[Numpy-discussion] .T Transpose shortcut for arrays again

[Tim Hochberg]

Tim Hochberg wrote:
> Sven Schreiber wrote:
>   
>> Travis Oliphant schrieb:
>>   
>>     
>>> Bill Baxter wrote:
>>>     
>>>       
>>>> So in short my proposal is to:
>>>> --  make a.T a property of array that returns a.swapaxes(-2,-1),
>>>> --  make a.H a property of array that returns 
>>>> a.conjugate().swapaxes(-2,-1)
>>>> and maybe
>>>> --  make a.M a property of array that returns numpy.asmatrix(a)
>>>>       
>>>>         
>>> I've tentatively implemented all of these suggestions as well as adding 
>>> the .A attribute to the ndarray as well (so that all sub-classes and 
>>> array scalars can get back a view as an ndarray). 
>>>
>>> I did this to make it easier to do matrix-like calculations with or 
>>> with-out matrices.   Matrix-calculation flexibility is still a sore-spot 
>>> for many and I think these syntatical-sugar attributes will help long term.
>>>
>>>     
>>>       
>> I think this is great, thanks to Bill for suggesting it and to Travis
>> for implementing it!
>>
>> So the only convenience of matrices over pure arrays that remains (afaics):
>>
>> -) .I for inverse; actually, why not add that to arrays as well as
>> "syntactic sugar"?
>>   
>>     
> Because it encourages people to do the wrong thing numerically speaking? 
> My understanding is that one almost never wants to compute the inverse 
> directly, at least not if you're subsequently going to multiply it with 
> something, instead you want to use linalg.solve or some other similar 
> approach.
>   
Also, having an attribute access do an expensive operation behind the 
scenes seems antisocial.

>   
>> -) * being the matrix product instead of element-wise; Now, I could live
>> with using dot and I don't want to push anything, but maybe this is the
>> right time to consider another operator symbol as a shortcut for the dot
>> function to be used with arrays? (Unfortunately right now I can't think
>> of any sensible character(s) for that...)
>>   
>>     
> See my earlier note on why using a method (which '*' is in essence) for 
> dot is not really a good idea. 
Actually, thinking about this some more, this isn't a very compelling 
reason since multiply suffers from the same effect. Better reasons:

1. Consistency. Unless '/' is overloaded as well, then some of the 
operators will be elementwise and some will be matrix (+/- go both ways) 
making the interface inconsistent and thus harder to learn.
2. Backwards compatibility.
3. I was going to say narrowness of interface, since I had this fantasy 
that dot allowed axis to be specified, and thus we'd still need a 
separate dot function in addition to '*'. However,  that must be 
something I have set up locally. (I realize that there's a separate 
multiply ufunc, but the ufuncs are all the same so I don't really count 
them against the overall complexity of numpy).

Curmudgeonly yours,

-tim


> We can do better. Oh, and this would 
> completely hose all the non matrix heads out there as well.
>
>   
>> -) ** analogously for powers. For me this is less important.
>>
>> -) Being able to distinguish between row and column vectors; I guess
>> this is just not possible with arrays...
>>   
>>     
> Why can't you distinguish between them the same way that the matrix 
> class does? Shape [1, N] is a row array, shape [N,1] is column array.
>   
>> If --apart from the previous changes-- numpy had the .I for arrays I
>> guess this would get me to say goodbye to matrices. The rest of the list
>> would be a welcome luxury. I believe this whole thing has the potential
>> to unify the use of numpy by eventually making the matrix subclass
>> redundant. IMO that would be more transparent for new users and would
>> increase the popularity of numpy!
>>     
>
>      >>> len(dir(numpy.ndarray)) - len(dir(int))
>     99
>
> I'm not particularly excited about the matrix class growing ever more 
> cruft, but c'est la vie. I really need to finish up arraykit.....
>
> -tim
>
>
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