What's wrong with:

    >>> import numpy as np
    >>> m = np.array([[0,1,0],[0,0,1],[1,0,0]])
    >>> v = np.array([2,3,1])
    >>> np.linalg.solve(m,v)

    array([ 1.,  2.,  3.])

No need to transpose, as numpy handles row and column vectors identically. Using the above variables `m` and `v`:

    >>> v @ m   # treated as row vector
    array([1, 2, 3])

    >>> m @ v   # treated as column vector
    array([3, 1, 2])

Where `@`, for those who don't know, is the matrix multiplication operator (`np.dot`) introduced in recent versions of python 3.

Sounds like a case of familiarity being confused for ease of use.

What's wrong with .... introduced in recent versions of python 3.

Exactly.

Python3 is ancient, can we stop pretending otherwise?

PEP 465 introduced the infix operator @: https://www.python.org/dev/peps/pep-0465/

Python 3.5 was the first version of python3 to implement the PEP: https://docs.python.org/3.5/whatsnew/3.5.html

If I am reading the release notes correctly, Python 3.5.0 was released last year (2015-09-13): https://www.python.org/downloads/release/python-350/

It's fair to say that this is a relatively new development.

It's true that infix multiply is new, but infix multiply if I'm not mistaken is not a breaking change.

True.

I mean in the context as @sampo described it above ...

In the Stanford/Coursera machine learning class, Andrew Ng said that his teaching experience is that students pick up octave/matlab quicker and the course can cover more actual machine learning, compared to python where more time is spent learning the language.

Then "which version of Python, and how is that different..." etc.

Code targeting Python 3.2 which was released five years code is still compatible with the latest versions of Python 3. What is the issue exactly?

There are still hundreds of projects that need to get ported to 3.x. http://portingdb.xyz

That link above seems to have broken. Here's an alternative link: https://portingdb-encukou.rhcloud.com/

Why not just: v = np.array([2,3,1]).T

is there something wrong with numpys convenience method for transpose but not matlabs postfix unary convenience operator?

You are right, T would be nicer than np.tranpose. But the first google hit on 'numpy transpose' [1] doesn't give that as as example.

Also you too made the same mistake: np.array([2,3,1]).T doesn't do anything, you need np.array([[2,3,1]]).T

[1] https://docs.scipy.org/doc/numpy/reference/generated/numpy.t...

I think that's kindasorta of reason for Ng's preference: students will focus on the subject of machine learning and not spend time on the ins and outs of Python and Numpy (or these days TensorFlow). It's the same sort of pedagogical approach that keeps SML as an active teaching language.

When someone googles Matlab/Octave there won't be the same mixture of contexts as there is for Python. With Python there's "2 or 3?" before even starting. Than Cython or Jython or PyPy? That's nothing against Python except that in one particular time limited context, the questions are a distraction {without even considering the complaints of students when the choice of Python implementation is contrary to their opinion}.

here is sagemath

    m = matrix([[0, 1, 0],
                [0, 0, 1],
                [1, 0, 0]])
    v = vector([2, 3, 1])
    m \ v

(1, 2, 3)

and then

    sol = m \ v
    m * sol

(2, 3, 1)

How about Julia and R?

R:

    m = matrix( c(0,0,1,1,0,0,0,1,0), nrow=3 )
    v = c(2,3,1)
    solve(m,v)

    [1] 1 2 3

Julia: for this code snippet the syntax is identical to Matlab:

    m = [0 1 0; 0 0 1; 1 0 0]
    v = [2 3 1]'
    m\v

    3×1 Array{Float64,2}:
    1.0
    2.0
    3.0