I found a matlab script that I want to translate into numpy, but have difficulties with understanding indexing in matlab. I haven't used matlab very much and I was hoping that someone could help with the following:
It says:
Uns = ones(1,m); ... and then later
Xijm(:,Uns);
m is a positive integer. The problem is that Xijm should be a 1-dimensional array (I think), so what is Uns doing in the second statement? Is this some way to expand Xijm into a second dimension? What would be a numpy equivalent?
Gabriel
Gabriel J.L. Beckers wrote:
I found a matlab script that I want to translate into numpy, but have difficulties with understanding indexing in matlab. I haven't used matlab very much and I was hoping that someone could help with the following:
It says:
Uns = ones(1,m);
... and then later
Xijm(:,Uns);
m is a positive integer. The problem is that Xijm should be a 1-dimensional array (I think), so what is Uns doing in the second statement? Is this some way to expand Xijm into a second dimension? What would be a numpy equivalent?
What this is doing depends on exactly what Xijm is. Matlab started out with 2-D arrays only--*everything* was a 2-D array (matrix) of double-precision numbers. (Even strings were represented as double-precision matrices.) Later, support for more dimensions was tacked on, but a vector in Matlab is still a 2-D array, and by default it is a single row:
Xijm = [2,3,4]
Xijm = 2 3 4
size(Xijm)
ans = 1 3
Xijm(:,ones(1,2))
ans = 2 2
To make a row into a column, you can transpose it:
XijmT = Xijm.'
XijmT = 2 3 4
XijmT(:,ones(1,2))
ans = 2 2 3 3 4 4
Numpy is much more general, and supports arrays of any number of dimensions right from the start.
My guess is that in your Matlab application, X is a column (XijmT), as in the second example above, and the indexing is repeating the columns as shown. Usually this is done to facilitate an array operation with another array that, in this example, has shape (3,2). In numpy it is not necessary to make a new array with the columns repeated because broadcasting achieves the same result more efficiently:
In [1]:import numpy
In [2]:XijmT = numpy.array([2,3,4])
In [3]:XijmT.shape Out[3]:(3,)
In [4]:A = numpy.arange(6)
In [5]:A.shape = 3,2
In [6]:A Out[6]: array([[0, 1], [2, 3], [4, 5]])
In [9]:XijmT[:,numpy.newaxis] + A Out[9]: array([[2, 3], [5, 6], [8, 9]])
The indexing with numpy.newaxis makes a 2-D view of the 1-D array, allowing the column dimension to be broadcast to match that of A.
Eric
Gabriel
Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
if m = 5, then Uns = ones(1,m) means 1x5 matrix(vector) of 1: [1 1 1 1 1] Xijm(:,Uns) is called Tony's trick where you actually replicate the Xijm (vector i guess) 5 times (according to the Uns.) e.g. http://xtargets.com/snippets/posts/show/55 Matlab introduced repmat() function that does the job "explicitly", using Tony's trick mostly.
Hope it helps, Alex
On Dec 23, 7:52 pm, "Gabriel J.L. Beckers" beck...@orn.mpg.de wrote:
I found a matlab script that I want to translate into numpy, but have difficulties with understanding indexing in matlab. I haven't used matlab very much and I was hoping that someone could help with the following:
It says:
Uns = ones(1,m);... and then later
Xijm(:,Uns);m is a positive integer. The problem is that Xijm should be a 1-dimensional array (I think), so what is Uns doing in the second statement? Is this some way to expand Xijm into a second dimension? What would be a numpy equivalent?
Gabriel
Numpy-discussion mailing list Numpy-discuss...@scipy.orghttp://projects.scipy.org/mailman/listinfo/numpy-discussion
participants (3)
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Alex -
Eric Firing -
Gabriel J.L. Beckers