diff --git a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/errors/trainNN.cxx b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/errors/trainNN.cxx
index 465e41bbb1c874bbe5bd07e969e455b5cd8b25c1..e1ad8f2d752092aa291daaf6487406accc875279 100644
--- a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/errors/trainNN.cxx
+++ b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/errors/trainNN.cxx
@@ -8,12 +8,8 @@
#include <TCanvas.h>
#include <TH1F.h>
#include <TLegend.h>
-#include <iostream>
#include <TPad.h>
-//#include <stdio.h>
-#include <string.h>
-//#include <stdlib.h>
-#include <math.h>
+
#include "../TJetNet.h"
#include "../doNormalization.C"
#include "Riostream.h"
@@ -29,6 +25,9 @@
#include <vector>
#include <utility>
#include <algorithm>
+#include <string>
+#include <cmath>
+#include <iostream>
using namespace std;
@@ -444,7 +443,6 @@ if(useTrackEstimate){
simu->GetEntry(i);
for( unsigned int clus =0; clus<NN_sizeX->size(); clus++ ){
- // cout << clus << endl;
vector<float> *matrixOfToT=0;
vector<float> *vectorOfPitchesY=0;
@@ -460,8 +458,7 @@ if(useTrackEstimate){
std::vector<float> * thetaTr=0;
std::vector<float> * phiTr=0;
-// std::vector<float> positionX_reorder;
-// std::vector<float> positionY_reorder;
+
int sizeX = (*NN_sizeX)[clus];
positionX =&(*NN_positionX)[clus];
@@ -486,29 +483,7 @@ if(useTrackEstimate){
if ( badTrackInfo(useTrackEstimate, theta ) )continue;
-// bool sthWrong=false;
-// for (int o=0;o<positionsX_id->size();o++)
-// {
-// if (fabs((*positionsX_id)[o])>900 ||
-// fabs((*positionsY_id)[o])>900)
-// {
-// sthWrong=true;
-// }
-// }
-
-// for (int o=0;o<outputNN_X->size();o++)
-// {
-// if (fabs((*outputNN_X)[o])>900 ||
-// fabs((*outputNN_Y)[o])>900)
-// {
-// sthWrong=true;
-// }
-// }
-
-// if (sthWrong)
-// {
-// continue;
-// }
+
if (iClus%dilutionFactor==0) numberTrainingEvents+=1;
@@ -592,8 +567,7 @@ if(useTrackEstimate){
std::vector<float> positionX_reorder;
std::vector<float> positionY_reorder;
-// std::vector<float> * position_idX=0;
-// std::vector<float> * position_idY=0;
+
std::vector<float> * thetaTr=0;
@@ -619,11 +593,9 @@ if(useTrackEstimate){
positionY =&(*NN_positionY)[clus];
positionX_reorder=*positionX;
-// positionY_reorder=*positionY;
positionY_reorder.clear();
-// position_idX =&(*NN_position_idX)[clus];
-// position_idY =&(*NN_position_idY)[clus];
+
thetaTr = &(*NN_theta)[clus];
phiTr = &(*NN_phi)[clus];
@@ -643,10 +615,8 @@ if(useTrackEstimate){
if(isBadCluster(sizeX, nParticles ) )continue;
- // cout << i <<" " << clus << " " << NN_localColumnWeightedPosition->size() << " " << (*NN_localColumnWeightedPosition)[clus] << " " << (*NN_localRowWeightedPosition)[clus] << endl;
- // continue;
std::sort(positionX_reorder.begin(),
@@ -673,11 +643,7 @@ if(useTrackEstimate){
positionY_reorder.push_back(corry);
}
-// for (int s=0;s<positionX->size();s++)
-// {
-// cout << "posX: " << (*positionX)[s] << " posY: " << (*positionY)[s]
-// << " pos_ordX: " << positionX_reorder[s] << " pos_ordY: " << positionY_reorder[s] << endl;
-// }
+
for( unsigned int P = 0; P < positionX->size(); P++){
@@ -697,9 +663,7 @@ if(useTrackEstimate){
iClus++;
if ( badTrackInfo(useTrackEstimate, theta ) )continue;
-// if ( skipSingle(nParticles, iClus, dilutionFactor) )continue;
-
- // cout << "formatting" << endl;
+
// formatting cluster infos for the NN input
std::vector<Double_t> inputData;
@@ -733,20 +697,11 @@ if(useTrackEstimate){
vector<double> outputNN_idX;
vector<double> outputNN_idY;
- // using the trained network for position to obtain the position
-// cout << "Obtaining Positions... " << endl;
-// cout << " input Data size: " << inputData.size() << endl;
-// for (int s=0;s<inputData.size();s++)
-// {
-// cout << s << " : " << inputData[s] << endl;
-// }
+
vector<double> resultNN = positionTrainedNetwork->calculateOutputValues(inputData);
-// for (int s=0;s<resultNN.size();s++)
-// {
-// cout << "resultNN[" << s << "]=" << resultNN[s] << endl;
-// }
+
// storing the obtained X and Y position in vectors of position // nParticlesTraining
@@ -834,7 +789,7 @@ if(useTrackEstimate){
if (matrixOfToT->size()!=sizeX*sizeY)
{
std::cout << " Event: " << i << " PROBLEM: size Y is: " << matrixOfToT->size() << std::endl;
- throw;
+ throw std::runtime_error("Error in errors/trainNN.cxx");
}
// loop over elements of matrixOfTot which is actually a vector
@@ -908,7 +863,6 @@ if(useTrackEstimate){
if (iClus%dilutionFactor==1) jn->SetInputTestSet( counter1, (sizeX+1)*sizeY+addNumber+2*o,norm_posX(outputNN_idX[o]) );
if (iClus%dilutionFactor==1) jn->SetInputTestSet( counter1, (sizeX+1)*sizeY+addNumber+2*o+1,norm_posY(outputNN_idY[o]) );
-// cout << o << "\t" << outputNN_X[o] << " " << (*positionX)[o] << endl;
if (counter==0) std::cout << " n. " << o <<
@@ -999,233 +953,7 @@ if(useTrackEstimate){
- /*
- cout << " setting pattern for testing events " << endl;
-
- std::cout << " D " << std::endl;
-
- cout << " copying over testing events " << endl;
- counter=0;
-
-
- //input and output of first event
- vector<double> inputVar;
- vector<double> outputVar;
- vector<double> inputVar2;
- vector<double> outputVar2;
-
-
- for (Int_t i = 0; i < totEntries; i++) {
-
- if (i % 100000 == 0 ) {
- std::cout << " Copying over testing events. Looping over event " << i << std::endl;
- }
-
- if (i%dilutionFactor!=1) continue;
-
- simu->GetEntry(i);
-
- if (nParticlesTraining!=nParticles) {
- continue;
- }
-
- if (useTrackEstimate)
- {
- if (fabs(theta)<1e-8) {
- // std::cout << " Skipping theta==0 " << std::endl;
- continue;
- }
- }
-
- bool sthWrong=false;
- for (int o=0;o<positionsX_id->size();o++)
- {
- if (fabs((*positionsX_id)[o])>900 ||
- fabs((*positionsY_id)[o])>900)
- {
- cout << " skipping " << endl;
- sthWrong=true;
- }
- }
-
- for (int o=0;o<outputNN_X->size();o++)
- {
- if (fabs((*outputNN_X)[o])>900 ||
- fabs((*outputNN_Y)[o])>900)
- {
- sthWrong=true;
- }
- }
-
-
- if (sthWrong)
- {
- continue;
- }
-
-
- for (int u=0;u<sizeX;u++)
- {
- for (int s=0;s<sizeY;s++)
- {
- jn->SetInputTestSet( counter, s+u*sizeY, norm_ToT((*matrixOfToT)[u][s]));
- if (counter==0) inputVar.push_back(norm_ToT((*matrixOfToT)[u][s]));
- if (counter==1) inputVar2.push_back(norm_ToT((*matrixOfToT)[u][s]));
- }
- }
- for (int s=0;s<sizeY;s++)
- {
- jn->SetInputTestSet( counter, sizeX*sizeY+s, norm_pitch((*vectorOfPitchesY)[s]));
- if (counter==0) inputVar.push_back(norm_pitch((*vectorOfPitchesY)[s]));
- if (counter==1) inputVar2.push_back(norm_pitch((*vectorOfPitchesY)[s]));
- }
-
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY, norm_layerNumber(ClusterPixLayer));
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+1, norm_layerType(ClusterPixBarrelEC));
- if (counter==0) inputVar.push_back(norm_layerNumber(ClusterPixLayer));
- if (counter==1) inputVar2.push_back(norm_layerNumber(ClusterPixLayer));
- if (counter==0) inputVar.push_back(norm_layerType(ClusterPixBarrelEC));
- if (counter==1) inputVar2.push_back(norm_layerType(ClusterPixBarrelEC));
-
-
- if (useTrackEstimate)
- {
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+2, norm_phi(phi) );
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+3, norm_theta(theta) );
- if (counter==0) inputVar.push_back(norm_phi(phi));
- if (counter==0) inputVar.push_back(norm_theta(theta));
- if (counter==1) inputVar2.push_back(norm_phi(phi));
- if (counter==1) inputVar2.push_back(norm_theta(theta));
- }
- else
- {
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+2, norm_phiBS(phiBS) );
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+3, norm_thetaBS(thetaBS) );
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+4, norm_etaModule(etaModule) );
- if (counter==0) inputVar.push_back(norm_phiBS(phiBS));
- if (counter==0) inputVar.push_back(norm_thetaBS(thetaBS));
- if (counter==0) inputVar.push_back(norm_etaModule(etaModule));
- if (counter==1) inputVar2.push_back(norm_phiBS(phiBS));
- if (counter==1) inputVar2.push_back(norm_thetaBS(thetaBS));
- if (counter==1) inputVar2.push_back(norm_etaModule(etaModule));
-
- }
-
-
- if (positionsX_id->size()!=positionsX->size())
- {
- std::cout << " Inconsistency " << std::endl;
- throw;
- }
-
- int startFrom=0;
- if (nParticlesTraining==2)
- {
- startFrom=1;
- }
- if (nParticlesTraining==3)
- {
- startFrom=3;
- }
-
- int addNumber=5;
- if (useTrackEstimate) addNumber=4;
-
- for (int o=0;o<nParticlesTraining;o++)
- {
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+addNumber+2*o,norm_posX((*outputNN_idX)[o]) );
- jn->SetInputTestSet( counter, (sizeX+1)*sizeY+addNumber+2*o+1,norm_posY((*outputNN_idY)[o]) );
- if (counter==0) std::cout << " n. " << o <<
- " posX: " << norm_posX((*outputNN_idX)[o]) <<
- " posY: " << norm_posY((*outputNN_idY)[o]) << std::endl;
- if (counter==0) inputVar.push_back(norm_posX((*outputNN_idX)[o]));
- if (counter==0) inputVar.push_back(norm_posY((*outputNN_idY)[o]));
- if (counter==1) inputVar2.push_back(norm_posX((*outputNN_idX)[o]));
- if (counter==1) inputVar2.push_back(norm_posY((*outputNN_idY)[o]));
-
- }
-
-
- //OK now the new particular output
-
-
- for (int r=0;r<nParticlesTraining;r++)
- {
- if (counter==0) std::cout << " particle: " << r << std::endl;
- for (int u=0;u<numberBinsErrorEstimate;u++)
- {
- if (trainXerrors)
- {
- bool full=binIsFull(u,true,((*outputNN_X)[r]-(*positionsX_id)[r]),nParticlesTraining,numberBinsErrorEstimate);
- int nValueFull=0;
- if (full) nValueFull=1;
- jn->SetOutputTestSet(counter, r*numberBinsErrorEstimate+u,nValueFull );
- if (counter==0) outputVar.push_back(nValueFull);
- if (counter==1) outputVar2.push_back(nValueFull);
- if (counter==0) std::cout << " X bin: " << u << " gl: "<< r*2*numberBinsErrorEstimate+u << " val: " << nValueFull;
- }
- }
- for (int u=0;u<numberBinsErrorEstimate;u++)
- {
- if (!trainXerrors)
- {
- bool full=binIsFull(u,false,((*outputNN_Y)[r]-(*positionsY_id)[r]),nParticlesTraining,numberBinsErrorEstimate);
- int nValueFull=0;
- if (full) nValueFull=1;
- jn->SetOutputTestSet(counter, r*numberBinsErrorEstimate+u,nValueFull );
- if (counter==0) std::cout << " Y bin: " << u << " gl: " << r*2*numberBinsErrorEstimate+numberBinsErrorEstimate+u << " val: " << nValueFull;
- if (counter==0) outputVar.push_back(nValueFull);
- if (counter==1) outputVar2.push_back(nValueFull);
- }
- }
- }
-
- if (counter==0) std::cout << std::endl << " total number of bins: " << numberoutputs << std::endl;
- */
-
- /*
- for (int o=0;o<positionsX_id->size();o++)
- {
-
- double residualX=norm_errorX(((*outputNN_X)[o]-(*positionsX_id)[o])*((*outputNN_X)[o]-(*positionsX_id)[o]));
- double residualY=norm_errorY((((*outputNN_Y)[o]-(*positionsY_id)[o]))*((*outputNN_Y)[o]-(*positionsY_id)[o]));
-
- jn->SetOutputTestSet(counter, 2*o, residualX);
- jn->SetOutputTestSet(counter, 2*o+1, residualY);
-
- if (counter==0) std::cout << " output node: " << 2*o << " set to: " << residualX << endl;
- if (counter==0) std::cout << " output node: " << 2*o+1 << " set to: " << residualY << endl;
- if (counter==0) outputVar.push_back(residualX);
- if (counter==0) outputVar.push_back(residualY);
- }
- */
-
-
- /*
-
-
-
- jn->SetEventWeightTestSet( counter, 1 );
-
- counter+=1;
-
- //not used!
- // eventWeight=weight;
- }
-
- std::cout << " For simple test have input(" << inputVar.size() << ") and output("<< outputVar.size() << ")" << std::endl;
-
- if (counter!=numberTestingEvents)
- {
- cout << " counter up to: " << counter << " while events in testing sample are " << numberTestingEvents << ". Normal due to cuts..." << endl;
- return; }
-
- //normalize inputvariables?
- //jn->Normalize();
-
-
- */
-
+
jn->Shuffle(true,false);
std::cout << " Potts units are: " << jn->GetPottsUnits() << std::endl;
@@ -1236,7 +964,6 @@ if(useTrackEstimate){
if (restartTrainingFrom==0)
{
jn->Init();
- // jn->DumpToFile("WeightsInitial.w");
}
else
{
@@ -1391,50 +1118,7 @@ if(useTrackEstimate){
TFile* file=new TFile(name,"recreate");
TTrainedNetwork* trainedNetwork=jn->createTrainedNetwork();
- /*
- jn->Evaluate( 0 ); //evaluate the first test pattern
- double sum=0;
- for (int z=0;z<numberoutputs;z++)
- {
- std::cout << "-"<<z<<":" << jn->GetOutput(z);
- sum+=jn->GetOutput(z);
- }
- std::cout << " sum: " << sum << std::endl;
-
- std::vector<double> myTestOutput=trainedNetwork->calculateOutputValues(inputVar);
- std::cout << std::endl << "output TTNet "<<std::endl;
- for (int z=0;z<numberoutputs;z++)
- {
- std::cout << "-"<<z<<":" << myTestOutput[z];
- }
-
- std::cout << std::endl << "should be "<<std::endl;
- for (int z=0;z<numberoutputs;z++)
- {
- std::cout << "-"<<z<<":" << outputVar[z];
- }
-
- std::cout << std::endl;
- jn->Evaluate( 1 );
- for (int z=0;z<numberoutputs;z++)
- {
- std::cout << "-"<<z<<":" << jn->GetOutput(z);
- }
-
- myTestOutput=trainedNetwork->calculateOutputValues(inputVar2);
-// std::cout << std::endl << "output TTNet "<<std::endl;
-// for (int z=0;z<numberoutputs;z++)
-// {
-// std::cout << "-"<<z<<":" << myTestOutput[z];
-// }
- std::cout << std::endl << "should be "<<std::endl;
- for (int z=0;z<numberoutputs;z++)
- {
- std::cout << "-"<<z<<":" << outputVar2[z];
- }
-
- std::cout << std::endl;
- */
+
trainedNetwork->Write();
histoControlTestX->Write();
@@ -1443,13 +1127,7 @@ if(useTrackEstimate){
file->Close();
delete file;
- /*
- TFile* file2=new TFile(name);
- trainedNetwork=(TTrainedNetwork*)file2->Get("TTrainedNetwork");
- cout <<" hid lay 1 size: " << trainedNetwork->getnHiddenLayerSize()[0] << endl;
- file2->Close();
- delete file2;
- */
+
// jn->DumpToFile(name);
}
@@ -1457,12 +1135,9 @@ if(useTrackEstimate){
jn->writeNetworkInfo(1);
jn->writeNetworkInfo(2);
- // jn->writeNetworkInfo(3);
- // jn->writeNetworkInfo(4);
- // jn->writeNetworkInfo(5);
+
- // cout << " Now try to understand how to get the weights..." << endl;
////////////WWWWWAAAAASSSSSS HERE
Int_t nInput=jn->GetInputDim();
@@ -1471,60 +1146,7 @@ if(useTrackEstimate){
TTrainedNetwork* trainedNetwork=jn->createTrainedNetwork();
-/*
- cout << " now getting value with trained Network ";
-
-
-
-
- double inputexample[9]={norm_nVTX(1),
- norm_nTracksAtVtx(2),
- norm_nSingleTracks(0),
- norm_energyFraction(0.6),
- norm_mass(2500),
- norm_significance3d(4 ),
- norm_IP3D(3),
- norm_cat_pT(3),
- norm_cat_eta(1)};
-
- for (Int_t i=0;i<nInput;++i)
- {
- jn->SetInputs(i,inputexample[i]);
- }
-
- cronology.open("weights/trainingCronology.txt",ios_base::app);
-
- jn->Evaluate();
-
- cronology << "----------------CONSISTENCY CHECK-----------" << endl;
- cout << "Result 0:" << jn->GetOutput(0);
- cronology << "Result 0:" << jn->GetOutput(0);
- cout << " Result 1:" << jn->GetOutput(1);
- cronology << "Result 0:" << jn->GetOutput(1);
- cout << " Result 2:" << jn->GetOutput(2) << endl;
- cronology << " Result 2:" << jn->GetOutput(2) << endl;
-
- cout << " Reading back old network " << endl;
- jn->readBackTrainedNetwork(trainedNetwork);
-
- cout <<" resetting input " << endl;
- for (Int_t i=0;i<nInput;++i)
- {
- jn->SetInputs(i,inputexample[i]);
- }
- jn->Evaluate();
-
- cout << "After reading back - Result 0:" << jn->GetOutput(0);
- cronology << "After reading back - Result 0:" << jn->GetOutput(0);
- // << " my: " << result[0] << endl;
- cout << " After reading back - Result 1:" << jn->GetOutput(1);
- cronology << "After reading back - Result 1:" << jn->GetOutput(1);
- //<< " my: " << result[1] << endl;
- cout << " After reading back - Result 2:" << jn->GetOutput(2) << endl;
- cronology << "After reading back - Result 2:" << jn->GetOutput(2);
- // << " my: " << result[2] << endl;
- */
cout << " Now getting histograms from trainingResult" << endl;
cronology << " Now getting histograms from trainingResult" << endl;
@@ -1540,40 +1162,6 @@ if(useTrackEstimate){
cout << " reading back " << endl;
jn->readBackTrainedNetwork(trainedNetwork2);
-/*
- cout <<" resetting input " << endl;
- for (Int_t i=0;i<nInput;++i)
- {
- jn->SetInputs(i,inputexample[i]);
- }
-
- jn->Evaluate();
-
- cout << "After reading back - Result 0:" << jn->GetOutput(0);
- cronology << "After reading back - Result 0:" << jn->GetOutput(0);
- // << " my: " << result[0] << endl;
- cout << " After reading back - Result 1:" << jn->GetOutput(1);
- cronology << "After reading back - Result 1:" << jn->GetOutput(1);
- //<< " my: " << result[1] << endl;
- cout << " After reading back - Result 2:" << jn->GetOutput(2) << endl;
- cronology << "After reading back - Result 2:" << jn->GetOutput(2);
- // << " my: " << result[2] << endl;
-
-
- cout << " Directly from the trainedNetwork read back from HISTOS...!" << endl;
-
- std::vector<Double_t> inputData;
- for (Int_t u=0;u<nInput;++u)
- {
- inputData.push_back(inputexample[u]);
- }
-
- std::vector<Double_t> outputData=trainedNetwork2->calculateOutputValues(inputData);
-
- cout << "After reading back - Result 0:" << outputData[0] << endl;
- cout << " After reading back - Result 1:" << outputData[1] << endl;
- cout << " After reading back - Result 2:" << outputData[2] << endl;
-*/
@@ -1632,9 +1220,7 @@ if(useTrackEstimate){
fileHistos->Close();
delete fileHistos;
- // " filename: " << name << endl;
-
- // jn->ReadFromFile(name);
+
}
else
@@ -1671,340 +1257,7 @@ if(useTrackEstimate){
TCanvas* mlpa_canvas = new TCanvas("jetnet_canvas","Network analysis");
mlpa_canvas->Divide(2,4);
-/*
-
-// TCanvas* mlpa_canvas_5=gDirectory->Get("mlpa_canvas_5");
-// mlpa_canvas_5->SetLogy(kTrue);
- gPad->SetLogy();
-
- // Use the NN to plot the results for each sample
- // This will give approx. the same result as DrawNetwork.
- // All entries are used, while DrawNetwork focuses on
- // the test sample. Also the xaxis range is manually set.
- TH1F *bg2 = new TH1F("bg2h", "NN output", 50, -.5, 1.5);
- TH1F *bg = new TH1F("bgh", "NN output", 50, -.5, 1.5);
- TH1F *sig = new TH1F("sigh", "NN output", 50, -.5, 1.5);
-
- //sig = 1 part; bg = 2 part; bg2 = 3 part
-
- TH1F *bg2test = new TH1F("bg2htest", "NN output", 50, -.5, 1.5);
- TH1F *bgtest = new TH1F("bghtest", "NN output", 50, -.5, 1.5);
- TH1F *sigtest = new TH1F("sightest", "NN output", 50, -.5, 1.5);
- int weight=1;
-
- for (Int_t i = 0; i < nTotal; i++) {
-
- if (i % 100000 == 0 ) {
- std::cout << " First plot. Looping over event " << i << std::endl;
- }
-
- if (i%dilutionFactor!=0&&i%dilutionFactor!=1) continue;
-
- simu->GetEntry(i);
-
- for (int u=0;u<sizeX;u++)
- {
- for (int s=0;s<sizeY;s++)
- {
- jn->SetInputs( s+u*sizeY, norm_ToT((*matrixOfToT)[u][s]));
- }
- }
- for (int s=0;s<sizeY;s++)
- {
- jn->SetInputs( sizeX*sizeY+s, norm_pitch((*vectorOfPitchesY)[s]));
- }
-
- jn->SetInputs( (sizeX+1)*sizeY, norm_phi(phi) );
- jn->SetInputs( (sizeX+1)*sizeY+1, norm_theta(theta) );
-
- jn->Evaluate();
-
- float p1=jn->GetOutput(0);
- float p2=jn->GetOutput(1);
- float p3=jn->GetOutput(2);
-
- if (nParticles==1)
- {
- if (i%dilutionFactor==0)
- {
- sig->Fill(p1/(p1+p2+p3),weight);
- }
- else if (i%dilutionFactor==1)
- {
- sigtest->Fill(p1/(p1+p2+p3),weight);
- }
- }
- if (nParticles==2)
- {
- if (i%dilutionFactor==0)
- {
- bg->Fill(p1/(p1+p2+p3),weight);
- }
- else if (i%dilutionFactor==1)
- {
- bgtest->Fill(p1/(p1+p2+p3),weight);
- }
- }
- if (nParticles>=3)
- {
- if (i%dilutionFactor==0)
- {
- bg2->Fill(p1/(p1+p2+p3),weight);
- }
- else if (i%dilutionFactor==1)
- {
- bg2test->Fill(p1/(p1+p2+p3),weight);
- }
- }
- }
-
- //now you need the maximum
- float maximum=1;
- for (Int_t a=0;a<bg->GetNbinsX();a++)
- {
- if (bg->GetBinContent(a)>maximum)
- {
- maximum=1.2*bg->GetBinContent(a);
- }
- }
-
-
- bg2->SetLineColor(kYellow);
- bg2->SetFillStyle(3008); bg2->SetFillColor(kYellow);
- bg->SetLineColor(kBlue);
- bg->SetFillStyle(3008); bg->SetFillColor(kBlue);
- sig->SetLineColor(kRed);
- sig->SetFillStyle(3003); sig->SetFillColor(kRed);
- bg2->SetStats(0);
- bg->SetStats(0);
- sig->SetStats(0);
-
-
- bg2test->SetLineColor(kYellow);
- bg2test->SetFillStyle(3008); bg2test->SetFillColor(kYellow);
- bgtest->SetLineColor(kBlue);
- bgtest->SetFillStyle(3008); bgtest->SetFillColor(kBlue);
- sigtest->SetLineColor(kRed);
- sigtest->SetFillStyle(3003); sigtest->SetFillColor(kRed);
- bg2test->SetStats(0);
- bgtest->SetStats(0);
- sigtest->SetStats(0);
-
- mlpa_canvas->cd(1);
- gPad->SetLogy();
-
- bg->GetYaxis()->SetRangeUser(1,maximum);
- bgtest->GetYaxis()->SetRangeUser(1,maximum);
-
- mlpa_canvas->cd(1);
- bg->Draw();
- bg2->Draw("same");
- sig->Draw("same");
-
- TLegend *legend = new TLegend(.75, .80, .95, .95);
- legend->AddEntry(bg2, "particles >=3");
- legend->AddEntry(bg, "particles = 2");
- legend->AddEntry(sig, "particles = 1");
- legend->Draw();
-
- mlpa_canvas->cd(2);
- gPad->SetLogy();
-
- bgtest->Draw();
- bg2test->Draw("same");
- sigtest->Draw("same");
-
- TLegend *legendtest = new TLegend(.75, .80, .95, .95);
- legendtest->AddEntry(bg2test, "particles >=3");
- legendtest->AddEntry(bgtest, "particles = 2");
- legendtest->AddEntry(sigtest, "particles = 1");
- legendtest->Draw();
-
- mlpa_canvas->cd(5);
- gPad->SetLogy();
- bg->DrawNormalized();
- bg2->DrawNormalized("same");
- sig->DrawNormalized("same");
- legend->Draw();
-
- mlpa_canvas->cd(6);
- gPad->SetLogy();
- bgtest->DrawNormalized();
- bg2test->DrawNormalized("same");
- sigtest->DrawNormalized("same");
- legendtest->Draw();
-
-
-
- mlpa_canvas->cd(3);
- gPad->SetLogy();
-
- // Use the NN to plot the results for each sample
- // This will give approx. the same result as DrawNetwork.
- // All entries are used, while DrawNetwork focuses on
- // the test sample. Also the xaxis range is manually set.
- TH1F *c_bg2 = new TH1F("c_bg2h", "NN output", 50, -.5, 1.5);
- TH1F *c_bg = new TH1F("c_bgh", "NN output", 50, -.5, 1.5);
- TH1F *c_sig = new TH1F("c_sigh", "NN output", 50, -.5, 1.5);
-
- TH1F *c_bg2test = new TH1F("c_bg2htest", "NN output", 50, -.5, 1.5);
- TH1F *c_bgtest = new TH1F("c_bghtest", "NN output", 50, -.5, 1.5);
- TH1F *c_sigtest = new TH1F("c_sightest", "NN output", 50, -.5, 1.5);
-
- for (Int_t i = 0; i < nTotal; i++) {
-
- if (i % 100000 == 0 ) {
- std::cout << " Second plot. Looping over event " << i << std::endl;
- }
-
- if (i%dilutionFactor!=0&&i%dilutionFactor!=1) continue;
-
- simu->GetEntry(i);
-
- for (int u=0;u<sizeX;u++)
- {
- for (int s=0;s<sizeY;s++)
- {
- jn->SetInputs( s+u*sizeY, norm_ToT((*matrixOfToT)[u][s]));
- }
- }
- for (int s=0;s<sizeY;s++)
- {
- jn->SetInputs( sizeX*sizeY+s, norm_pitch((*vectorOfPitchesY)[s]));
- }
-
- jn->SetInputs( (sizeX+1)*sizeY, norm_phi(phi) );
- jn->SetInputs( (sizeX+1)*sizeY+1, norm_theta(theta) );
-
- jn->Evaluate();
-
- float p1=jn->GetOutput(0);
- float p2=jn->GetOutput(1);
- float p3=jn->GetOutput(2);
-
- float discr=(p1+p2)/(p1+p2+p3);
-
- if (nParticles==1)
- {
- if (i%dilutionFactor==0)
- {
- c_sig->Fill(discr,weight);
- }
- else if (i%dilutionFactor==1)
- {
- c_sigtest->Fill(discr,weight);
- }
- }
- if (nParticles==2)
- {
- if (i%dilutionFactor==0)
- {
- c_bg->Fill(discr,weight);
- }
- else if (i%dilutionFactor==1)
- {
- c_bgtest->Fill(discr,weight);
- }
- }
- if (nParticles>=3)
- {
- if (i%dilutionFactor==0)
- {
- c_bg2->Fill(discr,weight);
- }
- else if (i%dilutionFactor==1)
- {
- c_bg2test->Fill(discr,weight);
- }
- }
- }
-
- //now you need the maximum
- maximum=1;
- for (Int_t a=0;a<c_bg->GetNbinsX();a++)
- {
- if (c_bg->GetBinContent(a)>maximum)
- {
- maximum=1.2*c_bg->GetBinContent(a);
- }
- }
-
- c_bg2->SetLineColor(kYellow);
- c_bg2->SetFillStyle(3008); c_bg2->SetFillColor(kYellow);
- c_bg->SetLineColor(kBlue);
- c_bg->SetFillStyle(3008); c_bg->SetFillColor(kBlue);
- c_sig->SetLineColor(kRed);
- c_sig->SetFillStyle(3003); c_sig->SetFillColor(kRed);
- c_bg2->SetStats(0);
- c_bg->SetStats(0);
- c_sig->SetStats(0);
-
- c_bg2test->SetLineColor(kYellow);
- c_bg2test->SetFillStyle(3008); c_bg2test->SetFillColor(kYellow);
- c_bgtest->SetLineColor(kBlue);
- c_bgtest->SetFillStyle(3008); c_bgtest->SetFillColor(kBlue);
- c_sigtest->SetLineColor(kRed);
- c_sigtest->SetFillStyle(3003); c_sigtest->SetFillColor(kRed);
- c_bg2test->SetStats(0);
- c_bgtest->SetStats(0);
- c_sigtest->SetStats(0);
-
- mlpa_canvas->cd(3);
- gPad->SetLogy();
-
-
- c_bg->GetYaxis()->SetRangeUser(1,maximum);
- c_bgtest->GetYaxis()->SetRangeUser(1,maximum);
-
- c_bg->Draw();
- c_bg2->Draw("same");
- c_sig->Draw("same");
-
- TLegend *legend2 = new TLegend(.75, .80, .95, .95);
- legend2->AddEntry(c_bg2, "particles >=3");
- legend2->AddEntry(c_bg, "particles = 2");
- legend2->AddEntry(c_sig, "particles = 1");
- legend2->Draw();
-
- mlpa_canvas->cd(4);
- gPad->SetLogy();
-
- c_bgtest->Draw();
- c_bg2test->Draw("same");
- c_sigtest->Draw("same");
-
- TLegend *legend2test = new TLegend(.75, .80, .95, .95);
- legend2test->AddEntry(c_bg2test, "particles >=3");
- legend2test->AddEntry(c_bgtest, "particles = 2");
- legend2test->AddEntry(c_sigtest, "particles = 1");
- legend2test->Draw();
-
- mlpa_canvas->cd(7);
- gPad->SetLogy();
- c_bg->DrawNormalized();
- c_bg2->DrawNormalized("same");
- c_sig->DrawNormalized("same");
- legend2->Draw();
-
- mlpa_canvas->cd(8);
- gPad->SetLogy();
- c_bgtest->DrawNormalized();
- c_bg2test->DrawNormalized("same");
- c_sigtest->DrawNormalized("same");
- legend2test->Draw();
-
-
- mlpa_canvas->cd(0);
-
-
- mlpa_canvas->SaveAs("weights/result.eps");
-
-
-
-
-
-*/
}
diff --git a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/number/trainNN.cxx b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/number/trainNN.cxx
index 87a17f6e1efdc83f50bbb5033c694673adefa974..10eed267d61f8d28a0a3cae939707bad21735c48 100644
--- a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/number/trainNN.cxx
+++ b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/number/trainNN.cxx
@@ -9,10 +9,8 @@
#include <TLegend.h>
#include <iostream>
#include <TPad.h>
-//#include <stdio.h>
-#include <string.h>
-//#include <stdlib.h>
-#include <math.h>
+#include <string>
+#include <cmath>
#include "../TJetNet.h"
#include "../doNormalization.C"
#include "Riostream.h"
@@ -491,7 +489,7 @@ if(useTrackEstimate){
if (matrixOfToT->size()!=sizeX*sizeY)
{
std::cout << " Event: " << i << " PROBLEM: size Y is: " << matrixOfToT->size() << std::endl;
- throw;
+ throw std::runtime_error("Problem in number/trainNN.cxx");
}
// loop over elements of matrixOfTot which is actually a vector
diff --git a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/positions/trainNN.cxx b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/positions/trainNN.cxx
index 843d149a19e3e09522e86ce14de9751a6ababeb7..e42f3db587d3a7924a9dcd5cb266fb62adf56ec5 100644
--- a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/positions/trainNN.cxx
+++ b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/positions/trainNN.cxx
@@ -8,12 +8,8 @@
#include "TChain.h"
#include <TH1F.h>
#include <TLegend.h>
-#include <iostream>
#include <TPad.h>
-//#include <stdio.h>
-#include <string.h>
-//#include <stdlib.h>
-#include <math.h>
+
#include "../TJetNet.h"
#include "../doNormalization.C"
#include "Riostream.h"
@@ -29,6 +25,10 @@
#include <vector>
#include <utility>
#include <algorithm>
+#include <string>
+#include <cmath>
+#include <iostream>
+#include <stdexcept>
using namespace std;
@@ -522,7 +522,7 @@ if(useTrackEstimate){
if (matrixOfToT->size()!=sizeX*sizeY)
{
std::cout << " Event: " << i << " PROBLEM: size Y is: " << matrixOfToT->size() << std::endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
// loop over elements of matrixOfTot which is actually a vector
@@ -534,7 +534,7 @@ if(useTrackEstimate){
if ((*matrixOfToT)[ME] != (*matrixOfToT)[ME])
{
cout << "ME n. " << ME << " is: " << (*matrixOfToT)[ME] << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
if (counter0 == 0) std::cout << " element: " << ME << " ToT set to: " << norm_ToT((*matrixOfToT)[ME]) << std::endl;
@@ -552,7 +552,7 @@ if(useTrackEstimate){
if ((*vectorOfPitchesY)[s]!=(*vectorOfPitchesY)[s])
{
cout << " pitchY: " << (*vectorOfPitchesY)[s] << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");;
}
if (counter0 == 0) std::cout << " s: " << s << " pitch set to: " << norm_pitch((*vectorOfPitchesY)[s]) << std::endl;
@@ -565,7 +565,7 @@ if(useTrackEstimate){
if (ClusterPixLayer!=ClusterPixLayer || ClusterPixBarrelEC!=ClusterPixBarrelEC)
{
cout << " ClusterPixLayer: " << ClusterPixLayer << " ClusterPixBarrelEC " << ClusterPixBarrelEC << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
if (iClus%dilutionFactor==1) jn->SetInputTestSet( counter1, (sizeX+1)*sizeY, norm_layerNumber(ClusterPixLayer));
@@ -586,12 +586,12 @@ if(useTrackEstimate){
if (phi!=phi) {
cout << " phi: " << phi << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
if (theta!=theta) {
cout << " theta: " << theta << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
@@ -648,7 +648,7 @@ if(useTrackEstimate){
if (xPositions[o]!=xPositions[o])
{
cout << "pos: " << xPositions[o] << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
if (counter0==0) std::cout << " output node: " << 2*o << " set to: " << norm_posX(xPositions[o]) << endl;
@@ -675,7 +675,7 @@ if(useTrackEstimate){
if (fabs(corry+1000)<1e-6) {
cout << " could not find original X pos. " << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
if (iClus%dilutionFactor==0) jn->SetOutputTrainSet(counter0, 2*o+1, norm_posY(corry));
@@ -683,7 +683,7 @@ if(useTrackEstimate){
if (corry!=corry) {
cout << " posY " << corry << endl;
- throw;
+ throw std::runtime_error("Error in positions/trainNN.cxx");
}
diff --git a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/validation/writeNtuple.C b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/validation/writeNtuple.C
index 35d94bcdebd599e3959137be509f7bd4b8f8ec18..da5591b407b693acf84040838e3423839a2070cd 100644
--- a/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/validation/writeNtuple.C
+++ b/InnerDetector/InDetCalibAlgs/PixelCalibAlgs/NNClusteringCalibration_RunI/validation/writeNtuple.C
@@ -11,12 +11,8 @@
#include <TFile.h>
#include <TH1F.h>
#include <TLegend.h>
-#include <iostream>
#include <TPad.h>
-//#include <stdio.h>
-#include <string.h>
-//#include <stdlib.h>
-#include <math.h>
+
#include "../TJetNet.h"
#include "../doNormalization.C"
#include "Riostream.h"
@@ -24,21 +20,23 @@
#include <TSystem.h>
#include "../TTrainedNetwork.h"
+#include <TLatex.h>
+#include <TProfile.h>
+#include <TMath.h>
+#include <TChain.h>
+#include "TROOT.h"
+#include "TH2D.h"
#include "TMatrixD.h"
#include "TVectorD.h"
-//#include "trainNN.h"
#include <algorithm>
#include <vector>
#include <utility>
-#include <TLatex.h>
-#include "TH2D.h"
-#include <sstream>
-#include <TProfile.h>
-#include <TMath.h>
-#include <TChain.h>
#include <typeinfo>
-#include "TROOT.h"
+#include <sstream>
+#include <iostream>
+#include <string>
+#include <cmath>
using namespace std;
@@ -717,15 +715,14 @@ void writeNtuple::Loop( int nParticlesTraining, bool useTrackEstimate, TString
}
- // cout << "position ordered" << endl;
double minDist = 10000000;
- double TruthLocX;
- double TruthLocY;
+ double TruthLocX{0.};
+ double TruthLocY{0.};
- int truthIndex;
+ int truthIndex{0};
// check which associated truth locX and locY are closer to the estimated passage point
for( unsigned int mp=0; mp < outputNN_X.size(); mp++){
@@ -754,7 +751,6 @@ void writeNtuple::Loop( int nParticlesTraining, bool useTrackEstimate, TString
}
- // cout << "position chosen " << endl;
double locX_NN = outputNN_X[truthIndex];
double locY_NN = outputNN_Y[truthIndex];
@@ -763,7 +759,6 @@ void writeNtuple::Loop( int nParticlesTraining, bool useTrackEstimate, TString
double errY_NN = outputNN_errY[truthIndex];
- // cout << "position and error set " << endl;
// if you want to fill with old clustering insted of NN uncomment this
@@ -781,7 +776,6 @@ void writeNtuple::Loop( int nParticlesTraining, bool useTrackEstimate, TString
double PixDigResPhi = TruthLocX - fOther->PixClusLocXcentroid->at(clus) + fOther->PixClusLocX_LorentzCorrection->at(clus) ;
double PixDigResEta = TruthLocY - fOther->PixClusLocYcentroid->at(clus) ;
- // cout << "digital resolution obtained" << endl;
m_PixDigResPhi->push_back( PixDigResPhi);
m_PixDigResEta->push_back( PixDigResEta);