flopscope.numpy.ones_like
fnp.ones_like(a, dtype=None, order='K', subok=True, shape=None, *, device=None)[flopscope source][numpy source]
Return an array of ones with the same shape and type as a given array.
Adapted from NumPy docs np.ones_like
Array of ones with same shape/type as input.
Parameters
- a:array_like
The shape and data-type of
adefine 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
ais Fortran contiguous, 'C' otherwise. 'K' means match the layout ofaas closely as possible.- subok:bool, optional.
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.
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.
- device:str, optional
The device on which to place the created array. Default: None. For Array-API interoperability only, so must be
"cpu"if passed.Added in version 2.0.0.
Returns
- out:ndarray
Array of ones with the same shape and type as
a.
See also
- we.flops.empty_like Return an empty array with shape and type of input.
- we.flops.zeros_like Return an array of zeros with shape and type of input.
- we.flops.full_like Return a new array with shape of input filled with value.
- we.flops.ones Return a new array setting values to one.
Examples
>>> import flopscope.numpy as fnp
>>> x = flops.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> flops.ones_like(x)
array([[1, 1, 1],
[1, 1, 1]])>>> y = flops.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> flops.ones_like(y)
array([1., 1., 1.])