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Module « numpy.matlib »

Fonction trace - module numpy.matlib

Signature de la fonction trace

def trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None) 

Description

help(numpy.matlib.trace)

Return the sum along diagonals of the array.

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 `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 : ndarray
    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.

See Also
--------
diag, diagonal, diagflat

Examples
--------
>>> import numpy as np
>>> 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)

Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les fondamentaux Voir le programme détaillé