mirror of
https://github.com/Doctorado-ML/Stree_datasets.git
synced 2025-08-18 08:56:01 +00:00
Commit Inicial
This commit is contained in:
2
data/tanveer/echocardiogram/conxuntos.dat
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2
data/tanveer/echocardiogram/conxuntos.dat
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8
data/tanveer/echocardiogram/conxuntos_kfold.dat
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8
data/tanveer/echocardiogram/conxuntos_kfold.dat
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63 51 64 120 113 121 96 78 110 92 101 74 100 59 91 43 127 128 79 39 8 29 130 98 69 85 44 17 46 62 48 65 23
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144
data/tanveer/echocardiogram/echocardiogram.arff
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144
data/tanveer/echocardiogram/echocardiogram.arff
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@relation echocardiogram
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@attribute 'f1' real
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@attribute 'f2' real
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@attribute 'f3' real
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@attribute 'f4' real
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@attribute 'f5' real
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@attribute 'f6' real
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@attribute 'f7' real
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@attribute 'f8' real
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@attribute 'f9' real
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-0.234147,-0.471791,-1.46504,-0.488061,-0.187727,-0.112217,-0.295066,0.11834,0.67578,-0.471791,0
|
||||
1.20517,-0.471791,0.215205,3.67939,1.22917,-0.0198545,0.0697935,-0.196432,0.67578,2.1034,1
|
||||
0.108549,-0.471791,0.475037,-0.412289,0.0137276,-0.0198545,-0.7887,1.05391,0.67578,2.1034,1
|
||||
0.108549,-0.471791,0.128594,0.155999,-0.322029,-0.574032,-0.7887,0.113968,-1.92796,-0.471791,0
|
||||
0.382705,-0.471791,0.301815,0.345429,-0.013133,1.45795,1.78678,-0.196432,-1.92796,-0.471791,0
|
||||
0.0400095,-0.471791,0.561648,-0.235488,-0.228017,-0.389306,-0.7887,0.428739,-1.92796,-0.471791,0
|
||||
-0.0970686,2.1034,1.68759,-0.210231,0.644951,-0.389306,-0.7887,0.428739,-1.92796,-0.471791,0
|
||||
-0.234147,-0.471791,0.734869,-0.184974,0.248758,-0.943483,-0.7887,-0.519946,-1.92796,-0.471791,0
|
||||
0.108549,2.1034,-0.131238,2.27761,1.49777,0.996137,1.25022,-0.196432,-1.92796,-0.471791,0
|
||||
-4.14087,-0.471791,0.475037,-1.37207,-0.013133,-0.943483,-0.7887,-0.519946,-1.92796,-0.471791,0
|
||||
-0.439764,-0.471791,1.94742,-0.197602,0.262188,-0.758757,-0.7887,-0.196432,-1.92796,-0.471791,0
|
||||
0.108549,2.1034,0.301815,2.23972,0.980708,1.36559,1.25022,0.11834,-1.92796,-0.471791,1
|
||||
-4.14087,-0.471791,0.215205,1.04,0.732248,-0.389306,-0.359453,-0.213919,-1.92796,-0.471791,0
|
||||
0.245627,-0.471791,-0.477682,-0.538576,-0.154151,-0.0198545,-0.209217,0.11834,-1.92796,-0.471791,0
|
||||
-0.234147,2.1034,-0.737514,-1.37207,-1.39645,0.44196,0.0161377,0.428739,-1.92796,-0.471791,0
|
||||
0.0400095,-0.471791,-0.217849,-1.37207,0.0540184,-0.574032,0.0161377,-0.825974,-1.92796,-0.471791,0
|
||||
0.0400095,2.1034,-0.131238,0.294914,0.430066,0.903774,0.77805,0.11834,-1.92796,-0.471791,0
|
||||
-0.850998,-0.471791,-0.477682,0.143371,-0.496623,-0.758757,-0.7887,-0.196432,-1.92796,-0.471791,0
|
||||
-4.14087,-0.471791,-0.997346,-0.513318,0.376345,-0.20458,-0.617001,0.428739,-1.92796,-0.471791,0
|
||||
0.0400095,-0.471791,-0.564292,1.84824,0.510648,-0.0198545,-0.209217,0.11834,-1.92796,-0.471791,0
|
||||
0.245627,-0.471791,0.301815,0.257028,0.215182,-0.389306,-0.7887,0.428739,-1.92796,-0.471791,1
|
||||
0.245627,-0.471791,0.648259,-0.690119,0.718817,-0.574032,-0.574076,-0.196432,-1.92796,-0.471791,0
|
||||
0.588322,-0.471791,-0.0446277,-0.488061,0.436781,0.0725084,-0.337991,0.428739,-1.92796,-0.471791,0
|
||||
-0.234147,-0.471791,-0.564292,0.661145,-0.0265633,0.164871,-0.0160558,0.11834,-1.92796,-0.471791,0
|
||||
0.108549,-0.471791,-0.477682,-1.37207,0.0741639,0.257234,0.0891096,0.11834,-1.92796,-0.471791,0
|
||||
5
data/tanveer/echocardiogram/echocardiogram.cost
Executable file
5
data/tanveer/echocardiogram/echocardiogram.cost
Executable file
@@ -0,0 +1,5 @@
|
||||
% Rows Columns
|
||||
2 2
|
||||
% Matrix elements
|
||||
0.0 1.0
|
||||
1.0 0.0
|
||||
131
data/tanveer/echocardiogram/echocardiogram.data
Executable file
131
data/tanveer/echocardiogram/echocardiogram.data
Executable file
@@ -0,0 +1,131 @@
|
||||
11 0 71 0 0.260 9 4.600 14 1 1 name 1 0
|
||||
19 0 72 0 0.380 6 4.100 14 1.700 0.588 name 1 0
|
||||
16 0 55 0 0.260 4 3.420 14 1 1 name 1 0
|
||||
57 0 60 0 0.253 12.062 4.603 16 1.450 0.788 name 1 0
|
||||
19 1 57 0 0.160 22 5.750 18 2.250 0.571 name 1 0
|
||||
26 0 68 0 0.260 5 4.310 12 1 0.857 name 1 0
|
||||
13 0 62 0 0.230 31 5.430 22.5 1.875 0.857 name 1 0
|
||||
50 0 60 0 0.330 8 5.250 14 1 1 name 1 0
|
||||
19 0 46 0 0.340 0 5.090 16 1.140 1.003 name 1 0
|
||||
25 0 54 0 0.140 13 4.490 15.5 1.190 0.930 name 1 0
|
||||
10 1 77 0 0.130 16 4.230 18 1.800 0.714 name 1 1
|
||||
52 0 62 1 0.450 9 3.600 16 1.140 1.003 name 1 0
|
||||
52 0 73 0 0.330 6 4 14 1 1 name 1 0
|
||||
44 0 60 0 0.150 10 3.730 14 1 1 name 1 0
|
||||
0.5 1 62 0 0.120 23 5.800 11.67 2.330 0.358 name 1 1
|
||||
24 0 55 1 0.250 12.063 4.290 14 1 1 name 1 0
|
||||
0.5 1 69 1 0.260 11 4.650 18 1.640 0.784 name 1 1
|
||||
0.5 1 62.529 1 0.070 20 5.200 24 2 0.857 name 1 1
|
||||
22 1 66 0 0.090 17 5.819 8 1.333 0.429 name 1 0
|
||||
1 1 66 1 0.220 15 5.400 27 2.250 0.857 name 1 1
|
||||
0.75 1 69 0 0.150 12 5.390 19.5 1.625 0.857 name 1 1
|
||||
0.75 1 85 1 0.180 19 5.460 13.83 1.380 0.710 name 1 1
|
||||
0.5 1 73 0 0.230 12.733 6.060 7.5 1.500 0.360 name 1 1
|
||||
5 1 71 0 0.170 0 4.650 8 1 0.570 name 1 1
|
||||
48 0 64 0 0.190 5.9 3.480 10 1.110 0.640 name 2 ?
|
||||
29 0 54 0 0.300 7 3.850 10 1.667 0.430 name 2 ?
|
||||
29 0 35 0 0.300 5 4.170 14 1 1 name 2 ?
|
||||
29 0 55 0 ? 7 ? 2 1 2 name 2 ?
|
||||
0.25 1 75 0 ? ? ? ? 1 ? name 2 ?
|
||||
36 0 55 1 0.210 4.2 4.160 14 1.560 0.640 name 2 0
|
||||
1 1 65 0 0.150 ? 5.050 10 1 0.710 name 2 ?
|
||||
1 1 52 1 0.170 17.200 5.320 14 1.170 0.857 name 2 ?
|
||||
03 1 ? 0 ? 12 ? 6 3 0.140 name 2 ?
|
||||
27 0 47 0 0.400 5.120 3.100 12 1 0.857 name 2 ?
|
||||
35 0 63 0 ? 10 ? 14 1.170 0.857 name 2 0
|
||||
26 0 61 0 0.610 13.100 4.070 13 1.625 0.571 name 2 0
|
||||
16 0 63 1 ? ? 5.310 5 1 0.357 name 2 0
|
||||
1 1 65 0 0.060 23.600 ? 21.500 2.150 0.714 name 2 1
|
||||
19 0 68 0 0.510 ? 3.880 15 1.670 0.640 name 2 0
|
||||
31 0 80 0 0.410 5.400 4.360 ? 1 ? name 2 ?
|
||||
32 0 54 0 0.350 9.300 3.630 11 1.222 0.640 name 2 0
|
||||
16 0 70 1 0.270 4.700 4.490 22 2 0.786 name 2 0
|
||||
40 0 79 0 0.150 17.500 4.270 13 1.300 0.714 name 2 0
|
||||
46 0 56 0 0.330 ? 3.590 14 1 1 name 2 0
|
||||
2 1 67 1 0.440 9 3.960 17.500 1.450 0.857 name 2 ?
|
||||
37 0 64 0 0.090 ? ? 12 2 0.428 name 2 ?
|
||||
19.5 1 81 0 0.120 ? ? 9 1.250 0.570 name 2 0
|
||||
20 1 59 0 0.030 21.300 6.290 17 1.310 0.928 name 2 0
|
||||
0.25 1 63 1 ? ? ? 23 2.300 0.714 name 2 1
|
||||
2 1 56 1 0.040 14 5 ? ? ? name 2 1
|
||||
7 1 61 1 0.270 ? ? 9 1.500 0.428 name 2 1
|
||||
10 0 57 0 0.240 14.800 5.260 18 1.380 0.812 name 2 ?
|
||||
12 0 58 0 0.300 9.400 3.490 14 1 1 name 2 0
|
||||
1 1 60 0 0.010 24.600 5.650 39 3 0.928 name 2 1
|
||||
10 0 66 0 0.290 15.600 6.150 14 1 1 name 2 0
|
||||
45 0 63 0 0.15 13 4.57 13 1.08 0.857 name 2 0
|
||||
22 0 57 0 0.13 18.6 4.37 12.33 1.37 0.642 name 2 0
|
||||
53 0 70 0 0.10 9.8 5.30 23 2.30 0.714 name 2 0
|
||||
38 0 68 0 0.29 ? 4.41 14 1.167 0.857 name 2 ?
|
||||
26 0 79 0 0.17 11.9 5.15 10.5 1.05 0.714 name 2 0
|
||||
9 0 73 0 0.12 ? 6.78 16.67 1.39 0.857 name 2 ?
|
||||
26 0 72 0 0.187 12 5.02 13 1.18 0.785 name 2 0
|
||||
0.5 1 59 0 0.13 16.4 4.96 17.83 1.37 0.928 name 2 ?
|
||||
12 0 67 1 0.11 10.3 4.68 11 1 0.785 name 2 ?
|
||||
49 0 51 0 0.16 13.2 5.26 11 1 0.786 name 2 0
|
||||
0.75 1 50 0 0.14 11.4 4.75 10 2.5 0.28 name 2 ?
|
||||
49 0 70 1 0.25 9.7 5.57 5.5 1.10 0.357 name 2 0
|
||||
47 0 65 0 0.36 8.8 5.78 12 1 0.857 name 2 0
|
||||
41 0 78 0 0.06 16.1 5.62 13.67 1.367 0.714 name 2 0
|
||||
.25 1 86 0 0.225 12.2 5.20 24 2.18 0.786 name 2 1
|
||||
33 0 56 0 0.25 11 4.72 11 1 0.785 name 2 0
|
||||
29 0 60 0 0.12 10.2 4.31 15 1.67 0.64 name 2 0
|
||||
41 0 59 0 0.29 7.5 4.75 13 1.08 0.857 name 2 0
|
||||
26 0 50 0 0.06 30.1 5.95 21.5 2.39 0.643 name 2 ?
|
||||
15 0 54 0 0.217 17.9 4.54 16.5 1.18 1 name 2 0
|
||||
.25 1 68 0 0.22 21.7 4.85 15 1.15 0.928 name 2 ?
|
||||
.03 1 ? 0 0.26 19.4 4.77 21 2.1 0.714 name 2 1
|
||||
12 0 64 0 0.20 7.1 4.58 14 1 1 name 2 0
|
||||
32 0 63 0 0.20 5 5.20 8 1 0.57 name 2 ?
|
||||
32 0 65 0 0.06 23.6 6.74 12 1.09 0.785 name 2 ?
|
||||
27 0 54 1 0.07 16.8 4.16 18 1.5 0.857 name 2 0
|
||||
23 0 62 0 0.25 6 4.48 11 1 0.786 name 2 ?
|
||||
0.75 1 78 0 0.05 10 4.44 15 1.36 0.786 name 2 1
|
||||
0.75 1 61 0 ? ? ? 28 2.33 0.857 name 2 1
|
||||
34 0 52 0 0.14 25 6.21 11.5 1.15 0.714 name 2 ?
|
||||
1 1 73 0 0.05 14.8 4.14 15.5 1.41 0.786 name 2 ?
|
||||
21 1 70 1 0.16 19.2 5.25 11 1 0.786 name 2 ?
|
||||
55 0 55 0 0.28 5.5 4.48 22 1.83 0.857 name 2 0
|
||||
15 1 60 0 0.18 8.7 4.56 13.5 1.04 0.928 name 2 ?
|
||||
0.5 1 67 0 0.155 11.3 5.16 13 1 0.928 name 2 ?
|
||||
35 0 64 0 0.30 6.6 4.36 14 1.27 0.786 name 2 ?
|
||||
53 0 59 0 0.344 9.1 4.04 9 1 0.643 name 2 0
|
||||
33 0 46 0 0.272 16.5 5.36 12.67 1.06 0.857 name 2 ?
|
||||
? 1 61 0 0.20 9.4 4.02 15.67 1.42 0.786 name 2 1
|
||||
33 0 63 0 0.25 5.6 3.87 18 1.50 0.857 name 2 ?
|
||||
40 1 74 0 0.20 4.8 4.56 12.5 1.04 0.857 name 2 0
|
||||
33 0 59 0 0.50 9.1 3.42 18 1.5 0.857 name 2 ?
|
||||
5 1 65 1 0.16 8.5 5.47 16 1.45 0.786 name 2 1
|
||||
4 1 58 0 0.17 28.9 6.73 26.08 2.01 0.928 name 2 1
|
||||
31 0 53 0 0.17 ? 4.69 10 1 0.71 name 2 ?
|
||||
33 0 66 0 0.20 ? 4.23 12 1 0.857 name 2 0
|
||||
22 0 70 0 0.38 0 4.55 10 1 0.714 name 2 0
|
||||
25 0 62 0 0.258 11.8 4.87 11 1 0.786 name 2 ?
|
||||
1.25 1 63 0 0.30 6.9 3.52 18.16 1.51 0.857 name 2 1
|
||||
24 0 59 0 0.17 14.3 5.49 13.5 1.50 0.643 name 2 0
|
||||
25 0 57 0 0.228 9.7 4.29 11 1 0.786 name 2 0
|
||||
24 0 57 0 0.036 7 4.12 13.5 1.23 0.786 name 2 ?
|
||||
.75 1 78 0 0.23 40 6.23 14 1.4 0.714 name 2 1
|
||||
3 1 62 0 0.26 7.6 4.42 14 1 1 name 2 1
|
||||
27 0 62 0 0.22 12.1 3.92 11 1 0.785 name ? ?
|
||||
13 0 66 0 0.24 13.6 4.38 22 2.20 0.714 name ? ?
|
||||
36 0 61 0 0.27 9.00 4.06 12 1 0.857 name ? ?
|
||||
25 0 59 1 0.40 9.20 5.36 12 1 0.857 name ? ?
|
||||
27 0 57 0 0.29 9.40 4.77 9 1 0.64 name ? ?
|
||||
34 0 62 1 0.19 28.9 6.63 19.5 1.95 0.714 name ? ?
|
||||
37 0 ? 0 0.26 0 4.38 9 1 0.64 name ? ?
|
||||
34 0 54 0 0.43 9.30 4.79 10 1 0.714 name ? ?
|
||||
28 1 62 1 0.24 28.6 5.86 21.5 1.95 0.786 name ? ?
|
||||
28 0 ? 0 0.23 19.1 5.49 12 1.20 0.71 name ? ?
|
||||
17 0 64 0 0.15 6.60 4.17 14 1.27 0.786 name ? ?
|
||||
38 0 57 1 0.12 0 2.32 16.5 1.375 0.857 name ? ?
|
||||
31 0 61 0 0.18 0 4.48 11 1.375 0.57 name ? ?
|
||||
12 0 61 1 0.19 13.2 5.04 19 1.73 0.786 name ? ?
|
||||
36 0 48 0 0.15 12 3.66 10 1 0.714 name ? ?
|
||||
17 0 ? 0 0.09 6.80 4.96 13 1.08 0.857 name ? ?
|
||||
21 0 61 0 0.14 25.5 5.16 14 1.27 0.786 name ? ?
|
||||
7.5 1 64 0 0.24 12.9 4.72 12 1 0.857 name ? ?
|
||||
41 0 64 0 0.28 5.40 5.47 11 1.10 0.714 name ? ?
|
||||
36 0 69 0 0.20 7.00 5.05 14.5 1.21 0.857 name ? ?
|
||||
22 0 57 0 0.14 16.1 4.36 15 1.36 0.786 name ? ?
|
||||
20 0 62 0 0.15 0 4.51 15.5 1.409 0.786 name ? ?
|
||||
116
data/tanveer/echocardiogram/echocardiogram.names
Executable file
116
data/tanveer/echocardiogram/echocardiogram.names
Executable file
@@ -0,0 +1,116 @@
|
||||
1. Title: Echocardiogram Data
|
||||
|
||||
2. Source Information:
|
||||
-- Donor: Steven Salzberg (salzberg@cs.jhu.edu)
|
||||
-- Collector:
|
||||
-- Dr. Evlin Kinney
|
||||
-- The Reed Institute
|
||||
-- P.O. Box 402603
|
||||
-- Maimi, FL 33140-0603
|
||||
-- Date Received: 28 February 1989
|
||||
|
||||
3. Past Usage:
|
||||
-- 1. Salzberg, S. (1988). Exemplar-based learning: Theory and
|
||||
implementation (Technical Report TR-10-88). Harvard University,
|
||||
Center for Research in Computing Technology, Aiken Computation
|
||||
Laboratory (33 Oxford Street; Cambridge, MA 02138).
|
||||
-- Steve applied his EACH program to predict survival (i.e., life
|
||||
or death), did not use the wall-motion attribute, and recorded 87
|
||||
correct and 29 incorrect in an incremental application to this
|
||||
database. He also showed that, by tuning EACH to this domain,
|
||||
EACH was able to derive (non-incrementally) a set of 28
|
||||
hyper-rectangles that could perfectly classify 119 instances.
|
||||
-- 2. Kan, G., Visser, C., Kooler, J., & Dunning, A. (1986). Short
|
||||
and long term predictive value of wall motion score in acute
|
||||
myocardial infarction. British Heart Journal, 56, 422-427.
|
||||
-- They predicted the same variable (whether patients will live
|
||||
one year after a heart attack) using a different set of 345
|
||||
instances. Their statistical test recorded a 61% accuracy
|
||||
in predicting that a patient will die (post-hoc fit).
|
||||
-- 3. Elvin Kinney (in communication with Steven Salzberg) reported
|
||||
that a Cox regression application recorded a 60% accuracy
|
||||
in predicting that a patient will die.
|
||||
|
||||
4. Relevant Information:
|
||||
-- All the patients suffered heart attacks at some point in the past.
|
||||
Some are still alive and some are not. The survival and still-alive
|
||||
variables, when taken together, indicate whether a patient survived
|
||||
for at least one year following the heart attack.
|
||||
|
||||
The problem addressed by past researchers was to predict from the
|
||||
other variables whether or not the patient will survive at least
|
||||
one year. The most difficult part of this problem is correctly
|
||||
predicting that the patient will NOT survive. (Part of the difficulty
|
||||
seems to be the size of the data set.)
|
||||
|
||||
5. Number of Instances: 132
|
||||
|
||||
6. Number of Attributes: 13 (all numeric-valued)
|
||||
|
||||
7. Attribute Information:
|
||||
1. survival -- the number of months patient survived (has survived,
|
||||
if patient is still alive). Because all the patients
|
||||
had their heart attacks at different times, it is
|
||||
possible that some patients have survived less than
|
||||
one year but they are still alive. Check the second
|
||||
variable to confirm this. Such patients cannot be
|
||||
used for the prediction task mentioned above.
|
||||
2. still-alive -- a binary variable. 0=dead at end of survival period,
|
||||
1 means still alive
|
||||
3. age-at-heart-attack -- age in years when heart attack occurred
|
||||
4. pericardial-effusion -- binary. Pericardial effusion is fluid
|
||||
around the heart. 0=no fluid, 1=fluid
|
||||
5. fractional-shortening -- a measure of contracility around the heart
|
||||
lower numbers are increasingly abnormal
|
||||
6. epss -- E-point septal separation, another measure of contractility.
|
||||
Larger numbers are increasingly abnormal.
|
||||
7. lvdd -- left ventricular end-diastolic dimension. This is
|
||||
a measure of the size of the heart at end-diastole.
|
||||
Large hearts tend to be sick hearts.
|
||||
8. wall-motion-score -- a measure of how the segments of the left
|
||||
ventricle are moving
|
||||
9. wall-motion-index -- equals wall-motion-score divided by number of
|
||||
segments seen. Usually 12-13 segments are seen
|
||||
in an echocardiogram. Use this variable INSTEAD
|
||||
of the wall motion score.
|
||||
10. mult -- a derivate var which can be ignored
|
||||
11. name -- the name of the patient (I have replaced them with "name")
|
||||
12. group -- meaningless, ignore it
|
||||
13. alive-at-1 -- Boolean-valued. Derived from the first two attributes.
|
||||
0 means patient was either dead after 1 year or had
|
||||
been followed for less than 1 year. 1 means patient
|
||||
was alive at 1 year.
|
||||
|
||||
8. Missing Attribute Values: (denoted by "?")
|
||||
Attribute #: Number of Missing Values: (total: 132)
|
||||
------------ -------------------------
|
||||
1 2
|
||||
2 1
|
||||
3 5
|
||||
4 1
|
||||
5 8
|
||||
6 15
|
||||
7 11
|
||||
8 4
|
||||
9 1
|
||||
10 4
|
||||
11 0
|
||||
12 22
|
||||
13 58
|
||||
|
||||
9. Distribution of attribute number 2: still-alive
|
||||
Value Number of instances with this value
|
||||
---- -----------------------------------
|
||||
0 88 (dead)
|
||||
1 43 (alive)
|
||||
? 1
|
||||
Total 132
|
||||
|
||||
|
||||
10. Distribution of attribute number 13: alive-at-1
|
||||
Value Number of instances with this value
|
||||
---- -----------------------------------
|
||||
0 50
|
||||
1 24
|
||||
? 58
|
||||
Total 132
|
||||
8
data/tanveer/echocardiogram/echocardiogram.txt
Executable file
8
data/tanveer/echocardiogram/echocardiogram.txt
Executable file
@@ -0,0 +1,8 @@
|
||||
n_entradas= 10
|
||||
n_clases= 2
|
||||
n_arquivos= 1
|
||||
fich1= echocardiogram_R.dat
|
||||
n_patrons1= 131
|
||||
n_patrons_entrena= 66
|
||||
n_patrons_valida= 65
|
||||
n_conxuntos= 1
|
||||
132
data/tanveer/echocardiogram/echocardiogram_R.dat
Executable file
132
data/tanveer/echocardiogram/echocardiogram_R.dat
Executable file
@@ -0,0 +1,132 @@
|
||||
f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 clase
|
||||
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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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|
||||
131 0.108549 -0.471791 -0.477682 -1.37207 0.0741639 0.257234 0.0891096 0.11834 -1.92796 -0.471791 0
|
||||
42
data/tanveer/echocardiogram/le_datos.m
Executable file
42
data/tanveer/echocardiogram/le_datos.m
Executable file
@@ -0,0 +1,42 @@
|
||||
printf('lendo problema %s ...\n', problema);
|
||||
|
||||
n_entradas= 10; n_clases= 2; n_fich= 1; fich{1}= 'echocardiogram.data'; n_patrons(1)= 131;
|
||||
|
||||
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);
|
||||
t=fscanf(f,'%c',1); % le e descarta survival
|
||||
if t~='?'
|
||||
fseek(f,-1,SEEK_CUR); fscanf(f, '%g',1);
|
||||
end
|
||||
fscanf(f,'%c',1);
|
||||
cl(i_fich,i)=fscanf(f,'%i',1); fscanf(f,'%c',1);
|
||||
% printf('i= %i cl= %i: ', i, cl(i_fich,i))
|
||||
for j = 1:n_entradas
|
||||
t = fscanf(f,'%c',1);
|
||||
if t ~= '?'
|
||||
fseek(f,-1,SEEK_CUR); t= fscanf(f, '%g',1); x(i_fich,i,j) = t;
|
||||
% printf('%g ', t)
|
||||
else
|
||||
% printf('? ')
|
||||
x(i_fich,i,j) = 0;
|
||||
end
|
||||
fscanf(f,'%c',1);
|
||||
if j==8
|
||||
fscanf(f,'%s',1); fscanf(f,'%c',1);
|
||||
end % le e descarta name
|
||||
end
|
||||
% printf('\n')
|
||||
% if i==2 exit end
|
||||
end
|
||||
fclose(f);
|
||||
end
|
||||
Reference in New Issue
Block a user