+++ /dev/null
-c===========================================================================
-c
-c This file is part of TISEAN
-c
-c Copyright (c) 1998-2007 Rainer Hegger, Holger Kantz, Thomas Schreiber
-c
-c TISEAN is free software; you can redistribute it and/or modify
-c it under the terms of the GNU General Public License as published by
-c the Free Software Foundation; either version 2 of the License, or
-c (at your option) any later version.
-c
-c TISEAN is distributed in the hope that it will be useful,
-c but WITHOUT ANY WARRANTY; without even the implied warranty of
-c MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-c GNU General Public License for more details.
-c
-c You should have received a copy of the GNU General Public License
-c along with TISEAN; if not, write to the Free Software
-c Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
-c
-c===========================================================================
-c part of the TISEAN randomize package for constraint surrogates
-c cost function
-c autocorrelation function
-c author T. Schreiber (1999)
-c
-c-------------------------------------------------------------------
-c get cost function specific options
-c
- subroutine opts_cost(ncol)
- parameter(mlag=100000)
- dimension c0(mlag), c(mlag)
- common /costcom/ nlag, c0, c, sd, sc, iweight
-
- nlag=imust('D')
- iweight=ican('W',0)
- ncol=1
- end
-
-c-------------------------------------------------------------------
-c print version information on cost function
-c
- subroutine what_cost()
- call ptext("Cost function: autocorrelation")
- end
-
-c-------------------------------------------------------------------
-c print cost function specific usage message
-c
- subroutine usage_cost()
- call ptext("Cost function options: -D# [-W#]")
- call popt("D","number of lags")
- call popt("W",
- . "average: 0=max(c) 1=|c|/lag 2=(c/lag)**2 3=max(c)/lag (0)")
- end
-
-c-------------------------------------------------------------------
-c initialise all that is needed for cost function
-c
- subroutine cost_init()
- parameter(mlag=100000)
- dimension c0(mlag), c(mlag)
- common /costcom/ nlag, c0, c, sd, sc, iweight
-
- if(nlag.gt.mlag) write(istderr(),'(a)')
- . "truncated to ", mlag," lags"
- nlag=min(mlag,nlag)
- call auto(nlag,c0)
- end
-
-c-------------------------------------------------------------------
-c initial transformation on time series and its inverse
-c
- subroutine cost_transform(nmax,mcmax,nxdum,x)
- dimension x(nmax)
- parameter(mlag=100000)
- dimension c0(mlag), c(mlag)
- common /costcom/ nlag, c0, c, sd, sc, iweight
-
- call normal1(nmax,x,sc,sd)
- end
-
- subroutine cost_inverse(nmax,mcmax,nxdum,x,y)
- dimension x(nmax), y(nmax)
- parameter(mlag=100000)
- dimension c0(mlag), c(mlag)
- common /costcom/ nlag, c0, c, sd, sc, iweight
-
- do 10 n=1,nmax
- 10 y(n)=x(n)*sd+sc
- end
-
-c-------------------------------------------------------------------
-c compute full cost function from scratch
-c
- subroutine cost_full(iv)
- parameter(mlag=100000)
- dimension c0(mlag), c(mlag)
- common /costcom/ nlag, c0, c, sd, sc, iweight
- common nmax,cost
-
- call auto(nlag,c)
- cc=0
- do 10 n=1,nlag
- 10 call aver(cc,c0(n)-c(n),n)
- cost=cc
- end
-
-c-------------------------------------------------------------------
-c compute changed cost function on exchange of n1 and n2
-c
- subroutine cost_update(nn1,nn2,cmax,iaccept,iv)
- parameter(mlag=100000,nx=100000)
- dimension c0(mlag), c(mlag), ccop(mlag), x(nx)
- common /costcom/ nlag, c0, c, sd, sc, iweight
- common nmax,cost,temp,cmin,rate,x
-
- n1=min(nn1,nn2)
- n2=max(nn1,nn2)
- comp=0
- iaccept=0
- do 10 n=1,nlag
- cc=c(n)
- dx=x(n2)-x(n1)
- if(n1-n.ge.1) cc=cc+dx*x(n1-n)
- if(n2+n.le.nmax) cc=cc-dx*x(n2+n)
- if(n2-n1.eq.n) goto 1
- if(n1+n.le.nmax) cc=cc+dx*x(n1+n)
- if(n2-n.ge.1) cc=cc-dx*x(n2-n)
- 1 call aver(comp,c0(n)-cc,n)
- if(comp.ge.cmax) return
- 10 ccop(n)=cc
- cost=comp ! if got here: accept
- iaccept=1
- call exch(n1,n2)
- do 20 n=1,nlag
- 20 c(n)=ccop(n)
- end
-
-c-------------------------------------------------------------------
-c compute autocorrelation from scratch
-c
- subroutine auto(nlag,c)
- parameter(nx=100000)
- dimension c(*), x(nx)
- common nmax,cost,temp,cmin,rate,x
-
- do 10 n=1,nlag
- cc=0
- do 20 i=n+1,nmax
- 20 cc=cc+x(i-n)*x(i)
- 10 c(n)=cc
- end
-
-c-------------------------------------------------------------------
-c weighted average of autocorrelation
-c
- subroutine aver(cav,dc,n)
- parameter(mlag=100000)
- dimension c0(mlag), c(mlag)
- common /costcom/ nlag, c0, c, sd, sc, iweight
- common nmax
-
- if(iweight.eq.0) then
- cav=max(cav,abs(dc)/real(nmax-n))
- else if(iweight.eq.1) then
- cav=cav+abs(dc)/real((nmax-n)*n)
- else if(iweight.eq.2) then
- cav=cav+(dc/real((nmax-n)*n))**2
- else
- cav=max(cav,abs(dc)/real((nmax-n)*n))
- endif
- end
-
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