clinton's output:
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
sample output:
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
sample input:
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRe
+gression, LogisticRegression, LogisticRegression, BayesPointMachine,
+BayesPointMachine, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, Platt
+LL, PlattLL, PlattLL, RBFN, PlattLL, WekaNaiveBayes, RBFN, RBFN, RBFN
+, RBFN, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaRotationF
+orest, WekaRotationForest, WekaBestFirstTree, WekaFURIA, PlattLL, Wek
+aBestFirstTree, WekaBestFirstTree, PlattLL, PlattLL, WekaRotationFore
+st, WekaBestFirstTree, WekaFURIA, PlattLL, WekaFURIA, RBFN, RBFN, Wek
+aBestFirstTree, PlattLL, RBFN, RBFN, RBFN, PlattLL, RBFN, RBFN, RBFN,
+ RBFN, RBFN, WekaRotationForest, WekaBestFirstTree, RBFN, WekaBestFir
+stTree, PlattLL, RBFN, WekaBestFirstTree
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRe
+gression, WekaRotationForest, PlattLL, PlattLL, PlattLL, WekaDescrimi
+nativeNaiveBayes, WekaDescriminativeNaiveBayes, PlattLL, PlattLL, Pla
+ttLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL,
+PlattLL, WekaNaiveBayes, PlattLL, RBFN, WekaRotationForest, WekaNaive
+Bayes, RBFN, WekaNaiveBayes, WekaNaiveBayes, WekaRotationForest, Plat
+tLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, P
+lattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL
+, PlattLL, PlattLL, WekaNaiveBayes, PlattLL, PlattLL, PlattLL, PlattL
+L, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRe
+gression, BayesPointMachine, BayesPointMachine, BayesPointMachine, Ba
+yesPointMachine, WekaComplimentNaiveBayes, RBFN, PlattLL, WekaRotatio
+nForest, WekaRotationForest, WekaBestFirstTree, WekaRotationForest, W
+ekaBestFirstTree, WekaBestFirstTree, WekaRotationForest, WekaFURIA, W
+ekaRotationForest, PlattLL, WekaBestFirstTree, WekaBestFirstTree, Wek
+aRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaRotationFo
+rest, PlattLL, WekaRotationForest, PlattLL, WekaBestFirstTree, WekaNa
+iveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree, We
+kaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaRotationF
+orest, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaBest
+FirstTree, WekaBestFirstTree, RBFN, WekaNaiveBayes, WekaBestFirstTree
+, WekaNaiveBayes, WekaFURIA, WekaBestFirstTree, WekaBestFirstTree, RB
+FN, WekaNaiveBayes, WekaBestFirstTree, WekaRotationForest, WekaBestFi
+rstTree, WekaBestFirstTree, WekaRotationForest, WekaRotationForest, W
+ekaBestFirstTree
LogisticRegression, LogisticRegression, WekaNaiveBayes, RBFN, RBFN, RB
+FN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, RBFN, RBFN, WekaRotat
+ionForest, RBFN, WekaFURIA, WekaFURIA, WekaFURIA, WekaRotationForest,
+ WekaBestFirstTree, WekaFURIA, WekaFURIA, WekaRotationForest, WekaFUR
+IA, WekaFURIA, WekaFURIA, WekaBestFirstTree, PlattLL, WekaBestFirstTr
+ee, WekaFURIA, RBFN, WekaRotationForest, WekaBestFirstTree, WekaNaive
+Bayes, WekaNaiveBayes, PlattLL, PlattLL, PlattLL, WekaBestFirstTree,
+RBFN, RBFN, WekaBestFirstTree, WekaFURIA, WekaRotationForest, WekaRot
+ationForest, WekaBestFirstTree, WekaBestFirstTree, WekaBestFirstTree,
+ WekaBestFirstTree, RBFN, PlattLL, WekaRotationForest, PlattLL, WekaB
+estFirstTree, WekaFURIA, WekaBestFirstTree, WekaBestFirstTree, WekaRo
+tationForest, RBFN, WekaBestFirstTree
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRe
+gression, RBFN, KSTAR, BayesPointMachine, RBFN, PlattLL, PlattLL, Pla
+ttLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL,
+PlattLL, PlattLL, PlattLL, WekaRotationForest, WekaRotationForest, Pl
+attLL, RBFN, PlattLL, PlattLL, WekaNaiveBayes, WekaRotationForest, Pl
+attLL, WekaRotationForest, WekaRotationForest, WekaRotationForest, We
+kaRotationForest, WekaNaiveBayes, WekaNaiveBayes, RBFN, WekaRotationF
+orest, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaRotationFo
+rest, WekaNaiveBayes, RBFN, WekaNaiveBayes, WekaRotationForest, WekaB
+estFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaRotationForest,
+WekaBestFirstTree, WekaBestFirstTree, RBFN, WekaBestFirstTree, WekaBe
+stFirstTree, WekaBestFirstTree, WekaNaiveBayes, RBFN, WekaNaiveBayes,
+ WekaRotationForest
LogisticRegression, LogisticRegression, LogisticRegression, LogisticRe
+gression, LogisticRegression, LogisticRegression, RBFN, BayesPointMac
+hine, RBFN, WekaRotationForest, WekaRotationForest, RBFN, RBFN, WekaR
+otationForest, RBFN, RBFN, RBFN, RBFN, RBFN, WekaBestFirstTree, RBFN,
+ WekaNaiveBayes, RBFN, WekaNaiveBayes, WekaNaiveBayes, RBFN, RBFN, We
+kaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNai
+veBayes, RBFN, WekaNaiveBayes, RBFN, WekaFURIA, WekaRotationForest, R
+BFN, RBFN, RBFN, RBFN, RBFN, RBFN, PlattLL, WekaRotationForest, RBFN,
+ RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, RB
+FN, WekaRotationForest, RBFN
RBFN, WekaRotationForest, KSTAR, KNN, BayesPointMachine, KSTAR, WekaNa
+iveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBa
+yes, WekaNaiveBayes, MultilayerPerceptron, RBFN, RBFN, WekaFURIA, RBF
+N, RBFN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, RBFN, WekaRotati
+onForest, WekaRotationForest, RBFN, RBFN, RBFN, WekaBestFirstTree, We
+kaBestFirstTree, WekaBestFirstTree, RBFN, RBFN, RBFN, WekaRotationFor
+est, PlattLL, RBFN, WekaRotationForest, WekaBestFirstTree, WekaBestFi
+rstTree, WekaBestFirstTree, RBFN, RBFN, WekaBestFirstTree, WekaRotati
+onForest, RBFN, RBFN, WekaBestFirstTree, RBFN, WekaRotationForest, RB
+FN, RBFN, WekaBestFirstTree, WekaBestFirstTree, RBFN, WekaBestFirstTr
+ee, RBFN, RBFN, RBFN
PlattLL, RBFN, RBFN, RBFN, RBFN, RBFN, WekaRotationForest, RBFN, RBFN,
+ RBFN, RBFN, RBFN, RBFN, RBFN, RBFN, WekaBestFirstTree, RBFN, RBFN, R
+BFN, RBFN, WekaRotationForest, WekaRotationForest, WekaNaiveBayes, RB
+FN, WekaFURIA, WekaBestFirstTree, RBFN, WekaBestFirstTree, RBFN, Weka
+RotationForest, WekaBestFirstTree, WekaFURIA, PlattLL, RBFN, WekaRota
+tionForest, RBFN, WekaFURIA, WekaFURIA, RBFN, RBFN, WekaFURIA, WekaRo
+tationForest, WekaFURIA, RBFN, WekaBestFirstTree, WekaFURIA, WekaBest
+FirstTree, RBFN, RBFN, RBFN, WekaBestFirstTree, RBFN, WekaBestFirstTr
+ee, WekaFURIA, WekaFURIA, RBFN, WekaRotationForest, RBFN, RBFN, RBFN
LogisticRegression, LogisticRegression, LogisticRegression, RBFN, Mult
+ilayerPerceptron, WekaNaiveBayes, WekaNaiveBayes, PlattLL, PlattLL, P
+lattLL, PlattLL, WekaNaiveBayes, WekaNaiveBayes, RBFN, RBFN, WekaNaiv
+eBayes, WekaNaiveBayes, RBFN, WekaBestFirstTree, WekaNaiveBayes, Weka
+BestFirstTree, WekaNaiveBayes, WekaNaiveBayes, WekaBestFirstTree, Wek
+aNaiveBayes, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, We
+kaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree
+, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, WekaBestF
+irstTree, WekaRotationForest, WekaBestFirstTree, WekaBestFirstTree, W
+ekaNaiveBayes, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree,
+WekaRotationForest, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstT
+ree, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFi
+rstTree, WekaNaiveBayes, WekaBestFirstTree, WekaBestFirstTree, WekaBe
+stFirstTree, WekaBestFirstTree, WekaNaiveBayes, WekaBestFirstTree, We
+kaBestFirstTree, WekaBestFirstTree
RBFN, BayesPointMachine, BayesPointMachine, WekaNaiveBayes, BayesPoint
+Machine, BayesPointMachine, KSTAR, WekaNaiveBayes, WekaNaiveBayes, We
+kaNaiveBayes, WekaNaiveBayes, RBFN, RBFN, WekaNaiveBayes, WekaNaiveBa
+yes, WekaNaiveBayes, PlattLL, RBFN, PlattLL, RBFN, WekaNaiveBayes, RB
+FN, WekaNaiveBayes, WekaRotationForest, PlattLL, WekaFURIA, WekaRotat
+ionForest, PlattLL, PlattLL, PlattLL, RBFN, PlattLL, PlattLL, PlattLL
+, PlattLL, PlattLL, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, W
+ekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNaiveBayes, WekaNa
+iveBayes, PlattLL, WekaRotationForest, WekaRotationForest, WekaNaiveB
+ayes, WekaNaiveBayes, WekaRotationForest, WekaNaiveBayes, PlattLL, RB
+FN, PlattLL, PlattLL, PlattLL, PlattLL, PlattLL, RBFN, WekaBestFirstT
+ree
#!/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};
}
|
