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Commit Inicial
This commit is contained in:
2
data/tanveer/teaching/conxuntos.dat
Executable file
2
data/tanveer/teaching/conxuntos.dat
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37 29 70 28 116 114 36 69 74 67 32 119 111 138 71 139 117 68 140 72 148 143 77 144 141 16 100 95 61 130 128 57 103 135 105 23 24 96 137 66 26 60 102 133 62 134 56 101 136 19 3 127 46 86 82 52 42 90 88 78 51 91 81 92 11 49 124 5 1 13 94 12 121 83 43 85
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113 149 109 35 107 34 112 115 118 120 31 108 73 38 147 145 110 76 75 142 30 33 146 150 106 55 22 59 21 98 14 58 15 65 27 104 131 64 17 63 129 132 18 25 99 97 20 54 53 122 84 6 8 10 50 4 7 39 80 40 79 123 9 2 44 0 41 87 48 126 45 47 93 125 89
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8
data/tanveer/teaching/conxuntos_kfold.dat
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8
data/tanveer/teaching/conxuntos_kfold.dat
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@@ -0,0 +1,8 @@
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74 73 28 141 138 30 109 144 116 36 142 70 117 115 68 110 113 148 69 146 38 31 147 71 119 149 108 139 34 32 107 118 29 77 140 145 112 65 16 104 54 15 14 137 63 21 56 19 129 99 25 66 58 97 59 133 132 55 130 136 53 57 95 61 60 20 101 102 17 98 131 23 128 96 22 4 126 92 121 5 48 10 127 50 11 83 82 123 1 78 41 87 91 122 86 88 2 12 9 90 3 52 13 89 84 42 81 8 94 46 6 43 124
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33 67 120 114 35 72 37 75 76 143 111 150 134 106 26 18 135 27 64 103 62 100 24 105 40 80 79 0 44 47 39 51 93 7 49 85 125 45
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28 148 143 67 142 139 75 115 145 35 110 38 36 32 33 108 112 117 118 76 109 30 120 72 146 34 119 144 68 116 141 107 71 111 140 37 70 101 20 137 100 56 99 23 19 136 21 53 65 27 25 131 97 62 129 24 59 105 17 61 63 64 95 54 98 96 104 134 55 128 103 18 58 14 57 87 94 51 40 39 85 9 7 45 86 5 50 11 8 122 81 4 91 10 90 82 88 43 3 6 13 47 84 52 93 83 42 126 1 79 46 78 48
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29 77 147 149 113 138 73 31 114 69 74 150 132 15 130 106 133 102 66 26 22 16 60 135 127 12 121 125 124 0 49 89 92 41 2 44 123 80
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140 31 145 116 73 148 32 35 74 118 110 108 113 71 109 36 144 67 107 70 75 141 117 119 142 143 149 138 29 30 139 33 69 114 34 68 77 132 14 53 57 21 131 64 59 18 23 19 96 106 130 55 62 22 102 17 133 105 26 97 99 63 103 54 61 100 129 15 104 66 134 58 95 16 27 4 44 10 49 86 51 39 48 6 45 81 5 85 125 40 52 83 90 89 82 94 2 9 43 84 126 0 11 127 46 7 3 91 88 41 12 124 47
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111 115 72 37 120 76 146 147 38 28 112 150 128 136 98 101 65 20 135 24 137 60 56 25 78 93 122 80 13 1 87 92 79 8 121 42 50 123
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148 143 108 36 74 71 31 111 28 118 30 34 115 72 112 77 35 67 32 145 107 120 38 70 109 33 75 138 73 110 113 119 149 139 117 114 141 22 96 56 20 104 23 103 64 133 130 17 61 97 98 58 129 101 135 25 63 66 132 100 57 102 55 53 134 131 95 54 99 16 136 21 19 15 137 94 44 78 84 42 1 39 51 89 7 8 81 85 0 13 40 90 12 43 11 82 6 83 50 5 86 10 47 123 92 46 41 80 2 124 9 4 93
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140 29 37 76 144 68 142 146 69 116 147 150 59 65 62 128 106 60 105 24 27 26 14 18 48 91 121 3 122 52 87 126 79 88 49 127 125 45
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22
data/tanveer/teaching/le_datos.m
Executable file
22
data/tanveer/teaching/le_datos.m
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@@ -0,0 +1,22 @@
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printf('lendo problema %s ...\n', problema);
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n_entradas= 5; n_clases= 3; n_fich= 1; fich{1}= 'tae.data'; n_patrons(1)= 151;
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n_max= max(n_patrons);
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x = zeros(n_fich, n_max, n_entradas); cl= zeros(n_fich, n_max);
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n_patrons_total = sum(n_patrons); n_iter=0;
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for i_fich=1:n_fich
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f=fopen(fich{i_fich}, 'r');
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if -1==f
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error('erro en fopen abrindo %s\n', fich{i_fich});
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end
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for i=1:n_patrons(i_fich)
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fprintf(2,'%5.1f%%\r', 100*n_iter++/n_patrons_total);
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for j = 1:n_entradas
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x(i_fich,i,j) = fscanf(f,'%i',1);
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end
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cl(i_fich,i) = fscanf(f,'%i',1) - 1; % lectura da clase
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end
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fclose(f);
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end
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151
data/tanveer/teaching/tae.data
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151
data/tanveer/teaching/tae.data
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@@ -0,0 +1,151 @@
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1 23 3 1 19 3
|
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2 15 3 1 17 3
|
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1 23 3 2 49 3
|
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1 5 2 2 33 3
|
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2 7 11 2 55 3
|
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2 23 3 1 20 3
|
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2 9 5 2 19 3
|
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2 10 3 2 27 3
|
||||
1 22 3 1 58 3
|
||||
2 15 3 1 20 3
|
||||
2 10 22 2 9 3
|
||||
2 13 1 2 30 3
|
||||
2 18 21 2 29 3
|
||||
2 6 17 2 39 3
|
||||
2 6 17 2 42 2
|
||||
2 6 17 2 43 2
|
||||
2 7 11 2 10 2
|
||||
2 22 3 2 46 2
|
||||
2 13 3 1 10 2
|
||||
2 7 25 2 42 2
|
||||
2 25 7 2 27 2
|
||||
2 25 7 2 23 2
|
||||
2 2 9 2 31 2
|
||||
2 1 15 1 22 2
|
||||
2 15 13 2 37 2
|
||||
2 7 11 2 13 2
|
||||
2 8 3 2 24 2
|
||||
2 14 15 2 38 2
|
||||
2 21 2 2 42 1
|
||||
2 22 3 2 28 1
|
||||
2 11 1 2 51 1
|
||||
2 18 5 2 19 1
|
||||
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|
||||
1 13 3 1 13 1
|
||||
2 5 2 2 37 1
|
||||
2 16 8 2 36 1
|
||||
2 4 16 2 21 1
|
||||
2 5 2 2 48 1
|
||||
2 14 15 2 38 1
|
||||
1 23 3 1 19 3
|
||||
2 15 3 1 17 3
|
||||
1 23 3 2 49 3
|
||||
1 5 2 2 33 3
|
||||
2 7 11 2 55 3
|
||||
2 23 3 1 20 3
|
||||
2 9 5 2 19 3
|
||||
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|
||||
1 22 3 2 58 3
|
||||
2 15 3 1 20 3
|
||||
2 10 22 2 9 3
|
||||
2 13 1 2 30 3
|
||||
2 18 21 2 29 3
|
||||
2 6 17 2 39 3
|
||||
2 6 17 2 42 2
|
||||
2 6 17 2 43 2
|
||||
2 7 11 2 10 2
|
||||
2 22 3 2 46 2
|
||||
2 13 3 1 10 2
|
||||
2 7 25 2 42 2
|
||||
2 25 7 2 27 2
|
||||
2 25 7 2 23 2
|
||||
2 2 9 2 31 2
|
||||
2 1 15 1 22 2
|
||||
2 15 13 2 37 2
|
||||
2 7 11 2 13 2
|
||||
2 8 3 2 24 2
|
||||
2 14 15 2 38 2
|
||||
2 21 2 2 42 1
|
||||
2 22 3 2 28 1
|
||||
2 11 1 2 51 1
|
||||
2 18 5 2 19 1
|
||||
2 13 1 2 31 1
|
||||
1 13 3 1 13 1
|
||||
2 5 2 2 37 1
|
||||
2 16 8 2 36 1
|
||||
2 4 16 2 21 1
|
||||
2 5 2 2 48 1
|
||||
2 14 15 2 38 1
|
||||
1 23 3 1 25 3
|
||||
1 13 3 1 17 3
|
||||
2 16 19 2 11 3
|
||||
2 9 2 2 39 3
|
||||
2 13 3 1 11 3
|
||||
2 18 21 2 19 3
|
||||
1 22 3 2 45 3
|
||||
2 7 11 1 20 3
|
||||
2 23 3 1 20 3
|
||||
1 23 3 1 20 3
|
||||
1 23 3 2 38 3
|
||||
2 14 22 2 17 3
|
||||
1 17 17 2 19 3
|
||||
2 9 5 2 24 3
|
||||
2 18 25 2 25 3
|
||||
1 17 17 2 31 3
|
||||
2 1 15 2 31 3
|
||||
2 1 8 2 18 2
|
||||
1 11 16 2 22 2
|
||||
1 22 13 2 27 2
|
||||
2 9 2 2 14 2
|
||||
2 13 1 2 20 2
|
||||
1 6 17 2 35 2
|
||||
2 23 3 1 20 2
|
||||
1 23 3 1 20 2
|
||||
2 6 17 2 37 2
|
||||
1 22 3 2 15 2
|
||||
2 20 2 2 25 2
|
||||
2 23 3 2 10 2
|
||||
2 20 2 2 14 1
|
||||
1 23 3 2 38 1
|
||||
2 13 1 2 29 1
|
||||
2 10 3 2 19 1
|
||||
2 7 11 2 30 1
|
||||
1 14 15 2 32 1
|
||||
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|
||||
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|
||||
2 8 7 2 23 1
|
||||
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|
||||
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|
||||
2 2 9 2 29 1
|
||||
2 15 1 2 19 1
|
||||
2 20 2 2 3 1
|
||||
2 13 14 2 17 3
|
||||
2 9 6 2 7 3
|
||||
1 10 3 2 21 3
|
||||
2 14 15 2 36 3
|
||||
1 13 1 2 54 3
|
||||
1 8 3 2 29 3
|
||||
2 20 2 2 45 3
|
||||
2 22 1 2 11 2
|
||||
2 18 12 2 16 2
|
||||
2 20 15 2 18 2
|
||||
1 17 18 2 44 2
|
||||
2 14 23 2 17 2
|
||||
2 24 26 2 21 2
|
||||
2 9 24 2 20 2
|
||||
2 12 8 2 24 2
|
||||
2 9 6 2 5 2
|
||||
2 22 1 2 42 2
|
||||
2 7 11 2 30 1
|
||||
2 10 3 2 19 1
|
||||
2 23 3 2 11 1
|
||||
2 17 18 2 29 1
|
||||
2 16 20 2 15 1
|
||||
2 3 2 2 37 1
|
||||
2 19 4 2 10 1
|
||||
2 23 3 2 24 1
|
||||
2 3 2 2 26 1
|
||||
2 10 3 2 12 1
|
||||
1 18 7 2 48 1
|
||||
2 22 1 2 51 1
|
||||
2 2 10 2 27 1
|
||||
39
data/tanveer/teaching/tae.names
Executable file
39
data/tanveer/teaching/tae.names
Executable file
@@ -0,0 +1,39 @@
|
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1. Title: Teaching Assistant Evaluation
|
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|
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2. Sources:
|
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(a) Collector: Wei-Yin Loh (Department of Statistics, UW-Madison)
|
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(b) Donor: Tjen-Sien Lim (limt@stat.wisc.edu)
|
||||
(b) Date: June 7, 1997
|
||||
|
||||
3. Past Usage:
|
||||
1. Loh, W.-Y. & Shih, Y.-S. (1997). Split Selection Methods for
|
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Classification Trees, Statistica Sinica 7: 815-840.
|
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2. Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of
|
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Prediction Accuracy, Complexity, and Training Time of
|
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Thirty-three Old and New Classification Algorithms. Machine
|
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Learning. Forthcoming.
|
||||
(ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or
|
||||
(http://www.stat.wisc.edu/~limt/mach1317.pdf)
|
||||
|
||||
4. Relevant Information:
|
||||
The data consist of evaluations of teaching performance over three
|
||||
regular semesters and two summer semesters of 151 teaching assistant
|
||||
(TA) assignments at the Statistics Department of the University of
|
||||
Wisconsin-Madison. The scores were divided into 3 roughly equal-sized
|
||||
categories ("low", "medium", and "high") to form the class variable.
|
||||
|
||||
5. Number of Instances: 151
|
||||
|
||||
6. Number of Attributes: 6 (including the class attribute)
|
||||
|
||||
7. Attribute Information:
|
||||
|
||||
1. Whether of not the TA is a native English speaker (binary)
|
||||
1=English speaker, 2=non-English speaker
|
||||
2. Course instructor (categorical, 25 categories)
|
||||
3. Course (categorical, 26 categories)
|
||||
4. Summer or regular semester (binary) 1=Summer, 2=Regular
|
||||
5. Class size (numerical)
|
||||
6. Class attribute (categorical) 1=Low, 2=Medium, 3=High
|
||||
|
||||
8. Missing Attribute Values: None
|
||||
159
data/tanveer/teaching/teaching.arff
Executable file
159
data/tanveer/teaching/teaching.arff
Executable file
@@ -0,0 +1,159 @@
|
||||
@relation teaching
|
||||
@attribute 'f1' real
|
||||
@attribute 'f2' real
|
||||
@attribute 'f3' real
|
||||
@attribute 'f4' real
|
||||
@attribute 'f5' real
|
||||
@attribute 'clase' {0,1,2}
|
||||
@data
|
||||
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|
||||
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||||
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|
||||
6
data/tanveer/teaching/teaching.cost
Executable file
6
data/tanveer/teaching/teaching.cost
Executable file
@@ -0,0 +1,6 @@
|
||||
% Rows Columns
|
||||
3 3
|
||||
% Matrix elements
|
||||
0.0 1.0 1.0
|
||||
1.0 0.0 1.0
|
||||
1.0 1.0 0.0
|
||||
8
data/tanveer/teaching/teaching.txt
Executable file
8
data/tanveer/teaching/teaching.txt
Executable file
@@ -0,0 +1,8 @@
|
||||
n_entradas= 5
|
||||
n_clases= 3
|
||||
n_arquivos= 1
|
||||
fich1= teaching_R.dat
|
||||
n_patrons1= 151
|
||||
n_patrons_entrena= 76
|
||||
n_patrons_valida= 75
|
||||
n_conxuntos= 1
|
||||
152
data/tanveer/teaching/teaching_R.dat
Executable file
152
data/tanveer/teaching/teaching_R.dat
Executable file
@@ -0,0 +1,152 @@
|
||||
f1 f2 f3 f4 f5 clase
|
||||
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|
||||
151 0.485933 -1.70565 0.269656 0.42249 -0.0672845 0
|
||||
Reference in New Issue
Block a user