def test_grad(self):
x = vector("x")
a = np.random.random(50).astype(config.floatX)
aesara.function([x], grad(aet_sum(diff(x)), x))
utt.verify_grad(self.op, [a])
for k in range(TestDiffOp.nb):
aesara.function([x], grad(aet_sum(diff(x, n=k)), x))
utt.verify_grad(DiffOp(n=k), [a], eps=7e-3)
def test_diffOp(self):
x = matrix("x")
a = np.random.random((30, 50)).astype(config.floatX)
f = aesara.function([x], diff(x))
assert np.allclose(np.diff(a), f(a))
for axis in range(len(a.shape)):
for k in range(TestDiffOp.nb):
g = aesara.function([x], diff(x, n=k, axis=axis))
assert np.allclose(np.diff(a, n=k, axis=axis), g(a))
def test_perform(self, axis, n):
rng = np.random.default_rng(4282)
x = matrix("x")
a = rng.random((30, 50)).astype(config.floatX)
f = aesara.function([x], diff(x))
assert np.allclose(np.diff(a), f(a))
g = aesara.function([x], diff(x, n=n, axis=axis))
assert np.allclose(np.diff(a, n=n, axis=axis), g(a))
def test_extra_ops():
a = matrix("a")
a.tag.test_value = np.arange(6, dtype=config.floatX).reshape((3, 2))
out = aet_extra_ops.cumsum(a, axis=0)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
out = aet_extra_ops.cumprod(a, axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
out = aet_extra_ops.diff(a, n=2, axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
out = aet_extra_ops.repeat(a, (3, 3), axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
# This function also cannot take symbolic input.
c = aet.as_tensor(5)
out = aet_extra_ops.bartlett(c)
fgraph = FunctionGraph([], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
with pytest.raises(NotImplementedError):
out = aet_extra_ops.fill_diagonal(a, c)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
with pytest.raises(NotImplementedError):
out = aet_extra_ops.fill_diagonal_offset(a, c, c)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
with pytest.raises(NotImplementedError):
out = aet_extra_ops.Unique(axis=1)(a)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
indices = np.arange(np.product((3, 4)))
out = aet_extra_ops.unravel_index(indices, (3, 4), order="C")
fgraph = FunctionGraph([], out)
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs],
must_be_device_array=False)
multi_index = np.unravel_index(np.arange(np.product((3, 4))), (3, 4))
out = aet_extra_ops.ravel_multi_index(multi_index, (3, 4))
fgraph = FunctionGraph([], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs],
must_be_device_array=False)
# The inputs are "concrete", yet it still has problems?
out = aet_extra_ops.Unique()(aet.as_tensor(
np.arange(6, dtype=config.floatX).reshape((3, 2))))
fgraph = FunctionGraph([], [out])
compare_jax_and_py(fgraph, [])
def test_basic(self):
rng = np.random.default_rng(4282)
x = matrix("x")
a = rng.random((30, 50)).astype(config.floatX)
f = aesara.function([x], diff(x))
assert np.allclose(np.diff(a), f(a))
def test_infer_shape(self):
x = matrix("x")
a = np.random.random((30, 50)).astype(config.floatX)
self._compile_and_check([x], [self.op(x)], [a], self.op_class)
for axis in range(len(a.shape)):
for k in range(TestDiffOp.nb):
self._compile_and_check([x], [diff(x, n=k, axis=axis)], [a],
self.op_class)
def test_output_type(self, x_type, axis, n):
x = x_type("x")
x_test = np.empty((10, 30))
out = diff(x, n=n, axis=axis)
out_test = np.diff(x_test, n=n, axis=axis)
for i in range(2):
if x.type.shape[i] is None:
assert out.type.shape[i] is None
else:
assert out.type.shape[i] == out_test.shape[i]
def test_extra_ops():
a = matrix("a")
a.tag.test_value = np.arange(6, dtype=config.floatX).reshape((3, 2))
out = at_extra_ops.cumsum(a, axis=0)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
out = at_extra_ops.cumprod(a, axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
out = at_extra_ops.diff(a, n=2, axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
out = at_extra_ops.repeat(a, (3, 3), axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
c = at.as_tensor(5)
with pytest.raises(NotImplementedError):
out = at_extra_ops.fill_diagonal(a, c)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
with pytest.raises(NotImplementedError):
out = at_extra_ops.fill_diagonal_offset(a, c, c)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
with pytest.raises(NotImplementedError):
out = at_extra_ops.Unique(axis=1)(a)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
indices = np.arange(np.product((3, 4)))
out = at_extra_ops.unravel_index(indices, (3, 4), order="C")
fgraph = FunctionGraph([], out)
compare_jax_and_py(
fgraph, [get_test_value(i) for i in fgraph.inputs], must_be_device_array=False
)
