def test_ops():
x, y, z, w = tn.meshgrid([32] * 4)
t = x + y + z + w + 1
assert tn.relative_error(1 / t.torch(), 1 / t) < 1e-4
assert tn.relative_error(torch.cos(t.torch()), tn.cos(t)) < 1e-4
assert tn.relative_error(torch.exp(t.torch()), tn.exp(t)) < 1e-4
def check(t1, t2):
x1 = t1.torch()
x2 = t2.torch()
assert tn.relative_error(t1 + t2, x1 + x2) <= 1e-7
assert tn.relative_error(t1 - t2, x1 - x2) <= 1e-7
assert tn.relative_error(t1 * t2, x1 * x2) <= 1e-7
assert tn.relative_error(-t1 + t2, -x1 + x2) <= 1e-7
def test_orthogonalization():
for i in range(100):
gt = tn.rand(np.random.randint(1, 8, np.random.randint(2, 6)))
t = gt.clone()
assert tn.relative_error(gt, t) <= 1e-7
t.left_orthogonalize(0)
assert tn.relative_error(gt, t) <= 1e-7
t.right_orthogonalize(t.dim() - 1)
assert tn.relative_error(gt, t) <= 1e-7
t.orthogonalize(np.random.randint(t.dim()))
assert tn.relative_error(gt, t) <= 1e-7
def store_metrics(self, full):
t = self.tn_weight
full = full.view(t.torch().size())
self.compression = (full.numel(), t.numel(), full.numel() / t.numel())
self.relative_error = tn.relative_error(full, t)
self.rmse = tn.rmse(full, t)
self.r_squared = tn.r_squared(full, t)
def test_round_tucker():
for i in range(100):
eps = np.random.rand()**2
gt = tn.rand([32] * 4, ranks_tt=8, ranks_tucker=8)
t = gt.clone()
t.round_tucker(eps=eps)
assert tn.relative_error(gt, t) <= eps
def test_tensors():
for i in range(100):
t = random_format([10] * 6)
t2 = tn.cross(function=lambda x: x,
tensors=t,
ranks_tt=15,
verbose=False)
assert tn.relative_error(t, t2) < 1e-6
def test_tensors():
for i in range(100):
t = random_format([10] * 6)
t2 = tn.cross(function=lambda x: x,
tensors=t,
ranks_tt=15,
verbose=False)
assert tn.relative_error(t, t2) < 1e-6
t = tn.rand([10] * 6, ranks_tt=10)
_, info = tn.cross(function=lambda x: x,
tensors=[t],
ranks_tt=15,
verbose=False,
return_info=True)
t2 = tn.cross_forward(info, function=lambda x: x, tensors=t)
assert tn.relative_error(t, t2) < 1e-6
def test_round_tt_eig():
for i in range(100):
gt = tn.rand(np.random.randint(1, 8, np.random.randint(8, 10)),
ranks_tt=np.random.randint(1, 10))
gt.round_tt(1e-8, algorithm='eig')
t = gt + gt
t.round_tt(1e-8, algorithm='eig')
assert tn.relative_error(gt, t / 2) <= 1e-7
def test_domain():
def function(Xs):
return 1. / torch.sum(Xs, dim=1)
domain = [torch.linspace(1, 10, 10) for n in range(3)]
t = tn.cross(function=function,
domain=domain,
ranks_tt=3,
function_arg='matrix')
gt = torch.meshgrid(domain)
gt = 1. / sum(gt)
assert tn.relative_error(gt, t) < 5e-2
def round(self, eps=1e-14, **kwargs):
"""
General recompression. Attempts to reduce TT ranks first; then does Tucker rounding with the remaining error
budget.
:param eps: this relative error will not be exceeded
:param kwargs: passed to `round_tt()` and `round_tucker()`
"""
copy = self.clone()
self.round_tt(eps, **kwargs)
reached = tn.relative_error(copy, self)
if reached < eps:
self.round_tucker((1+eps) / (1+reached) - 1, **kwargs)
def test_als_completion():
I = 8
train_x = torch.arange(I)[:, None].repeat(1, 2)
train_y = torch.ones(I)
t = tn.als_completion(train_x, train_y, ranks_tt=3)
assert tn.relative_error(train_y, t[train_x]) < 1e-5
def check():
x1 = t1.torch()
x2 = t2.torch()
gt = torch.dot(x1.flatten(), x2.flatten())
assert tn.relative_error(tn.dot(t1, t2), gt) <= 1e-7
def check():
x = t.torch()
assert tn.relative_error(tn.mean(t), torch.mean(x)) <= 1e-3
assert tn.relative_error(tn.var(t), torch.var(x)) <= 1e-3
assert tn.relative_error(tn.norm(t), torch.norm(x)) <= 1e-3
