[Numpy-discussion] Integer array indexing (numpy.take) as function composition
Jim Pivarski
jpivarski at gmail.com
Thu Sep 5 11:51:12 EDT 2019
Hi,
I'm a long-time user of Numpy; I had a question and I didn't know where
else to ask. (It's not a bug—otherwise I would have posted it at
https://github.com/numpy/numpy/issues).
Has anyone noticed that indexing an array with integer arrays (i.e.
numpy.take) is a function composition? For example, suppose you have any
two non-negative functions of integers:
def f(x):
return x**2 - 5*x + 10
def g(y):
return max(0, 2*y - 10) + 3
and you sample them as arrays, as well as their composition g(f(•)):
F = numpy.array([f(i) for i in range(10)]) # F is f at 10 elements
G = numpy.array([g(i) for i in range(100)]) # G is g at enough
elements to include max(f)
GoF = numpy.array([g(f(i)) for i in range(10)]) # GoF is g∘f at 10 elements
Indexing G by F (G[F]) returns the same result as the sampled composition (
GoF):
print("G\u2218F =", G[F]) # integer indexing
print("g\u2218f =", GoF) # array of the composed functions
G∘F = [13 5 3 3 5 13 25 41 61 85]
g∘f = [13 5 3 3 5 13 25 41 61 85]
This isn't a proof, but I think it's easy to see that it would be true for
any non-negative functions (negative index handling spoils this property).
It might sound like a purely academic point, but I've noticed that I've
been able to optimize and simplify some code by taking advantage of the
associative property of function composition, repeatedly applying
numpy.take on arrays of integers before applying the fully composed index
to my data. As an example of an optimization, if I have to do the same
thing to N data arrays, it helps to prepare a single integer index and
apply it to the N data arrays instead of modifying all N data arrays in
multiple steps. As an example of a simplification, if I need to modify
arrays in recursion, it's easier to reason about the recursion if only the
terminal case applies an index to data, with the non-terminal steps
applying indexes to indexes.
This is such a basic property that I bet it has a name, and there's
probably some literature on it, like what you could find if you were
interested in monads in Haskell. But I haven't been able to find the right
search strings—what would you call this property? Is there a literature on
it and its uses?
Thanks!
-- Jim
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20190905/5830b4f5/attachment.html>
More information about the NumPy-Discussion
mailing list