Commit Inicial

data/tanveer/chess-krvkp/chess-krvkp.cost

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+% Rows	Columns
+2	2
+% Matrix elements
+ 0.0	 1.0	
+ 1.0	 0.0	

data/tanveer/chess-krvkp/chess-krvkp.txt

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+n_entradas= 36
+n_clases= 2
+n_arquivos= 1
+fich1= chess-krvkp_R.dat
+n_patrons1= 3196
+n_patrons_entrena= 1598
+n_patrons_valida= 1598
+n_conxuntos= 1

data/tanveer/chess-krvkp/kr-vs-kp.names

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+1. Title: Chess End-Game -- King+Rook versus King+Pawn on a7
+   (usually abbreviated KRKPA7).  The pawn on a7 means it is one square
+   away from queening.  It is the King+Rook's side (white) to move.
+
+2. Sources:
+    (a) Database originally generated and described by Alen Shapiro.
+    (b) Donor/Coder: Rob Holte (holte@uottawa.bitnet).  The database
+        was supplied to Holte by Peter Clark of the Turing Institute
+        in Glasgow (pete@turing.ac.uk).
+    (c) Date: 1 August 1989
+
+3. Past Usage:
+     - Alen D. Shapiro (1983,1987), "Structured Induction in Expert Systems",
+       Addison-Wesley.  This book is based on Shapiro's Ph.D. thesis (1983)
+       at the University of Edinburgh entitled "The Role of Structured
+       Induction in Expert Systems".
+     - Stephen Muggleton (1987), "Structuring Knowledge by Asking Questions",
+       pp.218-229 in "Progress in Machine Learning", edited by I. Bratko
+       and Nada Lavrac, Sigma Press, Wilmslow, England  SK9 5BB.
+     - Robert C. Holte, Liane Acker, and Bruce W. Porter (1989),
+       "Concept Learning and the Problem of Small Disjuncts",
+       Proceedings of IJCAI.  Also available as technical report AI89-106,
+       Computer Sciences Department, University of Texas at Austin,
+       Austin, Texas 78712.
+
+4. Relevant Information:
+      The dataset format is described below.  Note: the format of this
+    database was modified on 2/26/90 to conform with the format of all
+    the other databases in the UCI repository of machine learning databases.
+
+5. Number of Instances: 3196 total
+
+6. Number of Attributes: 36
+
+7. Attribute Summaries:
+    Classes (2):  -- White-can-win ("won") and White-cannot-win ("nowin").
+          I believe that White is deemed to be unable to win if the Black pawn
+          can safely advance.
+    Attributes: see Shapiro's book.
+
+8. Missing Attributes: --  none
+
+9. Class Distribution:
+    In 1669 of the positions (52%), White can win.
+    In 1527 of the positions (48%), White cannot win.
+
+The format for instances in this database is a sequence of 37 attribute values.
+Each instance is a board-descriptions for this chess endgame.  The first
+36 attributes describe the board.  The last (37th) attribute is the
+classification: "win" or "nowin".  There are 0 missing values.
+A typical board-description is
+
+f,f,f,f,f,f,f,f,f,f,f,f,l,f,n,f,f,t,f,f,f,f,f,f,f,t,f,f,f,f,f,f,f,t,t,n,won
+
+The names of the features do not appear in the board-descriptions.
+Instead, each feature correponds to a particular position in the
+feature-value list.  For example, the head of this list is the value
+for the feature "bkblk".  The following is the list of features, in
+the order in which their values appear in the feature-value list:
+
+[bkblk,bknwy,bkon8,bkona,bkspr,bkxbq,bkxcr,bkxwp,blxwp,bxqsq,cntxt,dsopp,dwipd,
+ hdchk,katri,mulch,qxmsq,r2ar8,reskd,reskr,rimmx,rkxwp,rxmsq,simpl,skach,skewr,
+ skrxp,spcop,stlmt,thrsk,wkcti,wkna8,wknck,wkovl,wkpos,wtoeg]
+
+In the file, there is one instance (board position) per line.
+

data/tanveer/chess-krvkp/le_datos.m

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+printf('lendo problema %s ...\n', problema);
+
+n_entradas= 36; n_clases= 2; n_fich= 1; fich{1}= 'kr-vs-kp.data'; n_patrons(1)= 3196;
+
+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;
+clase={'nowin', 'won'};
+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
+	  t = fscanf(f,'%s',1);
+	  if j==13
+		val={'l','g'};
+	  elseif j==15
+		val={'b','n','w'};
+	  elseif j==36
+		val={'n','t'};
+	  else
+		val={'f','t'};
+	  end
+	  n=length(val);
+	  for k=1:n
+		if strcmp(t,val{k})
+		  x(i_fich,i,j)=k; break
+		end
+	  end
+	end	
+	t = fscanf(f,'%s',1);
+	for j=1:n_clases    	% lectura da clase
+	  if strcmp(t,clase{j})
+		cl(i_fich,i)=j-1; break
+	  end
+	end
+  end
+  fclose(f);
+end