Commit Inicial

data/tanveer/lenses/conxuntos.dat

@@ -0,0 +1,2 @@
+11 3 21 13 9 0 20 8 4 10 14 22 
+7 19 5 1 17 6 18 12 15 16 2 23 

data/tanveer/lenses/conxuntos_kfold.dat

@@ -0,0 +1,8 @@
+11 3 19 13 5 9 1 4 8 22 0 18 6 10 17 15 16 23 
+7 21 14 2 20 12 
+3 19 11 5 13 21 9 4 10 6 14 18 22 20 15 23 0 2 
+7 1 17 12 8 16 
+3 19 7 21 5 9 13 15 18 8 2 17 22 0 4 16 12 14 
+11 1 20 23 10 6 
+7 11 19 13 9 21 1 14 8 15 0 23 17 16 12 2 20 18 
+3 5 10 6 4 22 

data/tanveer/lenses/le_datos.m

@@ -0,0 +1,24 @@
+printf('lendo problema %s ...\n', problema);
+
+n_entradas= 4; n_clases= 3; n_fich= 1; fich{1}= 'lenses.data'; n_patrons(1)= 24;
+
+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);
+	fscanf(f,'%i',1);  % descarta o nº de patrón
+	for j = 1:n_entradas
+	  x(i_fich,i,j) = fscanf(f,'%i',1);
+	end	
+	cl(i_fich,i) = fscanf(f,'%i',1) - 1;  	% lectura da clase
+  end
+  fclose(f);
+end

data/tanveer/lenses/lenses.arff

@@ -0,0 +1,31 @@
+@relation lenses
+@attribute 'f1' real
+@attribute 'f2' real
+@attribute 'f3' real
+@attribute 'f4' real
+@attribute 'clase' {0,1,2}
+@data
+-1.19896,-0.978945,-0.978945,-0.978945,2
+-1.19896,-0.978945,-0.978945,0.978945,1
+-1.19896,-0.978945,0.978945,-0.978945,2
+-1.19896,-0.978945,0.978945,0.978945,0
+-1.19896,0.978945,-0.978945,-0.978945,2
+-1.19896,0.978945,-0.978945,0.978945,1
+-1.19896,0.978945,0.978945,-0.978945,2
+-1.19896,0.978945,0.978945,0.978945,0
+0,-0.978945,-0.978945,-0.978945,2
+0,-0.978945,-0.978945,0.978945,1
+0,-0.978945,0.978945,-0.978945,2
+0,-0.978945,0.978945,0.978945,0
+0,0.978945,-0.978945,-0.978945,2
+0,0.978945,-0.978945,0.978945,1
+0,0.978945,0.978945,-0.978945,2
+0,0.978945,0.978945,0.978945,2
+1.19896,-0.978945,-0.978945,-0.978945,2
+1.19896,-0.978945,-0.978945,0.978945,2
+1.19896,-0.978945,0.978945,-0.978945,2
+1.19896,-0.978945,0.978945,0.978945,0
+1.19896,0.978945,-0.978945,-0.978945,2
+1.19896,0.978945,-0.978945,0.978945,1
+1.19896,0.978945,0.978945,-0.978945,2
+1.19896,0.978945,0.978945,0.978945,2

data/tanveer/lenses/lenses.cost

@@ -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	

data/tanveer/lenses/lenses.data

@@ -0,0 +1,24 @@
+1  1  1  1  1  3
+2  1  1  1  2  2
+3  1  1  2  1  3
+4  1  1  2  2  1
+5  1  2  1  1  3
+6  1  2  1  2  2
+7  1  2  2  1  3
+8  1  2  2  2  1
+9  2  1  1  1  3
+10 2  1  1  2  2
+11 2  1  2  1  3
+12 2  1  2  2  1
+13 2  2  1  1  3
+14 2  2  1  2  2
+15 2  2  2  1  3
+16 2  2  2  2  3
+17 3  1  1  1  3
+18 3  1  1  2  3
+19 3  1  2  1  3
+20 3  1  2  2  1
+21 3  2  1  1  3
+22 3  2  1  2  2
+23 3  2  2  1  3
+24 3  2  2  2  3

data/tanveer/lenses/lenses.names

@@ -0,0 +1,48 @@
+1. Title: Database for fitting contact lenses
+
+2. Sources:
+     (a) Cendrowska, J. "PRISM: An algorithm for inducing modular rules",
+         International Journal of Man-Machine Studies, 1987, 27, 349-370
+     (b) Donor: Benoit Julien (Julien@ce.cmu.edu)
+     (c) Date: 1 August 1990
+
+3. Past Usage:
+      1. See above.
+      2. Witten, I. H. & MacDonald, B. A. (1988). Using concept
+         learning for knowledge acquisition. International Journal of
+         Man-Machine Studies, 27, (pp. 349-370).
+
+ Notes:  This database is complete (all possible combinations of
+         attribute-value pairs are represented).
+
+         Each instance is complete and correct.
+
+         9 rules cover the training set.
+
+4. Relevant Information Paragraph:
+    The examples are complete and noise free.
+    The examples highly simplified the problem. The attributes do not
+    fully describe all the factors affecting the decision as to which type,
+    if any, to fit.
+
+5. Number of Instances: 24
+
+6. Number of Attributes: 4 (all nominal)
+
+7. Attribute Information:
+    -- 3 Classes
+     1 : the patient should be fitted with hard contact lenses,
+     2 : the patient should be fitted with soft contact lenses,
+     3 : the patient should not be fitted with contact lenses.
+
+    1. age of the patient: (1) young, (2) pre-presbyopic, (3) presbyopic
+    2. spectacle prescription:  (1) myope, (2) hypermetrope
+    3. astigmatic:     (1) no, (2) yes
+    4. tear production rate:  (1) reduced, (2) normal
+
+8. Number of Missing Attribute Values:   0
+
+9. Class Distribution:
+   1. hard contact lenses: 4
+   2. soft contact lenses: 5
+   3. no contact lenses: 15

data/tanveer/lenses/lenses.txt

@@ -0,0 +1,8 @@
+n_entradas= 4
+n_clases= 3
+n_arquivos= 1
+fich1= lenses_R.dat
+n_patrons1= 24
+n_patrons_entrena= 12
+n_patrons_valida= 12
+n_conxuntos= 1

data/tanveer/lenses/lenses_R.dat

@@ -0,0 +1,25 @@
+	f1	f2	f3	f4	clase
+1	-1.19896	-0.978945	-0.978945	-0.978945	2
+2	-1.19896	-0.978945	-0.978945	0.978945	1
+3	-1.19896	-0.978945	0.978945	-0.978945	2
+4	-1.19896	-0.978945	0.978945	0.978945	0
+5	-1.19896	0.978945	-0.978945	-0.978945	2
+6	-1.19896	0.978945	-0.978945	0.978945	1
+7	-1.19896	0.978945	0.978945	-0.978945	2
+8	-1.19896	0.978945	0.978945	0.978945	0
+9	0	-0.978945	-0.978945	-0.978945	2
+10	0	-0.978945	-0.978945	0.978945	1
+11	0	-0.978945	0.978945	-0.978945	2
+12	0	-0.978945	0.978945	0.978945	0
+13	0	0.978945	-0.978945	-0.978945	2
+14	0	0.978945	-0.978945	0.978945	1
+15	0	0.978945	0.978945	-0.978945	2
+16	0	0.978945	0.978945	0.978945	2
+17	1.19896	-0.978945	-0.978945	-0.978945	2
+18	1.19896	-0.978945	-0.978945	0.978945	2
+19	1.19896	-0.978945	0.978945	-0.978945	2
+20	1.19896	-0.978945	0.978945	0.978945	0
+21	1.19896	0.978945	-0.978945	-0.978945	2
+22	1.19896	0.978945	-0.978945	0.978945	1
+23	1.19896	0.978945	0.978945	-0.978945	2
+24	1.19896	0.978945	0.978945	0.978945	2