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Ricardo Montañana Gómez
2020-11-20 11:23:40 +01:00
commit 5611e5bc01
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2
data/tanveer/monks-2/conxuntos.dat Executable file
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115 137 0 112 74 107 61 50 150 144 43 27 21 65 10 134 39 141 59 111 54 145 93 41 164 11 135 89 110 55 17 67 91 118 99 109 64 114 48 84 2 4 25 94 14 23 95 19 128 133 12 38 70 20 123 60 131 127 117 130 56 24 28 63 47 34 62 71 37 26 53 129 79 143 159 51 76 81 78 85 147 106 105 68 166
77 88 161 46 75 124 8 83 6 29 40 72 126 42 156 9 58 30 165 80 82 120 163 158 146 92 52 155 151 5 33 36 69 3 86 22 152 16 113 154 1 97 7 100 157 148 160 121 73 167 57 168 32 153 98 104 132 149 122 116 90 142 49 18 136 13 125 102 103 140 15 119 87 108 31 138 162 45 101 44 35 66 139 96

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data/tanveer/monks-2/conxuntos_kfold.dat Executable file
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40 23 1 100 86 36 121 156 43 72 65 83 52 41 30 16 89 54 111 10 22 109 58 14 157 46 88 134 93 25 148 69 126 8 113 0 82 57 107 17 70 145 64 91 124 9 77 97 144 150 11 21 112 27 155 141 158 42 3 99 128 74 94 19 48 133 110 115 4 55 61 154 120 163 114 146 135 59 73 44 37 18 136 45 103 49 87 142 76 34 104 56 47 130 51 119 24 53 159 63 66 166 106 143 79 140 116 90 32 102 60 127 108 123 125 68 149 15 117 20 28 122 139 78 62 162 147
152 118 50 67 12 75 92 33 39 160 29 7 84 167 6 151 164 80 137 165 161 5 38 2 95 168 153 26 81 98 138 71 105 131 101 85 132 129 96 35 13 31
58 115 97 121 107 92 23 156 99 30 54 118 14 128 124 21 3 164 135 74 100 158 134 120 114 152 157 73 155 88 38 36 5 112 50 109 42 86 61 65 133 82 41 150 93 69 126 154 70 17 16 39 40 94 2 33 6 167 145 111 95 89 72 22 160 0 113 161 25 75 83 55 84 27 80 77 144 141 29 140 49 26 60 66 102 143 98 108 37 159 116 79 78 149 153 119 68 138 101 35 81 24 127 117 123 85 51 28 32 34 147 131 20 45 90 166 142 13 63 47 31 130 125 129 139 96 53
64 151 4 19 59 137 46 110 67 57 91 10 48 7 146 43 9 11 12 165 52 163 148 1 168 8 122 71 106 18 44 105 87 76 56 136 132 162 104 103 15 62
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4 61 21 99 69 152 91 145 160 7 97 109 33 111 164 126 157 5 88 30 72 1 155 113 9 168 31 85 26 125 81 71 37 139 68 147 49 78 98 108 143 56

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data/tanveer/monks-2/le_datos.m Executable file
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printf('lendo problema %s ...\n', problema);
n_entradas= 6; n_clases= 2;
n_fich= 2; fich{1}= 'monks-2.train'; n_patrons(1)= 169; fich{2}= 'monks-2.test'; n_patrons(2)= 432;
n_max= max(n_patrons);
x = zeros(n_fich, n_max, n_entradas); cl= zeros(n_fich, n_max);
n_patrons_total = sum(n_patrons); n_iter=0;
for i_fich=1:n_fich
f=fopen(fich{i_fich}, 'r');
if -1==f
error('erro en fopen abrindo %s\n', fich{i_fich});
end
for i=1:n_patrons(i_fich)
fprintf(2,'%5.1f%%\r', 100*n_iter++/n_patrons_total);
cl(i_fich,i) = fscanf(f,'%i',1); % lectura da clase
for j = 1:n_entradas
x(i_fich,i,j) = fscanf(f,'%i',1);
end
fscanf(f,'%s',1); % le e descarta o ID
end
fclose(f);
end

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data/tanveer/monks-2/monks-2.cost Executable file
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% Rows Columns
2 2
% Matrix elements
0.0 1.0
1.0 0.0

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data/tanveer/monks-2/monks-2.test Executable file
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data/tanveer/monks-2/monks-2.train Executable file
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10
data/tanveer/monks-2/monks-2.txt Executable file
View File

@@ -0,0 +1,10 @@
n_entradas= 6
n_clases= 2
n_arquivos= 2
fich1= monks-2_train_R.dat
n_patrons1= 169
fich2= monks-2_test_R.dat
n_patrons2= 432
n_patrons_entrena= 85
n_patrons_valida= 84
n_conxuntos= 1

441
data/tanveer/monks-2/monks-2_test.arff Executable file
View File

@@ -0,0 +1,441 @@
@relation monks-2
@attribute 'f1' real
@attribute 'f2' real
@attribute 'f3' real
@attribute 'f4' real
@attribute 'f5' real
@attribute 'f6' real
@attribute 'clase' {0,1}
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178
data/tanveer/monks-2/monks-2_train.arff Executable file
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@@ -0,0 +1,178 @@
@relation monks-2
@attribute 'f1' real
@attribute 'f2' real
@attribute 'f3' real
@attribute 'f4' real
@attribute 'f5' real
@attribute 'f6' real
@attribute 'clase' {0,1}
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2.02573,2.05162,1.76011,1.9604,2.18315,1.76884,0

170
data/tanveer/monks-2/monks-2_train_R.dat Executable file
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f1 f2 f3 f4 f5 f6 clase
1 0.202573 0.209001 0.51156 0.179342 0.74327 1.76884 0
2 0.202573 0.209001 0.51156 0.179342 2.18315 0.516154 0
3 0.202573 0.209001 0.51156 1.06987 0.0233313 0.516154 0
4 0.202573 0.209001 0.51156 1.06987 0.0233313 1.76884 0
5 0.202573 0.209001 0.51156 1.06987 0.74327 0.516154 0
6 0.202573 0.209001 0.51156 1.06987 1.46321 0.516154 0
7 0.202573 0.209001 0.51156 1.06987 2.18315 0.516154 0
8 0.202573 0.209001 0.51156 1.9604 0.74327 0.516154 0
9 0.202573 0.209001 0.51156 1.9604 2.18315 0.516154 0
10 0.202573 0.209001 1.76011 0.179342 0.0233313 0.516154 0
11 0.202573 0.209001 1.76011 0.179342 0.0233313 1.76884 0
12 0.202573 0.209001 1.76011 1.06987 1.46321 0.516154 0
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17 0.202573 1.13031 0.51156 0.179342 0.0233313 1.76884 0
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24 0.202573 1.13031 0.51156 1.9604 0.0233313 1.76884 0
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28 0.202573 1.13031 0.51156 1.9604 2.18315 0.516154 0
29 0.202573 1.13031 0.51156 1.9604 2.18315 1.76884 1
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41 0.202573 2.05162 0.51156 0.179342 0.74327 1.76884 0
42 0.202573 2.05162 0.51156 0.179342 1.46321 0.516154 0
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45 0.202573 2.05162 0.51156 1.06987 0.74327 1.76884 1
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47 0.202573 2.05162 0.51156 1.06987 2.18315 0.516154 0
48 0.202573 2.05162 0.51156 1.9604 0.74327 1.76884 1
49 0.202573 2.05162 0.51156 1.9604 1.46321 0.516154 0
50 0.202573 2.05162 0.51156 1.9604 2.18315 1.76884 1
51 0.202573 2.05162 1.76011 0.179342 1.46321 0.516154 0
52 0.202573 2.05162 1.76011 0.179342 1.46321 1.76884 1
53 0.202573 2.05162 1.76011 0.179342 2.18315 0.516154 0
54 0.202573 2.05162 1.76011 1.06987 0.0233313 1.76884 1
55 0.202573 2.05162 1.76011 1.06987 1.46321 1.76884 0
56 0.202573 2.05162 1.76011 1.06987 2.18315 1.76884 0
57 0.202573 2.05162 1.76011 1.9604 0.74327 0.516154 1
58 1.11415 0.209001 0.51156 0.179342 0.0233313 0.516154 0
59 1.11415 0.209001 0.51156 0.179342 0.74327 1.76884 0
60 1.11415 0.209001 0.51156 0.179342 1.46321 0.516154 0
61 1.11415 0.209001 0.51156 1.06987 0.74327 1.76884 1
62 1.11415 0.209001 0.51156 1.9604 0.0233313 1.76884 0
63 1.11415 0.209001 0.51156 1.9604 0.74327 1.76884 1
64 1.11415 0.209001 0.51156 1.9604 1.46321 1.76884 1
65 1.11415 0.209001 0.51156 1.9604 2.18315 0.516154 0
66 1.11415 0.209001 1.76011 0.179342 0.0233313 0.516154 0
67 1.11415 0.209001 1.76011 0.179342 0.74327 1.76884 1
68 1.11415 0.209001 1.76011 0.179342 2.18315 0.516154 0
69 1.11415 0.209001 1.76011 1.06987 0.74327 0.516154 1
70 1.11415 0.209001 1.76011 1.06987 2.18315 1.76884 0
71 1.11415 0.209001 1.76011 1.9604 0.0233313 0.516154 0
72 1.11415 0.209001 1.76011 1.9604 0.0233313 1.76884 1
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75 1.11415 0.209001 1.76011 1.9604 2.18315 1.76884 0
76 1.11415 1.13031 0.51156 0.179342 1.46321 0.516154 0
77 1.11415 1.13031 0.51156 0.179342 2.18315 1.76884 1
78 1.11415 1.13031 0.51156 1.06987 0.0233313 0.516154 0
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80 1.11415 1.13031 0.51156 1.9604 1.46321 0.516154 1
81 1.11415 1.13031 0.51156 1.9604 1.46321 1.76884 0
82 1.11415 1.13031 0.51156 1.9604 2.18315 0.516154 1
83 1.11415 1.13031 1.76011 0.179342 0.0233313 0.516154 0
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86 1.11415 1.13031 1.76011 0.179342 2.18315 0.516154 1
87 1.11415 1.13031 1.76011 0.179342 2.18315 1.76884 0
88 1.11415 1.13031 1.76011 1.06987 0.0233313 0.516154 1
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91 1.11415 1.13031 1.76011 1.9604 0.0233313 0.516154 1
92 1.11415 1.13031 1.76011 1.9604 0.74327 0.516154 0
93 1.11415 1.13031 1.76011 1.9604 0.74327 1.76884 0
94 1.11415 1.13031 1.76011 1.9604 2.18315 1.76884 0
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96 1.11415 2.05162 0.51156 0.179342 0.0233313 1.76884 0
97 1.11415 2.05162 0.51156 0.179342 1.46321 1.76884 1
98 1.11415 2.05162 0.51156 1.06987 0.0233313 0.516154 0
99 1.11415 2.05162 0.51156 1.06987 1.46321 0.516154 1
100 1.11415 2.05162 0.51156 1.06987 1.46321 1.76884 0
101 1.11415 2.05162 0.51156 1.06987 2.18315 1.76884 0
102 1.11415 2.05162 0.51156 1.9604 0.0233313 1.76884 1
103 1.11415 2.05162 0.51156 1.9604 0.74327 0.516154 1
104 1.11415 2.05162 0.51156 1.9604 2.18315 0.516154 1
105 1.11415 2.05162 1.76011 0.179342 0.0233313 1.76884 1
106 1.11415 2.05162 1.76011 0.179342 0.74327 0.516154 1
107 1.11415 2.05162 1.76011 0.179342 1.46321 0.516154 1
108 1.11415 2.05162 1.76011 0.179342 2.18315 1.76884 0
109 1.11415 2.05162 1.76011 1.06987 0.0233313 0.516154 1
110 1.11415 2.05162 1.76011 1.06987 0.74327 0.516154 0
111 1.11415 2.05162 1.76011 1.06987 1.46321 1.76884 0
112 1.11415 2.05162 1.76011 1.9604 1.46321 0.516154 0
113 1.11415 2.05162 1.76011 1.9604 1.46321 1.76884 0
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155 2.02573 1.13031 1.76011 1.9604 1.46321 1.76884 0
156 2.02573 1.13031 1.76011 1.9604 2.18315 1.76884 0
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161 2.02573 2.05162 0.51156 1.06987 1.46321 1.76884 0
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164 2.02573 2.05162 1.76011 1.06987 0.74327 0.516154 0
165 2.02573 2.05162 1.76011 1.06987 1.46321 0.516154 0
166 2.02573 2.05162 1.76011 1.06987 1.46321 1.76884 0
167 2.02573 2.05162 1.76011 1.9604 0.0233313 0.516154 1
168 2.02573 2.05162 1.76011 1.9604 0.74327 0.516154 0
169 2.02573 2.05162 1.76011 1.9604 2.18315 1.76884 0

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data/tanveer/monks-2/monks.names Executable file
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1. Title: The Monk's Problems
2. Sources:
(a) Donor: Sebastian Thrun
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, USA
E-mail: thrun@cs.cmu.edu
(b) Date: October 1992
3. Past Usage:
- See File: thrun.comparison.ps.Z
- Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation,
School of Information Technology and Engineering, Reports of Machine
Learning and Inference Laboratory, MLI 93-2, Center for Artificial
Intelligence, George Mason University, March 1993.
- Wnek, J. and Michalski, R.S., "Comparing Symbolic and
Subsymbolic Learning: Three Studies," in Machine Learning: A
Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.),
Morgan Kaufmann, San Mateo, CA, 1993.
4. Relevant Information:
The MONK's problem were the basis of a first international comparison
of learning algorithms. The result of this comparison is summarized in
"The MONK's Problems - A Performance Comparison of Different Learning
algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B.
Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher,
R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S.
Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de
Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as
Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec.
1991.
One significant characteristic of this comparison is that it was
performed by a collection of researchers, each of whom was an advocate
of the technique they tested (often they were the creators of the
various methods). In this sense, the results are less biased than in
comparisons performed by a single person advocating a specific
learning method, and more accurately reflect the generalization
behavior of the learning techniques as applied by knowledgeable users.
There are three MONK's problems. The domains for all MONK's problems
are the same (described below). One of the MONK's problems has noise
added. For each problem, the domain has been partitioned into a train
and test set.
5. Number of Instances: 432
6. Number of Attributes: 8 (including class attribute)
7. Attribute information:
1. class: 0, 1
2. a1: 1, 2, 3
3. a2: 1, 2, 3
4. a3: 1, 2
5. a4: 1, 2, 3
6. a5: 1, 2, 3, 4
7. a6: 1, 2
8. Id: (A unique symbol for each instance)
8. Missing Attribute Values: None
9. Target Concepts associated to the MONK's problem:
MONK-1: (a1 = a2) or (a5 = 1)
MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1}
MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3)
(5% class noise added to the training set)