i had changed to use kmeans https://gist.github.com/hoyeunglee/2475391ad554e3d2b2a40ec24ab47940
i do not know whether write it correctly but it seems can cluster to find words in window, but not perfect On Wednesday, August 2, 2017 at 3:06:40 PM UTC+8, Peter Otten wrote: > Glenn Linderman wrote: > > > On 8/1/2017 2:10 PM, Piet van Oostrum wrote: > >> Ho Yeung Lee <jobmatt...@gmail.com> writes: > >> > >>> def isneighborlocation(lo1, lo2): > >>> if abs(lo1[0] - lo2[0]) < 7 and abs(lo1[1] - lo2[1]) < 7: > >>> return 1 > >>> elif abs(lo1[0] - lo2[0]) == 1 and lo1[1] == lo2[1]: > >>> return 1 > >>> elif abs(lo1[1] - lo2[1]) == 1 and lo1[0] == lo2[0]: > >>> return 1 > >>> else: > >>> return 0 > >>> > >>> > >>> sorted(testing1, key=lambda x: (isneighborlocation.get(x[0]), x[1])) > >>> > >>> return something like > >>> [(1,2),(3,3),(2,5)] > > >> I think you are trying to sort a list of two-dimensional points into a > >> one-dimensiqonal list in such a way thet points that are close together > >> in the two-dimensional sense will also be close together in the > >> one-dimensional list. But that is impossible. > > > It's not impossible, it just requires an appropriate distance function > > used in the sort. > > That's a grossly misleading addition. > > Once you have an appropriate clustering algorithm > > clusters = split_into_clusters(items) # needs access to all items > > you can devise a key function > > def get_cluster(item, clusters=split_into_clusters(items)): > return next( > index for index, cluster in enumerate(clusters) if item in cluster > ) > > such that > > grouped_items = sorted(items, key=get_cluster) > > but that's a roundabout way to write > > grouped_items = sum(split_into_clusters(items), []) > > In other words: sorting is useless, what you really need is a suitable > approach to split the data into groups. > > One well-known algorithm is k-means clustering: > > https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html > > Here is an example with pictures: > > https://dzone.com/articles/k-means-clustering-scipy -- https://mail.python.org/mailman/listinfo/python-list
