Perhaps this a bit of a thread hyjack; but this discussion got me thinking about how to arrive

at a more vectorized/tensorified way of specifying linear algebra operations, in an elegant manner.

I probably got a little carried away, but what about this syntax?

#declare some symbols
i,j,ij,k = 'i','j','ij','k'
#we may force evaluation of a (sub) TensorExpression by calling it
#this is trivial to translate to call to einsum
#but such special cases could be dispatched to BLAS as well
b = (A(ij) * x(j)) (i)
#alternatively, we can predeclare a LHS which is automatically sized later
#note that this translates into the same call as the above; just some syntactic sugar
b = np.empty(())                   
b[i] = A(ij) * x(j)
#more complex TensorExpression graphs of this form are trivial to translate to a call to einsum as well
#conceptually, there is no need to limit this scheme to multiplications only!
#although such generalizations would require a more complex execution engine
#however, the revamped nditer should make this quite managable to implement
a(i)*b(j) + c(k)
#if axes strings are omitted, standard numpy broadcasting rules are applied to the expressiongraph created
#this is identical to a*b+c; except that we have the opportunity to eliminate temporaries

Note that such an approach kills quite some birds with one stone
it allows for the elimination of temporaries along the lines of numexpr

But if i could write:

b[i] = A[ij] * x[j]
I would much prefer that over
b = A @ x
even though the latter is shorter

Now if i had n input and output vectors, it would be easy what to do with them:

b[ni] = A[ij] * x[nj]

As i argued earlier, I much prefer this form of explicitness over conventions about what constitutes a row or column vector. And vectorization of linear algebra is a trivial extension in this manner, which in itself is just a subset of even more general multilinear products, which themselves are a subset of more general expression involving things other than products

Its a somewhat ambitious idea, and there are probably reasons why it isnt a good idea as well, but it does not require python language modifications, and it does not clash with any other functionality or syntax of numpy, as far as i can tell. Calling of arrays is not yet defined, and alternatively array indexing could be overloaded on string type.

Either way, something to chew on when deciding on the best way to go forward.


On Tue, Mar 18, 2014 at 4:28 AM, Mark Daoust <> wrote:
On Mon, Mar 17, 2014 at 8:54 PM, Nathaniel Smith <> wrote:

But, this is actually a feature! Because obviously what *should* be
returned in this case is *not* (Mat @ vec) @ Mat, *or* Mat @ (vec @
Mat). Both of those answers are terrible; it's just, if you have an
ordinary left-/right-associative operator, those are your only
options. What *should* be returned is an error. And in this scheme we
get to see the whole @ expression at once, so we actually can raise an
error for such things.

Sorry if this is a little off topic.

But there's still something about the "vector" examples that bugs me, "matrix@vector" and "vector@@2", keep popping up (this also applies to the matrix@matrix examples to a lesser extent).

I'm a little unconformable looking at the shape to to decide what's a matrix and what's a vector. (Matlab has some problems like this)

If it only has one or two dimensions it's easy, but I always find that if I've written code that works for 1 matrix or vector, 5 minutes later I want it to work for fields of matrices or vectors. If we're just going by shape there's no way to distinguish between a 2d field of matrices and a 3d field of vectors.

I guess this is a repeat of part of what Eelco Hoogendoorn saying a few posts back

I was just wondering if anyone sees a place, to get @ a little closer to Einsum, for some sort of array class that understands the difference between a 4D array of scalars, a 3D array of vectors, and a 2D array of matrices... The difference between the axes that broad-cast and the axes that can sum when you hit them with an @ ... or something like that. 

Just a thought.

Einsum is fantastic by the way, totally worth learning and using.

Mark Daoust

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