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Re: Euclidean distance of all pairwise combinations (redundants)

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  • Subject: [mg128690] Re: Euclidean distance of all pairwise combinations (redundants)
  • From: Joseph Gwinn <joegwinn at comcast.net>
  • Date: Sun, 18 Nov 2012 03:55:58 -0500 (EST)
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  • References: <k87j6e$s4u$1@smc.vnet.net>

In article <k87j6e$s4u$1 at smc.vnet.net>, Dana DeLouis <dana01 at me.com> 
wrote:

> > Any ideas on how to get the euclidean distance between all the points 
> including redundants and self references?
> 
> Just for fun, and to add complexity, heres one more but with the output 
> in table form.   :>)
> 
> 
> list={{1,1},{2,2},{3,3},{4,5}};
> 
> (* Edge List - el  *)
> 
> el=UndirectedEdge@@@Subsets[list,{2}];
> 
> g=Graph[el,EdgeWeight->EuclideanDistance@@@el];
> 
> 
> MatrixForm[GraphDistanceMatrix[g],TableHeadings->{VertexList[g],VertexList[g]}
> ]
> 
> 
> = = = = = = = = = =
> HTH   :>)
> Mac & Math 8
> =E2=80=A8Dana DeLouis
> =E2=80=A8= = = = = = = = = =
> 
> 
> 
> 
> On Tuesday, November 13, 2012 10:35:53 PM UTC-8, Jesse Pisel wrote:
> > I have been having a tough time trying to figure out how to include =
> all red undant pairwise combinations in my results for the euclidean =
> distance between a set of points. I have a set of points with xy =
> coordinates and want the euclidean distance between each point including =
> the point and itself. So if my points in xy space are list = {{1, 1}, =
> {2, 2}, {3, 3}} for example, I want the distance from {1, 1} to {1, 1}, =
> {1, 1} to {2, 2}, and {2, 2} to {3, 3} etc. for each point for a total =
> of 9 distances all together. The EuclideanDistance function removes the =
> redundant distances that I want retained in the results. I have been =
> using this code just to play with data but would like to be able to =
> expand up to 500+ points:
> >
> >
> >
> > list = {{1, 1}, {2, 2}, {3, 3}}
> >
> > EuclideanDistance @@@ Subsets[list, {2}]
> >
> >
> >
> > Any ideas on how to get the euclidean distance between all the points =
> including redundants and self references?

What I've used is Outer[EuclideanDistance,list,list,1]. 

Joe Gwinn



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