C H K D H Bhadeshia, Cambridge University, 1994 C C Program used to normalise a data file for testing an trained network C C Modified Feb 94 to print out maximum and minimum values C Analysis of secondary and Widmanstatten not included C Normalisation of data into a range -0.5 to 0.5 C flags submerged arc as 0.5 in a(1,16) and MMA as -0.5 C TEST contains test data of the type used for making predictions with C a trained network C use original training data as input, in order to set max and min values C C *********************************************************************** C C Change OPEN FILE names and irow1 as appropriate. C K. Ichikawa on 22 March 1996 C C *********************************************************************** C C implicit real*8(a-h,k-z), integer(i,j) double precision a(2000,14),b(2000,2) OPEN(3,FILE='MINMAX2') OPEN(10,FILE='testcA.norm') irow1=351 icol=14 do 1 i=1, irow1 read(*,*,end=121)a(i,1),a(i,2),a(i,3),a(i,4),a(i,5), & a(i,6),a(i,7),a(i,8),a(i,9),a(i,10), & a(i,11),a(i,12),a(i,13),a(i,14) C C note stucture of matrix a(irow,icol) C irow=i 1 continue read(3,*) 121 do 4 j=1,icol read(3,*)ik,bmin,bmax b(ik,1)=bmin b(ik,2)=bmax 4 continue do 3 i=1,icol do 5 j=1,irow a(j,i)=((a(j,i)-b(i,1))/(b(i,2)-b(i,1))) - 0.5 5 continue C 3 continue do 7 i=1,irow write(10,222)a(i,2),a(i,3),a(i,4),a(i,5), & a(i,6),a(i,7),a(i,8),a(i,9),a(i,10),a(i,11), & a(i,12),a(i,13),a(i,14) C 7 continue 222 format(20f8.2) C stop end C C**************************************************************** C SUBROUTINE PIKSRT(IN,ARR) implicit real*8(a-h,k-z), integer(i,j) DOUBLE PRECISION ARR(IN) DO 12 J=2,IN A=ARR(J) DO 11 I=J-1,1,-1 IF(ARR(I).LE.A)GO TO 10 ARR(I+1)=ARR(I) 11 CONTINUE I=0 10 ARR(I+1)=A 12 CONTINUE RETURN END