[Numpy-discussion] ndarray.T2 for 2D transpose

josef.pktd at gmail.com josef.pktd at gmail.com
Thu Apr 7 11:35:12 EDT 2016

On Thu, Apr 7, 2016 at 11:13 AM, Todd <toddrjen at gmail.com> wrote:
> On Wed, Apr 6, 2016 at 5:20 PM, Nathaniel Smith <njs at pobox.com> wrote:
>> On Wed, Apr 6, 2016 at 10:43 AM, Todd <toddrjen at gmail.com> wrote:
>> >
>> > My intention was to make linear algebra operations easier in numpy.
>> > With
>> > the @ operator available, it is now very easy to do basic linear algebra
>> > on
>> > arrays without needing the matrix class.  But getting an array into a
>> > state
>> > where you can use the @ operator effectively is currently pretty verbose
>> > and
>> > confusing.  I was trying to find a way to make the @ operator more
>> > useful.
>> Can you elaborate on what you're doing that you find verbose and
>> confusing, maybe paste an example? I've never had any trouble like
>> this doing linear algebra with @ or dot (which have similar semantics
>> for 1d arrays), which is probably just because I've had different use
>> cases, but it's much easier to talk about these things with a concrete
>> example in front of us to put everyone on the same page.
> Let's say you want to do a simple matrix multiplication example.  You create
> two example arrays like so:
>    a = np.arange(20)
>    b = np.arange(10, 50, 10)
> Now you want to do
>     a.T @ b
> First you need to turn a into a 2D array.  I can think of 10 ways to do this
> off the top of my head, and there may be more:
>     1a) a[:, None]
>     1b) a[None]
>     1c) a[None, :]
>     2a) a.shape = (1, -1)
>     2b) a.shape = (-1, 1)
>     3a) a.reshape(1, -1)
>     3b) a.reshape(-1, 1)
>     4a) np.reshape(a, (1, -1))
>     4b) np.reshape(a, (-1, 1))
>     5) np.atleast_2d(a)
> 5 is pretty clear, and will work fine with any number of dimensions, but is
> also long to type out when trying to do a simple example.  The different
> variants of 1, 2, 3, and 4, however, will only work with 1D arrays (making
> them less useful for functions), are not immediately obvious to me what the
> result will be (I always need to try it to make sure the result is what I
> expect), and are easy to get mixed up in my opinion.  They also require
> people keep a mental list of lots of ways to do what should be a very simple
> task.
> Basically, my argument here is the same as the argument from pep465 for the
> inclusion of the @ operator:
> https://www.python.org/dev/peps/pep-0465/#transparent-syntax-is-especially-crucial-for-non-expert-programmers
> "A large proportion of scientific code is written by people who are experts
> in their domain, but are not experts in programming. And there are many
> university courses run each year with titles like "Data analysis for social
> scientists" which assume no programming background, and teach some
> combination of mathematical techniques, introduction to programming, and the
> use of programming to implement these mathematical techniques, all within a
> 10-15 week period. These courses are more and more often being taught in
> Python rather than special-purpose languages like R or Matlab.
> For these kinds of users, whose programming knowledge is fragile, the
> existence of a transparent mapping between formulas and code often means the
> difference between succeeding and failing to write that code at all."

This doesn't work because of the ambiguity between column and row vector.

In most cases 1d vectors in statistics/econometrics are column
vectors. Sometime it takes me a long time to figure out whether an
author uses row or column vector for transpose.

i.e. I often need x.T dot y   which works for 1d and 2d to produce
inner product.
but the outer product would require most of the time a column vector
so it's defined as x dot x.T.

I think keeping around explicitly 2d arrays if necessary is less error
prone and confusing.

But I wouldn't mind a shortcut for atleast_2d   (although more often I
need atleast_2dcol to translate formulas)


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