1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
|
#!/usr/bin/perl
use Data::Dumper;
use POSIX;
use Math::BigFloat;
$path = "/home/lukesky/rl-thesis/doc";
@algorithms = ({ALGO => 'fann_cascade_rprop_one_activation', NAME => 'C2 RPROP One'},
{ALGO => 'fann_cascade_rprop_multi_activation', NAME => 'C2 RPROP Multi'},
{ALGO => 'fann_cascade_quickprop_one_activation', NAME => 'C2 Quickprop One'},
{ALGO => 'fann_cascade_quickprop_multi_activation', NAME => 'C2 Quickprop Multi'},
{ALGO => 'fann_rprop', NAME => 'iRPROP$^{-}$'},
{ALGO => 'fann_quickprop', NAME => 'Quickprop'},
{ALGO => 'fann_batch', NAME => 'Batch'},
{ALGO => 'fann_incremental_momentum', NAME => 'Incremental'},
{ALGO => 'lwnn', NAME => 'Lwnn Incr.'},
{ALGO => 'jneural', NAME => 'Jneural Incr.'});
#This is not used yet
%dataSetType = {'abelone' => 'R',
'bank32fm' => 'R',
'bank32nh' => 'R',
'kin32fm' => 'R',
'census-house' => 'R',
'building' => 'R',
'diabetes' => 'C',
'gene' => 'C',
'mushroom' => 'C',
'parity8' => 'C',
'parity13' => 'C',
'pumadyn-32fm' => 'R',
'robot' => 'R',
'soybean' => 'C',
'thyroid' => 'C',
'two-spiral' => 'C'};
$oldDataSet = "";
$numDataSet = 0;
while(<>)
{
chop;
@data = split / /;
@words = split(/\./, @data[0]);
$dataSet = @words[0];
$algoritm = @words[1];
$test_train = @words[2];
$mse = Math::BigFloat->new(@data[2]);
$meanbitfail = Math::BigFloat->new(@data[5]);
if($oldDataSet ne $dataSet && $oldDataSet ne "")
{
$numDataSet++;
print "**$oldDataSet**\n";
@test = keys(%summary);
#calculate min and max mse
$min_test_mse = 100;
$min_train_mse = 100;
$max_test_mse = -100;
$max_train_mse = -100;
while(($algo, $foo) = each(%summary))
{
if($foo->{'test'} < $min_test_mse)
{
$min_test_mse = $foo->{'test'};
}
if($foo->{'train'} < $min_train_mse)
{
$min_train_mse = $foo->{'train'};
}
if($foo->{'test'} > $max_test_mse)
{
$max_test_mse = $foo->{'test'};
}
if($foo->{'train'} > $max_train_mse)
{
$max_train_mse = $foo->{'train'};
}
}
#Adds rank to each set and calculates percent etc.
for($i = 1; $i <= scalar(%summary)+2; $i++)
{
$min_algo_test = 0;
$min_algo_train = 0;
while(($algo, $foo) = each(%summary))
{
if(!exists($foo->{'test_rank'}) && (!$min_algo_test || $foo->{'test'} < $summary{$min_algo_test}->{'test'}))
{
$min_algo_test = $algo;
}
if(!exists($foo->{'train_rank'}) && (!$min_algo_train || $foo->{'train'} < $summary{$min_algo_train}->{'train'}))
{
$min_algo_train = $algo;
}
}
$summary{$min_algo_test}->{'test_rank'} = $i;
$summary{$min_algo_train}->{'train_rank'} = $i;
$summary{$min_algo_test}->{'test_percent'} = (($summary{$min_algo_test}->{'test'}-$min_test_mse)*100)/($max_test_mse-$min_test_mse);
$summary{$min_algo_train}->{'train_percent'} = (($summary{$min_algo_train}->{'train'}-$min_train_mse)*100)/($max_train_mse-$min_train_mse);
}
#write tex to file
open(OUT, "> $path/".$oldDataSet."_table.tex") or die("unable to open file $path/".$oldDataSet."_table.tex");
print OUT '\begin{tabular} {|l|r|r|r|r|r|r|}'."\n";
print OUT '\hline'."\n";
print OUT '& \multicolumn{3}{c|}{\textbf{Best Train}} & \multicolumn{3}{c|}{\textbf{Best Test}} \\\\'."\n";
print OUT '\hline'."\n";
print OUT '\textbf{Configuration} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} \\\\'."\n";
print OUT '\hline'."\n";
foreach $algo (@algorithms)
{
$foo = $summary{$algo->{ALGO}};
printf OUT ("%s & %.8f & %d & %.2f & %.8f & %d & %.2f\\\\\n\\hline\n",
$algo->{NAME},
$foo->{'train'}, $foo->{'train_rank'}, $foo->{'train_percent'},
$foo->{'test'}, $foo->{'test_rank'}, $foo->{'test_percent'});
$total{$algo->{ALGO}}->{'train'} += $foo->{'train'};
$total{$algo->{ALGO}}->{'test'} += $foo->{'test'};
$total{$algo->{ALGO}}->{'train_percent'} += $foo->{'train_percent'};
$total{$algo->{ALGO}}->{'test_percent'} += $foo->{'test_percent'};
$total{$algo->{ALGO}}->{'train_rank'} += $foo->{'train_rank'};
$total{$algo->{ALGO}}->{'test_rank'} += $foo->{'test_rank'};
}
print OUT '\end{tabular}'."\n";
close(OUT);
undef(%summary);
}
$summary{$algoritm}->{$test_train} = $mse;
$oldDataSet = $dataSet;
}
print "**Average**\n";
open(OUT, "> $path/average_table.tex") or die("unable to open file $path/average_table.tex");
print OUT '\begin{tabular} {|l|r|r|r|r|r|r|}'."\n";
print OUT '\hline'."\n";
print OUT '& \multicolumn{3}{c|}{\textbf{Best Train}} & \multicolumn{3}{c|}{\textbf{Best Test}} \\\\'."\n";
print OUT '\hline'."\n";
print OUT '\textbf{Configuration} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} & \textbf{MSE} & \textbf{Rank} & \textbf{\%} \\\\'."\n";
print OUT '\hline'."\n";
foreach $algo (@algorithms)
{
$foo = $total{$algo->{ALGO}};
printf OUT ("%s & %.6f & %.2f & %.2f & %.6f & %.2f & %.2f\\\\\n\\hline\n",
$algo->{NAME},
$foo->{'train'}/$numDataSet, $foo->{'train_rank'}/$numDataSet, $foo->{'train_percent'}/$numDataSet,
$foo->{'test'}/$numDataSet, $foo->{'test_rank'}/$numDataSet, $foo->{'test_percent'}/$numDataSet);
}
print OUT '\end{tabular}'."\n";
close(OUT);
|
