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subroutine funkmin_neural(ndim,beta,fvalue)
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implicit none
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include 'NeuralNetRegres.h'
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integer ndim
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double precision beta(ndim),fvalue
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!(in) ndim: the dimension of the parameter vector
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!(in) beta: the parameters
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!(out) fvalue: the value of the cost function at beta
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!-----------------------------------------------------
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integer i,j,k,idowhat
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double precision w(maxnx,maxnh),bph(maxnh),q(maxnh),
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&bend,annfunc,ypred
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!
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! check to see if parameters are out of bounds
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if(betamin(1).lt.betamax(1))then
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do i=1,ndim
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if(beta(i).lt.betamin(i).or.
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& beta(i).gt.betamax(i))then
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! parameter out of bound
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fvalue=1.0d+100
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return
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endif
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enddo
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endif
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idowhat=1
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call coeff_beta(idowhat,nx,nh,beta,w(1:nx,1:nh),bph,q,bend)
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fvalue=0.0d0
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do i=1,nobs
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ypred=annfunc(nx,xsamp(i:i,1:nx),nh,q,w(1:nx,1:nh),bph,bend)
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fvalue=fvalue+(ysamp(i)-ypred)*(ysamp(i)-ypred)
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enddo
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return
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end subroutine funkmin_neural
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!$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
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double precision function f1dim_neural(x)
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implicit none
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double precision x
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CU USES funkmin_neural
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INTEGER j
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!((((((((((((((((((((((((((((((((((((((((((((((((((((
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integer NMAX,ncom
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parameter(NMAX=1000)
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double precision pcom(NMAX),xicom(NMAX)
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COMMON /f1com/ pcom,xicom,ncom
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save /f1com/
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!))))))))))))))))))))))))))))))))))))))))))))))))))))
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double precision xt(NMAX)
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!-----------------------------------------------------
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do 11 j=1,ncom
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xt(j)=pcom(j)+x*xicom(j)
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11 continue
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call funkmin_neural(ncom,xt,f1dim_neural)
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return
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END
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!&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
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SUBROUTINE FCN_neural(N,M,NP,NQ,
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+ LDN,LDM,LDNP,
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+ BETA,XPLUSD,
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+ IFIXB,IFIXX,LDIFX,
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+ IDEVAL,F,FJACB,FJACD,
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+ ISTOP)
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implicit none
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C SUBROUTINE ARGUMENTS
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C ==> N NUMBER OF OBSERVATIONS
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C ==> M NUMBER OF COLUMNS IN EXPLANATORY VARIABLE
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C ==> NP NUMBER OF PARAMETERS
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C ==> NQ NUMBER OF RESPONSES PER OBSERVATION
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C ==> LDN LEADING DIMENSION DECLARATOR EQUAL OR EXCEEDING N
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C ==> LDM LEADING DIMENSION DECLARATOR EQUAL OR EXCEEDING M
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C ==> LDNP LEADING DIMENSION DECLARATOR EQUAL OR EXCEEDING NP
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C ==> BETA CURRENT VALUES OF PARAMETERS
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C ==> XPLUSD CURRENT VALUE OF EXPLANATORY VARIABLE, I.E., X + DELTA
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C ==> IFIXB INDICATORS FOR "FIXING" PARAMETERS (BETA)
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C ==> IFIXX INDICATORS FOR "FIXING" EXPLANATORY VARIABLE (X)
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C ==> LDIFX LEADING DIMENSION OF ARRAY IFIXX
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C ==> IDEVAL INDICATOR FOR SELECTING COMPUTATION TO BE PERFORMED
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C <== F PREDICTED FUNCTION VALUES
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C <== FJACB JACOBIAN WITH RESPECT TO BETA
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C <== FJACD JACOBIAN WITH RESPECT TO ERRORS DELTA
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C <== ISTOP STOPPING CONDITION, WHERE
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C 0 MEANS CURRENT BETA AND X+DELTA WERE
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C ACCEPTABLE AND VALUES WERE COMPUTED SUCCESSFULLY
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C 1 MEANS CURRENT BETA AND X+DELTA ARE
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C NOT ACCEPTABLE; ODRPACK SHOULD SELECT VALUES
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C CLOSER TO MOST RECENTLY USED VALUES IF POSSIBLE
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C -1 MEANS CURRENT BETA AND X+DELTA ARE
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C NOT ACCEPTABLE; ODRPACK SHOULD STOP
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C INPUT ARGUMENTS, NOT TO BE CHANGED BY THIS ROUTINE:
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INTEGER II,IDEVAL,ISTOP,L,LDIFX,LDM,LDN,LDNP,M,N,NP,NQ
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DOUBLE PRECISION BETA(NP),XPLUSD(LDN,M)
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INTEGER IFIXB(NP),IFIXX(LDIFX,M)
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C OUTPUT ARGUMENTS:
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DOUBLE PRECISION F(LDN,NQ),FJACB(LDN,LDNP,NQ),FJACD(LDN,LDM,NQ)
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!
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integer k,i,j,s,t,ierr,idowhat
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include 'NeuralNetRegres.h'
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double precision w(maxnx,maxnh),bph(maxnh),
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& q(maxnh),bend,xnew(maxnx),annfunc,
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& derBETA(NP),derw(maxnx,maxnh),derbph(maxnh),
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& derq(maxnh),derbend
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C CHECK FOR UNACCEPTABLE VALUES FOR THIS PROBLEM
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c
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do I=1,NP
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if(BETA(I).lt.betamin(I).or.BETA(I).gt.betamax(I))then
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ISTOP = 1
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RETURN
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endif
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enddo
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idowhat=1
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call coeff_beta(idowhat,nx,nh,BETA,
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& w(1:nx,1:nh),bph,q,bend)
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!---------------- find the ann function values--------------------------
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IF (MOD(IDEVAL,10).GE.1) THEN
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DO 110 L = 1,NQ
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DO 100 I = 1,N
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do k=1,M
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xnew(k)=XPLUSD(I,k)
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enddo
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F(I,L)=annfunc(nx,xnew,nh,q,w(1:nx,1:nh),bph,bend)
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100 CONTINUE
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110 CONTINUE
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END IF
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!----------------------------------------------------------------------
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C COMPUTE DERIVATIVES WITH RESPECT TO BETA
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IF (MOD(IDEVAL/10,10).GE.1) THEN
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idowhat=2
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DO 200 I = 1,N
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do k=1,M
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xnew(k)=XPLUSD(I,k)
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enddo
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call derannfunc(nx,xnew,nh,q,w(1:nx,1:nh),
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& bph,bend,derq,derw(1:nx,1:nh),derbph,derbend)
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call coeff_beta(idowhat,nx,nh,derBETA,
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& derw(1:nx,1:nh),derbph,derq,derbend)
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DO 210 L = 1,NQ
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do k=1,NP
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FJACB(I,k,L)=derBETA(k)
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enddo
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210 CONTINUE
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200 CONTINUE
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END IF
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RETURN
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END
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!
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!$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
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