mygrad.maximum#

class mygrad.maximum(x1: ArrayLike, x2: ArrayLike, out: Tensor | ndarray | None = None, *, where: Mask = True, dtype: DTypeLikeReals = None, constant: bool | None = None)#

Pair-wise maximum of tensor elements.

This docstring was adapted from that of numpy.maximum [1]

Parameters:
x1, x2ArrayLike

The tensors holding the elements to be compared. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

outOptional[Union[Tensor, ndarray]]

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned.

constantOptional[bool]

If True, this tensor is treated as a constant, and thus does not facilitate back propagation (i.e. constant.grad will always return None).

Defaults to False for float-type data. Defaults to True for integer-type data.

Integer-type tensors must be constant.

whereMask

This condition is broadcast over the input. At locations where the condition is True, the out tensor will be set to the ufunc result. Elsewhere, the out tensor will retain its original value. Note that if an uninitialized out tensor is created via the default out=None, locations within it where the condition is False will remain uninitialized.

dtypeOptional[DTypeLikeReals]

The dtype of the resulting tensor.

Returns:
yTensor

The maximum of x1 and x2, element-wise.

See also

minimum

Element-wise minimum of two arrays, propagates NaNs.

Notes

The maximum is equivalent to mg.where(x1 >= x2, x1, x2) when neither x1 nor x2 are nans, but it is faster and does proper broadcasting.

References

Examples

>>> import mygrad as mg
>>> mg.maximum([2, 3, 4], [1, 5, 2])
Tensor([2, 5, 4])

>>> mg.maximum(mg.eye(2), [0.5, 2]) # broadcasting
Tensor([[ 1. ,  2. ],
       [ 0.5,  2. ]])

>>> mg.maximum([mg.nan, 0, mg.nan], [0, mg.nan, mg.nan])
Tensor([nan, nan, nan])
>>> mg.maximum(mg.Inf, 1)
Tensor(inf)
Attributes:
identity
signature

Methods

accumulate([axis, dtype, out, constant])

Not implemented

at(indices[, b, constant])

Not implemented

outer(b, *[, dtype, out])

Not Implemented

reduce([axis, dtype, out, keepdims, ...])

Not Implemented

reduceat(indices[, axis, dtype, out])

Not Implemented

resolve_dtypes(dtypes, *[, signature, ...])

Find the dtypes NumPy will use for the operation.
__init__(*args, **kwargs)#

Methods

__init__(*args, **kwargs)

accumulate([axis, dtype, out, constant])

Not implemented

at(indices[, b, constant])

Not implemented

outer(b, *[, dtype, out])

Not Implemented

reduce([axis, dtype, out, keepdims, ...])

Not Implemented

reduceat(indices[, axis, dtype, out])

Not Implemented

resolve_dtypes(dtypes, *[, signature, ...])

Find the dtypes NumPy will use for the operation.

Attributes

identity

nargs

nin

nout

ntypes

signature

types