Implementation of matrix norm for order greater than 2

When using numpy.linalg.norm with ord different from 2, for example 3, then I expected the norm to be computed according to the documentation, i.e. as sum(abs(x)**ord)**(1./ord)

But instead I got:

ValueError                                Traceback (most recent call last)
<ipython-input-784-3344f4d8d663> in <module>()
----> 1 nnorm((distpdfs_diverg['BC']/n_samples) - np.eye(n_bundles), 3)

/usr/lib/python3.5/site-packages/numpy/linalg/linalg.py in norm(x, ord, axis, keepdims)
   2247             ret = _multi_svd_norm(x, row_axis, col_axis, sum)
   2248         else:
-> 2249             raise ValueError("Invalid norm order for matrices.")
   2250         if keepdims:
   2251             ret_shape = list(x.shape)

ValueError: Invalid norm order for matrices.

Looking at the source code (numpy 1.13, numpy 1.14, and current) it seems it is simply the norm is simply not implemented for ord greater than 2 contrarily to what the doc says. Would that be something difficult to do?

[pv]

Yes, ord=(p, q) could also be possible. The implementation is in principle one-liner.

[eric-wieser]

This is as documented:

=====  ============================  ==========================
ord    norm for matrices             norm for vectors
=====  ============================  ==========================
other  --                            sum(abs(x)**ord)**(1./ord)
=====  ============================  ==========================

Note the column for matrices is blank