/* A data sample describing decisions on loan applications
   from bank clients. "emp" means employed and "buy" describes
   the loan purpose: computer (comp) or car.  
*/

/* Experiments:
   1. vs (inductive bias):  Exchange 4 and 5.
   2. id3 (effect of noise): switch classes of 12 and 6.
*/

:-dynamic(example/3).

example(1,  approve, [emp=yes, buy=comp, sex=f, married=no]).
example(2,  reject,  [emp=no,  buy=comp, sex=f, married=yes]).
example(3,  approve, [emp=yes, buy=comp, sex=m, married=no]).
example(4,  approve, [emp=yes, buy=car,  sex=f, married=yes]).
example(5,  reject,  [emp=yes, buy=car,  sex=f, married=no]).
example(6,  approve, [emp=yes, buy=comp, sex=f, married=yes]).
example(7,  approve, [emp=yes, buy=comp, sex=f, married=no]).
example(8,  approve, [emp=yes, buy=comp, sex=m, married=no]).
example(9,  approve, [emp=yes, buy=comp, sex=m, married=yes]).
example(10, approve, [emp=yes, buy=comp, sex=m, married=yes]).
example(11, reject,  [emp=no,  buy=comp, sex=m, married=yes]).
example(12, reject,  [emp=no,  buy=car,  sex=f, married=yes]).


/* For classification (assume that the class is unknown):
example(13, reject,  [emp=yes, buy=car,  sex=m, married=no]).
*/


/*-----------------------------------------------------------*/
/* representing nominal attributes as structural (for vs.pl) */
son(yes,?).
son(no,?).
son(comp,?).
son(car,?).
son(f,?).
son(m,?).

/* The data below are not needed for VS */

/*----------------------------------------------------------*/
/* Belief network (runs with bn.pl)                         */
/*----------------------------------------------------------*/

/*----------------------------------------------------------*/
/* List of variables                                        */
/*----------------------------------------------------------*/
variables([class, emp, buy, sex, married]).

/*----------------------------------------------------------*/
/* Structure of the graph                                   */
/*----------------------------------------------------------*/
parents(emp,[class]).
parents(buy,[class]).
parents(sex,[class]).
parents(married,[class]).
parents(class,[]).

/*---------------------------------------------------------*/
/* Values for variables                                    */
/*---------------------------------------------------------*/
values(emp,[yes,no]).
values(buy,[comp,car]).
values(sex,[m,f]).
values(married,[yes,no]).
values(class,[approve,reject]).

/*---------------------------------------------------------*/
/* conditional probabilities                               */
/*---------------------------------------------------------*/
pr(emp,[class=approve],[1.000,0.000]).
pr(emp,[class=reject],[0.250,0.750]).

pr(buy,[class=approve],[0.875,0.125]).
pr(buy,[class=reject],[0.500,0.500]).

pr(sex,[class=approve],[0.500,0.500]).
pr(sex,[class=reject],[0.250,0.750]).

pr(married,[class=approve],[0.500,0.500]).
pr(married,[class=reject],[0.750,0.250]).

pr(class,[],[0.667,0.333]).