Re: [Numpy-discussion] Extracting values from one array corresponding to argmax elements in another array
2010/4/5 Ken Basye kbas...@jhu.edu: 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. Maybe: def select(A, B, axis): # Extract incomplete index tuples: argmax = a.argmax(axis = axis) # Create the selection list to be handed over to B.__getitem__() ... advanced_index = [] # Decompose the index tuples. for dimi in xrange(argmax.shape[1]): advanced_index.append(argmax[:, dimi]) # Insert the missing dimension. advanced_index.insert(axis, numpy.arange(0, B.shape[axis])) # Perform advanced (integer) selection ... return B[advanced_index] a array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) select(a, a, 0) array([3, 7]) select(a, a, 1) array([5, 7]) select(a, a, 2) array([6, 7]) It seems to work. Friedrich ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Extracting values from one array corresponding to argmax elements in another array
On 04/06/2010 03:22 PM, Ken Basye wrote: 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 Basyekbas...@jhu.edu wrote: snip 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 I've tracked it down. I have created the following quick'n dirty function: def selectFromDimension(data, selection, dimension): ogridStatement = numpy.ogrid[ slices = [] for d in data.shape: slices.append(0: + str(d)) ogridStatement += ,.join(slices) ogridStatement += ] grid = eval(ogridStatement) grid[dimension] = selection result = data[grid] return result which you call with the array to select from, the result of argmin/max(axis=...), and the axis to select from (usually the same as the axis argument to the argmin/max) It uses eval to be able to create the ogrid independent of dimensionality. There's probably a much cleaner way, but this worked for me :-) Now I think about it, probably another way could be to transpose your axes such that the axis to select on becomes the first axis. Then just index with the result of argmin/max, and transpose back if necessary. However, I have not tried this and I might miss something obvious here... Vincent. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Extracting values from one array corresponding to argmax elements in another array
On 04/05/2010 06:06 PM, Keith Goodman wrote: On Mon, Apr 5, 2010 at 8:44 AM, Ken Basyekbas...@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! :-) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Extracting values from one array corresponding to argmax elements in another array
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 Basyekbas...@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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Extracting values from one array corresponding to argmax elements in another array
On Tue, Apr 6, 2010 at 9:22 AM, Ken Basye kbas...@jhu.edu wrote: 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 Basyekbas...@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 an alternative to Vincent's general solution, if you have unique max or want all argmax is using a mask a array([[ 100.,0.,0.], [ 0., 100., 100.], [ 0.,0.,0.]]) b array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) ax=0; b[a==np.expand_dims(a.max(ax),ax)] array([0, 4, 5]) ax=1; b[a==np.expand_dims(a.max(ax),ax)] array([0, 4, 5, 6, 7, 8]) aa=np.eye(3) ax=1; b[aa==np.expand_dims(aa.max(ax),ax)] array([0, 4, 8]) ax=0; b[aa==np.expand_dims(aa.max(ax),ax)] array([0, 4, 8]) Josef ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Extracting values from one array corresponding to argmax elements in another array
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.]) I tried several approaches with take() but now that I understand how take() works when you give it an axis argument it seems like this isn't going to do what I want. Still, it seems like there should be some shortcut... TIA, Ken ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Extracting values from one array corresponding to argmax elements in another array
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]) ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
