morning Boyd, morning list, if your similarity values are (or can be reduced to) vector cosines, you could run a singular value decomposition (e.g. via PDL::MatrixOps::svd()) on your raw data and plot the decomposed U (or V) -- that way, data points "close" to one another will wind up close together in the decomposition. If you're using Euclidean distance (over similarity vectors) you might try PDL::Stats::Kmeans to cluster your data, and then group the clusters together in your plot. there's probably a better way to do what you want, though, but it's not occurring to me at the moment.
marmosets, Bryan On Mon, Nov 28, 2016 at 11:21 PM, Boyd Duffee <[email protected]> wrote: > Hi All, > > I have a list of objects and a measure of similarity between each of them > such that I can define a matrix that holds the similarity between objects > $i and $j as > $similarity->[$i]->[$j] = sim($i, $j); > > I'd like to display that matrix as a heatmap but with the list of objects > sorted so that when I plot it, objects that are similar are next to each > other. I'm getting a lot of heatmap results from TFW and some on CPAN, but > they all seem to be concerned with overlays on maps rather than the > ordering of the columns. I can display the result with gnuplot, but I'm > getting bogged down in the sorting. > > I'm in the middle of some kind of selection sort based on minimizing the > sum of the differences between the rows, but I thought I should ask if > anyone has any experience with pretty-ing up heatmaps or if the problem has > already been solved. > > thanks, > -- > Boyd Duffee > Brexit has made Europe the new rock and roll, > that thing the older generation just doesn't understand. > > ------------------------------------------------------------ > ------------------ > > _______________________________________________ > pdl-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/pdl-general > > -- Bryan Jurish "There is *always* one more bug." [email protected] -Lubarsky's Law of Cybernetic Entomology
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