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.