[Numpy-discussion] arrays of matrices
Geoffrey Irving
irving@pixar....
Thu Feb 28 18:43:08 CST 2008
On Thu, Feb 28, 2008 at 05:57:29PM -0600, Robert Kern wrote:
> On Thu, Feb 28, 2008 at 4:34 PM, Geoffrey Irving <irving@pixar.com> wrote:
> > Hello,
> >
> > I have a large number of points (shape (n,3)), and a matching
> > number of 3x3 matrices (shape (n,3,3)), and I want to compute
> > the product of each matrix times the corresponding point.
> >
> > I can't see a way to do this operation with dot or tensordot,
> > since these routines either sum across an index or treat it
> > as independent between the two arguments.
> >
> > Is this case, I can use the fact that 3 is small to do it, but
> > is there a clean way for handle this kind of "array of matrix"
> > situation in general?
>
> For dot-products, yes. We can use broadcasting followed by axis summation.
>
>
> In [20]: from numpy import *
>
> In [21]: n = 5
>
> In [22]: A = arange(n*3*3).reshape([n,3,3])
>
> In [23]: b = 10*arange(n*3).reshape([n,3])
>
> In [24]: A
> Out[24]:
> array([[[ 0, 1, 2],
> [ 3, 4, 5],
> [ 6, 7, 8]],
>
> [[ 9, 10, 11],
> [12, 13, 14],
> [15, 16, 17]],
>
> [[18, 19, 20],
> [21, 22, 23],
> [24, 25, 26]],
>
> [[27, 28, 29],
> [30, 31, 32],
> [33, 34, 35]],
>
> [[36, 37, 38],
> [39, 40, 41],
> [42, 43, 44]]])
>
> In [25]: b
> Out[25]:
> array([[ 0, 10, 20],
> [ 30, 40, 50],
> [ 60, 70, 80],
> [ 90, 100, 110],
> [120, 130, 140]])
>
> In [26]: for i in range(n):
> ....: print dot(A[i], b[i])
> ....:
> ....:
> [ 50 140 230]
> [1220 1580 1940]
> [4010 4640 5270]
> [ 8420 9320 10220]
> [14450 15620 16790]
>
> In [27]: (A * b.reshape([n, 1, 3])).sum(axis=-1)
> Out[27]:
> array([[ 50, 140, 230],
> [ 1220, 1580, 1940],
> [ 4010, 4640, 5270],
> [ 8420, 9320, 10220],
> [14450, 15620, 16790]])
>
>
> The magic is in In[27]. 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.
> 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!
Geoffrey
More information about the Numpy-discussion
mailing list