nums.numpy.ones_like

nums.numpy.ones_like(prototype, dtype=None, order='K', shape=None)[source]

Return an array of ones with the same shape and type as a given array.

This docstring was copied from numpy.ones_like.

Some inconsistencies with the NumS version may exist.

Parameters

prototype (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.
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.
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 ones with the same shape and type as a.

Return type

BlockArray

See also

empty_like: Return an empty array 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.
ones: Return a new array setting values to one.

Notes

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().

>>> x = nps.arange(6)  
>>> x = x.reshape((2, 3))  
>>> x.get()  
array([[0, 1, 2],
       [3, 4, 5]])
>>> nps.ones_like(x).get()  
array([[1, 1, 1],
       [1, 1, 1]])

>>> y = nps.arange(3, dtype=float)  
>>> y.get()  
array([0., 1., 2.])
>>> nps.ones_like(y).get()  
array([1.,  1.,  1.])

nums.numpy.zeros_like

nums.numpy.concatenate