def test_minimum_invalid_dtypes(device, dtype):
shape = (3, 2)
bool_array = chainerx.array(array_utils.uniform(shape, 'bool_'))
numeric_array = chainerx.array(array_utils.uniform(shape, dtype))
with pytest.raises(chainerx.DtypeError):
chainerx.minimum(bool_array, numeric_array)
with pytest.raises(chainerx.DtypeError):
chainerx.minimum(numeric_array, bool_array)
def clip(a, a_min, a_max):
"""Clips the values of an array to a given interval.
Given an interval, values outside the interval are clipped to the
interval edges. For example, if an interval of ``[0, 1]`` is specified,
values smaller than 0 become 0, and values larger than 1 become 1.
Args:
a (~chainerx.ndarray): Array containing elements to clip.
a_min (scalar): Maximum value.
a_max (scalar): Minimum value.
Returns:
~chainerx.ndarray: An array with the elements of ``a``, but where
values < ``a_min`` are replaced with ``a_min``,
and those > ``a_max`` with ``a_max``.
Note:
The :class:`~chainerx.ndarray` typed ``a_min`` and ``a_max`` are
not supported yet.
Note:
During backpropagation, this function propagates the gradient
of the output array to the input array ``a``.
.. seealso:: :func:`numpy.clip`
"""
if a_min is None and a_max is None:
raise ValueError('Must set either a_min or a_max.')
if a_min is not None:
a = chainerx.maximum(a, a_min)
if a_max is not None:
a = chainerx.minimum(a, a_max)
return a
def forward_chainerx(self, inputs):
x, = inputs
return chainerx.minimum(chainerx.maximum(0, x), self.cap),
def forward_chainerx(self, inputs):
x, = inputs
return chainerx.minimum(chainerx.maximum(0, x), self.cap),