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 of prototype 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

BlockArray

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]])

nums.numpy.ones

nums.numpy.top_k