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()
# ************************************************************
