def test_dtype_failure(self):
arr = TensorType(FLOATX, [False] * 3)("arr")
indices = TensorType(FLOATX, [False] * 3)("indices")
arr.tag.test_value = np.zeros((1, ) * arr.ndim, dtype=FLOATX)
indices.tag.test_value = np.zeros((1, ) * indices.ndim, dtype=FLOATX)
with pytest.raises(IndexError):
take_along_axis(arr, indices)
def test_dtype_failure(self, floatX):
with aesara.config.change_flags(floatX=floatX):
arr = TensorType(floatX, [False] * 3)("arr")
indices = TensorType(floatX, [False] * 3)("indices")
arr.tag.test_value = np.zeros((1, ) * arr.ndim, dtype=floatX)
indices.tag.test_value = np.zeros((1, ) * indices.ndim,
dtype=floatX)
with pytest.raises(IndexError):
take_along_axis(arr, indices)
def test_ndim_failure(self, floatX):
with aesara.config.change_flags(floatX=floatX):
intX = str(_conversion_map[floatX])
arr = TensorType(floatX, [False] * 3)("arr")
indices = TensorType(intX, [False] * 2)("indices")
arr.tag.test_value = np.zeros((1, ) * arr.ndim, dtype=floatX)
indices.tag.test_value = np.zeros((1, ) * indices.ndim, dtype=intX)
with pytest.raises(ValueError):
take_along_axis(arr, indices)
def __init__(self, w, comp_dists, mixture_axis=-1, *args, **kwargs):
self.w = at.as_tensor_variable(w)
if not isinstance(comp_dists, Distribution):
raise TypeError(
"The MixtureSameFamily distribution only accepts Distribution "
f"instances as its components. Got {type(comp_dists)} instead."
)
self.comp_dists = comp_dists
if mixture_axis < 0:
mixture_axis = len(comp_dists.shape) + mixture_axis
if mixture_axis < 0:
raise ValueError(
"`mixture_axis` is supposed to be in shape of components' distribution. "
f"Got {mixture_axis + len(comp_dists.shape)} axis instead out of the bounds."
)
comp_shape = to_tuple(comp_dists.shape)
self.shape = comp_shape[:mixture_axis] + comp_shape[mixture_axis + 1:]
self.mixture_axis = mixture_axis
kwargs.setdefault("dtype", self.comp_dists.dtype)
# Compute the mode so we don't always have to pass a initval
defaults = kwargs.pop("defaults", [])
event_shape = self.comp_dists.shape[mixture_axis + 1:]
_w = at.shape_padleft(
at.shape_padright(w, len(event_shape)),
len(self.comp_dists.shape) - w.ndim - len(event_shape),
)
mode = take_along_axis(
self.comp_dists.mode,
at.argmax(_w, keepdims=True),
axis=mixture_axis,
)
self.mode = mode[(..., 0) + (slice(None), ) * len(event_shape)]
if not all_discrete(comp_dists):
mean = at.as_tensor_variable(self.comp_dists.mean)
self.mean = (_w * mean).sum(axis=mixture_axis)
if "mean" not in defaults:
defaults.append("mean")
defaults.append("mode")
super().__init__(defaults=defaults, *args, **kwargs)
def test_axis_failure(self, axis, floatX):
with aesara.config.change_flags(floatX=floatX):
arr, indices = self.get_input_tensors((3, 1), floatX)
with pytest.raises(ValueError):
take_along_axis(arr, indices, axis=axis)
def _output_tensor(self, arr, indices, axis):
return take_along_axis(arr, indices, axis)
def test_axis_failure(self, axis):
arr, indices = self.get_input_tensors((3, 1))
with pytest.raises(ValueError):
take_along_axis(arr, indices, axis=axis)