On Thu, Apr 15, 2010 at 1:48 PM, Keith Goodman <kwgood...@gmail.com> wrote: > On Thu, Apr 15, 2010 at 12:41 PM, Nikolaus Rath <nikol...@rath.org> wrote: >> Keith Goodman <kwgood...@gmail.com> writes: >>> On Wed, Apr 14, 2010 at 12:39 PM, Nikolaus Rath <nikol...@rath.org> wrote: >>>> Keith Goodman <kwgood...@gmail.com> writes: >>>>> On Wed, Apr 14, 2010 at 8:49 AM, Keith Goodman <kwgood...@gmail.com> >>>>> wrote: >>>>>> On Wed, Apr 14, 2010 at 8:16 AM, Nikolaus Rath <nikol...@rath.org> wrote: >>>>>>> Hello, >>>>>>> >>>>>>> How do I best find out the indices of the largest x elements in an >>>>>>> array? >>>>>>> >>>>>>> Example: >>>>>>> >>>>>>> a = [ [1,8,2], [2,1,3] ] >>>>>>> magic_function(a, 2) == [ (0,1), (1,2) ] >>>>>>> >>>>>>> Since the largest 2 elements are at positions (0,1) and (1,2). >>>>>> >>>>>> Here's a quick way to rank the data if there are no ties and no NaNs: >>>>> >>>>> ...or if you need the indices in order: >>>>> >>>>>>> shape = (3,2) >>>>>>> x = np.random.rand(*shape) >>>>>>> x >>>>> array([[ 0.52420123, 0.43231286], >>>>> [ 0.97995333, 0.87416228], >>>>> [ 0.71604075, 0.66018382]]) >>>>>>> r = x.reshape(-1).argsort().argsort() >>>> >>>> I don't understand why this works. Why do you call argsort() twice? >>>> Doesn't that give you the indices of the sorted indices? >>> >>> It is confusing. Let's look at an example: >>> >>>>> x = np.random.rand(4) >>>>> x >>> array([ 0.37412289, 0.68248559, 0.12935131, 0.42510212]) >>> >>> If we call argsort once we get the index that will sort x: >>> >>>>> idx = x.argsort() >>>>> idx >>> array([2, 0, 3, 1]) >>>>> x[idx] >>> array([ 0.12935131, 0.37412289, 0.42510212, 0.68248559]) >>> >>> Notice that the first element of idx is 2. That's because element x[2] >>> is the min of x. But that's not what we want. >> >> I think that's exactly what I want, the index of the smallest element. >> It also seems to work: >> >> In [3]: x = np.random.rand(3,3) >> In [4]: x >> Out[4]: >> array([[ 0.49064281, 0.54989584, 0.05319183], >> [ 0.50510206, 0.39683101, 0.22801874], >> [ 0.04595144, 0.3329171 , 0.61156205]]) >> In [5]: idx = x.reshape(-1).argsort() >> In [6]: [ np.unravel_index(i, x.shape) for i in idx[-3:] ] >> Out[6]: [(1, 0), (0, 1), (2, 2)] > > Yes, you are right. My first thought was to approach the problem by > ranking the data. But that is not needed here since the position in > the argsorted index tells us the rank. I guess my approach was to rank > first and then ask questions later. Well, at least we got to see > Anne's fast ranking method.
I see now that the first method I tried in this thread requires ranking. But the second method, the one that uses unravel_index, doesn't. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
