+ SUBROUTINE myTestFunc(rw,r,tu,tl,hu,hl,l,kw,yhat)
+C --- *********************************************
+C ---
+C --- File used by unit test
+C --- PMMLBasicsTest1::testExportNeuralNetworkFortran
+C ---
+C --- *********************************************
+ IMPLICIT DOUBLE PRECISION (V)
+ DOUBLE PRECISION rw
+ DOUBLE PRECISION r
+ DOUBLE PRECISION tu
+ DOUBLE PRECISION tl
+ DOUBLE PRECISION hu
+ DOUBLE PRECISION hl
+ DOUBLE PRECISION l
+ DOUBLE PRECISION kw
+ DOUBLE PRECISION yhat
+
+C --- Preprocessing of the inputs
+ VXNrw = ( rw - 0.099999D0 ) / 0.028899D0
+ VXNr = ( r - 25048.9D0 ) / 14419.8D0
+ VXNtu = ( tu - 89334.9D0 ) / 15180.8D0
+ VXNtl = ( tl - 89.5523D0 ) / 15.2866D0
+ VXNhu = ( hu - 1050D0 ) / 34.6793D0
+ VXNhl = ( hl - 760.001D0 ) / 34.6718D0
+ VXNl = ( l - 1400.02D0 ) / 161.826D0
+ VXNkw = ( kw - 10950D0 ) / 632.913D0
+
+C --- Values of the weights
+ VW1 = -1.74548
+ VW2 = 6.96551
+ VW3 = -1.26357
+ VW4 = 0.753663
+ VW5 = 0.00165366
+ VW6 = 0.004725
+ VW7 = 0.00996979
+ VW8 = 0.178798
+ VW9 = -0.180981
+ VW10 = -0.173569
+ VW11 = 0.0855967
+
+C --- hidden neural number 1
+ VAct1 = VW3
+ 1 + VW4 * VXNrw
+ 1 + VW5 * VXNr
+ 1 + VW6 * VXNtu
+ 1 + VW7 * VXNtl
+ 1 + VW8 * VXNhu
+ 1 + VW9 * VXNhl
+ 1 + VW10 * VXNl
+ 1 + VW11 * VXNkw
+
+ VPot1 = 1.D0 / (1.D0 + DEXP(-1.D0 * VAct1))
+
+C --- Output
+ VOut = VW1
+ 1 + VW2 * VPot1
+
+C --- Pretraitment of the output
+ yhat = 77.8117D0 + 45.7061D0 * VOut;
+
+C ---
+ RETURN
+ END