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2
data/tanveer/hayes-roth/conxuntos.dat
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data/tanveer/hayes-roth/conxuntos.dat
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17 46 49 50 55 58 10 94 15 28 43 128 47 24 125 131 88 76 82 112 12 23 78 9 65 85 71 67 69 20 66 90 13 38 102 37 100 25 106 48 16 118 72 86 56 40 122 111 99 92 87 4 115 70 84 3 60 64 116 62 127 2 74 11 34 126
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33 29 96 73 54 114 79 97 63 8 21 110 103 22 31 109 124 0 53 18 44 19 83 113 57 101 61 26 32 81 130 27 45 51 59 35 129 42 52 91 117 1 6 89 41 5 105 93 36 120 95 98 7 77 68 108 30 107 121 123 39 104 14 119 80 75
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8
data/tanveer/hayes-roth/conxuntos_kfold.dat
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data/tanveer/hayes-roth/conxuntos_kfold.dat
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76 43 57 103 82 58 15 10 31 125 97 44 63 19 54 18 65 49 28 12 112 109 94 8 0 33 114 78 53 24 21 110 22 96 101 9 124 36 81 37 25 41 5 26 38 67 51 117 91 32 61 69 35 87 66 13 86 100 45 95 120 40 105 129 1 93 16 6 92 99 42 72 106 59 85 56 74 64 39 3 34 70 116 7 60 84 14 30 77 98 121 119 108 62 68 126 80 4 123
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113 83 17 29 23 79 73 55 47 128 88 131 50 46 102 27 118 89 48 71 90 130 111 52 20 122 104 107 11 127 75 2 115
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79 46 8 9 63 125 31 114 15 128 49 50 82 110 54 53 83 23 28 76 17 33 24 12 124 10 103 58 94 47 0 88 29 101 55 73 78 51 67 87 37 61 41 102 27 52 89 95 40 5 90 26 105 91 92 66 59 71 6 56 20 122 118 120 85 72 100 32 25 1 106 35 38 81 48 36 2 116 64 77 4 121 98 70 107 126 115 123 127 11 60 34 75 80 74 108 119 68 62
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65 57 113 112 21 22 18 96 44 43 109 19 97 131 45 42 117 111 16 93 13 99 86 129 130 69 104 30 39 7 3 14 84
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55 103 21 114 124 83 17 110 128 22 0 15 49 23 73 50 78 79 109 8 19 97 96 112 82 33 65 94 44 101 18 10 76 57 54 24 125 52 59 93 27 16 81 85 92 13 72 69 61 6 102 105 51 45 120 25 42 118 40 66 99 100 36 111 1 32 122 35 89 86 38 91 26 71 95 67 14 30 84 108 127 98 3 104 75 64 74 77 2 70 119 121 126 115 60 116 62 39 4
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53 47 113 9 12 31 29 58 63 43 88 28 46 131 117 56 5 20 106 41 48 90 87 129 130 37 68 80 34 107 11 123 7
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||||
23 88 0 50 113 55 94 15 97 12 63 22 103 24 57 18 73 83 109 29 17 9 110 125 21 114 8 46 47 124 76 53 82 28 78 65 112 16 56 52 71 111 42 48 89 86 72 6 129 117 120 87 93 69 105 26 61 40 91 81 37 35 5 118 106 85 122 66 67 27 90 92 41 99 95 36 62 60 70 127 39 7 121 11 74 80 104 64 68 84 14 4 34 119 115 30 116 2 3
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79 49 19 96 58 101 44 128 43 10 33 54 31 131 59 25 20 130 13 45 1 51 38 32 100 102 108 77 98 126 123 107 75
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2
data/tanveer/hayes-roth/copias/conxuntos.dat
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2
data/tanveer/hayes-roth/copias/conxuntos.dat
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83 73 23 31 46 10 128 101 21 15 97 28 29 79 8 43 96 50 12 44 47 53 82 78 112 89 38 102 5 85 36 6 25 72 45 35 99 20 106 51 130 69 105 86 122 16 120 91 87 52 37 126 115 77 11 34 2 121 98 75 62 64 60 30 68 80
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114 18 55 65 17 103 9 88 131 33 0 19 110 125 94 63 49 124 22 113 109 54 76 24 57 58 42 13 111 32 41 90 26 1 67 81 40 100 92 95 71 93 59 129 48 118 61 27 56 117 66 4 14 7 70 119 127 104 116 107 123 84 3 39 74 108
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8
data/tanveer/hayes-roth/copias/conxuntos_kfold.dat
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data/tanveer/hayes-roth/copias/conxuntos_kfold.dat
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96 18 47 53 124 22 19 94 44 49 54 125 50 101 55 58 33 65 57 0 9 73 83 12 43 79 113 46 15 29 110 63 76 112 82 10 21 51 26 105 42 13 16 118 120 117 87 1 20 61 35 67 129 95 102 92 72 106 69 100 130 40 111 45 66 5 90 93 36 41 99 122 91 71 59 56 4 115 7 104 107 119 121 84 60 14 108 2 70 75 116 11 3 80 30 34 68 64 74
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78 97 8 31 128 23 17 103 114 24 28 88 109 131 81 27 89 37 32 48 38 52 86 25 6 85 98 127 39 123 77 62 126
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73 24 114 29 78 22 43 19 54 76 50 28 125 23 33 79 55 112 46 103 63 44 53 113 21 49 97 9 65 88 10 15 0 128 94 96 12 92 20 52 35 6 71 25 118 59 41 37 66 129 48 95 13 32 130 120 38 61 51 102 100 91 27 93 85 87 42 111 99 72 36 117 90 86 67 89 80 68 75 60 64 127 126 104 98 74 116 84 2 7 77 11 121 3 70 30 108 34 107
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110 57 101 18 58 17 82 109 47 124 31 8 83 131 26 1 69 122 81 105 40 56 16 5 45 106 115 119 14 123 39 4 62
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19 31 47 76 18 28 17 23 88 49 33 24 58 57 78 96 50 63 113 83 53 101 94 22 79 131 112 8 125 12 128 110 73 124 46 109 82 91 35 56 69 38 71 40 67 72 48 81 42 51 25 1 99 102 37 66 5 32 130 85 20 27 61 59 36 6 26 13 95 117 16 105 122 52 86 120 77 3 80 62 30 7 121 127 116 68 4 2 11 70 14 108 64 126 115 104 84 60 98
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0 55 44 9 114 103 10 97 29 65 21 43 54 15 87 41 90 89 106 129 92 100 118 111 93 45 34 123 39 75 107 74 119
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55 8 49 33 103 12 44 53 58 17 46 0 28 76 79 125 109 43 29 21 88 82 114 47 57 15 23 113 50 78 110 96 112 128 65 101 63 69 37 72 42 61 6 32 81 56 25 91 13 129 106 20 92 66 67 48 1 89 99 102 41 85 36 35 59 90 105 45 95 86 100 51 27 118 120 5 4 126 75 104 115 60 30 84 80 39 34 74 62 64 119 14 7 2 121 77 11 107 123
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18 31 24 97 19 124 73 94 9 83 22 10 54 131 87 52 26 130 16 93 40 122 117 71 111 38 108 116 3 127 98 70 68
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6
data/tanveer/hayes-roth/hayes-roth.cost
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data/tanveer/hayes-roth/hayes-roth.cost
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% Rows Columns
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3 3
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% Matrix elements
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0.0 1.0 1.0
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1.0 0.0 1.0
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1.0 1.0 0.0
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132
data/tanveer/hayes-roth/hayes-roth.data
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132
data/tanveer/hayes-roth/hayes-roth.data
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92 2 1 1 2 1
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10 2 1 3 2 2
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83 3 1 4 1 3
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61 2 4 2 2 3
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107 1 1 3 4 3
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113 1 1 3 2 2
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80 3 1 3 2 2
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125 3 4 2 4 3
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36 2 2 1 1 1
|
||||
105 3 2 1 1 1
|
||||
81 1 2 1 1 1
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122 2 2 3 4 3
|
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94 1 1 2 1 1
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60 2 1 2 2 2
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8 2 4 1 4 3
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20 1 1 3 3 1
|
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85 3 2 1 2 2
|
||||
50 1 2 1 1 1
|
||||
68 3 3 2 1 1
|
||||
89 3 1 3 2 1
|
||||
52 1 2 2 1 2
|
||||
19 3 2 1 3 1
|
||||
118 2 1 2 1 1
|
||||
16 3 2 1 3 1
|
||||
91 2 3 2 1 1
|
||||
79 3 2 2 1 2
|
||||
23 3 2 1 3 2
|
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25 2 1 2 2 2
|
||||
30 1 1 3 2 1
|
||||
57 3 2 1 1 1
|
||||
3 1 4 1 1 3
|
||||
114 2 2 1 3 1
|
||||
37 1 2 1 3 2
|
||||
66 1 1 1 2 1
|
||||
110 2 4 3 1 3
|
||||
116 3 1 2 2 2
|
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88 1 1 2 2 2
|
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77 3 2 2 1 2
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82 1 2 1 2 2
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64 3 4 3 2 3
|
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84 2 2 2 1 2
|
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86 2 2 1 2 2
|
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6 3 2 1 3 2
|
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74 3 2 1 1 1
|
||||
106 3 1 2 1 1
|
||||
115 1 2 1 3 2
|
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130 2 1 1 2 1
|
||||
54 1 1 1 2 1
|
||||
33 1 2 2 3 2
|
||||
67 3 3 1 1 1
|
||||
69 3 3 3 1 1
|
||||
39 3 2 1 2 2
|
||||
53 3 2 1 2 2
|
||||
127 3 1 2 1 1
|
||||
96 1 1 1 2 1
|
||||
121 2 1 3 2 1
|
||||
70 2 2 2 1 2
|
||||
123 2 1 2 1 1
|
||||
42 2 2 1 3 1
|
||||
78 2 1 2 2 2
|
||||
11 1 2 4 2 3
|
||||
129 2 2 1 2 2
|
||||
128 1 1 2 4 3
|
||||
5 1 3 2 1 1
|
||||
4 2 4 4 2 3
|
||||
95 2 3 2 1 1
|
||||
73 3 1 2 2 2
|
||||
26 1 1 2 2 2
|
||||
48 1 3 2 4 3
|
||||
104 1 1 2 2 2
|
||||
102 3 1 4 2 3
|
||||
2 2 1 3 2 2
|
||||
41 1 1 3 2 2
|
||||
119 3 1 3 2 1
|
||||
75 1 2 4 4 3
|
||||
47 1 4 2 1 3
|
||||
93 2 1 2 1 1
|
||||
46 3 4 1 2 3
|
||||
132 2 2 1 1 1
|
||||
108 1 1 2 1 1
|
||||
18 2 2 4 3 3
|
||||
62 3 1 2 2 2
|
||||
120 1 1 3 2 1
|
||||
35 1 2 1 3 1
|
||||
27 1 4 4 1 3
|
||||
98 3 3 3 2 2
|
||||
109 2 2 1 3 2
|
||||
31 3 3 2 1 2
|
||||
112 1 1 1 3 1
|
||||
34 2 2 1 2 2
|
||||
63 2 2 2 1 2
|
||||
65 2 3 2 3 2
|
||||
117 1 3 2 1 2
|
||||
56 2 2 1 2 2
|
||||
59 1 1 1 2 1
|
||||
76 3 2 2 1 2
|
||||
1 3 2 1 1 1
|
||||
28 1 1 2 1 1
|
||||
22 3 1 4 4 3
|
||||
29 3 3 2 1 2
|
||||
111 2 3 2 1 2
|
||||
97 2 1 3 1 1
|
||||
49 1 2 1 2 2
|
||||
51 3 1 1 2 1
|
||||
87 2 2 4 1 3
|
||||
58 1 2 2 1 2
|
||||
32 2 3 2 1 2
|
||||
72 2 2 1 4 3
|
||||
55 1 4 2 3 3
|
||||
103 2 2 1 1 1
|
||||
7 1 2 1 1 1
|
||||
99 2 2 3 2 2
|
||||
15 1 3 2 1 1
|
||||
126 3 1 2 1 1
|
||||
45 3 1 1 2 1
|
||||
101 3 3 1 4 3
|
||||
100 2 3 4 1 3
|
||||
24 1 2 3 3 2
|
||||
124 3 3 2 2 2
|
||||
13 3 3 4 2 3
|
||||
14 1 2 2 1 2
|
||||
38 2 1 1 4 3
|
||||
71 3 1 2 2 2
|
||||
43 3 2 2 4 3
|
||||
131 2 3 1 3 1
|
||||
17 2 1 1 2 1
|
||||
12 3 4 1 3 3
|
||||
44 1 1 4 3 3
|
||||
40 2 1 2 1 1
|
||||
90 1 2 1 2 2
|
||||
21 1 2 2 1 2
|
||||
9 3 1 1 2 1
|
||||
130
data/tanveer/hayes-roth/hayes-roth.names
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130
data/tanveer/hayes-roth/hayes-roth.names
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|
||||
1. Title: Hayes-Roth & Hayes-Roth (1977) Database
|
||||
|
||||
2. Source Information:
|
||||
(a) Creators: Barbara and Frederick Hayes-Roth
|
||||
(b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779
|
||||
(c) Date: March, 1989
|
||||
|
||||
3. Past Usage:
|
||||
1. Hayes-Roth, B., & Hayes-Roth, F. (1977). Concept learning and the
|
||||
recognition and classification of exemplars. Journal of Verbal Learning
|
||||
and Verbal Behavior, 16, 321-338.
|
||||
-- Results:
|
||||
-- Human subjects classification and recognition performance:
|
||||
1. decreases with distance from the prototype,
|
||||
2. is better on unseen prototypes than old instances, and
|
||||
3. improves with presentation frequency during learning.
|
||||
2. Anderson, J.R., & Kline, P.J. (1979). A learning system and its
|
||||
psychological implications. In Proceedings of the Sixth International
|
||||
Joint Conference on Artificial Intelligence (pp. 16-21). Tokyo, Japan:
|
||||
Morgan Kaufmann.
|
||||
-- Partitioned the results into 4 classes:
|
||||
1. prototypes
|
||||
2. near-prototypes with high presentation frequency during learning
|
||||
3. near-prototypes with low presentation frequency during learning
|
||||
4. instances that are far from protoypes
|
||||
-- Described evidence that ACT's classification confidence and
|
||||
recognition behaviors closely simulated human subjects' behaviors.
|
||||
3. Aha, D.W. (1989). Incremental learning of independent, overlapping, and
|
||||
graded concept descriptions with an instance-based process framework.
|
||||
Manuscript submitted for publication.
|
||||
-- Used same partition as Anderson & Kline
|
||||
-- Described evidence that Bloom's classification confidence behavior
|
||||
is similar to the human subjects' behavior. Bloom fitted the data
|
||||
more closely than did ACT.
|
||||
|
||||
4. Relevant Information:
|
||||
This database contains 5 numeric-valued attributes. Only a subset of
|
||||
3 are used during testing (the latter 3). Furthermore, only 2 of the
|
||||
3 concepts are "used" during testing (i.e., those with the prototypes
|
||||
000 and 111). I've mapped all values to their zero-indexing equivalents.
|
||||
|
||||
Some instances could be placed in either category 0 or 1. I've followed
|
||||
the authors' suggestion, placing them in each category with equal
|
||||
probability.
|
||||
|
||||
I've replaced the actual values of the attributes (i.e., hobby has values
|
||||
chess, sports and stamps) with numeric values. I think this is how
|
||||
the authors' did this when testing the categorization models described
|
||||
in the paper. I find this unfair. While the subjects were able to bring
|
||||
background knowledge to bear on the attribute values and their
|
||||
relationships, the algorithms were provided with no such knowledge. I'm
|
||||
uncertain whether the 2 distractor attributes (name and hobby) are
|
||||
presented to the authors' algorithms during testing. However, it is clear
|
||||
that only the age, educational status, and marital status attributes are
|
||||
given during the human subjects' transfer tests.
|
||||
|
||||
5. Number of Instances: 132 training instances, 28 test instances
|
||||
|
||||
6. Number of Attributes: 5 plus the class membership attribute. 3 concepts.
|
||||
|
||||
7. Attribute Information:
|
||||
-- 1. name: distinct for each instance and represented numerically
|
||||
-- 2. hobby: nominal values ranging between 1 and 3
|
||||
-- 3. age: nominal values ranging between 1 and 4
|
||||
-- 4. educational level: nominal values ranging between 1 and 4
|
||||
-- 5. marital status: nominal values ranging between 1 and 4
|
||||
-- 6. class: nominal value between 1 and 3
|
||||
|
||||
9. Missing Attribute Values: none
|
||||
|
||||
10. Class Distribution: see below
|
||||
|
||||
11. Detailed description of the experiment:
|
||||
1. 3 categories (1, 2, and neither -- which I call 3)
|
||||
-- some of the instances could be classified in either class 1 or 2, and
|
||||
they have been evenly distributed between the two classes
|
||||
2. 5 Attributes
|
||||
-- A. name (a randomly-generated number between 1 and 132)
|
||||
-- B. hobby (a randomly-generated number between 1 and 3)
|
||||
-- C. age (a number between 1 and 4)
|
||||
-- D. education level (a number between 1 and 4)
|
||||
-- E. marital status (a number between 1 and 4)
|
||||
3. Classification:
|
||||
-- only attributes C-E are diagnostic; values for A and B are ignored
|
||||
-- Class Neither: if a 4 occurs for any attribute C-E
|
||||
-- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E
|
||||
-- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E
|
||||
-- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E
|
||||
4. Prototypes:
|
||||
-- Class 1: 111
|
||||
-- Class 2: 222
|
||||
-- Class Either: 333
|
||||
-- Class Neither: 444
|
||||
5. Number of training instances: 132
|
||||
-- Each instance presented 0, 1, or 10 times
|
||||
-- None of the prototypes seen during training
|
||||
-- 3 instances from each of categories 1, 2, and either are repeated
|
||||
10 times each
|
||||
-- 3 additional instances from the Either category are shown during
|
||||
learning
|
||||
5. Number of test instances: 28
|
||||
-- All 9 class 1
|
||||
-- All 9 class 2
|
||||
-- All 6 class Either
|
||||
-- All 4 prototypes
|
||||
--------------------
|
||||
-- 28 total
|
||||
|
||||
Observations of interest:
|
||||
1. Relative classification confidence of
|
||||
-- prototypes for classes 1 and 2 (2 instances)
|
||||
(Anderson calls these Class 1 instances)
|
||||
-- instances of class 1 with frequency 10 during training and
|
||||
instances of class 2 with frequency 10 during training that
|
||||
are 1 value away from their respective prototypes (6 instances)
|
||||
(Anderson calls these Class 2 instances)
|
||||
-- instances of class 1 with frequency 1 during training and
|
||||
instances of class 2 with frequency 1 during training that
|
||||
are 1 value away from their respective prototypes (6 instances)
|
||||
(Anderson calls these Class 3 instances)
|
||||
-- instances of class 1 with frequency 1 during training and
|
||||
instances of class 2 with frequency 1 during training that
|
||||
are 2 values away from their respective prototypes (6 instances)
|
||||
(Anderson calls these Class 4 instances)
|
||||
2. Relative classification recognition of them also
|
||||
|
||||
Some Expected results:
|
||||
Both frequency and distance from prototype will effect the classification
|
||||
accuracy of instances. Greater the frequency, higher the classification
|
||||
confidence. Closer to prototype, higher the classification confidence.
|
||||
28
data/tanveer/hayes-roth/hayes-roth.test
Executable file
28
data/tanveer/hayes-roth/hayes-roth.test
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|
||||
1 1 1 2 1
|
||||
1 1 2 1 1
|
||||
1 2 1 1 1
|
||||
1 1 1 3 1
|
||||
1 1 3 1 1
|
||||
1 3 1 1 1
|
||||
1 1 3 3 1
|
||||
1 3 1 3 1
|
||||
1 3 3 1 1
|
||||
1 2 2 1 2
|
||||
1 2 1 2 2
|
||||
1 1 2 2 2
|
||||
1 2 2 3 2
|
||||
1 2 3 2 2
|
||||
1 3 2 2 2
|
||||
1 2 3 3 2
|
||||
1 3 2 3 2
|
||||
1 3 3 2 2
|
||||
1 1 3 2 1
|
||||
1 3 2 1 2
|
||||
1 2 1 3 1
|
||||
1 2 3 1 2
|
||||
1 1 2 3 1
|
||||
1 3 1 2 2
|
||||
1 1 1 1 1
|
||||
1 2 2 2 2
|
||||
1 3 3 3 1
|
||||
1 4 4 4 3
|
||||
10
data/tanveer/hayes-roth/hayes-roth.txt
Executable file
10
data/tanveer/hayes-roth/hayes-roth.txt
Executable file
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|
||||
n_entradas= 3
|
||||
n_clases= 3
|
||||
n_arquivos= 2
|
||||
fich1= hayes-roth_train_R.dat
|
||||
n_patrons1= 132
|
||||
fich2= hayes-roth_test_R.dat
|
||||
n_patrons2= 28
|
||||
n_patrons_entrena= 66
|
||||
n_patrons_valida= 66
|
||||
n_conxuntos= 1
|
||||
34
data/tanveer/hayes-roth/hayes-roth_test.arff
Executable file
34
data/tanveer/hayes-roth/hayes-roth_test.arff
Executable file
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|
||||
@relation hayes-roth
|
||||
@attribute 'f1' real
|
||||
@attribute 'f2' real
|
||||
@attribute 'f3' real
|
||||
@attribute 'clase' {0,1,2}
|
||||
@data
|
||||
1,1,2,0
|
||||
1,2,1,0
|
||||
2,1,1,0
|
||||
1,1,3,0
|
||||
1,3,1,0
|
||||
3,1,1,0
|
||||
1,3,3,0
|
||||
3,1,3,0
|
||||
3,3,1,0
|
||||
2,2,1,1
|
||||
2,1,2,1
|
||||
1,2,2,1
|
||||
2,2,3,1
|
||||
2,3,2,1
|
||||
3,2,2,1
|
||||
2,3,3,1
|
||||
3,2,3,1
|
||||
3,3,2,1
|
||||
1,3,2,0
|
||||
3,2,1,1
|
||||
2,1,3,0
|
||||
2,3,1,1
|
||||
1,2,3,0
|
||||
3,1,2,1
|
||||
1,1,1,0
|
||||
2,2,2,1
|
||||
3,3,3,0
|
||||
4,4,4,2
|
||||
29
data/tanveer/hayes-roth/hayes-roth_test_R.dat
Executable file
29
data/tanveer/hayes-roth/hayes-roth_test_R.dat
Executable file
@@ -0,0 +1,29 @@
|
||||
f1 f2 f3 clase
|
||||
1 1 1 2 0
|
||||
2 1 2 1 0
|
||||
3 2 1 1 0
|
||||
4 1 1 3 0
|
||||
5 1 3 1 0
|
||||
6 3 1 1 0
|
||||
7 1 3 3 0
|
||||
8 3 1 3 0
|
||||
9 3 3 1 0
|
||||
10 2 2 1 1
|
||||
11 2 1 2 1
|
||||
12 1 2 2 1
|
||||
13 2 2 3 1
|
||||
14 2 3 2 1
|
||||
15 3 2 2 1
|
||||
16 2 3 3 1
|
||||
17 3 2 3 1
|
||||
18 3 3 2 1
|
||||
19 1 3 2 0
|
||||
20 3 2 1 1
|
||||
21 2 1 3 0
|
||||
22 2 3 1 1
|
||||
23 1 2 3 0
|
||||
24 3 1 2 1
|
||||
25 1 1 1 0
|
||||
26 2 2 2 1
|
||||
27 3 3 3 0
|
||||
28 4 4 4 2
|
||||
138
data/tanveer/hayes-roth/hayes-roth_train.arff
Executable file
138
data/tanveer/hayes-roth/hayes-roth_train.arff
Executable file
@@ -0,0 +1,138 @@
|
||||
@relation hayes-roth
|
||||
@attribute 'f1' real
|
||||
@attribute 'f2' real
|
||||
@attribute 'f3' real
|
||||
@attribute 'clase' {0,1,2}
|
||||
@data
|
||||
-1.00692,-1.00692,0.0479484,0
|
||||
-1.00692,1.10281,0.0479484,1
|
||||
-1.00692,2.15768,-1.00692,2
|
||||
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|
||||
-1.00692,1.10281,2.15768,2
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
2.15768,1.10281,0.0479484,2
|
||||
0.0479484,0.0479484,-1.00692,1
|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
0.0479484,-1.00692,1.10281,1
|
||||
0.0479484,-1.00692,-1.00692,0
|
||||
-1.00692,0.0479484,-1.00692,0
|
||||
0.0479484,-1.00692,1.10281,1
|
||||
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|
||||
-1.00692,-1.00692,0.0479484,0
|
||||
0.0479484,0.0479484,1.10281,1
|
||||
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|
||||
1.10281,1.10281,-1.00692,0
|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.0479484,-1.00692,1.10281,0
|
||||
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|
||||
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|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
-1.00692,0.0479484,2.15768,2
|
||||
1.10281,0.0479484,-1.00692,0
|
||||
2.15768,2.15768,0.0479484,2
|
||||
1.10281,0.0479484,-1.00692,0
|
||||
-1.00692,0.0479484,0.0479484,1
|
||||
-1.00692,0.0479484,0.0479484,1
|
||||
1.10281,0.0479484,2.15768,2
|
||||
-1.00692,0.0479484,0.0479484,1
|
||||
-1.00692,2.15768,0.0479484,2
|
||||
-1.00692,1.10281,0.0479484,1
|
||||
-1.00692,1.10281,0.0479484,1
|
||||
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|
||||
0.0479484,2.15768,2.15768,2
|
||||
2.15768,0.0479484,-1.00692,2
|
||||
-1.00692,0.0479484,-1.00692,0
|
||||
2.15768,-1.00692,0.0479484,2
|
||||
0.0479484,-1.00692,-1.00692,0
|
||||
-1.00692,0.0479484,-1.00692,0
|
||||
0.0479484,2.15768,1.10281,2
|
||||
-1.00692,0.0479484,0.0479484,1
|
||||
-1.00692,1.10281,0.0479484,0
|
||||
0.0479484,-1.00692,1.10281,0
|
||||
2.15768,2.15768,-1.00692,2
|
||||
1.10281,1.10281,0.0479484,1
|
||||
0.0479484,-1.00692,1.10281,1
|
||||
1.10281,0.0479484,-1.00692,1
|
||||
-1.00692,-1.00692,1.10281,0
|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
0.0479484,0.0479484,-1.00692,1
|
||||
1.10281,0.0479484,1.10281,1
|
||||
1.10281,0.0479484,-1.00692,1
|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
-1.00692,-1.00692,0.0479484,0
|
||||
0.0479484,0.0479484,-1.00692,1
|
||||
0.0479484,-1.00692,-1.00692,0
|
||||
-1.00692,0.0479484,-1.00692,0
|
||||
-1.00692,2.15768,2.15768,2
|
||||
1.10281,0.0479484,-1.00692,1
|
||||
1.10281,0.0479484,-1.00692,1
|
||||
-1.00692,1.10281,-1.00692,0
|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
-1.00692,-1.00692,0.0479484,0
|
||||
0.0479484,2.15768,-1.00692,2
|
||||
0.0479484,0.0479484,-1.00692,1
|
||||
1.10281,0.0479484,-1.00692,1
|
||||
0.0479484,-1.00692,2.15768,2
|
||||
2.15768,0.0479484,1.10281,2
|
||||
0.0479484,-1.00692,-1.00692,0
|
||||
0.0479484,-1.00692,-1.00692,0
|
||||
0.0479484,1.10281,0.0479484,1
|
||||
1.10281,0.0479484,-1.00692,0
|
||||
-1.00692,0.0479484,-1.00692,0
|
||||
-1.00692,-1.00692,0.0479484,0
|
||||
1.10281,-1.00692,2.15768,2
|
||||
1.10281,2.15768,-1.00692,2
|
||||
0.0479484,1.10281,1.10281,1
|
||||
1.10281,0.0479484,0.0479484,1
|
||||
1.10281,2.15768,0.0479484,2
|
||||
0.0479484,0.0479484,-1.00692,1
|
||||
-1.00692,-1.00692,2.15768,2
|
||||
-1.00692,0.0479484,0.0479484,1
|
||||
0.0479484,0.0479484,2.15768,2
|
||||
1.10281,-1.00692,1.10281,0
|
||||
-1.00692,-1.00692,0.0479484,0
|
||||
2.15768,-1.00692,1.10281,2
|
||||
-1.00692,2.15768,1.10281,2
|
||||
-1.00692,0.0479484,-1.00692,0
|
||||
0.0479484,-1.00692,0.0479484,1
|
||||
0.0479484,0.0479484,-1.00692,1
|
||||
-1.00692,-1.00692,0.0479484,0
|
||||
133
data/tanveer/hayes-roth/hayes-roth_train_R.dat
Executable file
133
data/tanveer/hayes-roth/hayes-roth_train_R.dat
Executable file
@@ -0,0 +1,133 @@
|
||||
f1 f2 f3 clase
|
||||
1 -1.00692 -1.00692 0.0479484 0
|
||||
2 -1.00692 1.10281 0.0479484 1
|
||||
3 -1.00692 2.15768 -1.00692 2
|
||||
4 2.15768 0.0479484 0.0479484 2
|
||||
5 -1.00692 1.10281 2.15768 2
|
||||
6 -1.00692 1.10281 0.0479484 1
|
||||
7 -1.00692 1.10281 0.0479484 1
|
||||
8 2.15768 0.0479484 2.15768 2
|
||||
9 0.0479484 -1.00692 -1.00692 0
|
||||
10 0.0479484 -1.00692 -1.00692 0
|
||||
11 0.0479484 -1.00692 -1.00692 0
|
||||
12 0.0479484 1.10281 2.15768 2
|
||||
13 -1.00692 0.0479484 -1.00692 0
|
||||
14 -1.00692 0.0479484 0.0479484 1
|
||||
15 2.15768 -1.00692 2.15768 2
|
||||
16 -1.00692 1.10281 1.10281 0
|
||||
17 0.0479484 -1.00692 0.0479484 1
|
||||
18 0.0479484 -1.00692 -1.00692 0
|
||||
19 1.10281 0.0479484 -1.00692 0
|
||||
20 -1.00692 1.10281 0.0479484 0
|
||||
21 0.0479484 0.0479484 -1.00692 1
|
||||
22 0.0479484 -1.00692 1.10281 0
|
||||
23 -1.00692 0.0479484 -1.00692 0
|
||||
24 0.0479484 -1.00692 1.10281 0
|
||||
25 1.10281 0.0479484 -1.00692 0
|
||||
26 0.0479484 0.0479484 -1.00692 1
|
||||
27 0.0479484 -1.00692 1.10281 1
|
||||
28 -1.00692 0.0479484 0.0479484 1
|
||||
29 -1.00692 1.10281 0.0479484 0
|
||||
30 0.0479484 -1.00692 -1.00692 0
|
||||
31 2.15768 -1.00692 -1.00692 2
|
||||
32 0.0479484 -1.00692 1.10281 0
|
||||
33 0.0479484 -1.00692 1.10281 1
|
||||
34 -1.00692 -1.00692 0.0479484 0
|
||||
35 2.15768 1.10281 -1.00692 2
|
||||
36 -1.00692 0.0479484 0.0479484 1
|
||||
37 -1.00692 0.0479484 0.0479484 1
|
||||
38 0.0479484 0.0479484 -1.00692 1
|
||||
39 0.0479484 -1.00692 0.0479484 1
|
||||
40 2.15768 1.10281 0.0479484 2
|
||||
41 0.0479484 0.0479484 -1.00692 1
|
||||
42 0.0479484 -1.00692 0.0479484 1
|
||||
43 0.0479484 -1.00692 1.10281 1
|
||||
44 0.0479484 -1.00692 -1.00692 0
|
||||
45 -1.00692 0.0479484 -1.00692 0
|
||||
46 0.0479484 -1.00692 1.10281 1
|
||||
47 -1.00692 -1.00692 0.0479484 0
|
||||
48 -1.00692 -1.00692 0.0479484 0
|
||||
49 0.0479484 0.0479484 1.10281 1
|
||||
50 1.10281 -1.00692 -1.00692 0
|
||||
51 1.10281 1.10281 -1.00692 0
|
||||
52 0.0479484 -1.00692 0.0479484 1
|
||||
53 0.0479484 -1.00692 0.0479484 1
|
||||
54 -1.00692 0.0479484 -1.00692 0
|
||||
55 -1.00692 -1.00692 0.0479484 0
|
||||
56 -1.00692 1.10281 0.0479484 0
|
||||
57 0.0479484 0.0479484 -1.00692 1
|
||||
58 -1.00692 0.0479484 -1.00692 0
|
||||
59 0.0479484 -1.00692 1.10281 0
|
||||
60 -1.00692 0.0479484 0.0479484 1
|
||||
61 0.0479484 2.15768 0.0479484 2
|
||||
62 0.0479484 -1.00692 0.0479484 1
|
||||
63 -1.00692 0.0479484 2.15768 2
|
||||
64 1.10281 0.0479484 -1.00692 0
|
||||
65 2.15768 2.15768 0.0479484 2
|
||||
66 1.10281 0.0479484 -1.00692 0
|
||||
67 -1.00692 0.0479484 0.0479484 1
|
||||
68 -1.00692 0.0479484 0.0479484 1
|
||||
69 1.10281 0.0479484 2.15768 2
|
||||
70 -1.00692 0.0479484 0.0479484 1
|
||||
71 -1.00692 2.15768 0.0479484 2
|
||||
72 -1.00692 1.10281 0.0479484 1
|
||||
73 -1.00692 1.10281 0.0479484 1
|
||||
74 -1.00692 1.10281 0.0479484 0
|
||||
75 0.0479484 2.15768 2.15768 2
|
||||
76 2.15768 0.0479484 -1.00692 2
|
||||
77 -1.00692 0.0479484 -1.00692 0
|
||||
78 2.15768 -1.00692 0.0479484 2
|
||||
79 0.0479484 -1.00692 -1.00692 0
|
||||
80 -1.00692 0.0479484 -1.00692 0
|
||||
81 0.0479484 2.15768 1.10281 2
|
||||
82 -1.00692 0.0479484 0.0479484 1
|
||||
83 -1.00692 1.10281 0.0479484 0
|
||||
84 0.0479484 -1.00692 1.10281 0
|
||||
85 2.15768 2.15768 -1.00692 2
|
||||
86 1.10281 1.10281 0.0479484 1
|
||||
87 0.0479484 -1.00692 1.10281 1
|
||||
88 1.10281 0.0479484 -1.00692 1
|
||||
89 -1.00692 -1.00692 1.10281 0
|
||||
90 0.0479484 -1.00692 0.0479484 1
|
||||
91 0.0479484 0.0479484 -1.00692 1
|
||||
92 1.10281 0.0479484 1.10281 1
|
||||
93 1.10281 0.0479484 -1.00692 1
|
||||
94 0.0479484 -1.00692 0.0479484 1
|
||||
95 -1.00692 -1.00692 0.0479484 0
|
||||
96 0.0479484 0.0479484 -1.00692 1
|
||||
97 0.0479484 -1.00692 -1.00692 0
|
||||
98 -1.00692 0.0479484 -1.00692 0
|
||||
99 -1.00692 2.15768 2.15768 2
|
||||
100 1.10281 0.0479484 -1.00692 1
|
||||
101 1.10281 0.0479484 -1.00692 1
|
||||
102 -1.00692 1.10281 -1.00692 0
|
||||
103 0.0479484 -1.00692 0.0479484 1
|
||||
104 -1.00692 -1.00692 0.0479484 0
|
||||
105 0.0479484 2.15768 -1.00692 2
|
||||
106 0.0479484 0.0479484 -1.00692 1
|
||||
107 1.10281 0.0479484 -1.00692 1
|
||||
108 0.0479484 -1.00692 2.15768 2
|
||||
109 2.15768 0.0479484 1.10281 2
|
||||
110 0.0479484 -1.00692 -1.00692 0
|
||||
111 0.0479484 -1.00692 -1.00692 0
|
||||
112 0.0479484 1.10281 0.0479484 1
|
||||
113 1.10281 0.0479484 -1.00692 0
|
||||
114 -1.00692 0.0479484 -1.00692 0
|
||||
115 -1.00692 -1.00692 0.0479484 0
|
||||
116 1.10281 -1.00692 2.15768 2
|
||||
117 1.10281 2.15768 -1.00692 2
|
||||
118 0.0479484 1.10281 1.10281 1
|
||||
119 1.10281 0.0479484 0.0479484 1
|
||||
120 1.10281 2.15768 0.0479484 2
|
||||
121 0.0479484 0.0479484 -1.00692 1
|
||||
122 -1.00692 -1.00692 2.15768 2
|
||||
123 -1.00692 0.0479484 0.0479484 1
|
||||
124 0.0479484 0.0479484 2.15768 2
|
||||
125 1.10281 -1.00692 1.10281 0
|
||||
126 -1.00692 -1.00692 0.0479484 0
|
||||
127 2.15768 -1.00692 1.10281 2
|
||||
128 -1.00692 2.15768 1.10281 2
|
||||
129 -1.00692 0.0479484 -1.00692 0
|
||||
130 0.0479484 -1.00692 0.0479484 1
|
||||
131 0.0479484 0.0479484 -1.00692 1
|
||||
132 -1.00692 -1.00692 0.0479484 0
|
||||
29
data/tanveer/hayes-roth/le_datos.m
Executable file
29
data/tanveer/hayes-roth/le_datos.m
Executable file
@@ -0,0 +1,29 @@
|
||||
printf('lendo problema %s ...\n', problema);
|
||||
|
||||
n_entradas= 3; n_clases= 3;
|
||||
n_fich= 2; fich{1}= 'hayes-roth.data'; n_patrons(1)= 132; fich{2}= 'hayes-roth.test'; n_patrons(2)= 28;
|
||||
|
||||
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);
|
||||
if i_fich==1
|
||||
fscanf(f,'%i',2); % le e descarta as columnas 1 e 2
|
||||
else
|
||||
fscanf(f,'%i',1);
|
||||
end
|
||||
for j = 1:n_entradas
|
||||
x(i_fich,i,j) = fscanf(f,'%i',1);
|
||||
end
|
||||
cl(i_fich,i)=fscanf(f,'%i',1)-1;
|
||||
end
|
||||
fclose(f);
|
||||
end
|
||||
Reference in New Issue
Block a user