def test_mini_h5_w():
y, parent, xt, lam, root = load_mini_h5()
n = len(y)
mua = np.ones(n)
lama = lam * np.ones(n)
x2 = tl.tree_dp(y=y, parent=parent, lam=lama, mu=mua, root=root)
assert (x2.reshape(xt.shape) == xt).all()
def test_mini_h5():
y, parent, xt, lam, root = load_mini_h5()
x = tl.tree_dp(y, parent, lam, root)
assert (x.reshape(xt.shape) == xt).all()
assert sys.getrefcount(parent) <= 2
assert sys.getrefcount(y) <= 2
assert sys.getrefcount(x) <= 2
def average_tree(fname: str, lam: float, rep: int):
with h5py.File(fname) as io:
y = io["y"][()]
grid_opt = fname.replace(".img", f"_lam{lam}.grid_opt")
if path.exists(grid_opt):
with h5py.File(grid_opt) as io:
print(grid_opt)
grid_opt = io["xgrid"][()]
else:
grid_opt = None
graph = BiAdjacent(GridGraph(*y.shape))
yvec = y.reshape(-1, order='F')
xsol = list()
for s in range(rep):
print("seed =", s)
pi = random_spanning_tree(graph, seed=s)
assert len(pi) == len(yvec)
assert pi.max() < len(pi)
assert pi.min() >= 0
x = treelas.tree_dp(y=yvec, parent=pi, lam=8 * lam, verbose=False)
xsol.append(x.reshape(*y.shape, order='F'))
xopt = np.mean(xsol, axis=0)
return xsol, xopt, grid_opt
def test_grid_2x2():
graph = BiAdjacent(GridGraph(2, 2))
pi = random_spanning_tree(graph, seed=0)
if os.getenv("show"):
Tree(pi).show()
np.random.seed(0)
y = np.random.randn(len(pi))
x = tree_dp(y=y, parent=pi, lam=0.2)
def test_grid_3x3():
graph = BiAdjacent(GridGraph(3, 3))
pi = random_spanning_tree(graph, seed=0)
if os.getenv("show"):
Tree(pi).show()
assert all(pi == [3, 0, 1, 6, 7, 8, 6, 6, 7])
np.random.seed(0)
y = np.random.randn(len(pi))
x = tree_dp(y=y, parent=pi, lam=0.2)
def test_grid(width):
graph = BiAdjacent(GridGraph(width, width))
pi = random_spanning_tree(graph, seed=0)
np.random.seed(0)
y = np.random.randn(len(pi))
x = tree_dp(y=y, parent=pi, lam=0.2)