def test_norm(self):
a = Tensor(numpy.array([1, 2, 3]))
e = Tensor(numpy.array([1j, 2j, 3j]))
self.assertAlmostEqual(a.norm_squared, 14)
self.assertAlmostEqual(e.norm_squared, 14)
self.assertAlmostEqual((a + 2 * e).norm_squared, 70)
c = normalize(Tensor(numpy.random.rand(*self.a)))
self.assertAlmostEqual(c.norm_squared, 1)
def test_copy(self):
a = Tensor(numpy.array([1, 2]))
b = a.copy()
c = deepcopy(a)
self.assertTrue(b._data is a._data)
self.assertFalse(c._data is a._data)
testing.assert_allclose(a.contract(), b.contract())
testing.assert_allclose(a.contract(), c.contract())
def test_mul_add_tran_conj(self):
res_c = numpy.zeros(self.a[:2], dtype=complex)
res = Tensor(copy(res_c))
for _ in range(100):
c1 = numpy.random.rand() + 1j * numpy.random.rand()
a = numpy.random.rand(*(self.a[:2])) + 1j * \
numpy.random.rand(*(self.a[:2]))
c2 = numpy.random.rand() + 1j * numpy.random.rand()
axes = numpy.random.permutation(2)
res_c += c1 * numpy.transpose(numpy.conj(c2 * a), axes)
res += c1 * (~(Tensor(a) * c2) % axes)
testing.assert_allclose(res.contract(), res_c)
def test_conjugation(self):
a_R = numpy.random.rand(*(self.a))
a_I = numpy.random.rand(*(self.a))
a = a_R + a_I * 1j
b = Tensor(a)
testing.assert_allclose((~b).contract(), numpy.conj(a))
testing.assert_allclose((~(~b)).contract(), a)
def setUp(self):
self.a = TensorNetwork()
for i in range(5):
self.a.open_edge(i)
for i in range(5):
for j in range(5, 10):
self.a.add_node((i, j), [i, j], numpy.random.rand(2, 2))
self.b = TensorNetwork()
for i in range(5):
self.b.open_edge(i)
for i in range(5):
for j in range(5, 10):
self.b.add_node((i, j), [i, j], 1j * numpy.random.rand(2, 3))
self.c = Tensor(numpy.random.rand(2, 2, 2, 2, 2))
def test_multiplication_addition(self):
a = numpy.random.rand(10, *(self.a))
c = numpy.random.rand(10)
b = [c[i] * Tensor(a[i]) for i in range(10)]
res = TensorSum()
res_c = numpy.zeros(self.a)
for i in range(2):
res.add_term(i, b[i])
res_c += c[i] * a[i]
testing.assert_allclose(res.contract(), res_c)
def test_copy(self):
c = TensorSum()
a = Tensor(numpy.random.rand(2, 2, 2, 2, 2))
for i in range(2):
c.add_term(i, (a % numpy.random.permutation(5)).expand())
d = c.copy()
testing.assert_allclose(c.contract(), d.contract())
e = deepcopy(c)
testing.assert_allclose(c.contract(), e.contract())
d.update_term(0, numpy.random.rand(2, 2, 2, 2, 2))
self.assertFalse(numpy.allclose(c.contract(), d.contract()))
def test_transpose(self):
a = numpy.random.rand(*(self.a))
axes = numpy.random.permutation(10)
b = Tensor(a) % axes
testing.assert_allclose(b.contract(), numpy.transpose(a, axes))
def test_multiplication(self):
a = numpy.random.rand(*(self.a))
b = Tensor(a)
c = numpy.random.rand()
testing.assert_allclose(c * a, (c * b).contract())
def test_data(self):
a = numpy.random.rand(*(self.a))
b = Tensor(a)
testing.assert_allclose(b.contract(), a)
self.assertEqual(self.a, b.shape)
def test_equal(self):
a = Tensor(numpy.array([2, 4]))
b = 2 * Tensor(numpy.array([1, 2]))
c = Tensor(numpy.array([1, 1])) + Tensor(numpy.array([1, 3]))
testing.assert_allclose(a.contract(), c.contract())
testing.assert_allclose(a.contract(), b.contract())
def test_incompatible_dimensions(self):
a = Tensor(numpy.array([1, 2]))
b = Tensor(numpy.array([1, 2, 3]))
with self.assertRaises(ValueError):
a + b
def test_identifier(self):
cnt = TensorValued.id_count
for _ in range(100):
a = Tensor()
self.assertEqual(a.identifier, cnt + 100)
class TensorSumTestCase(unittest.TestCase):
def setUp(self):
self.a = TensorNetwork()
for i in range(5):
self.a.open_edge(i)
for i in range(5):
for j in range(5, 10):
self.a.add_node((i, j), [i, j], numpy.random.rand(2, 2))
self.b = TensorNetwork()
for i in range(5):
self.b.open_edge(i)
for i in range(5):
for j in range(5, 10):
self.b.add_node((i, j), [i, j], 1j * numpy.random.rand(2, 3))
self.c = Tensor(numpy.random.rand(2, 2, 2, 2, 2))
def test_addition(self):
c = self.a + ~(self.b) + 8 * self.c
self.assertEqual(type(c), TensorSum)
self.assertTrue(numpy.allclose(c.contract(),
self.a.contract()
+ numpy.conj(self.b.contract())
+ 8 * self.c.contract()))
def test_associativity(self):
c = (self.a + self.b) + self.c
d = self.a + (self.b + self.c)
e = (self.a + (self.c - self.b)) + (self.a + (2 * self.b - self.a))
self.assertTrue(numpy.allclose(c.contract(), d.contract()))
self.assertTrue(numpy.allclose(c.contract(), e.contract()))
def test_transpose(self):
axesA = (2, 4, 0, 1, 3)
axesB = (3, 4, 1, 0, 2)
axesC = (3, 0, 1, 2, 4)
axes_ = (1, 3, 0, 4, 2)
c = (self.a % axesA
+ self.b % axesB
+ self.c % axesC) % axes_
data = numpy.transpose(numpy.transpose(self.a.contract(), axesA)
+ numpy.transpose(self.b.contract(), axesB)
+ numpy.transpose(self.c.contract(), axesC), axes_)
self.assertTrue(numpy.allclose(c.contract(), data))
def test_add_and_remove_term(self):
c = TensorSum()
for i in range(100):
c.add_term(i, self.a % numpy.random.permutation(5))
choice = numpy.random.choice(100, 30, replace=False)
for j in choice:
c.remove_term(j)
with self.assertRaises(KeyError):
c.remove_term(choice[0])
with self.assertRaises(KeyError):
c.add_term(0, None)
c.add_term(0, None)
def test_shape_cache(self):
c = TensorSum()
misshaped = numpy.random.rand(2, 2, 3, 2, 2)
c.add_term(0, self.a)
c.add_term(1, self.a)
c.remove_term(0)
with self.assertRaises(ValueError):
c.add_term(2, misshaped)
c.add_term(2, self.a)
c.remove_term(1)
c.remove_term(2)
# Now that C is empty we can add any shape to it
c.add_term(0, misshaped)
c.add_term(1, misshaped)
with self.assertRaises(ValueError):
c.add_term(2, self.a)
# The function update_term does not fail early, so the user can update all tensors one-by-one
c.update_term(0, self.a)
c.update_term(1, self.a)
c.add_term(2, self.a)
def test_copy(self):
c = TensorSum()
a = Tensor(numpy.random.rand(2, 2, 2, 2, 2))
for i in range(2):
c.add_term(i, (a % numpy.random.permutation(5)).expand())
d = c.copy()
testing.assert_allclose(c.contract(), d.contract())
e = deepcopy(c)
testing.assert_allclose(c.contract(), e.contract())
d.update_term(0, numpy.random.rand(2, 2, 2, 2, 2))
self.assertFalse(numpy.allclose(c.contract(), d.contract()))