print("curv: ", curv) if curv == 'both': edge_feat_list = [ollivier_curv_vals, forman_curv_vals] elif curv == 'ollivier': edge_feat_list = [ollivier_curv_vals] else: edge_feat_list = [forman_curv_vals] ########################################### END ########################################### vals = [] for mat in edge_feat_list: vals.append((mat + mat.transpose() + EYE > 0).astype(np.float32)) if args.encode_edge_direction: vals.append((mat + EYE > 0).astype(np.float32)) vals.append((mat.transpose() + EYE > 0).astype(np.float32)) vals = [sparse.as_coo(x) for x in vals] vals = sparse.stack(vals, axis=0) vals = vals.todense() vals = aml_graph.normalize_adj(vals, args.edge_norm, assume_symmetric_input=False) temp_vals = vals vals = [vals] ret = np.concatenate(vals, 1) edges = np.transpose(ret, [1, 2, 0]) #edges = edges.todense() # ************************************************************