ShowRec_Alg_N3.h File Reference

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Functions
void	N3_Create_ALG_MLP (TTree *inputtree, Int_t parasetnr)
void	N3_Dump_ALG_MLP_weights (TMultiLayerPerceptron *mlp, Int_t parasetnr)
void	N3_FindBestCompliments (EdbSegP *seg, EdbPattern *TestPattern, EdbPVRec *local_gAli, Int_t Downstream, Float_t &mindeltaZ, Float_t &mindT, Float_t &mindR, Float_t &mindMinDist, Int_t &nDifferentSegs)
void	N3_Load_ALG_MLP_weights (TMultiLayerPerceptron *mlp, Int_t parasetnr)
void	N3_ReadOptionFile ()

Variables
Int_t	N3_ANN_EQUALIZESGBG
Int_t	N3_ANN_INPUTNEURONS
TString	N3_ANN_Layout =""
Int_t	N3_ANN_NHIDDENLAYER
Int_t	N3_ANN_NInput
Int_t	N3_ANN_NTRAINEPOCHS
Double_t	N3_ANN_OUTPUTTHRESHOLD
Int_t	N3_ANN_PLATE_DELTANMAX
Bool_t	N3_DoTrain =kTRUE
Double_t	N3_Inputvar [24]
Double_t	N3_OutputValue =0
TMultiLayerPerceptron *	N3_TMLP_ANN
Int_t	N3_TrainNMax
Int_t	N3_Type

Function Documentation

N3_Create_ALG_MLP()

void N3_Create_ALG_MLP	(	TTree *	inputtree,
		Int_t	parasetnr
	)

{
    Log(2, "ShowRec.cpp", "--- void N3_Create_ALG_MLP() ---");
 
    if (!gROOT->GetClass("TMultiLayerPerceptron")) {
        gSystem->Load("libMLP");
    }
 
    N3_Create_Layout();
    cout << "N3_ANN_Layout:   " << N3_ANN_Layout.Data() << endl;
    // Full layout would look like:
//     N3_ANN_Layout:   N3_Inputvar[0],N3_Inputvar[1],N3_Inputvar[2],N3_Inputvar[3],N3_Inputvar[4],N3_Inputvar[5],N3_Inputvar[6],N3_Inputvar[7],N3_Inputvar[8],N3_Inputvar[9],N3_Inputvar[10],N3_Inputvar[11],N3_Inputvar[12],N3_Inputvar[13],N3_Inputvar[14],N3_Inputvar[15],N3_Inputvar[16],N3_Inputvar[17],N3_Inputvar[18],N3_Inputvar[19],N3_Inputvar[20],N3_Inputvar[21],N3_Inputvar[22],N3_Inputvar[23],N3_Inputvar[24],N3_Inputvar[25],N3_Inputvar[26],N3_Inputvar[27],N3_Inputvar[28]:29:29:29:29:29:N3_Type
 
    // Create the network:
    // Attention: for training case, the tree "simu" has already to be filled with the entries.
    N3_TMLP_ANN = new TMultiLayerPerceptron(N3_ANN_Layout, simu, "(Entry$)%2", "(Entry$+1)%2");
//     N3_TMLP_ANN = new TMultiLayerPerceptron(N3_ANN_Layout, simu, "(Entry$)%5", "(Entry$+1)%5");
    // This network does use the dZ to InBT (variable N3_Inputvar[0]) as primary weight
// N3_TMLP_ANN = new TMultiLayerPerceptron(N3_ANN_Layout, "N3_Inputvar[0]", simu, "(Entry$)%2", "(Entry$+1)%2");
    //N3_TMLP_ANN->Print();
 
    Log(2, "ShowRec.cpp", "--- void N3_Create_ALG_MLP() done.");
    return;
}

N3_Dump_ALG_MLP_weights()

void N3_Dump_ALG_MLP_weights	(	TMultiLayerPerceptron *	mlp,
		Int_t	parasetnr
	)

{
    Log(2, "ShowRec.cpp", "--- void N3_Dump_ALG_MLP_weights() ---");
    cout << "parasetnr = " << parasetnr << endl;
    cout << "weightfilestring = " << Form("N3_ANN_WEIGHTS_PARASET_%d.txt",parasetnr) << endl;
    mlp->DumpWeights(Form("N3_ANN_WEIGHTS_PARASET_%d.txt",parasetnr));
    mlp->GetStructure();
    mlp->Print();
    Log(2, "ShowRec.cpp", "--- void N3_Dump_ALG_MLP_weights() done.");
    return;
}

N3_FindBestCompliments()

void N3_FindBestCompliments	(	EdbSegP *	seg,
		EdbPattern *	TestPattern,
		EdbPVRec *	local_gAli,
		Int_t	Downstream,
		Float_t &	mindeltaZ,
		Float_t &	mindT,
		Float_t &	mindR,
		Float_t &	mindMinDist,
		Int_t &	nDifferentSegs
	)

                                                                                                                                                                                                                {
 
    Log(4, "ShowRec.cpp", "--- void N3_FindBestCompliments() ---");
 
    if (gEDBDEBUGLEVEL>3) {
        cout << "Looking for best compliments for segment " << seg << " of pattern " << TestPattern << " ... " << endl;
        cout << "Looking for pattern with Downstream = " << Downstream << endl;
        seg->PrintNice();
    }
 
    EdbPattern* OtherPattern = NULL;
    EdbPattern* OtherPatternInterim = NULL;
 
    // Speculative calculation of delta Z value. To be refined later.
    // See further down below.
    // It is basically seg->Z() - OtherPattern->Z().
    mindeltaZ = 0.0 - Downstream * 1300.0;
 
    // Dumb workaround for the selection method, but working.
    // Quick and dirty approach:
    if (Downstream==0) {
        // Will be the same plate.
        // Take care not to compare segment with segment itself further down!!
        OtherPattern = TestPattern;
    }
    else if (Downstream==1) {
        OtherPattern = local_gAli->GetPatternSucceding(TestPattern);
    }
    else if (Downstream==2) {
        OtherPatternInterim = local_gAli->GetPatternSucceding(TestPattern);
        if (NULL==OtherPatternInterim) {
            OtherPattern=NULL;
        }
        else {
            OtherPattern = local_gAli->GetPatternSucceding(OtherPatternInterim);
        }
    }
    else if (Downstream==-1) {
        OtherPattern = local_gAli->GetPatternPreceding(TestPattern);
    }
    else if (Downstream==-2) {
        OtherPatternInterim = local_gAli->GetPatternPreceding(TestPattern);
        if (NULL==OtherPatternInterim) {
            OtherPattern=NULL;
        }
        else {
            OtherPattern = local_gAli->GetPatternPreceding(OtherPatternInterim);
        }
    }
    else {
//         cout << "WARNING Downstream variable out of bounds. Return NULL as OtherPattern." << endl;
    }
 
    if (NULL == OtherPattern) {
//         cout << "WARNING NULL == OtherPattern. Return now." << endl;
        return;
    }
 
//     cout << "TestPattern has Z() of: " << TestPattern << " and next/overnext pattern has Z of: " <<  OtherPattern->Z() << endl;
 
    // How many segments are there in the check_pattern?
    Int_t nSeg = OtherPattern->N();
//     cout << "TestPattern has N entries: " << nSeg << " (one maybe the test segment)" <<  endl;
    // Whats the Z difference for segment and actual pattern?
    mindeltaZ = seg->Z() - OtherPattern->Z();
 
    Double_t testval_dR=0;
    Double_t testval_dT=0;
    Double_t testval_dminDins=0;
    Int_t    IDmindR = -1;
    Int_t    IDmindT = -1;
    Int_t    IDmindminDist = -1;
 
    // Reset minimal values to incredibly large values for first time check:
    mindT=9999999;
    mindR=9999999;
    mindMinDist=9999999;
 
    // Do finding routine now, simple comparison...
    for (Int_t i=0; i<nSeg; ++i) {
        EdbSegP* otherSeg = OtherPattern->GetSegment(i);
        // dont match segment with itsself:
        if (otherSeg==seg) continue;
        
        // Quick Preselection: we aussume, that large away segments do not 
        // contribute to any of the minimum selection segments.
        if (TMath::Abs(seg->X() - otherSeg->X()) > 1000 || TMath::Abs(seg->Y() - otherSeg->Y()) > 1000 ) continue;
//          cout << "seg = " << seg << " and otherSeg = " << otherSeg << endl;
//          cout << "deltaX = seg->X() - otherSeg->X() " <<  seg->X() - otherSeg->X()  << endl;
//          cout << "deltaY = seg->Y() - otherSeg->Y() " <<  seg->Y() - otherSeg->Y()  << endl;
        
        // always propagate from seg to other seg (from i to j)
        testval_dR = GetdR(seg, otherSeg);
        testval_dT = GetdeltaThetaSingleAngles(seg, otherSeg);
        testval_dminDins = GetdMinDist(seg, otherSeg);
        //printf("i otherSeg->ID()  testval_dR  testval_dT testval_dminDins %d, %d, %.03f  %.03f  %.03f \n",i,otherSeg->ID(), testval_dR ,testval_dT ,testval_dminDins );
        if (testval_dR < mindR ) {
            mindR = testval_dR;
            IDmindR = otherSeg->ID();
        }
        if (testval_dT < mindT ) {
            mindT = testval_dT;
            IDmindT = otherSeg->ID();
        }
        if (testval_dminDins < mindMinDist ) {
            mindMinDist = testval_dminDins;
            IDmindminDist = otherSeg->ID();
        }
    }
 
    /*
    cout << "Comparison for all segments gives following results: " << endl;
    printf("IDmindR =  %d, mindR = %.03f\n", IDmindR , mindR);
    printf("IDmindT =  %d, mindT = %.03f\n", IDmindT , mindT);
    printf("IDmindminDist =  %d, mindMinDist = %.03f\n", IDmindminDist , mindMinDist);
    */
 
    // Now check, how many different otherSeg ID() values we have...
//     cout << "Now check, how many different otherSeg ID() values we have..." << endl;
    // Comparing ID of mindR mit mindT
    Int_t ndifferent=3;
    if (IDmindR==IDmindT || IDmindR==IDmindminDist || IDmindminDist==IDmindT) {
        ndifferent=2;
        if (IDmindR==IDmindT && IDmindR==IDmindminDist && IDmindminDist==IDmindT) ndifferent=1;
    }
    if (nSeg==0) ndifferent=0; // no, if no other segment was around...
    if (nSeg==1 && OtherPattern==TestPattern)  ndifferent=0; // no, if no other segment was around...
    // Hand over new value:
    nDifferentSegs= ndifferent;
//     cout << "nDifferentSegs contributing to min values " << nDifferentSegs    <<   endl;
 
    Log(4, "ShowRec.cpp", "--- void N3_FindBestCompliments() done.");
    return;
}

N3_Load_ALG_MLP_weights()

void N3_Load_ALG_MLP_weights	(	TMultiLayerPerceptron *	mlp,
		Int_t	parasetnr
	)

{
    Log(2, "ShowRec.cpp", "--- void N3_Load_ALG_MLP_weights() ---");
    cout << "parasetnr = " << parasetnr << endl;
    cout << "weightfilestring = " << Form("N3_ANN_WEIGHTS_PARASET_%d.txt",parasetnr) << endl;
    mlp->LoadWeights(Form("N3_ANN_WEIGHTS_PARASET_%d.txt",parasetnr));
    mlp->GetStructure();
    mlp->Print();
    Log(2, "ShowRec.cpp", "--- void N3_Load_ALG_MLP_weights() done.");
    return;
}

N3_ReadOptionFile()

void N3_ReadOptionFile

(

)

{
    Log(2, "ShowRec.cpp", "--- void N3_ReadOptionFile() ---");
    cout << "Read OptionFile N3_Options.txt (no change of name possible currently)" << endl;
 
    // read parameters from par-file
    // return: 0 if ok
    //        -1 if file access failed
    char buf[256];
    char key[256];
    char name[256];
    const char *file="N3_Options.txt";
 
    FILE *fp = fopen(file,"r");
    if (!fp) {
        Log(1,"N3_ReadOptionFile","ERROR open file: %s", file);
        return;
    }
    else Log(2,"N3_ReadOptionFile","Read shower parameters from file: %s", file );
 
    Int_t id;
    Int_t int_eUse;
    int   dospec;
    TString tmpString;
    char tmpchar[256];
 
    while (fgets(buf, sizeof(buf), fp)) {
        for (Int_t i = 0; i < (Int_t)strlen(buf); ++i)
            if (buf[i]=='#')  {
                buf[i]='\0';                       // cut out comments starting from #
                break;
            }
 
        if ( sscanf(buf,"%s",key)!=1 ) continue;
 
        if ( !strcmp(key,"N3_TRAIN")  )
        {
            int train;
            sscanf(buf+strlen(key),"%d",&train);
            cout << "N3_ReadOptionFile   train = " << train << endl;
            N3_DoTrain=train;
        }
        else if ( !strcmp(key,"N3_TRAIN_NMAX")  )
        {
            int trainNMax;
            sscanf(buf+strlen(key),"%d",&trainNMax);
            cout << "N3_ReadOptionFile   trainNMax = " << trainNMax << endl;
            N3_TrainNMax=trainNMax;
        }
 
    } // end of while (fgets(buf, sizeof(buf), fp)) loop
    fclose(fp);
 
 
    Log(2, "ShowRec.cpp", "--- void N3_ReadOptionFile() done.");
    return;
}

Variable Documentation

N3_ANN_EQUALIZESGBG

Int_t N3_ANN_EQUALIZESGBG

N3_ANN_INPUTNEURONS

Int_t N3_ANN_INPUTNEURONS

N3_ANN_Layout

TString N3_ANN_Layout =""

N3_ANN_NHIDDENLAYER

Int_t N3_ANN_NHIDDENLAYER

N3_ANN_NInput

Int_t N3_ANN_NInput

N3_ANN_NTRAINEPOCHS

Int_t N3_ANN_NTRAINEPOCHS

N3_ANN_OUTPUTTHRESHOLD

Double_t N3_ANN_OUTPUTTHRESHOLD

N3_ANN_PLATE_DELTANMAX

Int_t N3_ANN_PLATE_DELTANMAX

N3_DoTrain

Bool_t N3_DoTrain =kTRUE

N3_Inputvar

Double_t N3_Inputvar[24]

N3_OutputValue

Double_t N3_OutputValue =0

N3_TMLP_ANN

TMultiLayerPerceptron* N3_TMLP_ANN

N3_TrainNMax

Int_t N3_TrainNMax

N3_Type

Int_t N3_Type