remove dump files.

packages/CLPBN/clpbn/bp/out.txt

@@ -1,762 +0,0 @@
-Variable: Burglar
-Domain:   b1, b2
-Parents:  
-Childs:   Alarm
-cpt             
-----------------
-b1         0.005
-b2         0.995
-
-Variable: FreightTruck
-Domain:   f1, f2
-Parents:  
-Childs:   Alarm
-cpt             
-----------------
-f1          0.03
-f2          0.97
-
-Variable: Alarm
-Domain:   a1, a2
-Parents:  Burglar, FreightTruck
-Childs:   
-cpt        b1,f1       b1,f2       b2,f1       b2,f2
-----------------------------------------------------
-a1         0.992        0.99         0.2       0.003
-a2         0.008        0.01         0.8       0.997
-
-Variable: Burglar
-Domain:   b1, b2
-Parents:  
-Childs:   Alarm
-cpt             
-----------------
-b1         0.005
-b2         0.995
-
-Variable: FreightTruck
-Domain:   f1, f2
-Parents:  
-Childs:   Alarm, _Jn
-cpt             
-----------------
-f1          0.03
-f2          0.97
-
-Variable: Alarm
-Domain:   a1, a2
-Parents:  Burglar, FreightTruck
-Childs:   _Jn
-cpt        b1,f1       b1,f2       b2,f1       b2,f2
-----------------------------------------------------
-a1         0.992        0.99         0.2       0.003
-a2         0.008        0.01         0.8       0.997
-
-Variable: _Jn
-Domain:   _jn0, _jn1, _jn2, _jn3
-Parents:  FreightTruck, Alarm
-Childs:   
-cpt        f1,a1       f1,a2       f2,a1       f2,a2
-----------------------------------------------------
-_jn0           1           0           0           0
-_jn1           0           1           0           0
-_jn2           0           0           1           0
-_jn3           0           0           0           1
-
-The graph is not single connected. Iterative belief propagation will be used.
-
-Initializing solver
--> schedule           = parallel
--> max iters          = 100
--> stable threashold  = 1e-20
--> query vars         = FreightTruck Alarm 
-domain    π(Burglar)         λ(Burglar)         belief          
-----------------------------------------------------------------
-b1        1                  1                  0.5             
-b2        1                  1                  0.5             
-
-domain    πAlarm(Burglar)            λAlarm(Burglar)            
-----------------------------------------------------------------
-b1        0.5                        0.5                        
-b2        0.5                        0.5                        
-
-domain    π(FreightTruck)    λ(FreightTruck)    belief          
-----------------------------------------------------------------
-f1        1                  1                  0.5             
-f2        1                  1                  0.5             
-
-domain    πAlarm(FreightTruck)       λAlarm(FreightTruck)       
-----------------------------------------------------------------
-f1        0.5                        0.5                        
-f2        0.5                        0.5                        
-
-domain    π_Jn(FreightTruck)         λ_Jn(FreightTruck)         
-----------------------------------------------------------------
-f1        0.5                        0.5                        
-f2        0.5                        0.5                        
-
-domain    π(Alarm)           λ(Alarm)           belief          
-----------------------------------------------------------------
-a1        1                  1                  0.5             
-a2        1                  1                  0.5             
-
-domain    π_Jn(Alarm)                λ_Jn(Alarm)                
-----------------------------------------------------------------
-a1        0.5                        0.5                        
-a2        0.5                        0.5                        
-
-domain    π(_Jn)             λ(_Jn)             belief          
-----------------------------------------------------------------
-_jn0      1                  1                  0.25            
-_jn1      1                  1                  0.25            
-_jn2      1                  1                  0.25            
-_jn3      1                  1                  0.25            
-
-
-
-********************************************************************************
- Iteration 1
-********************************************************************************
-λ message Alarm --> Burglar
-λAlarm(b1)
-= [p(a1|b1,f1).πAlarm(f1) + p(a1|b1,f2).πAlarm(f2)].λ(a1)
-+ [p(a2|b1,f1).πAlarm(f1) + p(a2|b1,f2).πAlarm(f2)].λ(a2)
-= [(0.992x0.5) + (0.99x0.5)]x1
-+ [(0.008x0.5) + (0.01x0.5)]x1 = 1
-λAlarm(b2)
-= [p(a1|b2,f1).πAlarm(f1) + p(a1|b2,f2).πAlarm(f2)].λ(a1)
-+ [p(a2|b2,f1).πAlarm(f1) + p(a2|b2,f2).πAlarm(f2)].λ(a2)
-= [(0.2x0.5) + (0.003x0.5)]x1
-+ [(0.8x0.5) + (0.997x0.5)]x1 = 1
-
-λ message Alarm --> FreightTruck
-λAlarm(f1)
-= [p(a1|b1,f1).πAlarm(b1) + p(a1|b2,f1).πAlarm(b2)].λ(a1)
-+ [p(a2|b1,f1).πAlarm(b1) + p(a2|b2,f1).πAlarm(b2)].λ(a2)
-= [(0.992x0.5) + (0.2x0.5)]x1
-+ [(0.008x0.5) + (0.8x0.5)]x1 = 1
-λAlarm(f2)
-= [p(a1|b1,f2).πAlarm(b1) + p(a1|b2,f2).πAlarm(b2)].λ(a1)
-+ [p(a2|b1,f2).πAlarm(b1) + p(a2|b2,f2).πAlarm(b2)].λ(a2)
-= [(0.99x0.5) + (0.003x0.5)]x1
-+ [(0.01x0.5) + (0.997x0.5)]x1 = 1
-
-λ message _Jn --> FreightTruck
-λ_Jn(f1)
-= [p(_jn0|f1,a1).π_Jn(a1) + p(_jn0|f1,a2).π_Jn(a2)].λ(_jn0)
-+ [p(_jn1|f1,a1).π_Jn(a1) + p(_jn1|f1,a2).π_Jn(a2)].λ(_jn1)
-+ [p(_jn2|f1,a1).π_Jn(a1) + p(_jn2|f1,a2).π_Jn(a2)].λ(_jn2)
-+ [p(_jn3|f1,a1).π_Jn(a1) + p(_jn3|f1,a2).π_Jn(a2)].λ(_jn3)
-= [(1x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (1x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1 = 1
-λ_Jn(f2)
-= [p(_jn0|f2,a1).π_Jn(a1) + p(_jn0|f2,a2).π_Jn(a2)].λ(_jn0)
-+ [p(_jn1|f2,a1).π_Jn(a1) + p(_jn1|f2,a2).π_Jn(a2)].λ(_jn1)
-+ [p(_jn2|f2,a1).π_Jn(a1) + p(_jn2|f2,a2).π_Jn(a2)].λ(_jn2)
-+ [p(_jn3|f2,a1).π_Jn(a1) + p(_jn3|f2,a2).π_Jn(a2)].λ(_jn3)
-= [(0x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1
-+ [(1x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (1x0.5)]x1 = 1
-
-λ message _Jn --> Alarm
-λ_Jn(a1)
-= [p(_jn0|f1,a1).π_Jn(f1) + p(_jn0|f2,a1).π_Jn(f2)].λ(_jn0)
-+ [p(_jn1|f1,a1).π_Jn(f1) + p(_jn1|f2,a1).π_Jn(f2)].λ(_jn1)
-+ [p(_jn2|f1,a1).π_Jn(f1) + p(_jn2|f2,a1).π_Jn(f2)].λ(_jn2)
-+ [p(_jn3|f1,a1).π_Jn(f1) + p(_jn3|f2,a1).π_Jn(f2)].λ(_jn3)
-= [(1x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (1x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1 = 1
-λ_Jn(a2)
-= [p(_jn0|f1,a2).π_Jn(f1) + p(_jn0|f2,a2).π_Jn(f2)].λ(_jn0)
-+ [p(_jn1|f1,a2).π_Jn(f1) + p(_jn1|f2,a2).π_Jn(f2)].λ(_jn1)
-+ [p(_jn2|f1,a2).π_Jn(f1) + p(_jn2|f2,a2).π_Jn(f2)].λ(_jn2)
-+ [p(_jn3|f1,a2).π_Jn(f1) + p(_jn3|f2,a2).π_Jn(f2)].λ(_jn3)
-= [(0x0.5) + (0x0.5)]x1
-+ [(1x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (1x0.5)]x1 = 1
-
-π message Burglar --> Alarm
-πAlarm(b1) = π(b1) = 0.005 = 0.005
-πAlarm(b2) = π(b2) = 0.995 = 0.995
-
-π message FreightTruck --> Alarm
-πAlarm(f1) = π(f1).λ_Jn(f1) = 0.03 x 0.5 = 0.015
-πAlarm(f2) = π(f2).λ_Jn(f2) = 0.97 x 0.5 = 0.485
-
-π message FreightTruck --> _Jn
-π_Jn(f1) = π(f1).λAlarm(f1) = 0.03 x 0.5 = 0.015
-π_Jn(f2) = π(f2).λAlarm(f2) = 0.97 x 0.5 = 0.485
-
-π message Alarm --> _Jn
-π_Jn(a1) = π(a1) = 1 = 1
-π_Jn(a2) = π(a2) = 1 = 1
-
-var Burglar:
-π(b1)
-= p(b1)
-= (0.005) = 0.005
-π(b2)
-= p(b2)
-= (0.995) = 0.995
-λ(b1) = λAlarm(b1) = 0.5 = 0.5
-λ(b2) = λAlarm(b2) = 0.5 = 0.5
-belief change = 1
-
-var FreightTruck:
-π(f1)
-= p(f1)
-= (0.03) = 0.03
-π(f2)
-= p(f2)
-= (0.97) = 0.97
-λ(f1) = λAlarm(f1).λ_Jn(f1) = 0.5 x 0.5 = 0.25
-λ(f2) = λAlarm(f2).λ_Jn(f2) = 0.5 x 0.5 = 0.25
-belief change = 1
-
-var Alarm:
-π(a1)
-= p(a1|b1,f1).πAlarm(b1).πAlarm(f1)
-+ p(a1|b1,f2).πAlarm(b1).πAlarm(f2)
-+ p(a1|b2,f1).πAlarm(b2).πAlarm(f1)
-+ p(a1|b2,f2).πAlarm(b2).πAlarm(f2)
-= (0.992x0.005x0.03)
-+ (0.99x0.005x0.97)
-+ (0.2x0.995x0.03)
-+ (0.003x0.995x0.97) = 0.01381575
-π(a2)
-= p(a2|b1,f1).πAlarm(b1).πAlarm(f1)
-+ p(a2|b1,f2).πAlarm(b1).πAlarm(f2)
-+ p(a2|b2,f1).πAlarm(b2).πAlarm(f1)
-+ p(a2|b2,f2).πAlarm(b2).πAlarm(f2)
-= (0.008x0.005x0.03)
-+ (0.01x0.005x0.97)
-+ (0.8x0.995x0.03)
-+ (0.997x0.995x0.97) = 0.98618425
-λ(a1) = λ_Jn(a1) = 0.5 = 0.5
-λ(a2) = λ_Jn(a2) = 0.5 = 0.5
-belief change = 1
-
-var _Jn:
-π(_jn0)
-= p(_jn0|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn0|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn0|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn0|f2,a2).π_Jn(f2).π_Jn(a2)
-= (1x0.03x0.5)
-+ (0x0.03x0.5)
-+ (0x0.97x0.5)
-+ (0x0.97x0.5) = 0.015
-π(_jn1)
-= p(_jn1|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn1|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn1|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn1|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.5)
-+ (1x0.03x0.5)
-+ (0x0.97x0.5)
-+ (0x0.97x0.5) = 0.015
-π(_jn2)
-= p(_jn2|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn2|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn2|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn2|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.5)
-+ (0x0.03x0.5)
-+ (1x0.97x0.5)
-+ (0x0.97x0.5) = 0.485
-π(_jn3)
-= p(_jn3|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn3|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn3|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn3|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.5)
-+ (0x0.03x0.5)
-+ (0x0.97x0.5)
-+ (1x0.97x0.5) = 0.485
-λ(_jn0) = 1
-λ(_jn1) = 1
-λ(_jn2) = 1
-λ(_jn3) = 1
-belief change = 1
-
-domain    π(Burglar)         λ(Burglar)         belief          
-----------------------------------------------------------------
-b1        0.005              0.5                0.005           
-b2        0.995              0.5                0.995           
-
-domain    πAlarm(Burglar)            λAlarm(Burglar)            
-----------------------------------------------------------------
-b1        0.005                      0.5                        
-b2        0.995                      0.5                        
-
-domain    π(FreightTruck)    λ(FreightTruck)    belief          
-----------------------------------------------------------------
-f1        0.03               0.25               0.03            
-f2        0.97               0.25               0.97            
-
-domain    πAlarm(FreightTruck)       λAlarm(FreightTruck)       
-----------------------------------------------------------------
-f1        0.03                       0.5                        
-f2        0.97                       0.5                        
-
-domain    π_Jn(FreightTruck)         λ_Jn(FreightTruck)         
-----------------------------------------------------------------
-f1        0.03                       0.5                        
-f2        0.97                       0.5                        
-
-domain    π(Alarm)           λ(Alarm)           belief          
-----------------------------------------------------------------
-a1        0.01381575         0.5                0.01381575      
-a2        0.98618425         0.5                0.98618425      
-
-domain    π_Jn(Alarm)                λ_Jn(Alarm)                
-----------------------------------------------------------------
-a1        0.5                        0.5                        
-a2        0.5                        0.5                        
-
-domain    π(_Jn)             λ(_Jn)             belief          
-----------------------------------------------------------------
-_jn0      0.015              1                  0.015           
-_jn1      0.015              1                  0.015           
-_jn2      0.485              1                  0.485           
-_jn3      0.485              1                  0.485           
-
-
-
-********************************************************************************
- Iteration 2
-********************************************************************************
-λ message Alarm --> Burglar
-λAlarm(b1)
-= [p(a1|b1,f1).πAlarm(f1) + p(a1|b1,f2).πAlarm(f2)].λ(a1)
-+ [p(a2|b1,f1).πAlarm(f1) + p(a2|b1,f2).πAlarm(f2)].λ(a2)
-= [(0.992x0.03) + (0.99x0.97)]x0.5
-+ [(0.008x0.03) + (0.01x0.97)]x0.5 = 0.5
-λAlarm(b2)
-= [p(a1|b2,f1).πAlarm(f1) + p(a1|b2,f2).πAlarm(f2)].λ(a1)
-+ [p(a2|b2,f1).πAlarm(f1) + p(a2|b2,f2).πAlarm(f2)].λ(a2)
-= [(0.2x0.03) + (0.003x0.97)]x0.5
-+ [(0.8x0.03) + (0.997x0.97)]x0.5 = 0.5
-
-λ message Alarm --> FreightTruck
-λAlarm(f1)
-= [p(a1|b1,f1).πAlarm(b1) + p(a1|b2,f1).πAlarm(b2)].λ(a1)
-+ [p(a2|b1,f1).πAlarm(b1) + p(a2|b2,f1).πAlarm(b2)].λ(a2)
-= [(0.992x0.005) + (0.2x0.995)]x0.5
-+ [(0.008x0.005) + (0.8x0.995)]x0.5 = 0.5
-λAlarm(f2)
-= [p(a1|b1,f2).πAlarm(b1) + p(a1|b2,f2).πAlarm(b2)].λ(a1)
-+ [p(a2|b1,f2).πAlarm(b1) + p(a2|b2,f2).πAlarm(b2)].λ(a2)
-= [(0.99x0.005) + (0.003x0.995)]x0.5
-+ [(0.01x0.005) + (0.997x0.995)]x0.5 = 0.5
-
-λ message _Jn --> FreightTruck
-λ_Jn(f1)
-= [p(_jn0|f1,a1).π_Jn(a1) + p(_jn0|f1,a2).π_Jn(a2)].λ(_jn0)
-+ [p(_jn1|f1,a1).π_Jn(a1) + p(_jn1|f1,a2).π_Jn(a2)].λ(_jn1)
-+ [p(_jn2|f1,a1).π_Jn(a1) + p(_jn2|f1,a2).π_Jn(a2)].λ(_jn2)
-+ [p(_jn3|f1,a1).π_Jn(a1) + p(_jn3|f1,a2).π_Jn(a2)].λ(_jn3)
-= [(1x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (1x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1 = 1
-λ_Jn(f2)
-= [p(_jn0|f2,a1).π_Jn(a1) + p(_jn0|f2,a2).π_Jn(a2)].λ(_jn0)
-+ [p(_jn1|f2,a1).π_Jn(a1) + p(_jn1|f2,a2).π_Jn(a2)].λ(_jn1)
-+ [p(_jn2|f2,a1).π_Jn(a1) + p(_jn2|f2,a2).π_Jn(a2)].λ(_jn2)
-+ [p(_jn3|f2,a1).π_Jn(a1) + p(_jn3|f2,a2).π_Jn(a2)].λ(_jn3)
-= [(0x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (0x0.5)]x1
-+ [(1x0.5) + (0x0.5)]x1
-+ [(0x0.5) + (1x0.5)]x1 = 1
-
-λ message _Jn --> Alarm
-λ_Jn(a1)
-= [p(_jn0|f1,a1).π_Jn(f1) + p(_jn0|f2,a1).π_Jn(f2)].λ(_jn0)
-+ [p(_jn1|f1,a1).π_Jn(f1) + p(_jn1|f2,a1).π_Jn(f2)].λ(_jn1)
-+ [p(_jn2|f1,a1).π_Jn(f1) + p(_jn2|f2,a1).π_Jn(f2)].λ(_jn2)
-+ [p(_jn3|f1,a1).π_Jn(f1) + p(_jn3|f2,a1).π_Jn(f2)].λ(_jn3)
-= [(1x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (1x0.97)]x1
-+ [(0x0.03) + (0x0.97)]x1 = 1
-λ_Jn(a2)
-= [p(_jn0|f1,a2).π_Jn(f1) + p(_jn0|f2,a2).π_Jn(f2)].λ(_jn0)
-+ [p(_jn1|f1,a2).π_Jn(f1) + p(_jn1|f2,a2).π_Jn(f2)].λ(_jn1)
-+ [p(_jn2|f1,a2).π_Jn(f1) + p(_jn2|f2,a2).π_Jn(f2)].λ(_jn2)
-+ [p(_jn3|f1,a2).π_Jn(f1) + p(_jn3|f2,a2).π_Jn(f2)].λ(_jn3)
-= [(0x0.03) + (0x0.97)]x1
-+ [(1x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (1x0.97)]x1 = 1
-
-π message Burglar --> Alarm
-πAlarm(b1) = π(b1) = 0.005 = 0.005
-πAlarm(b2) = π(b2) = 0.995 = 0.995
-
-π message FreightTruck --> Alarm
-πAlarm(f1) = π(f1).λ_Jn(f1) = 0.03 x 0.5 = 0.015
-πAlarm(f2) = π(f2).λ_Jn(f2) = 0.97 x 0.5 = 0.485
-
-π message FreightTruck --> _Jn
-π_Jn(f1) = π(f1).λAlarm(f1) = 0.03 x 0.5 = 0.015
-π_Jn(f2) = π(f2).λAlarm(f2) = 0.97 x 0.5 = 0.485
-
-π message Alarm --> _Jn
-π_Jn(a1) = π(a1) = 0.0138158 = 0.01381575
-π_Jn(a2) = π(a2) = 0.986184 = 0.98618425
-
-var Burglar:
-π(b1)
-= p(b1)
-= (0.005) = 0.005
-π(b2)
-= p(b2)
-= (0.995) = 0.995
-λ(b1) = λAlarm(b1) = 0.5 = 0.5
-λ(b2) = λAlarm(b2) = 0.5 = 0.5
-belief change = 8.67361738e-19
-
-var FreightTruck:
-π(f1)
-= p(f1)
-= (0.03) = 0.03
-π(f2)
-= p(f2)
-= (0.97) = 0.97
-λ(f1) = λAlarm(f1).λ_Jn(f1) = 0.5 x 0.5 = 0.25
-λ(f2) = λAlarm(f2).λ_Jn(f2) = 0.5 x 0.5 = 0.25
-belief change = 0
-
-var Alarm:
-π(a1)
-= p(a1|b1,f1).πAlarm(b1).πAlarm(f1)
-+ p(a1|b1,f2).πAlarm(b1).πAlarm(f2)
-+ p(a1|b2,f1).πAlarm(b2).πAlarm(f1)
-+ p(a1|b2,f2).πAlarm(b2).πAlarm(f2)
-= (0.992x0.005x0.03)
-+ (0.99x0.005x0.97)
-+ (0.2x0.995x0.03)
-+ (0.003x0.995x0.97) = 0.01381575
-π(a2)
-= p(a2|b1,f1).πAlarm(b1).πAlarm(f1)
-+ p(a2|b1,f2).πAlarm(b1).πAlarm(f2)
-+ p(a2|b2,f1).πAlarm(b2).πAlarm(f1)
-+ p(a2|b2,f2).πAlarm(b2).πAlarm(f2)
-= (0.008x0.005x0.03)
-+ (0.01x0.005x0.97)
-+ (0.8x0.995x0.03)
-+ (0.997x0.995x0.97) = 0.98618425
-λ(a1) = λ_Jn(a1) = 0.5 = 0.5
-λ(a2) = λ_Jn(a2) = 0.5 = 0.5
-belief change = 0
-
-var _Jn:
-π(_jn0)
-= p(_jn0|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn0|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn0|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn0|f2,a2).π_Jn(f2).π_Jn(a2)
-= (1x0.03x0.0138158)
-+ (0x0.03x0.986184)
-+ (0x0.97x0.0138158)
-+ (0x0.97x0.986184) = 0.0004144725
-π(_jn1)
-= p(_jn1|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn1|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn1|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn1|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.0138158)
-+ (1x0.03x0.986184)
-+ (0x0.97x0.0138158)
-+ (0x0.97x0.986184) = 0.0295855275
-π(_jn2)
-= p(_jn2|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn2|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn2|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn2|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.0138158)
-+ (0x0.03x0.986184)
-+ (1x0.97x0.0138158)
-+ (0x0.97x0.986184) = 0.0134012775
-π(_jn3)
-= p(_jn3|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn3|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn3|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn3|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.0138158)
-+ (0x0.03x0.986184)
-+ (0x0.97x0.0138158)
-+ (1x0.97x0.986184) = 0.9565987225
-λ(_jn0) = 1
-λ(_jn1) = 1
-λ(_jn2) = 1
-λ(_jn3) = 1
-belief change = 0.9723685
-
-domain    π(Burglar)         λ(Burglar)         belief          
-----------------------------------------------------------------
-b1        0.005              0.5                0.005           
-b2        0.995              0.5                0.995           
-
-domain    πAlarm(Burglar)            λAlarm(Burglar)            
-----------------------------------------------------------------
-b1        0.005                      0.5                        
-b2        0.995                      0.5                        
-
-domain    π(FreightTruck)    λ(FreightTruck)    belief          
-----------------------------------------------------------------
-f1        0.03               0.25               0.03            
-f2        0.97               0.25               0.97            
-
-domain    πAlarm(FreightTruck)       λAlarm(FreightTruck)       
-----------------------------------------------------------------
-f1        0.03                       0.5                        
-f2        0.97                       0.5                        
-
-domain    π_Jn(FreightTruck)         λ_Jn(FreightTruck)         
-----------------------------------------------------------------
-f1        0.03                       0.5                        
-f2        0.97                       0.5                        
-
-domain    π(Alarm)           λ(Alarm)           belief          
-----------------------------------------------------------------
-a1        0.01381575         0.5                0.01381575      
-a2        0.98618425         0.5                0.98618425      
-
-domain    π_Jn(Alarm)                λ_Jn(Alarm)                
-----------------------------------------------------------------
-a1        0.01381575                 0.5                        
-a2        0.98618425                 0.5                        
-
-domain    π(_Jn)             λ(_Jn)             belief          
-----------------------------------------------------------------
-_jn0      0.0004144725       1                  0.0004144725    
-_jn1      0.0295855275       1                  0.0295855275    
-_jn2      0.0134012775       1                  0.0134012775    
-_jn3      0.9565987225       1                  0.9565987225    
-
-
-
-********************************************************************************
- Iteration 3
-********************************************************************************
-λ message Alarm --> Burglar
-λAlarm(b1)
-= [p(a1|b1,f1).πAlarm(f1) + p(a1|b1,f2).πAlarm(f2)].λ(a1)
-+ [p(a2|b1,f1).πAlarm(f1) + p(a2|b1,f2).πAlarm(f2)].λ(a2)
-= [(0.992x0.03) + (0.99x0.97)]x0.5
-+ [(0.008x0.03) + (0.01x0.97)]x0.5 = 0.5
-λAlarm(b2)
-= [p(a1|b2,f1).πAlarm(f1) + p(a1|b2,f2).πAlarm(f2)].λ(a1)
-+ [p(a2|b2,f1).πAlarm(f1) + p(a2|b2,f2).πAlarm(f2)].λ(a2)
-= [(0.2x0.03) + (0.003x0.97)]x0.5
-+ [(0.8x0.03) + (0.997x0.97)]x0.5 = 0.5
-
-λ message Alarm --> FreightTruck
-λAlarm(f1)
-= [p(a1|b1,f1).πAlarm(b1) + p(a1|b2,f1).πAlarm(b2)].λ(a1)
-+ [p(a2|b1,f1).πAlarm(b1) + p(a2|b2,f1).πAlarm(b2)].λ(a2)
-= [(0.992x0.005) + (0.2x0.995)]x0.5
-+ [(0.008x0.005) + (0.8x0.995)]x0.5 = 0.5
-λAlarm(f2)
-= [p(a1|b1,f2).πAlarm(b1) + p(a1|b2,f2).πAlarm(b2)].λ(a1)
-+ [p(a2|b1,f2).πAlarm(b1) + p(a2|b2,f2).πAlarm(b2)].λ(a2)
-= [(0.99x0.005) + (0.003x0.995)]x0.5
-+ [(0.01x0.005) + (0.997x0.995)]x0.5 = 0.5
-
-λ message _Jn --> FreightTruck
-λ_Jn(f1)
-= [p(_jn0|f1,a1).π_Jn(a1) + p(_jn0|f1,a2).π_Jn(a2)].λ(_jn0)
-+ [p(_jn1|f1,a1).π_Jn(a1) + p(_jn1|f1,a2).π_Jn(a2)].λ(_jn1)
-+ [p(_jn2|f1,a1).π_Jn(a1) + p(_jn2|f1,a2).π_Jn(a2)].λ(_jn2)
-+ [p(_jn3|f1,a1).π_Jn(a1) + p(_jn3|f1,a2).π_Jn(a2)].λ(_jn3)
-= [(1x0.0138158) + (0x0.986184)]x1
-+ [(0x0.0138158) + (1x0.986184)]x1
-+ [(0x0.0138158) + (0x0.986184)]x1
-+ [(0x0.0138158) + (0x0.986184)]x1 = 1
-λ_Jn(f2)
-= [p(_jn0|f2,a1).π_Jn(a1) + p(_jn0|f2,a2).π_Jn(a2)].λ(_jn0)
-+ [p(_jn1|f2,a1).π_Jn(a1) + p(_jn1|f2,a2).π_Jn(a2)].λ(_jn1)
-+ [p(_jn2|f2,a1).π_Jn(a1) + p(_jn2|f2,a2).π_Jn(a2)].λ(_jn2)
-+ [p(_jn3|f2,a1).π_Jn(a1) + p(_jn3|f2,a2).π_Jn(a2)].λ(_jn3)
-= [(0x0.0138158) + (0x0.986184)]x1
-+ [(0x0.0138158) + (0x0.986184)]x1
-+ [(1x0.0138158) + (0x0.986184)]x1
-+ [(0x0.0138158) + (1x0.986184)]x1 = 1
-
-λ message _Jn --> Alarm
-λ_Jn(a1)
-= [p(_jn0|f1,a1).π_Jn(f1) + p(_jn0|f2,a1).π_Jn(f2)].λ(_jn0)
-+ [p(_jn1|f1,a1).π_Jn(f1) + p(_jn1|f2,a1).π_Jn(f2)].λ(_jn1)
-+ [p(_jn2|f1,a1).π_Jn(f1) + p(_jn2|f2,a1).π_Jn(f2)].λ(_jn2)
-+ [p(_jn3|f1,a1).π_Jn(f1) + p(_jn3|f2,a1).π_Jn(f2)].λ(_jn3)
-= [(1x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (1x0.97)]x1
-+ [(0x0.03) + (0x0.97)]x1 = 1
-λ_Jn(a2)
-= [p(_jn0|f1,a2).π_Jn(f1) + p(_jn0|f2,a2).π_Jn(f2)].λ(_jn0)
-+ [p(_jn1|f1,a2).π_Jn(f1) + p(_jn1|f2,a2).π_Jn(f2)].λ(_jn1)
-+ [p(_jn2|f1,a2).π_Jn(f1) + p(_jn2|f2,a2).π_Jn(f2)].λ(_jn2)
-+ [p(_jn3|f1,a2).π_Jn(f1) + p(_jn3|f2,a2).π_Jn(f2)].λ(_jn3)
-= [(0x0.03) + (0x0.97)]x1
-+ [(1x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (0x0.97)]x1
-+ [(0x0.03) + (1x0.97)]x1 = 1
-
-π message Burglar --> Alarm
-πAlarm(b1) = π(b1) = 0.005 = 0.005
-πAlarm(b2) = π(b2) = 0.995 = 0.995
-
-π message FreightTruck --> Alarm
-πAlarm(f1) = π(f1).λ_Jn(f1) = 0.03 x 0.5 = 0.015
-πAlarm(f2) = π(f2).λ_Jn(f2) = 0.97 x 0.5 = 0.485
-
-π message FreightTruck --> _Jn
-π_Jn(f1) = π(f1).λAlarm(f1) = 0.03 x 0.5 = 0.015
-π_Jn(f2) = π(f2).λAlarm(f2) = 0.97 x 0.5 = 0.485
-
-π message Alarm --> _Jn
-π_Jn(a1) = π(a1) = 0.0138158 = 0.01381575
-π_Jn(a2) = π(a2) = 0.986184 = 0.98618425
-
-var Burglar:
-π(b1)
-= p(b1)
-= (0.005) = 0.005
-π(b2)
-= p(b2)
-= (0.995) = 0.995
-λ(b1) = λAlarm(b1) = 0.5 = 0.5
-λ(b2) = λAlarm(b2) = 0.5 = 0.5
-belief change = 0
-
-var FreightTruck:
-π(f1)
-= p(f1)
-= (0.03) = 0.03
-π(f2)
-= p(f2)
-= (0.97) = 0.97
-λ(f1) = λAlarm(f1).λ_Jn(f1) = 0.5 x 0.5 = 0.25
-λ(f2) = λAlarm(f2).λ_Jn(f2) = 0.5 x 0.5 = 0.25
-belief change = 0
-
-var Alarm:
-π(a1)
-= p(a1|b1,f1).πAlarm(b1).πAlarm(f1)
-+ p(a1|b1,f2).πAlarm(b1).πAlarm(f2)
-+ p(a1|b2,f1).πAlarm(b2).πAlarm(f1)
-+ p(a1|b2,f2).πAlarm(b2).πAlarm(f2)
-= (0.992x0.005x0.03)
-+ (0.99x0.005x0.97)
-+ (0.2x0.995x0.03)
-+ (0.003x0.995x0.97) = 0.01381575
-π(a2)
-= p(a2|b1,f1).πAlarm(b1).πAlarm(f1)
-+ p(a2|b1,f2).πAlarm(b1).πAlarm(f2)
-+ p(a2|b2,f1).πAlarm(b2).πAlarm(f1)
-+ p(a2|b2,f2).πAlarm(b2).πAlarm(f2)
-= (0.008x0.005x0.03)
-+ (0.01x0.005x0.97)
-+ (0.8x0.995x0.03)
-+ (0.997x0.995x0.97) = 0.98618425
-λ(a1) = λ_Jn(a1) = 0.5 = 0.5
-λ(a2) = λ_Jn(a2) = 0.5 = 0.5
-belief change = 0
-
-var _Jn:
-π(_jn0)
-= p(_jn0|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn0|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn0|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn0|f2,a2).π_Jn(f2).π_Jn(a2)
-= (1x0.03x0.0138158)
-+ (0x0.03x0.986184)
-+ (0x0.97x0.0138158)
-+ (0x0.97x0.986184) = 0.0004144725
-π(_jn1)
-= p(_jn1|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn1|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn1|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn1|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.0138158)
-+ (1x0.03x0.986184)
-+ (0x0.97x0.0138158)
-+ (0x0.97x0.986184) = 0.0295855275
-π(_jn2)
-= p(_jn2|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn2|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn2|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn2|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.0138158)
-+ (0x0.03x0.986184)
-+ (1x0.97x0.0138158)
-+ (0x0.97x0.986184) = 0.0134012775
-π(_jn3)
-= p(_jn3|f1,a1).π_Jn(f1).π_Jn(a1)
-+ p(_jn3|f1,a2).π_Jn(f1).π_Jn(a2)
-+ p(_jn3|f2,a1).π_Jn(f2).π_Jn(a1)
-+ p(_jn3|f2,a2).π_Jn(f2).π_Jn(a2)
-= (0x0.03x0.0138158)
-+ (0x0.03x0.986184)
-+ (0x0.97x0.0138158)
-+ (1x0.97x0.986184) = 0.9565987225
-λ(_jn0) = 1
-λ(_jn1) = 1
-λ(_jn2) = 1
-λ(_jn3) = 1
-belief change = 0
-
-domain    π(Burglar)         λ(Burglar)         belief          
-----------------------------------------------------------------
-b1        0.005              0.5                0.005           
-b2        0.995              0.5                0.995           
-
-domain    πAlarm(Burglar)            λAlarm(Burglar)            
-----------------------------------------------------------------
-b1        0.005                      0.5                        
-b2        0.995                      0.5                        
-
-domain    π(FreightTruck)    λ(FreightTruck)    belief          
-----------------------------------------------------------------
-f1        0.03               0.25               0.03            
-f2        0.97               0.25               0.97            
-
-domain    πAlarm(FreightTruck)       λAlarm(FreightTruck)       
-----------------------------------------------------------------
-f1        0.03                       0.5                        
-f2        0.97                       0.5                        
-
-domain    π_Jn(FreightTruck)         λ_Jn(FreightTruck)         
-----------------------------------------------------------------
-f1        0.03                       0.5                        
-f2        0.97                       0.5                        
-
-domain    π(Alarm)           λ(Alarm)           belief          
-----------------------------------------------------------------
-a1        0.01381575         0.5                0.01381575      
-a2        0.98618425         0.5                0.98618425      
-
-domain    π_Jn(Alarm)                λ_Jn(Alarm)                
-----------------------------------------------------------------
-a1        0.01381575                 0.5                        
-a2        0.98618425                 0.5                        
-
-domain    π(_Jn)             λ(_Jn)             belief          
-----------------------------------------------------------------
-_jn0      0.0004144725       1                  0.0004144725    
-_jn1      0.0295855275       1                  0.0295855275    
-_jn2      0.0134012775       1                  0.0134012775    
-_jn3      0.9565987225       1                  0.9565987225    
-
-
-Iterative belief propagation converged in 3 iterations
-

packages/CLPBN/clpbn/bp/simple-loop.seq.txt

@@ -1,85 +0,0 @@
-Variable: A
-Domain:   a1, a2
-Parents:  
-Childs:   B, C
-cpt             
-----------------
-a1          0.01
-a2          0.09
-
-Variable: B
-Domain:   b1, b2
-Parents:  A
-Childs:   D
-cpt          a1,         a2,
-----------------------------
-b1          0.03         0.6
-b2          0.97         0.4
-
-Variable: C
-Domain:   c1, c2
-Parents:  A
-Childs:   D
-cpt          a1,         a2,
-----------------------------
-c1          0.24        0.12
-c2          0.76        0.88
-
-Variable: D
-Domain:   d1, d2
-Parents:  B, C
-Childs:   
-cpt       b1,c1,      b1,c2,      b2,c1,      b2,c2,
-----------------------------------------------------
-d1           0.2         0.7        0.45        0.22
-d2           0.8         0.3        0.55        0.78
-
-initializing solver
-    schedule = sequential
-    maxIter  = 100
-    accuracy = 0
-sending pi message from `A' to `B'
-    πB(a1) = π(a1).λC(a1) = 0.01 x 1 = 0.01
-    πB(a2) = π(a2).λC(a2) = 0.09 x 1 = 0.09
-    π(b1) = p(b1|a1).πB(a1) + p(b1|a2).πB(a2) = (0.03 x 0.01) + (0.6 x 0.09) = 0.0543
-    π(b2) = p(b2|a1).πB(a1) + p(b2|a2).πB(a2) = (0.97 x 0.01) + (0.4 x 0.09) = 0.0457
-sending pi message from `B' to `D'
-    πD(b1) = π(b1) = 0.0543 = 0.0543
-    πD(b2) = π(b2) = 0.0457 = 0.0457
-    π(d1) = p(d1|b1,c1).πD(b1).πD(c1) + p(d1|b1,c2).πD(b1).πD(c2) + p(d1|b2,c1).πD(b2).πD(c1) + p(d1|b2,c2).πD(b2).πD(c2) = (0.2 x 0.0543 x 1) + (0.7 x 0.0543 x 1) + (0.45 x 0.0457 x 1) + (0.22 x 0.0457 x 1) = 0.079489
-    π(d2) = p(d2|b1,c1).πD(b1).πD(c1) + p(d2|b1,c2).πD(b1).πD(c2) + p(d2|b2,c1).πD(b2).πD(c1) + p(d2|b2,c2).πD(b2).πD(c2) = (0.8 x 0.0543 x 1) + (0.3 x 0.0543 x 1) + (0.55 x 0.0457 x 1) + (0.78 x 0.0457 x 1) = 0.120511
-sending pi message from `A' to `C'
-    πC(a1) = π(a1).λB(a1) = 0.01 x 1 = 0.01
-    πC(a2) = π(a2).λB(a2) = 0.09 x 1 = 0.09
-    π(c1) = p(c1|a1).πC(a1) + p(c1|a2).πC(a2) = (0.24 x 0.01) + (0.12 x 0.09) = 0.0132
-    π(c2) = p(c2|a1).πC(a1) + p(c2|a2).πC(a2) = (0.76 x 0.01) + (0.88 x 0.09) = 0.0868
-sending pi message from `C' to `D'
-    πD(c1) = π(c1) = 0.0132 = 0.0132
-    πD(c2) = π(c2) = 0.0868 = 0.0868
-    π(d1) = p(d1|b1,c1).πD(b1).πD(c1) + p(d1|b1,c2).πD(b1).πD(c2) + p(d1|b2,c1).πD(b2).πD(c1) + p(d1|b2,c2).πD(b2).πD(c2) = (0.2 x 0.0543 x 0.0132) + (0.7 x 0.0543 x 0.0868) + (0.45 x 0.0457 x 0.0132) + (0.22 x 0.0457 x 0.0868) = 0.00458677
-    π(d2) = p(d2|b1,c1).πD(b1).πD(c1) + p(d2|b1,c2).πD(b1).πD(c2) + p(d2|b2,c1).πD(b2).πD(c1) + p(d2|b2,c2).πD(b2).πD(c2) = (0.8 x 0.0543 x 0.0132) + (0.3 x 0.0543 x 0.0868) + (0.55 x 0.0457 x 0.0132) + (0.78 x 0.0457 x 0.0868) = 0.00541323
-
-beliefs for variable `A':
-    domain                  belief
-----------------------------------
-        a1                     0.1
-        a2                     0.9
-
-beliefs for variable `B':
-    domain                  belief
-----------------------------------
-        b1                   0.543
-        b2                   0.457
-
-beliefs for variable `C':
-    domain                  belief
-----------------------------------
-        c1                   0.132
-        c2                   0.868
-
-beliefs for variable `D':
-    domain                  belief
-----------------------------------
-        d1              0.45867652
-        d2              0.54132348
-