Sasha wrote:
> On 7/6/06, Bill Baxter <[EMAIL PROTECTED]> wrote:
>
>> ...
>> Yep, like Tim said. The usage is say a N sets of basis vectors. Each set
>> of basis vectors is a matrix.
>>
>
> This brings up a feature that I really miss from numpy: an ability to do
>
> array([f(x) for x in a])
>
Please note that there is now a fromiter function so that much of the
overhead of the above function can be removed by using:
numpy.fromiter((f(x) for x in a), float)
This won't generate an intermediate list or use significantly extra
storage. I doubt it's a full replacement for adverbs as you've described
below though.
-tim
> without python overhead. APL-like languages have a notion of "adverb"
> - a higher level operator that maps a function to a function. Numpy
> has some adverbs implemented as attributes to ufuncs: for example
> add.reduce is the same as +/ in K and add.accumulate is the same as +\
> ('/' and '\' are 'over' and 'scan' adverbs in K). However, there is no
> way to do f/ or f\ where f is an arbitrary dyadic function.
>
> The equivalent of array([f(x) for x in a]) is spelled f'(a) in K (' is
> an adverb 'each'). The transpose operator (+) is swaps the first two
> axes, so in order to apply to the array of matrices, one would have to
> do +:'a (: in +: disambiguates + as a unary operator).
>
> I don't know of a good way to introduce adverbs in numpy, nor can I
> think of a good way to do list comprehensions, but array friendly
> versions of map, filter and reduce may be a good addition. These
> higher order functions may take an optional axes argument to deal with
> the higher rank arrays and may be optimized to recognize ufuncs so
> that map(f, a) could call f(a) and reduce(f, a) could do f.reduce(a)
> when f is a ufunc.
>
> [snip]
>
>> Either way swapaxes(-2,-1) is likely more likely to be what you want than
>> .transpose().
>>
>>
>
> Agree, but swapaxes(0, 1) is a close runner-up which is also known as
> zip in python.
>
>
>> Well, I would be really happy for .T to return an (N,1) column vector if
>> handed an (N,) 1-d array. But I'm pretty sure that would raise more furuor
>> among the readers of the list than leaving it 1-d.
>>
>>
>
> Would you be even happier if .T would return a matrix? I hope not
> because my .M objection will apply. Maybe we can compromize by
> implementing a.T so that it raises ValueError unless rank(a) == 2 or
> at least unless rank(a) <= 2?
>
>
>> I have serious reservations about a function called t(). x,y,z, and t are
>> probably all in the top 10 variable names in scientific computing.
>>
>>
>
> What about T()?
>
>
>>> K (an APL-like language) overloads
>>> unary '+' to do swapaxes(0,1) for rank>=2 and nothing for lower rank.
>>>
>> Hmm. That's kind of interesting, it seems like an abuse of notation to me.
>> And precedence might be an issue too. The precedence of unary + isn't as
>> high as attribute access.
>>
>
> It is high enough AFAICT - higher than any binary operator.
>
>
>> Anyway, as far as the meaning of + in K, I'm
>> guessing K's arrays are in Fortran order, so (0,1) axes vary the fastest.
>>
>
> No, K has 1d arrays only, but they can be nested. Matrices are arrays
> of arrays and tensors are arrays of arrays of arrays ..., but you are
> right (0,1) swap is faster than (-2,-1) swap and this motivated the
> choice for the primitive.
>
>
>> I couldn't find any documentation for the K language from a quick search,
>> though.
>>
>
> Kx Systems, the company behind K has replaced K with Q and pulled old
> manuals from the web. Q is close enough to K: see
> http://kx.com/q/d/k.txt for a terse summary.
>
> [snip]
>
>>> Why would anyone do that if b was a matrix?
>>>
>> Maybe because, like you, they think "that a.T is fairly cryptic".
>>
>>
> If they are like me, they will not use numpy.matrix to begin with :-).
>
>
>>>> But probably a better solution
>>>> would be to have matrix versions of these in the library as an optional
>>>> module to import so people could, say, import them as M and use
>>>>
>> M.ones(2,2).
>>
>>> This is the solution used by ma, which is another argument for it.
>>>
>> Yeh, I'm starting to think that's better than slapping an M attribute on
>> arrays, too. Is it hard to write a module like that?
>>
>>
>
> Writing matrixutils with
>
> def zeros(shape, dtype=float):
> return asmatrix(zeros(shape, dtype))
>
> is trivial, but matrixutils.zeros will have two python function calls
> overhead. This may be a case for making zeros a class method of
> ndarray that can be written in a way that will make inherited
> matrix.zeros do the right thing with no overhead.
>
> [snip]
>
>> * +A implies addition.
>>
> No, it does not. Unary '+' is a noop. Does * imply multiplication or
> ** imply pow in f(*args, **kwds) to you?
>
>
>> The general rule with operator overloading is that
>> the overload should have the same general meaning as the original operator.
>>
> Unary '+' has no preset meaning in plain python. It can be interpreted
> as transpose if you think of scalars as 1x1 matrices.
>
>
>> So overloading * for matrix multiplication makes sense.
>>
>
> It depends on what you consider part of "general meaning". If the
> commutativity property is part of it then overloading * for matrix
> multiplication doesn't make sense. If the "general meaning" of unary +
> includes x = +x invariant, then you are right, but I am willing to
> relax that to x = ++x invariant when x is a non-symmetric matrix.
>
>
>> ... New users looking at something like A + +B are pretty
>> certain to be confused because they think they know what + means, but
>> they're wrong.
>>
>
> In my experience new users don't realize that unary + is defined for
> arrays. Use of unary + with non-literal numbers is exotic enough that
> new users seeing "something like A + +B" will not assume that they
> know what it means.
>
> [snip]
>
>> * +A has different precedence than the usual transpose operator. (But I
>> can't think of a case where that would make a difference now.)
>>
>>
> Maybe you can't because it doesn't? :-)
>
>
>> I would be willing to accept a .T that just threw an exception if ndim were
>>
>>> 2.
>>>
>
> Aha! Let's start with an error unless ndim != 2. It is always easier
> to add good features than to remove bad ones.
>
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Numpy-discussion@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>
>
Using Tomcat but need to do more? Need to support web services, security?
Get stuff done quickly with pre-integrated technology to make your job easier
Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/numpy-discussion
