PS - If you're really interested, prior to the clustering command you
can use 'debug <#>' (where <#> is greater than 0) to print more
potentially helpful information. In the output you will see 'DBG: Max
dist=' which will show the maximum distance observed between points;
epsilon should be less than this. I should probably have that printed
by default.
Thanks for the report by the way.
On Fri, Jan 17, 2020 at 9:34 AM Daniel Roe <daniel.r.roe.gmail.com> wrote:
>
> OK - I've been looking at this for a bit. I think that the problem
> must be that all your points too close i.e. all points are within
> epsilon from each other. Your dvdfile backs that up - the first column
> is '#Density', which just means # of points that are within epsilon
> from that point. In each case the #Density is 1249, indicating that
> everyone is too tight. I think if you lower epsilon you'll start to
> get better results.
>
> This is probably a case that cpptraj should trap. In my (limited)
> defense, it does state that the 'dpeaks' implementation is under
> development...
>
> So in summary, try lowering epsilon and see if that helps. I'll work
> on an update to trap the case where epsilon is too large.
>
> Hope this helps,
>
> -Dan
>
> On Tue, Jan 14, 2020 at 12:34 PM <yinglong.miao.gmail.com> wrote:
> >
> > I have also tried the gauss option. It gave the following output:
> > ACTION OUTPUT:
> >
> > ANALYSIS: Performing 1 analyses:
> > 0: [cluster C0 dpeaks epsilon 4 dvdfile dvdfile choosepoints auto runavg
> > runavg.dat deltafile delta.dat sieve 200 gauss]
> > Starting clustering.
> > Mask [*] corresponds to 15 atoms.
> > Estimated pair-wise matrix memory usage: > 3.123 MB
> > Pair-wise matrix set up with sieve, 250000 frames, 1250 sieved frames.
> > Calculating pair-wise distances.
> > 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
> >
> > No error message was given but also no further output ...
> >
> > Thanks,
> > Yinglong
> >
> >
> > On Tue, Jan 14, 2020 at 9:47 AM Daniel Roe <daniel.r.roe.gmail.com> wrote:
> >
> > > Can you provide me (either in reply to this or off list) your entire
> > > cpptraj output and the contents of dvdfile?
> > >
> > > This could happen with very sparse density I think, although its
> > > difficult to say without exactly replicating. You could potentially
> > > try the 'gauss' keyword for Gaussian density instead of discrete
> > > density.
> > >
> > > -Dan
> > >
> > > On Mon, Jan 13, 2020 at 8:10 PM Yinglong Miao <yinglong.miao.gmail.com>
> > > wrote:
> > > >
> > > > Hi Dan,
> > > >
> > > > It’s the latest version as in AMBER git repository.
> > > >
> > > > Thanks,
> > > > Yinglong
> > > >
> > > >
> > > > > On Jan 13, 2020, at 6:20 PM, Daniel Roe <daniel.r.roe.gmail.com>
> > > wrote:
> > > > >
> > > > > What version of cpptraj are you using?
> > > > >
> > > > > -Dan
> > > > >
> > > > > On Mon, Jan 13, 2020 at 6:51 PM <yinglong.miao.gmail.com> wrote:
> > > > >>
> > > > >> Hello,
> > > > >>
> > > > >> I tried to use the dpeaks algorithm for clustering with the following
> > > > >> command:
> > > > >> cluster C0 dpeaks epsilon 4 dvdfile dvdfile choosepoints auto runavg
> > > > >> runavg.dat deltafile delta.dat sieve 200
> > > > >>
> > > > >> But keep getting the following output with error:
> > > > >> ...
> > > > >> Finding closest neighbor point with higher density for each point.
> > > > >> 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
> > > > >> Internal Error: In Cluster_DPeaks::AssignClusterNum nearest neighbor
> > > is -1.
> > > > >> Segmentation fault (core dumped)
> > > > >>
> > > > >> I will appreciate any suggestions that would fix this ...
> > > > >>
> > > > >> Thanks,
> > > > >> Yinglong
> > > > >>
> > > > >> Yinglong Miao, Ph.D.
> > > > >> Assistant Professor
> > > > >> Center for Computational Biology and
> > > > >> Department of Molecular Biosciences
> > > > >> University of Kansas
> > > > >> http://miao.compbio.ku.edu
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Received on Fri Jan 17 2020 - 07:00:03 PST
