flopscope.numpy.trace
fnp.trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)[flopscope source][numpy source]
Return the sum along diagonals of the array.
Adapted from NumPy docs np.trace
Diagonal sum; cost = min(n,m).
If a is 2-D, the sum along its diagonal with the given offset
is returned, i.e., the sum of elements a[i,i+offset] for all i.
If a has more than two dimensions, then the axes specified by axis1 and
axis2 are used to determine the 2-D sub-arrays whose traces are returned.
The shape of the resulting array is the same as that of a with axis1
and axis2 removed.
Parameters
- a:array_like
Input array, from which the diagonals are taken.
- offset:int, optional
Offset of the diagonal from the main diagonal. Can be both positive and negative. Defaults to 0.
- axis1, axis2:int, optional
Axes to be used as the first and second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults are the first two axes of
a.- dtype:dtype, optional
Determines the data-type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and
ais of integer type of precision less than the default integer precision, then the default integer precision is used. Otherwise, the precision is the same as that ofa.- out:ndarray, optional
Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output.
Returns
- sum_along_diagonals:ndarray
If
ais 2-D, the sum along the diagonal is returned. Ifahas larger dimensions, then an array of sums along diagonals is returned.
See also
Examples
>>> import flopscope.numpy as fnp
>>> flops.trace(flops.eye(3))
3.0
>>> a = flops.arange(8).reshape((2,2,2))
>>> flops.trace(a)
array([6, 8])>>> a = flops.arange(24).reshape((2,2,2,3))
>>> flops.trace(a).shape
(2, 3)