/* 

  Healthiness in Picat.

  From BLOG examples/healtiness.blog:
  """
  healthiness model
  This BLOG program describes the model for healthiness in 
  a person.
  See exercise 3.5 in:
  Probabilistic Graphical Models: Principles and Techniques
  Daphne Koller, Nir Friedman, MIT 2009
  Each person is described by whether they are health
  conscious, have free time, exercise, have a good diet, 
  have normal weight, have high cholesterol, and whether
  they tested positive for high cholesterol.
  @author: jnieh
  @date: 2012-09-13
  """


  Cf my Gamble model gamble_healthyness.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.
% import ordset.

main => go.

/*

  * Observation: Not tested high cholesterol

var : health conscious
Probabilities:
true: 0.5706327099084759
false: 0.4293672900915241
mean = [true = 0.570633,false = 0.429367]

var : little free time
Probabilities:
true: 0.5041782729805014
false: 0.4958217270194986
mean = [true = 0.504178,false = 0.495822]

var : exercise
Probabilities:
true: 0.5254675686430561
false: 0.4745324313569439
mean = [true = 0.525468,false = 0.474532]

var : good diet
Probabilities:
true: 0.6625547154795066
false: 0.3374452845204934
mean = [true = 0.662555,false = 0.337445]

var : normal weight
Probabilities:
true: 0.5584958217270195
false: 0.4415041782729805
mean = [true = 0.558496,false = 0.441504]

var : high cholesterol
Probabilities:
false: 0.8963390370075607
true: 0.1036609629924393
mean = [false = 0.896339,true = 0.103661]

var : tested high cholesterol
Probabilities:
false: 1.0000000000000000
mean = [false = 1.0]

  * Observation: Exercise

var : health conscious
Probabilities:
true: 0.6962484872932634
false: 0.3037515127067366
mean = [true = 0.696248,false = 0.303752]

var : little free time
Probabilities:
false: 0.6926179911254539
true: 0.3073820088745462
mean = [false = 0.692618,true = 0.307382]

var : exercise
Probabilities:
true: 1.0000000000000000
mean = [true = 1.0]

var : good diet
Probabilities:
true: 0.5917708753529649
false: 0.4082291246470351
mean = [true = 0.591771,false = 0.408229]

var : normal weight
Probabilities:
true: 0.6855586930213796
false: 0.3144413069786204
mean = [true = 0.685559,false = 0.314441]

var : high cholesterol
Probabilities:
false: 0.5417507059298104
true: 0.4582492940701896
mean = [false = 0.541751,true = 0.458249]

var : tested high cholesterol
Probabilities:
false: 0.5348931020572811
true: 0.4651068979427188
mean = [false = 0.534893,true = 0.465107]

  * observation: not normal weight
var : health conscious
Probabilities:
false: 0.6241317721770192
true: 0.3758682278229807
mean = [false = 0.624132,true = 0.375868]

var : little free time
Probabilities:
true: 0.5419726136138122
false: 0.4580273863861877
mean = [true = 0.541973,false = 0.458027]

var : exercise
Probabilities:
false: 0.6662036118277436
true: 0.3337963881722564
mean = [false = 0.666204,true = 0.333796]

var : good diet
Probabilities:
false: 0.6773169279618972
true: 0.3226830720381028
mean = [false = 0.677317,true = 0.322683]

var : normal weight
Probabilities:
false: 1.0000000000000000
mean = [false = 1.0]

var : high cholesterol
Probabilities:
true: 0.5763048223853939
false: 0.4236951776146061
mean = [true = 0.576305,false = 0.423695]

var : tested high cholesterol
Probabilities:
true: 0.5701528080968447
false: 0.4298471919031554
mean = [true = 0.570153,false = 0.429847]

*/
go ?=>
  reset_store,
  run_model(10_000,$model,[show_probs_trunc,mean % ,
                           % show_percentiles,show_histogram,
                           % show_hpd_intervals,hpd_intervals=[0.84],
                           % min_accepted_samples=1000,show_accepted_samples=true
                          ]),
  nl,
  % show_store_lengths,nl,
  % fail,
  nl.
go => true.
  
  
model() =>


  HealthConscious = flip(0.5),
  LittleFreeTime = flip(0.5),
  
  Exercise = cases([[(HealthConscious,LittleFreeTime),flip(0.5)],
                    [(HealthConscious,not LittleFreeTime),flip(0.9)],
                    [(not HealthConscious,LittleFreeTime),flip(0.1)],
                    [(not HealthConscious,not LittleFreeTime),flip(0.5)]
                   ]),

  GoodDiet = condt(HealthConscious,flip(0.7),flip(0.3)),
  NormalWeight = cases([[(GoodDiet,Exercise),flip(0.8)],
                        [(GoodDiet,not Exercise),flip(0.5)],
                        [(not GoodDiet,Exercise),flip(0.5)],
                        [(not GoodDiet,not Exercise),flip(0.2)]
                       ]),

  HighCholesterol = condt(GoodDiet, flip(0.3), flip(0.7)),

  TestedHighCholesterol = condt(HighCholesterol,flip(0.9),flip(0.1)),

  % Evidence
  % observe((not TestedHighCholesterol)),
  % observe(Exercise),
  % observe(LittleFreeTime),
  observe(not NormalWeight),
  
  if observed_ok then
    add("health conscious",HealthConscious),
    add("little free time",LittleFreeTime),
    add("exercise",Exercise),
    add("good diet",GoodDiet),
    add("normal weight",NormalWeight),
    add("high cholesterol",HighCholesterol),
    add("tested high cholesterol",TestedHighCholesterol),                        
  end.