nums.numpy.empty_like
-
nums.numpy.
empty_like
(prototype, dtype=None, order='K', shape=None)[source] Return a new array with the same shape and type as a given array.
This docstring was copied from numpy.empty_like.
Some inconsistencies with the NumS version may exist.
- Parameters
prototype (BlockArray) – The shape and data-type of prototype define these same attributes of the returned array.
dtype (data-type, optional) – Overrides the data type of the result.
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 ofprototype
as closely as possible.shape (int or sequence of ints, optional.) – 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.
- Returns
out – Array of uninitialized (arbitrary) data with the same shape and type as prototype.
- Return type
See also
ones_like
Return an array of ones with shape and type of input.
zeros_like
Return an array of zeros with shape and type of input.
full_like
Return a new array with shape of input filled with value.
empty
Return a new uninitialized array.
Notes
This function does not initialize the returned array; to do that use zeros_like or ones_like instead. It may be marginally faster than the functions that do set the array values.
Only order=’K’ is supported.
Examples
The doctests shown below are copied from NumPy. They won’t show the correct result until you operate
get()
.>>> a = ([1,2,3], [4,5,6]) # a is array-like >>> nps.empty_like(a, shape=(2, 3), dtype=float).get() array([[-1073741821, -1073741821, 3], # uninitialized [ 0, 0, -1073741821]]) >>> a = nps.array([[1., 2., 3.],[4.,5.,6.]]).get() >>> nps.empty_like(a, shape=(2, 3), dtype=float).get() array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])