Commit Inicial

data/tanveer/statlog-image/le_datos.m

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+printf('lendo problema %s ...\n', problema);
+
+n_entradas= 19; n_clases= 7; n_fich= 1; fich{1}= 'segment.dat'; n_patrons(1)= 2310;
+
+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);
+	for j = 1:n_entradas
+	  x(i_fich,i,j) = fscanf(f,'%g',1);
+	end	
+	cl(i_fich,i) = fscanf(f,'%i',1) - 1;  	% lectura da clase
+  end
+  fclose(f);
+end

data/tanveer/statlog-image/segment.doc

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+Description of Datasets
+
+
+1. Title: Image Segmentation data
+
+2. Source Information
+   -- Creators: Vision Group, University of Massachusetts
+   -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu)
+   -- Date: November, 1990
+ 
+3. Past Usage: None yet published
+
+4. Relevant Information:
+
+   The instances were drawn randomly from a database of 7 outdoor 
+   images.  The images were handsegmented to create a classification
+   for every pixel.  
+
+   Each instance is a 3x3 region.
+
+5. Number of Instances:  2310
+
+6. Number of Attributes: 19 continuous attributes
+
+7. Attribute Information:
+
+    1.  region-centroid-col:  the column of the center pixel of the region.
+    2.  region-centroid-row:  the row of the center pixel of the region.
+    3.  region-pixel-count:  the number of pixels in a region = 9.
+    4.  short-line-density-5:  the results of a line extractoin algorithm that 
+         counts how many lines of length 5 (any orientation) with
+         low contrast, less than or equal to 5, go through the region.
+    5.  short-line-density-2:  same as short-line-density-5 but counts lines
+         of high contrast, greater than 5.
+    6.  vedge-mean:  measure the contrast of horizontally
+         adjacent pixels in the region.  There are 6, the mean and 
+         standard deviation are given.  This attribute is used as
+        a vertical edge detector.
+    7.  vegde-sd:  (see 6)
+    8.  hedge-mean:  measures the contrast of vertically adjacent
+          pixels. Used for horizontal line detection. 
+    9.  hedge-sd: (see 8).
+    10. intensity-mean:  the average over the region of (R + G + B)/3
+    11. rawred-mean: the average over the region of the R value.
+    12. rawblue-mean: the average over the region of the B value.
+    13. rawgreen-mean: the average over the region of the G value.
+    14. exred-mean: measure the excess red:  (2R - (G + B))
+    15. exblue-mean: measure the excess blue:  (2B - (G + R))
+    16. exgreen-mean: measure the excess green:  (2G - (R + B))
+    17. value-mean:  3-d nonlinear transformation
+         of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals
+         of Interactive Computer Graphics)
+    18. saturatoin-mean:  (see 17)
+    19. hue-mean:  (see 17)
+
+8. Missing Attribute Values: None
+
+9. Class Distribution: 
+
+   Classes: 1 = brickface, 
+	    2 = sky, 
+	    3 = foliage, 
+	    4 = cement, 
+	    5 = window, 
+	    6 = path, 
+	    7 = grass.
+
+

data/tanveer/statlog-image/statlog-image.cost

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+% Rows	Columns
+7	7
+% Matrix elements
+ 0.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	
+ 1.0	 0.0	 1.0	 1.0	 1.0	 1.0	 1.0	
+ 1.0	 1.0	 0.0	 1.0	 1.0	 1.0	 1.0	
+ 1.0	 1.0	 1.0	 0.0	 1.0	 1.0	 1.0	
+ 1.0	 1.0	 1.0	 1.0	 0.0	 1.0	 1.0	
+ 1.0	 1.0	 1.0	 1.0	 1.0	 0.0	 1.0	
+ 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 0.0	

data/tanveer/statlog-image/statlog-image.txt

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+n_entradas= 18
+n_clases= 7
+n_arquivos= 1
+fich1= statlog-image_R.dat
+n_patrons1= 2310
+n_patrons_entrena= 1155
+n_patrons_valida= 1155
+n_conxuntos= 1