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Top Forums UNIX for Dummies Questions & Answers A question about Unix Architecture. Post 302688031 by jim mcnamara on Friday 17th of August 2012 12:24:21 PM
Old 08-17-2012
Another way to look at UNIX/Linux:

A range of scale from small to ridiculous:
It runs some kids toys, your refrigerator, your android, your car, most electric utility billing systems, and very LARGE computer clusters that have thousands of individual computers working on a single problem.

Some of the world's fastest installations are UNIX/Linux clusters.

So it is literally everywhere.
 

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clm mate(1)							  USER COMMANDS 						       clm mate(1)

  NAME
      clm mate - compute best matches between two clusterings

      clmmate  is not in actual fact a program. This manual page documents the behaviour and options of the clm program when invoked in mode mate.
      The options -h, --apropos, --version, -set, --nop are accessible in all clm modes. They are described in the clm manual page.

  SYNOPSIS
      clm mate [-o fname (output file name)] [-b (omit headers)] [--one-to-many (require multiple hits in <clfile1>)] [-h (print synopsis,  exit)]
      [--apropos (print synopsis, exit)] [--version (print version, exit)] <clfile1> <clfile2>

  DESCRIPTION
      clm  mate computes for each cluster X in clfile1 all clusters Y in clfile2 that have non-empty intersection and outputs a line with the data
      points listed below.

	 overlap(X,Y)		    # 2 * size(meet(X,Y)) / (size(X)+size(Y))
	 index(X)		    # name of cluster
	 index(Y)		    # name of cluster
	 size(meet(X,Y))
	 size(X-Y)		    # size of left difference
	 size(Y-X)		    # size of right difference
	 size(X)
	 size(Y)
	 projection(X, clfile2)     # see below
	 projection(Y, clfile1)     # see below

      The projected size of a cluster X relative to a clustering K is simply the sum of all the nodes shared between any cluster Y  in	K  and	X,
      duplications allowed. For example, the projected size of (0,1) relative to {(0,2,4), (1,4,9), (1,3,5)} equals 3.

      The  overlap  between X and Y is exactly 1.0 if the two clusters are identical, and for nearly identical clusterings the score will be close
      to 1.0.

      All of this information can also be obtained from the contingency matrix defined for two clusterings.  The [i,j] row-column entry in a  con-
      tigency  matrix  between	to clusterings gives the number of entries in the intersection between cluster i and cluster j from the respective
      clusterings. The other information is implicitly present; the total number of nodes in clusters i and j for example can be obtained  as  the
      sum  of  entries	in row i and column j respectively, and the difference counts can then be obtained by substracting the intersection count.
      The contingency matrix can easily be computed using mcx; e.g.

      mcx /clfile2 lm /clfile1 lm tp mul /ting wm

      will create the contingency matrix in mcl matrix format in the file ting, where columns range over the clusters in clfile1.

      The output can be put to good use by sorting it numerically on that first score field. It is advisable to use a stable sort routine (use the
      -s option for UNIX sort) From this information one can quickly extract the closest clusters between two clusterings.

  OPTIONS
      -o fname (output file name)
	Specify the name of the output file.

      -b (omit headers)
	Batch mode, omit column names.

      --one-to-many (require multiple hits in <clfile1>)
	Do not output information for clusters in the first file that are subset of a cluster in the second file.

  AUTHOR
      Stijn van Dongen.

  SEE ALSO
      mclfamily(7) for an overview of all the documentation and the utilities in the mcl family.

  clm mate 12-068						      8 Mar 2012							 clm mate(1)
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