#!/usr/perl/perl580/bin/perl -w
use strict;
use Algorithm::Cluster qw/kmedoids distancematrix/;
my $file = "../../data/cyano.txt";
my $i = 0;
my $j = 0;
my (@orfname,@orfdata,@weight,@mask);
open(DATA,"<$file") or die "Can't open file $file: $!";
#------------------
# Read in the data file, and save the data to @orfdata
# We know that the file is intact and has no holes,
# so just set the mask to 1 for every item.
# We don't check for errors in this case, because the file
# is short and we can spot errors by eye.
#
my $firstline = <DATA>; # Skip the title line
while(<DATA>) {
chomp(my $line = $_);
my @field = split /\t/, $line;
$orfname[$i] = $field[0];
$orfdata[$i] = [ @field[2..5] ];
$mask[$i] = [ 1,1,1,1 ];
++$i;
}
close(DATA);
#------------------
# Make a reverse-lookup index of the @orfnames hash:
#
my %orfname_by_rowid;
$i=0;
$orfname_by_rowid{$i++} = $_, foreach(@orfname);
@weight = (1.0) x 4;
#------------------
# Define the params we want to pass to distancematrix
my %params1 = (
transpose => 0,
dist => 'e',
data => \@orfdata,
mask => \@mask,
weight => \@weight,
);
#------------------
# Here is where we invoke the library function!
#
printf("Calculating the distance matrix\n");
my $matrix = distancematrix(%params1);
#
#------------------
my %params2 = (
nclusters => 6,
distances => $matrix,
npass => 1000,
);
printf("Executing k-medoids clustering 1000 times, using random initial clusterings\n");
my ($clusters, $error, $found) = kmedoids(%params2);
my $item;
$i = 0;
foreach $item (@{$clusters}) {
print $i, ": ", $item, "\n";
++$i;
}
#------------------
# Print out the resulting within-cluster sum of distances.
#
print "------------------\n";
printf("Within-cluster sum of distances: %f; solution was found %d times\n\n", $error, $found);
#------------------
# Try this again with a specified initial clustering solution
#
my @initialid = (0,1,2,3,4,5) x 15;
# choice for the initial clustering; the data file contains 90 genes.
%params2 = (
nclusters => 6,
distances => $matrix,
initialid => \@initialid,
);
printf("Executing k-medoids clustering with a specified initial clustering\n");
($clusters, $error, $found) = kmedoids(%params2);
printf("Within-cluster sum of distances: %f\n\n", $error);