From: Vincent Schut <sc...@sarvision.nl>
On 04/05/2010 06:06 PM, Keith Goodman wrote:On Mon, Apr 5, 2010 at 8:44 AM, Ken Basye<kbas...@jhu.edu> wrote:Hi Folks, I have two arrays, A and B, with the same shape. I want to find the highest values in A along some axis, then extract the corresponding values from B. I can get the highest values in A with A.max(axis=0) and the indices of these highest values with A.argmax(axis=0). I'm trying to figure out a loop-free way to extract the corresponding elements from B using these indices. Here's code with a loop that will do what I want for two-dimensional arrays:>>> a array([[ 100., 0., 0.], [ 0., 100., 100.], [ 0., 0., 0.]]) >>> a.max(axis=0) array([ 100., 100., 100.]) >>> sel = a.argmax(axis=0) >>>sel array([0, 1, 1]) >>> b = np.arange(9).reshape((3,3)) >>> b array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> b_best = np.empty(3) >>> for i in xrange(3): ... b_best[i] = b[sel[i], i] ... >>> b_best array([ 0., 4., 5.])Here's one way:b[a.argmax(axis=0), range(3)]array([0, 4, 5])Which does not work anymore when your arrays become more-dimensional (like in my case: 4 or more) and the axis you want to select on is not the first/last one. If I recall correctly, I needed to construct the full index arrays for the other dimensions too (like with ogrid I think). So: create the ogrid, replace the one for the dimensions you want the argmax selection to take place on with the argmax parameter, and use those index arrays to index your b array. I'd need to look up my source code to be more sure/precise. If anyone would like me to, please let me know. If anyone knows a less elaborate way, also please let us know! :-)
Hi Vincent,I'd like to see more about your solution. For my present purposes, Keith's solution was sufficient, but I'm still very interested in a solution that's independent of dimension and axis. Thanks (and thanks, Keith),
Ken
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