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Go to the source code of this file.
Classes | |
| struct | divers_ |
| struct | learn_ |
| struct | net_ |
| struct | pat_ |
| struct | stat_ |
Defines | |
| #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 |
| #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(), 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(), 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(), DeDwSum(), FreePatterns(), MLP_Epoch(), MLP_PrintInputStat(), MLP_ResLin(), MLP_Stochastic(), MLP_Test(), MLP_Test_MM(), MLP_Train(), NormalizeInputs(), and ReadPatterns().
| #define STAT stat_ |
Definition at line 60 of file mlp_gen.h.
Referenced by MLP_PrCFun(), MLP_PrFFun(), and NormalizeInputs().
| int AllocNetwork | ( | int | Nlayer, |
| int * | Neurons | ||
| ) |
Definition at line 3452 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 3082 of file mlp_gen.cc.
References FreePatterns(), indexGen::ifile, j, PAT, and tmp.
Referenced by ReadPatterns().
{
int j, ierr;
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)
{
ierr = 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 1345 of file mlp_gen.cc.
References g, i, recoMuon::in, j, LEARN, NET, and asciidump::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 1271 of file mlp_gen.cc.
References recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
| int CountLexemes | ( | char * | string | ) |
Definition at line 2590 of file mlp_gen.cc.
Referenced by getLexemes(), and ReadPatterns().
Definition at line 1616 of file mlp_gen.cc.
References recoMuon::in, LEARN, MLP_Line(), MLP_Test(), NET, and metsig::tau.
{
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;
MLP_Line(w0,alpha2);
err2 = MLP_Test(0,0);
(*Ntest) ++;
if(err2<err1)
{
*alpmin = alpha2;
}
else
{
alpha3 = alpha2;
err3 = err2;
for(icount=1;icount<=100;icount++)
{
alpha2 = alpha2/tau;
MLP_Line(w0,alpha2);
err2 = MLP_Test(0,0);
(*Ntest) ++;
if(err1>err2) break;
alpha3 = alpha2;
err3 = err2;
}
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 1003 of file mlp_gen.cc.
References createTree::dd, recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
| dbl DeDwProd | ( | ) |
Definition at line 1025 of file mlp_gen.cc.
References createTree::dd, recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
| void DeDwSave | ( | ) |
| void DeDwSaveZero | ( | ) |
Definition at line 1102 of file mlp_gen.cc.
References recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
| void DeDwScale | ( | int | Nexamples | ) |
Definition at line 1065 of file mlp_gen.cc.
References recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
Definition at line 1128 of file mlp_gen.cc.
References a, b, 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 1046 of file mlp_gen.cc.
References recoMuon::in, LEARN, and NET.
| dbl DerivDir | ( | ) |
Definition at line 1293 of file mlp_gen.cc.
References recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
| void EtaDecay | ( | ) |
| int FixedStep | ( | dbl | alpha | ) |
Definition at line 1709 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 3534 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 3237 of file mlp_gen.cc.
References i, indexGen::ifile, and PAT.
Referenced by AllocPatterns().
{
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 1423 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 1318 of file mlp_gen.cc.
References i, recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
| void getLexemes | ( | char * | s, |
| char ** | ss | ||
| ) |
Definition at line 2620 of file mlp_gen.cc.
References CountLexemes(), i, n, NULL, and tmp.
| int GetNetStructure | ( | char * | s, |
| int * | Nlayer, | ||
| int * | Nneur | ||
| ) |
Definition at line 3595 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 | ||
| ) |
| void InitBFGSH | ( | int | Nweights | ) |
| void InitWeights | ( | ) |
Definition at line 2152 of file mlp_gen.cc.
References i, MLP_Rand(), and NET.
Referenced by PhysicsTools::MLP::MLP().
| int LearnAlloc | ( | ) |
Definition at line 2690 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 2645 of file mlp_gen.cc.
References recoMuon::in, and NET.
Referenced by LearnAlloc().
{
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); */
}
Definition at line 1478 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 1786 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 3027 of file mlp_gen.cc.
References i, NET, L1TEmulatorMonitor_cff::p, and asciidump::s.
{
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;
}
Definition at line 710 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(), CrabTask::prod, SetLambda(), ShuffleExamples(), SteepestDir(), and StochStep().
{
dbl err, Norm, ONorm, beta, prod, ddir;
/* int *index;*/
int Nweights, Nlinear, ipat, ierr;
int nn;
err = 0;
Norm = 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);
}
Definition at line 1762 of file mlp_gen.cc.
References recoMuon::in, and NET.
Referenced by DecreaseSearch(), FixedStep(), and LineSearch().
Definition at line 1928 of file mlp_gen.cc.
References recoMuon::in, MLP_ResLin(), and NET.
Referenced by LineSearchHyb().
Definition at line 3751 of file mlp_gen.cc.
References trackerHits::c, i, j, m, n, and csvReporter::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;
}
}
Definition at line 3685 of file mlp_gen.cc.
References trackerHits::c, i, j, m, n, and csvReporter::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;
}
}
Definition at line 3820 of file mlp_gen.cc.
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;
}
}
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(), and NET.
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 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 2883 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 2774 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 3337 of file mlp_gen.cc.
References j, plotscripts::mean(), MLP_StatInputs(), NET, PAT, and ExpressReco_HICollisions_FallBack::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;
}
Definition at line 2136 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 1976 of file mlp_gen.cc.
References a, 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;
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];
}
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 3642 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 3280 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(), recoMuon::in, LEARN, MLP_Out2(), NET, PAT, ShuffleExamples(), and ExpressReco_HICollisions_FallBack::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, epseta;
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;
epseta = eps/eta;
/* 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 indexGen::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 900 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 3383 of file mlp_gen.cc.
References j, plotscripts::mean(), MLP_StatInputs(), NET, PAT, ExpressReco_HICollisions_FallBack::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 2173 of file mlp_gen.cc.
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 2234 of file mlp_gen.cc.
References AllocPatterns(), CLEN, CountLexemes(), DIVERS, GetNetStructure(), getnLexemes(), i, indexGen::ifile, prof2calltree::l, LEARN, geometryCSVtoXML::line, MLP_SetNet(), NET, NLMAX, runTheMatrix::np, L1TEmulatorMonitor_cff::p, PAT, ReadPatterns(), asciidump::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 2977 of file mlp_gen.cc.
{
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 1228 of file mlp_gen.cc.
References recoMuon::in, and NET.
Referenced by MLP_SetNet().
| void SetLambda | ( | double | Wmax | ) |
Definition at line 1954 of file mlp_gen.cc.
References LEARN, MLP_Test(), and NET.
Referenced by MLP_Epoch().
| int SetTransFunc | ( | int | layer, |
| int | neuron, | ||
| int | func | ||
| ) |
| int ShuffleExamples | ( | int | n, |
| int * | index | ||
| ) |
| void SteepestDir | ( | ) |
Definition at line 1249 of file mlp_gen.cc.
References recoMuon::in, LEARN, and NET.
Referenced by MLP_Epoch().
| int StochStep | ( | ) |
Definition at line 970 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 931 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);
}
1.7.3