xorbits.numpy.zeros_like#
- xorbits.numpy.zeros_like(a, dtype=None, gpu=None, order='K')[source]#
Return an array of zeros with the same shape and type as a given array.
- Parameters
a (array_like) – The shape and data-type of a define these same attributes of the returned array.
dtype (data-type, optional) –
Overrides the data type of the result.
New in version 1.6.0(numpy).
order ({'C', 'F', 'A', or 'K'}, optional) –
Overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible.
New in version 1.6.0(numpy).
subok (bool, optional. (Not supported yet)) – If True, then the newly created array will use the sub-class type of a, otherwise it will be a base-class array. Defaults to True.
shape (int or sequence of ints, optional. (Not supported yet)) –
Overrides the shape of the result. If order=’K’ and the number of dimensions is unchanged, will try to keep order, otherwise, order=’C’ is implied.
New in version 1.17.0(numpy).
- Returns
out – Array of zeros with the same shape and type as a.
- Return type
ndarray
See also
empty_like
Return an empty array with shape and type of input.
ones_like
Return an array of ones with shape and type of input.
full_like
Return a new array with shape of input filled with value.
zeros
Return a new array setting values to zero.
Examples
>>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.zeros_like(x) array([[0, 0, 0], [0, 0, 0]])
>>> y = np.arange(3, dtype=float) >>> y array([0., 1., 2.]) >>> np.zeros_like(y) array([0., 0., 0.])
- gpubool, optional
Allocate the tensor on GPU if True, None as default
This docstring was copied from numpy.