mygrad.where#

mygrad.where(condition[, x, y])[source]#

Return elements chosen from x or y depending on condition.

Note

When only condition is provided, this function is a shorthand for np.asarray(condition).nonzero(). The rest of this documentation covers only the case where all three arguments are provided.

This docstring was adapted from that of numpy.where.

Parameters:

conditionArrayLike, bool

Where True, yield x, otherwise yield y. x, y and condition need to be broadcastable to some shape.

xArrayLike

Values from which to chosen where condition is True.

yArrayLike

Values from which to chosen where condition is False.

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.

Returns:

outmygrad.Tensor: A tensor with elements from x where condition is True, and elements from y elsewhere.

Examples

>>> import mygrad as mg
>>> a = mg.arange(10)
>>> a
Tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> mg.where(a < 5, a, 10*a)
Tensor([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

This can be used on multidimensional tensors too:

>>> mg.where([[True, False], [True, True]],
...          [[1, 2], [3, 4]],
...          [[9, 8], [7, 6]])
Tensor([[1, 8],
        [3, 4]])

The shapes of x, y, and the condition are broadcast together:

>>> x, y = np.ogrid[:3, :4]
>>> mg.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
Tensor([[10,  0,  0,  0],
        [10, 11,  1,  1],
        [10, 11, 12,  2]])

>>> a = mg.Tensor([[0, 1, 2],
...                [0, 2, 4],
...                [0, 3, 6]])
>>> mg.where(a < 4, a, -1)  # -1 is broadcast
Tensor([[ 0,  1,  2],
        [ 0,  2, -1],
        [ 0,  3, -1]])