[Numpy-discussion] arrays of matrices
Thu Feb 28 19:21:27 CST 2008
On Thu, Feb 28, 2008 at 06:55:11PM -0600, Robert Kern wrote:
> On Thu, Feb 28, 2008 at 6:43 PM, Geoffrey Irving <firstname.lastname@example.org> wrote:
> > > The magic is in In. We reshape the array of vectors to be
> > > compatible with the shape of the array of matrices. When we multiply
> > > the two together, it is as if we multiplied two (n,3,3) matrices, the
> > > latter being the vectors repeated 3 times. Then we sum along the rows
> > > of each of the product matrices to get the desired dot product.
> > Thanks! That'll do nicely.
> > For large matrices, that could be problematic due to the blowup in
> > intermediate memory, but on the other hand for large matrices a loop
> > through the toplevel index wouldn't add much cost.
> If you really want to save memory and you can destroy A, then you
> could do the multiplication in-place. If you really want to get fancy
> and can destroy b, you can use it as storage for the summation output,
> In : A *= b.reshape([n,1,3])
> In : c = A.sum(axis=-1, out=b)
> In : b
> array([[ 50, 140, 230],
> [ 1220, 1580, 1940],
> [ 4010, 4640, 5270],
> [ 8420, 9320, 10220],
> [14450, 15620, 16790]])
> In : c is b
> Out: True
By large I meant if the array shapes are (n,i,j), (n,j,k), where all indices
are large. The permanent memory cost is O(nij+njk), but O(nijk) flops are
required, and the broadcast/sum solution would require intermediate storage
for each one.
It doesn't matter in my case though, so it's just a curiosity.
> > > PS: Are you perchance the Geoffrey Irving I knew at CalTech, class of '03?
> > Yep. That would answer the question I had when I started reading this email.
> > However, it's spelled Caltech, not CalTech!
> Yeah, yeah, yeah. The Wikis, they have taken over my finger ReFlexes.
> NumPy Rudds += 1. Take that, Tim Hochberg! :-)
There are a bunch of techers here (from further back than us), but I may be the
only one actively using numpy at the moment. It would be very painful to have
to lug large mesh data in python around without it. And I wouldn't have the
pleasure of writing stuff like this:
# triangulate a polygon mesh, given as (counts,vertices,points)
# counts is the number of vertices in each polygon, and vertices
# are the concatenated vertex indices of each polygon
triangleFace = numpy.arange(len(counts)).repeat(counts-2)
vertex1 = (numpy.add.accumulate(counts)-counts)[triangleFace]
vertex2 = numpy.arange(len(triangleFace)) + 2*triangleFace + 1
triangles = vertices[numpy.vstack([vertex1,vertex2,vertex2+1]).transpose()]
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