def test_simple_tensors():
"""
tons of tests relating to reaxeing tensors.
variables names have a postfix integer which represents the dimensionality
of the value. Views have x_y postfix which means they are y dimensional
views of x dimensional buffers.
I started refactoring into smaller pieces as seen in tests above, but
stopped ...
"""
# A simple vector
td1 = TensorDescription(axes=[ax.A])
e1 = random(td1)
td2 = TensorDescription(axes=[ax.A, ax.B])
e2 = random(td2)
# Reaxes
e1_1 = tensorview(td1.broadcast([ax.A, ax.B]), e1)
e1_2 = tensorview(td1.broadcast([ax.B, ax.A]), e1)
e1_3 = tensorview(td1.broadcast([(ax.B, ax.C), ax.A]), e1)
e2_1 = tensorview(td2.broadcast([ax.B, ax.A]), e2)
e2_2 = tensorview(td2.broadcast([ax.A, ax.B]), e2)
e2_3 = tensorview(td2.flatten((
FlattenedAxis((ax.A, ax.B)),
)), e2_2)
assert e1_1.shape == (ax.A.length, ax.B.length)
assert e1_2.shape == (ax.B.length, ax.A.length)
for i in range(ax.A.length):
e1_1[i] = i
for i in range(ax.A.length):
assert e1[i] == i
for j in range(ax.B.length):
assert e1_1[i, j] == i
assert e1_2[j, i] == i
for j in range(ax.B.length * ax.C.length):
assert e1_3[j, i] == i
def val2(i, j):
return (i + 1) * (j + 2)
for i in range(ax.A.length):
for j in range(ax.B.length):
e2[i, j] = val2(i, j)
for i in range(ax.A.length):
for j in range(ax.B.length):
assert e2_1[j, i] == val2(i, j)
assert e2_2[i, j] == val2(i, j)
assert e2_3[i * ax.B.length + j] == val2(i, j)
def test_reaxe_0d_to_2d():
td = TensorDescription(axes=())
x = random(td)
x_view = tensorview(td.broadcast([ax_A, ax_B]), x)
# set x
x[()] = 3
assert x_view.shape == (ax_A.length, ax_B.length)
assert np.all(x_view == 3)
def test_reaxe_0d_to_1d():
td = TensorDescription(())
x = random(td)
# create view of x
x_view = tensorview(td.broadcast([ax_A]), x)
# set x
x[()] = 3
# setting e also sets x_view
assert x_view.shape == (ax_A.length, )
assert np.all(x_view == 3)