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glmnet.pyf
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! -*- f90 -*-
! Note: the context of this file is case sensitive.
python module _glmnet ! in
interface ! in :_glmnet
subroutine elnet(ka,parm,no,ni,x,y,w,jd,vp,ne,nx,nlam,flmin,ulam,thr,isd,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) ! in :glmnet:glmnet.f
integer optional :: ka=1 ! Use covariance updates over naive by default
real*8 :: parm
integer intent(hide),check(shape(x,0)==no),depend(x) :: no=shape(x,0)
integer intent(hide),check(shape(x,1)==ni),depend(x) :: ni=shape(x,1)
real*8 dimension(no,ni) :: x
real*8 dimension(no),depend(no) :: y
real*8 dimension(no),depend(no) :: w
integer dimension(*) :: jd
real*8 dimension(ni),depend(ni) :: vp
integer optional,depend(x) :: ne=min(shape(x,1), nx)
integer :: nx
integer optional,check((flmin < 1.0 || len(ulam)==nlam)),depend(flmin,ulam) :: nlam=len(ulam)
real*8 :: flmin
real*8 dimension(nlam) :: ulam
real*8 :: thr
integer optional :: isd=1 ! Standardize predictors by default
! Outputs
integer intent(out) :: lmu
real*8 intent(out),dimension(nlam),depend(nlam) :: a0
real*8 intent(out),dimension(nx,nlam),depend(nlam) :: ca
integer intent(out),dimension(nx),depend(nx) :: ia
integer intent(out),dimension(nlam),depend(nlam) :: nin
real*8 intent(out),dimension(nlam),depend(nlam) :: rsq
real*8 intent(out),dimension(nlam),depend(nlam) :: alm
integer intent(out) :: nlp
integer intent(out) :: jerr
end subroutine elnet
subroutine uncomp(ni,ca,ia,nin,a) ! in :glmnet:glmnet.f
integer :: ni
real*8 dimension(*) :: ca
integer dimension(*) :: ia
integer :: nin
real*8 intent(out),dimension(ni),depend(ni) :: a
end subroutine uncomp
subroutine modval(a0,ca,ia,nin,n,x,f) ! in :glmnet:glmnet.f
real :: a0
real dimension(*) :: ca
integer dimension(*), check(len(ca) == len(ia)), depend(ca) :: ia
integer check(len(ca)>=nin),depend(ca) :: nin
integer optional,check(shape(x,0)==n),depend(x) :: n=shape(x,0)
real dimension(n,*) :: x
real dimension(n),depend(n),intent(out) :: f
end subroutine modval
subroutine spelnet(ka,parm,no,ni,x,ix,jx,y,w,jd,vp,ne,nx,nlam,flmin,ulam,thr,isd,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) ! in :glmnet:glmnet.f
integer optional :: ka=1 ! Use covariance updates over naive by default
real*8 :: parm
integer intent(hide),check(len(y)>=no),depend(y) :: no=len(y)
integer intent(hide),check(len(vp)>=ni),depend(vp) :: ni=len(vp)
real*8 dimension(*) :: x
integer dimension(*) :: ix
integer dimension(*) :: jx
real*8 dimension(no) :: y
real*8 dimension(no),depend(no) :: w
integer dimension(*) :: jd
real*8 dimension(ni) :: vp
integer optional, depend(x) :: ne=shape(x,1)
integer optional,check(shape(ca,0)==nx),depend(ca) :: nx=shape(ca,0)
integer optional,check((flmin < 1.0 || len(ulam)==nlam)),depend(flmin,ulam) :: nlam=len(ulam)
real*8 :: flmin
real*8 dimension(nlam) :: ulam
real*8 optional :: thr=1.0e-4
integer optional :: isd=1 ! Standardize predictors by default
! Output arguments
integer intent(out) :: lmu
real*8 intent(out),dimension(nlam),depend(nlam) :: a0
real*8 intent(out),dimension(nx,nlam),depend(nlam) :: ca
integer intent(out),dimension(nx),depend(nx) :: ia
integer intent(out),dimension(nlam),depend(nlam) :: nin
real*8 intent(out),dimension(nlam),depend(nlam) :: rsq
real*8 intent(out),dimension(nlam),depend(nlam) :: alm
integer intent(out) :: nlp
integer intent(out) :: jerr
end subroutine spelnet
subroutine cmodval(a0,ca,ia,nin,x,ix,jx,n,f) ! in :glmnet:glmnet.f
real*8 :: a0
real*8 dimension(*) :: ca
integer dimension(*) :: ia
integer :: nin
real*8 dimension(*) :: x
integer dimension(*) :: ix
integer dimension(*) :: jx
integer optional,check(len(f)>=n),depend(f) :: n=len(f)
real*8 dimension(n) :: f
end subroutine cmodval
subroutine lognet(parm,no,ni,nc,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thr,isd,maxit,kopt,lmu,a0,ca,ia,nin,dev,alm,nlp,jerr) ! in :glmnet:glmnet.f
real*8 :: parm
integer intent(hide),check(shape(x,0)==no),depend(x) :: no=shape(x,0)
integer intent(hide),check(shape(x,1)==ni),depend(x) :: ni=shape(x,1)
integer optional,check(shape(y,0)==max(2,nc)),depend(y) :: nc = ((shape(y,0) == 2) ? 1 : shape(y, 0))
real*8 dimension(no,ni) :: x
real*8 dimension(no,max(2,nc)),depend(no) :: y
integer dimension(*) :: jd
real*8 dimension(ni),depend(ni) :: vp
integer optional,depend(x) :: ne=min(shape(x,1), nx)
integer :: nx
integer optional,check((flmin < 1.0 || len(ulam)<=nlam)),depend(flmin,ulam) :: nlam=len(ulam)
real*8 :: flmin
real*8 dimension(nlam) :: ulam
real*8 :: thr
integer optional :: isd=1 ! Standardize predictors by default
integer :: maxit
integer optional :: kopt=1 ! Use modified Newton-Raphson by default
integer intent(out) :: lmu
real*8 intent(out),dimension(nc,nlam),depend(nlam) :: a0
real*8 intent(out),dimension(nx,nc,nlam),depend(nlam) :: ca
integer intent(out),dimension(nx),depend(nx) :: ia
integer intent(out),dimension(nlam),depend(nlam) :: nin
real*8 intent(out),dimension(nlam),depend(nlam) :: dev
real*8 intent(out),dimension(nlam),depend(nlam) :: alm
integer intent(out) :: nlp
integer intent(out) :: jerr
end subroutine lognet
subroutine luncomp(ni,nx,nc,ca,ia,nin,a) ! in :glmnet:glmnet.f
integer optional,check(shape(a,0)==ni),depend(a) :: ni=shape(a,0)
integer optional,check(shape(ca,0)==nx),depend(ca) :: nx=shape(ca,0)
integer optional,check(shape(ca,1)==nc),depend(ca) :: nc=shape(ca,1)
real*8 dimension(nx,nc) :: ca
integer dimension(nx),depend(nx) :: ia
integer :: nin
real*8 dimension(ni,nc),depend(nc) :: a
end subroutine luncomp
subroutine lmodval(nt,x,nc,nx,a0,ca,ia,nin,ans) ! in :glmnet:glmnet.f
integer optional,check(shape(x,0)==nt),depend(x) :: nt=shape(x,0)
real*8 dimension(nt,*) :: x
integer optional,check(len(a0)>=nc),depend(a0) :: nc=len(a0)
integer optional,check(shape(ca,0)==nx),depend(ca) :: nx=shape(ca,0)
real*8 dimension(nc) :: a0
real*8 dimension(nx,nc),depend(nc) :: ca
integer dimension(nx),depend(nx) :: ia
integer :: nin
real*8 dimension(nc,nt),depend(nc,nt) :: ans
end subroutine lmodval
subroutine splognet(parm,no,ni,nc,x,ix,jx,y,jd,vp,ne,nx,nlam,flmin,ulam,thr,isd,maxit,kopt,lmu,a0,ca,ia,nin,dev,alm,nlp,jerr) ! in :glmnet:glmnet.f
real*8 :: parm
integer intent(hide),check(shape(y,0)==no),depend(y) :: no=shape(y,0)
integer intent(hide),check(len(vp)>=ni),depend(vp) :: ni=len(vp)
integer optional,check(shape(a0,0)==nc),depend(a0) :: nc=shape(a0,0)
real*8 dimension(*) :: x
integer dimension(*) :: ix
integer dimension(*) :: jx
real*8 dimension(no,max(2,nc)),depend(nc) :: y
integer dimension(*) :: jd
real*8 dimension(ni) :: vp
integer :: ne
integer optional,check(shape(ca,0)==nx),depend(ca) :: nx=shape(ca,0)
integer optional,check((flmin < 1.0 || len(ulam)==nlam)),depend(flmin,ulam) :: nlam=len(ulam)
real*8 :: flmin
real*8 dimension(nlam) :: ulam
real*8 :: thr
integer optional :: isd=1 ! Standardize predictors by default
integer :: maxit
integer optional :: kopt=1 ! Use modified Newton-Raphson by default
integer intent(out) :: lmu
real*8 intent(out),dimension(nc,nlam),depend(nlam) :: a0
real*8 intent(out),dimension(nx,nc,nlam),depend(nc,nlam) :: ca
integer intent(out),dimension(nx),depend(nx) :: ia
integer intent(out),dimension(nlam),depend(nlam) :: nin
real*8 intent(out),dimension(nlam),depend(nlam) :: dev
real*8 intent(out),dimension(nlam),depend(nlam) :: alm
integer intent(out) :: nlp
integer intent(out) :: jerr
end subroutine splognet
subroutine lcmodval(nc,nx,a0,ca,ia,nin,x,ix,jx,n,f) ! in :glmnet:glmnet.f
integer optional,check(len(a0)>=nc),depend(a0) :: nc=len(a0)
integer optional,check(shape(ca,0)==nx),depend(ca) :: nx=shape(ca,0)
real*8 dimension(nc) :: a0
real*8 dimension(nx,nc),depend(nc) :: ca
integer dimension(*) :: ia
integer :: nin
real*8 dimension(*) :: x
integer dimension(*) :: ix
integer dimension(*) :: jx
integer optional,check(shape(f,1)==n),depend(f) :: n=shape(f,1)
real*8 dimension(nc,n),depend(nc) :: f
end subroutine lcmodval
end interface
end python module glmnet
! This file was auto-generated with f2py (version:2).
! See http://cens.ioc.ee/projects/f2py2e/