Commit Inicial

data/tanveer/chess-krvk/chess-krvk.cost

@@ -0,0 +1,21 @@
+% Rows	Columns
+18	18
+% Matrix elements
+ 0.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	
+ 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	 1.0	 1.0	 1.0	
+ 1.0	 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	 1.0	 1.0	
+ 1.0	 1.0	 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	 1.0	
+ 1.0	 1.0	 1.0	 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	
+ 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 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	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 1.0	 0.0	

data/tanveer/chess-krvk/chess-krvk.txt

@@ -0,0 +1,8 @@
+n_entradas= 6
+n_clases= 18
+n_arquivos= 1
+fich1= chess-krvk_R.dat
+n_patrons1= 28056
+n_patrons_entrena= 14028
+n_patrons_valida= 14028
+n_conxuntos= 1

data/tanveer/chess-krvk/krkopt.info

@@ -0,0 +1,183 @@
+1. Title:
+   Chess Endgame Database for White King and Rook against Black King (KRK) -
+   Black-to-move Positions Drawn or Lost in N Moves.
+
+2. Source Information:
+   -- Creators: Database generated by Michael Bain and Arthur van Hoff
+      at the Turing Institute, Glasgow, UK.
+   -- Donor: Michael Bain (mike@cse.unsw.edu.au), AI Lab, Computer Science,
+      University of New South Wales, Sydney 2052, Australia.
+      (tel) +61 2 385 3939
+      (fax) +61 2 663 4576
+   -- Date: June, 1994.
+
+3. Past Usage:
+
+	Chess endgames are complex domains which are enumerable. Endgame
+   databases are tables of stored game-theoretic values for the enumerated
+   elements (legal positions) of the domain. The game-theoretic values stored
+   denote whether or not positions are won for either side, or include also
+   the depth of win (number of moves) assuming minimax-optimal play. From the
+   point of view of experiments on computer induction such databases provide
+   not only a source of examples but also an oracle (Roycroft, 1986) for
+   testing induced rules. However a chess endgame database differs from, say,
+   a relational database containing details of parts and suppliers in the
+   following important respect. The combinatorics of computing the required
+   game-theoretic values for individual position entries independently would
+   be prohibitive. Therefore all the database entries are generated in a single
+   iterative process using the ``standard backup'' algorithm (Thompson, 1986).
+
+   A KRK database was described by Clarke (1977). The current database was
+   described and used for machine learning experiments in Bain (1992; 1994). It
+   should be noted that our database is not guaranteed correct, but the class
+   distribution is the same as Clarke's database. In (Bain 1992; 1994) the
+   task was classification of positions in the database as won for white in a
+   fixed number of moves, assuming optimal play by both sides. The problem was
+   structured into separate sub-problems by depth-of-win ordered draw, zero,
+   one, ..., sixteen. When learning depth d all examples at depths > d are
+   used as negatives. Quinlan (1994) applied Foil to learn a complete and
+   correct solution for this task.
+
+   	The typical complexity of induced classifiers in this domain suggest
+   that the task is demanding when background knowledge is restricted.
+
+
+4. Relevant Information:
+   An Inductive Logic Programming (ILP) or relational learning framework is
+   assumed (Muggleton, 1992). The learning system is provided with examples
+   of chess positions described only by the coordinates of the pieces on the
+   board. Background knowledge in the form of row and column differences is
+   also supplied. The relations necessary to form a correct and concise
+   classifier for the target concept must be discovered by the learning system
+   (the examples already provide a complete extensional definition).
+   The task is closely related to Quinlan's (1983) application of ID3 to
+   classify White King and Rook against Black King and Knight (KRKN) positions
+   as lost 2-ply or lost 3-ply. The framework is similar in that the example
+   positions supply only low-grade data. An important difference is that
+   additional background predicates of the kind supplied in the KRKN study via
+   hand-crafted attributes are not provided for this KRK domain.
+
+5. Number of Instances: 28056
+
+6. Number of Attributes:
+   There are six attribute variables and one class variable.
+
+7. Attribute Information:
+   1. White King file (column)
+   2. White King rank (row)
+   3. White Rook file
+   4. White Rook rank
+   5. Black King file
+   6. Black King rank
+   7. optimal depth-of-win for White in 0 to 16 moves, otherwise drawn
+	{draw, zero, one, two, ..., sixteen}.
+
+
+8. Missing Attribute Values: None
+
+9. Class Distribution:
+
+   draw       2796
+   zero         27
+   one          78
+   two         246
+   three        81
+   four        198
+   five        471
+   six         592
+   seven       683
+   eight      1433
+   nine       1712
+   ten        1985
+   eleven     2854
+   twelve     3597
+   thirteen   4194
+   fourteen   4553
+   fifteen    2166
+   sixteen     390
+
+   Total     28056
+
+
+10. Note: Foil is available by anonymous ftp from ftp.cs.su.oz.au, file
+    pub/foil6.sh.
+
+
+References: (BibTeX format)
+
+@incollection{bain_1992,
+        AUTHOR = "M. Bain",
+        TITLE = "Learning optimal chess strategies",
+        BOOKTITLE = "{ILP 92}: {P}roc. {I}ntl. {W}orkshop on
+		{I}nductive {L}ogic {P}rogramming",
+        YEAR = 1992,
+        VOLUME = "ICOT TM-1182",
+        EDITOR = "S. Muggleton",
+        PUBLISHER = "Institute for New Generation Computer Technology",
+        ADDRESS = "Tokyo, Japan"}
+
+@phdthesis{bain_1994,
+        TITLE = "Learning {L}ogical {E}xceptions in {C}hess",
+        AUTHOR = "M. Bain",
+        SCHOOL = "University of Strathclyde",
+        YEAR = "1994"}
+
+@incollection{clarke_1977,
+        AUTHOR = "M. R. B. Clarke",
+        TITLE = "A {Q}uantitative {S}tudy of {K}ing and {P}awn
+		{A}gainst {K}ing",
+	BOOKTITLE = "Advances in Computer Chess",
+        VOLUME = 1,
+        PAGES = "108--118",
+        EDITOR = "M. R. B. Clarke",
+        PUBLISHER = "Edinburgh University Press",
+        ADDRESS = "Edinburgh",
+        YEAR = "1977"}
+
+@incollection{muggleton_1992,
+        AUTHOR = "S. Muggleton",
+        TITLE = "Inductive {L}ogic {P}rogramming",
+        BOOKTITLE = "Inductive {L}ogic {P}rogramming",
+        PAGES = "3--27",
+        EDITOR = "S. Muggleton",
+        PUBLISHER = "Academic Press",
+        ADDRESS = "London",
+        YEAR = "1992"}
+
+@incollection{quinlan_1983,
+        AUTHOR = "J. R. Quinlan",
+        TITLE = "Learning {E}fficient {C}lassification {P}rocedures and their
+                {A}pplication to {C}hess {E}nd {G}ames",
+        YEAR = 1983,
+        PAGES = "464--482",
+        BOOKTITLE = "Machine Learning: An Artificial Intelligence
+                        Approach",
+        EDITOR = "R. Michalski and J. Carbonnel and T. Mitchell",
+        PUBLISHER = "Tioga",
+        ADDRESS = "Palo Alto, CA"}
+
+@misc{quinlan_1994,
+        AUTHOR = "J. R. Quinlan",
+        YEAR = 1994,
+        NOTE = "Personal Communication"}
+
+@article{roycroft_1986,
+        AUTHOR = "A. J. Roycroft",
+        TITLE = "Database ``{O}racles'': {N}ecessary and desirable features",
+        JOURNAL = "International Computer Chess Association Journal",
+        YEAR = "1986",
+        VOLUME = 8,
+        NUMBER = 2,
+        PAGES = "100--104"}
+
+@article{thompson_1986,
+        AUTHOR = "K. Thompson",
+        TITLE  = "Retrograde {A}nalysis of {C}ertain {E}ndgames",
+        JOURNAL = "International Computer Chess Association Journal",
+        YEAR = "1986",
+        VOLUME = "8",
+        NUMBER = "3",
+        PAGES = "131--139"}
+
+
+

data/tanveer/chess-krvk/le_datos.m

@@ -0,0 +1,41 @@
+printf('lendo problema %s ...\n', problema);
+
+n_entradas= 6; n_clases= 18; n_fich= 1; fich{1}= 'krkopt.data'; n_patrons(1)= 28056;
+
+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;
+val={'a','b','c','d','e','f','g','h'};
+n=length(val); a=2/(n-1); b=(1+n)/(1-n);
+clase={'draw','zero','one','two','three','four','five','six','seven','eight','nine','ten','eleven','twelve','thirteen','fourteen','fifteen','sixteen'};
+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
+	  if mod(j,2)==1
+		t = fscanf(f,'%c',1);
+		for k=1:n
+		  if strcmp(t,val{k})
+			x(i_fich,i,j)=a*k+b; break
+		  end
+		end
+	  else
+		x(i_fich,i,j) = fscanf(f, '%i',1);
+	  end
+	  fscanf(f,'%c',1);  % le e descarta a coma
+	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
+	fscanf(f,'%c',1);
+  end
+  fclose(f);
+end