mars.tensor.empty_like#

mars.tensor.empty_like(a, dtype=None, gpu=None, order='K')[source]#

Return a new tensor with the same shape and type as a given tensor.

Parameters

  • a (array_like) – The shape and data-type of a define these same attributes of the returned tensor.

  • dtype (data-type, optional) – Overrides the data type of the result.

  • gpu (bool, optional) – Allocate the tensor on GPU if True, None as default

  • 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 prototype is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of prototype as closely as possible.

Returns

out – Array of uninitialized (arbitrary) data with the same shape and type as a.

Return type

Tensor

See also

ones_like

Return a tensor of ones with shape and type of input.

zeros_like

Return a tensor of zeros with shape and type of input.

empty

Return a new uninitialized tensor.

ones

Return a new tensor setting values to one.

zeros

Return a new tensor setting values to zero.

Notes

This function does not initialize the returned tensor; to do that use zeros_like or ones_like instead. It may be marginally faster than the functions that do set the array values.

Examples

>>> import mars.tensor as mt
>>> a = ([1,2,3], [4,5,6])                         # a is array-like
>>> mt.empty_like(a).execute()
array([[-1073741821, -1073741821,           3],    #ranm
       [          0,           0, -1073741821]])
>>> a = mt.array([[1., 2., 3.],[4.,5.,6.]])
>>> mt.empty_like(a).execute()
array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000],#random
       [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])