mlp_gen.h File Reference

Go to the source code of this file.

Classes
struct	divers_
struct	learn_
struct	net_
struct	pat_
struct	stat_

Macros
#define	DIVERS   divers_
#define	LEARN   learn_
#define	NET   net_
#define	PAT   pat_
#define	STAT   stat_

Typedefs
typedef double	dbl
typedef double	type_pat

Functions
int	AllocNetwork (int Nlayer, int *Neurons) MLP_HIDDEN
int	AllocPatterns (int ifile, int npat, int nin, int nout, int iadd) MLP_HIDDEN
void	AllocWeights () MLP_HIDDEN
void	BFGSdir (int Nweights) MLP_HIDDEN
void	CGDir (dbl beta) MLP_HIDDEN
int	CountLexemes (char *string) MLP_HIDDEN
int	DecreaseSearch (dbl *alpmin, int *Ntest, dbl Err0) MLP_HIDDEN
dbl	DeDwNorm () MLP_HIDDEN
dbl	DeDwProd () MLP_HIDDEN
void	DeDwSave () MLP_HIDDEN
void	DeDwSaveZero () MLP_HIDDEN
void	DeDwScale (int Nexamples) MLP_HIDDEN
int	DeDwSum (type_pat *ans, dbl *out, int ipat) MLP_HIDDEN
void	DeDwZero () MLP_HIDDEN
dbl	DerivDir () MLP_HIDDEN
void	EtaDecay () MLP_HIDDEN
int	FixedStep (dbl alpha) MLP_HIDDEN
void	FreeNetwork () MLP_HIDDEN
int	FreePatterns (int ifile) MLP_HIDDEN
void	FreeWeights () MLP_HIDDEN
int	GetBFGSH (int Nweights) MLP_HIDDEN
void	GetGammaDelta () MLP_HIDDEN
void	getLexemes (char *s, char **ss) MLP_HIDDEN
int	GetNetStructure (char *s, int *Nlayer, int *Nneur) MLP_HIDDEN
void	getnLexemes (int n, char *s, char **ss) MLP_HIDDEN
void	InitBFGSH (int Nweights) MLP_HIDDEN
void	InitWeights () MLP_HIDDEN
int	LearnAlloc () MLP_HIDDEN
void	LearnFree () MLP_HIDDEN
int	LineSearch (dbl *alpmin, int *Ntest, dbl Err0) MLP_HIDDEN
int	LineSearchHyb (dbl *alpmin, int *Ntest) MLP_HIDDEN
int	LoadWeights (char *filename, int *iepoch) MLP_HIDDEN
dbl	MLP_Epoch (int iepoch, dbl *alpmin, int *ntest) MLP_HIDDEN
void	MLP_Line (dbl ***w0, dbl alpha) MLP_HIDDEN
void	MLP_LineHyb (dbl ***w0, dbl alpha) MLP_HIDDEN
void	MLP_MatrixVector (dbl *M, type_pat *v, dbl *r, int n, int m) MLP_HIDDEN
void	MLP_MatrixVectorBias (dbl *M, dbl *v, dbl *r, int n, int m) MLP_HIDDEN
void	MLP_MM2rows (dbl *c, type_pat *a, dbl *b, int Ni, int Nj, int Nk, int NaOffs, int NbOffs) MLP_HIDDEN
void	MLP_Out (type_pat *rrin, dbl *rrout) MLP_HIDDEN
void	MLP_Out2 (type_pat *rrin) MLP_HIDDEN
void	MLP_Out_T (type_pat *rrin) MLP_HIDDEN
int	MLP_PrCFun (char *filename) MLP_HIDDEN
int	MLP_PrFFun (char *filename) MLP_HIDDEN
int	MLP_PrintInputStat () MLP_HIDDEN
double	MLP_Rand (dbl min, dbl max) MLP_HIDDEN
void	MLP_ResLin () MLP_HIDDEN
int	MLP_SetNet (int *nl, int *nn) MLP_HIDDEN
int	MLP_StatInputs (int Nexamples, int Ninputs, type_pat **inputs, dbl *mean, dbl *sigma, dbl *minimum, dbl *maximum) MLP_HIDDEN
dbl	MLP_Stochastic () MLP_HIDDEN
dbl	MLP_Test (int ifile, int regul) MLP_HIDDEN
int	MLP_Train (int *ipat, dbl *err) MLP_HIDDEN
int	NormalizeInputs () MLP_HIDDEN
void	PrintWeights () MLP_HIDDEN
int	ReadPatterns (char *filename, int ifile, int *inet, int *ilearn, int *iexamples) MLP_HIDDEN
int	SaveWeights (char *filename, int iepoch) MLP_HIDDEN
void	SetDefaultFuncs () MLP_HIDDEN
void	SetLambda (double Wmax) MLP_HIDDEN
int	SetTransFunc (int layer, int neuron, int func) MLP_HIDDEN
int	ShuffleExamples (int n, int *index) MLP_HIDDEN
void	SteepestDir () MLP_HIDDEN
int	StochStep () MLP_HIDDEN
int	StochStepHyb () MLP_HIDDEN

Variables
struct net_	MLP_HIDDEN

Macro Definition Documentation

#define DIVERS   divers_

Definition at line 54 of file mlp_gen.h.

Referenced by MLP_PrCFun(), MLP_PrFFun(), and ReadPatterns().

#define LEARN   learn_

Definition at line 36 of file mlp_gen.h.

Referenced by AllocNetwork(), BFGSdir(), CGDir(), DecreaseSearch(), DeDwNorm(), DeDwProd(), DeDwSave(), DeDwSaveZero(), DeDwScale(), DeDwSum(), DeDwZero(), DerivDir(), EtaDecay(), FixedStep(), FreeNetwork(), GetGammaDelta(), LearnAlloc(), LineSearch(), LineSearchHyb(), MLP_Epoch(), MLP_ResLin(), MLP_Stochastic(), MLP_Test(), ReadPatterns(), SetLambda(), PhysicsTools::MLP::setLearn(), SteepestDir(), StochStep(), and StochStepHyb().

#define NET   net_

Definition at line 25 of file mlp_gen.h.

Referenced by AllocNetwork(), BFGSdir(), CGDir(), DecreaseSearch(), DeDwNorm(), DeDwProd(), DeDwSave(), DeDwSaveZero(), DeDwScale(), DeDwSum(), DeDwZero(), DerivDir(), PhysicsTools::MLP::eval(), FixedStep(), FreeNetwork(), GetGammaDelta(), InitWeights(), LearnAlloc(), LearnFree(), LineSearch(), LineSearchHyb(), LoadWeights(), MLP_Epoch(), MLP_Line(), MLP_LineHyb(), MLP_Out(), MLP_Out2(), MLP_Out_T(), MLP_PrCFun(), MLP_PrFFun(), MLP_PrintInputStat(), MLP_ResLin(), MLP_SetNet(), MLP_Stochastic(), MLP_Test(), MLP_Test_MM(), MLP_Train(), NormalizeInputs(), PrintWeights(), ReadPatterns(), SaveWeights(), SetDefaultFuncs(), SetLambda(), SetTransFunc(), SteepestDir(), StochStep(), and StochStepHyb().

#define PAT   pat_

Definition at line 45 of file mlp_gen.h.

Referenced by AllocPatterns(), PhysicsTools::MLP::clear(), DeDwSum(), FreePatterns(), MLP_Epoch(), MLP_PrintInputStat(), MLP_ResLin(), MLP_Stochastic(), MLP_Test(), MLP_Test_MM(), MLP_Train(), NormalizeInputs(), ReadPatterns(), PhysicsTools::MLP::set(), and PhysicsTools::MLP::setNPattern().

#define STAT   stat_

Definition at line 60 of file mlp_gen.h.

Referenced by MLP_PrCFun(), MLP_PrFFun(), and NormalizeInputs().

Typedef Documentation

typedef double dbl

Definition at line 12 of file mlp_gen.h.

typedef double type_pat

Definition at line 13 of file mlp_gen.h.

Function Documentation

int AllocNetwork	(	int	Nlayer,
		int *	Neurons
	)

Definition at line 3445 of file mlp_gen.cc.

References FreeNetwork(), i, j, gen::k, prof2calltree::l, LEARN, and NET.

Referenced by MLP_SetNet().

{
    int i, j, k, l;
    
    if(NetMemory != 0) FreeNetwork();
    NetMemory = 1;
    
    NET.Nneur = (int *) malloc(Nlayer*sizeof(int));
    if(NET.Nneur == 0) return -111;
    
    NET.T_func = (int **) malloc(Nlayer*sizeof(int *));
    NET.Deriv1 = (dbl **) malloc(Nlayer*sizeof(dbl *));
    NET.Inn = (dbl **) malloc(Nlayer*sizeof(dbl *));
    NET.Outn = (dbl **) malloc(Nlayer*sizeof(dbl *));
    NET.Delta = (dbl **) malloc(Nlayer*sizeof(dbl *));
    if(NET.T_func == 0 || NET.Deriv1 == 0
        || NET.Inn == 0 || NET.Outn == 0
        || NET.Delta == 0) return -111;
    
    for(i=0; i<Nlayer; i++)
        {
        NET.T_func[i] = (int *) malloc(Neurons[i]*sizeof(int));
        NET.Deriv1[i] = (dbl *) malloc(Neurons[i]*sizeof(dbl));
        NET.Inn[i] = (dbl *) malloc(Neurons[i]*sizeof(dbl));
        NET.Outn[i] = (dbl *) malloc(Neurons[i]*sizeof(dbl));
        NET.Delta[i] = (dbl *) malloc(Neurons[i]*sizeof(dbl));
        if(NET.T_func[i] == 0 || NET.Deriv1[i] == 0 
            || NET.Inn[i] == 0 || NET.Outn[i] == 0
            || NET.Delta[i] ==0 ) return -111;
        }
        
    NET.Weights = (dbl ***) malloc(Nlayer*sizeof(dbl **));
    NET.vWeights = (dbl **) malloc(Nlayer*sizeof(dbl *));
    LEARN.Odw = (dbl ***) malloc(Nlayer*sizeof(dbl **));
    LEARN.ODeDw = (dbl ***) malloc(Nlayer*sizeof(dbl **));
    LEARN.DeDw = (dbl ***) malloc(Nlayer*sizeof(dbl **));
    if(NET.Weights == 0 || NET.vWeights == 0 
      || LEARN.Odw == 0 || LEARN.ODeDw == 0
      || LEARN.DeDw == 0)  return -111;
      
    for(i=1; i<Nlayer; i++)
        {
        k = Neurons[i-1]+1;
        NET.vWeights[i] = (dbl *) malloc(k * Neurons[i] *
                        sizeof(dbl));
        NET.Weights[i] = (dbl **) malloc(Neurons[i]*sizeof(dbl *));
        LEARN.Odw[i] = (dbl **) malloc(Neurons[i]*sizeof(dbl *));
        LEARN.ODeDw[i] = (dbl **) malloc(Neurons[i]*sizeof(dbl *));
        LEARN.DeDw[i] = (dbl **) malloc(Neurons[i]*sizeof(dbl *));
        if(NET.Weights[i] == 0 || NET.vWeights[i] == 0 
          || LEARN.Odw[i] == 0 || LEARN.ODeDw[i] == 0
          || LEARN.DeDw[i] == 0)  return -111;
          
        for(j=0; j<Neurons[i]; j++)
            {
            NET.Weights[i][j] = &(NET.vWeights[i][j*k]);
            LEARN.Odw[i][j] = (dbl *) malloc(k*sizeof(dbl));
            LEARN.ODeDw[i][j] = (dbl *) malloc(k*sizeof(dbl));
            LEARN.DeDw[i][j] = (dbl *) malloc(k*sizeof(dbl));
            if(LEARN.Odw[i][j] == 0 
              || LEARN.ODeDw[i][j] == 0
              || LEARN.DeDw[i][j] == 0)  return -111;
            
            for(l=0; l<k; l++)
                {
                LEARN.Odw[i][j][l] = 0;
                LEARN.ODeDw[i][j][l] = 0;
                }
            }
        }
    return 0;   
}

int AllocPatterns	(	int	ifile,
		int	npat,
		int	nin,
		int	nout,
		int	iadd
	)

Definition at line 3075 of file mlp_gen.cc.

References FreePatterns(), compare_using_db::ifile, j, PAT, and tmp.

Referenced by PhysicsTools::MLP::init(), and ReadPatterns().

{
    int j;
    type_pat *tmp, *tmp3;
    type_pat **tmp2;
    int ntot;
    
    if(ifile>1 || ifile<0) return(1);
/*  scanf("%d",&j); */
    if(ExamplesMemory==0) 
        {
        ExamplesMemory=1;
            PAT.Pond = (type_pat **) malloc(2*sizeof(dbl*));
            PAT.Rin = (type_pat***) malloc(2*sizeof(type_pat**));
            PAT.Rans = (type_pat***) malloc(2*sizeof(type_pat**));
        PAT.vRin = (type_pat**) malloc(2*sizeof(type_pat*));
        if(PAT.Pond == 0 || PAT.Rin == 0
           || PAT.Rans == 0 || PAT.vRin == 0) return -111; 
        } 
    

/* if iadd=0, check that memory not already allocated. Otherwise free it */
    if(iadd==0 && PatMemory[ifile]!=0)
        {
         FreePatterns(ifile);
        }

/* allocate memory and initialize ponderations */
    if(iadd==0 || PatMemory[ifile]==0)
    {
    PatMemory[ifile] = 1;       
        PAT.Pond[ifile] = (type_pat*) malloc(npat*sizeof(type_pat));
    if(PAT.Pond[ifile] == 0) return -111;
    for(j=0; j<npat; j++)
            PAT.Pond[ifile][j] = 1;
            
    PAT.Rin[ifile] = (type_pat**) malloc(npat*sizeof(type_pat*));
    if(PAT.Rin[ifile] == 0) return -111;
    PAT.Rans[ifile] = (type_pat**) malloc(npat*sizeof(type_pat*));
    if(PAT.Rans[ifile] == 0) return -111;

    PAT.vRin[ifile] = (type_pat *) malloc(npat*(nin+1)*
                        sizeof(type_pat));
    if(PAT.vRin[ifile] == 0) return -111;
                        
    for(j=0; j<npat; j++)
        {
        PAT.Rin[ifile][j] = &(PAT.vRin[ifile][j*(nin+1)+1]);
        PAT.vRin[ifile][j*(nin+1)] = 1;
        }
    for(j=0; j<npat; j++)
        {
        PAT.Rans[ifile][j] = (type_pat*) malloc(nout*sizeof(type_pat));
        if(PAT.Rans[ifile][j] == 0) return -111;
        }
    PAT.Npat[ifile] = npat;
    
    if(ifile==0)
        {   
        ExamplesIndex = (int *) malloc(npat*sizeof(int));
        if(ExamplesIndex == 0) return -111;
        for(j=0; j<npat; j++) ExamplesIndex[j] = j;
        }
    }
    else        /* add examples */
    {
    ntot = PAT.Npat[ifile]+npat;
    
/* event weighting */   
    tmp = (type_pat *) malloc(ntot*sizeof(type_pat));
    if(tmp == 0) return -111;
    
    for(j=0; j<PAT.Npat[ifile]; j++) 
        {
        tmp[j] = PAT.Pond[ifile][j];
        }
    for(j=PAT.Npat[ifile];j<ntot;j++)
        {
        tmp[j] = 1;
        }
    if(PatMemory[ifile]==1) free(PAT.Pond[ifile]);
    PAT.Pond[ifile] = tmp;  
    
/* examples */
/*  tmp2 = (type_pat **) malloc(ntot*sizeof(type_pat*));        
    for(j=0; j<PAT.Npat[ifile]; j++) 
        {
        tmp2[j] = PAT.Rin[ifile][j];
        }
    for(j=PAT.Npat[ifile];j<ntot;j++)
        {
        tmp2[j] = (type_pat*) malloc(nin*sizeof(type_pat));
        }
    if(PatMemory[ifile]==1) free(PAT.Rin[ifile]);
    PAT.Rin[ifile] = tmp2;  */
    
    tmp3 = (type_pat *) malloc(ntot*(nin+1)*sizeof(type_pat));
    if(tmp3 == 0) return -111;
    
    for(j=0; j<PAT.Npat[ifile]*(nin+1); j++)
        {
        tmp3[j] = PAT.vRin[ifile][j];
        }
    if(PatMemory[ifile]==1) free(PAT.vRin[ifile]);
    PAT.vRin[ifile] = tmp3;
    for(j=0; j<ntot; j++)
        {
        PAT.Rin[ifile][j] = &(PAT.vRin[ifile][j*(nin+1)+1]);
        PAT.vRin[ifile][j*(nin+1)] = 1;
        }
        
    tmp2 = (type_pat **) malloc(ntot*sizeof(type_pat*));
    if(tmp2 == 0) return -111;      
    for(j=0; j<PAT.Npat[ifile]; j++) 
        {
        tmp2[j] = PAT.Rans[ifile][j];
        }
    for(j=PAT.Npat[ifile];j<ntot;j++)
        {
        tmp2[j] = (type_pat*) malloc(nout*sizeof(type_pat));
        if(tmp2[j] == 0) return -111;
        }
    if(PatMemory[ifile]==1) free(PAT.Rans[ifile]);
    PAT.Rans[ifile] = tmp2; 
    PAT.Npat[ifile] = ntot;
    PatMemory[ifile] = 1;       
    
/* indices */   
    if(ifile==0)
        {
        free(ExamplesIndex);    
        ExamplesIndex = (int *) malloc(ntot*sizeof(int));
        if(ExamplesIndex == 0) return -111;
        for(j=0; j<ntot; j++) ExamplesIndex[j] = j;
        }
    }
        
    return 0;
} 

void AllocWeights

(

)

void BFGSdir

(

int

Nweights

)

Definition at line 1343 of file mlp_gen.cc.

References g, i, recoMuon::in, j, LEARN, NET, and alignCSCRings::s.

Referenced by MLP_Epoch().

{
    dbl *g, *s;
    int kk=0;
    int il,i,j,in,jn;
    
    g = (dbl*) malloc(NET.Nweights*sizeof(dbl));
    s = (dbl*) malloc(Nweights*sizeof(dbl));
    
    for(il=1; kk<Nweights; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                g[kk] = LEARN.DeDw[il][in][jn];
                kk++;
                }
    for(i=0; i<Nweights; i++)
        {
        s[i] = 0;
        for(j=0; j<Nweights; j++)
            {
            s[i] += BFGSH[i][j] * g[j];
            }
        }
    
    kk = 0;
    for(il=1; kk<Nweights; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                dir[il][in][jn] = -s[kk];
                kk++;
                }
    free(g);
    free(s);
}

void CGDir

(

dbl

beta

)

Definition at line 1269 of file mlp_gen.cc.

References recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
        int il,in,jn;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                dir[il][in][jn] = -LEARN.DeDw[il][in][jn]+
                    beta*dir[il][in][jn];
                }
}

int CountLexemes

(

char *

string

)

Definition at line 2583 of file mlp_gen.cc.

References i, NULL, and tmp.

Referenced by getLexemes(), and ReadPatterns().

{
  char tmp[1024];
  int i=0;
  
  strcpy(tmp,s);
  if (strtok(tmp," "))
    {
      i=1;
      while (strtok(NULL," ")) i++;
    }
  return i;
}

int DecreaseSearch	(	dbl *	alpmin,
		int *	Ntest,
		dbl	Err0
	)

Definition at line 1614 of file mlp_gen.cc.

References recoMuon::in, LEARN, MLP_Line(), MLP_Test(), NET, and metsig::tau.

{
    dbl ***w0;
    dbl alpha2;
    dbl err1, err2;
    dbl tau;
    int icount, il, in, jn;
    
    tau=LEARN.Tau;

/* store weights before line search */  

    *Ntest = 0;
    w0 = (dbl ***) malloc(NET.Nlayer*sizeof(dbl**));
    for(il=1; il<NET.Nlayer; il++)
        {
            w0[il] = (dbl **) malloc(NET.Nneur[il]*sizeof(dbl*));
        for(in=0; in<NET.Nneur[il]; in++)
            {
            w0[il][in] = (dbl *) malloc((NET.Nneur[il-1]+1)*
                sizeof(dbl));
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                w0[il][in][jn] = NET.Weights[il][in][jn];
                }
            }
        }
    
/* compute error(w0) */

/*  err1 = MLP_Test(0,0); 
    (*Ntest) ++;*/
    err1 = Err0;
    
    if(NET.Debug>=4) printf("err depart= %f\n",err1);   
    
    *alpmin = 0;
    alpha2 = 0.05;
    MLP_Line(w0,alpha2);
    err2 = MLP_Test(0,0);
    (*Ntest) ++;
    
    if(err2<err1) 
        {
        *alpmin = alpha2;
        }
    else
        {   
    
   
        for(icount=1;icount<=100;icount++)
            {
            alpha2 = alpha2/tau;
            MLP_Line(w0,alpha2);
            err2 = MLP_Test(0,0);
            (*Ntest) ++;
            if(err1>err2) break;
            }
        if(icount>=100)         /* line search fails */
            {
            MLP_Line(w0,0);     /* reset weights */
            free(w0);
            return(1);
            }
        *alpmin = alpha2;   
        }

/* set the weights */
        MLP_Line(w0,*alpmin);
    
/* store weight changes */
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                LEARN.Odw[il][in][jn] = NET.Weights[il][in][jn]
                - w0[il][in][jn];

    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            free(w0[il][in]);           
    for(il=1; il<NET.Nlayer; il++)
        free(w0[il]);   
    free(w0);
    
    return(0);
}

dbl DeDwNorm

(

)

Definition at line 1001 of file mlp_gen.cc.

References createTree::dd, recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
        int il,in,jn;
    dbl dd=0;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                dd += LEARN.DeDw[il][in][jn]*
                    LEARN.DeDw[il][in][jn];
    return(dd);
}

dbl DeDwProd

(

)

Definition at line 1023 of file mlp_gen.cc.

References createTree::dd, recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
        int il,in,jn;
    dbl dd=0;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                dd += LEARN.DeDw[il][in][jn]*
                    LEARN.ODeDw[il][in][jn];
    return(dd);
}

void DeDwSave

(

)

Definition at line 1081 of file mlp_gen.cc.

References recoMuon::in, LEARN, and NET.

{
    int il, in, jn;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                LEARN.ODeDw[il][in][jn] = LEARN.DeDw[il][in][jn];
}   

void DeDwSaveZero

(

)

Definition at line 1100 of file mlp_gen.cc.

References recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
    int il, in, jn;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                LEARN.ODeDw[il][in][jn] = LEARN.DeDw[il][in][jn];
                LEARN.DeDw[il][in][jn] = 0;
                }
}   

void DeDwScale

(

int

Nexamples

)

Definition at line 1063 of file mlp_gen.cc.

References recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
    int il, in, jn;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                LEARN.DeDw[il][in][jn] /= (dbl) Nexamples;
}   

int DeDwSum	(	type_pat *	ans,
		dbl *	out,
		int	ipat
	)

Definition at line 1126 of file mlp_gen.cc.

References a, b, cuy::ii, recoMuon::in, LEARN, NET, and PAT.

Referenced by MLP_Train().

{
    int il, in1, in, ii;
/*  dbl err[NMAX][4]; */
    dbl deriv;
    dbl *pout, *pdedw, *pdelta;
    register dbl a, b;
/*  char buf[50];*/

/* output layer */ 
    b =  (dbl) PAT.Pond[0][ipat];  
    for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++) 
    {              
        deriv = NET.Deriv1[NET.Nlayer-1][in];
        NET.Delta[NET.Nlayer-1][in] = 
            (out[in] - (dbl) ans[in])*deriv*b;
    }
    
    for(il=NET.Nlayer-2; il>0; il--) 
        {

    for(in=0; in<NET.Nneur[il]; in++) 
    {                  
        deriv = NET.Deriv1[il][in];
        a = NET.Delta[il+1][0] * NET.Weights[il+1][0][in+1];
        pdelta = &(NET.Delta[il+1][1]);
        for(in1=1; in1<NET.Nneur[il+1]; in1++, pdelta++) 
        {
            a += *pdelta * NET.Weights[il+1][in1][in+1];
        }
        NET.Delta[il][in] = a * deriv; 
    }
    }
        
        for(il=1; il<NET.Nlayer; il++) 
            {
        ii = NET.Nneur[il-1];
        for(in=0; in<NET.Nneur[il]; in++) 
            {
            a = NET.Delta[il][in];  
            LEARN.DeDw[il][in][0] += a;
            LEARN.DeDw[il][in][1] += a * NET.Outn[il-1][0];
            pout = &(NET.Outn[il-1][1]);
            pdedw = &(LEARN.DeDw[il][in][2]);
            for(in1=1; in1<ii; ++in1, ++pout, ++pdedw) 
                {
                (*pdedw) += a * (*pout);
                }
            }
        }       

    return(0); 
}

void DeDwZero

(

)

Definition at line 1044 of file mlp_gen.cc.

References recoMuon::in, LEARN, and NET.

{
    int il, in, jn;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                LEARN.DeDw[il][in][jn] = 0;
}

dbl DerivDir

(

)

Definition at line 1291 of file mlp_gen.cc.

References recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
        int il,in,jn;
    dbl ddir = 0;
    
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                ddir += LEARN.DeDw[il][in][jn]*dir[il][in][jn];
                }
    return(ddir);           
}

void EtaDecay

(

)

Definition at line 2084 of file mlp_gen.cc.

References LEARN.

Referenced by MLP_Stochastic().

{
    LEARN.eta *= LEARN.Decay;
}

int FixedStep

(

dbl

alpha

)

Definition at line 1702 of file mlp_gen.cc.

References recoMuon::in, LEARN, MLP_Line(), and NET.

{
    dbl ***w0;
    int il, in, jn;
    
    w0 = (dbl ***) malloc(NET.Nlayer*sizeof(dbl**));
    for(il=1; il<NET.Nlayer; il++)
        {
            w0[il] = (dbl **) malloc(NET.Nneur[il]*sizeof(dbl*));
        for(in=0; in<NET.Nneur[il]; in++)
            {
            w0[il][in] = (dbl *) malloc((NET.Nneur[il-1]+1)*
                sizeof(dbl));
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                w0[il][in][jn] = NET.Weights[il][in][jn];
                }
            }
        }
    

/* set the weights */
        MLP_Line(w0,alpha);
    
/* store weight changes */
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                LEARN.Odw[il][in][jn] = NET.Weights[il][in][jn]
                - w0[il][in][jn];

    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            free(w0[il][in]);           
    for(il=1; il<NET.Nlayer; il++)
        free(w0[il]);   
    free(w0);
    
    return(0);
}

void FreeNetwork

(

)

Definition at line 3527 of file mlp_gen.cc.

References i, j, LEARN, and NET.

Referenced by AllocNetwork().

{
    int i, j;
    for(i=1; i<NET.Nlayer; i++)
        {
        for(j=0; j<NET.Nneur[i]; j++)
            {
/*          free(NET.Weights[i][j]); */
            free(LEARN.Odw[i][j]);
            free(LEARN.ODeDw[i][j]);
            free(LEARN.DeDw[i][j]);
            }
        free(NET.vWeights[i]);
        free(NET.Weights[i]);
        free(LEARN.Odw[i]);
        free(LEARN.ODeDw[i]);
        free(LEARN.DeDw[i]);
        }
    free(NET.Weights);
    free(LEARN.Odw);
    free(LEARN.ODeDw);
    free(LEARN.DeDw);   
    
    free(NET.Nneur);
    
    for(i=0; i<NET.Nlayer; i++)
        {
        free(NET.T_func[i]);
        free(NET.Deriv1[i]);
        free(NET.Inn[i]);
        free(NET.Outn[i]);
        free(NET.Delta[i]);
        }
    free(NET.T_func);
    free(NET.Deriv1);
    free(NET.Inn);
    free(NET.Outn);
    free(NET.Delta);
    
    NetMemory = 0;  
}

int FreePatterns

(

int

ifile

)

Definition at line 3230 of file mlp_gen.cc.

References i, compare_using_db::ifile, and PAT.

Referenced by AllocPatterns(), and PhysicsTools::MLP::clear().

{
    int i;

    if(ifile>1 || ifile<0) return 1;
/*  printf("%d %d \n",ifile,PatMemory[ifile]);*/
    if(PatMemory[ifile]==0) return 2;
    
    free(PAT.Pond[ifile]);
    for(i=0; i<PAT.Npat[ifile]; i++)
        {
/*      free(PAT.Rin[ifile][i]); */
        free(PAT.Rans[ifile][i]); 
        }
    free(PAT.Rin[ifile]);
    free(PAT.Rans[ifile]);
    free(PAT.vRin[ifile]);
    PatMemory[ifile] = 0;
    PAT.Npat[ifile] = 0;
    
    return 0;
}       

void FreeWeights

(

)

int GetBFGSH

(

int

Nweights

)

Definition at line 1421 of file mlp_gen.cc.

References a, b, i, j, and tmp.

Referenced by MLP_Epoch().

{
        typedef double dble;
    dble deltaTgamma=0;
    dble factor=0; 
    dble *Hgamma;
    dble *tmp;
    register dble a, b;
    int i,j;
    
    Hgamma = (dble *) malloc(Nweights*sizeof(dble));
    tmp = (dble *) malloc(Nweights*sizeof(dble));
    
    for(i=0; i<Nweights; i++)
        {
        deltaTgamma += (dble) delta[i] * (dble) Gamma[i];
        a = 0;
        b = 0;
        for(j=0; j<Nweights; j++)
            {
            a += (dble) BFGSH[i][j] * (dble) Gamma[j];
            b += (dble) Gamma[j] * (dble) BFGSH[j][i];
            }
        Hgamma[i] = a;
        tmp[i] = b; 
        factor += (dble) Gamma[i]*Hgamma[i];
        }
    if(deltaTgamma == 0) return 1;
    a = 1 / deltaTgamma;    
    factor = 1 + factor*a;
    
    for(i=0; i<Nweights; i++)
        {
        b = (dble) delta[i];
        for(j=0; j<Nweights; j++)
            BFGSH[i][j] += (dbl) (factor*b* (dble) 
            delta[j]-(tmp[j]*b+Hgamma[i]*(dble)delta[j]))*a;    
        }   
    free(Hgamma);
    free(tmp);
    return 0;
}

void GetGammaDelta

(

)

Definition at line 1316 of file mlp_gen.cc.

References i, recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
    int i=0;
        int il,in,jn;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                Gamma[i] = LEARN.DeDw[il][in][jn]-
                    LEARN.ODeDw[il][in][jn];
                delta[i] = LEARN.Odw[il][in][jn];
                i++;
                }
}

void getLexemes	(	char *	s,
		char **	ss
	)

Definition at line 2613 of file mlp_gen.cc.

References CountLexemes(), i, n, NULL, and tmp.

{
  char tmp[1024];
  int i,n;   
    
  strcpy(tmp,s);
  n=CountLexemes(tmp);
  if (n>0)
    {
      strcpy(ss[0],strtok(tmp," "));
      for (i=1;i<n;i++)
        strcpy(ss[i],strtok(NULL," "));
    }
}

int GetNetStructure	(	char *	s,
		int *	Nlayer,
		int *	Nneur
	)

Definition at line 3588 of file mlp_gen.cc.

References i, NLMAX, NULL, and tmp.

Referenced by ReadPatterns().

{
    int i=0;
    char tmp[1024];

    if(strlen(s)==0) return -1;
    if(strlen(s)>1024) return -2;

    strcpy(tmp,s);
    if (strtok(tmp,","))
            {
            i=1;
            while (strtok(NULL,",")) i++;
            }
    *Nlayer = i;
    if(i > NLMAX) return -3;

    strcpy(tmp,s);
    if (*Nlayer>0)
            {
            sscanf(strtok(tmp,","),"%d",&(Nneur[0]));
            for (i=1;i<*Nlayer;i++)
                sscanf(strtok(NULL,","),"%d",&(Nneur[i]));
            }

    return 0;
}

void getnLexemes	(	int	n,
		char *	s,
		char **	ss
	)

Definition at line 2599 of file mlp_gen.cc.

References i, n, NULL, and tmp.

Referenced by ReadPatterns().

{
  char tmp[1024];
  int i;     
  strcpy(tmp,s);
  if (n>0)
    {
      strcpy(ss[0],strtok(tmp," "));
      for (i=1;i<n;i++)
        strcpy(ss[i],strtok(NULL," "));
    }
}

void InitBFGSH

(

int

Nweights

)

Definition at line 1391 of file mlp_gen.cc.

References i, and j.

Referenced by MLP_Epoch().

{
    int i,j;
    for(i=0; i<Nweights; i++)
        for(j=0; j<Nweights; j++)
            {
            BFGSH[i][j] = 0;
            if(i==j) BFGSH[i][j] = 1;
            }
}

void InitWeights

(

)

Definition at line 2145 of file mlp_gen.cc.

References i, MLP_Rand(), and NET.

Referenced by PhysicsTools::MLP::MLP().

{
        int ilayer,ineur,i;
    
        for(ilayer=1;ilayer<NET.Nlayer;ilayer++)
        for(ineur=0;ineur<NET.Nneur[ilayer];ineur++)
            for(i=0;i<=NET.Nneur[ilayer-1];i++)
                NET.Weights[ilayer][ineur][i]=
                    (dbl) MLP_Rand(-0.5, 0.5);
}

int LearnAlloc

(

)

Definition at line 2683 of file mlp_gen.cc.

References i, recoMuon::in, LEARN, LearnFree(), and NET.

Referenced by PhysicsTools::MLP::MLP().

{
        int il,in,i; 
    int Nweights = 0;
    
    if(LearnMemory != 0) LearnFree();
    LearnMemory = 1;
    dir = (dbl ***) malloc(NET.Nlayer*sizeof(dbl**));
    if(dir == 0) return -111;
    
    for(il=0; il<NET.Nlayer; il++)
        {
            dir[il] = (dbl **) malloc(NET.Nneur[il]*sizeof(dbl*));
        if(dir[il] == 0) return -111;
        for(in=0; in<NET.Nneur[il]; in++)
            {
            if(il==0) 
                {
/* TODO: understand implications of hard-coded 101 */               
                dir[0][in] = (dbl *)
                malloc(101*sizeof(dbl));
                if(dir[0][in] == 0) return -111;
                }
            else
                {
                dir[il][in] = (dbl *) 
                malloc((NET.Nneur[il-1]+1)*sizeof(dbl));
                if(dir[il][in] == 0) return -111;
                    Nweights += NET.Nneur[il-1]+1;
                }
            }
        }
    NET.Nweights = Nweights;
        
    if(BFGSMemory==0 && LEARN.Meth>= 6) 
        {
        BFGSMemory = 1;
        Gamma = (dbl*) malloc(Nweights*sizeof(dbl));
        delta = (dbl*) malloc(Nweights*sizeof(dbl));
        BFGSH = (dbl**) malloc(Nweights*sizeof(dbl*));
        if(Gamma == 0 || delta == 0 || BFGSH == 0)
           return -111;
           
        for(i=0; i<Nweights; i++)
            {
            BFGSH[i] = (dbl*) malloc(Nweights*sizeof(dbl));
            if(BFGSH[i] == 0) return -111;
            }
        }
        
/*  if(JacobianMemory==0)
        {
        JacobianMemory = 1;
        printf("JacobianMemory = %d\n",JacobianMemory);
        JacobianMatrix = (dbl **) malloc(PAT.Npat[0]*sizeof(dbl *));
        for(i=0; i<PAT.Npat[0]; i++)
            JacobianMatrix[i] = 
                (dbl*) malloc(Nweights*sizeof(dbl));
        printf("end memory alloc\n");
        }   
    
    if(DIVERS.Ihess==1) HessianAlloc(Nweights);*/
    
    return 0;
}

void LearnFree

(

)

Definition at line 2638 of file mlp_gen.cc.

References recoMuon::in, and NET.

Referenced by LearnAlloc(), and PhysicsTools::MLP::~MLP().

{
        int il,in; 
    if(LearnMemory==0) return;
    LearnMemory = 0;
    for(il=0; il<NET.Nlayer; il++)
        {
        for(in=0; in<NET.Nneur[il]; in++)
            {
            free(dir[il][in]);
            }
        free(dir[il]);
        }
    free(dir);
    if(BFGSMemory==0) return;
    BFGSMemory = 0;
    for(il=0; il<NET.Nweights; il++)
        {
        free(BFGSH[il]);
        }       
    free(BFGSH);
    free(Gamma);
    free(delta);
    
/*  if(JacobianMemory == 0) return;
    JacobianMemory = 0;
    for(il=0; il<PAT.Npat[0]; il++) free(JacobianMatrix[il]);
    free(JacobianMatrix);   */
}

int LineSearch	(	dbl *	alpmin,
		int *	Ntest,
		dbl	Err0
	)

Definition at line 1476 of file mlp_gen.cc.

References recoMuon::in, LastAlpha, LEARN, MLP_Line(), MLP_Test(), NET, and metsig::tau.

Referenced by MLP_Epoch().

{
    dbl ***w0;
    dbl alpha1, alpha2, alpha3;
    dbl err1, err2, err3;
    dbl tau;
    int icount, il, in, jn;
    
    tau=LEARN.Tau;

/* store weights before line search */  

    *Ntest = 0;
    w0 = (dbl ***) malloc(NET.Nlayer*sizeof(dbl**));
    for(il=1; il<NET.Nlayer; il++)
        {
            w0[il] = (dbl **) malloc(NET.Nneur[il]*sizeof(dbl*));
        for(in=0; in<NET.Nneur[il]; in++)
            {
            w0[il][in] = (dbl *) malloc((NET.Nneur[il-1]+1)*
                sizeof(dbl));
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                w0[il][in][jn] = NET.Weights[il][in][jn];
                }
            }
        }
    
/* compute error(w0) */

/*  err1 = MLP_Test(0,0); 
    (*Ntest) ++;*/
    err1 = Err0;
    
    if(NET.Debug>=4) printf("err depart= %f\n",err1);   
    
    *alpmin = 0;
    alpha1 = 0;
/*  alpha2 = 0.05;
    if(LastAlpha != 0) alpha2 = LastAlpha;*/
    alpha2 = LastAlpha;
    if(alpha2 < 0.01) alpha2 = 0.01;
    if(alpha2 > 2.0) alpha2 = 2.0;
    MLP_Line(w0,alpha2);
    err2 = MLP_Test(0,0);
    (*Ntest) ++;
    if(NET.Debug>=4) printf("alpha, err= %e %e\n",alpha2,err2);   
    
    alpha3 = alpha2;
    err3 = err2;
    
/* try to find a triplet (alpha1, alpha2, alpha3) such that 
   Error(alpha1)>Error(alpha2)<Error(alpha3)                 */
   
    if(err1>err2) 
        {
        for(icount=1;icount<=100;icount++)
            {
            alpha3 = alpha3*tau;
            MLP_Line(w0,alpha3);
            err3 =MLP_Test(0,0);
    if(NET.Debug>=4) printf("alpha, err= %e %e\n",alpha3,err3);   
            (*Ntest) ++;
            if(err3>err2) break;
            alpha1 = alpha2;
            err1 = err2;
            alpha2 = alpha3;
            err2 = err3;
            }
        if(icount>=100)         /* line search fails */
            {
            MLP_Line(w0,0);     /* reset weights */
            free(w0);
            return(1);
            }
        }
    else
        {
        for(icount=1;icount<=100;icount++)
            {
            alpha2 = alpha2/tau;
            MLP_Line(w0,alpha2);
            err2 = MLP_Test(0,0);
    if(NET.Debug>=4) printf("alpha, err= %e %e\n",alpha2,err2);   
            (*Ntest) ++;
            if(err1>err2) break;
            alpha3 = alpha2;
            err3 = err2;
            }
        if(icount>=100)         /* line search fails */
            {
            MLP_Line(w0,0);     /* reset weights */
            free(w0);
            LastAlpha = 0.05;       /* try to be safe */
            return(1);
            }
        }

/* find bottom of parabola */
    
    *alpmin = 0.5*(alpha1+alpha3-(err3-err1)/((err3-err2)/(alpha3-alpha2)
        -(err2-err1)/(alpha2-alpha1)));
    if(*alpmin>10000) *alpmin=10000;

/* set the weights */
        MLP_Line(w0,*alpmin);
    LastAlpha = *alpmin;
    
/* store weight changes */
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                LEARN.Odw[il][in][jn] = NET.Weights[il][in][jn]
                - w0[il][in][jn];

    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            free(w0[il][in]);           
    for(il=1; il<NET.Nlayer; il++)
        free(w0[il]);   
    free(w0);
    
    return(0);
}

int LineSearchHyb	(	dbl *	alpmin,
		int *	Ntest
	)

Definition at line 1779 of file mlp_gen.cc.

References recoMuon::in, LastAlpha, LEARN, MLP_LineHyb(), MLP_Test(), NET, and metsig::tau.

Referenced by MLP_Epoch().

{
    dbl ***w0;
    dbl alpha1, alpha2, alpha3;
    dbl err1, err2, err3;
    dbl tau;
    int icount, il, in, jn;

/*  char buf [50];
  sprintf (buf,"entree linesearchhyb\n");
  MessageBoxA (0,buf,"dans FreePatterns",MB_OK);*/
    
    if(NET.Debug>=4){
        printf(" entry LineSearchHyb \n");
        }
    tau=LEARN.Tau;

/* store weights before line search */  

    *Ntest = 0;
    w0 = (dbl ***) malloc((NET.Nlayer-1)*sizeof(dbl**));
    for(il=1; il<NET.Nlayer-1; il++)
        {
            w0[il] = (dbl **) malloc(NET.Nneur[il]*sizeof(dbl*));
        for(in=0; in<NET.Nneur[il]; in++)
            {
            w0[il][in] = (dbl *) malloc((NET.Nneur[il-1]+1)*
                sizeof(dbl));
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                {
                w0[il][in][jn] = NET.Weights[il][in][jn];
                }
            }
        }
    
/* compute error(w0) */
    err1 = MLP_Test(0,1);
    (*Ntest) ++; 
    if(NET.Debug>=4) printf("LinesearchHyb err depart= %f\n",err1);   
    
    *alpmin = 0;
    alpha1 = 0;
/*  alpha2 = 0.05;
    if(LastAlpha != 0) alpha2 = LastAlpha;*/
    alpha2 = LastAlpha;
    if(alpha2 < 0.01) alpha2 = 0.01;
    if(alpha2 > 2.0) alpha2 = 2.0;
    MLP_LineHyb(w0,alpha2);
    err2 = MLP_Test(0,1);
    (*Ntest) ++; 
    
    alpha3 = alpha2;
    err3 = err2;
    
/* try to find a triplet (alpha1, alpha2, alpha3) such that 
   Error(alpha1)>Error(alpha2)<Error(alpha3)                 */
   
    if(err1>err2) 
        {
        for(icount=1;icount<=100;icount++)
            {
            alpha3 = alpha3*tau;
            MLP_LineHyb(w0,alpha3);
            err3 = MLP_Test(0,1);
            (*Ntest) ++; 
            if(err3>err2) break;
            alpha1 = alpha2;
            err1 = err2;
            alpha2 = alpha3;
            err2 = err3;
            }
        if(icount>=100)         /* line search fails */
            {
            MLP_LineHyb(w0,0);      /* reset weights */
            free(w0);
            return(1);
            }
        }
    else
        {
        for(icount=1;icount<=100;icount++)
            {
            alpha2 = alpha2/tau;
            MLP_LineHyb(w0,alpha2);
            err2 = MLP_Test(0,1);
            (*Ntest) ++; 
            if(err1>err2) break;
            alpha3 = alpha2;
            err3 = err2;
            }
        if(icount>=100)         /* line search fails */
            {
            MLP_LineHyb(w0,0);      /* reset weights */
            free(w0);
            return(1);
            }
        }

/* find bottom of parabola */
    
    *alpmin = 0.5*(alpha1+alpha3-(err3-err1)/((err3-err2)/(alpha3-alpha2)
        -(err2-err1)/(alpha2-alpha1)));
    if(*alpmin>10000) *alpmin=10000;

/* set the weights */
        MLP_LineHyb(w0,*alpmin);
    LastAlpha = *alpmin;
    
/* store weight changes */
    for(il=1; il<NET.Nlayer-1; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                LEARN.Odw[il][in][jn] = NET.Weights[il][in][jn]
                - w0[il][in][jn];

    for(il=1; il<NET.Nlayer-1; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            free(w0[il][in]);           
    for(il=1; il<NET.Nlayer-1; il++)
        free(w0[il]);   
    free(w0);
    if(NET.Debug>=4){
        printf(" exit LineSearchHyb \n");
        }

    return(0);
}

int LoadWeights	(	char *	filename,
		int *	iepoch
	)

Definition at line 3020 of file mlp_gen.cc.

References i, NET, AlCaHLTBitMon_ParallelJobs::p, and alignCSCRings::s.

Referenced by PhysicsTools::MLP::load().

{
    FILE *W;
    int ilayer,ineur,i;
    double p;
    char s[80];

    W=fopen(filename,"r");
    if(W==0) return -1;
    do
        {
        fgets(s,80,W);
        }
    while(*s == '#');   
        sscanf(s," %d",iepoch);
        for(ilayer=1; ilayer<NET.Nlayer; ilayer++)
            {
            for(ineur=0; ineur<NET.Nneur[ilayer]; ineur++)
                {
                for(i=0; i<=NET.Nneur[ilayer-1]; i++)
                    {
                    fscanf(W," %le",&p);
                    NET.Weights[ilayer][ineur][i] = (dbl) p;
                    }
                }
            }
        
    fclose(W);
    return 0;
}

dbl MLP_Epoch	(	int	iepoch,
		dbl *	alpmin,
		int *	ntest
	)

Definition at line 709 of file mlp_gen.cc.

References beta, BFGSdir(), CGDir(), DeDwNorm(), DeDwProd(), DeDwSaveZero(), DeDwScale(), DerivDir(), GetBFGSH(), GetGammaDelta(), InitBFGSH(), LEARN, LineSearch(), LineSearchHyb(), MLP_ResLin(), MLP_Stochastic(), MLP_Train(), NET, PAT, PrintWeights(), parseEventContent::prod, SetLambda(), ShuffleExamples(), SteepestDir(), and StochStep().

Referenced by PhysicsTools::MLP::train().

{
    dbl err, ONorm, beta, prod, ddir;   
/*  int *index;*/
    int Nweights, Nlinear, ipat, ierr;
    int nn;
    
    err = 0;
    *alpmin = 0.;
    
    Nweights = NET.Nweights;
    Nlinear = NET.Nneur[NET.Nlayer-2] + 1;
    
    if(NET.Debug>=5) printf(" Entry MLP_Epoch\n");      
/* stochastic minimization */       
    if(LEARN.Meth==1) 
        {

        err = MLP_Stochastic();
            
        }
    else
        {
        if(iepoch==1 && LEARN.Meth==7)
            {
            SetLambda(10000);
            MLP_ResLin();
            if(NET.Debug>=2) PrintWeights();
            }
            
/* save previous gradient and reset current one */
        DeDwSaveZero();
        if(LEARN.Meth==16) 
            {           
            ShuffleExamples(PAT.Npat[0],ExamplesIndex);
            nn = PAT.Npat[0];
            PAT.Npat[0] = nn/10;
            for(ipat=0;ipat<nn;ipat++)
                {
                ierr = MLP_Train(&ExamplesIndex[ipat],&err);
                if(ierr!=0) printf("Epoch: ierr= %d\n",ierr);
                }
            } 
        else
            {
            for(ipat=0;ipat<PAT.Npat[0];ipat++)
                {
                ierr = MLP_Train(&ipat,&err);
                if(ierr!=0) printf("Epoch: ierr= %d\n",ierr);
                }
            }
        DeDwScale(PAT.Npat[0]);
        if(LEARN.Meth==2) StochStep();
        if(LEARN.Meth==3) 
            {
            SteepestDir(); 
            if(LineSearch(alpmin,Ntest,err)==1) StochStep();
            }

/* Conjugate Gradients Ribiere - Polak */
        if(LEARN.Meth==4) 
            {
            if((iepoch-1)%LEARN.Nreset==0) 
                {
                LEARN.Norm = DeDwNorm(); /* for next epoch */
                SteepestDir();
                }
            else
                {
                ONorm = LEARN.Norm;
                LEARN.Norm = DeDwNorm();
                prod = DeDwProd();
                beta = (LEARN.Norm-prod)/ONorm;
                CGDir(beta);
                }
            if(LineSearch(alpmin,Ntest,err)==1) StochStep();
            }

/* Conjugate Gradients Fletcher - Reeves */
        if(LEARN.Meth==5) 
            {
            if((iepoch-1)%LEARN.Nreset==0) 
                {
                LEARN.Norm = DeDwNorm(); /* for next epoch */
                SteepestDir();
                }
            else
                {
                ONorm = LEARN.Norm;
                LEARN.Norm = DeDwNorm();
                beta = LEARN.Norm/ONorm;
                CGDir(beta);
                }
            if(LineSearch(alpmin,Ntest,err)==1) StochStep();
            }
        if(LEARN.Meth==6)
            {
            if((iepoch-1)%LEARN.Nreset==0)
                {
                SteepestDir();
                InitBFGSH(Nweights);
                }
            else
                {
                GetGammaDelta();
                ierr = GetBFGSH(Nweights);
                if(ierr)
                    {
                    SteepestDir();
                    InitBFGSH(Nweights);
                    }
                else
                    {   
                    BFGSdir(Nweights);
                    }
                }
            ddir = DerivDir();
            if(ddir>0)
                {
                SteepestDir();
                InitBFGSH(Nweights);
                ddir = DerivDir();
                }
            if(LineSearch(alpmin,Ntest,err)==1) 
                {
                InitBFGSH(Nweights);
                SteepestDir();
                if(LineSearch(alpmin,Ntest,err)==1) 
                    {
                    printf("Line search fail \n");
                    }
                }
            }
        if(LEARN.Meth==7)
            {
            if((iepoch-1)%LEARN.Nreset==0)
                {
                SteepestDir();
                InitBFGSH(Nweights-Nlinear);
                }
            else
                {
            if(NET.Debug>=5) printf("Before GetGammaDelta \n");
                GetGammaDelta();
            if(NET.Debug>=5) printf("After GetGammaDelta \n");
                ierr = GetBFGSH(Nweights-Nlinear);
            if(NET.Debug>=5) printf("After GetBFGSH \n");
                if(ierr)
                    {
                    SteepestDir();
                    InitBFGSH(Nweights-Nlinear);
                    }
                else
                    {   
                    BFGSdir(Nweights-Nlinear);
                    }
            if(NET.Debug>=5) printf("After BFGSdir \n");
                }
            SetLambda(10000);
            if(LineSearchHyb(alpmin,Ntest)==1)
                {
                InitBFGSH(Nweights-Nlinear);
                SteepestDir();
                if(LineSearchHyb(alpmin,Ntest)==1) 
                    {
                    printf("Line search fail \n");
                    }
                }
            }
        }
    
    if(NET.Debug>=5) printf(" End MLP_Epoch\n");        
    return(err);
}

void MLP_Line	(	dbl ***	w0,
		dbl	alpha
	)

Definition at line 1755 of file mlp_gen.cc.

References recoMuon::in, and NET.

Referenced by DecreaseSearch(), FixedStep(), and LineSearch().

{
    register int il,in,jn;
    
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                NET.Weights[il][in][jn] = w0[il][in][jn]+
                alpha*dir[il][in][jn];
                
}

void MLP_LineHyb	(	dbl ***	w0,
		dbl	alpha
	)

Definition at line 1921 of file mlp_gen.cc.

References recoMuon::in, MLP_ResLin(), and NET.

Referenced by LineSearchHyb().

{
    int il,in,jn;
    for(il=1; il<NET.Nlayer-1; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
            {
                NET.Weights[il][in][jn] = w0[il][in][jn]+
                alpha*dir[il][in][jn];
            }
    MLP_ResLin();
}

void MLP_MatrixVector	(	dbl *	M,
		type_pat *	v,
		dbl *	r,
		int	n,
		int	m
	)

Definition at line 3744 of file mlp_gen.cc.

References trackerHits::c, i, j, m, n, and alignCSCRings::r.

Referenced by MLP_Test_MM().

{
    int i,j;
    register dbl a1, a2, a3, a4, c, d;
    dbl *pM1 = M;
    dbl *pM2 = &(M[m]);
    dbl *pM3 = &(M[2*m]);
    dbl *pM4 = &(M[3*m]);
    dbl *pr = r;
    int mp1 = m;
    
    for(i=0; i<n-3; 
        i+=4, pM1 += 3*mp1, pM2 += 3*mp1, pM3 += 3*mp1, pM4 += 3*mp1, 
        pr+=4)
        {
        a1 = 0;
        a2 = 0;
        a3 = 0;
        a4 = 0;
        for(j=0; j<m-1; j+=2, pM1+=2, pM2+=2, pM3+=2, pM4+=2) 
            {
            c = v[j];
            d = v[j+1];
            a1 = a1 + *pM1 * c + *(pM1+1) * d;
            a2 = a2 + *pM2 * c + *(pM2+1) * d; 
            a3 = a3 + *pM3 * c + *(pM3+1) * d;
            a4 = a4 + *pM4 * c + *(pM4+1) * d; 
            }
        for(j=j; j<m; j++, pM1++, pM2++, pM3++, pM4++)
            {
            c = v[j];
            a1 = a1 + *pM1 * c;
            a2 = a2 + *pM2 * c; 
            a3 = a3 + *pM3 * c;
            a4 = a4 + *pM4 * c; 
            }   
        *pr = a1; *(pr+1) = a2; *(pr+2) = a3; *(pr+3) = a4;
        }
    for(i=i; i<n; i++)
        {
        pM1 = &(M[i*m]);
        a1 = 0;
        for(j=0; j<m; j++, pM1++)
            {
            a1 = a1 + *pM1 * v[j];
            }
        r[i] = a1;  
        }   
}

void MLP_MatrixVectorBias	(	dbl *	M,
		dbl *	v,
		dbl *	r,
		int	n,
		int	m
	)

Definition at line 3678 of file mlp_gen.cc.

References trackerHits::c, i, j, m, n, and alignCSCRings::r.

Referenced by MLP_Out(), and MLP_Out2().

{
    int i,j;
    register dbl a1, a2, a3, a4, c, d;
    dbl *pM1 = M;
    dbl *pM2 = &(M[m+1]);
    dbl *pM3 = &(M[2*(m+1)]);
    dbl *pM4 = &(M[3*(m+1)]);
    dbl *pr = r;
    int mp1 = m+1;
    
    for(i=0; i<n-3; 
        i+=4, pM1 += 3*mp1, pM2 += 3*mp1, pM3 += 3*mp1, pM4 += 3*mp1, 
        pr+=4)
        {
        a1 = *pM1;
        a2 = *pM2;
        a3 = *pM3;
        a4 = *pM4;
        pM1++; pM2++; pM3++; pM4++;
        for(j=0; j<m-1; j+=2, pM1+=2, pM2+=2, pM3+=2, pM4+=2) 
            {
            c = v[j];
            d = v[j+1];
            a1 = a1 + *pM1 * c + *(pM1+1) * d;
            a2 = a2 + *pM2 * c + *(pM2+1) * d; 
            a3 = a3 + *pM3 * c + *(pM3+1) * d;
            a4 = a4 + *pM4 * c + *(pM4+1) * d; 
            }
        for(j=j; j<m; j++, pM1++, pM2++, pM3++, pM4++)
            {
            c = v[j];
            a1 = a1 + *pM1 * c;
            a2 = a2 + *pM2 * c; 
            a3 = a3 + *pM3 * c;
            a4 = a4 + *pM4 * c; 
            }   
        *pr = a1; *(pr+1) = a2; *(pr+2) = a3; *(pr+3) = a4;
        }
    for(i=i; i<n; i++)
        {
        pM1 = &(M[i*(m+1)]);
        a1 = *pM1;
        pM1++;
        for(j=0; j<m; j++, pM1++)
            {
            a1 = a1 + *pM1 * v[j];
            }
        r[i] = a1;  
        }   
}

void MLP_MM2rows	(	dbl *	c,
		type_pat *	a,
		dbl *	b,
		int	Ni,
		int	Nj,
		int	Nk,
		int	NaOffs,
		int	NbOffs
	)

Definition at line 3813 of file mlp_gen.cc.

References j, and gen::k.

Referenced by MLP_Test_MM().

{
//int i,j,k;
int j,k;
dbl s00,s01,s10,s11;
type_pat *pa0,*pa1;
dbl *pb0,*pb1,*pc0,*pc1;

  for (j=0; j<=Nj-2; j+=2)
   {
    pc0 = c+j;
    pc1 = c+j+Nj;
    s00 = 0.0; s01 = 0.0; s10 = 0.0; s11 = 0.0;

    for (k=0,pb0=b+k+NbOffs*j,
             pb1=b+k+NbOffs*(j+1),
             pa0=a+k,
             pa1=a+k+NaOffs;
         k<Nk;
         k++,pa0++,
             pa1++,
             pb0++,
             pb1++)
     {
      s00 += (*pa0)*(*pb0);
      s01 += (*pa0)*(*pb1);
      s10 += (*pa1)*(*pb0);
      s11 += (*pa1)*(*pb1);
     }
    *pc0 = s00; *(pc0+1) = s01; *pc1 = s10; *(pc1+1) = s11;
   }
  for (j=j; j<Nj; j++)
   {
    pc0 = c+j;
    pc1 = c+j+Nj;
    s00 = 0.0; s10 = 0.0;
    for (k=0,pb0=b+k+NbOffs*j,
             pa0=a+k,
             pa1=a+k+NaOffs;
         k<Nk;
         k++,pa0++,
             pa1++,
             pb0++)
     {
      s00 += (*pa0)*(*pb0);
      s10 += (*pa1)*(*pb0);
     }
    *pc0 = s00; *pc1 = s10;
   }
}

void MLP_Out	(	type_pat *	rrin,
		dbl *	rrout
	)

Definition at line 63 of file mlp_gen.cc.

References i, recoMuon::in, j, m, MLP_MatrixVectorBias(), MLP_vSigmoideDeriv(), and NET.

Referenced by MLP_ResLin().

{
//      static int i, il, in, j, ilm1, m, mp1;  
    static int i, il, in, j, m, mp1;  
    dbl **deriv1;

/* input layer */  

    deriv1 = NET.Deriv1;
    m = NET.Nneur[0]%4;
    if(m==0) goto L10;
    for(j=0;j<m;j++) NET.Outn[0][j] = rrin[j];
L10:    
    mp1 = m+1;
    for(i=mp1; i<=NET.Nneur[0]; i+=4)
        {
            NET.Outn[0][i-1] = rrin[i-1];
            NET.Outn[0][i] = rrin[i];
            NET.Outn[0][i+1] = rrin[i+1];
            NET.Outn[0][i+2] = rrin[i+2];
        }
  
/* hidden and output layers */

    MLP_MatrixVectorBias(NET.vWeights[1],NET.Outn[0],
            NET.Outn[1],NET.Nneur[1],NET.Nneur[0]);
            
    for(il=2; il<NET.Nlayer; il++)
        {
        MLP_vSigmoideDeriv(NET.Outn[il-1],
                deriv1[il-1],NET.Nneur[il-1]);
        MLP_MatrixVectorBias(NET.vWeights[il],NET.Outn[il-1],
                NET.Outn[il],NET.Nneur[il],
                NET.Nneur[il-1]);           
        }    
    for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++)
        {                                
        deriv1[NET.Nlayer-1][in] = 1;
        }       
}

void MLP_Out2

(

type_pat *

rrin

)

Definition at line 186 of file mlp_gen.cc.

References i, recoMuon::in, m, MLP_MatrixVectorBias(), MLP_vSigmoideDeriv(), NET, and nin.

Referenced by MLP_Stochastic(), and MLP_Train().

{
//      static int il, in, m, mp1, i0, ilm1;  
    static int il, in, m, mp1;
    register int i;
    dbl **rrout, **deriv1;
    register dbl *prrout;
    type_pat *prrin;
    int nhid = NET.Nneur[1];
    int nin = NET.Nneur[0];

    rrout = NET.Outn;
    deriv1 = NET.Deriv1;
    
    m = NET.Nneur[0]%4;
    if(m==0) goto L10;
    if(m==1) 
        {
        rrout[0][0] = rrin[1];
        goto L10;
        }
    else if(m==2)
        {
        rrout[0][0] = rrin[1];
        rrout[0][1] = rrin[2];
        goto L10;
        }   
    else if(m==3)
        {
        rrout[0][0] = rrin[1];
        rrout[0][1] = rrin[2];
        rrout[0][2] = rrin[3];
        goto L10;
        }   
L10:    
    mp1 = m+1;
    prrout = &(rrout[0][mp1]);
    prrin = &(rrin[mp1+1]);
    for(i=mp1; i<=NET.Nneur[0]; i+=4, prrout+=4, prrin+=4)
        {
        *(prrout-1) = *(prrin-1);
        *prrout = *prrin;
        *(prrout+1)= *(prrin+1);
        *(prrout+2) = *(prrin+2);
        }
        
/* input layer */ 

    MLP_MatrixVectorBias(NET.vWeights[1],NET.Outn[0],
        NET.Outn[1],nhid,nin);
    
          
/* hidden and output layers */

    for(il=2; il<NET.Nlayer; il++)
        {
        MLP_vSigmoideDeriv(NET.Outn[il-1],deriv1[il-1],NET.Nneur[il-1]);
        MLP_MatrixVectorBias(NET.vWeights[il],NET.Outn[il-1],
            NET.Outn[il],NET.Nneur[il],NET.Nneur[il-1]);
        }   
    for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++)
        deriv1[NET.Nlayer-1][in] = 1;    
}

void MLP_Out_T

(

type_pat *

rrin

)

Definition at line 115 of file mlp_gen.cc.

References a, i, recoMuon::in, j, m, MLP_Sigmoide(), and NET.

Referenced by PhysicsTools::MLP::eval(), and MLP_Test().

{
    static int i, il, in, j, ilm1, m, mp1;  
    register dbl a;

/* input layer */  

    m = NET.Nneur[0]%4;
    if(m==0) goto L10;
    for(j=0;j<m;j++) NET.Outn[0][j] = rrin[j];
L10:    
    mp1 = m+1;
    for(i=mp1; i<=NET.Nneur[0]; i+=4)
        {
            NET.Outn[0][i-1] = rrin[i-1];
            NET.Outn[0][i] = rrin[i];
            NET.Outn[0][i+1] = rrin[i+1];
            NET.Outn[0][i+2] = rrin[i+2];
        }
  
/* hidden and output layers */

/*  for(in=0;in<NET.Nneur[0]; in++) printf("%e %e\n",
        NET.Outn[0][in],NET.Weights[1][0][in]);
        printf("\n");  */
    for(il=1; il<NET.Nlayer; il++)
        {
        ilm1 = il-1;
        m = NET.Nneur[ilm1]%4;
        for(in=0; in<NET.Nneur[il]; in++)
            {
                a = NET.Weights[il][in][0];
            if(m==0) goto L20;
            for(j=1;j<=m;j++) a += 
                NET.Weights[il][in][j]*NET.Outn[ilm1][j-1];
L20:            
            mp1 = m+1;
                for(j=mp1; j<=NET.Nneur[ilm1]; j+=4)
                {
                a += 
                NET.Weights[il][in][j+3]*NET.Outn[ilm1][j+2]+
                NET.Weights[il][in][j+2]*NET.Outn[ilm1][j+1]+
                NET.Weights[il][in][j+1]*NET.Outn[ilm1][j]+
                NET.Weights[il][in][j]*NET.Outn[ilm1][j-1];
                }
            switch(NET.T_func[il][in])
                {
                case 2: NET.Outn[il][in] = MLP_Sigmoide(a);
                    break;                  
                case 1: NET.Outn[il][in] = a; 
                    break; 
                case 0: NET.Outn[il][in] = 0; 
                    break; 
                }   
                }
        }    
}

int MLP_PrCFun

(

char *

filename

)

Definition at line 2876 of file mlp_gen.cc.

References DIVERS, recoMuon::in, NET, and STAT.

{
    int il,in,jn;
    FILE *W;

    W=fopen(filename,"w");
    if(W==0) return -1;
    
    fprintf(W,"double sigmoid(double x)\n");
    fprintf(W,"{\n");
    fprintf(W,"return 1/(1+exp(-x));\n");
    fprintf(W,"}\n");
    fprintf(W,"void rnnfun(double *rin,double *rout)\n");
    fprintf(W,"{\n");
    fprintf(W,"      double out1[%d];\n",NET.Nneur[0]);
    fprintf(W,"      double out2[%d];\n",NET.Nneur[1]);
    if(NET.Nlayer>=3) fprintf(W,"      double out3[%d];\n",NET.Nneur[2]);
    if(NET.Nlayer>=4) fprintf(W,"      double out4[%d];\n",NET.Nneur[3]);
    fprintf(W,"\n");
    
    for(in=0; in<NET.Nneur[0]; in++)
        {
        if(DIVERS.Norm==0)
            { 
            fprintf(W,"      out1[%d] = rin[%d];\n",in,in);
            }
        else
            {
            fprintf(W,"      out1[%d] = (rin[%d]-%e)/%e;\n",
                    in,in,
                    STAT.mean[in],STAT.sigma[in]);
            }   
        }
        
    for(il=1; il<=NET.Nlayer-1; il++)
        {
        fprintf(W,"\n");
        fprintf(W,"/*     layer %d */\n",il+1);
        for(in=0; in<NET.Nneur[il]; in++)
            {
            fprintf(W,"      out%d[%d] = %e\n",il+1,in,
                    (double) NET.Weights[il][in][0]);
            for(jn=1;jn<=NET.Nneur[il-1]; jn++)
                fprintf(W,"      +(%e) * out%d[%d]\n",
                    (double) NET.Weights[il][in][jn],il,jn-1);
                fprintf(W,"      ;\n");
            }
        fprintf(W,"\n");
        for(in=0; in<NET.Nneur[il]; in++)
            {
            if(NET.T_func[il][in]==0) 
                {
                fprintf(W,"      out%d[%d] = 0;\n",il+1,in);
                }
            else if(NET.T_func[il][in]==1)
                {
                }
            else if(NET.T_func[il][in]==2)
                {
                fprintf(W,"      out%d[%d] = sigmoid(out%d[%d]);\n",
                    il+1,in,il+1,in);
                }
            }
        }
    il = NET.Nlayer-1;
    for(in=0; in<NET.Nneur[il]; in++)
        {
        fprintf(W,"      rout[%d] = out%d[%d];\n",in,il+1,in);
        }
    fprintf(W,"}\n");   
    fclose(W);
    return 0;
}

int MLP_PrFFun

(

char *

filename

)

Definition at line 2767 of file mlp_gen.cc.

References DIVERS, recoMuon::in, NET, and STAT.

{
    int il,in,jn;
    FILE *W;

    W=fopen(filename,"w");
    if(W==0) return -1;
    fprintf(W,"      SUBROUTINE RNNFUN(rin,rout)\n");
    fprintf(W,"      DIMENSION RIN(%d)\n",NET.Nneur[0]);
    fprintf(W,"      DIMENSION ROUT(%d)\n",NET.Nneur[NET.Nlayer-1]);
    fprintf(W,"C\n");
    
    for(in=0; in<NET.Nneur[0]; in++)
        {
        if(DIVERS.Norm==0)
            { 
            fprintf(W,"      OUT%d = RIN(%d)\n",in+1,in+1);
            }
        else
            {
            fprintf(W,"      OUT%d = (RIN(%d)-%e)/%e\n",in+1,in+1,
                    STAT.mean[in],STAT.sigma[in]);
            }   
        }
    for(il=1; il<NET.Nlayer-1; il++)
        {
        fprintf(W,"C\n");
        fprintf(W,"C     layer %d\n",il+1);
        for(in=0; in<NET.Nneur[il]; in++)
            {
            fprintf(W,"      RIN%d = %e\n",in+1,
                    (double) NET.Weights[il][in][0]);
            for(jn=1;jn<=NET.Nneur[il-1]; jn++)
                fprintf(W,"     > +(%e) * OUT%d\n",
                    (double) NET.Weights[il][in][jn],jn);
            }
        fprintf(W,"C\n");
        for(in=0; in<NET.Nneur[il]; in++)
            {
            if(NET.T_func[il][in]==0) 
                {
                fprintf(W,"      OUT%d = 0\n",in+1);
                }
            else if(NET.T_func[il][in]==1)
                {
                fprintf(W,"      OUT%d = RIN%d\n",in+1,in+1);
                }
            else if(NET.T_func[il][in]==2)
                {
                fprintf(W,"      OUT%d = SIGMOID(RIN%d)\n",
                    in+1,in+1);
                }
            }
        }
    il = NET.Nlayer-1;
    fprintf(W,"C\n");
    fprintf(W,"C     layer %d\n",il+1);
    for(in=0; in<NET.Nneur[il]; in++)
        {
        fprintf(W,"      RIN%d = %e\n",in+1,
                (double) NET.Weights[il][in][0]);
        for(jn=1;jn<=NET.Nneur[il-1]; jn++)
            fprintf(W,"     > +(%e) * OUT%d\n",
                (double) NET.Weights[il][in][jn],jn);
        }
    fprintf(W,"C\n");
    for(in=0; in<NET.Nneur[il]; in++)
        {
        if(NET.T_func[il][in]==0) 
            {
            fprintf(W,"      ROUT(%d) = 0\n",in+1);
            }
        else if(NET.T_func[il][in]==1)
            {
            fprintf(W,"      ROUT(%d) = RIN%d\n",in+1,in+1);
            }
        else if(NET.T_func[il][in]==2)
            {
            fprintf(W,"      ROUT(%d) = SIGMOID(RIN%d)\n",
                in+1,in+1);
            }
        }
        
    fprintf(W,"C\n");
    fprintf(W,"      END\n");
    fprintf(W,"      REAL FUNCTION SIGMOID(X)\n");
    fprintf(W,"      SIGMOID = 1./(1.+EXP(-X))\n");
    fprintf(W,"      END\n");
    
    fclose(W);
    return 0;
}

int MLP_PrintInputStat

(

)

Definition at line 3330 of file mlp_gen.cc.

References j, timingPdfMaker::mean, MLP_StatInputs(), NET, PAT, and stat_::sigma.

{
    int j;
    dbl *mean, *sigma, *minimum, *maximum;

/* allocate memory */
    mean = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    sigma = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    minimum = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    maximum = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));

    if(mean == 0 || sigma == 0 || minimum == 0
       || maximum == 0) return -111;

    MLP_StatInputs(PAT.Npat[0],NET.Nneur[0],PAT.Rin[0],
            mean,sigma,minimum,maximum);

    printf("\t mean \t\t RMS \t\t min \t\t max\n");
    for(j=0;j<NET.Nneur[0];j++)
        {
        printf("var%d \t %e \t %e \t %e \t %e\n",j+1,
                    mean[j],sigma[j],minimum[j],maximum[j]);
        }
    
    free(mean);
    free(sigma);
    free(minimum);
    free(maximum);  
    printf("\n");
    return 0;
}

double MLP_Rand	(	dbl	min,
		dbl	max
	)

Definition at line 2129 of file mlp_gen.cc.

References random.

Referenced by InitWeights(), and ShuffleExamples().

{
return mini+(maxi-mini)*random()/RAND_MAX;
}

void MLP_ResLin

(

)

Definition at line 1969 of file mlp_gen.cc.

References dgels_(), recoMuon::in, LEARN, MLP_Out(), MLP_Test(), NET, PAT, and mathSSE::sqrt().

Referenced by MLP_Epoch(), MLP_LineHyb(), and StochStepHyb().

{
/*  dbl rrans[NMAX], rrout[NMAX];*/
/*  type_pat rrin[NMAX];*/
    doublereal *HR,*dpat; //,*wlin,*SV;
    double err,lambda,lambda2;
    integer Nl,M,Nhr,khr,nrhs,iret,ierr;
    int   il, in, inl, ipat;
    /*register dbl a;*/ //a unused
    char Trans = 'N';

    
/*  integer rank; */
//  doublereal rcond = -1;  /* use machine precision */
    
    lambda2 = LEARN.Alambda;
    
/* Nl = number of linear weights
   M = number of terms in linear system = number of examples + regularisation*/
    Nl = NET.Nneur[NET.Nlayer-2] + 1;
    M = PAT.Npat[0]+Nl;

    integer Lwork = 5 * M;
        doublereal *Work = (doublereal*) malloc((int) Lwork*sizeof(doublereal));
    
/* memory allocation */
    dpat = (doublereal*) malloc((int) M*sizeof(doublereal));
//  wlin = (doublereal*) malloc((int) Nl*sizeof(doublereal));
//  SV = (doublereal*) malloc((int) Nl*sizeof(doublereal));
    
    Nhr = M * Nl;
    HR = (doublereal*) malloc((int) Nhr*sizeof(doublereal));
    err = 0.;
    for(ipat=0;ipat<PAT.Npat[0];ipat++)
        {
/* *** Filling dpat and HR *** */
/*      for(in=0; in<NET.Nneur[0]; in++)
            {
            rrin[in] = PAT.Rin[0][ipat][in];
            }*/

        MLP_Out(PAT.Rin[0][ipat],NET.Outn[NET.Nlayer-1]);
/*      MLP_Out(rrin,rrout);*/
        /*for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++)
            { 
              a = (dbl) PAT.Rans[0][ipat][in]; //a was not used
            } */
        il = NET.Nlayer-2;
        dpat[ipat] = (dbl) PAT.Rans[0][ipat][0]*sqrt(PAT.Pond[0][ipat]);
        khr = ipat;
        HR[khr] = (dbl) sqrt(PAT.Pond[0][ipat]);
        for(in=0;in<NET.Nneur[il];in++)
            {
            khr =  M *(in+1) + ipat;
            HR[khr] = NET.Outn[il][in]* 
                (dbl) sqrt(PAT.Pond[0][ipat]);
            }
        }
    il = NET.Nlayer-2;
    lambda = sqrt(lambda2);
    for(ipat=0;ipat<=NET.Nneur[il];ipat++)
        {
        dpat[ipat+PAT.Npat[0]] = 0;
        for(in=0;in<=NET.Nneur[il];in++)
            {
            khr =  M *in + ipat + PAT.Npat[0];
            HR[khr] = 0;
            if(in==ipat) HR[khr]=lambda;
            }
        }
    if(NET.Debug>=4) 
        {
        err = MLP_Test(0,0);
        printf("entry ResLin, err=MLP_Test(0,0), err= %f\n",err); 
        }        
/*                                                                */
/*      Trouve les poids lineaires par resolution lineaire        */
/*                                                                */
    nrhs = 1;
    ierr = dgels_(&Trans,&M,&Nl,&nrhs,HR,&M,dpat,&M,Work,
            &Lwork,&iret);
    if(iret != 0) printf("Warning from dgels: iret = %d\n",(int)iret);
    if(ierr != 0) printf("Warning from dgels: ierr = %d\n",(int)ierr);
    
/*  ierr = dgelss_(&M,&Nl,&nrhs,HR,&M,dpat,&M,SV,&rcond,&rank,Work,&Lwork,
        &iret);
    if(iret != 0) printf("Warning from dgelss: iret = %d\n",iret);
    if(ierr != 0) printf("Warning from dgelss: ierr = %d\n",ierr);*/
    
    il = NET.Nlayer-1;
    for (inl=0; inl<=NET.Nneur[il-1];inl++)
        {
        NET.Weights[il][0][inl] = dpat[inl];
        }
    if(NET.Debug>=4) 
        {
        err = MLP_Test(0,0);
        printf("ResLin, apres tlsfor, err= %f\n",err); 
        }        
    free(Work);
    free(dpat);
//  free(wlin);
    free(HR);
//  free(SV);
}

int MLP_SetNet	(	int *	nl,
		int *	nn
	)

Definition at line 3635 of file mlp_gen.cc.

References AllocNetwork(), NET, NLMAX, and SetDefaultFuncs().

Referenced by PhysicsTools::MLP::MLP(), and ReadPatterns().

{
    int il,ierr;

    if((*nl)>NLMAX) return(1);
    if((*nl)<2) return(2);
    
/*    LearnFree(); */
/* allocate memory */      
    ierr = AllocNetwork(*nl,nn);
    if(ierr != 0) return ierr;
      
/* set number of layers */               
    NET.Nlayer = (int) *nl;

/* set number of neurons */               
    for(il=0; il<NET.Nlayer; il++) {
       NET.Nneur[il] = nn[il];
      }

/* set transfer functions */      
    SetDefaultFuncs();
/*    LearnAlloc(); */
      
    return(0); 
}

int MLP_StatInputs	(	int	Nexamples,
		int	Ninputs,
		type_pat **	inputs,
		dbl *	mean,
		dbl *	sigma,
		dbl *	minimum,
		dbl *	maximum
	)

Definition at line 3273 of file mlp_gen.cc.

References j, and mathSSE::sqrt().

Referenced by MLP_PrintInputStat(), and NormalizeInputs().

{
    dbl *fmean;
    int j, ipat, nmax;
    
/* first compute a fast rough mean using the first 100 events */
    fmean = (dbl*) malloc(Ninputs*sizeof(dbl));
    nmax = 100;
    if(Nexamples<100) nmax=Nexamples;
    
    for(j=0;j<Ninputs;j++)
        {
        fmean[j] = 0;
        for(ipat=0;ipat<nmax;ipat++)
            {
            fmean[j] += (dbl) inputs[ipat][j];
            }
        fmean[j] = fmean[j]/(dbl) nmax;
        
/* now compute real mean and sigma, min and max */      
        mean[j] = 0;
        sigma[j] = 0;
        minimum[j] = 99999;
        maximum[j] = -99999;
        for(ipat=0;ipat<Nexamples;ipat++)
            {
            mean[j] += (dbl) inputs[ipat][j];
            sigma[j] += ((dbl) inputs[ipat][j]-fmean[j])*
                    ((dbl) inputs[ipat][j]-fmean[j]);
            if((dbl) inputs[ipat][j] > maximum[j]) 
                maximum[j]=(dbl) inputs[ipat][j];       
            if((dbl) inputs[ipat][j] < minimum[j]) 
                minimum[j]=(dbl) inputs[ipat][j];       
            }
        mean[j] = mean[j]/(dbl) Nexamples;
        sigma[j] = sqrt(sigma[j]/ (dbl) Nexamples - 
                    (mean[j]-fmean[j])*
                (mean[j]-fmean[j]));    
        }
    free(fmean);
    return 0;
}

dbl MLP_Stochastic

(

)

Definition at line 521 of file mlp_gen.cc.

References a, b, createTree::dd, eta(), EtaDecay(), cuy::ii, recoMuon::in, LEARN, MLP_Out2(), NET, PAT, ShuffleExamples(), and create_public_pileup_plots::weights.

Referenced by MLP_Epoch().

{
    int ipat, ii, inm1;
    dbl err = 0;
    int il, in1, in, itest2;
    dbl deriv, deriv1, deriv2, deriv3, deriv4, pond;
    dbl eta, eps;
    register dbl a, b, dd, a1, a2, a3, a4;
    dbl *pout, *pdelta, *pw1, *pw2, *pw3, *pw4;
    dbl ***weights;
    
    if(NET.Debug>=5) printf(" Entry MLP_Stochastic\n");     
    weights = NET.Weights;
/* shuffle patterns */      
    ShuffleExamples(PAT.Npat[0],ExamplesIndex); 
        
/* reduce learning parameter */
    if(LEARN.Decay<1) EtaDecay();
        
    eta = -LEARN.eta;
    eps = LEARN.epsilon;
    
/* loop on the examples */      
    for(ipat=0;ipat<PAT.Npat[0];ipat++)
        {
        ii = ExamplesIndex[ipat];
        pond = PAT.Pond[0][ii];
        
        MLP_Out2(&(PAT.vRin[0][ii*(NET.Nneur[0]+1)])); 
            
/* next lines are equivalent to DeDwSum */              
        for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++) 
            {
            deriv = NET.Deriv1[NET.Nlayer-1][in]; 
            a = (dbl) PAT.Rans[0][ii][in];
            b = NET.Outn[NET.Nlayer-1][in]-a;
            err  += b*b*pond;
            NET.Delta[NET.Nlayer-1][in] = b*deriv*pond*eta;
            }
            
        for(il=NET.Nlayer-2; il>0; il--) 
            {
            dd = NET.Delta[il+1][0];
            for(in=0; in<NET.Nneur[il]-3; in+=4) 
                {
                deriv1 = NET.Deriv1[il][in]; 
                deriv2 = NET.Deriv1[il][in+1]; 
                deriv3 = NET.Deriv1[il][in+2]; 
                deriv4 = NET.Deriv1[il][in+3]; 
                itest2 = (NET.Nneur[il+1]==1);
                a1 = dd*weights[il+1][0][in+1];
                a2 = dd*weights[il+1][0][in+2];
                a3 = dd*weights[il+1][0][in+3];
                a4 = dd*weights[il+1][0][in+4];
                if(itest2) goto L1;
                pdelta = &(NET.Delta[il+1][1]);
                for(in1=1; in1<NET.Nneur[il+1];
                    in1++, pdelta++) 
                    {
                    a1 += *pdelta * weights[il+1][in1][in+1];
                    a2 += *pdelta * weights[il+1][in1][in+2];
                    a3 += *pdelta * weights[il+1][in1][in+3];
                    a4 += *pdelta * weights[il+1][in1][in+4];
                    }
L1:             NET.Delta[il][in] = a1*deriv1;
                NET.Delta[il][in+1] = a2*deriv2;
                NET.Delta[il][in+2] = a3*deriv3;
                NET.Delta[il][in+3] = a4*deriv4;
                } 
            for(in=in; in<NET.Nneur[il]; in++) 
                {
                deriv = NET.Deriv1[il][in]; 
                itest2 = (NET.Nneur[il+1]==1);
                a = dd*weights[il+1][0][in+1];
                if(itest2) goto L2;
                pdelta = &(NET.Delta[il+1][1]);
                for(in1=1; in1<NET.Nneur[il+1];
                    in1++, pdelta++) 
                    {
                    a += *pdelta * 
                    weights[il+1][in1][in+1];
                    }
L2:             NET.Delta[il][in] = a*deriv;
                } 
                
            }           /* end of loop on layers */


/* update the weights */
        if(eps==0)
            {
            for(il=1; il<NET.Nlayer; il++)
            {
            inm1 = NET.Nneur[il-1];
            for(in=0; in<NET.Nneur[il]-3; in+=4)
                { 
                a1 = NET.Delta[il][in];
                a2 = NET.Delta[il][in+1];
                a3 = NET.Delta[il][in+2];
                a4 = NET.Delta[il][in+3];
                pout = &(NET.Outn[il-1][0]);
                weights[il][in][0] += a1;               
                weights[il][in+1][0] += a2;             
                weights[il][in+2][0] += a3;             
                weights[il][in+3][0] += a4;             
                    weights[il][in][1] += a1* (*pout);
                    weights[il][in+1][1] += a2* (*pout);
                    weights[il][in+2][1] += a3* (*pout);
                    weights[il][in+3][1] += a4* (*pout);
                pout++;
                pw1 = &(weights[il][in][2]);
                pw2 = &(weights[il][in+1][2]);
                pw3 = &(weights[il][in+2][2]);
                pw4 = &(weights[il][in+3][2]);
                for(in1=2; in1<=inm1; 
                    ++in1, ++pout, ++pw1, ++pw2,
                    ++pw3, ++pw4)
                    {
                        *pw1 += a1 * *pout;
                        *pw2 += a2 * *pout;
                        *pw3 += a3 * *pout;
                        *pw4 += a4 * *pout;
                    }
                }
            for(in=in; in<NET.Nneur[il]; in++)
                { 
                a1 = NET.Delta[il][in];
                pout = &(NET.Outn[il-1][0]);
                weights[il][in][0] += a1;               
                    weights[il][in][1] += a1* (*pout);
                pout++;
                pw1 = &(weights[il][in][2]);
                for(in1=2; in1<=inm1; 
                    ++in1, ++pout, ++pw1)
                    {
                        *pw1 += a1 * *pout;
                    }
                }
            }
            }
        else
            {           
            for(il=1; il<NET.Nlayer; il++)
            {
            for(in=0; in<NET.Nneur[il]; in++)
                { 
                
                a = NET.Delta[il][in];
                LEARN.Odw[il][in][0] = a + eps * LEARN.Odw[il][in][0];
                NET.Weights[il][in][0] += LEARN.Odw[il][in][0];
                
                b = a*NET.Outn[il-1][0];
                LEARN.Odw[il][in][1] = b + eps*LEARN.Odw[il][in][1];
                    NET.Weights[il][in][1] += LEARN.Odw[il][in][1];
                
                for(in1=2; in1<=NET.Nneur[il-1]; in1++)
                    {
                    b = a*NET.Outn[il-1][in1-1];
                    LEARN.Odw[il][in][in1] = b + eps*LEARN.Odw[il][in][in1];
                        NET.Weights[il][in][in1] += LEARN.Odw[il][in][in1];
                    }
                }
            }
            }       
            
        }           /* end of loop on examples */
    return(err);        
}

dbl MLP_Test	(	int	ifile,
		int	regul
	)

Definition at line 449 of file mlp_gen.cc.

References compare_using_db::ifile, recoMuon::in, LEARN, MLP_Out_T(), MLP_Test_MM(), NET, PAT, and tmp.

Referenced by DecreaseSearch(), LineSearch(), LineSearchHyb(), MLP_ResLin(), and SetLambda().

{
    dbl err, rrans;
    int in,jn,ipat,ipati;
    
    dbl *tmp;
    
    tmp = (dbl *) malloc(2 * NET.Nneur[1] * sizeof(dbl)); 
    if(tmp == 0)    /* not enough memory */
    {
    printf("not enough memory in MLP_Test\n");              
    err = 0;
    for(ipat=0; ipat<PAT.Npat[ifile]; ipat++)
        {
        if(ifile==0)
            {
            ipati = ExamplesIndex[ipat];
            }
        else
            {
            ipati = ipat;
            }   
        MLP_Out_T(PAT.Rin[ifile][ipati]);
        for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++) 
            {
            rrans = (dbl) PAT.Rans[ifile][ipati][in];
            err  += (rrans-NET.Outn[NET.Nlayer-1][in])*
                    (rrans-NET.Outn[NET.Nlayer-1][in])*
                PAT.Pond[ifile][ipati];
            }
        }

    if(regul>=1) 
        {
        for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++)
            for(jn=0; jn<=NET.Nneur[NET.Nlayer-2]; jn++)
            {
            err += LEARN.Alambda*NET.Weights[NET.Nlayer-1][in][jn]*
                NET.Weights[NET.Nlayer-1][in][jn];
            }
        }
    free(tmp);  
    return(err);
    }
    else    /* computation using matrix - matrix multiply */
    {
    err = MLP_Test_MM(ifile, tmp);
    if(regul>=1) 
        {
        for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++)
            for(jn=0; jn<=NET.Nneur[NET.Nlayer-2]; jn++)
            {
            err += LEARN.Alambda*NET.Weights[NET.Nlayer-1][in][jn]*
                NET.Weights[NET.Nlayer-1][in][jn];
            }
        }
    free(tmp);
    return(err);
    }       
}

int MLP_Train	(	int *	ipat,
		dbl *	err
	)

Definition at line 898 of file mlp_gen.cc.

References DeDwSum(), recoMuon::in, MLP_Out2(), NET, and PAT.

Referenced by MLP_Epoch().

{
    int in;
    
/*      if(*ipat>=PAT.Npat[0]) return(1);*/
    if(*ipat<0) return(2);
        
/*      MLP_Out(PAT.Rin[0][*ipat],NET.Outn[NET.Nlayer-1]);*/
        MLP_Out2(&(PAT.vRin[0][*ipat*(NET.Nneur[0]+1)]));
    for(in=0; in<NET.Nneur[NET.Nlayer-1]; in++) 
        {
        *err  += ((dbl) PAT.Rans[0][*ipat][in]-NET.Outn[NET.Nlayer-1][in])
            *((dbl) PAT.Rans[0][*ipat][in]-NET.Outn[NET.Nlayer-1][in])*
            PAT.Pond[0][*ipat];
        }
        DeDwSum(PAT.Rans[0][*ipat],NET.Outn[NET.Nlayer-1],*ipat);   
        return(0); 
} 

int NormalizeInputs

(

)

Definition at line 3376 of file mlp_gen.cc.

References j, timingPdfMaker::mean, MLP_StatInputs(), NET, PAT, stat_::sigma, and STAT.

{
    int j, ipat;
    dbl *mean, *sigma, *minimum, *maximum;

/* allocate memory */
    mean = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    sigma = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    STAT.mean = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    STAT.sigma = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    minimum = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    maximum = (dbl *) malloc(NET.Nneur[0]*sizeof(dbl));
    
    if(mean == 0 || sigma == 0 || minimum == 0
       || maximum == 0 || STAT.mean == 0 ||
       STAT.sigma == 0) return -111;

    MLP_StatInputs(PAT.Npat[0],NET.Nneur[0],PAT.Rin[0],
            mean,sigma,minimum,maximum);

    if(NET.Debug>=1) printf("\t mean \t\t RMS \t\t min \t\t max\n");
    for(j=0;j<NET.Nneur[0];j++)
        {
        if(NET.Debug>=1)
            printf("var%d \t %e \t %e \t %e \t %e\n",j+1,
                    mean[j],sigma[j],minimum[j],maximum[j]);
        
/* store mean and sigma for output function */      
        STAT.mean[j] = mean[j];         
        STAT.sigma[j] = sigma[j];
                    
/* finally apply the normalization */
        for(ipat=0;ipat<PAT.Npat[0];ipat++)
            {
            PAT.Rin[0][ipat][j] =
            (PAT.Rin[0][ipat][j]-(float) mean[j])/
            (float) sigma[j];
            }   
        for(ipat=0;ipat<PAT.Npat[1];ipat++)
            {
            PAT.Rin[1][ipat][j] =
            (PAT.Rin[1][ipat][j]-(float) mean[j])/
            (float) sigma[j];
            }   
        }
    
    free(mean);
    free(sigma);
    free(minimum);
    free(maximum);  
    if(NET.Debug>=1) printf("\n");
    return 0;
}

void PrintWeights

(

)

Definition at line 2166 of file mlp_gen.cc.

References i, and NET.

Referenced by MLP_Epoch().

{
        int ilayer,ineur,i;

    for(ilayer=1; ilayer<NET.Nlayer; ilayer++)
        {
        if(MessLang==1) 
            {
            printf("Couche %d\n",ilayer);
            }
        else
            {
            printf("Layer %d\n",ilayer);
            }
        for(ineur=0; ineur<NET.Nneur[ilayer]; ineur++)
            {
            if(MessLang==1) 
                {
                printf("Neurone %d",ineur);
                }
            else
                {
                printf("Neuron %d",ineur);
                }
            for(i=0; i<=NET.Nneur[ilayer-1]; i++)
                {
                printf(" %f",
                    (double) NET.Weights[ilayer][ineur][i]);
                }
            printf("\n");
            }
        printf("\n");
        }
}

int ReadPatterns	(	char *	filename,
		int	ifile,
		int *	inet,
		int *	ilearn,
		int *	iexamples
	)

Definition at line 2227 of file mlp_gen.cc.

References AllocPatterns(), CLEN, CountLexemes(), DIVERS, GetNetStructure(), getnLexemes(), i, compare_using_db::ifile, prof2calltree::l, LEARN, geometryCSVtoXML::line, MLP_SetNet(), NET, nin, NLMAX, nout, np, AlCaHLTBitMon_ParallelJobs::p, PAT, ReadPatterns(), alignCSCRings::s, and indexGen::s2.

Referenced by ReadPatterns().

{
char s[CLEN], s2[CLEN], cc[6], cc2[6];
char otherfile[CLEN];
double p;
//int line,i,j;
int line,i;
//int l,ll,ipat,nmax,il,in,tf;
int l,ll,ipat,nmax;
int np=0;       /* nombre d'exemples */
int nin=0;      /* nombre d'entrees */
int nout=0;     /* nombre de sorties */
int npon=0;
int ntot, ierr;
//char **ss;
char **ss=0;
FILE *LVQpat;
int nlayer, nneur[NLMAX];

printf("\nLoading file %s\n",filename);
LVQpat=fopen(filename,"r");
if(LVQpat == 0) return -1;

line=0;

while(fgets(s,CLEN,LVQpat))
    {
    if(*s=='N')                                 
        {
        if(*(s+1)=='N')         /* NNEU */
            {
            printf("Number of neurons %s",s);
            *inet = 1;
            sscanf(s,"%s %s",cc,s2);
            ierr = GetNetStructure(s2,&nlayer,nneur);
            if(ierr != 0) return ierr;
            ierr = MLP_SetNet(&nlayer,nneur);
            if(ierr != 0) return ierr;
            }
        else
            {   
            sscanf(s,"%s %d",cc,&l);
            if(*(cc+1)=='P')    /* NPAT */
                {
                np=l;
                printf("Number of patterns %d\n",np);
                }
            else if(*(cc+1)=='I')   /* NINP */
                {
                nin=l;
                PAT.Nin = nin;
                printf("Number of inputs %d\n",nin);
                }
            else if(*(cc+1)=='O' && *(cc+2)=='U')   /* NOUT */
                {
                nout=l;
                PAT.Nout = nout;
                printf("Number of outputs %d\n",nout);
                }
            else if(*(cc+1)=='O' && *(cc+2)=='R')   /* NORM */
                {
                DIVERS.Norm=l;
                if(l==1) printf("Normalize inputs\n");
                }
/* obsolete datacard     NLAY */            
            else if(*(cc+1)=='L')   
                {
                printf("NLAY datacard is no longer needed\n");
                }               
            else if(*(cc+1)=='E')   /* NEPO */ 
                {
                LEARN.Nepoch=l;
                printf("Number of epochs %d\n",l);
                }
            else if(*(cc+1)=='R')   /* NRES */
                {
                LEARN.Nreset=l;
                printf(
                "Reset to steepest descent every %d epochs\n",
                l);
                }
            }
        }
    else if(*s=='L')        
        {
        if(*(s+1)=='P')         /* LPAR */
            {
            sscanf(s,"%s %le",cc,&p);
            printf("Learning parameter %f\n",p);
            LEARN.eta = (dbl) p;
            }
        else if(*(s+1)=='M')        /* LMET */
            {
            *ilearn = 1;
            sscanf(s,"%s %d",cc,&(LEARN.Meth));
            printf("Learning method = ");
            switch(LEARN.Meth)
                    {
                case 1: printf("Stochastic Minimization\n");
                        break;
                case 2: printf("Steepest descent with fixed step\n");
                        break;
                case 3: printf("Steepest descent with line search\n"); break;
                case 4: printf("Polak-Ribiere Conjugate Gradients\n"); break;
                case 5: printf("Fletcher-Reeves Conjugate Gradients\n");
                        break;
                case 6: printf("BFGS\n");
                        break;
                case 7: printf("Hybrid BFGS-linear\n");
                        break;
                default: printf("Error: unknown method\n"); break;
                }

            }
        else if(*(s+1)=='T')    /* LTAU */
            {
            sscanf(s,"%s %lf",cc,&p);
            printf("Tau %f\n",p);
            LEARN.Tau = (dbl) p;
            }
        else if(*(s+1)=='A')    /* LAMB */
            {
            sscanf(s,"%s %lf",cc,&p);
            printf("Lambda %f\n",p);
            LEARN.Lambda = (dbl) p;
            }
        }
    else if(*s=='F')        
        {
        if(*(s+1)=='S')     /* FSPO */
            {
            sscanf(s,"%s %le",cc,&p);
            printf("Flat spot elimination parameter %f\n",p);
            LEARN.delta = (dbl) p;
            }
        else if(*(s+1)=='I')    /* FILE */
            {
            sscanf(s,"%s %s",cc,otherfile);
            ierr = ReadPatterns(otherfile,ifile, inet, ilearn, iexamples);
            if(ierr != 0) return ierr;
            }   
        }
    else if(*s=='M')        /* momentum */
        {
        sscanf(s,"%s %le",cc,&p);
        printf("Momentum term %f\n",p);
        LEARN.epsilon = (dbl) p;
        }       
    else if(*s=='O')                /* OUTx */
        {
        if(*(s+3)=='W')     /* OUTW */
            {
            sscanf(s,"%s %d",cc,&OutputWeights);
            if(OutputWeights == 0)
                {
                printf("Never write file weights.out\n");
                }
            else if(OutputWeights == -1)
                {
                printf("Write weights to output file at the end\n");
                }
            else 
                {
                printf("Write weights to file every %d epochs\n",
                    OutputWeights);
                }
            }
        else if(*(s+3)=='F')    /* OUTF */
            {
            sscanf(s,"%s %s",cc,cc2);
            if(*cc2=='F' || *cc2=='C')
                {
                DIVERS.Outf = *cc2;
                }
            else
                {
            printf(" *** Error while loading file %s at line %s :",
                filename,s);
            printf(" unknown language\n");
                }   
            }
        else
            {
            printf(" *** Error while loading file %s at line %s\n",
                filename,s);
            }                   
        }       
    else if(*s=='R')                /* RDWT */
        {
        sscanf(s,"%s %d",cc,&(NET.Rdwt));
        if(NET.Rdwt == 0)
            {
            printf("Random weights \n");
            }
        else
            {
            printf("Read weights from file weights.in\n");
            }   
        }       
    else if(*s=='S')                /* STAT */
        {
        sscanf(s,"%s %d",cc,&(DIVERS.Stat));
        }
/*  else if(*s=='T')                 TFUN 
        {
        sscanf(s,"%s %d %d %d",cc,&il,&in,&tf);
        SetTransFunc(il,in,tf);
        } */    
    else if(*s=='H')                /* HESS */
        {
        sscanf(s,"%s %d",cc,&(DIVERS.Ihess));
        }       
    else if(*s=='D')                
        {
        if(*(s+1)=='C')     /* DCAY */
            {   
            sscanf(s,"%s %le",cc,&p);
            LEARN.Decay = p;
            printf("Learning parameter decay %f\n",
                        (double) LEARN.Decay);
            }
        if(*(s+1)=='B')     /* DBIN */  
            {   
            sscanf(s,"%s %d",cc,&(DIVERS.Dbin));
            printf("Fill histogram every %d epochs\n",DIVERS.Dbin);
            }
        if(*(s+1)=='E')     /* DEBU */  
            {   
            sscanf(s,"%s %d",cc,&(NET.Debug));
            printf("Debug mode %d\n",NET.Debug);
            }
        }       
    else if(*s=='P')                /* POND */
        {
        npon = CountLexemes(s);
        if(npon==2) 
            {
            sscanf(s,"%s %d",cc,&(PAT.Iponde));
            }
        else
            {
            ss = (char**) malloc((npon+1)*sizeof(char*)); 
            for(i=0;i<=npon;i++)
                ss[i]=(char*) malloc(40*sizeof(char));
            getnLexemes(npon,s,ss);
            sscanf(ss[1],"%d",&(PAT.Iponde));
            for(i=2;i<npon;i++)
                {   
                sscanf(ss[i],"%le",&(PAT.Ponds[i-2]));
                }
            }
        if(PAT.Iponde==0) 
            {
            npon = 0;
            }
        else
            {
            npon = 1;
            }
        }       
    else if(*s=='#')                /* comments */
        {
        }
    else                                        /* exemple itself */
        {
        if(np==0) return 1;
        if(nin==0) return 2;
        if(nout==0) return 3;
        

/* store number of exemples and allocate memory*/       
        if(line==0)
            {
            PAT.Npat[ifile] = np;
            ierr = AllocPatterns(ifile,np,nin,nout,0);
            if(ierr != 0) return ierr;
            *iexamples = 1;
            }

/* now get exemple */
                
            line++;
        ll = (line-1)%2;
        ipat = (line-1)/2;
        /*      printf("Loading event \t %d\r",ipat);*/
/*      if(ipat>NPMAX) 
            {
            printf("Too many examples in file\n");
            printf("Loading %d examples\n",NPMAX);
            PAT.Npat[ifile] = NPMAX;
            break;
            }
*/          
        
/* allocate the number of lines */
        
        if(line==1) 
            {
            
            nmax = nin;
            if(nout>nin) nmax=nout;
            ss = (char**) malloc((nmax+1)*sizeof(char*));
            if(ss == 0) return -111; 
            for(i=0;i<=nmax;i++)
                {
                ss[i]=(char*) malloc(40*sizeof(char));
                if(ss[i] == 0) return -111;
                }
            }
            
        if(ll==0)    /* inputs */
            {
            getnLexemes(nin,s,ss);
            for(i=0;i<nin;i++)
                {
                sscanf(ss[i],"%le",&p);
                PAT.Rin[ifile][ipat][i] = (type_pat) p;
                }
            }
        else         /* answers */
            {
            ntot=nout+npon;
            getnLexemes(ntot,s,ss);
            for(i=0;i<ntot;i++)
                {
                sscanf(ss[i],"%le",&p);
                if(i<nout)
                    {
                    PAT.Rans[ifile][ipat][i] = (type_pat) p;
                    }
                else
                    {
                    if(PAT.Iponde==1)
                        {
                        PAT.Pond[ifile][ipat] = 
                            (type_pat) p;
                        }
                    else
                        {
                        PAT.Pond[ifile][ipat] = 
                        (type_pat) PAT.Ponds[(int) p -1];
                        }
                    }
                }
            }               
        }
    }
    printf("%d examples loaded    \n\n",PAT.Npat[ifile]);
    fclose(LVQpat);
    return 0;
}

int SaveWeights	(	char *	filename,
		int	iepoch
	)

Definition at line 2970 of file mlp_gen.cc.

References i, and NET.

Referenced by PhysicsTools::MLP::save().

{
    FILE *W;
    int ilayer,ineur,i;

    W=fopen(filename,"w");
    if(W==0) return -1;
    
    fprintf(W,"# network structure ");
    for(ilayer=0; ilayer<NET.Nlayer; ilayer++)
        {
        fprintf(W,"%d ",NET.Nneur[ilayer]);
        }
        
    fprintf(W,"\n %d\n",iepoch);
    for(ilayer=1; ilayer<NET.Nlayer; ilayer++)
        {
        for(ineur=0; ineur<NET.Nneur[ilayer]; ineur++)
            {
            for(i=0; i<=NET.Nneur[ilayer-1]; i++)
                {
                fprintf(W," %1.15e\n",
                (double) NET.Weights[ilayer][ineur][i]);
                }
            }
        }
    fclose(W);
    return 0;
}

void SetDefaultFuncs

(

)

Definition at line 1226 of file mlp_gen.cc.

References recoMuon::in, and NET.

Referenced by MLP_SetNet().

{
    int il,in;
    for(il=0; il<NET.Nlayer; il++) {
       for(in=0; in<NET.Nneur[il]; in++) {
          NET.T_func[il][in] = 2; 
      if(il==NET.Nlayer-1) NET.T_func[il][in] = 1;
     }
      }

}

void SetLambda

(

double

Wmax

)

Definition at line 1947 of file mlp_gen.cc.

References LEARN, MLP_Test(), and NET.

Referenced by MLP_Epoch().

{
    dbl err;
    err = MLP_Test(0,0);
    LEARN.Alambda =
    LEARN.Lambda*err/(Wmax*Wmax*(dbl)(NET.Nneur[NET.Nlayer-2]+1));
}

int SetTransFunc	(	int	layer,
		int	neuron,
		int	func
	)

Definition at line 1203 of file mlp_gen.cc.

References NET, and NLMAX.

{    
    if(layer>NLMAX) return(1);
/*    if(neuron>NMAX) return(2);*/
         
    NET.T_func[layer-1][neuron-1] = func;

    return(0); 
}             

int ShuffleExamples	(	int	n,
		int *	index
	)

Definition at line 2100 of file mlp_gen.cc.

References a, i, cuy::ii, MLP_Rand(), and n.

Referenced by MLP_Epoch(), and MLP_Stochastic().

{
    int i,ii,itmp;
    dbl a = (dbl) (n-1);
    
    for(i=0;i<n;i++)
        {
        ii = (int) MLP_Rand(0.,a);
        itmp = index[ii];
        index[ii] = index[i];
        index[i] = itmp;
        }
    return 0;
}

void SteepestDir

(

)

Definition at line 1247 of file mlp_gen.cc.

References recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
        int il,in,jn;
    for(il=1; il<NET.Nlayer; il++)
        for(in=0; in<NET.Nneur[il]; in++)
            for(jn=0; jn<=NET.Nneur[il-1]; jn++)
                dir[il][in][jn] = -LEARN.DeDw[il][in][jn];
}

int StochStep

(

)

Definition at line 968 of file mlp_gen.cc.

References eta(), recoMuon::in, LEARN, and NET.

Referenced by MLP_Epoch().

{
    int il, in1, in;
    dbl eta, eps, epseta;
    
        eta = -LEARN.eta;
        eps = LEARN.epsilon;
        epseta = eps/eta;
    for(il=NET.Nlayer-1; il>0; il--) {
        for(in1=0; in1<=NET.Nneur[il-1]; in1++) {

        /* compute delta weights */
    for(in=0; in<NET.Nneur[il]; in++) {
            LEARN.Odw[il][in][in1] = eta * (LEARN.DeDw[il][in][in1]
             + epseta * LEARN.Odw[il][in][in1]);
            NET.Weights[il][in][in1] += LEARN.Odw[il][in][in1];
        }
        
    }   
    }   

    return(0); 
}             

int StochStepHyb

(

)

Definition at line 929 of file mlp_gen.cc.

References eta(), recoMuon::in, LEARN, MLP_ResLin(), and NET.

{
    int il, in1, in;
    dbl eta, eps;
    
    eta = LEARN.eta;
    eps = LEARN.epsilon;
    for(il=NET.Nlayer-2; il>0; il--) {

    for(in=0; in<NET.Nneur[il]; in++) {

        /* compute delta weights */
        for(in1=0; in1<=NET.Nneur[il-1]; in1++) {
            LEARN.Odw[il][in][in1] = -eta * LEARN.DeDw[il][in][in1]
             + eps * LEARN.Odw[il][in][in1];
        }
        
        /* update weights */
        for(in1=0; in1<=NET.Nneur[il-1]; in1++) {
            NET.Weights[il][in][in1] += LEARN.Odw[il][in][in1];
        }
    }   
    }   
    MLP_ResLin();
    return(0); 
} 

Variable Documentation

dbl** Hessian MLP_HIDDEN

Definition at line 62 of file mlp_gen.h.