Commit Inicial

This commit is contained in:
2020-11-20 11:23:40 +01:00
commit 5611e5bc01
2914 changed files with 2625178 additions and 0 deletions

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printf('lendo problema %s ...\n', problema);
n_entradas= 56; n_clases= 3; n_fich= 1; fich{1}= 'lung-cancer.data'; n_patrons(1)= 32;
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) - 1; % lectura da clase
% printf('cl= %i: ', cl(i_fich,i))
for j = 1:n_entradas
t = fscanf(f,'%s',1);
if t ~= '?'
fseek(f,-1,SEEK_CUR); x(i_fich,i,j) = fscanf(f, '%i',1);
else
x(i_fich,i,j) = 0;
end
% printf('%g ', x(i_fich,i,j))
end
% printf('\n')
% if i==2 exit end
end
fclose(f);
end

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@relation lung-cancer
@attribute 'f1' real
@attribute 'f2' real
@attribute 'f3' real
@attribute 'f4' real
@attribute 'f5' real
@attribute 'f6' real
@attribute 'f7' real
@attribute 'f8' real
@attribute 'f9' real
@attribute 'f10' real
@attribute 'f11' real
@attribute 'f12' real
@attribute 'f13' real
@attribute 'f14' real
@attribute 'f15' real
@attribute 'f16' real
@attribute 'f17' real
@attribute 'f18' real
@attribute 'f19' real
@attribute 'f20' real
@attribute 'f21' real
@attribute 'f22' real
@attribute 'f23' real
@attribute 'f24' real
@attribute 'f25' real
@attribute 'f26' real
@attribute 'f27' real
@attribute 'f28' real
@attribute 'f29' real
@attribute 'f30' real
@attribute 'f31' real
@attribute 'f32' real
@attribute 'f33' real
@attribute 'f34' real
@attribute 'f35' real
@attribute 'f36' real
@attribute 'f37' real
@attribute 'f38' real
@attribute 'f39' real
@attribute 'f40' real
@attribute 'f41' real
@attribute 'f42' real
@attribute 'f43' real
@attribute 'f44' real
@attribute 'f45' real
@attribute 'f46' real
@attribute 'f47' real
@attribute 'f48' real
@attribute 'f49' real
@attribute 'f50' real
@attribute 'f51' real
@attribute 'f52' real
@attribute 'f53' real
@attribute 'f54' real
@attribute 'f55' real
@attribute 'f56' real
@attribute 'clase' {0,1,2}
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@@ -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

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@@ -0,0 +1,33 @@
1 0 3 0 ? 0 2 2 2 1 1 1 1 3 2 2 1 2 2 0 2 2 2 2 1 2 2 2 3 2 1 1 1 3 3 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2
1 0 3 3 1 0 3 1 3 1 1 1 1 1 3 3 1 2 2 0 0 2 2 2 1 2 1 3 2 3 1 1 1 3 3 2 2 2 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 1 2 2
1 0 3 3 2 0 3 3 3 1 1 1 0 3 3 3 1 2 1 0 0 2 2 2 1 2 2 3 2 3 1 3 3 3 1 2 2 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2
1 0 2 3 2 1 3 3 3 1 2 1 0 3 3 1 1 2 2 0 0 2 2 2 2 1 3 2 3 3 1 3 3 3 1 1 1 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2
1 0 3 2 1 1 3 3 3 2 2 2 1 1 2 2 2 2 2 0 0 2 2 2 1 1 2 3 2 2 1 1 1 3 2 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2
1 0 3 3 2 0 3 3 3 1 2 2 0 3 3 3 2 2 1 0 0 1 2 2 2 1 3 3 1 2 2 3 3 3 2 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2
1 0 3 2 1 0 3 3 3 1 2 1 2 3 3 3 3 2 2 0 0 2 2 2 2 1 3 2 2 2 2 3 3 3 2 1 1 2 2 1 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 1 2
1 0 2 2 1 0 3 1 3 3 3 3 2 1 3 3 1 2 2 0 0 1 1 2 1 2 1 3 2 1 1 3 3 3 2 2 1 2 1 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 1 2 2
1 0 3 1 1 0 3 1 3 1 1 1 3 2 3 3 1 2 2 0 0 2 2 2 1 2 1 2 1 1 1 3 3 3 3 2 2 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2
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2 0 2 0 ? 1 3 3 3 1 2 1 1 3 3 3 1 2 2 0 0 2 2 2 2 1 1 2 3 2 1 1 1 3 1 3 1 1 2 2 2 1 2 2 1 2 2 2 2 2 2 1 2 2 1 2 2
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3 0 2 3 1 1 2 2 1 1 1 1 1 1 2 2 1 2 2 2 2 1 2 1 1 1 1 2 2 3 1 3 3 3 1 1 1 3 1 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 2 1
3 0 2 3 1 1 1 2 1 1 1 2 1 1 1 2 2 1 1 1 2 1 2 1 1 2 2 2 2 2 1 3 3 3 2 2 2 3 3 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 1
3 0 3 3 1 0 3 3 1 1 1 2 1 1 2 2 2 2 2 2 2 1 1 1 1 1 2 2 2 2 3 3 3 3 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 1
3 0 2 3 2 0 1 2 2 1 2 1 2 1 1 1 2 1 2 2 1 2 1 2 2 1 3 2 1 1 2 2 2 2 1 1 2 2 ? 2 1 1 1 2 2 2 1 2 2 2 1 3 1 2 2 1 2
3 0 2 2 2 0 2 1 2 1 1 1 0 2 2 3 1 2 2 2 2 2 2 2 3 3 3 2 2 1 2 2 2 2 3 1 2 2 2 2 1 2 1 1 2 2 1 2 2 2 2 2 2 2 1 2 1
3 0 2 2 1 0 2 2 2 1 1 2 0 2 2 2 1 2 2 2 2 2 2 2 1 2 1 3 3 3 1 3 3 2 2 3 1 2 1 3 2 2 3 2 2 2 3 3 3 2 2 3 2 2 2 2 1
3 0 3 2 2 0 2 2 2 1 1 2 0 2 2 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 1 2 1 1 3 1 3 3 3 2 3 2 2 2 2 2 2 3 1 2 2 2 2 2
3 0 2 1 1 0 2 2 1 1 1 1 0 1 1 1 2 1 2 0 2 1 1 1 1 1 2 2 1 2 1 3 3 3 1 1 3 3 3 2 3 1 2 2 3 3 2 2 2 3 2 2 2 2 2 2 1
3 0 2 3 2 1 2 2 3 1 1 2 1 2 2 2 1 2 2 0 2 2 2 1 1 2 2 2 2 2 1 2 2 3 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2
3 0 2 3 1 0 2 3 3 1 1 1 1 2 2 2 1 2 2 0 2 2 2 2 1 2 1 2 2 2 1 1 1 1 1 2 2 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2

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@@ -0,0 +1,61 @@
1. Title: Lung Cancer Data
2. Source Information:
- Data was published in :
Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small
Number of Samples and Design Method of Classifier on the Plane",
Pattern Recognition, Vol. 24, No. 4, pp. 317-324, 1991.
- Donor: Stefan Aeberhard, stefan@coral.cs.jcu.edu.au
- Date : May, 1992
3. Past Usage:
- Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small
Number of Samples and Design Method of Classifier on the Plane",
Pattern Recognition, Vol. 24, No. 4, pp. 317-324, 1991.
- Aeberhard, S., Coomans, D, De Vel, O. "Comparisons of
Classification Methods in High Dimensional Settings",
submitted to Technometrics.
- Aeberhard, S., Coomans, D, De Vel, O. "The Dangers of
Bias in High Dimensional Settings", submitted to
pattern Recognition.
4. Relevant Information:
- This data was used by Hong and Young to illustrate the
power of the optimal discriminant plane even in ill-posed
settings. Applying the KNN method in the resulting plane
gave 77% accuracy. However, these results are strongly
biased (See Aeberhard's second ref. above, or email to
stefan@coral.cs.jcu.edu.au). Results obtained by
Aeberhard et al. are :
RDA : 62.5%, KNN 53.1%, Opt. Disc. Plane 59.4%
The data described 3 types of pathological lung cancers.
The Authors give no information on the individual
variables nor on where the data was originally used.
- In the original data 4 values for the fifth attribute were -1.
These values have been changed to ? (unknown). (*)
- In the original data 1 value for the 39 attribute was 4. This
value has been changed to ? (unknown). (*)
5. Number of Instances: 32
6. Number of Attributes: 57 (1 class attribute, 56 predictive)
7. Attribute Information:
attribute 1 is the class label.
- All predictive attributes are nominal, taking on integer
values 0-3
8. Missing Attribute Values: Attributes 5 and 39 (*)
9. Class Distribution:
- 3 classes,
1.) 9 observations
2.) 13 "
3.) 10 "

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n_entradas= 56
n_clases= 3
n_arquivos= 1
fich1= lung-cancer_R.dat
n_patrons1= 32
n_patrons_entrena= 16
n_patrons_valida= 16
n_conxuntos= 1

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@@ -0,0 +1,33 @@
f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15 f16 f17 f18 f19 f20 f21 f22 f23 f24 f25 f26 f27 f28 f29 f30 f31 f32 f33 f34 f35 f36 f37 f38 f39 f40 f41 f42 f43 f44 f45 f46 f47 f48 f49 f50 f51 f52 f53 f54 f55 f56 clase
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