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 check(shape, index_ndim, mode, order):
multi_index = np.unravel_index(np.arange(np.product(shape)),
shape,
order=order)
# create some invalid indices to test the mode
if mode in ("wrap", "clip"):
multi_index = (multi_index[0] - 1, ) + multi_index[1:]
# test with scalars and higher-dimensional indices
if index_ndim == 0:
multi_index = tuple(i[-1] for i in multi_index)
elif index_ndim == 2:
multi_index = tuple(i[:, np.newaxis] for i in multi_index)
multi_index_symb = [aesara.shared(i) for i in multi_index]
# reference result
ref = np.ravel_multi_index(multi_index, shape, mode, order)
def fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))
# shape given as a tuple
f_array_tuple = fn(multi_index, shape)
f_symb_tuple = fn(multi_index_symb, shape)
np.testing.assert_equal(ref, f_array_tuple())
np.testing.assert_equal(ref, f_symb_tuple())
# shape given as an array
shape_array = np.array(shape)
f_array_array = fn(multi_index, shape_array)
np.testing.assert_equal(ref, f_array_array())
# shape given as an Aesara variable
shape_symb = aesara.shared(shape_array)
f_array_symb = fn(multi_index, shape_symb)
np.testing.assert_equal(ref, f_array_symb())
# shape testing
self._compile_and_check(
[],
[ravel_multi_index(multi_index, shape_symb, mode, order)],
[],
RavelMultiIndex,
)
def test_extra_ops_omni():
a = matrix("a")
a.tag.test_value = np.arange(6, dtype=config.floatX).reshape((3, 2))
# 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])
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 fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))
def test_ravel_multi_index(self):
def check(shape, index_ndim, mode, order):
multi_index = np.unravel_index(np.arange(np.product(shape)),
shape,
order=order)
# create some invalid indices to test the mode
if mode in ("wrap", "clip"):
multi_index = (multi_index[0] - 1, ) + multi_index[1:]
# test with scalars and higher-dimensional indices
if index_ndim == 0:
multi_index = tuple(i[-1] for i in multi_index)
elif index_ndim == 2:
multi_index = tuple(i[:, np.newaxis] for i in multi_index)
multi_index_symb = [aesara.shared(i) for i in multi_index]
# reference result
ref = np.ravel_multi_index(multi_index, shape, mode, order)
def fn(mi, s):
return function([], ravel_multi_index(mi, s, mode, order))
# shape given as a tuple
f_array_tuple = fn(multi_index, shape)
f_symb_tuple = fn(multi_index_symb, shape)
np.testing.assert_equal(ref, f_array_tuple())
np.testing.assert_equal(ref, f_symb_tuple())
# shape given as an array
shape_array = np.array(shape)
f_array_array = fn(multi_index, shape_array)
np.testing.assert_equal(ref, f_array_array())
# shape given as an Aesara variable
shape_symb = aesara.shared(shape_array)
f_array_symb = fn(multi_index, shape_symb)
np.testing.assert_equal(ref, f_array_symb())
# shape testing
self._compile_and_check(
[],
[ravel_multi_index(multi_index, shape_symb, mode, order)],
[],
RavelMultiIndex,
)
for mode in ("raise", "wrap", "clip"):
for order in ("C", "F"):
for index_ndim in (0, 1, 2):
check((3, ), index_ndim, mode, order)
check((3, 4), index_ndim, mode, order)
check((3, 4, 5), index_ndim, mode, order)
# must provide integers
with pytest.raises(TypeError):
ravel_multi_index((fvector(), ivector()), (3, 4))
with pytest.raises(TypeError):
ravel_multi_index(((3, 4), ivector()), (3.4, 3.2))
# dims must be a 1D sequence
with pytest.raises(TypeError):
ravel_multi_index(((3, 4), ), ((3, 4), ))
