! Below is an example whereby a network with 2 inputs, 1 output and 
! two hidden layers each with 3 nodes is trained to output the XOR of the two inputs:

! The training files should start with the number of inputs, followed by the number of outputs, each on a new lines. 
! The rest of the data should then follow, with inputs before their outputs. 
! For example, if there are two inputs and one output, the following file suggests that 1 and 0 should result in 1:

! following file suggests that 1 and 0 should result in 1:

use neural

type(network) :: testNet

!Integer :: Mxvar, Myvar, Nspl
!Integer :: i,j
!Real, Allocatable :: X_inp(:,:), Y_out(:,:), Yest(:,:), X(:)
 double precision, Allocatable :: tmp(:)
 Real :: a,b,c,d,PC,POD
!-----------------------------------------------------------------------------
call neural_init_network(testNet, (/ 5, 3, 2, 1 /))
                                 !  4 so ung voi 4 layers va so node cua tung layer 
                                 ! layer 1=input, layer 2,3 hidden, layer va so node trong layer, layer 4=output
call neural_train_network_file(testNet, "data_test_spei.txt", 10000,0.05d00)
                 ! xor.dat la input data filename:
                 ! Dong 1: So bien dau vao (X1,X2,..,Xm)
                 ! Dong 2: So bien dau ra (y1, Y2, Yk)
                 ! m+k dong tiep theo: m gia tri cua X va k gia tri cua Y
                 ! Neu dung luong mau bang N thi se co N khoi m+k tren day.
print*,"neural_train_network_file ... Done"
! call neural_save_network(testNet, "network.dat")
print*,"neural_save_network ---"
! call neural_load_network(testNet, "network.dat")
print*,"neural_load_network...Done"
!print *, "1 and 1 = ", neural_use_network(testNet, (/ 1.0d00, 1.0d00 /))  ! Test for x1=1, x2=1
!print *, "0 and 1 = ", neural_use_network(testNet, (/ 0.0d00, 1.0d00 /))  ! Test for x1=0, x2=1

Allocate (X(Mxvar),tmp(Myvar))
  a=0.;b=0.;c=0.;d=0.
!Do j=1,Myvar
   Do i=1,Nspl
      X(1:Mxvar)=X_inp(i,1:Mxvar)
      tmp(:) = neural_use_network(testNet,X)
      tmp(:) = tmp(:)*(Ymax(:)-Ymin(:))+Ymin(:)
      !print*,neural_use_network(testNet,X)
      print "(10F6.2)",tmp(:),Y_out(i,:)*(Ymax(:)-Ymin(:))+Ymin(:)
      if (tmp(1)==1..AND.Y_out(i,1)==1.) a=a+1.
      if (tmp(1)==0..AND.Y_out(i,1)==0.) d=d+1.
      if (tmp(1)==0..AND.Y_out(i,1)==1.) c=c+1.
      if (tmp(1)==1..AND.Y_out(i,1)==0.) b=b+1.
!      print*,a,b,c,d
   Enddo
   PC  = (a+d)/(a+b+c+d)
   POD = (a+c)/(a+b+c+d)
   print*,PC,POD
!Enddo
End