jax.numpy.traceΒΆ

jax.numpy.trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)[source]ΒΆ

Return the sum along diagonals of the array.

LAX-backend implementation of trace(). Original docstring below.

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.

  • axis2 (axis1,) – 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 a is 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 of a.

  • 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 – If a is 2-D, the sum along the diagonal is returned. If a has larger dimensions, then an array of sums along diagonals is returned.

Return type

ndarray

Examples

>>> np.trace(np.eye(3))
3.0
>>> a = np.arange(8).reshape((2,2,2))
>>> np.trace(a)
array([6, 8])
>>> a = np.arange(24).reshape((2,2,2,3))
>>> np.trace(a).shape
(2, 3)
Parameters
  • axis1 (int) –

  • axis2 (int) –