bool EyeTrackerCalibration::calibrate(const CalibrationData & calibrationData)
{
if(calibrationData.size() == 0)
return false;
size_t totalCalibrationPointsNum = 0;
for(const auto & p: calibrationData)
totalCalibrationPointsNum += p.eyePositions.size();
if(totalCalibrationPointsNum < 10)
return false;
std::vector<cv::Point2d> eyePositions;
std::vector<cv::Point2d> screenPositions;
if(m_dataPreprocessingType == NoPreprocessing)
{
eyePositions.resize(totalCalibrationPointsNum);
screenPositions.resize(totalCalibrationPointsNum);
size_t i = 0;
for(const auto & p: calibrationData)
{
const auto screenPos = p.screenPoint;
for(const auto & eyePos: p.eyePositions)
{
eyePositions[i] = eyePos;
screenPositions[i] = screenPos;
++i;
}
}
}
else if(m_dataPreprocessingType == MeanPoint)
{
eyePositions.resize(calibrationData.size());
screenPositions.resize(calibrationData.size());
size_t i = 0;
for(const auto & p: calibrationData)
{
double mean_x = 0.0;
double mean_y = 0.0;
for(const auto & eyePos: p.eyePositions)
{
mean_x += eyePos.x;
mean_y += eyePos.y;
}
mean_x /= p.eyePositions.size();
mean_y /= p.eyePositions.size();
eyePositions[i] = cv::Point2d(mean_x, mean_y);
screenPositions[i] = p.screenPoint;
i++;
}
}
return estimateParameters(eyePositions, screenPositions);
}
bool processFrame(NUI_Controller* mNui)
{
optimizeDepthMap();
getSphereSizes(mNui);
measureBody(mNui);
estimateParameters();
return true;
}
void SQFitting::estimateInitialParameters(pcl::PointCloud<pcl::PointXYZ>& cloud, SQParameters& sqParams) {
double minError = 1000000;
int maxEigenVector = 0;
int retVal;
for (int i = 0; i < 3; i++) {
retVal = estimateParameters(cloud, sqParams, i);
if (retVal == -1) {
printf("Error: At least 13 data points must support the fit!\n");
}
double error = 0;
error = minProcess.errorFunc(cloud, sqParams);
if (error < minError) {
minError = error;
maxEigenVector = i;
}
}
retVal = estimateParameters(cloud, sqParams, maxEigenVector);
}
void SQFitting::performShapeFitting(const pcl::PointCloud<pcl::PointXYZ>& cloud, SQParameters& initParams, SQParameters& bestParams) {
pcl::PointCloud<pcl::PointXYZ> tempSQCloud;
SQParameters unrefinedParams, refinedParams;
double error, minError = 1.0e13;;
int ret = -1, eigenVect;
int iterations;
// Get best eigenvector
iterations = initParams.min.iterations;
initParams.min.iterations = 20;
for (int i = 0; i < 3; i++) {
ret = estimateParameters(cloud, initParams, i);
if (ret == -1) {
printf("Error: Cannot perform superquadric fitting!\n");
}
error = recoverParameters(cloud, initParams);
if (error < minError) {
minError = error;
eigenVect = i;
}
}
initParams.min.iterations = iterations;
initParams.principalAxis = eigenVect;
ret = estimateParameters(cloud, initParams, eigenVect);
if (ret == -1) {
printf("Error: Cannot perform superquadric fitting!\n");
error = 100000;
} else {
error = recoverParameters(cloud, initParams);
}
initParams.copyTo(bestParams);
}
bool processFrame(xn::DepthGenerator* dpg,xn::UserGenerator* ug,XnUserID userID)
{
if (!dImage)
{
dImage=cvCreateImage(dSize,IPL_DEPTH_16U,1);
uImage=cvCreateImage(dSize,IPL_DEPTH_8U,1);
showImage=cvCreateImage(dSize,IPL_DEPTH_8U,1);
}
cvSetZero(dImage);
cvSetZero(uImage);
if (!convertMetaDataToIpl( dpg,ug,userID)) //Convert Xn Matrices to OpenCV Matrices for easier calculation.
return false;
optimizeDepthMap();
getSphereSizes(dpg,ug,userID);
measureBody(dpg,ug,userID);
estimateParameters();
return true;
}
// estimate the HMM parameters
void MAPEstimator::estimateParameters(MAccumulatorPhysical &mAccumulator, float fPriorKnowledgeWeight) {
for(int i=0 ; i < m_iHMMStates ; ++i) {
bool bData = false;
int iComponents = m_hmmStates[i]->getMixture().getNumberComponents();
Accumulator **accumulators = new Accumulator*[iComponents];
for(int g=0 ; g < iComponents ; ++g) {
MAccumulatorPhysical::iterator it = mAccumulator.find(Accumulator::getPhysicalAccumulatorKey(i,g));
if (it != mAccumulator.end()) {
accumulators[g] = it->second;
bData = true;
} else {
accumulators[g] = NULL;
}
}
// is there data to update the HMM-state
if (bData) {
estimateParameters(m_hmmStates[i],accumulators,fPriorKnowledgeWeight);
}
delete [] accumulators;
}
}
int CExpNet::estimateNodeProb(int nnode, int *parnodes, int numpars, double *psamples, int nsamples) {
int res, i, j, ic, iC;
int *pcats, numcats;
double fdiff, fdiffsum, fsum, *paux;
double *pnodeprob;
PROB_LIST<double>* pProbList;
if(nnode < 0 || nnode >= m_numNodes || !m_loglambdas ||
!psamples || nsamples < 1)
return ERR_CATNET_PARAM;
pProbList = (PROB_LIST<double>*)getNodeProb(nnode);
pnodeprob = pProbList->pProbs;
numcats = m_numCategories[nnode];
if(numpars <= 0 || !parnodes) {
fsum = 0;
for(ic = 0; ic < numcats; ic++) {
fdiffsum = 0;
for(j = 0; j < nsamples; j++) {
fdiff = (psamples[j * m_numNodes + nnode] * m_loglambdas[nnode][numcats+ic] + m_loglambdas[nnode][ic]);
// P(Y_i|X_i=c)
fdiffsum += exp(-fdiff);
}
pnodeprob[ic] = fdiffsum;
fsum += fdiffsum;
}
if(fsum > 0)
fsum = 1/fsum;
for(ic = 0; ic < numcats; ic++)
pnodeprob[ic] *= fsum;
return ERR_CATNET_OK;
}
// at this point m_nBlockSizes == numnodes and m_pBlockSizes are filled
m_parCatSetSize = 1;
for(i = 0; i < numpars; i++) {
m_parCatSetSize *= m_numCategories[parnodes[i]];
}
if(m_pcC)
CATNET_FREE(m_pcC);
m_pcC = 0;
if(m_qcC)
CATNET_FREE(m_qcC);
m_qcC = 0;
// m_pcC returns marginal P(X_{Pa_i})
m_pcC = (double*)CATNET_MALLOC(m_parCatSetSize*sizeof(double));
pcats = (int*)CATNET_MALLOC(m_parCatSetSize*numpars*sizeof(int));
if(!m_pcC || !pcats)
return ERR_CATNET_MEM;
memset(m_pcC, 0, m_parCatSetSize*sizeof(double));
if(!m_pCatnetSamples || m_nCatnetSamples < 1) {
// find the exact joint probability for small sets
res = marginalProb(parnodes, numpars);
if(res != ERR_CATNET_OK) {
CATNET_FREE(pcats);
return res;
}
if(m_margProbSize != m_parCatSetSize) {
CATNET_FREE(pcats);
return ERR_CATNET_PROC;
}
for(ic = 0; ic < m_parCatSetSize; ic++) {
// determine the category indices
fsum = ic;
for(i = 0; i < numpars; i++) {
pcats[ic * numpars + i] = (int)(fsum/m_pBlockSizes[i]);
fsum -= pcats[ic*numpars + i] * m_pBlockSizes[i];
}
m_pcC[ic] = getCatProb(pcats + ic * numpars, numpars);
}
}
else {
if(!m_pBlockSizes || m_nBlockSizes < numpars) {
if(m_pBlockSizes)
CATNET_FREE(m_pBlockSizes);
m_nBlockSizes = numpars;
m_pBlockSizes = (int*) CATNET_MALLOC(m_nBlockSizes * sizeof(int));
}
m_pBlockSizes[numpars - 1] = 1;
for (i = numpars - 2; i >= 0; i--) {
m_pBlockSizes[i] = m_pBlockSizes[i + 1] * m_numCategories[parnodes[i + 1]];
}
for(j = 0; j < m_nCatnetSamples; j++) {
ic = 0;
for (i = 0; i < numpars; i++)
ic += (m_pBlockSizes[i] * m_pCatnetSamples[j * m_numNodes + parnodes[i]]);
m_pcC[ic] += 1;
}
fsum = 1/(double)m_nCatnetSamples;
for(ic = 0; ic < m_parCatSetSize; ic++)
m_pcC[ic] *= fsum;
for(ic = 0; ic < m_parCatSetSize; ic++) {
// determine the category indices
fsum = ic;
for(i = 0; i < numpars; i++) {
pcats[ic * numpars + i] = (int)(fsum/m_pBlockSizes[i]);
fsum -= pcats[ic*numpars + i] * m_pBlockSizes[i];
}
}
}
paux = (double*)CATNET_MALLOC(m_parCatSetSize*sizeof(double));
m_qcC = (double*)CATNET_MALLOC(m_parCatSetSize*nsamples*sizeof(double));
for (j = 0; j < nsamples; j++) {
fsum = 0;
for(ic = 0; ic < m_parCatSetSize; ic++) {
fdiffsum = 0;
for(i = 0; i < numpars; i++) {
fdiffsum += (psamples[j * m_numNodes + parnodes[i]] * m_loglambdas[parnodes[i]][m_numCategories[parnodes[i]]+pcats[ic*numpars + i]] + m_loglambdas[parnodes[i]][pcats[ic*numpars + i]]);
}
paux[ic] = m_pcC[ic]*exp(-fdiffsum);
fsum += paux[ic];
}
if(fsum > 0)
fsum = 1/fsum;
else
fsum = FLT_MAX;
// set P(X_{Pa_i}|Y_{Pa_i}) in m_qcC
for(ic = 0; ic < m_parCatSetSize; ic++)
m_qcC[j*m_parCatSetSize + ic] = fsum*paux[ic];
}
for(iC = 0; iC < m_parCatSetSize; iC++) {
fsum = 0;
for(ic = 0; ic < numcats; ic++) {
fdiffsum = 0;
for(j = 0; j < nsamples; j++) {
fdiff = (psamples[j * m_numNodes + nnode] * m_loglambdas[nnode][numcats+ic] + m_loglambdas[nnode][ic]);
// P(Y_i|X_i=c)P(Pa_i=C|Y_{Pa_i})
fdiffsum += exp(-fdiff)*m_qcC[j*m_parCatSetSize + iC];
}
pnodeprob[iC*numcats + ic] = fdiffsum;
fsum += fdiffsum;
}
if(fsum > 0)
fsum = 1/fsum;
for(ic = 0; ic < numcats; ic++)
pnodeprob[iC*numcats + ic] *= fsum;
}
CATNET_FREE(pcats);
CATNET_FREE(paux);
// we don't need them any more
if(m_pcC)
CATNET_FREE(m_pcC);
m_pcC = 0;
if(m_qcC)
CATNET_FREE(m_qcC);
m_qcC = 0;
// update the betas and sigma
res = findNodeMarginalProb(nnode, psamples, nsamples);
if(res != ERR_CATNET_OK)
return res;
estimateParameters(nnode, psamples, nsamples, m_lambdas[nnode], m_loglambdas[nnode]);
if(m_pc)
CATNET_FREE(m_pc);
m_pc = 0;
if(m_qc)
CATNET_FREE(m_qc);
m_qc = 0;
return ERR_CATNET_OK;
}
int main(int argc, char* argv[])
{
// parameters that you can set.
string infF = "";
string infR = "";
string infPath = "";
string outI = "oIter.txt";
string outP = "oPar.txt";
string outCorr = "oCorr.txt";
string outCnt = "oCnt.txt";
string outPath = "";
string outStub = "";
//bool useemp = false;
double truncLimit = 0.99;
int verbose = 3;
int lambdaD = 147;
int minD = 130;
int maxD = 180;
int sampleSize = -1;
int burnins = 1000;
int iterations = 10000;
int seed = -1;
// Unknowns
double lambdaF[2];
double lambdaR[2];
double pF[2];
double pR[2];
/*
* Read and check input parameters
*/
string errorLine = "usage " +
string(argv[0]) +
" [parameters] \n" +
"\t-iF <forward reads binary file> \n" +
"\t-iR <reverse reads binary file> \n" +
"\t-iPath <path to forward and reverse reads binary files \n" +
" (overrides iR and iF if set)> \n" +
"\t-oPath <outdirectory, where all output files are put. \n" +
"\t NOT created - needs to exists. Defaults to where \n"+
"\t the program is executed from.>\n" +
"\t-oStub <outfile names stub, \n" +
"\t defaults -oIter to <-oStub_>oIter.txt, \n" +
"\t defaults -oPar to <-oStub_>oPar.txt> \n" +
"\t defaults -oCorr to <-oStub_>oCorr.txt> \n" +
"\t defaults -oCnt to <-oStub_>oCnt.txt> \n" +
"\t-oIter <outfile, where to write MCMC parameter\n" +
"\t estimates for each iteration,default generated from -oStub>\n" +
"\t-oPar <parameter-file, where to write MCMC\n" +
"\t final parameter estimates, default generated from -oStub> \n" +
/*
"\t-oCorr <out-file, lists correlation coefficients between forward\n" +
"\t and reverse reads in [mind,maxd] \n" +
"\t default generated from -oStub> \n" +
*/
"\t-oCnt <out-file, lists forward and reverse read count frequencies\n" +
"\t default generated from -oStub> \n" +
"\t-trunc <truncation limit in (0,1], default 0.99.> \n" +
"\t-size <sample size, default all observations.> \n" +
"\t-burnin <number of MCMC burnin iterations,\n" +
"\t default 1000.> \n" +
"\t-iter <number of MCMC iterations\n" +
"\t (non-burnin iterations), default 10000.> \n" +
"\t-seed <set seed to random number generator.> \n" +
"\t-ld <distance-lambda, default 147 bp.> \n" +
"\t-mind <min distance, default 130 bp.> \n" +
"\t-maxd <max distance, default 180 bp.> \n" +
/*
"\t-useemp <toggle use of data-driven distance distribution, or poisson\n" +
"\t around distance-lambda. default off.>\n" +
*/
"\t-v <verbose level 0-3. default 2>";
bool fail = false;
string failmessage = "";
for (int i = 1; i < argc; i++)
{
if(strcmp(argv[i],"-iF") == 0)
infF.assign(argv[++i]);
else if(strcmp(argv[i],"-iR") == 0)
infR.assign(argv[++i]);
else if(strcmp(argv[i],"-iPath") == 0)
infPath.assign(argv[++i]);
else if(strcmp(argv[i],"-oPath") == 0)
outPath.assign(argv[++i]);
else if(strcmp(argv[i],"-oStub") == 0)
outStub.assign(argv[++i]);
else if(strcmp(argv[i],"-oIter") == 0)
outI.assign(argv[++i]);
else if(strcmp(argv[i],"-oPar") == 0)
outP.assign(argv[++i]);
/*
else if(strcmp(argv[i],"-oCorr") == 0)
outCorr.assign(argv[++i]);
*/
else if(strcmp(argv[i],"-oCnt") == 0)
outCnt.assign(argv[++i]);
/*
else if (strcmp(argv[i],"-useemp") == 0)
useemp = true;
*/
else if (strcmp(argv[i],"-v") == 0)
verbose = atoi(argv[++i]);
else if (strcmp(argv[i],"-seed") == 0)
seed = atoi(argv[++i]);
else if (strcmp(argv[i],"-trunc") == 0)
truncLimit = atof(argv[++i]);
else if (strcmp(argv[i],"-size") == 0)
sampleSize = atoi(argv[++i]);
else if (strcmp(argv[i],"-burnin") == 0)
burnins = atoi(argv[++i]);
else if (strcmp(argv[i],"-iter") == 0)
iterations = atoi(argv[++i]);
else if (strcmp(argv[i],"-ld") == 0)
lambdaD = atoi(argv[++i]);
else if (strcmp(argv[i],"-mind") == 0)
minD = atoi(argv[++i]);
else if (strcmp(argv[i],"-maxd") == 0)
maxD = atoi(argv[++i]);
else
{
failmessage.assign("Unknown argument: ");
failmessage.append(argv[i]);
failmessage.append("\n");
fail = true;
}
}
if (truncLimit <= 0 || truncLimit > 1)
{
failmessage.append("-trunc value does not make sense.\n");
fail = true;
}
bool infPathSpec = false;
if (strcmp(infPath.c_str(), "") != 0)
{
infPathSpec = true;
DIR *d = opendir(infPath.c_str());
if(d)
{
closedir(d);
}
else
{
failmessage.append("-iPath does not exist.\n");
fail = true;
}
}
if (strcmp(infF.c_str(), "") == 0)
{
if (!infPathSpec)
{
failmessage.append("-iF or -iPath must be specified.\n");
fail = true;
}
}
if (strcmp(infR.c_str(), "") == 0)
{
if (!infPathSpec)
{
failmessage.append("-iR or -iPath must be specified.\n");
fail = true;
}
}
if (strcmp(outI.c_str(), "") == 0)
{
failmessage.append("invalid -oIter.\n");
fail = true;
}
if (strcmp(outP.c_str(), "") == 0)
{
failmessage.append("invalid -oPar.\n");
fail = true;
}
if (strcmp(outCorr.c_str(), "") == 0)
{
failmessage.append("invalid -oCorr.\n");
fail = true;
}
if (strcmp(outCnt.c_str(), "") == 0)
{
failmessage.append("invalid -oCnt.\n");
fail = true;
}
if (strcmp(outPath.c_str(), "") != 0)
{
DIR* d = opendir(outPath.c_str());
if(d)
{
closedir(d);
}
else
{
failmessage.append("-oPath does not exist.\n");
fail = true;
}
}
int infFCnt = 1;
if (infPathSpec)
{
infFCnt = countFiles(infPath);
if (infFCnt < 1)
{
failmessage.append("ERROR: infile path \"");
failmessage.append(infPath.c_str());
failmessage.append("\" does not contain a positive number of F and R binary files, aborting.\n");
fail = true;
}
}
if (fail)
{
cerr << endl << failmessage.c_str() << endl << errorLine << endl;
return(-1);
}
if(strcmp(outStub.c_str(),"") != 0)
{
outI = outStub + "_" + outI;
outP = outStub + "_" + outP;
outCorr = outStub + "_" + outCorr;
outCnt = outStub + "_" + outCnt;
}
if(strcmp(outPath.c_str(),"") != 0)
{
outI = outPath + outI;
outP = outPath + outP;
outCorr = outPath + outCorr;
outCnt = outPath + outCnt;
}
if (seed < -1)
seed = -1;
ifstream iff[infFCnt];
ifstream ifr[infFCnt];
string fileNames[infFCnt];
if (infPathSpec)
{
if (openFiles(infPath, iff, ifr, fileNames) != infFCnt)
{
failmessage.append("ERROR: all files in \"");
failmessage.append(infPath.c_str());
failmessage.append("\" could not be opened, aborting.\n");
fail = true;
}
}
else
{
iff[0].open(infF.c_str(),ios::binary);
ifr[0].open(infR.c_str(),ios::binary);
if (iff[0].fail())
{
failmessage.append("ERROR: Forward reads binary file \"");
failmessage.append(infF.c_str());
failmessage.append("\" could not be opened, aborting.\n");
fail = true;
}
if (ifr[0].fail())
{
failmessage.append("ERROR: Reverse reads binary file \"");
failmessage.append(infR.c_str());
failmessage.append("\" could not be opened, aborting.\n");
fail = true;
}
}
/*
iff.open(infF.c_str(),ios::binary);
if (iff.fail())
{
failmessage.append("ERROR: Forward reads binary file \"");
failmessage.append(infF.c_str());
failmessage.append("\" could not be opened, aborting.\n");
fail = true;
}
ifstream ifr;
ifr.open(infR.c_str(),ios::binary);
if (ifr.fail())
{
failmessage.append("ERROR: Reverse reads binary file \"");
failmessage.append(infR.c_str());
failmessage.append("\" could not be opened, aborting.\n");
fail = true;
}
*/
ofstream ofi;
ofi.open(outI.c_str(),ios::trunc);
if (ofi.fail())
{
failmessage.append("ERROR: Output file \"");
failmessage.append(outI.c_str());
failmessage.append("\" could not be created, aborting.\n");
fail = true;
}
ofstream ofc;
ofc.open(outCorr.c_str(),ios::trunc);
if (ofc.fail())
{
failmessage.append("ERROR: Output file \"");
failmessage.append(outCorr.c_str());
failmessage.append("\" could not be created, aborting.\n");
fail = true;
}
ofstream ofcnt;
ofcnt.open(outCnt.c_str(),ios::trunc);
if (ofcnt.fail())
{
failmessage.append("ERROR: Output file \"");
failmessage.append(outCnt.c_str());
failmessage.append("\" could not be created, aborting.\n");
fail = true;
}
ofstream ofp;
int truncValF,truncValR;
int estMinD = minD, estMaxD = maxD, estLambdaD = lambdaD;
double* distDens;
vector<string> distDensV;
ofp.open(outP.c_str(),ios::trunc);
if (ofp.fail())
{
failmessage.append("ERROR: Paramater file \"");
failmessage.append(outP.c_str());
failmessage.append("\" could not be created, aborting.\n");
fail = true;
}
if (fail)
{
cerr << endl << failmessage.c_str() << endl << errorLine << endl;
return(-1);
}
ofi << "! Command:";
for (int i = 0; i < argc; i++)
ofi << " " << argv[i];
ofi << endl;
vlevel = verbose;
/*
* Check file lengths
*/
int minPos[infFCnt], maxPos[infFCnt], minF[infFCnt], maxF[infFCnt], minR[infFCnt], maxR[infFCnt];
checkFileLengths(iff, ifr, minPos, maxPos, minR, minF, maxF, maxR, infFCnt);
/*
* Estimate parameters
*/
ofp << "! Command:";
for (int i = 0; i < argc; i++)
ofp << " " << argv[i];
ofp << endl;
vcerr(1) << "*** Identifying truncation limits and data distributions ***" << endl;
calculateTruncVal(iff,ifr,&ofcnt,
&truncValF, &truncValR, truncLimit,
minPos, maxPos,
minR, minF,
maxF, maxR, infFCnt);
ofcnt.close();
distDens = new double[maxD-minD+1];
if (infFCnt == 1)
{
estimateDistance(&iff[0],&ifr[0],&ofc,
&estMinD, &estMaxD, &estLambdaD,
minD, maxD,
minPos[0], maxPos[0],
minR[0], minF[0],
maxF[0], maxR[0],
truncValF, truncValR,distDens,0.3);
ofc.close();
}
/*
if (useemp)
{
vcerr(2) << "\t* Estimating forward-reverse distance" << endl;
estimateDistance(&iff,&ifr,&ofc,
&estMinD, &estMaxD, &estLambdaD,
minD, maxD,
minPos, maxPos,
minR, minF,
maxF, maxR,
truncValF, truncValR,distDens,0.3);
ofc.close();
}
else
{
*/
for (int dist = minD; dist <= maxD; dist++)
distDens[dist-minD] = gsl_ran_poisson_pdf(dist, lambdaD);
/*
}
*/
vcerr(1) << "*** Parameter estimation ***" << endl;
if (estimateParameters(iff,ifr,&ofi,
estMinD,estMaxD,estLambdaD,
pF, pR, lambdaF, lambdaR,
sampleSize,burnins,iterations,
seed,
minPos, maxPos,
minR, minF,
maxF, maxR,
truncValF, truncValR, infFCnt) < 0)
{
cerr << "ERROR: estimateParameters failed, aborting." << endl;
for (int i=0; i<infFCnt; i++)
{
iff[i].close();
ifr[i].close();
}
ofi.close();
ofp.close();
delete[] distDens;
return(-1);
}
else
{
if (writeParameters(&ofp, pF, pR, lambdaF, lambdaR, lambdaD,
truncValF, truncValR,
minD, maxD, estLambdaD,
estMinD, estMaxD, distDens) < 0)
{
cerr << "WARNING: could not write parameters to file, skipping." << endl;
}
}
ofi.close();
ofp.close();
for (int i=0; i<infFCnt; i++)
{
iff[i].close();
ifr[i].close();
}
delete[] distDens;
vcerr(3) << setprecision(3);
vcerr(3) << endl << "\t\tParameter estimates:" << endl;
vcerr(3) << "\t\tpF: S = " << pF[0] << " NotS = " << pF[1] << endl;
vcerr(3) << "\t\tpR: E = " << pR[0] << " NotE = " << pR[1] << endl;
vcerr(3) << "\t\tlambdaF: S = " << lambdaF[0] << " NotS = " << lambdaF[1] << endl;
vcerr(3) << "\t\tlambdaR: E = " << lambdaR[0] << " NotE = " << lambdaR[1] << endl;
vcerr(3) << "\t\testMinD: = " << estMinD << " estMaxd = " << estMaxD << endl;
}
