BayesPointMachine: 	0	0.1	0.1	0	0.3	0.2	0.3	0.3	0	0	0	0	0	0	0	0	0	0
BestFirstTree: 	0	0	0	0	0	0	0	0	0	0	0	0	0	0.1	0	0.2	0.1	0	0.1	0.2	0.1	0.1	0.1	0.1	0.1	0.3	0.1	0.2	0.3	0.1	0.4	0.3	0.3	0.2	0.2	0.1	0.2	0.2	0.2	0.1	0.2	0.4	0.3	0.1	0.3	0.1	0.5	0.3	0.2	0.2	0.4	0.1	0.2	0.5	0.4	0.4	0.4	0.1	0.1	0.5
ComplimentNaiveBayes: 	0	0	0	0	0	0	0	0	0.1	0	0	0	0	0	0	0	0	0
DescriminativeNaiveBayes: 	0	0	0	0	0	0	0	0	0.1	0.1	0	0	0	0	0	0	0
FURIA: 	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.2	0.1	0.1	0.1	0.1	0.1	0	0.1	0.2	0.2	0	0	0	0.2	0	0.1	0	0	0	0.1	0.1	0.2	0	0.1	0.1	0	0.2	0	0	0.1	0	0	0.1	0	0	0	0	0.1	0.2	0	0	0	0	0
KNN: 	0	0	0	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
KSTAR: 	0	0	0.1	0	0	0.2	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0
LogisticRegression: 	0.7	0.7	0.6	0.5	0.2	0.2	0	0	0	0	0	0	0	0	0	0	0	0
MultilayerPerceptron: 	0	0	0	0	0.1	0	0	0	0	0	0	0	0.1	0	0	0	0	0
NaiveBayes: 	0	0	0.1	0.1	0	0.1	0.2	0.2	0.2	0.2	0.2	0.2	0.1	0.1	0.1	0.2	0.1	0	0.1	0.1	0.1	0.3	0.3	0.2	0.3	0.2	0.1	0.3	0.2	0.2	0.1	0.2	0	0.2	0.2	0.1	0.1	0.1	0.2	0.4	0.3	0.1	0.2	0.1	0.1	0.2	0	0.2	0.4	0	0.1	0.1	0.1	0	0	0	0.2	0	0.1	0
PlattLL: 	0.1	0	0	0	0	0.1	0.1	0.2	0.3	0.3	0.5	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.2	0.3	0	0.1	0.1	0.1	0.1	0.1	0.3	0.1	0.3	0.2	0.2	0.3	0.3	0.3	0.3	0.3	0.2	0.2	0.1	0.1	0.1	0.1	0.3	0.2	0.1	0.1	0.2	0	0.1	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.3	0.1	0.1
RBFN: 	0.2	0.1	0.1	0.3	0.3	0.2	0.2	0.3	0.3	0.3	0.1	0.4	0.4	0.3	0.5	0.1	0.4	0.5	0.3	0.4	0.3	0.3	0.2	0.3	0.1	0.1	0.4	0.1	0.2	0	0.2	0	0.2	0.2	0.2	0.1	0.1	0.2	0.2	0.3	0.3	0.2	0.2	0.3	0.2	0.2	0.3	0.3	0.3	0.5	0.2	0.5	0.5	0.1	0.1	0.4	0.1	0.4	0.5	0.3
RotationForest: 	0	0.1	0	0	0.1	0	0.1	0	0	0.1	0.2	0.1	0.1	0.2	0.1	0	0	0.1	0.1	0.1	0.1	0.2	0.3	0.2	0.2	0.1	0.3	0.1	0.2	0.2	0.1	0.2	0.2	0.1	0.1	0.3	0.2	0.1	0.2	0	0	0.2	0	0.2	0.2	0.3	0.1	0	0	0.2	0.1	0.1	0	0.1	0.1	0	0.1	0.2	0.2	0.1


##</code><code>##

LogisticRegression = 1
BayesPointMachine = 2
PlattLL = 3
RBFN = 4
NaiveBayes = 5
RotationForest = 6
BestFirstTree = 7
FURIA = 8
DescriminativeNaiveBayes = 9
ComplimentNaiveBayes = 10
KSTAR = 11
KNN = 12
MultilayerPerceptron = 13
max is 13, md = 60

0.7	0.7	0.6	0.5	0.2	0.2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
(should be more entries here, 60 columns)
0	0.1	0.1	0	0.3	0.2	0.3	0.3	0	0	0	0	0	0	0	0	0	0	0	0	0
(should be more entries here, 60 columns)
0.1	0	0	0	0	0.1	0.1	0.2	0.3	0.3	0.5	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.3	0.2	0.3	0	0.1	0.1	0.1	0.1	0.1	0.3	0.1	0.3	0.2	0.2	0.3	0.3	0.3	0.3	0.3	0.2	0.2	0.1	0.1	0.1	0.1	0.3	0.2	0.1	0.1	0.2	0	0.1	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.3	0.1	0.1

0.2	0.1	0.1	0.3	0.3	0.2	0.2	0.3	0.3	0.3	0.1	0.4	0.4	0.3	0.5	0.1	0.4	0.5	0.3	0.4	0.3	0.3	0.2	0.3	0.1	0.1	0.4	0.1	0.2	0	0.2	0	0.2	0.2	0.2	0.1	0.1	0.2	0.2	0.3	0.3	0.2	0.2	0.3	0.2	0.2	0.3	0.3	0.3	0.5	0.2	0.5	0.5	0.1	0.1	0.4	0.1	0.4	0.5	0.3

0	0	0.1	0.1	0	0.1	0.2	0.2	0.2	0.2	0.2	0.2	0.1	0.1	0.1	0.2	0.1	0	0.1	0.1	0.1	0.3	0.3	0.2	0.3	0.2	0.1	0.3	0.2	0.2	0.1	0.2	0	0.2	0.2	0.1	0.1	0.1	0.2	0.4	0.3	0.1	0.2	0.1	0.1	0.2	0	0.2	0.4	0	0.1	0.1	0.1	0	0	0	0.2	0	0.1	0

0	0.1	0	0	0.1	0	0.1	0	0	0.1	0.2	0.1	0.1	0.2	0.1	0	0	0.1	0.1	0.1	0.1	0.2	0.3	0.2	0.2	0.1	0.3	0.1	0.2	0.2	0.1	0.2	0.2	0.1	0.1	0.3	0.2	0.1	0.2	0	0.2	0	0.2	0.2	0.3	0.1	0	0	0.2	0.1	0.1	0	0.1	0.1	0	0.1	0.2	0.2	0.1

0	0	0	0	0	0	0	0	0	0	0	0	0	0.1	0	0.2	0.1	0	0.1	0.2	0.1	0.1	0.1	0.1	0.1	0.3	0.1	0.2	0.3	0.1	0.4	0.3	0.3	0.2	0.2	0.1	0.2	0.2	0.2	0.1	0.2	0.4	0.3	0.1	0.3	0.1	0.5	0.3	0.2	0.2	0.4	0.1	0.2	0.5	0.4	0.4	0.4	0.1	0.1	0.5

0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.2	0.1	0.1	0.1	0	0.1	0.1	0	0.1	0.2	0.2	0	0	0	0.2	0	0.1	0	0	0	0.1	0.1	0.2	0	0.1	0.1	0	0.2	0	0	0.1	0	0	0.1	0	0	0	0	0.1	0.2	0	0	0	0	0

0	0	0	0	0	0	0	0	0.1	0.1	0	0	0	0	0	0	0	0	0	0	0

0	0	0	0	0	0	0	0	0.1	0	0	0	0	0	0	0	0	0	0	0	0

0	0	0.1	0	0	0.2	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0

0	0	0	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

0	0	0	0	0.1	0	0	0	0	0	0	0	0.1	0	0	0	0	0	0	0	0


##</code><code>##

LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, BayesPointMachine, BayesPointMachine, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, RBFN, PlattLL, WekaNaiveBayes, RBFN, RBFN, RBFN, RBFN, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaRotationForest, WekaRotationForest, WekaBestFirstTree, WekaFURIA, PlattLL, WekaBestFirstTree, WekaBestFirstTree, PlattLL, PlattLL, WekaRotationForest, WekaBestFirstTree, WekaFURIA, PlattLL, WekaFURIA, RBFN, RBFN, WekaBestFirstTree, PlattLL, RBFN, RBFN, RBFN, PlattLL, RBFN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, WekaBestFirstTree, RBFN, WekaBestFirstTree, PlattLL, RBFN, WekaBestFirstTree
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, WekaRotationForest, PlattLL, PlattLL, PlattLL, WekaDescriminativeNaiveBayes, WekaDescriminativeNaiveBayes, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, WekaNaiveBayes, PlattLL, RBFN, WekaRotationForest, WekaNaiveBayes, RBFN, WekaNaiveBayes, WekaNaiveBayes, WekaRotationForest, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, WekaNaiveBayes, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, BayesPointMachine, BayesPointMachine, BayesPointMachine, BayesPointMachine, WekaComplimentNaiveBayes, RBFN, PlattLL, WekaRotationForest, WekaRotationForest, WekaBestFirstTree, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaFURIA, WekaRotationForest, PlattLL, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, PlattLL, WekaRotationForest, PlattLL, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, RBFN, WekaNaiveBayes, WekaBestFirstTree, WekaNaiveBayes, WekaFURIA, WekaBestFirstTree, WekaBestFirstTree, RBFN, WekaNaiveBayes, WekaBestFirstTree, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaRotationForest, WekaBestFirstTree
LogisticRegression, LogisticRegression, WekaNaiveBayes, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, RBFN, RBFN, WekaRotationForest, RBFN, WekaFURIA, WekaFURIA, WekaFURIA, WekaRotationForest, WekaBestFirstTree, WekaFURIA, WekaFURIA, WekaRotationForest, WekaFURIA, WekaFURIA, WekaFURIA, WekaBestFirstTree, PlattLL, WekaBestFirstTree, WekaFURIA, RBFN, WekaRotationForest, WekaBestFirstTree, WekaNaiveBayes, WekaNaiveBayes, PlattLL, PlattLL, PlattLL, WekaBestFirstTree, RBFN, RBFN, WekaBestFirstTree, WekaFURIA, WekaRotationForest, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, RBFN, PlattLL, WekaRotationForest, PlattLL, WekaBestFirstTree, WekaFURIA, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, RBFN, WekaBestFirstTree
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, RBFN, KSTAR, BayesPointMachine, RBFN, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, WekaRotationForest, WekaRotationForest, PlattLL, RBFN, PlattLL, PlattLL, WekaNaiveBayes, WekaRotationForest, PlattLL, WekaRotationForest, WekaRotationForest, WekaRotationForest, WekaRotationForest, WekaNaiveBayes, WekaNaiveBayes, RBFN, WekaRotationForest, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaRotationForest, WekaNaiveBayes, RBFN, WekaNaiveBayes, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, RBFN, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, WekaNaiveBayes, RBFN, WekaNaiveBayes, WekaRotationForest
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, LogisticRegression, RBFN, BayesPointMachine, RBFN, WekaRotationForest, WekaRotationForest, RBFN, RBFN, WekaRotationForest, RBFN, RBFN, RBFN, RBFN, RBFN, WekaBestFirstTree, RBFN, WekaNaiveBayes, RBFN, WekaNaiveBayes, WekaNaiveBayes, RBFN, RBFN, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, RBFN, WekaNaiveBayes, RBFN, WekaFURIA, WekaRotationForest, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, PlattLL, WekaRotationForest, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, RBFN
RBFN, WekaRotationForest, KSTAR, KNN, BayesPointMachine, KSTAR, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, MultilayerPerceptron, RBFN, RBFN, WekaFURIA, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, RBFN, WekaRotationForest, WekaRotationForest, RBFN, RBFN, RBFN, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, RBFN, RBFN, RBFN, WekaRotationForest, PlattLL, RBFN, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, RBFN, RBFN, WekaBestFirstTree, WekaRotationForest, RBFN, RBFN, WekaBestFirstTree, RBFN, WekaRotationForest, RBFN, RBFN, WekaBestFirstTree, WekaBestFirstTree, RBFN, WekaBestFirstTree, RBFN, RBFN, RBFN
PlattLL, RBFN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, WekaBestFirstTree, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, WekaRotationForest, WekaNaiveBayes, RBFN, WekaFURIA, WekaBestFirstTree, RBFN, WekaBestFirstTree, RBFN, WekaRotationForest, WekaBestFirstTree, WekaFURIA, PlattLL, RBFN, WekaRotationForest, RBFN, WekaFURIA, WekaFURIA, RBFN, RBFN, WekaFURIA, WekaRotationForest, WekaFURIA, RBFN, WekaBestFirstTree, WekaFURIA, WekaBestFirstTree, RBFN, RBFN, RBFN, WekaBestFirstTree, RBFN, WekaBestFirstTree, WekaFURIA, WekaFURIA, RBFN, WekaRotationForest, RBFN, RBFN, RBFN
LogisticRegression, LogisticRegression, LogisticRegression, RBFN, MultilayerPerceptron, WekaNaiveBayes, WekaNaiveBayes, PlattLL, PlattLL, PlattLL, PlattLL, WekaNaiveBayes, WekaNaiveBayes, RBFN, RBFN, WekaNaiveBayes, WekaNaiveBayes, RBFN, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaNaiveBayes, WekaNaiveBayes, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree
RBFN, BayesPointMachine, BayesPointMachine, WekaNaiveBayes, BayesPointMachine, BayesPointMachine, KSTAR, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, RBFN, RBFN, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, PlattLL, RBFN, PlattLL, RBFN, WekaNaiveBayes, RBFN, WekaNaiveBayes, WekaRotationForest, PlattLL, WekaFURIA, WekaRotationForest, PlattLL, PlattLL, PlattLL, RBFN, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, PlattLL, WekaRotationForest, WekaRotationForest, WekaNaiveBayes, WekaNaiveBayes, WekaRotationForest, WekaNaiveBayes, PlattLL, RBFN, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, RBFN, WekaBestFirstTree

##</code><code>##

#!/usr/bin/perl
use strict;
use warnings;

my $md = 0;
my %M;
my @r;
for(my $i = 0; $i <= 13; $i++)
{
    for(my $j = 0; $j <= 60; $j++)
    {
	$r[$i][$j] = 0;
    }

}
my $ct = 0;
while(<>)
{
    chomp; 
    s/weka//ig; 
    my @p = split /, /; 
foreach my $m (@p)
{
    if(!exists $M{$m})
    {
	$ct++; 
	$M{$m} = $ct;
    }
} 
for(my $i = 0; $i < @p; $i++)
{
    my $m = $p[$i]; 
    $r[$M{$m}][$i]++;
    #print "$m\t$M{$m}\t$i\t$r[$M{$m}][$i]\n";

}
if(@p >$md)
{
    $md = @p
}
}


my $max = 0; 
foreach my $k (sort hashValueAscendingNum keys %M)
{
    print "$k = $M{$k}\n"; 
    if($M{$k} > $max)
    {
	$max = $M{$k}
    } 
}
print "max is $max, md = $md\n";
for(my $j = 1; $j <=$max; $j++)
{
    my $out = "";
    for(my $i = 0; $i < $md; $i++)
    {
	my $tmp = 0;
	$tmp += $r[$j][$i];
	$out .= $tmp/10;
$out .= "\t";
    }
    print "$out\n\n";
}


sub hashValueAscendingNum
{
    $M{$a} <=> $M{$b};
}