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; Artificial neural network example by Balrog Soft
; Two layers with backprogpagation 

Global NUM_INPUT, NUM_OUTPUT, NUM_HIDDEN, NUM_NETWORK

NUM_INPUT = 2
NUM_OUTPUT = 1
NUM_HIDDEN = 4
NUM_NETWORK = 1

Dim patternin(NUM_INPUT-1)
Dim patternout(NUM_OUTPUT-1)

    Global Dim W_IN_HIDDEN.f(NUM_NETWORK-1, NUM_INPUT-1, NUM_HIDDEN-1)
    Global Dim W_HIDDEN_OUT.f(NUM_NETWORK-1, NUM_HIDDEN-1, NUM_OUTPUT-1)
    Global Dim N_IN.f(NUM_NETWORK-1, NUM_INPUT-1)
    Global Dim N_HIDDEN.f(NUM_NETWORK-1, NUM_HIDDEN-1)
    Global Dim N_OUT.f(NUM_NETWORK-1, NUM_OUTPUT-1)
    Global Dim N_TEACH.f(NUM_NETWORK-1, NUM_OUTPUT-1)
    Global Dim E_HIDDEN.f(NUM_NETWORK-1, NUM_HIDDEN-1)
    Global Dim E_OUT.f(NUM_NETWORK-1, NUM_OUTPUT-1)
    Global Dim LEARN.f(NUM_NETWORK-1)

#e=2.71828182

Procedure Init_Network(n,i,h,o) 
 NUM_NETWORK = n
 NUM_INPUT = i
 NUM_OUTPUT = o
 NUM_HIDDEN = h 

 Dim patternin(NUM_INPUT-1)
 Dim patternout(NUM_OUTPUT-1)

 Dim W_IN_HIDDEN.f(NUM_NETWORK-1, NUM_INPUT-1, NUM_HIDDEN-1) 
 Dim W_HIDDEN_OUT.f(NUM_NETWORK-1, NUM_HIDDEN-1, NUM_OUTPUT-1)
 Dim N_IN.f(NUM_NETWORK-1, NUM_INPUT-1) 
 Dim N_HIDDEN.f(NUM_NETWORK-1, NUM_HIDDEN-1) 
 Dim N_OUT.f(NUM_NETWORK-1, NUM_OUTPUT-1)
 Dim N_TEACH.f(NUM_NETWORK-1, NUM_OUTPUT-1) 
 Dim E_HIDDEN.f(NUM_NETWORK-1, NUM_HIDDEN-1)
 Dim E_OUT.f(NUM_NETWORK-1, NUM_OUTPUT-1)
 Dim LEARN.f(NUM_NETWORK-1)
 For n=0 To NUM_NETWORK-1
 For h=0 To NUM_HIDDEN-1 
   For o=0 To NUM_OUTPUT-1
    W_HIDDEN_OUT(n, h, o) =(Random(5)-3)
   Next o
  Next h
  For i=0 To NUM_INPUT-1
   For h=0 To NUM_HIDDEN-1
    W_IN_HIDDEN(n, i, h) =(Random(5)-3) 
   Next h
  Next i
 Next n
EndProcedure


Procedure Set_Input(n, i, value.f)
  N_IN(n, i) = value.f
EndProcedure 

Procedure Set_Output(n, o, value.f)
  N_TEACH(n, o) = value.f
EndProcedure 

Procedure.f f_logic(X.f)

 ret.f = 0

 ret = 1 / (1 + Pow(#e,-x))

 ProcedureReturn ret
EndProcedure

Procedure Calculate_Network(n)

 For h=0 To NUM_HIDDEN-1
  N_HIDDEN(n,h) = 0.0
 Next
 
 For o=0 To NUM_OUTPUT-1
  N_OUT(n,o) = 0.0
 Next

 For h=0 To NUM_HIDDEN-1
  For i=0 To NUM_INPUT-1
   N_HIDDEN(n,h) + (N_IN(n,i) * W_IN_HIDDEN(n,i, h))
  Next i
  N_HIDDEN(n,h) = f_logic(N_HIDDEN(n,h))
 Next h

 For o=0 To NUM_OUTPUT-1
  For h=0 To NUM_HIDDEN-1
   N_OUT(n,o) + (N_HIDDEN(n,h) * W_HIDDEN_OUT(n,h, o))
  Next h
  N_OUT(n,o) = f_logic(N_OUT(n,o))
 Next o
EndProcedure

Procedure Calculate_Errors(n)
 err.f

 For o=0 To NUM_OUTPUT-1
  E_OUT(n, o) = (N_TEACH(n, o) - N_OUT(n, o))
 Next o
 
 For h=0 To NUM_HIDDEN-1
  err = 0.0
  For o=0 To NUM_OUTPUT-1
   err + (E_OUT(n, o) * W_HIDDEN_OUT(n, h, o))
  Next o
  E_HIDDEN(n, h) = N_HIDDEN(n, h) * (1 - N_HIDDEN(n, h)) * err
 Next h
EndProcedure

Procedure Train_Network(n)
  For h=0 To NUM_HIDDEN-1
   For o=0 To NUM_OUTPUT-1
    W_HIDDEN_OUT(n, h, o) + (LEARN(n) * N_HIDDEN(n, h) * E_OUT(n, o))
   Next o
  Next h
  For i=0 To NUM_INPUT-1
   For h=0 To NUM_HIDDEN-1
    W_IN_HIDDEN(n, i, h) + (LEARN(n) * N_IN(n, i) * E_HIDDEN(n, h)) 
   Next h
  Next i
EndProcedure

Procedure.f Get_Network_Output(n, o)
  ProcedureReturn N_OUT(n, o)
EndProcedure

Procedure Learn_Constant(n,cte.f)
LEARN(n)=cte
EndProcedure

OpenConsole()

Init_Network(1, 2, 4, 1)

Learn_Constant(0, 0.45)

For RP=1 To 2000
  Inp$=""
  For In=0 To 1
    InputN.f=Random(1)
    Inp.l=InputN
    ;- Set random inputs
    Set_Input(0,In,InputN)
    Inp$+Str(Inp)
  Next In
  
  ; here you must put the combinations that must have a 1.0 output.
  If Inp$="11" Or Inp$="10"
    CorrectOutput.f=1.0
  Else
    CorrectOutput.f=0.0
  EndIf
  Set_Output(0,0,CorrectOutput)
  ; and get an Output #TRUE or #FALSE
  Calculate_Network(0)

  Calculate_Errors(0)
  
  Train_Network(0)

  output.f=Get_Network_Output(0,0)
  If output>=0.5
    PrintN("I: "+Inp$+" - O: 1 - "+StrF(output.f))
  Else
    PrintN("I: "+Inp$+" - O: 0 - "+StrF(output.f))
  EndIf
  Input()
Next RP
Input()

CloseConsole()