added cplint directory
git-svn-id: https://yap.svn.sf.net/svnroot/yap/trunk@1966 b08c6af1-5177-4d33-ba66-4b1c6b8b522a
This commit is contained in:
rzf
parent
ff4aa369be
commit
5a19adf881
27
cplint/Makefile
Normal file
27
cplint/Makefile
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@ -0,0 +1,27 @@
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OBJECTS=cplint_yap.o cplint_Prob.o
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.PHONY : clean
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# -lutil -lst -lepd libutil.a /usr/local/bin/yap.dll \
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cplint: $(OBJECTS)
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gcc -mno-cygwin -shared -o cplint.dll $(OBJECTS) "C:\Software\BDD\cudd-2.4.1 mingw\cudd-2.4.1\cudd\libcudd.a" \
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"C:\Software\BDD\cudd-2.4.1 mingw\cudd-2.4.1\mtr\libmtr.a" "C:\Software\BDD\cudd-2.4.1 mingw\cudd-2.4.1\st\libst.a" \
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"C:\Software\BDD\cudd-2.4.1 mingw\cudd-2.4.1\util\libutil.a" "C:\Software\BDD\cudd-2.4.1 mingw\cudd-2.4.1\epd\libepd.a" \
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"C:\Software\BDD\glu-2.1\libglu.a" "C:\Yap\bin\yap.dll" \
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"C:\zipped\Glib windows\glib-2.12.11\bin\libglib-2.0-0.dll"
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cplint_yap.o: cplint_yap.c cplint.h
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gcc -mno-cygwin -I/usr/local \
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-I/usr/include/glib-1.2 -I/lib/glib/include -I"c:/Yap/include" \
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-I"C:\Software\BDD\Somenzi\glu-2.1\include" \
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-I"C:\Software\BDD\cudd-2.4.1\cudd-2.4.1\include" \
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-c -shared cplint_yap.c
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cplint_Prob.o: cplint_Prob.c cplint.h
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gcc -mno-cygwin -I/usr/local \
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-I/usr/include/glib-1.2 -I/lib/glib/include -I"c:/Yap/include" \
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-I/cygdrive/c/Software/BDD/Somenzi/glu-2.1/include \
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-I"C:\Software\BDD\cudd-2.4.1\cudd-2.4.1\include" \
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-c -shared cplint_Prob.c
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clean:
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rm $(OBJECTS) cpint.so
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19
cplint/README
Normal file
19
cplint/README
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@ -0,0 +1,19 @@
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This directory contains the code of the LPAD anc CP-logic interpreter cplint
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COMPILATION:
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This package requires CUDD, GLU (a subpackage of VIS) and GLIB.
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You can download CUDD from http://vlsi.colorado.edu/~fabio/
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You can download GLU from http://vlsi.colorado.edu/~vis/getting_VIS_2.1.html
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You can download GLIB from http://www.gtk.org/. This is a standard Linux package
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so it is easy to install using the package management software of your Linux
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distribution
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FEEDBACK:
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Send feedback to:
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Fabrizio Riguzzi
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University of Ferrara
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Dept. of Engineering
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fabrizio.riguzzi@unife.it
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http://www.ing.unife.it/Docenti/FabrizioRiguzzi/
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80
cplint/cplint.h
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80
cplint/cplint.h
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/*
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LPAD and CP-Logic interpreter
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Copyright (c) 2007, Fabrizio Riguzzi
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For the use of cudd:
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Copyright (c) 1995-2004, Regents of the University of Colorado
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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Neither the name of the University of Colorado nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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POSSIBILITY OF SUCH DAMAGE.
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*/
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#include "util.h"
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#include "cuddInt.h"
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#include "array.h"
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#include "mtr.h"
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#include "avl.h"
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#include "Yap/YapInterface.h"
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#include <glib.h>
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typedef struct
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{
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int var,value;
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} factor;
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typedef struct
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{
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int nVal,nBit;
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array_t * probabilities;
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array_t * booleanVars;
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} variable;
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void createVars(array_t * vars, YAP_Term t,DdManager * mgr, array_t * bVar2mVar, char inames[1000][20]);
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void createExpression(array_t * expression, YAP_Term t);
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static int compute_prob(void);
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void init_my_predicates();
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int compare(char *a, char *b);
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gint my_equal(gconstpointer v,gconstpointer v2);
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guint my_hash(gconstpointer key);
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void dealloc(gpointer key,gpointer value,gpointer user_data);
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DdNode * retFunction(DdManager * mgr, array_t * expression,array_t * v);
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DdNode * retTerm(DdManager * mgr,array_t *factors,array_t * v);
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DdNode * retFactor(DdManager * mgr, factor f, array_t * v);
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double Prob(DdNode *node, array_t * vars,array_t * bVar2mVar, GHashTable * nodes);
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double ProbBool(DdNode *node, int bits, int nBit,int posBVar,variable v,
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array_t * vars,array_t * bVar2mVar, GHashTable * nodes);
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722
cplint/cplint.yap
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722
cplint/cplint.yap
Normal file
@ -0,0 +1,722 @@
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/*
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LPAD and CP-Logic interpreter
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Copyright (c) 2007, Fabrizio Riguzzi
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*/
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:-dynamic rule/4,def_rule/2,setting/2.
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:-use_module(library(lists)).
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:-use_module(library(ugraphs)).
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:-load_foreign_files(['cplint'],[],init_my_predicates).
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/* start of list of parameters that can be set by the user with
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set(Parameter,Value) */
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setting(epsilon_parsing,0.00001).
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setting(savedot,false).
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/* end of list of parameters */
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/* s(GoalsLIst,Prob) compute the probability of a list of goals
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GoalsLis can have variables, s returns in backtracking all the solutions with their
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corresponding probability */
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s(GoalsList,Prob):-
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solve(GoalsList,Prob).
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solve(GoalsList,Prob):-
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setof(Deriv,find_deriv(GoalsList,Deriv),LDup),
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rem_dup_lists(LDup,L),
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build_formula(L,Formula,[],Var),
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var2numbers(Var,0,NewVar),
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(setting(savedot,true)->
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format("Variables: ~p~n",[Var]),
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compute_prob(NewVar,Formula,Prob,1)
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;
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compute_prob(NewVar,Formula,Prob,0)
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).
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solve(GoalsList,0):-
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\+ find_deriv(GoalsList,_Deriv).
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find_deriv(GoalsList,Deriv):-
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solve(GoalsList,[],DerivDup),
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remove_duplicates(DerivDup,Deriv).
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/* duplicate can appear in the C set because two different unistantiated clauses may become the
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same clause when instantiated */
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/* sc(Goals,Evidence,Prob) compute the conditional probability of the list of goals
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Goals given the list of goals Evidence
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Goals and Evidence can have variables, sc returns in backtracking all the solutions with their
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corresponding probability
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if it fails, the conditional probability is undefined
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*/
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sc(Goals,Evidence,Prob):-
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solve_cond(Goals,Evidence,Prob).
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solve_cond(Goals,Evidence,Prob):-
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setof(DerivE,find_deriv(Evidence,DerivE),LDupE),
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rem_dup_lists(LDupE,LE),
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build_formula(LE,FormulaE,[],VarE),
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var2numbers(VarE,0,NewVarE),
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compute_prob(NewVarE,FormulaE,ProbE,0),
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solve_cond_goals(Goals,LE,ProbGE),
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Prob is ProbGE/ProbE.
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solve_cond_goals(Goals,LE,ProbGE):-
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setof(DerivGE,find_deriv_GE(LE,Goals,DerivGE),LDupGE),
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rem_dup_lists(LDupGE,LGE),
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build_formula(LGE,FormulaGE,[],VarGE),
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var2numbers(VarGE,0,NewVarGE),
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call_compute_prob(NewVarGE,FormulaGE,ProbGE).
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solve_cond_goals(Goals,LE,0):-
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\+ find_deriv_GE(LE,Goals,_DerivGE).
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call_compute_prob(NewVarGE,FormulaGE,ProbGE):-
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(setting(savedot,true)->
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format("Variables: ~p~n",[NewVarGE]),
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compute_prob(NewVarGE,FormulaGE,ProbGE,1)
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;
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compute_prob(NewVarGE,FormulaGE,ProbGE,0)
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).
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find_deriv_GE(LD,GoalsList,Deriv):-
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member(D,LD),
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solve(GoalsList,D,DerivDup),
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remove_duplicates(DerivDup,Deriv).
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/* solve(GoalsList,CIn,COut) takes a list of goals and an input C set
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and returns an output C set
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The C set is a list of triple (N,R,S) where
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- N is the index of the head atom used, starting from 0
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- R is the index of the non ground rule used, starting from 1
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- S is the substitution of rule R, in the form of a list whose elements
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are of the form 'VarName'=value
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*/
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solve([],C,C):-!.
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solve([bagof(V,EV^G,L)|T],CIn,COut):-!,
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list2and(GL,G),
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bagof((V,C),EV^solve(GL,CIn,C),LD),
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length(LD,N),
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build_initial_graph(N,GrIn),
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build_graph(LD,0,GrIn,Gr),
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clique(Gr,Clique),
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build_Cset(LD,Clique,L,[],C1),
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remove_duplicates_eq(C1,C2),
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solve(T,C2,COut).
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solve([bagof(V,G,L)|T],CIn,COut):-!,
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list2and(GL,G),
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bagof((V,C),solve(GL,CIn,C),LD),
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length(LD,N),
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build_initial_graph(N,GrIn),
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build_graph(LD,0,GrIn,Gr),
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clique(Gr,Clique),
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build_Cset(LD,Clique,L,[],C1),
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remove_duplicates_eq(C1,C2),
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solve(T,C2,COut).
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solve([setof(V,EV^G,L)|T],CIn,COut):-!,
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list2and(GL,G),
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setof((V,C),EV^solve(GL,CIn,C),LD),
|
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length(LD,N),
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build_initial_graph(N,GrIn),
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build_graph(LD,0,GrIn,Gr),
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clique(Gr,Clique),
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build_Cset(LD,Clique,L1,[],C1),
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remove_duplicates(L1,L),
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solve(T,C1,COut).
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|
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solve([setof(V,G,L)|T],CIn,COut):-!,
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list2and(GL,G),
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setof((V,C),solve(GL,CIn,C),LD),
|
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length(LD,N),
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build_initial_graph(N,GrIn),
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build_graph(LD,0,GrIn,Gr),
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clique(Gr,Clique),
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build_Cset(LD,Clique,L1,[],C1),
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remove_duplicates(L1,L),
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solve(T,C1,COut).
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|
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solve([\+ H |T],CIn,COut):-!,
|
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list2and(HL,H),
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(setof(D,find_deriv(HL,D),LDup)->
|
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rem_dup_lists(LDup,L),
|
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choose_clauses(CIn,L,C1),
|
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solve(T,C1,COut)
|
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;
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solve(T,CIn,COut)
|
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).
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|
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solve([H|T],CIn,COut):-
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builtin(H),!,
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call(H),
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solve(T,CIn,COut).
|
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|
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solve([H|T],CIn,COut):-
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def_rule(H,B),
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append(B,T,NG),
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solve(NG,CIn,COut).
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solve([H|T],CIn,COut):-
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find_rule(H,(R,S,N),B,CIn),
|
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solve_pres(R,S,N,B,T,CIn,COut).
|
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solve_pres(R,S,N,B,T,CIn,COut):-
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member_eq((N,R,S),CIn),!,
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append(B,T,NG),
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solve(NG,CIn,COut).
|
||||
|
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solve_pres(R,S,N,B,T,CIn,COut):-
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append(CIn,[(N,R,S)],C1),
|
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append(B,T,NG),
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solve(NG,C1,COut).
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||||
|
||||
build_initial_graph(N,G):-
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listN(0,N,Vert),
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add_vertices([],Vert,G).
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|
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|
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build_graph([],_N,G,G).
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|
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build_graph([(_V,C)|T],N,GIn,GOut):-
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N1 is N+1,
|
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compatible(C,T,N,N1,GIn,G1),
|
||||
build_graph(T,N1,G1,GOut).
|
||||
|
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compatible(_C,[],_N,_N1,G,G).
|
||||
|
||||
compatible(C,[(_V,H)|T],N,N1,GIn,GOut):-
|
||||
(compatible(C,H)->
|
||||
add_edges(GIn,[N-N1,N1-N],G1)
|
||||
;
|
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G1=GIn
|
||||
),
|
||||
N2 is N1 +1,
|
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compatible(C,T,N,N2,G1,GOut).
|
||||
|
||||
compatible([],_C).
|
||||
|
||||
compatible([(N,R,S)|T],C):-
|
||||
not_present_with_a_different_head(N,R,S,C),
|
||||
compatible(T,C).
|
||||
|
||||
not_present_with_a_different_head(_N,_R,_S,[]).
|
||||
|
||||
not_present_with_a_different_head(N,R,S,[(N,R,S)|T]):-!,
|
||||
not_present_with_a_different_head(N,R,S,T).
|
||||
|
||||
not_present_with_a_different_head(N,R,S,[(_N1,R,S1)|T]):-
|
||||
S\=S1,!,
|
||||
not_present_with_a_different_head(N,R,S,T).
|
||||
|
||||
not_present_with_a_different_head(N,R,S,[(_N1,R1,_S1)|T]):-
|
||||
R\=R1,
|
||||
not_present_with_a_different_head(N,R,S,T).
|
||||
|
||||
|
||||
|
||||
build_Cset(_LD,[],[],C,C).
|
||||
|
||||
build_Cset(LD,[H|T],[V|L],CIn,COut):-
|
||||
nth0(H,LD,(V,C)),
|
||||
append(C,CIn,C1),
|
||||
build_Cset(LD,T,L,C1,COut).
|
||||
|
||||
|
||||
/* find_rule(G,(R,S,N),Body,C) takes a goal G and the current C set and
|
||||
returns the index R of a disjunctive rule resolving with G together with
|
||||
the index N of the resolving head, the substitution S and the Body of the
|
||||
rule */
|
||||
find_rule(H,(R,S,N),Body,C):-
|
||||
rule(R,S,_,Head,Body),
|
||||
member_head(H,Head,0,N),
|
||||
not_already_present_with_a_different_head(N,R,S,C).
|
||||
|
||||
find_rule(H,(R,S,Number),Body,C):-
|
||||
rule(R,S,_,uniform(H:1/_Num,_P,Number),Body),
|
||||
not_already_present_with_a_different_head(Number,R,S,C).
|
||||
|
||||
not_already_present_with_a_different_head(_N,_R,_S,[]).
|
||||
|
||||
not_already_present_with_a_different_head(N,R,S,[(N1,R,S1)|T]):-
|
||||
not_different(N,N1,S,S1),!,
|
||||
not_already_present_with_a_different_head(N,R,S,T).
|
||||
|
||||
not_already_present_with_a_different_head(N,R,S,[(_N1,R1,_S1)|T]):-
|
||||
R\==R1,
|
||||
not_already_present_with_a_different_head(N,R,S,T).
|
||||
|
||||
not_different(_N,_N1,S,S1):-
|
||||
S\=S1,!.
|
||||
|
||||
not_different(N,N1,S,S1):-
|
||||
N\=N1,!,
|
||||
dif(S,S1).
|
||||
|
||||
not_different(N,N,S,S).
|
||||
|
||||
|
||||
member_head(H,[(H:_P)|_T],N,N).
|
||||
|
||||
member_head(H,[(_H:_P)|T],NIn,NOut):-
|
||||
N1 is NIn+1,
|
||||
member_head(H,T,N1,NOut).
|
||||
|
||||
/* choose_clauses(CIn,LC,COut) takes as input the current C set and
|
||||
the set of C sets for a negative goal and returns a new C set that
|
||||
excludes all the derivations for the negative goals */
|
||||
choose_clauses(C,[],C).
|
||||
|
||||
choose_clauses(CIn,[D|T],COut):-
|
||||
member((N,R,S),D),
|
||||
instantiation_present_with_the_same_head(N,R,S,CIn),
|
||||
choose_a_different_head(N,R,S,T,CIn,COut).
|
||||
|
||||
choose_a_different_head(N,R,S,D,CIn,COut):-
|
||||
/* cases 1 and 2 of Select */
|
||||
choose_a_head(N,R,S,CIn,C1),
|
||||
choose_clauses(C1,D,COut).
|
||||
|
||||
choose_a_different_head(N,R,S,D,CIn,COut):-
|
||||
/* case 3 of Select */
|
||||
new_head(N,R,S,N1),
|
||||
\+ already_present(N1,R,S,CIn),
|
||||
choose_clauses([(N1,R,S)|CIn],D,COut).
|
||||
|
||||
/* instantiation_present_with_the_same_head(N,R,S,C)
|
||||
takes rule R with substitution S and selected head N and a C set
|
||||
and asserts dif constraints for all the clauses in C of which RS
|
||||
is an instantitation and have the same head selected */
|
||||
instantiation_present_with_the_same_head(_N,_R,_S,[]).
|
||||
|
||||
instantiation_present_with_the_same_head(N,R,S,[(NH,R,SH)|T]):-
|
||||
\+ \+ S=SH,
|
||||
dif(N,NH),
|
||||
instantiation_present_with_the_same_head(N,R,S,T).
|
||||
|
||||
instantiation_present_with_the_same_head(N,R,S,[(NH,R,SH)|T]):-
|
||||
\+ \+ S=SH,
|
||||
N=NH,!,
|
||||
dif(S,SH),
|
||||
instantiation_present_with_the_same_head(N,R,S,T).
|
||||
|
||||
instantiation_present_with_the_same_head(N,R,S,[_H|T]):-
|
||||
instantiation_present_with_the_same_head(N,R,S,T).
|
||||
|
||||
/* case 1 of Select: a more general rule is present in C with
|
||||
a different head, instantiate it */
|
||||
choose_a_head(N,R,S,[(NH,R,SH)|T],[(NH,R,SH)|T]):-
|
||||
S=SH,
|
||||
dif(N,NH).
|
||||
|
||||
/* case 2 of Select: a more general rule is present in C with
|
||||
a different head, ensure that they do not generate the same
|
||||
ground clause */
|
||||
choose_a_head(N,R,S,[(NH,R,SH)|T],[(NH,R,S),(NH,R,SH)|T]):-
|
||||
\+ \+ S=SH, S\==SH,
|
||||
dif(N,NH),
|
||||
dif(S,SH).
|
||||
|
||||
choose_a_head(N,R,S,[H|T],[H|T1]):-
|
||||
choose_a_head(N,R,S,T,T1).
|
||||
|
||||
/* select a head different from N for rule R with
|
||||
substitution S, return it in N1 */
|
||||
new_head(N,R,S,N1):-
|
||||
rule(R,S,Numbers,Head,_Body),
|
||||
Head\=uniform(_,_,_),!,
|
||||
nth0(N, Numbers, _Elem, Rest),
|
||||
member(N1,Rest).
|
||||
|
||||
new_head(N,R,S,N1):-
|
||||
rule(R,S,Numbers,uniform(_A:1/Tot,_L,_Number),_Body),
|
||||
listN(0,Tot,Numbers),
|
||||
nth0(N, Numbers, _Elem, Rest),
|
||||
member(N1,Rest).
|
||||
|
||||
/* checks that a rule R with head N and selection S is already
|
||||
present in C (or a generalization of it is in C) */
|
||||
already_present(N,R,S,[(N,R,SH)|_T]):-
|
||||
S=SH.
|
||||
|
||||
already_present(N,R,S,[_H|T]):-
|
||||
already_present(N,R,S,T).
|
||||
|
||||
/* rem_dup_lists removes the C sets that are a superset of
|
||||
another C sets further on in the list of C sets */
|
||||
rem_dup_lists([],[]).
|
||||
|
||||
rem_dup_lists([H|T],T1):-
|
||||
member_subset(H,T),!,
|
||||
rem_dup_lists(T,T1).
|
||||
|
||||
rem_dup_lists([H|T],[H|T1]):-
|
||||
rem_dup_lists(T,T1).
|
||||
|
||||
member_subset(E,[H|_T]):-
|
||||
subset_my(H,E),!.
|
||||
|
||||
member_subset(E,[_H|T]):-
|
||||
member_subset(E,T).
|
||||
|
||||
/* predicates for building the formula to be converted into a BDD */
|
||||
|
||||
/* build_formula(LC,Formula,VarIn,VarOut) takes as input a set of C sets
|
||||
LC and a list of Variables VarIn and returns the formula and a new list
|
||||
of variables VarOut
|
||||
Formula is of the form [Term1,...,Termn]
|
||||
Termi is of the form [Factor1,...,Factorm]
|
||||
Factorj is of the form (Var,Value) where Var is the index of
|
||||
the multivalued variable Var and Value is the index of the value
|
||||
*/
|
||||
build_formula([],[],Var,Var).
|
||||
|
||||
build_formula([D|TD],[F|TF],VarIn,VarOut):-
|
||||
build_term(D,F,VarIn,Var1),
|
||||
build_formula(TD,TF,Var1,VarOut).
|
||||
|
||||
build_term([],[],Var,Var).
|
||||
|
||||
build_term([(N,R,S)|TC],[[NVar,N]|TF],VarIn,VarOut):-
|
||||
(nth0_eq(0,NVar,VarIn,(R,S))->
|
||||
Var1=VarIn
|
||||
;
|
||||
append(VarIn,[(R,S)],Var1),
|
||||
length(VarIn,NVar)
|
||||
),
|
||||
build_term(TC,TF,Var1,VarOut).
|
||||
|
||||
/* nth0_eq(PosIn,PosOut,List,El) takes as input a List,
|
||||
an element El and an initial position PosIn and returns in PosOut
|
||||
the position in the List that contains an element exactly equal to El
|
||||
*/
|
||||
nth0_eq(N,N,[H|_T],El):-
|
||||
H==El,!.
|
||||
|
||||
nth0_eq(NIn,NOut,[_H|T],El):-
|
||||
N1 is NIn+1,
|
||||
nth0_eq(N1,NOut,T,El).
|
||||
|
||||
/* var2numbers converts a list of couples (Rule,Substitution) into a list
|
||||
of triples (N,NumberOfHeadsAtoms,ListOfProbabilities), where N is an integer
|
||||
starting from 0 */
|
||||
var2numbers([],_N,[]).
|
||||
|
||||
var2numbers([(R,S)|T],N,[[N,ValNumber,Probs]|TNV]):-
|
||||
find_probs(R,S,Probs),
|
||||
length(Probs,ValNumber),
|
||||
N1 is N+1,
|
||||
var2numbers(T,N1,TNV).
|
||||
|
||||
find_probs(R,S,Probs):-
|
||||
rule(R,S,_N,Head,_Body),
|
||||
get_probs(Head,Probs).
|
||||
|
||||
get_probs(uniform(_A:1/Num,_P,_Number),ListP):-
|
||||
Prob is 1/Num,
|
||||
list_el(Num,Prob,ListP).
|
||||
|
||||
get_probs([],[]).
|
||||
|
||||
get_probs([_H:P|T],[P1|T1]):-
|
||||
P1 is P,
|
||||
get_probs(T,T1).
|
||||
|
||||
list_el(0,_P,[]):-!.
|
||||
|
||||
list_el(N,P,[P|T]):-
|
||||
N1 is N-1,
|
||||
list_el(N1,P,T).
|
||||
|
||||
/* end of predicates for building the formula to be converted into a BDD */list_el(0,_P,[]):-!.
|
||||
|
||||
|
||||
/* start of predicates for parsing an input file containing a program */
|
||||
|
||||
/* p(File) parses the file File.cpl. It can be called more than once without
|
||||
exiting yap */
|
||||
p(File):-
|
||||
parse(File).
|
||||
|
||||
parse(File):-
|
||||
atom_concat(File,'.cpl',FilePl),
|
||||
open(FilePl,read,S),
|
||||
read_clauses(S,C),
|
||||
close(S),
|
||||
retractall(rule(_,_,_,_,_)),
|
||||
retractall(def_rule(_,_)),
|
||||
process_clauses(C,1).
|
||||
|
||||
process_clauses([(end_of_file,[])],_N).
|
||||
|
||||
process_clauses([((H:-B),V)|T],N):-
|
||||
H=uniform(A,P,L),!,
|
||||
list2and(BL,B),
|
||||
process_body(BL,V,V1),
|
||||
remove_vars([P],V1,V2),
|
||||
append(BL,[length(L,Tot),nth0(Number,L,P)],BL1),
|
||||
append(V2,['Tot'=Tot],V3),
|
||||
assertz(rule(N,V3,_NH,uniform(A:1/Tot,L,Number),BL1)),
|
||||
N1 is N+1,
|
||||
process_clauses(T,N1).
|
||||
|
||||
process_clauses([((H:-B),V)|T],N):-
|
||||
H=(_;_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,HL),
|
||||
list2and(BL,B),
|
||||
process_body(BL,V,V1),
|
||||
length(HL,LH),
|
||||
listN(0,LH,NH),
|
||||
assertz(rule(N,V1,NH,HL,BL)),
|
||||
N1 is N+1,
|
||||
process_clauses(T,N1).
|
||||
|
||||
process_clauses([((H:-B),V)|T],N):-
|
||||
H=(_:_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,HL),
|
||||
list2and(BL,B),
|
||||
process_body(BL,V,V1),
|
||||
length(HL,LH),
|
||||
listN(0,LH,NH),
|
||||
assertz(rule(N,V1,NH,HL,BL)),
|
||||
N1 is N+1,
|
||||
process_clauses(T,N1).
|
||||
|
||||
process_clauses([((H:-B),_V)|T],N):-!,
|
||||
list2and(BL,B),
|
||||
assert(def_rule(H,BL)),
|
||||
process_clauses(T,N).
|
||||
|
||||
process_clauses([(H,V)|T],N):-
|
||||
H=(_;_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,HL),
|
||||
length(HL,LH),
|
||||
listN(0,LH,NH),
|
||||
assertz(rule(N,V,NH,HL,[])),
|
||||
N1 is N+1,
|
||||
process_clauses(T,N1).
|
||||
|
||||
process_clauses([(H,V)|T],N):-
|
||||
H=(_:_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,HL),
|
||||
length(HL,LH),
|
||||
listN(0,LH,NH),
|
||||
assertz(rule(N,V,NH,HL,[])),
|
||||
N1 is N+1,
|
||||
process_clauses(T,N1).
|
||||
|
||||
process_clauses([(H,_V)|T],N):-
|
||||
assert(def_rule(H,[])),
|
||||
process_clauses(T,N).
|
||||
|
||||
/* if the annotation in the head are not ground, the null atom is not added
|
||||
and the eventual formulas are not evaluated */
|
||||
|
||||
process_head(HL,NHL):-
|
||||
(ground_prob(HL)->
|
||||
process_head_ground(HL,0,NHL)
|
||||
;
|
||||
NHL=HL
|
||||
).
|
||||
|
||||
ground_prob([]).
|
||||
|
||||
ground_prob([_H:PH|T]):-
|
||||
ground(PH),
|
||||
ground_prob(T).
|
||||
|
||||
process_head_ground([H:PH],P,[H:PH1|Null]):-
|
||||
PH1 is PH,
|
||||
PNull is 1-P-PH1,
|
||||
setting(epsilon_parsing,Eps),
|
||||
EpsNeg is - Eps,
|
||||
PNull > EpsNeg,
|
||||
(PNull>Eps->
|
||||
Null=['':PNull]
|
||||
;
|
||||
Null=[]
|
||||
).
|
||||
|
||||
process_head_ground([H:PH|T],P,[H:PH1|NT]):-
|
||||
PH1 is PH,
|
||||
P1 is P+PH1,
|
||||
process_head_ground(T,P1,NT).
|
||||
|
||||
/* setof must have a goal of the form B^G where B is a term containing the existential variables */
|
||||
process_body([],V,V).
|
||||
|
||||
process_body([setof(A,B^_G,_L)|T],VIn,VOut):-!,
|
||||
get_var(A,VA),
|
||||
get_var(B,VB),
|
||||
remove_vars(VA,VIn,V1),
|
||||
remove_vars(VB,V1,V2),
|
||||
process_body(T,V2,VOut).
|
||||
|
||||
process_body([setof(A,_G,_L)|T],VIn,VOut):-!,
|
||||
get_var(A,VA),
|
||||
remove_vars(VA,VIn,V1),
|
||||
process_body(T,V1,VOut).
|
||||
|
||||
process_body([bagof(A,B^_G,_L)|T],VIn,VOut):-!,
|
||||
get_var(A,VA),
|
||||
get_var(B,VB),
|
||||
remove_vars(VA,VIn,V1),
|
||||
remove_vars(VB,V1,V2),
|
||||
process_body(T,V2,VOut).
|
||||
|
||||
process_body([bagof(A,_G,_L)|T],VIn,VOut):-!,
|
||||
get_var(A,VA),
|
||||
remove_vars(VA,VIn,V1),
|
||||
process_body(T,V1,VOut).
|
||||
|
||||
process_body([_H|T],VIn,VOut):-!,
|
||||
process_body(T,VIn,VOut).
|
||||
|
||||
get_var_list([],[]).
|
||||
|
||||
get_var_list([H|T],[H|T1]):-
|
||||
var(H),!,
|
||||
get_var_list(T,T1).
|
||||
|
||||
get_var_list([H|T],VarOut):-!,
|
||||
get_var(H,Var),
|
||||
append(Var,T1,VarOut),
|
||||
get_var_list(T,T1).
|
||||
|
||||
get_var(A,[A]):-
|
||||
var(A),!.
|
||||
|
||||
get_var(A,V):-
|
||||
A=..[_F|Args],
|
||||
get_var_list(Args,V).
|
||||
|
||||
remove_vars([],V,V).
|
||||
|
||||
remove_vars([H|T],VIn,VOut):-
|
||||
delete_var(H,VIn,V1),
|
||||
remove_vars(T,V1,VOut).
|
||||
|
||||
delete_var(_H,[],[]).
|
||||
|
||||
delete_var(V,[VN=Var|T],[VN=Var|T1]):-
|
||||
V\==Var,!,
|
||||
delete_var(V,T,T1).
|
||||
|
||||
delete_var(_V,[_H|T],T).
|
||||
|
||||
read_clauses(S,[(Cl,V)|Out]):-
|
||||
read_term(S,Cl,[variable_names(V)]),
|
||||
(Cl=end_of_file->
|
||||
Out=[]
|
||||
;
|
||||
read_clauses(S,Out)
|
||||
).
|
||||
|
||||
listN(N,N,[]):-!.
|
||||
|
||||
listN(NIn,N,[NIn|T]):-
|
||||
N1 is NIn+1,
|
||||
listN(N1,N,T).
|
||||
/* end of predicates for parsing an input file containing a program */
|
||||
|
||||
/* start of utility predicates */
|
||||
list2or([X],X):-
|
||||
X\=;(_,_),!.
|
||||
|
||||
list2or([H|T],(H ; Ta)):-!,
|
||||
list2or(T,Ta).
|
||||
|
||||
list2and([X],X):-
|
||||
X\=(_,_),!.
|
||||
|
||||
list2and([H|T],(H,Ta)):-!,
|
||||
list2and(T,Ta).
|
||||
|
||||
member_eq(A,[H|_T]):-
|
||||
A==H.
|
||||
|
||||
member_eq(A,[_H|T]):-
|
||||
member_eq(A,T).
|
||||
|
||||
subset_my([],_).
|
||||
|
||||
subset_my([H|T],L):-
|
||||
member_eq(H,L),
|
||||
subset_my(T,L).
|
||||
|
||||
remove_duplicates_eq([],[]).
|
||||
|
||||
remove_duplicates_eq([H|T],T1):-
|
||||
member_eq(H,T),!,
|
||||
remove_duplicates_eq(T,T1).
|
||||
|
||||
remove_duplicates_eq([H|T],[H|T1]):-
|
||||
remove_duplicates_eq(T,T1).
|
||||
|
||||
builtin(_A is _B).
|
||||
builtin(_A > _B).
|
||||
builtin(_A < _B).
|
||||
builtin(_A >= _B).
|
||||
builtin(_A =< _B).
|
||||
builtin(_A =:= _B).
|
||||
builtin(_A =\= _B).
|
||||
builtin(true).
|
||||
builtin(false).
|
||||
builtin(_A = _B).
|
||||
builtin(_A==_B).
|
||||
builtin(_A\=_B).
|
||||
builtin(_A\==_B).
|
||||
builtin(length(_L,_N)).
|
||||
builtin(member(_El,_L)).
|
||||
builtin(average(_L,_Av)).
|
||||
builtin(max_list(_L,_Max)).
|
||||
builtin(min_list(_L,_Max)).
|
||||
builtin(nth0(_,_,_)).
|
||||
builtin(nth(_,_,_)).
|
||||
average(L,Av):-
|
||||
sum_list(L,Sum),
|
||||
length(L,N),
|
||||
Av is Sum/N.
|
||||
|
||||
clique(Graph,Clique):-
|
||||
vertices(Graph,Candidates),
|
||||
extend_cycle(Graph,Candidates,[],[],Clique).
|
||||
|
||||
extend_cycle(G,[H|T],Not,CS,CSOut):-
|
||||
neighbours(H, G, Neigh),
|
||||
intersection(Neigh,T,NewCand),
|
||||
intersection(Neigh,Not,NewNot),
|
||||
extend(G,NewCand,NewNot,[H|CS],CSOut).
|
||||
|
||||
extend_cycle(G,[H|T],Not,CS,CSOut):-
|
||||
extend_cycle(G,T,[H|Not],CS,CSOut).
|
||||
|
||||
extend(_G,[],[],CompSub,CompSub):-!.
|
||||
|
||||
extend(G,Cand,Not,CS,CSOut):-
|
||||
extend_cycle(G,Cand,Not,CS,CSOut).
|
||||
|
||||
intersection([],_Y,[]).
|
||||
|
||||
intersection([H|T],Y,[H|Z]):-
|
||||
member(H,Y),!,
|
||||
intersection(T,Y,Z).
|
||||
|
||||
intersection([_H|T],Y,Z):-
|
||||
intersection(T,Y,Z).
|
||||
|
||||
/* set(Par,Value) can be used to set the value of a parameter */
|
||||
set(Parameter,Value):-
|
||||
retract(setting(Parameter,_)),
|
||||
assert(setting(Parameter,Value)).
|
||||
|
||||
/* end of utility predicates */
|
||||
239
cplint/cplint_Prob.c
Normal file
239
cplint/cplint_Prob.c
Normal file
@ -0,0 +1,239 @@
|
||||
/*
|
||||
LPAD and CP-Logic interpreter
|
||||
|
||||
Copyright (c) 2007, Fabrizio Riguzzi
|
||||
|
||||
For the use of cudd:
|
||||
Copyright (c) 1995-2004, Regents of the University of Colorado
|
||||
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
Neither the name of the University of Colorado nor the names of its
|
||||
contributors may be used to endorse or promote products derived from
|
||||
this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||||
FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||||
ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
This file contains the definition of Prob and ProbBool plus the functions
|
||||
for building the BDD
|
||||
*/
|
||||
|
||||
|
||||
#include "cplint.h"
|
||||
#include <stdlib.h>
|
||||
|
||||
|
||||
|
||||
|
||||
DdNode * retFunction(DdManager * mgr,array_t *expression, array_t *v)
|
||||
/* given an expression term1+term2+...+termn, returns the BDD that implements that function */
|
||||
{
|
||||
array_t * term;
|
||||
DdNode * tNode, * tmp, *tmp1;
|
||||
int i;
|
||||
|
||||
i=0;
|
||||
tNode=Cudd_ReadLogicZero(mgr);
|
||||
Cudd_Ref(tNode);
|
||||
while(i<array_n(expression))
|
||||
{
|
||||
term=array_fetch(array_t * ,expression,i);
|
||||
tmp=retTerm(mgr,term,v);
|
||||
Cudd_Ref(tmp);
|
||||
tmp1=Cudd_bddOr(mgr,tNode,tmp);
|
||||
Cudd_Ref(tmp1);
|
||||
Cudd_RecursiveDeref(mgr,tNode);
|
||||
tNode=tmp1;
|
||||
i++;
|
||||
}
|
||||
return tNode;
|
||||
}
|
||||
|
||||
DdNode * retTerm(DdManager * mgr,array_t *term, array_t * v)
|
||||
/* given a term V1=v1 and V2=v2 ... Vn=vn, returns the BDD that implements that function */
|
||||
{
|
||||
factor f;
|
||||
DdNode * fNode, * tmp, *tmp1;
|
||||
int i;
|
||||
|
||||
i=0;
|
||||
fNode=Cudd_ReadOne(mgr);
|
||||
Cudd_Ref(fNode);
|
||||
while (i<array_n(term))
|
||||
{
|
||||
f=array_fetch(factor, term, i);
|
||||
tmp=retFactor(mgr,f,v);
|
||||
Cudd_Ref(tmp);
|
||||
tmp1= Cudd_bddAnd(mgr,fNode,tmp);
|
||||
Cudd_Ref(tmp1);
|
||||
Cudd_RecursiveDeref(mgr,fNode);
|
||||
fNode=tmp1;
|
||||
i++;
|
||||
}
|
||||
return fNode;
|
||||
}
|
||||
|
||||
DdNode * retFactor(DdManager * mgr, factor f, array_t * vars)
|
||||
/* given a factor V=v, returns the BDD that implements that function */
|
||||
{
|
||||
int varIndex;
|
||||
int value;
|
||||
int i;
|
||||
int bit;
|
||||
variable v;
|
||||
DdNode * node, *booleanVar, * tmp;
|
||||
array_t * booleanVars;
|
||||
|
||||
|
||||
varIndex=f.var;
|
||||
value=f.value;
|
||||
v=array_fetch(variable, vars, varIndex);
|
||||
booleanVars=v.booleanVars;
|
||||
i=v.nBit-1;
|
||||
node=Cudd_ReadOne(mgr);
|
||||
Cudd_Ref(node);
|
||||
/* booelan var with index 0 in v.booleanVars is the most significant */
|
||||
do {
|
||||
booleanVar=array_fetch(DdNode *,booleanVars,i);
|
||||
bit=value & 01;
|
||||
if (bit)
|
||||
{
|
||||
tmp=Cudd_bddAnd(mgr,node,booleanVar);
|
||||
Cudd_Ref(tmp);
|
||||
}
|
||||
else
|
||||
{
|
||||
tmp=Cudd_bddAnd(mgr,node,Cudd_Not(booleanVar));
|
||||
Cudd_Ref(tmp);
|
||||
}
|
||||
value=value>>1;
|
||||
i--;
|
||||
Cudd_RecursiveDeref(mgr,node);
|
||||
node=tmp;
|
||||
} while (i>=0);
|
||||
return node;
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
double Prob(DdNode *node, array_t * vars,array_t * bVar2mVar, GHashTable * nodes)
|
||||
/* compute the probability of the expression rooted at node
|
||||
nodes is used to store nodes for which the probability has alread been computed
|
||||
so that it is not recomputed
|
||||
*/
|
||||
{
|
||||
DdNode *T,*F;
|
||||
int index,mVarIndex,nBit;
|
||||
variable v;
|
||||
double p,res;
|
||||
double value;
|
||||
double * value_p;
|
||||
DdNode **key;
|
||||
double *rp;
|
||||
|
||||
index=node->index;
|
||||
if (Cudd_IsConstant(node))
|
||||
{
|
||||
value=node->type.value;
|
||||
return value;
|
||||
}
|
||||
else
|
||||
{
|
||||
|
||||
value_p=g_hash_table_lookup(nodes,&node);
|
||||
if (value_p!=NULL)
|
||||
{
|
||||
return *value_p;
|
||||
}
|
||||
else
|
||||
{
|
||||
mVarIndex=array_fetch(int,bVar2mVar,index);
|
||||
v=array_fetch(variable,vars,mVarIndex);
|
||||
nBit=v.nBit;
|
||||
res=ProbBool(node,0,nBit,0,v,vars,bVar2mVar,nodes);
|
||||
key=(DdNode **)malloc(sizeof(DdNode *));
|
||||
*key=node;
|
||||
rp=(double *)malloc(sizeof(double));
|
||||
*rp=res;
|
||||
g_hash_table_insert(nodes, key, rp);
|
||||
return res;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
double ProbBool(DdNode *node, int bits, int nBit,int posBVar,variable v,
|
||||
array_t * vars,array_t * bVar2mVar, GHashTable * nodes)
|
||||
/* explores a group of binary variables making up the multivalued variable v */
|
||||
{
|
||||
DdNode *T,*F;
|
||||
double p,res;
|
||||
array_t * probs;
|
||||
double value;
|
||||
|
||||
probs=v.probabilities;
|
||||
if (nBit==0)
|
||||
{
|
||||
if (bits>=array_n(probs))
|
||||
return 0;
|
||||
else
|
||||
{
|
||||
p=array_fetch(double,probs,bits);
|
||||
res=p*Prob(node,vars,bVar2mVar,nodes);
|
||||
return res;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if (correctPosition(node->index,v,node,posBVar))
|
||||
{
|
||||
T = node->type.kids.T;
|
||||
F = node->type.kids.E;
|
||||
bits=bits<<1;
|
||||
|
||||
res=ProbBool(T,bits+1,nBit-1,posBVar+1,v,vars,bVar2mVar,nodes)+
|
||||
ProbBool(F,bits,nBit-1,posBVar+1,v,vars,bVar2mVar,nodes);
|
||||
return res;
|
||||
}
|
||||
else
|
||||
{
|
||||
|
||||
bits=bits<<1;
|
||||
res=ProbBool(node,bits+1,nBit-1,posBVar+1,v,vars,bVar2mVar,nodes)+
|
||||
ProbBool(node,bits,nBit-1,posBVar+1,v,vars,bVar2mVar,nodes);
|
||||
return res;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
int correctPosition(int index,variable v, DdNode * node,int posBVar)
|
||||
/* returns 1 is the boolean variable with index posBVar is in the correct position
|
||||
currently explored by ProbBool */
|
||||
{
|
||||
DdNode * bvar;
|
||||
|
||||
bvar=array_fetch(DdNode *,v.booleanVars,posBVar);
|
||||
return bvar->index==index;
|
||||
}
|
||||
275
cplint/cplint_yap.c
Normal file
275
cplint/cplint_yap.c
Normal file
@ -0,0 +1,275 @@
|
||||
/*
|
||||
LPAD and CP-Logic interpreter
|
||||
|
||||
Copyright (c) 2007, Fabrizio Riguzzi
|
||||
|
||||
For the use of cudd:
|
||||
Copyright (c) 1995-2004, Regents of the University of Colorado
|
||||
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
|
||||
Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
|
||||
Neither the name of the University of Colorado nor the names of its
|
||||
contributors may be used to endorse or promote products derived from
|
||||
this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||||
FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||||
ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
This file contains the functions for interfacing Yap and C
|
||||
The arguments of the predicate compute_prob are parsed and translated into C data
|
||||
structures
|
||||
*/
|
||||
|
||||
#include "cplint.h"
|
||||
#include <math.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
|
||||
|
||||
unsigned long dividend;
|
||||
|
||||
FILE *open_file (char *filename, const char *mode);
|
||||
void reverse(char s[]);
|
||||
|
||||
void createVars(array_t * vars, YAP_Term t,DdManager * mgr, array_t * bVar2mVar, char inames[1000][20])
|
||||
/* adds the boolean variables to the BDD and returns
|
||||
an array_t containing them (array_t is defined in the util library of glu)
|
||||
returns also the names of the variables to be used to save the ADD in dot format
|
||||
*/
|
||||
{
|
||||
YAP_Term varTerm,probTerm;
|
||||
int varIndex,nVal,i,b;
|
||||
variable v;
|
||||
char numberVar[10],numberBit[10];
|
||||
double p;
|
||||
b=0;
|
||||
|
||||
while(YAP_IsPairTerm(t))
|
||||
{
|
||||
varTerm=YAP_HeadOfTerm(t);
|
||||
varIndex=YAP_IntOfTerm(YAP_HeadOfTerm(varTerm));
|
||||
|
||||
varTerm=YAP_TailOfTerm(varTerm);
|
||||
nVal=YAP_IntOfTerm(YAP_HeadOfTerm(varTerm));
|
||||
varTerm=YAP_TailOfTerm(varTerm);
|
||||
probTerm=YAP_HeadOfTerm(varTerm);
|
||||
v.nVal=nVal;
|
||||
v.nBit=(int)ceil(log(nVal)/log(2));
|
||||
v.probabilities=array_alloc(double,0);
|
||||
v.booleanVars=array_alloc(DdNode *,0);
|
||||
for (i=0;i<nVal;i++)
|
||||
{
|
||||
strcpy(inames[b+i],"X");
|
||||
sprintf(numberVar,"%d",varIndex);
|
||||
strcat(inames[b+i],numberVar);
|
||||
strcat(inames[b+i],"_");
|
||||
sprintf(numberBit,"%d",i);
|
||||
strcat(inames[b+i],numberBit);
|
||||
p=YAP_FloatOfTerm(YAP_HeadOfTerm(probTerm));
|
||||
array_insert(double,v.probabilities,i,p);
|
||||
probTerm=YAP_TailOfTerm(probTerm);
|
||||
array_insert(DdNode *,v.booleanVars,i,Cudd_bddIthVar(mgr,b+i));
|
||||
array_insert(int,bVar2mVar,b+i,varIndex);
|
||||
}
|
||||
Cudd_MakeTreeNode(mgr,b,nVal,MTR_FIXED);
|
||||
b=b+nVal;
|
||||
array_insert(variable,vars,varIndex,v);
|
||||
t=YAP_TailOfTerm(t);
|
||||
}
|
||||
}
|
||||
|
||||
void createExpression(array_t * expression, YAP_Term t)
|
||||
/* returns the expression as an array_t of terms (cubes) starting from the prolog lists of terms
|
||||
each term is an array_t of factors obtained from a prolog list of factors
|
||||
each factor is a couple (index of variable, index of value) obtained from a prolog list containing
|
||||
two integers
|
||||
*/
|
||||
{
|
||||
YAP_Term termTerm,factorTerm,tail;
|
||||
factor f;
|
||||
int i,j;
|
||||
array_t * term;
|
||||
|
||||
i=0;
|
||||
while(YAP_IsPairTerm(t))
|
||||
{
|
||||
term=array_alloc(factor,0);
|
||||
termTerm=YAP_HeadOfTerm(t);
|
||||
j=0;
|
||||
while(YAP_IsPairTerm(termTerm))
|
||||
{
|
||||
factorTerm=YAP_HeadOfTerm(termTerm);
|
||||
f.var=YAP_IntOfTerm(YAP_HeadOfTerm(factorTerm));
|
||||
f.value=YAP_IntOfTerm(YAP_HeadOfTerm(YAP_TailOfTerm(factorTerm)));
|
||||
array_insert(factor,term,j,f);
|
||||
termTerm=YAP_TailOfTerm(termTerm);
|
||||
j++;
|
||||
}
|
||||
array_insert(array_t *,expression,i,term);
|
||||
t=YAP_TailOfTerm(t);
|
||||
i++;
|
||||
}
|
||||
}
|
||||
|
||||
static int compute_prob(void)
|
||||
/* this is the function that implements the compute_prob predicate used in pp.pl
|
||||
*/
|
||||
{
|
||||
YAP_Term out,arg1,arg2,arg3,arg4;
|
||||
array_t * variables,* expression, * term, * bVar2mVar;
|
||||
DdNode * function, * add;
|
||||
DdManager * mgr;
|
||||
factor f;
|
||||
variable v;
|
||||
double p;
|
||||
int var,val,nBVar,i,j,intBits;
|
||||
FILE * file;
|
||||
DdNode * array[1];
|
||||
char * onames[1];
|
||||
char inames[1000][20];
|
||||
char * names[1000];
|
||||
GHashTable * nodes; /* hash table that associates nodes with their probability if already
|
||||
computed, it is defined in glib */
|
||||
Cudd_ReorderingType * method;
|
||||
|
||||
arg1=YAP_ARG1;
|
||||
arg2=YAP_ARG2;
|
||||
arg3=YAP_ARG3;
|
||||
arg4=YAP_ARG4;
|
||||
|
||||
mgr=Cudd_Init(0,0,CUDD_UNIQUE_SLOTS,CUDD_CACHE_SLOTS,0);
|
||||
|
||||
variables=array_alloc(variable,0);
|
||||
bVar2mVar=array_alloc(int,0);
|
||||
createVars(variables,arg1,mgr,bVar2mVar,inames);
|
||||
|
||||
/* automatic variable reordering, default method CUDD_REORDER_SIFT used */
|
||||
Cudd_AutodynEnable(mgr,CUDD_REORDER_SAME);
|
||||
|
||||
|
||||
expression=array_alloc(array_t *,0);
|
||||
createExpression(expression,arg2);
|
||||
|
||||
function=retFunction(mgr,expression,variables);
|
||||
/* the BDD build by retFunction is converted to an ADD (algebraic decision diagram)
|
||||
because it is easier to interpret and to print */
|
||||
add=Cudd_BddToAdd(mgr,function);
|
||||
|
||||
if (YAP_IntOfTerm(arg4))
|
||||
/* if specified by the user, a dot file for the BDD is written to cpl.dot */
|
||||
{
|
||||
nBVar=array_n(bVar2mVar);
|
||||
for(i=0;i<nBVar;i++)
|
||||
names[i]=inames[i];
|
||||
array[0]=add;
|
||||
onames[0]="Out";
|
||||
file = open_file("cpl.dot", "w");
|
||||
Cudd_DumpDot(mgr,1,array,names,onames,file);
|
||||
fclose(file);
|
||||
}
|
||||
|
||||
nodes=g_hash_table_new(my_hash,my_equal);
|
||||
intBits=sizeof(unsigned int)*8;
|
||||
/* dividend is a global variable used by my_hash
|
||||
it is equal to an unsigned int with binary representation 11..1 */
|
||||
dividend=1;
|
||||
for(j=1;j<intBits;j++)
|
||||
{
|
||||
dividend=(dividend<<1)+1;
|
||||
}
|
||||
out=YAP_MkFloatTerm(Prob(add,variables,bVar2mVar,nodes));
|
||||
g_hash_table_foreach (nodes,dealloc,NULL);
|
||||
g_hash_table_destroy(nodes);
|
||||
Cudd_Quit(mgr);
|
||||
array_free(variables);
|
||||
array_free(bVar2mVar);
|
||||
array_free(expression);
|
||||
return(YAP_Unify(out,arg3));
|
||||
}
|
||||
/*
|
||||
int compare(char *a, char *b)
|
||||
{
|
||||
int aval,bval;
|
||||
aval=(int) *((DdNode **)a);
|
||||
aval=(int) *((DdNode **)b);
|
||||
|
||||
if (aval<bval)
|
||||
return -1;
|
||||
else
|
||||
if (aval>bval)
|
||||
return 1;
|
||||
else
|
||||
return 0;
|
||||
}
|
||||
*/
|
||||
void init_my_predicates()
|
||||
/* function required by YAP for intitializing the predicates defined by a C function*/
|
||||
{
|
||||
YAP_UserCPredicate("compute_prob",compute_prob,4);
|
||||
}
|
||||
FILE *
|
||||
open_file(char *filename, const char *mode)
|
||||
/* opens a file */
|
||||
{
|
||||
FILE *fp;
|
||||
|
||||
if ((fp = fopen(filename, mode)) == NULL) {
|
||||
perror(filename);
|
||||
exit(1);
|
||||
}
|
||||
return fp;
|
||||
|
||||
}
|
||||
void reverse(char s[])
|
||||
/* reverses a string */
|
||||
{
|
||||
int i,c,j;
|
||||
for (i=0,j=strlen(s)-1;i<j;i++,j--)
|
||||
{
|
||||
c=s[i];
|
||||
s[i]=s[j];
|
||||
s[j]=c;
|
||||
}
|
||||
}
|
||||
|
||||
gint my_equal(gconstpointer v,gconstpointer v2)
|
||||
/* function used by GHashTable to compare two keys */
|
||||
{
|
||||
DdNode *a,*b;
|
||||
a=*(DdNode **)v;
|
||||
b=*(DdNode **)v2;
|
||||
return (a==b);
|
||||
}
|
||||
guint my_hash(gconstpointer key)
|
||||
/* function used by GHashTable to hash a key */
|
||||
{
|
||||
unsigned int h;
|
||||
h=(unsigned int)((unsigned long) *((DdNode **)key) % dividend);
|
||||
return h;
|
||||
}
|
||||
void dealloc(gpointer key,gpointer value,gpointer user_data)
|
||||
{
|
||||
free(key);
|
||||
free(value);
|
||||
}
|
||||
546
cplint/doc/bib.bib
Normal file
546
cplint/doc/bib.bib
Normal file
@ -0,0 +1,546 @@
|
||||
@Book{Lloyd,
|
||||
author = {J. W. Lloyd},
|
||||
title = {Foundations of Logic Programming},
|
||||
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|
||||
isbn = {3-540-18199-7},
|
||||
publisher = {Springer-Verlag},
|
||||
year = {1987},
|
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|
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|
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@MISC{Str05-pc,
|
||||
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|
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year = {2005},
|
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note = {Personal communication},
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|
||||
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|
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title = {Aleph},
|
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year = {2004},
|
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note = {http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/aleph_toc.html},
|
||||
}
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||||
|
||||
|
||||
@article{RaeDeh97-ML,
|
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|
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@BOOK{GreVal85-book,
|
||||
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|
||||
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@BOOK{NiedeW97-book,
|
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|
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@techreport{VenVer03-TR,
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@BOOK{LobMinRaj92-book,
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|
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|
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|
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|
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|
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@inProceedings{VenVer04-ICLP04-IC,
|
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|
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|
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@techreport{Hol03-TR,
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|
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|
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|
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|
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|
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|
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|
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|
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number={2157}
|
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}
|
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|
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@inProceedings{KerDeR00-ILP00-IC,
|
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|
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|
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}
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@techreport{KerDeR01-TR,
|
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}
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@incollection{GetFri01-BC,
|
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@ARTICLE{NgoHad97-TheoCS-IJ,
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author = {L. Ngo and P. Haddaway},
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@ARTICLE{Poo97-ArtInt-IJ,
|
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author = {D. Poole},
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title = {The {I}ndependent {C}hoice {L}ogic for Modelling Multiple Agents under
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||||
@article{mugg:mi17:slplearn,
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|
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|
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|
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|
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|
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@inProceedings{Sat98-MD-IW,
|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
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||||
number = {3201},
|
||||
pages = {205-216},
|
||||
ee = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3201{\&}spage=205},
|
||||
bibsource = {DBLP, http://dblp.uni-trier.de}
|
||||
}
|
||||
@inproceedings{KerstingECML06,
|
||||
author = {B. Gutmann and K. Kersting},
|
||||
title = {{TildeCRF}: Conditional Random Fields for Logical Sequences.},
|
||||
booktitle = {Machine Learning, ({ECML} 2006)},
|
||||
year = {2006},
|
||||
publisher = {Springer},
|
||||
series = {LNCS},
|
||||
}
|
||||
@article{KerstingJAIR06,
|
||||
author = {K. Kersting and L. De Raedt and T. Raik},
|
||||
title = {Logical Hidden Markov Models.},
|
||||
journal = {Journal of Artificial Intelligence Research},
|
||||
volume = {25},
|
||||
year = {2006},
|
||||
pages = {425-456},
|
||||
}
|
||||
@inproceedings{Rig04-ILP04-IC-short,
|
||||
author = "F. Riguzzi",
|
||||
title = "Learning Logic Programs with Annotated Disjunctions",
|
||||
booktitle = "Inductive Logic Programming, ({ILP} 2004)",
|
||||
year = "2004",
|
||||
month={September},
|
||||
publisher={Springer},
|
||||
series ={LNCS},
|
||||
number = {3194},
|
||||
pages={270--287},
|
||||
url = {http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/Rig-ILP04.pdf},
|
||||
isbn={3-540-22941-8},
|
||||
issn={0302-9743},
|
||||
doi={10.1007/b10011},
|
||||
}
|
||||
@inproceedings{VenDenBru-JELIA06,
|
||||
author = "J. Vennekens and M. Denecker and M. Bruynooghe",
|
||||
title = "Representing Causal Information about a
|
||||
Probabilistic Process",
|
||||
booktitle = "10th European Conference on Logics in Artificial Intelligence, JELIA 2006",
|
||||
year = "2006",
|
||||
month={September},
|
||||
publisher={Springer},
|
||||
series ={LNAI},
|
||||
}
|
||||
|
||||
@article{Bry-TC86,
|
||||
author={R. E. Bryant},
|
||||
title={Graph-based algorithms for boolean function manipulation},
|
||||
journal={ IEEE Trans. on Computers},
|
||||
volume={35},
|
||||
number={8},
|
||||
pages={677-691},
|
||||
year={1986},
|
||||
}
|
||||
|
||||
@inproceedings{MilDre-ISML02-IC,
|
||||
title={On the construction of multiple-valued decision diagrams},
|
||||
author={Miller, D. M. and Drechsler, R.},
|
||||
booktitle={Proceedings 32nd IEEE International Symposium on
|
||||
Multiple-Valued Logic},
|
||||
year={2002},
|
||||
pages={245-253},
|
||||
}
|
||||
|
||||
@article{LauSpi-JRS88,
|
||||
author={Lauritzen, S. and Spiegelhalter, D. J.},
|
||||
year={1988},
|
||||
title={Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems},
|
||||
journal={Journal of the Royal Statistical Society},
|
||||
volume={B, 50},
|
||||
number={2},
|
||||
pages={157-224},
|
||||
}
|
||||
@inproceedings{Rig-AIIA07-IC,
|
||||
author={
|
||||
Fabrizio Riguzzi },
|
||||
title={A Top Down Interpreter for LPAD and CP-logic},
|
||||
booktitle={10th Congress of the Italian Association for Artificial Intelligence},
|
||||
year={2007},
|
||||
publisher={Springer},
|
||||
note={\href{http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/Rig-AIIA07.pdf}{http://www.ing.unife.it/docenti/FabrizioRiguzzi/Papers/Rig-AIIA07.pdf}}
|
||||
}
|
||||
|
||||
}
|
||||
@inproceedings{Rig-RCRA07-IC,
|
||||
author={
|
||||
Fabrizio Riguzzi },
|
||||
title={A Top Down Interpreter for LPAD and CP-logic},
|
||||
booktitle={The 14th RCRA workshop
|
||||
Experimental Evaluation of Algorithms for
|
||||
Solving Problems with Combinatorial Explosion},
|
||||
year={2007},
|
||||
note={\href{http://pst.istc.cnr.it/RCRA07/articoli/P19-riguzzi-RCRA07.pdf}{http://pst.istc.cnr.it/RCRA07/articoli/P19-riguzzi-RCRA07.pdf}}
|
||||
}
|
||||
|
||||
@unpublished{CP-logic-unp,
|
||||
author={Joost Vennekens and Marc Denecker and Maurice Bruynooge},
|
||||
title={Extending the Role of Causality in Probabilistic Modeling},
|
||||
year={2006},
|
||||
note={\href{http://www.cs.kuleuven.ac.be/\%7Ejoost/cplogic.pdf}{http://www.cs.kuleuven.ac.be/\%7Ejoost/cplogic.pdf}},
|
||||
}
|
||||
396
cplint/doc/manual.tex
Normal file
396
cplint/doc/manual.tex
Normal file
@ -0,0 +1,396 @@
|
||||
% preambolo per doppia compilazione HTML/PDF
|
||||
%\ifx\pdfoutput\undefined % compilazione htlatex
|
||||
\documentclass{article}
|
||||
\usepackage{graphicx}
|
||||
\DeclareGraphicsExtensions{.png, .gif, .jpg}
|
||||
\newcommand{\href}[2]{\Link[#1]{}{} #2 \EndLink}
|
||||
\newcommand{\hypertarget}[2]{\Link[]{}{#1} #2 \EndLink}
|
||||
\newcommand{\hyperlink}[2]{\Link[]{#1}{} #2 \EndLink}
|
||||
%\else % compilazione pdflatex
|
||||
%\documentclass[a4paper,12pt]{article}
|
||||
%\usepackage[pdftex]{graphicx}
|
||||
%\DeclareGraphicsExtensions{.pdf,.png,.jpg}
|
||||
%\RequirePackage[hyperindex]{hyperref}
|
||||
%\fi
|
||||
|
||||
%\documentclass{article}
|
||||
|
||||
|
||||
\begin{document}
|
||||
\title{\texttt{cplint} Version 1.0 Manual}
|
||||
|
||||
|
||||
\author{Fabrizio Riguzzi\\
|
||||
fabrizio.riguzzi@unife.it}
|
||||
|
||||
\maketitle
|
||||
|
||||
|
||||
\section{Introduction}
|
||||
|
||||
\texttt{cplint} is an interpreter for LPADs \cite{VenVer03-TR,VenVer04-ICLP04-IC} and CP-logic programs \cite{VenDenBru-JELIA06,CP-logic-unp}. It is described in \cite{Rig-AIIA07-IC} and \cite{Rig-RCRA07-IC}. It is an adaptation of the interpreter for ProbLog \cite{DBLP:conf/ijcai/RaedtKT07}.
|
||||
|
||||
It was proved correct \cite{Rig-RCRA07-IC} for range restricted acyclic programs \cite{DBLP:journals/ngc/AptB91} without function symbols.
|
||||
|
||||
It is also able to deal with extensions of LPADs and CP-logic: the clause bodies can contain \texttt{setof} and \texttt{bagof}, the probabilities in the head may be depend on variables in the body and it is possible to specify a uniform distribution in the head with reference to a \texttt{setof} or \texttt{bagof} operator. These extended features have been introduced in order to represent CLP(BN) \cite{SanPagQaz03-UAI-IC} programs and PRM models \cite{Getoor+al:JMLR02}:
|
||||
\texttt{setof} and \texttt{bagof} allow to express dependency of an attribute from an aggregate function of another attribute, as in CLP(BN) and PRM, while the possibility of specifying a uniform distribution allows the use of the reference uncertainty feature of PRM.
|
||||
|
||||
These extensions are work in progress: they have been implemented but there is no paper yet that describes the semantics of the extended language.
|
||||
|
||||
%For program with function symbols, the semantics of LPADs and CP-logic are not defined. However, the interpreter accepts programs with function symbols and, if it does not go into a loop, it returns an answer. What is the meaning of this answer is subject of current study.
|
||||
|
||||
\section{Installation}
|
||||
\texttt{cplint} is distributed to be run an a Linux machine. It has been tested under Debian 4.0 and Suse 10.2. Please contact the author for versions for Windows. Please contact the author also for the complete source code.
|
||||
Download and install Yap Prolog version 5.1.2 from \href{http://www.ncc.up.pt/\%7Evsc/Yap/}{http://www.ncc.up.pt/\%7Evsc/Yap/}.
|
||||
Make sure libglib1.2 is installed in your machine. It is a standard Linux package so, if it is not installed, you can use your package manager to install it.
|
||||
Download \texttt{cplint.zip} from \href{http://www.ing.unife.it/software/cplint/cplint.zip}{http://www.ing.unife.it/software/cplint/cplint.zip} and uncompress it.
|
||||
By downloading \texttt{cplint.zip} you agree to the BSD-style license reported in section \ref{license}.
|
||||
|
||||
\section{Syntax}
|
||||
|
||||
Disjunction in the head is represented with a semicolon and atoms in the head are separated from probabilities by a colon. For the rest, the usual syntax of Prolog is used.
|
||||
For example, the CP-logic clause
|
||||
$$h_1:p_1\vee \ldots \vee h_n:p_n\leftarrow b_1,\dots,b_m ,\neg c_1,\ldots,\neg c_l$$
|
||||
is represented by
|
||||
\begin{verbatim}
|
||||
h1:p1 ; ... ; hn:pn :- b1,...,bm,\+ c1,....,\+ cl
|
||||
\end{verbatim}
|
||||
No parentheses are necessary. The \texttt{pi} are numeric expressions that can involve
|
||||
variables appearing in the body. It is up to the user to ensure that the numeric expressions are legal, i.e. that they sum up to less than one for every instantiation of the clause for which the body is true in an instance.
|
||||
|
||||
If the clause has an empty body, it can be represented like this
|
||||
\begin{verbatim}
|
||||
h1:p1 ; ... ;hn:pn.
|
||||
\end{verbatim}
|
||||
If the clause has a single head with probability 1, the annotation can be omitted and the clause takes the form of a normal prolog clause, i.e.
|
||||
\begin{verbatim}
|
||||
h1:- b1,...,bm,\+ c1,...,cl.
|
||||
\end{verbatim}
|
||||
stands for
|
||||
\begin{verbatim}
|
||||
h1:1 :- b1,...,bm,\+ c1,...,cl.
|
||||
\end{verbatim}
|
||||
|
||||
The coin example of \cite{VenVer04-ICLP04-IC} is represented as (see file \texttt{coin.cpl})
|
||||
\begin{verbatim}
|
||||
heads(Coin):1/2 ; tails(Coin):1/2:-
|
||||
toss(Coin),\+biased(Coin).
|
||||
|
||||
heads(Coin):0.6 ; tails(Coin):0.4:-
|
||||
toss(Coin),biased(Coin).
|
||||
|
||||
fair(Coin):0.9 ; biased(Coin):0.1.
|
||||
|
||||
toss(coin).
|
||||
\end{verbatim}
|
||||
The first clause states that if we toss a coin that is not biased it has equal probability of landing heads and tails. The second states that if the coin is biased it is slightly more probable that it lands heads. The third states that the coin is fair with probability 0.9 and biased with probability 0.1 and the last clause states that we toss a coin with certainty.
|
||||
|
||||
|
||||
|
||||
\section{Commands}
|
||||
The program must be stored in a text file with extension \texttt{.cpl}. Suppose you have stored the example above in file \texttt{coin.cpl}.
|
||||
Suppose also that \texttt{coin.cpl} is in the directory \texttt{cplint} that you obtained when you uncompressed \texttt{cplint.zip}. In order to answer queries from this program, you run \texttt{yap} from directory \texttt{cplint} and then load the interpreter with the command
|
||||
\begin{verbatim}
|
||||
compile(cplint).
|
||||
\end{verbatim}
|
||||
If \texttt{coin.cpl} is not in directory \texttt{cplint} then you must use the command
|
||||
\begin{verbatim}
|
||||
compile('path_to_cplint/cplint.pl').
|
||||
\end{verbatim}
|
||||
from the directory where \texttt{coin.cpl} is.
|
||||
In this case \verb|path_to_cplint| is the path to \texttt{cplint.pl}.
|
||||
Then you must parse the source file \texttt{coin.cpl} with the command
|
||||
\begin{verbatim}
|
||||
p(coin).
|
||||
\end{verbatim}
|
||||
At this point you can pose query to the program. You have to use the predicate \texttt{s/2} (for solve) that takes as its first argument a conjunction of goals in the form of a list and returns the computed probability as its second argument. For example, the probability of the conjunction \texttt{head(coin), biased(coin)} can be asked with the query
|
||||
\begin{verbatim}
|
||||
s([head(coin),biased(coin)],P).
|
||||
\end{verbatim}
|
||||
For computing the probability of a conjunction given another conjunction you have to use the predicate \texttt{sc/3} (for solve conditional) that take takes as input the query conjunction as its first argument, the evidence conjunction as its second argument and returns the probability in its third argument.
|
||||
For example, the probability of the query \texttt{heads(coin)} given the evidence \texttt{biased(coin)} can be asked with the query
|
||||
\begin{verbatim}
|
||||
sc([heads(coin)],[biased(coin)],P).
|
||||
\end{verbatim}
|
||||
The package contains also a program \texttt{semantics.pl} that computes the probability of queries by using directly the semantics of LPADs (i.e. by generating all the ground instances and then testing the query with each of them). This is provided for testing purposes only. After having compiled the program, you can use the same commands of \texttt{cplint.pl}. \texttt{semantics.pl} requires an extra file in the directory where \texttt{coin.cpl} is: a file with extension \texttt{.uni} (for universe) that contains, for each variable, the list of constants to which the variable can be instantiated. For example, in our case the current directory will contain a file \texttt{coin.uni} that is a Prolog file containing facts of the form
|
||||
\begin{verbatim}
|
||||
universe(VarList,ConstList).
|
||||
\end{verbatim}
|
||||
where \texttt{VarList} is a list of variables names (each must be included in single quotes) and \texttt{ConstList} is a list of constants. \texttt{semantics.pl} generates the grounding by instantiating in all possible ways the variables of \texttt{VarList} with the constants of \texttt{ConstList}. Note that the variables are identified by name, so a variable with the same name in two different clauses will be instantiated with the same constants.
|
||||
|
||||
You can test your installation of \texttt{cplint} by using the program \texttt{test.pl}:
|
||||
compile \texttt{cplint.pl}, compile \texttt{test.pl} and exectue the query \texttt{t}.
|
||||
A number of queries are executed against the example programs and the returned probabilities are checked: if \texttt{t.} succeeds, then \texttt{cplint} is working.
|
||||
|
||||
|
||||
\section{Extensions}
|
||||
In this section we will present the extensions to the syntax of LPADs and CP-logic programs that \texttt{cplint} can handle.
|
||||
|
||||
The first is the use of some standard Prolog predicates.
|
||||
The bodies can contain the built-in predicates:
|
||||
\begin{verbatim}
|
||||
is/2
|
||||
>/2
|
||||
</2
|
||||
>=/2
|
||||
=</2
|
||||
=:=/2
|
||||
=\=/2
|
||||
true/0
|
||||
false/0
|
||||
=/2
|
||||
==/2
|
||||
\=/2
|
||||
\==/2
|
||||
length/2
|
||||
\end{verbatim}
|
||||
The bodies can also contain the following
|
||||
library predicates:
|
||||
\begin{verbatim}
|
||||
member/2
|
||||
max_list/2
|
||||
min_list/2
|
||||
nth0/3
|
||||
nth/3
|
||||
\end{verbatim}
|
||||
plus the predicate
|
||||
\begin{verbatim}
|
||||
average/2
|
||||
\end{verbatim}
|
||||
that, given a list of numbers, computes its arithmetic mean.
|
||||
|
||||
Moreover, the bodies can contain the predicates \texttt{setof/3} and \texttt{bagof/3} with the same meaning as in Prolog. Existential quantifiers are allowed in both, so for example the query
|
||||
\begin{verbatim}
|
||||
setof(Z, (term(X,Y))^foo(X,Y,Z), L).
|
||||
\end{verbatim}
|
||||
returns all the instantiations of \texttt{Z} such that there exists an instantiation of \texttt{X} and \texttt{Y} for which \texttt{foo(X,Y,Z)} is true.
|
||||
|
||||
An example of the use of \texttt{setof} and \texttt{bagof} is in the file \texttt{female.cpl}:
|
||||
\begin{verbatim}
|
||||
male(C):M/P ; female(C):F/P:-
|
||||
person(C),
|
||||
setof(Male,known_male(Male),LM),
|
||||
length(LM,M),
|
||||
setof(Female,known_female(Female),LF),
|
||||
length(LF,F),
|
||||
P is F+M.
|
||||
|
||||
person(f).
|
||||
|
||||
known_female(a).
|
||||
|
||||
known_female(b).
|
||||
|
||||
known_female(c).
|
||||
|
||||
known_male(d).
|
||||
|
||||
known_male(e).
|
||||
\end{verbatim}
|
||||
The disjunctive rule expresses the probability of a person of unknown sex of being male or female depending on the number of males and females that are known.
|
||||
This is an example of the use of expressions in the probabilities in the head that depend on variables in the body. The probabilities are well defined because they always sum to 1 (unless \texttt{P} is 0).
|
||||
|
||||
Another use of \texttt{setof} and \texttt{bagof} is to have an attribute depend on an aggregate function of another attribute, similarly to what is done in PRM and CLP(BN).
|
||||
|
||||
So, in the classical school example (available in \texttt{student.cpl}) you can find the following
|
||||
clauses:
|
||||
\begin{verbatim}
|
||||
student_rank(S,h):0.6 ; student_rank(S,l):0.4:-
|
||||
bagof(G,R^(registr_stu(R,S),registr_gr(R,G)),L),
|
||||
average(L,Av),Av>1.5.
|
||||
|
||||
student_rank(S,h):0.4 ; student_rank(S,l):0.6:-
|
||||
bagof(G,R^(registr_stu(R,S),registr_gr(R,G)),L),
|
||||
average(L,Av),Av =< 1.5.
|
||||
\end{verbatim}
|
||||
where \verb|registr_stu(R,S)| expresses that registration \texttt{R} refers to student \texttt{S} and \verb|registr_gr(R,G)| expresses that registration \texttt{R} reports grade \texttt{G} which is a natural number. The two clauses express a dependency of the rank of the student from the average of her grades.
|
||||
|
||||
Another extension has been introduced in order to be able to represent reference uncertainty of PRMs. Reference uncertainty means that the link structure of a relational model is not fixed but is uncertain: this is represented by having the instance referenced in a relationship be chosen uniformly from a set. For example, consider a domain modeling scientific papers: you have a single entity, paper, and a relationship, cites, between paper and itself that connects the citing paper to the cited paper. To represent the fact that the cited paper and the citing paper are selected uniformly from certain sets, the following clauses can be used (see file \verb|paper_ref_simple.cpl|):
|
||||
\begin{verbatim}
|
||||
uniform(cites_cited(C,P),P,L):-
|
||||
bagof(Pap,paper_topic(Pap,theory),L).
|
||||
|
||||
uniform(cites_citing(C,P),P,L):-
|
||||
bagof(Pap,paper_topic(Pap,ai),L).
|
||||
\end{verbatim}
|
||||
The first clauses states that the paper \texttt{P} cited in a citation \texttt{C} is selected uniformly from the set of all papers with topic theory.
|
||||
The second clauses expresses that the citing paper is selected uniformly from the papers with
|
||||
topic ai.
|
||||
|
||||
These clauses make use of the predicate
|
||||
\begin{verbatim}
|
||||
uniform(Atom,Variable,List)
|
||||
\end{verbatim}
|
||||
in the head, where \texttt{Atom} must contain \texttt{Variable}. The meaning is the following: the set of all the atoms obtained by instantiating \texttt{Variable} of \texttt{Atom} with a term taken from \texttt{List} is generated and the head is obtained by having a disjunct for each instantiation with probability $1/N$ where $N$ is the length of \texttt{List}.
|
||||
|
||||
|
||||
A more elaborate example is present in file \verb|paper_ref.cpl|:
|
||||
\begin{verbatim}
|
||||
uniform(cites_citing(C,P),P,L):-
|
||||
setof(Pap,paper(Pap),L).
|
||||
|
||||
cites_cited_group(C,theory):0.9 ; cites_cited_group(C,ai):0.1:-
|
||||
cites_citing(C,P),paper_topic(P,theory).
|
||||
|
||||
cites_cited_group(C,theory):0.01;cites_cited_group(C,ai):0.99:-
|
||||
cites_citing(C,P),paper_topic(P,ai).
|
||||
|
||||
uniform(cites_cited(C,P),P,L):-
|
||||
cites_cited_group(C,T),bagof(Pap,paper_topic(Pap,T),L).
|
||||
\end{verbatim}
|
||||
where the cited paper depends on the topic of the citing paper. In particular, if the topic is theory, the cited paper is selected uniformly from the papers about theory with probability 0.9 and from the papers about ai with probability 0.1. if the topic is ai, the cited paper is selected uniformly from the papers about theory with probability 0.01 and from the papers about ai with probability 0.99.
|
||||
|
||||
PRMs take into account as well existence uncertainty, where the existence of instances is also probabilistic. For example, in the paper domain, the total number of citations may be unknown and a citation between any two paper may have a probability of existing. For example, a citation between two paper may be more probable if they are about the same topic:
|
||||
\begin{verbatim}
|
||||
cites(X,Y):0.005 :-
|
||||
paper_topic(X,theory),paper_topic(Y,theory).
|
||||
|
||||
cites(X,Y):0.001 :-
|
||||
paper_topic(X,theory),paper_topic(Y,ai).
|
||||
|
||||
cites(X,Y):0.003 :-
|
||||
paper_topic(X,ai),paper_topic(Y,theory).
|
||||
|
||||
cites(X,Y):0.008 :-
|
||||
paper_topic(X,ai),paper_topic(Y,ai).
|
||||
\end{verbatim}
|
||||
This is an example of a CP-logic program, because the probabilities in the head do not sum up to one.
|
||||
The first clause states that, if the topic of a paper \texttt{X} is theory and of paper \texttt{Y} is theory, there is a probability of 0.005 that there is a citation from \texttt{X} to \texttt{Y}. The other clauses consider the remaining cases for the topics.
|
||||
|
||||
\section{Parameters}
|
||||
The proof procedure has two parameters that can be set with the command
|
||||
\begin{verbatim}
|
||||
set(parameter,value).
|
||||
\end{verbatim}
|
||||
from the Yap prompt after having compiled \texttt{cplint.pl}.
|
||||
The current value can be read with
|
||||
\begin{verbatim}
|
||||
setting(parameter,Value).
|
||||
\end{verbatim}
|
||||
from the Yap prompt.
|
||||
Available parameters:
|
||||
\begin{itemize}
|
||||
\item
|
||||
\verb|epsilon_parsing|: if (1 - the sum of the probabilities of all the head atoms) is smaller than
|
||||
\verb|epsilon_parsing|
|
||||
then the clause is considered as an LPAD clause, otherwise it is considered as a CP-logic
|
||||
clause. Default value 0.00001
|
||||
\item \texttt{savedot}: if true a graph representing the BDD is saved in the file \texttt{cpl.dot} in the current directory in dot format.
|
||||
The variables names are of the form \verb|Xn_m| where \texttt{n} is the number of the multivalued
|
||||
variable and \texttt{m} is the number of the binary variable. The correspondence of variables to
|
||||
clauses can be evinced from the list printed on the screen of the form
|
||||
\begin{verbatim}
|
||||
Variables: [(2,[X=2,X1=1]),(2,[X=1,X1=0]),(1,[])]
|
||||
\end{verbatim}
|
||||
where the first element of each couple is the clause number of the input file (starting from 1).
|
||||
In the example above variable \texttt{X0} corresponds to clause \texttt{2} with the substitutions \texttt{X=2,X1=1},
|
||||
variable \texttt{X1} corresponds to clause \texttt{2} with the substitutions \texttt{X=1,X1=0} and
|
||||
variable \texttt{X2} corresponds to clause \texttt{1} with the empty substitution.
|
||||
You can view the graph with \texttt{graphviz} (\href{www.graphviz.org}{www.graphviz.org}) using the
|
||||
command
|
||||
\begin{verbatim}
|
||||
dotty cpl.dot &
|
||||
\end{verbatim}
|
||||
\end{itemize}
|
||||
|
||||
|
||||
\section{Content of the Directory}
|
||||
\begin{itemize}
|
||||
\item \texttt{cplint.pl}: Prolog code of the interpreter.
|
||||
\item \verb|cplint.so|: dynamic library that includes CUDD. It is called by \texttt{cplint.pl}.
|
||||
\item \texttt{semantics.pl}: Prolog program for computing the probability according to the semantics.
|
||||
\item \texttt{test.pl}: Prolog program for testing the system.
|
||||
\item Examples:
|
||||
\begin{itemize}
|
||||
\item \texttt{alarm.cpl}: representation of the Bayesian network in Figure 2 of
|
||||
\cite{VenVer04-ICLP04-IC}.
|
||||
\item \texttt{coin.cpl}: coin example from \cite{VenVer04-ICLP04-IC}.
|
||||
\item \texttt{coin2.cpl}: coin example with two coins.
|
||||
\item \texttt{dice.cpl}: dice example from \cite{VenVer04-ICLP04-IC}.
|
||||
\item \verb|twosideddice.cpl,threesideddice.cpl| game with idealized dice with two or three sides. Used in the experiments in \cite{Rig-RCRA07-IC}.
|
||||
\item \texttt{es.cpl}: first example in \cite{Rig-RCRA07-IC}.
|
||||
\item \texttt{esapprox.cpl}: example showing the problems of approximate inference (see \cite{Rig-RCRA07-IC}).
|
||||
\item \texttt{esrange.cpl}: example showing the problems with non range restricted programs (see \cite{Rig-RCRA07-IC}).
|
||||
\item \texttt{female.cpl}: example showing the dependence of probabilities in the head from variables in the body (from \cite{VenVer04-ICLP04-IC}).
|
||||
\item \texttt{mendel.cpl}: program describing the Mendelian rules of inheritance, taken from \cite{Blo04-ILP04WIP-IC}.
|
||||
\item \verb|paper_ref.cpl,paper_ref_simple.cpl|: paper citations examples, showing reference uncertainty, inspired by \cite{Getoor+al:JMLR02}.
|
||||
\item \verb|paper_ref_not.cpl|: paper citations example showing that negation can be used also for predicates defined by clauses with \texttt{uniform} in the head.
|
||||
\item \texttt{school.cpl}: example inspired by the example \verb|school_32.yap| from the
|
||||
source distribution of Yap in the \texttt{CLPBN} directory.
|
||||
\item \verb|school_simple.cpl|: simplified version of \texttt{school.cpl}.
|
||||
\item \verb|student.cpl|: student example from Figure 1.3 of \cite{GetFri01-BC}.
|
||||
\end{itemize}
|
||||
The files \texttt{*.uni} that are present for some of the examples are used by \texttt{semantics.pl}. Some of the example files contain in an initial comment some queries together with their result.
|
||||
\item \verb|doc|: directory that contains this manual in pdf and html
|
||||
\end{itemize}
|
||||
\section{License}
|
||||
\label{license}
|
||||
|
||||
Copyright (c) 2007, Fabrizio Riguzzi
|
||||
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
|
||||
\begin{itemize}
|
||||
\item Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
|
||||
\item Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
|
||||
\item Neither the name of the University of Ferrara nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
|
||||
\end{itemize}
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
||||
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
||||
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
\vspace{3mm}
|
||||
|
||||
The program uses the library \href{http://vlsi.colorado.edu/\%7Efabio/}{CUDD} for manipulating BDDs.
|
||||
For the use of CUDD, the following license must be accepted:
|
||||
|
||||
\vspace{3mm}
|
||||
|
||||
Copyright (c) 1995-2004, Regents of the University of Colorado
|
||||
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions
|
||||
are met:
|
||||
|
||||
\begin{itemize}
|
||||
\item
|
||||
Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
\item
|
||||
Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
\item
|
||||
Neither the name of the University of Colorado nor the names of its
|
||||
contributors may be used to endorse or promote products derived from
|
||||
this software without specific prior written permission.
|
||||
\end{itemize}
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
||||
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
||||
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
||||
FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
||||
COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
||||
INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||||
ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
|
||||
\bibliographystyle{plain}
|
||||
\bibliography{bib}
|
||||
|
||||
\end{document}
|
||||
7
cplint/examples/alarm.cpl
Normal file
7
cplint/examples/alarm.cpl
Normal file
@ -0,0 +1,7 @@
|
||||
|
||||
burg(t):0.1; burg(f):0.9.
|
||||
earthq(t):0.2; earthq(f):0.8.
|
||||
alarm(t):-burg(t),earthq(t).
|
||||
alarm(t):0.8 ; alarm(f):0.2:-burg(t),earthq(f).
|
||||
alarm(t):0.8 ; alarm(f):0.2:-burg(f),earthq(t).
|
||||
alarm(t):0.1 ; alarm(f):0.9:-burg(f),earthq(f).
|
||||
17
cplint/examples/coin.cpl
Normal file
17
cplint/examples/coin.cpl
Normal file
@ -0,0 +1,17 @@
|
||||
/*
|
||||
s([heads(C)],P).
|
||||
C = coin
|
||||
P = 0.51 ?;
|
||||
no
|
||||
s([tails(C)],P).
|
||||
C = coin
|
||||
P = 0.49 ?;
|
||||
no
|
||||
|
||||
|
||||
*/
|
||||
|
||||
heads(Coin): 1/2; tails(Coin) : 1/2:-toss(Coin),\+biased(Coin).
|
||||
heads(Coin): 0.6 ; tails(Coin) : 0.4:-toss(Coin),biased(Coin).
|
||||
fair(Coin):0.9 ; biased(Coin):0.1.
|
||||
toss(coin).
|
||||
0
cplint/examples/coin.uni
Normal file
0
cplint/examples/coin.uni
Normal file
23
cplint/examples/coin2.cpl
Normal file
23
cplint/examples/coin2.cpl
Normal file
@ -0,0 +1,23 @@
|
||||
/*
|
||||
?- s([heads(C)],P).
|
||||
C = coin1
|
||||
P = 0.51 ? ;
|
||||
C = coin2
|
||||
P = 0.51 ? ;
|
||||
no
|
||||
?- s([tails(C)],P).
|
||||
C = coin1
|
||||
P = 0.49 ? ;
|
||||
C = coin2
|
||||
P = 0.49 ? ;
|
||||
no
|
||||
|
||||
s([tails(coin1)],P).
|
||||
P = 0.49 ?
|
||||
*/
|
||||
|
||||
heads(Coin): 0.5; tails(Coin) : 0.5:-toss(Coin),fair(Coin).
|
||||
heads(Coin): 0.6 ; tails(Coin) : 0.4:-toss(Coin),biased(Coin).
|
||||
fair(Coin):0.9 ; biased(Coin):0.1.
|
||||
toss(coin1).
|
||||
toss(coin2).
|
||||
1
cplint/examples/coin2.uni
Normal file
1
cplint/examples/coin2.uni
Normal file
@ -0,0 +1 @@
|
||||
universe(['Coin'],[coin1,coin2]).
|
||||
29
cplint/examples/dice.cpl
Normal file
29
cplint/examples/dice.cpl
Normal file
@ -0,0 +1,29 @@
|
||||
/*
|
||||
s([on(0,1)],P).
|
||||
P = 0.166666666666667 ?;
|
||||
s([\+ on(0,6)],P).
|
||||
P = 0.833333333333333 ?;
|
||||
|
||||
|
||||
s([on(1,1)],P).
|
||||
P = 0.138888888888889 ?
|
||||
s([on(1,6)],P).
|
||||
P = 0.138888888888889 ?
|
||||
s([on(2,1)],P).
|
||||
out of memory
|
||||
s([on(0,1),on(1,1)],P).
|
||||
P = 0.0277777777777778 ?
|
||||
s([on(0,1),on(1,1),on(2,1)],P).
|
||||
P = 0.00462962962962963 ?
|
||||
|
||||
|
||||
|
||||
|
||||
*/
|
||||
on(0,1):1/6;on(0,2):1/6;on(0,3):1/6;
|
||||
on(0,4):1/6;on(0,5):1/6;on(0,6):1/6.
|
||||
|
||||
on(X,1):1/6;on(X,2):1/6;on(X,3):1/6;
|
||||
on(X,4):1/6;on(X,5):1/6;on(X,6):1/6:-
|
||||
X1 is X-1,X1>=0,on(X1,_),
|
||||
\+ on(X1,6).
|
||||
2
cplint/examples/dice.uni
Normal file
2
cplint/examples/dice.uni
Normal file
@ -0,0 +1,2 @@
|
||||
universe(['X'],[1,2]).
|
||||
universe(['X1'],[0,1]).
|
||||
13
cplint/examples/es.cpl
Normal file
13
cplint/examples/es.cpl
Normal file
@ -0,0 +1,13 @@
|
||||
/*
|
||||
s([a],P).
|
||||
Variables: [(1,[]),(2,[]),(3,[])]
|
||||
P = 0.226 ?
|
||||
*/
|
||||
|
||||
a:0.1.
|
||||
|
||||
|
||||
|
||||
b:0.3;c:0.6.
|
||||
|
||||
a:0.2:- \+ b.
|
||||
12
cplint/examples/esapprox.cpl
Normal file
12
cplint/examples/esapprox.cpl
Normal file
@ -0,0 +1,12 @@
|
||||
/*
|
||||
s([a],P).
|
||||
Variables: [(1,[]),(2,[]),(3,[])]
|
||||
P = 0.1719 ? ;
|
||||
|
||||
*/
|
||||
|
||||
a:0.1:-p(X).
|
||||
|
||||
p(1):0.9.
|
||||
|
||||
p(2):0.9.
|
||||
21
cplint/examples/esrange.cpl
Normal file
21
cplint/examples/esrange.cpl
Normal file
@ -0,0 +1,21 @@
|
||||
/*
|
||||
s([a(X)],P).
|
||||
Variables: [(1,[]),(5,[]),(2,[]),(6,[])]
|
||||
P = 0.2775
|
||||
X = 1 ? ;
|
||||
Variables: [(3,[]),(5,[]),(4,[]),(6,[])]
|
||||
P = 0.36
|
||||
X = 2 ? ;
|
||||
*/
|
||||
|
||||
a(1):0.3:-p(1).
|
||||
|
||||
a(1):0.3:-p(2).
|
||||
|
||||
a(2):0.4:-p(1).
|
||||
|
||||
a(2):0.4:-p(2).
|
||||
|
||||
p(1):0.5.
|
||||
|
||||
p(2):0.5.
|
||||
26
cplint/examples/female.cpl
Normal file
26
cplint/examples/female.cpl
Normal file
@ -0,0 +1,26 @@
|
||||
/*
|
||||
s([female(f)],P).
|
||||
P = 0.6 ?
|
||||
s([male(f)],P).
|
||||
P = 0.4 ?
|
||||
|
||||
*/
|
||||
|
||||
|
||||
male(C):M/P;female(C):F/P:-
|
||||
person(C),
|
||||
setof(Male,known_male(Male),LM),
|
||||
length(LM,M),
|
||||
setof(Female,known_female(Female),LF),
|
||||
length(LF,F),
|
||||
P is F+M.
|
||||
|
||||
|
||||
person(f).
|
||||
|
||||
known_female(a).
|
||||
known_female(b).
|
||||
known_female(c).
|
||||
|
||||
known_male(d).
|
||||
known_male(e).
|
||||
38
cplint/examples/mendel.cpl
Normal file
38
cplint/examples/mendel.cpl
Normal file
@ -0,0 +1,38 @@
|
||||
/*
|
||||
this file can not be used with s.pl: it generates too many programs
|
||||
s([cg(s,1,p)],P).
|
||||
P = 0.75 ?
|
||||
s([cg(s,1,w)],P).
|
||||
P = 0.25 ?
|
||||
s([cg(s,2,p)],P).
|
||||
P = 0.25 ?
|
||||
s([cg(s,2,w)],P).
|
||||
P = 0.75 ?
|
||||
s([cg(f,2,w)],P).
|
||||
P = 0.5
|
||||
s([cg(s,2,w)],P).
|
||||
P = 0.75
|
||||
|
||||
*/
|
||||
|
||||
mother(m,s).
|
||||
mother(mm,m).
|
||||
mother(mf,f).
|
||||
father(f,s).
|
||||
father(fm,m).
|
||||
father(ff,f).
|
||||
|
||||
|
||||
cg(mm,1,p).
|
||||
cg(mm,2,w).
|
||||
cg(fm,1,p).
|
||||
cg(fm,2,p).
|
||||
cg(mf,1,w).
|
||||
cg(mf,2,w).
|
||||
cg(ff,1,w).
|
||||
cg(ff,2,p).
|
||||
|
||||
|
||||
cg(X,1,A):0.5 ; cg(X,1,B):0.5 :- mother(Y,X),cg(Y,1,A) , cg(Y,2,B).
|
||||
|
||||
cg(X,2,A):0.5 ; cg(X,2,B):0.5 :- father(Y,X),cg(Y,1,A) , cg(Y,2,B).
|
||||
51
cplint/examples/paper_ref.cpl
Normal file
51
cplint/examples/paper_ref.cpl
Normal file
@ -0,0 +1,51 @@
|
||||
/*
|
||||
|
||||
?- s([cites_cited(c1,Pap)],P).
|
||||
P = 0.181333333333333,
|
||||
Pap = p1 ? ;
|
||||
P = 0.181333333333333,
|
||||
Pap = p2 ? ;
|
||||
P = 0.228,
|
||||
Pap = p3 ? ;
|
||||
P = 0.181333333333333,
|
||||
Pap = p4 ? ;
|
||||
P = 0.228,
|
||||
Pap = p5 ? ;
|
||||
no
|
||||
|
||||
*/
|
||||
paper(p1).
|
||||
|
||||
paper(p2).
|
||||
|
||||
paper(p3).
|
||||
|
||||
paper(p4).
|
||||
|
||||
paper(p5).
|
||||
|
||||
paper_topic(p1,theory).
|
||||
|
||||
paper_topic(p2,theory).
|
||||
|
||||
paper_topic(p4,theory).
|
||||
|
||||
paper_topic(p3,ai).
|
||||
|
||||
paper_topic(p5,ai).
|
||||
|
||||
cites(c1).
|
||||
|
||||
|
||||
uniform(cites_citing(C,P),P,L):-
|
||||
setof(Pap,paper(Pap),L).
|
||||
|
||||
cites_cited_group(C,theory):0.9;cites_cited_group(C,ai):0.1:-
|
||||
cites_citing(C,P),paper_topic(P,theory).
|
||||
|
||||
cites_cited_group(C,theory):0.01;cites_cited_group(C,ai):0.99:-
|
||||
cites_citing(C,P),paper_topic(P,ai).
|
||||
|
||||
uniform(cites_cited(C,P),P,L):-
|
||||
cites_cited_group(C,T),bagof(Pap,paper_topic(Pap,T),L).
|
||||
|
||||
41
cplint/examples/paper_ref_not.cpl
Normal file
41
cplint/examples/paper_ref_not.cpl
Normal file
@ -0,0 +1,41 @@
|
||||
/*
|
||||
s([\+ cites_cited(c1,p1)],P).
|
||||
P = 0.7 ?
|
||||
|
||||
s([cites_citing(c1,p1)],P).
|
||||
P = 0.14 ?
|
||||
|
||||
*/
|
||||
paper(p1).
|
||||
|
||||
paper(p2).
|
||||
|
||||
paper(p3).
|
||||
|
||||
paper(p4).
|
||||
|
||||
paper(p5).
|
||||
|
||||
paper_topic(p1,theory).
|
||||
|
||||
paper_topic(p2,theory).
|
||||
|
||||
paper_topic(p4,theory).
|
||||
|
||||
paper_topic(p3,ai).
|
||||
|
||||
paper_topic(p5,ai).
|
||||
|
||||
cites(c1).
|
||||
|
||||
|
||||
cites_cited_group(C,theory):0.9;cites_cited_group(C,ai):0.1.
|
||||
|
||||
uniform(cites_cited(C,P),P,L):-
|
||||
cites_cited_group(C,T),bagof(Pap,paper_topic(Pap,T),L).
|
||||
|
||||
|
||||
uniform(cites_citing(C,P),P,L):-
|
||||
setof(Paper,paper(Paper),L),
|
||||
cites_cited(C,Pap),
|
||||
\+ cites_cited(C,p1).
|
||||
45
cplint/examples/paper_ref_simple.cpl
Normal file
45
cplint/examples/paper_ref_simple.cpl
Normal file
@ -0,0 +1,45 @@
|
||||
/*
|
||||
?- s([cites_cited(c1,Pap)],P).
|
||||
P = 0.333333333333333,
|
||||
Pap = p1 ? ;
|
||||
P = 0.333333333333333,
|
||||
Pap = p2 ? ;
|
||||
P = 0.333333333333333,
|
||||
Pap = p4 ? ;
|
||||
no
|
||||
?- Action (h for help): c
|
||||
| s([cites_citing(c1,Pap)],P).
|
||||
P = 0.5,
|
||||
Pap = p3 ? ;
|
||||
P = 0.5,
|
||||
Pap = p5 ? ;
|
||||
no
|
||||
|
||||
*/
|
||||
paper(p1).
|
||||
|
||||
paper(p2).
|
||||
|
||||
paper(p3).
|
||||
|
||||
paper(p4).
|
||||
|
||||
paper(p5).
|
||||
|
||||
paper_topic(p1,theory).
|
||||
|
||||
paper_topic(p2,theory).
|
||||
|
||||
paper_topic(p4,theory).
|
||||
|
||||
paper_topic(p3,ai).
|
||||
|
||||
paper_topic(p5,ai).
|
||||
|
||||
cites(c1).
|
||||
|
||||
uniform(cites_cited(C,P),P,L):-
|
||||
bagof(Pap,paper_topic(Pap,theory),L).
|
||||
|
||||
uniform(cites_citing(C,P),P,L):-
|
||||
bagof(Pap,paper_topic(Pap,ai),L).
|
||||
796
cplint/examples/school.cpl
Normal file
796
cplint/examples/school.cpl
Normal file
@ -0,0 +1,796 @@
|
||||
/*
|
||||
Yap run on school_32.yap
|
||||
?- s([professor_ability(p0,X)],P).
|
||||
P = 0.5,
|
||||
X = h ? ;
|
||||
P = 0.1,
|
||||
X = l ? ;
|
||||
P = 0.4,
|
||||
X = m ? ;
|
||||
no
|
||||
?- s([professor_popularity(p0,X)],P).
|
||||
P = 0.531,
|
||||
X = h ? ;
|
||||
P = 0.175,
|
||||
X = l ? ;
|
||||
P = 0.294,
|
||||
X = m ? ;
|
||||
no
|
||||
ok
|
||||
|
||||
sc([professor_ability(p0,X)],[professor_popularity(p0,h)],P).
|
||||
P = 0.847457627118644,
|
||||
X = h ? ;
|
||||
P = 0.00188323917137476,
|
||||
X = l ? ;
|
||||
P = 0.150659133709981,
|
||||
X = m ?
|
||||
ok
|
||||
|
||||
sc([professor_popularity(p0,X)],[professor_ability(p0,h)],P).
|
||||
P = 0.9,
|
||||
X = h ? ;
|
||||
P = 0.01,
|
||||
X = l ? ;
|
||||
P = 0.09,
|
||||
X = m ? ;
|
||||
no
|
||||
ok
|
||||
?- s([registration_grade(r0,X)],P).
|
||||
P = 0.06675,
|
||||
X = 1 ? ;
|
||||
P = 0.16575,
|
||||
X = 2 ? ;
|
||||
Action (";" for more choices, <return> for exit) ? ;
|
||||
P = 0.356,
|
||||
X = 3 ? ;
|
||||
P = 0.4115,
|
||||
X = 4 ?
|
||||
no
|
||||
ok
|
||||
|
||||
sc([registration_grade(r0,X)],[registration_course(r0,C), course_difficulty(C,h)],P).
|
||||
C = c16,
|
||||
P = 0.15,
|
||||
X = 1 ? ;
|
||||
C = c16,
|
||||
P = 0.285,
|
||||
X = 2 ? ;
|
||||
C = c16,
|
||||
P = 0.424,
|
||||
X = 3 ? ;
|
||||
C = c16,
|
||||
P = 0.141,
|
||||
X = 4 ? ;
|
||||
no
|
||||
ok (a=4, ...,d=1)
|
||||
|
||||
sc([registration_grade(r0,X)], [registration_course(r0,C), course_difficulty(C,h), registration_student(r0,S), student_intelligence(S,h)],P).
|
||||
C = c16,
|
||||
P = 0.05,
|
||||
S = s0,
|
||||
X = 1 ? ;
|
||||
C = c16,
|
||||
P = 0.15,
|
||||
S = s0,
|
||||
X = 2 ? ;
|
||||
C = c16,
|
||||
P = 0.6,
|
||||
S = s0,
|
||||
X = 3 ? ;
|
||||
C = c16,
|
||||
P = 0.2,
|
||||
S = s0,
|
||||
X = 4 ? ;
|
||||
no
|
||||
ok
|
||||
|
||||
sc([registration_grade(r0,X)],[registration_course(r0,C), course_difficulty(C,l), registration_student(r0,S), student_intelligence(S,h)],P).
|
||||
C = c16,
|
||||
P = 0.01,
|
||||
S = s0,
|
||||
X = 1 ? ;
|
||||
C = c16,
|
||||
P = 0.02,
|
||||
S = s0,
|
||||
X = 2 ? ;
|
||||
C = c16,
|
||||
P = 0.12,
|
||||
S = s0,
|
||||
X = 3 ? ;
|
||||
C = c16,
|
||||
P = 0.85,
|
||||
S = s0,
|
||||
X = 4 ? ;
|
||||
no
|
||||
ok
|
||||
|
||||
s([registration_satisfaction(r0,X)],P).
|
||||
P = 0.15197525,
|
||||
X = 1 ? ;
|
||||
P = 0.15331025,
|
||||
X = 2 ? ;
|
||||
P = 0.6947145,
|
||||
X = 3 ? ;
|
||||
no
|
||||
ok
|
||||
sc([registration_satisfaction(r0,X)],[ registration_student(r0,S), student_intelligence(S,h)],P).
|
||||
P = 0.0959225,
|
||||
S = s0,
|
||||
X = 1 ? ;
|
||||
P = 0.124515,
|
||||
S = s0,
|
||||
X = 2 ? ;
|
||||
P = 0.7795625,
|
||||
S = s0,
|
||||
X = 3 ? ;
|
||||
no
|
||||
ok (h=3, l=1)
|
||||
|
||||
sc([registration_satisfaction(r0,X)],[registration_grade(r0,4)],P).
|
||||
P = 0.04,
|
||||
X = 1 ? ;
|
||||
P = 0.06,
|
||||
X = 2 ? ;
|
||||
P = 0.9,
|
||||
X = 3 ? ;
|
||||
in Yap:
|
||||
?- registration_satisfaction(r0,X), registration_grade(r0,a).
|
||||
% YAP: no handler for error error(domain_error(bayesian_domain),bind_clpbns(var(grade(r0),[a,b,c,d],[0.2,0.7,0.85,0.1,0.2,0.5,0.01,0.05,0.1,0.6,0.25,0.12,0.3,0.6,0.35,0.04,0.15,0.4,0.15,0.04,0.02,0.4,0.15,0.12,0.5,0.6,0.4,0.05,0.01,0.01,0.2,0.05,0.03,0.45,0.2,0.1],[_D11,_D12]),var(grade(r0),[a,b,c,d],[0.2,0.7,0.85,0.1,0.2,0.5,0.01,0.05,0.1,0.6,0.25,0.12,0.3,0.6,0.35,0.04,0.15,0.4,0.15,0.04,0.02,0.4,0.15,0.12,0.5,0.6,0.4,0.05,0.01,0.01,0.2,0.05,0.03,0.45,0.2,0.1],[_D13,_D12])))
|
||||
|
||||
sc([registration_satisfaction(r0,X)],[registration_grade(r0,1)],P).
|
||||
P = 0.528,
|
||||
X = 1 ? ;
|
||||
P = 0.167,
|
||||
X = 2 ? ;
|
||||
P = 0.305,
|
||||
X = 3 ? ;
|
||||
no
|
||||
?- sc([ registration_grade(r0,X)],[registration_satisfaction(r0,3)],P).
|
||||
P = 0.0293052037923492,
|
||||
X = 1 ? ;
|
||||
P = 0.114760451955444,
|
||||
X = 2 ? ;
|
||||
P = 0.322837654892765,
|
||||
X = 3 ? ;
|
||||
P = 0.533096689359442,
|
||||
X = 4 ? ;
|
||||
no
|
||||
?- registration_satisfaction(r0,X), registration_grade(r0,d).
|
||||
% YAP: no handler for error error(domain_error(bayesian_domain),bind_clpbns(var(grade(r0),[a,b,c,d],[0.2,0.7,0.85,0.1,0.2,0.5,0.01,0.05,0.1,0.6,0.25,0.12,0.3,0.6,0.35,0.04,0.15,0.4,0.15,0.04,0.02,0.4,0.15,0.12,0.5,0.6,0.4,0.05,0.01,0.01,0.2,0.05,0.03,0.45,0.2,0.1],[_D11,_D12]),var(grade(r0),[a,b,c,d],[0.2,0.7,0.85,0.1,0.2,0.5,0.01,0.05,0.1,0.6,0.25,0.12,0.3,0.6,0.35,0.04,0.15,0.4,0.15,0.04,0.02,0.4,0.15,0.12,0.5,0.6,0.4,0.05,0.01,0.01,0.2,0.05,0.03,0.45,0.2,0.1],[_D13,_D12])))
|
||||
|
||||
sc([ registration_grade(r0,X)],[registration_satisfaction(r0,3)],P).
|
||||
?- registration_satisfaction(r0,h), registration_grade(r0,X).
|
||||
% YAP: no handler for error error(domain_error(bayesian_domain),bind_clpbns(var(grade(r0),[a,b,c,d],[0.2,0.7,0.85,0.1,0.2,0.5,0.01,0.05,0.1,0.6,0.25,0.12,0.3,0.6,0.35,0.04,0.15,0.4,0.15,0.04,0.02,0.4,0.15,0.12,0.5,0.6,0.4,0.05,0.01,0.01,0.2,0.05,0.03,0.45,0.2,0.1],[_D11,_D12]),var(grade(r0),[a,b,c,d],[0.2,0.7,0.85,0.1,0.2,0.5,0.01,0.05,0.1,0.6,0.25,0.12,0.3,0.6,0.35,0.04,0.15,0.4,0.15,0.04,0.02,0.4,0.15,0.12,0.5,0.6,0.4,0.05,0.01,0.01,0.2,0.05,0.03,0.45,0.2,0.1],[_D13,_D12])))
|
||||
|
||||
s([course_rating(c0,X)],P).
|
||||
P = 0.5392099,
|
||||
X = h ? ;
|
||||
P = 0.2,
|
||||
X = l ? ;
|
||||
P = 0.2607901,
|
||||
X = m ? ;
|
||||
no
|
||||
yap does not end, starts thrashing
|
||||
sc([course_difficulty(c0,X)],[course_rating(c0,h)],P).
|
||||
P = 0.235185778302661,
|
||||
X = h ? ;
|
||||
P = 0.259096503977393,
|
||||
X = l ? ;
|
||||
P = 0.505717717719945,
|
||||
X = m ? ;
|
||||
no
|
||||
yap does not end, starts thrashing
|
||||
s([course_difficulty(c0,X)],P).
|
||||
P = 0.25,
|
||||
X = h ? ;
|
||||
P = 0.25,
|
||||
X = l ? ;
|
||||
P = 0.5,
|
||||
X = m ? ;
|
||||
no
|
||||
ok
|
||||
student_ranking(s0,X).
|
||||
| ?- s([student_ranking(s0,X)],P).
|
||||
P = 0.33105221125,
|
||||
X = h ? ;
|
||||
P = 0.204865630625,
|
||||
X = l ? ;
|
||||
P = 0.46408215812500003,
|
||||
X = m ?
|
||||
not checked with s
|
||||
yap:
|
||||
true ?
|
||||
yes
|
||||
|
||||
|
||||
sc([student_ranking(s0,X)],[student_intelligence(s0,h)],P).
|
||||
P = 0.42922046875000003,
|
||||
X = h ? ;
|
||||
P = 0.20003319999999997,
|
||||
X = l ? ;
|
||||
P = 0.3707463312499999,
|
||||
X = m ? ;
|
||||
no
|
||||
not checked with s
|
||||
yap:
|
||||
true
|
||||
*/
|
||||
|
||||
|
||||
|
||||
|
||||
professor_ability(Key,h):0.50;professor_ability(Key,m): 0.40;professor_ability(Key,l): 0.10.
|
||||
|
||||
professor_popularity(Key, h):0.9; professor_popularity(Key, m):0.09; professor_popularity(Key, l):0.01 :-
|
||||
professor_ability(Key, h).
|
||||
|
||||
professor_popularity(Key, h):0.2; professor_popularity(Key, m):0.6; professor_popularity(Key, l):0.2:-
|
||||
professor_ability(Key, m).
|
||||
|
||||
professor_popularity(Key, h):0.01; professor_popularity(Key, m):0.09; professor_popularity(Key, l):0.9 :-
|
||||
professor_ability(Key,l).
|
||||
|
||||
|
||||
|
||||
registration_course(Key, CKey) :-
|
||||
registration(Key, CKey, _).
|
||||
|
||||
registration_student(Key, SKey) :-
|
||||
registration(Key, _, SKey).
|
||||
|
||||
registration_grade(Key, 4): 0.2;registration_grade(Key, 3): 0.6;registration_grade(Key, 2): 0.15 ;
|
||||
registration_grade(Key, 1): 0.05 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, h) ,
|
||||
course_difficulty(CKey, h).
|
||||
|
||||
registration_grade(Key, 4): 0.7;registration_grade(Key, 3): 0.25;registration_grade(Key, 2): 0.04 ;
|
||||
registration_grade(Key, 1): 0.01 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, h) ,
|
||||
course_difficulty(CKey, m).
|
||||
|
||||
|
||||
registration_grade(Key, 4): 0.85;registration_grade(Key, 3): 0.12;registration_grade(Key, 2): 0.02 ;
|
||||
registration_grade(Key, 1): 0.01 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, h) ,
|
||||
course_difficulty(CKey, l).
|
||||
|
||||
|
||||
registration_grade(Key, 4): 0.1;registration_grade(Key, 3): 0.3;registration_grade(Key, 2): 0.4 ;
|
||||
registration_grade(Key, 1): 0.2 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, m) ,
|
||||
course_difficulty(CKey, h).
|
||||
|
||||
|
||||
registration_grade(Key, 4): 0.2;registration_grade(Key, 3): 0.6;registration_grade(Key, 2): 0.15 ;
|
||||
registration_grade(Key, 1): 0.05 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, m) ,
|
||||
course_difficulty(CKey, m).
|
||||
|
||||
registration_grade(Key, 4): 0.5;registration_grade(Key, 3): 0.35;registration_grade(Key, 2): 0.12;
|
||||
registration_grade(Key, 1): 0.03 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, m) ,
|
||||
course_difficulty(CKey, l).
|
||||
|
||||
registration_grade(Key, 4): 0.01;registration_grade(Key, 3): 0.04;registration_grade(Key, 2): 0.5 ;
|
||||
registration_grade(Key, 1): 0.45 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, l) ,
|
||||
course_difficulty(CKey, h).
|
||||
|
||||
registration_grade(Key, 4): 0.05;registration_grade(Key, 3): 0.15;registration_grade(Key, 2): 0.6 ;
|
||||
registration_grade(Key, 1): 0.2 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, l) ,
|
||||
course_difficulty(CKey, m).
|
||||
|
||||
registration_grade(Key, 4): 0.1;registration_grade(Key, 3): 0.4;registration_grade(Key, 2): 0.4 ;
|
||||
registration_grade(Key, 1): 0.1 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
student_intelligence(SKey, l) ,
|
||||
course_difficulty(CKey, l).
|
||||
|
||||
|
||||
registration_satisfaction(Key, 3): 0.98 ; registration_satisfaction(Key, 2): 0.01 ;
|
||||
registration_satisfaction(Key, 1): 0.01 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, h),
|
||||
registration_grade(Key, 4).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.9 ; registration_satisfaction(Key, 2): 0.09 ;
|
||||
registration_satisfaction(Key, 1): 0.01 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, h),
|
||||
registration_grade(Key, 3).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.8 ; registration_satisfaction(Key, 2): 0.15 ;
|
||||
registration_satisfaction(Key, 1): 0.05 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, h),
|
||||
registration_grade(Key, 2).
|
||||
|
||||
|
||||
|
||||
registration_satisfaction(Key, 3): 0.6 ; registration_satisfaction(Key, 2): 0.3 ;
|
||||
registration_satisfaction(Key, 1): 0.1 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, h),
|
||||
registration_grade(Key, 1).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.9 ; registration_satisfaction(Key, 2): 0.05 ;
|
||||
registration_satisfaction(Key, 1): 0.05 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, m),
|
||||
registration_grade(Key, 4).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.4 ; registration_satisfaction(Key, 2): 0.4 ;
|
||||
registration_satisfaction(Key, 1): 0.2 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, m),
|
||||
registration_grade(Key, 3).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.2 ; registration_satisfaction(Key, 2): 0.3 ;
|
||||
registration_satisfaction(Key, 1): 0.5 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, m),
|
||||
registration_grade(Key, 2).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.01 ; registration_satisfaction(Key, 2): 0.04 ;
|
||||
registration_satisfaction(Key, 1): 0.95 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, m),
|
||||
registration_grade(Key, 1).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.5 ; registration_satisfaction(Key, 2): 0.35 ;
|
||||
registration_satisfaction(Key, 1): 0.15 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, l),
|
||||
registration_grade(Key, 4).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.2 ; registration_satisfaction(Key, 2): 0.3 ;
|
||||
registration_satisfaction(Key, 1): 0.5 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, l),
|
||||
registration_grade(Key, 3).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.01 ; registration_satisfaction(Key, 2): 0.09 ;
|
||||
registration_satisfaction(Key, 1): 0.9 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, l),
|
||||
registration_grade(Key, 2).
|
||||
|
||||
registration_satisfaction(Key, 3): 0.01 ; registration_satisfaction(Key, 2): 0.01 ;
|
||||
registration_satisfaction(Key, 1): 0.98 :-
|
||||
registration(Key, CKey,_),
|
||||
course(CKey, PKey),
|
||||
professor_ability(PKey, l),
|
||||
registration_grade(Key, 1).
|
||||
|
||||
course_rating(CKey, h):0.2; course_rating(CKey, m):0.2;course_rating(CKey, l):0.6:-
|
||||
bagof(Sat, RKey^(registration_course(RKey,CKey), registration_satisfaction(RKey,Sat)), Sats),
|
||||
average(Sats, Av),Av<1.
|
||||
|
||||
course_rating(CKey, h):0.2; course_rating(CKey, m):0.6;course_rating(CKey, l):0.2:-
|
||||
bagof(Sat, RKey^(registration_course(RKey,CKey), registration_satisfaction(RKey,Sat)), Sats),
|
||||
average(Sats, Av),Av<2,Av>=1.
|
||||
|
||||
course_rating(CKey, h):0.6; course_rating(CKey,m):0.2;course_rating(CKey, l):0.2:-
|
||||
bagof(Sat, RKey^(registration_course(RKey,CKey), registration_satisfaction(RKey,Sat)), Sats),
|
||||
average(Sats, Av),Av>=2.
|
||||
|
||||
course_difficulty(Key, h) :0.25 ; course_difficulty(Key, m): 0.5 ; course_difficulty(Key, l) : 0.25.
|
||||
|
||||
student_intelligence(Key, h): 0.5; student_intelligence(Key,m):0.4; student_intelligence(Key,l):0.1.
|
||||
|
||||
student_ranking(Key, h):0.2; student_ranking(Key, m):0.2;student_ranking(Key, l):0.6:-
|
||||
bagof(Grade, CKey^(registration_student(CKey,Key),
|
||||
registration_grade(CKey, Grade)), Grades),
|
||||
average(Grades, Av),Av<1.5.
|
||||
|
||||
student_ranking(Key, h):0.2; student_ranking(Key, m):0.6;student_ranking(Key, l):0.2:-
|
||||
bagof(Grade, CKey^(registration_student(CKey,Key),
|
||||
registration_grade(CKey, Grade)), Grades),
|
||||
average(Grades, Av),Av>=1.5,Av<3.5.
|
||||
|
||||
student_ranking(Key, h):0.6; student_ranking(Key, m):0.2;student_ranking(Key, l):0.2:-
|
||||
bagof(Grade, CKey^(registration_student(CKey,Key),
|
||||
registration_grade(CKey, Grade)), Grades),
|
||||
average(Grades, Av),Av>=3.5.
|
||||
|
||||
|
||||
professor(p0).
|
||||
professor(p1).
|
||||
professor(p2).
|
||||
professor(p3).
|
||||
professor(p4).
|
||||
professor(p5).
|
||||
professor(p6).
|
||||
professor(p7).
|
||||
professor(p8).
|
||||
professor(p9).
|
||||
professor(p10).
|
||||
professor(p11).
|
||||
professor(p12).
|
||||
professor(p13).
|
||||
professor(p14).
|
||||
professor(p15).
|
||||
professor(p16).
|
||||
professor(p17).
|
||||
professor(p18).
|
||||
professor(p19).
|
||||
professor(p20).
|
||||
professor(p21).
|
||||
professor(p22).
|
||||
professor(p23).
|
||||
professor(p24).
|
||||
professor(p25).
|
||||
professor(p26).
|
||||
professor(p27).
|
||||
professor(p28).
|
||||
professor(p29).
|
||||
professor(p30).
|
||||
professor(p31).
|
||||
|
||||
|
||||
course(c0,p24).
|
||||
course(c1,p7).
|
||||
course(c2,p16).
|
||||
course(c3,p27).
|
||||
course(c4,p25).
|
||||
course(c5,p6).
|
||||
course(c6,p28).
|
||||
course(c7,p1).
|
||||
course(c8,p29).
|
||||
course(c9,p23).
|
||||
course(c10,p17).
|
||||
course(c11,p16).
|
||||
course(c12,p11).
|
||||
course(c13,p28).
|
||||
course(c14,p13).
|
||||
course(c15,p7).
|
||||
course(c16,p21).
|
||||
course(c17,p15).
|
||||
course(c18,p8).
|
||||
course(c19,p30).
|
||||
course(c20,p1).
|
||||
course(c21,p23).
|
||||
course(c22,p11).
|
||||
course(c23,p9).
|
||||
course(c24,p0).
|
||||
course(c25,p30).
|
||||
course(c26,p15).
|
||||
course(c27,p4).
|
||||
course(c28,p26).
|
||||
course(c29,p29).
|
||||
course(c30,p31).
|
||||
course(c31,p19).
|
||||
course(c32,p5).
|
||||
course(c33,p14).
|
||||
course(c34,p14).
|
||||
course(c35,p25).
|
||||
course(c36,p21).
|
||||
course(c37,p10).
|
||||
course(c38,p2).
|
||||
course(c39,p20).
|
||||
course(c40,p3).
|
||||
course(c41,p18).
|
||||
course(c42,p9).
|
||||
course(c43,p20).
|
||||
course(c44,p17).
|
||||
course(c45,p19).
|
||||
course(c46,p6).
|
||||
course(c47,p4).
|
||||
course(c48,p12).
|
||||
course(c49,p10).
|
||||
course(c50,p2).
|
||||
course(c51,p22).
|
||||
course(c52,p31).
|
||||
course(c53,p24).
|
||||
course(c54,p0).
|
||||
course(c55,p5).
|
||||
course(c56,p22).
|
||||
course(c57,p13).
|
||||
course(c58,p18).
|
||||
course(c59,p12).
|
||||
course(c60,p27).
|
||||
course(c61,p3).
|
||||
course(c62,p8).
|
||||
course(c63,p26).
|
||||
|
||||
|
||||
student(s0).
|
||||
student(s1).
|
||||
student(s2).
|
||||
student(s3).
|
||||
student(s4).
|
||||
student(s5).
|
||||
student(s6).
|
||||
student(s7).
|
||||
student(s8).
|
||||
student(s9).
|
||||
student(s10).
|
||||
student(s11).
|
||||
student(s12).
|
||||
student(s13).
|
||||
student(s14).
|
||||
student(s15).
|
||||
student(s16).
|
||||
student(s17).
|
||||
student(s18).
|
||||
student(s19).
|
||||
student(s20).
|
||||
student(s21).
|
||||
student(s22).
|
||||
student(s23).
|
||||
student(s24).
|
||||
student(s25).
|
||||
student(s26).
|
||||
student(s27).
|
||||
student(s28).
|
||||
student(s29).
|
||||
student(s30).
|
||||
student(s31).
|
||||
student(s32).
|
||||
student(s33).
|
||||
student(s34).
|
||||
student(s35).
|
||||
student(s36).
|
||||
student(s37).
|
||||
student(s38).
|
||||
student(s39).
|
||||
student(s40).
|
||||
student(s41).
|
||||
student(s42).
|
||||
student(s43).
|
||||
student(s44).
|
||||
student(s45).
|
||||
student(s46).
|
||||
student(s47).
|
||||
student(s48).
|
||||
student(s49).
|
||||
student(s50).
|
||||
student(s51).
|
||||
student(s52).
|
||||
student(s53).
|
||||
student(s54).
|
||||
student(s55).
|
||||
student(s56).
|
||||
student(s57).
|
||||
student(s58).
|
||||
student(s59).
|
||||
student(s60).
|
||||
student(s61).
|
||||
student(s62).
|
||||
student(s63).
|
||||
student(s64).
|
||||
student(s65).
|
||||
student(s66).
|
||||
student(s67).
|
||||
student(s68).
|
||||
student(s69).
|
||||
student(s70).
|
||||
student(s71).
|
||||
student(s72).
|
||||
student(s73).
|
||||
student(s74).
|
||||
student(s75).
|
||||
student(s76).
|
||||
student(s77).
|
||||
student(s78).
|
||||
student(s79).
|
||||
student(s80).
|
||||
student(s81).
|
||||
student(s82).
|
||||
student(s83).
|
||||
student(s84).
|
||||
student(s85).
|
||||
student(s86).
|
||||
student(s87).
|
||||
student(s88).
|
||||
student(s89).
|
||||
student(s90).
|
||||
student(s91).
|
||||
student(s92).
|
||||
student(s93).
|
||||
student(s94).
|
||||
student(s95).
|
||||
student(s96).
|
||||
student(s97).
|
||||
student(s98).
|
||||
student(s99).
|
||||
student(s100).
|
||||
student(s101).
|
||||
student(s102).
|
||||
student(s103).
|
||||
student(s104).
|
||||
student(s105).
|
||||
student(s106).
|
||||
student(s107).
|
||||
student(s108).
|
||||
student(s109).
|
||||
student(s110).
|
||||
student(s111).
|
||||
student(s112).
|
||||
student(s113).
|
||||
student(s114).
|
||||
student(s115).
|
||||
student(s116).
|
||||
student(s117).
|
||||
student(s118).
|
||||
student(s119).
|
||||
student(s120).
|
||||
student(s121).
|
||||
student(s122).
|
||||
student(s123).
|
||||
student(s124).
|
||||
student(s125).
|
||||
student(s126).
|
||||
student(s127).
|
||||
student(s128).
|
||||
student(s129).
|
||||
student(s130).
|
||||
student(s131).
|
||||
student(s132).
|
||||
student(s133).
|
||||
student(s134).
|
||||
student(s135).
|
||||
student(s136).
|
||||
student(s137).
|
||||
student(s138).
|
||||
student(s139).
|
||||
student(s140).
|
||||
student(s141).
|
||||
student(s142).
|
||||
student(s143).
|
||||
student(s144).
|
||||
student(s145).
|
||||
student(s146).
|
||||
student(s147).
|
||||
student(s148).
|
||||
student(s149).
|
||||
student(s150).
|
||||
student(s151).
|
||||
student(s152).
|
||||
student(s153).
|
||||
student(s154).
|
||||
student(s155).
|
||||
student(s156).
|
||||
student(s157).
|
||||
student(s158).
|
||||
student(s159).
|
||||
student(s160).
|
||||
student(s161).
|
||||
student(s162).
|
||||
student(s163).
|
||||
student(s164).
|
||||
student(s165).
|
||||
student(s166).
|
||||
student(s167).
|
||||
student(s168).
|
||||
student(s169).
|
||||
student(s170).
|
||||
student(s171).
|
||||
student(s172).
|
||||
student(s173).
|
||||
student(s174).
|
||||
student(s175).
|
||||
student(s176).
|
||||
student(s177).
|
||||
student(s178).
|
||||
student(s179).
|
||||
student(s180).
|
||||
student(s181).
|
||||
student(s182).
|
||||
student(s183).
|
||||
student(s184).
|
||||
student(s185).
|
||||
student(s186).
|
||||
student(s187).
|
||||
student(s188).
|
||||
student(s189).
|
||||
student(s190).
|
||||
student(s191).
|
||||
student(s192).
|
||||
student(s193).
|
||||
student(s194).
|
||||
student(s195).
|
||||
student(s196).
|
||||
student(s197).
|
||||
student(s198).
|
||||
student(s199).
|
||||
student(s200).
|
||||
student(s201).
|
||||
student(s202).
|
||||
student(s203).
|
||||
student(s204).
|
||||
student(s205).
|
||||
student(s206).
|
||||
student(s207).
|
||||
student(s208).
|
||||
student(s209).
|
||||
student(s210).
|
||||
student(s211).
|
||||
student(s212).
|
||||
student(s213).
|
||||
student(s214).
|
||||
student(s215).
|
||||
student(s216).
|
||||
student(s217).
|
||||
student(s218).
|
||||
student(s219).
|
||||
student(s220).
|
||||
student(s221).
|
||||
student(s222).
|
||||
student(s223).
|
||||
student(s224).
|
||||
student(s225).
|
||||
student(s226).
|
||||
student(s227).
|
||||
student(s228).
|
||||
student(s229).
|
||||
student(s230).
|
||||
student(s231).
|
||||
student(s232).
|
||||
student(s233).
|
||||
student(s234).
|
||||
student(s235).
|
||||
student(s236).
|
||||
student(s237).
|
||||
student(s238).
|
||||
student(s239).
|
||||
student(s240).
|
||||
student(s241).
|
||||
student(s242).
|
||||
student(s243).
|
||||
student(s244).
|
||||
student(s245).
|
||||
student(s246).
|
||||
student(s247).
|
||||
student(s248).
|
||||
student(s249).
|
||||
student(s250).
|
||||
student(s251).
|
||||
student(s252).
|
||||
student(s253).
|
||||
student(s254).
|
||||
student(s255).
|
||||
|
||||
|
||||
registration(r0,c16,s0).
|
||||
registration(r1,c10,s0).
|
||||
registration(r2,c57,s0).
|
||||
registration(r3,c22,s1).
|
||||
registration(r4,c55,s1).
|
||||
registration(r5,c27,s1).
|
||||
registration(r6,c14,s2).
|
||||
registration(r7,c52,s2).
|
||||
registration(r8,c10,s2).
|
||||
registration(r9,c47,s3).
|
||||
registration(r10,c16,s3).
|
||||
registration(r11,c62,s3).
|
||||
registration(r12,c12,s4).
|
||||
registration(r13,c11,s4).
|
||||
registration(r14,c17,s4).
|
||||
registration(r15,c52,s5).
|
||||
registration(r16,c1,s5).
|
||||
registration(r17,c35,s5).
|
||||
registration(r18,c0,s6).
|
||||
registration(r19,c7,s6).
|
||||
registration(r20,c40,s6).
|
||||
52
cplint/examples/school_simple.cpl
Normal file
52
cplint/examples/school_simple.cpl
Normal file
@ -0,0 +1,52 @@
|
||||
/*
|
||||
?- s([registration_grade(r0,X)],P).
|
||||
|
||||
|
||||
|
||||
|
||||
student_ranking(s0,X).
|
||||
s([student_ranking(s0,h)],P).
|
||||
P = 0,6646250000000005 ?
|
||||
s([student_ranking(s0,l)],P).
|
||||
P = 0,33537499999999987
|
||||
ok, checked with s.pl
|
||||
*/
|
||||
|
||||
|
||||
|
||||
registration_course(Key, CKey) :-
|
||||
registration(Key, CKey, _).
|
||||
|
||||
registration_student(Key, SKey) :-
|
||||
registration(Key, _, SKey).
|
||||
|
||||
registration_grade(Key, 4): 0.2;
|
||||
registration_grade(Key, 1): 0.8 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
course_difficulty(CKey, h).
|
||||
|
||||
|
||||
registration_grade(Key, 4): 0.85;
|
||||
registration_grade(Key, 1): 0.15 :-
|
||||
registration(Key, CKey, SKey) ,
|
||||
course_difficulty(CKey, l).
|
||||
|
||||
|
||||
course_difficulty(Cou, h) :0.5 ; course_difficulty(Cou, l) : 0.5.
|
||||
|
||||
|
||||
student_ranking(Stu, h):0.2; student_ranking(Stu, l):0.8:-
|
||||
bagof(Grade, CKey^(registration_student(CKey,Stu),
|
||||
registration_grade(CKey, Grade)), Grades),
|
||||
average(Grades, Av),Av<2.
|
||||
|
||||
student_ranking(Stu, h):0.8; student_ranking(Stu, l):0.2:-
|
||||
bagof(Grade, CKey^(registration_student(CKey,Stu),
|
||||
registration_grade(CKey, Grade)), Grades),
|
||||
average(Grades, Av),Av>=2.
|
||||
|
||||
|
||||
|
||||
|
||||
registration(r0,c16,s0).
|
||||
registration(r1,c10,s0).
|
||||
3
cplint/examples/school_simple.uni
Normal file
3
cplint/examples/school_simple.uni
Normal file
@ -0,0 +1,3 @@
|
||||
universe(['Stu'],[s0]).
|
||||
universe(['Key'],[r0,r1]).
|
||||
universe(['Cou'],[c16,c10]).
|
||||
95
cplint/examples/student.cpl
Normal file
95
cplint/examples/student.cpl
Normal file
@ -0,0 +1,95 @@
|
||||
/*
|
||||
s([student_rank(jane_doe,h)],P).
|
||||
P = 0.465
|
||||
|
||||
s([student_rank(jane_doe,l)],P).
|
||||
P = 0.535
|
||||
|
||||
s([course_rat(C,h)],P).
|
||||
C = phil101,
|
||||
P = 0.330656
|
||||
|
||||
s([course_rat(C,l)],P).
|
||||
C = phil101,
|
||||
P = 0.669344
|
||||
|
||||
*/
|
||||
professor(gump).
|
||||
%1
|
||||
professor_teach_ab(P,h):0.2;professor_teach_ab(P,l):0.8:-
|
||||
professor(P).
|
||||
%2
|
||||
professor_pop(P,h):0.7;professor_pop(P,l):0.3:-
|
||||
professor_teach_ab(P,h).
|
||||
%3
|
||||
professor_pop(P,h):0.2;professor_pop(P,l):0.8:-
|
||||
professor_teach_ab(P,l).
|
||||
|
||||
student(jane_doe).
|
||||
%4
|
||||
student_int(S,h):0.6;student_int(S,l):0.4:-
|
||||
student(S).
|
||||
%5
|
||||
student_rank(S,h):0.6;student_rank(S,l):0.4:-
|
||||
bagof(G,R^(registr_stu(R,S),registr_gr(R,G)),L),
|
||||
average(L,Av),Av>1.5.
|
||||
%6
|
||||
student_rank(S,h):0.4;student_rank(S,l):0.6:-
|
||||
bagof(G,R^(registr_stu(R,S),registr_gr(R,G)),L),
|
||||
average(L,Av),Av =< 1.5.
|
||||
|
||||
registr(5639).
|
||||
|
||||
registr_cou(5639,phil101).
|
||||
|
||||
registr_stu(5639,jane_doe).
|
||||
|
||||
registr(5640).
|
||||
|
||||
registr_cou(5640,phil101).
|
||||
|
||||
registr_stu(5640,jane_doe).
|
||||
|
||||
%7
|
||||
registr_gr(Reg,1):0.5;registr_gr(Reg,2):0.5:-
|
||||
registr_stu(Reg,S),student_int(S,h),registr_cou(Reg,C),course_dif(C,h).
|
||||
%8
|
||||
registr_gr(Reg,1):0.2;registr_gr(Reg,2):0.8:-
|
||||
registr_stu(Reg,S),student_int(S,h),registr_cou(Reg,C),course_dif(C,l).
|
||||
%9
|
||||
registr_gr(Reg,1):0.8;registr_gr(Reg,2):0.2:-
|
||||
registr_stu(Reg,S),student_int(S,l),registr_cou(Reg,C),course_dif(C,h).
|
||||
%10
|
||||
registr_gr(Reg,1):0.5;registr_gr(Reg,2):0.5:-
|
||||
registr_stu(Reg,S),student_int(S,l),registr_cou(Reg,C),course_dif(C,l).
|
||||
%11
|
||||
registr_sat(Reg,1):0.2;registr_sat(Reg,2):0.8:-
|
||||
registr_gr(Reg,2),registr_cou(Reg,C),course_prof(C,P),
|
||||
professor_teach_ab(P,h).
|
||||
%12
|
||||
registr_sat(Reg,1):0.4;registr_sat(Reg,2):0.6:-
|
||||
registr_gr(Reg,2),registr_cou(Reg,C),course_prof(C,P),
|
||||
professor_teach_ab(P,l).
|
||||
%13
|
||||
registr_sat(Reg,1):0.6;registr_sat(Reg,2):0.4:-
|
||||
registr_gr(Reg,1),registr_cou(Reg,C),course_prof(C,P),
|
||||
professor_teach_ab(P,h).
|
||||
%14
|
||||
registr_sat(Reg,1):0.8;registr_sat(Reg,2):0.2:-
|
||||
registr_gr(Reg,1),registr_cou(Reg,C),course_prof(C,P),
|
||||
professor_teach_ab(P,l).
|
||||
|
||||
course(phil101).
|
||||
|
||||
course_prof(phil101,gump).
|
||||
%15
|
||||
course_dif(C,h):0.5;course_dif(C,l):0.5:-
|
||||
course(C).
|
||||
%16
|
||||
course_rat(C,h):0.8;course_rat(C,l):0.2:-
|
||||
bagof(Stu,R^(registr_cou(R,C),registr_sat(R,Stu)),L),
|
||||
average(L,Av),Av>1.5.
|
||||
%17
|
||||
course_rat(C,h):0.2;course_rat(C,l):0.8:-
|
||||
bagof(Stu,R^(registr_cou(R,C),registr_sat(R,Stu)),L),
|
||||
average(L,Av),Av=< 1.5.
|
||||
4
cplint/examples/student.uni
Normal file
4
cplint/examples/student.uni
Normal file
@ -0,0 +1,4 @@
|
||||
universe(['S'],[jane_doe]).
|
||||
universe(['P'],[gump]).
|
||||
universe(['Reg'],[5639,5640]).
|
||||
universe(['C'],[phil101]).
|
||||
39
cplint/examples/threesideddice.cpl
Normal file
39
cplint/examples/threesideddice.cpl
Normal file
@ -0,0 +1,39 @@
|
||||
/*
|
||||
s([on(0,1)],P).
|
||||
P = 0.333333333333333 ?
|
||||
|
||||
s([on(1,1)],P).
|
||||
P = 0.222222222222222
|
||||
s([on(2,1)],P).
|
||||
P = 0.148148147703704
|
||||
s([on(3,1)],P).
|
||||
% OUT OF DATABASE SPACE ERROR- Database crashed against Stacks
|
||||
s([on(4,1)],P).
|
||||
% OUT OF DATABASE SPACE ERROR- Database crashed against Stacks
|
||||
s([on(5,1)],P).
|
||||
% OUT OF DATABASE SPACE ERROR- Database crashed against Stacks
|
||||
|
||||
s([on(0,1),on(1,1)],P).
|
||||
P = 0.111111111111111
|
||||
s([on(0,1),on(1,1),on(2,1)],P).
|
||||
P = 0.037037037037037
|
||||
sc([on(1,1)],[on(0,1)],P).
|
||||
P = 0.333333333333333
|
||||
|
||||
sc([on(2,1)],[on(0,1)],P).
|
||||
P = 0.222222222222222
|
||||
|
||||
sc([on(3,1)],[on(0,1)],P).
|
||||
P = 0.148148148148148
|
||||
|
||||
sc([on(2,1)],[on(1,1)],P).
|
||||
P = 0.333333333333333 ?
|
||||
|
||||
|
||||
*/
|
||||
on(0,1):1/3;on(0,2):1/3;on(0,3):1/3.
|
||||
|
||||
on(X,1):1/3;on(X,2):1/3;on(X,3):1/3:-
|
||||
X1 is X-1,X1>=0,
|
||||
on(X1,Y),
|
||||
\+ on(X1,3).
|
||||
3
cplint/examples/threesideddice.uni
Normal file
3
cplint/examples/threesideddice.uni
Normal file
@ -0,0 +1,3 @@
|
||||
universe(['X'],[1,2]).
|
||||
universe(['X1'],[0,1]).
|
||||
universe(['Y'],[1,2,3]).
|
||||
21
cplint/examples/twosideddice.cpl
Normal file
21
cplint/examples/twosideddice.cpl
Normal file
@ -0,0 +1,21 @@
|
||||
/*
|
||||
|
||||
s([on(3,1)],P).
|
||||
P = 0.0625
|
||||
sc([on(3,1)],[on(2,1)],P).
|
||||
P = 0.5 ?
|
||||
|
||||
sc([on(4,1)],[on(2,1)],P).
|
||||
P = 0.25
|
||||
|
||||
sc([on(10,1)],[on(2,1)],P).
|
||||
P = 0.00390625
|
||||
|
||||
|
||||
*/
|
||||
on(0,1):0.5;on(0,2):0.5.
|
||||
|
||||
on(X,1):0.5;on(X,2):0.5:-
|
||||
X1 is X-1,X1>=0,
|
||||
on(X1,_),
|
||||
\+ on(X1,2).
|
||||
263
cplint/semantics.yap
Normal file
263
cplint/semantics.yap
Normal file
@ -0,0 +1,263 @@
|
||||
/*
|
||||
|
||||
Program for computing the probability of a query directly according to the
|
||||
semantics
|
||||
|
||||
Copyright (c) 2007, Fabrizio Riguzzi
|
||||
|
||||
|
||||
*/
|
||||
:-use_module(library(lists)).
|
||||
:-dynamic setting/2.
|
||||
:-set_prolog_flag(unknown,fail).
|
||||
|
||||
|
||||
setting(epsilon,0.00001).
|
||||
setting(test_builtins,false).
|
||||
|
||||
solve(GoalsList,Prob):-
|
||||
s(GoalsList,Prob).
|
||||
|
||||
|
||||
s(GoalsList,Prob):-
|
||||
program_names(L),
|
||||
list2and(GoalsList,Goals),
|
||||
run_query(L,Goals,0,Prob).
|
||||
|
||||
run_query([],_G,P,P).
|
||||
|
||||
run_query([Prog|T],Goal,PIn,POut):-
|
||||
elab_conj(Prog,Goal,Goal1),
|
||||
call(Goal1),
|
||||
prob(Prog,P),
|
||||
P1 is PIn+P,
|
||||
run_query(T,Goal,P1,POut).
|
||||
|
||||
run_query([Prog|T],Goal,PIn,POut):-
|
||||
elab_conj(Prog,Goal,Goal1),
|
||||
\+ call(Goal1),
|
||||
run_query(T,Goal,PIn,POut).
|
||||
|
||||
p(File):-
|
||||
atom_concat(File,'.uni',FileUni),
|
||||
consult(FileUni),
|
||||
atom_concat(File,'.cpl',FilePl),
|
||||
open(FilePl,read,S),
|
||||
read_clauses(S,C),
|
||||
close(S),
|
||||
process_clauses(C,ClausesVar),
|
||||
instantiate(ClausesVar,[],Clauses),
|
||||
assert(program(1)),
|
||||
assert(program_names([])),
|
||||
create_programs(Clauses).
|
||||
|
||||
|
||||
|
||||
create_programs(Clauses):-
|
||||
create_single_program(Clauses,1,Program),
|
||||
retract(program(N)),
|
||||
number_codes(N,NC),
|
||||
atom_codes(NA,NC),
|
||||
atom_concat(p,NA,Name),
|
||||
N1 is N+1,
|
||||
assert(program(N1)),
|
||||
format("Writing program ~d~n",[N]),
|
||||
write_program(Name,Program),
|
||||
retract(program_names(L)),
|
||||
append(L,[Name],L1),
|
||||
assert(program_names(L1)),
|
||||
fail.
|
||||
|
||||
create_programs(_).
|
||||
|
||||
write_program(_Name,[]).
|
||||
|
||||
write_program(Name,[(H:-B)|T]):-
|
||||
elab_conj(Name,H,H1),
|
||||
elab_conj(Name,B,B1),
|
||||
assertz((H1:-B1)),
|
||||
write_program(Name,T).
|
||||
|
||||
elab_conj(_Name,true,true):-!.
|
||||
|
||||
elab_conj(Name,\+(B),\+(B1)):-!,
|
||||
elab_conj(Name,B,B1).
|
||||
|
||||
elab_conj(Name,(BL,Rest),(BL1,Rest1)):-!,
|
||||
elab_conj(Name,BL,BL1),
|
||||
elab_conj(Name,Rest,Rest1).
|
||||
|
||||
elab_conj(Name,bagof(V,EV^G,L),bagof(V,EV^GL,L)):-!,
|
||||
elab_conj(Name,G,GL).
|
||||
|
||||
elab_conj(Name,bagof(V,G,L),bagof(V,GL,L)):-!,
|
||||
elab_conj(Name,G,GL).
|
||||
|
||||
elab_conj(Name,setof(V,EV^G,L),setof(V,EV^GL,L)):-!,
|
||||
elab_conj(Name,G,GL).
|
||||
|
||||
elab_conj(Name,setof(V,G,L),setof(V,GL,L)):-!,
|
||||
elab_conj(Name,G,GL).
|
||||
|
||||
elab_conj(Name,findall(V,G,L),findall(V,GL,L)):-!,
|
||||
elab_conj(Name,G,GL).
|
||||
|
||||
elab_conj(_Name,A,A):-
|
||||
bg(A),!.
|
||||
|
||||
elab_conj(_Name,A,A):-
|
||||
builtin(A),!.
|
||||
|
||||
elab_conj(Name,Lit,Lit1):-
|
||||
Lit\=(_,_),
|
||||
Lit=..[Pred|Args],
|
||||
Lit1=..[Pred,Name|Args].
|
||||
|
||||
|
||||
create_single_program([],P,[(prob(P):-true)]).
|
||||
|
||||
create_single_program([r(H,B)|T],PIn,[(HA:-B)|T1]):-
|
||||
member((HA:P),H),
|
||||
P1 is PIn*P,
|
||||
create_single_program(T,P1,T1).
|
||||
|
||||
instantiate([],C,C).
|
||||
|
||||
instantiate([r(V,H,B)|T],CIn,COut):-
|
||||
findall(r(H,BOut),instantiate_clause(V,H,B,BOut),L),
|
||||
append(CIn,L,C1),
|
||||
instantiate(T,C1,COut).
|
||||
|
||||
check_body([],[]).
|
||||
|
||||
check_body([H|T],TOut):-
|
||||
builtin(H),setting(test_builtins,true),!,
|
||||
call(H),
|
||||
check_body(T,TOut).
|
||||
|
||||
check_body([H|T],[H|TOut]):-
|
||||
check_body(T,TOut).
|
||||
|
||||
|
||||
instantiate_clause([],_H,B,BOut):-
|
||||
list2and(BL,B),
|
||||
check_body(BL,BLOut),
|
||||
list2and(BLOut,BOut).
|
||||
|
||||
|
||||
instantiate_clause([VarName=Var|T],H,BIn,BOut):-
|
||||
universe(VarNames,U),
|
||||
member(VarName,VarNames),
|
||||
member(Var,U),
|
||||
instantiate_clause(T,H,BIn,BOut).
|
||||
|
||||
instantiate_clause([VarName=_Var|T],H,BIn,BOut):-
|
||||
\+ varName_present(VarName),!,
|
||||
instantiate_clause(T,H,BIn,BOut).
|
||||
|
||||
varName_present(VarName):-
|
||||
universe(VarNames,_U), member(VarName,VarNames).
|
||||
|
||||
process_clauses([(end_of_file,[])],[]).
|
||||
|
||||
process_clauses([((H:-B),V)|T],[r(V,HL,B)|T1]):-
|
||||
H=(_;_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,0,HL),
|
||||
process_clauses(T,T1).
|
||||
|
||||
process_clauses([((H:-B),V)|T],[r(V,HL,B)|T1]):-
|
||||
H=(_:_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,0,HL),
|
||||
process_clauses(T,T1).
|
||||
|
||||
process_clauses([((H:-B),V)|T],[r(V,[H:1],B)|T1]):-!,
|
||||
process_clauses(T,T1).
|
||||
|
||||
process_clauses([(H,V)|T],[r(V,HL,true)|T1]):-
|
||||
H=(_;_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,0,HL),
|
||||
process_clauses(T,T1).
|
||||
|
||||
process_clauses([(H,V)|T],[r(V,HL,true)|T1]):-
|
||||
H=(_:_),!,
|
||||
list2or(HL1,H),
|
||||
process_head(HL1,0,HL),
|
||||
process_clauses(T,T1).
|
||||
|
||||
process_clauses([(H,V)|T],[r(V,[H:1],true)|T1]):-
|
||||
process_clauses(T,T1).
|
||||
|
||||
process_head([H:PH],P,[H:PH1|Null]):-
|
||||
PH1 is PH,
|
||||
PNull is 1-P-PH1,
|
||||
setting(epsilon,Eps),
|
||||
EpsNeg is - Eps,
|
||||
PNull > EpsNeg,
|
||||
(PNull>Eps->
|
||||
Null=['':PNull]
|
||||
;
|
||||
Null=[]
|
||||
).
|
||||
|
||||
process_head([H:PH|T],P,[H:PH1|NT]):-
|
||||
PH1 is PH,
|
||||
P1 is P+PH1,
|
||||
process_head(T,P1,NT).
|
||||
|
||||
read_clauses(S,[(Cl,V)|Out]):-
|
||||
read_term(S,Cl,[variable_names(V)]),
|
||||
(Cl=end_of_file->
|
||||
Out=[]
|
||||
;
|
||||
read_clauses(S,Out)
|
||||
).
|
||||
|
||||
|
||||
|
||||
list2or([X],X):-
|
||||
X\=;(_,_),!.
|
||||
|
||||
list2or([H|T],(H ; Ta)):-!,
|
||||
list2or(T,Ta).
|
||||
|
||||
|
||||
list2and([],true):-!.
|
||||
|
||||
list2and([X],X):-
|
||||
X\=(_,_),!.
|
||||
|
||||
list2and([H|T],(H,Ta)):-!,
|
||||
list2and(T,Ta).
|
||||
|
||||
builtin(_A is _B).
|
||||
builtin(_A > _B).
|
||||
builtin(_A < _B).
|
||||
builtin(_A >= _B).
|
||||
builtin(_A =< _B).
|
||||
builtin(_A =:= _B).
|
||||
builtin(_A =\= _B).
|
||||
builtin(true).
|
||||
builtin(false).
|
||||
builtin(_A = _B).
|
||||
builtin(_A==_B).
|
||||
builtin(_A\=_B).
|
||||
builtin(_A\==_B).
|
||||
|
||||
bg(member(_El,_L)).
|
||||
bg(average(_L,_Av)).
|
||||
bg(max_list(_L,_Max)).
|
||||
bg(min_list(_L,_Max)).
|
||||
|
||||
average(L,Av):-
|
||||
sum_list(L,Sum),
|
||||
length(L,N),
|
||||
Av is Sum/N.
|
||||
|
||||
/* set(Par,Value) can be used to set the value of a parameter */
|
||||
set(Parameter,Value):-
|
||||
retract(setting(Parameter,_)),
|
||||
assert(setting(Parameter,Value)).
|
||||
|
||||
177
cplint/test.yap
Normal file
177
cplint/test.yap
Normal file
@ -0,0 +1,177 @@
|
||||
/*
|
||||
LPAD and CP-Logic interpreter test program
|
||||
|
||||
Copyright (c) 2007, Fabrizio Riguzzi
|
||||
|
||||
use
|
||||
:-t.
|
||||
to execute the test
|
||||
|
||||
*/
|
||||
%:-use_module(library(cplint)).
|
||||
|
||||
:-compile(cplint).
|
||||
|
||||
epsilon(0.000001).
|
||||
|
||||
close_to(V,T):-
|
||||
epsilon(E),
|
||||
TLow is T-E,
|
||||
THigh is T+E,
|
||||
TLow<V,
|
||||
V<THigh.
|
||||
|
||||
|
||||
t:-
|
||||
files(F),
|
||||
statistics(runtime,[_,_]),
|
||||
test_files(F),
|
||||
statistics(runtime,[_,T]),
|
||||
T1 is T /1000,
|
||||
format("Time ~f secs~n",[T1]).
|
||||
|
||||
test_files([]).
|
||||
|
||||
test_files([H|T]):-
|
||||
format("~a~n",[H]),
|
||||
atom_concat('examples/',H,HN),
|
||||
p(HN),
|
||||
findall(A,test(A,H),L),
|
||||
test_all(H,L),
|
||||
test_files(T).
|
||||
|
||||
test_all(_F,[]).
|
||||
|
||||
test_all(F,[H|T]):-
|
||||
format("~a ~p.~n",[F,H]),
|
||||
call(H),
|
||||
test_all(F,T).
|
||||
|
||||
|
||||
files([paper_ref_not,paper_ref,female,esapprox,esrange,threesideddice,
|
||||
mendel,student,school_simple,school,coin2,es]).
|
||||
|
||||
test((s([\+ cites_cited(c1,p1)],P),close_to(P,0.7)),paper_ref_not).
|
||||
test((s([cites_citing(c1,p1)],P),close_to(P,0.14)),paper_ref_not).
|
||||
|
||||
|
||||
test((s([cites_cited(c1,p1)],P),close_to(P,0.181333333)),paper_ref).
|
||||
test((s([cites_cited(c1,p2)],P),close_to(P,0.181333333)),paper_ref).
|
||||
test((s([cites_cited(c1,p4)],P),close_to(P,0.181333333)),paper_ref).
|
||||
test((s([cites_cited(c1,p3)],P),close_to(P,0.228)),paper_ref).
|
||||
test((s([cites_cited(c1,p5)],P),close_to(P,0.228)),paper_ref).
|
||||
|
||||
|
||||
test((s([female(f)],P),close_to(P,0.6)),female).
|
||||
test((s([male(f)],P),close_to(P,0.4)),female).
|
||||
|
||||
test((s([a],P),close_to(P,0.1719)),esapprox).
|
||||
|
||||
test((s([a(1)],P),close_to(P,0.2775)),esrange).
|
||||
test((s([a(2)],P),close_to(P,0.36)),esrange).
|
||||
|
||||
test((s([on(0,1)],P),close_to(P,0.333333333333333)),threesideddice).
|
||||
test((s([on(1,1)],P),close_to(P,0.222222222222222)),threesideddice).
|
||||
test((s([on(2,1)],P),close_to(P,0.148148147703704)),threesideddice).
|
||||
|
||||
test((sc([on(2,1)],[on(0,1)],P),close_to(P,0.222222222222222)),threesideddice).
|
||||
test((sc([on(2,1)],[on(1,1)],P),close_to(P,0.333333333333333)),threesideddice).
|
||||
|
||||
|
||||
test((s([cg(s,1,p)],P),close_to(P,0.75)),mendel).
|
||||
test((s([cg(s,1,w)],P),close_to(P,0.25)),mendel).
|
||||
test((s([cg(s,2,p)],P),close_to(P,0.25)),mendel).
|
||||
test((s([cg(s,2,w)],P),close_to(P,0.75)),mendel).
|
||||
test((s([cg(f,2,w)],P),close_to(P,0.5)),mendel).
|
||||
test((s([cg(s,2,w)],P),close_to(P,0.75)),mendel).
|
||||
|
||||
test((s([a],P),close_to(P,0.226)),es).
|
||||
|
||||
test((s([heads(coin1)],P),close_to(P,0.51)),coin2).
|
||||
test((s([heads(coin2)],P),close_to(P,0.51)),coin2).
|
||||
|
||||
test((s([tails(coin1)],P),close_to(P,0.49)),coin2).
|
||||
test((s([tails(coin2)],P),close_to(P,0.49)),coin2).
|
||||
|
||||
|
||||
|
||||
test((s([student_rank(jane_doe,h)],P),close_to(P,0.465)),student).
|
||||
test((s([student_rank(jane_doe,l)],P),close_to(P,0.535)),student).
|
||||
|
||||
test((s([course_rat(phil101,h)],P),close_to(P,0.330656)),student).
|
||||
test((s([course_rat(phil101,l)],P),close_to(P,0.669344)),student).
|
||||
|
||||
|
||||
test((s([professor_ability(p0,h)],P),close_to(P,0.5)),school).
|
||||
test((s([professor_ability(p0,m)],P),close_to(P,0.4)),school).
|
||||
test((s([professor_ability(p0,l)],P),close_to(P,0.1)),school).
|
||||
|
||||
|
||||
test((s([professor_popularity(p0,h)],P),close_to(P,0.531)),school).
|
||||
test((s([professor_popularity(p0,l)],P),close_to(P,0.175)),school).
|
||||
test((s([professor_popularity(p0,m)],P),close_to(P,0.294)),school).
|
||||
|
||||
test((sc([professor_ability(p0,h)],[professor_popularity(p0,h)],P),close_to(P,0.847457627118644)),school).
|
||||
test((sc([professor_ability(p0,l)],[professor_popularity(p0,h)],P),close_to(P,0.00188323917137476)),school).
|
||||
test((sc([professor_ability(p0,m)],[professor_popularity(p0,h)],P),close_to(P,0.150659133709981)),school).
|
||||
|
||||
test((sc([professor_popularity(p0,h)],[professor_ability(p0,h)],P),close_to(P,0.9)),school).
|
||||
test((sc([professor_popularity(p0,l)],[professor_ability(p0,h)],P),close_to(P,0.01)),school).
|
||||
test((sc([professor_popularity(p0,m)],[professor_ability(p0,h)],P),close_to(P,0.09)),school).
|
||||
|
||||
test(( s([registration_grade(r0,1)],P),close_to(P,0.06675)),school).
|
||||
test(( s([registration_grade(r0,2)],P),close_to(P,0.16575)),school).
|
||||
test(( s([registration_grade(r0,3)],P),close_to(P, 0.356)),school).
|
||||
test(( s([registration_grade(r0,4)],P),close_to(P,0.4115)),school).
|
||||
|
||||
test((sc([registration_grade(r0,1)],[registration_course(r0,C), course_difficulty(C,h)],P),close_to(P,0.15)),school).
|
||||
test((sc([registration_grade(r0,2)],[registration_course(r0,C), course_difficulty(C,h)],P),close_to(P,0.285)),school).
|
||||
test((sc([registration_grade(r0,3)],[registration_course(r0,C), course_difficulty(C,h)],P),close_to(P,0.424)),school).
|
||||
test((sc([registration_grade(r0,4)],[registration_course(r0,C), course_difficulty(C,h)],P),close_to(P,0.141)),school).
|
||||
|
||||
test((sc([registration_grade(r0,1)], [registration_course(r0,C), course_difficulty(C,h), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.05)),school).
|
||||
test((sc([registration_grade(r0,2)], [registration_course(r0,C), course_difficulty(C,h), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.15)),school).
|
||||
test((sc([registration_grade(r0,3)], [registration_course(r0,C), course_difficulty(C,h), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.6)),school).
|
||||
test((sc([registration_grade(r0,4)], [registration_course(r0,C), course_difficulty(C,h), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.2)),school).
|
||||
|
||||
test((sc([registration_grade(r0,1)],[registration_course(r0,C), course_difficulty(C,l), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.01)),school).
|
||||
test((sc([registration_grade(r0,2)],[registration_course(r0,C), course_difficulty(C,l), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.02)),school).
|
||||
test((sc([registration_grade(r0,3)],[registration_course(r0,C), course_difficulty(C,l), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.12)),school).
|
||||
test((sc([registration_grade(r0,4)],[registration_course(r0,C), course_difficulty(C,l), registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.85)),school).
|
||||
|
||||
test((s([registration_satisfaction(r0,1)],P),close_to(P,0.15197525)),school).
|
||||
test((s([registration_satisfaction(r0,2)],P),close_to(P,0.1533102)),school).
|
||||
test((s([registration_satisfaction(r0,3)],P),close_to(P,0.6947145)),school).
|
||||
|
||||
test((sc([registration_satisfaction(r0,1)],[ registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.0959225)),school).
|
||||
test((sc([registration_satisfaction(r0,2)],[ registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.124515)),school).
|
||||
test((sc([registration_satisfaction(r0,3)],[ registration_student(r0,S), student_intelligence(S,h)],P),close_to(P,0.7795625)),school).
|
||||
|
||||
test((sc([registration_satisfaction(r0,1)],[registration_grade(r0,4)],P),close_to(P,0.04)),school).
|
||||
test((sc([registration_satisfaction(r0,2)],[registration_grade(r0,4)],P),close_to(P,0.06)),school).
|
||||
test((sc([registration_satisfaction(r0,3)],[registration_grade(r0,4)],P),close_to(P,0.9)),school).
|
||||
|
||||
test((sc([registration_satisfaction(r0,1)],[registration_grade(r0,1)],P),close_to(P,0.528)),school).
|
||||
test((sc([registration_satisfaction(r0,2)],[registration_grade(r0,1)],P),close_to(P,0.167)),school).
|
||||
test((sc([registration_satisfaction(r0,3)],[registration_grade(r0,1)],P),close_to(P,0.305)),school).
|
||||
|
||||
test((sc([ registration_grade(r0,1)],[registration_satisfaction(r0,3)],P),close_to(P,0.0293052037923492)),school).
|
||||
test((sc([ registration_grade(r0,2)],[registration_satisfaction(r0,3)],P),close_to(P, 0.114760451955444)),school).
|
||||
test((sc([ registration_grade(r0,3)],[registration_satisfaction(r0,3)],P),close_to(P,0.322837654892765)),school).
|
||||
test((sc([ registration_grade(r0,4)],[registration_satisfaction(r0,3)],P),close_to(P,0.533096689359442)),school).
|
||||
|
||||
test((s([course_rating(c0,h)],P),close_to(P,0.5392099)),school).
|
||||
test((s([course_rating(c0,l)],P),close_to(P, 0.2)),school).
|
||||
test((s([course_rating(c0,m)],P),close_to(P,0.2607901)),school).
|
||||
|
||||
test((sc([course_difficulty(c0,h)],[course_rating(c0,h)],P),close_to(P,0.235185778302661)),school).
|
||||
test((sc([course_difficulty(c0,l)],[course_rating(c0,h)],P),close_to(P,0.259096503977393)),school).
|
||||
test((sc([course_difficulty(c0,m)],[course_rating(c0,h)],P),close_to(P,0.505717717719945)),school).
|
||||
|
||||
test((s([course_difficulty(c0,h)],P),close_to(P,0.25)),school).
|
||||
test((s([course_difficulty(c0,l)],P),close_to(P,0.25)),school).
|
||||
test((s([course_difficulty(c0,m)],P),close_to(P,0.5)),school).
|
||||
|
||||
|
||||
test((s([student_ranking(s0,h)],P),close_to(P,0.6646250000000005)),school_simple).
|
||||
test((s([student_ranking(s0,l)],P),close_to(P,0.33537499999999987)),school_simple).
|
||||
Reference in New Issue
Block a user