/* 

  Noisy OR in Picat.

  From https://github.com/SHoltzen/dice/blob/master/benchmarks/baselines/noisyOr.psi
  https://github.com/SHoltzen/dice/blob/master/benchmarks/baselines/noisyOr.dice

  Cf my Gamble model gamble_noisy_or.rkt

  This program was created by Hakan Kjellerstrand, hakank@gmail.com
  See also my Picat page: http://www.hakank.org/picat/

*/

import ppl_distributions, ppl_utils.
import util.

main => go.

/*

  var : expected value:
  Probabilities:
  0.814419: 0.8169000000000000
  0.185581: 0.1831000000000000
  mean = 0.699279

  var : n3
  Probabilities:
  true: 0.8169000000000000
  false: 0.1831000000000000
  mean = [true = 0.8169,false = 0.1831]

*/
go ?=>
  reset_store,
  run_model(10_000,$model,[show_probs_trunc,mean]),
  nl,
  % show_store_lengths,
  % fail,
  nl.
go => true.
  
  
model() =>
  N0 = flip(),
  N4 = flip(),
  N1 = condt(N0,flip(4/5), flip(1/10)),

  N21 = condt(N0,flip(4/5),flip(1/10)),

  N22 = condt(N4,flip(4/5),flip(1/10)),
  N33 = condt(N4,flip(4/5),flip(1/10)),

  N2  = check((N21 ; N22)),
  N31 = condt(N1,flip(4/5),flip(1/10)),
  N32 = condt(N2,flip(4/5),flip(1/10)),

  N3 = check((N31 ; N32 ; N33)),

  % return n3 ;; PSI:  expected: 130307/160000·δ(1)(n3)+29693/160000·δ(0)(n3)
  add("n3",N3),
  add("expected value:",
         ((130307/160000)*condt(N3,1,0)) + ((29693/160000) * condt(N3,0,1)) % the expected value
    ).