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piscal/dataassim/math/numrec/PrinCompAna.f
2022-09-12 16:40:28 +00:00

113 lines
3.7 KiB
FortranFixed

! program main
! implicit none
! integer nsamp,mdim,mpc
! double precision sample(100,100),princomp(100,100),
! &transdata(100,100),x(100),eigenvector(100,100),eigenvalue(100)
! integer i,j,k
! open(unit=1,file='Table8.3.txt')
! read(1,*)
! nsamp=0
!10 read(1,*,end=100)i,(x(j),j=1,6)
! if(i.le.1)goto 10
! nsamp=nsamp+1
! do j=1,6
! sample(nsamp,j)=x(j)
! enddo
! goto 10
!100 close(1)
! mdim=6
! mpc=2
! call princompana(nsamp,mdim,sample(1:nsamp,1:mdim),mpc,
! &princomp(1:nsamp,1:mpc),transdata(1:nsamp,1:mdim),
! &eigenvector(1:mdim,1:mdim),eigenvalue)
! do i=1,mpc
! do j=1,nsamp
! write(*,*)j,princomp(j,i)
! enddo
! enddo
! do i=1,mdim
! do j=1,nsamp
! write(*,*)j,transdata(j,i)
! enddo
! enddo
! end
subroutine princompana(nsamp,mdim,sample,mpc,princomp,transdata,
&eigenvector,eigenvalue)
implicit none
!-----------Inputs----------------------------------------
!mpc is the number of principal components to keep
integer nsamp,mdim,mpc
double precision sample(nsamp,mdim)
!-----------Outputs---------------------------------------
!princomp is the projection of a sample on the principal axes
!transdata is the data of the orginal sample filtered with mpc principal components
double precision eigenvalue(mdim),eigenvector(mdim,mdim),
&sampmean(mdim),princomp(nsamp,mpc),transdata(nsamp,mdim),
&sampadj(nsamp,mdim)
!---------------------------------------------------------
integer i,j,k
call geteigen(nsamp,mdim,sample(1:nsamp,1:mdim),eigenvalue,
&eigenvector(1:mdim,1:mdim),sampmean,sampadj(1:nsamp,1:mdim))
do i=1,mpc
do j=1,nsamp
princomp(j,i)=0.0d0
do k=1,mdim
princomp(j,i)=princomp(j,i)+eigenvector(k,i)*sampadj(j,k)
enddo
enddo
enddo
do j=1,mdim
do i=1,nsamp
transdata(i,j)=sampmean(j)
do k=1,mpc
transdata(i,j)=transdata(i,j)+eigenvector(j,k)*princomp(i,k)
enddo
enddo
enddo
return
end
subroutine geteigen(nsamp,mdim,sample,eigenvalue,eigenvector,
&sampmean,sampadj)
integer nsamp,mdim
double precision sample(nsamp,mdim),eigenvalue(mdim),
&eigenvector(mdim,mdim),sampmean(mdim),sampadj(nsamp,mdim)
!Each column is an eigenvector. The first column corresponds to the largest eigenvalue and the last column corresponds
!to the smallest eigenvalue
call covariancematrix(nsamp,mdim,sample(1:nsamp,1:mdim),
&eigenvector(1:mdim,1:mdim),sampmean,sampadj(1:nsamp,1:mdim))
call eigen_sym_up(mdim,eigenvector(1:mdim,1:mdim),eigenvalue)
return
end
subroutine covariancematrix(nsamp,mdim,sample,covarmatrix,
&sampmean,sampadj)
implicit none
!covarmatrix is an upper trangle
integer nsamp,mdim
double precision sample(nsamp,mdim),covarmatrix(mdim,mdim),
&sampmean(mdim),sampadj(nsamp,mdim)
integer i,j,k
do j=1,mdim
sampmean(j)=0.0d0
do i=1,nsamp
sampmean(j)=sampmean(j)+sample(i,j)
enddo
sampmean(j)=sampmean(j)/dble(nsamp)
do i=1,nsamp
sampadj(i,j)=sample(i,j)-sampmean(j)
enddo
enddo
do i=1,mdim
do j=i,mdim
covarmatrix(i,j)=0.0d0
do k=1,nsamp
covarmatrix(i,j)=covarmatrix(i,j)+sampadj(k,i)*sampadj(k,j)
enddo
covarmatrix(i,j)=covarmatrix(i,j)/dble(nsamp-1)
enddo
enddo
return
end