void SOGP::reduceBasisVectorSet(unsigned int index) {
unsigned int end = this->current_size-1;
VectorXd zero_vector = VectorXd::Zero(this->current_size);
VectorXd alpha_star = this->alpha.row(index);
VectorXd last_item = this->alpha.row(end);
alpha.block(index,0,1,this->output_dim) = last_item.transpose();
alpha.block(end,0,1, this->output_dim) = VectorXd::Zero(this->output_dim).transpose();
double cstar = this->C(index, index);
VectorXd Cstar = this->C.col(index);
Cstar(index) = Cstar(end);
Cstar.conservativeResize(end);
VectorXd Crep = C.col(end);
Crep(index) = Crep(end);
C.block(index, 0, 1, this->current_size) = Crep.transpose();
C.block(0, index, this->current_size, 1) = Crep;
C.block(end, 0, 1, this->current_size) = zero_vector.transpose();
C.block(0, end, this->current_size,1) = zero_vector;
double qstar = this->Q(index, index);
VectorXd Qstar = this->Q.col(index);
Qstar(index) = Qstar(end);
Qstar.conservativeResize(end);
VectorXd Qrep = Q.col(end);
Qrep(index) = Qrep(end);
Q.block(index, 0, 1, this->current_size) = Qrep.transpose();
Q.block(0, index, this->current_size, 1) = Qrep;
Q.block(end, 0, 1, this->current_size) = zero_vector.transpose();
Q.block(0, end, this->current_size,1) = zero_vector;
VectorXd qc = (Qstar + Cstar)/(qstar + cstar);
for (unsigned int i=0; i<this->output_dim; i++) {
VectorXd diffAlpha = alpha.block(0,i,end,1) - alpha_star(i)*qc;
alpha.block(0,i,end,1) = diffAlpha;
}
MatrixXd oldC = C.block(0,0, end, end);
C.block(0,0, end,end) = oldC + (Qstar*Qstar.transpose())/qstar -
((Qstar + Cstar)*((Qstar + Cstar).transpose()))/(qstar+cstar);
MatrixXd oldQ = Q.block(0,0,end,end);
Q.block(0,0, end, end) = oldQ - (Qstar*Qstar.transpose())/qstar;
this->basis_vectors[index] = this->basis_vectors[end];
this->basis_vectors.pop_back();
this->current_size = end;
}
void removeElem(VectorXd &vec, const int elemToRemove)
{
int elems = vec.size() - 1;
if (elemToRemove <= elems) {
// Copy the last element into the element to remove
if (elemToRemove < elems) {
vec(elemToRemove) = vec(elems);
}
vec.conservativeResize(elems);
}
}
// phi contains mu1, mu2, rho
// prior contains mu0, T0 and S0
double bbivF(VectorXd eps, VectorXd phi, List prior) {
// BVN density in terms of rho
const double rho=phi[2], rho2=pow(rho, 2.);
MatrixXd T;
T.setConstant(2, 2, pow(1.-rho2, -1.));
T(0, 1) *= -rho;
T(1, 0) *= -rho;
VectorXd mu=phi;
mu.conservativeResize(2);
eps.conservativeResize(2); // semi
eps -= mu;
return pow(1.-rho2, -0.5)*exp(-0.5*(eps.transpose()*T*eps)[0]);
}
void addConstraint(const measurement& _meas)
{
t_managing_ = clock();
assert(_meas.jacobians.size() == _meas.nodes_idx.size());
assert(_meas.error.size() == _meas.dim);
n_measurements++;
measurements_.push_back(_meas);
measurements_.back().location = A_.rows();
// Resize problem
A_.conservativeResize(A_.rows() + _meas.dim, A_.cols());
b_.conservativeResize(b_.size() + _meas.dim);
A_nodes_.conservativeResize(n_measurements,n_nodes_);
// ADD MEASUREMENTS
first_ordered_node_ = n_nodes_;
for (unsigned int j = 0; j < _meas.nodes_idx.size(); j++)
{
assert(acc_node_permutation_.indices()(_meas.nodes_idx.at(j)) == nodes_.at(_meas.nodes_idx.at(j)).order);
int ordered_node = nodes_.at(_meas.nodes_idx.at(j)).order;//acc_permutation_nodes_.indices()(_nodes_idx.at(j));
addSparseBlock(_meas.jacobians.at(j), A_, A_.rows()-_meas.dim, nodes_.at(_meas.nodes_idx.at(j)).location);
A_nodes_.coeffRef(A_nodes_.rows()-1, ordered_node) = 1;
assert(_meas.jacobians.at(j).cols() == nodes_.at(_meas.nodes_idx.at(j)).dim);
assert(_meas.jacobians.at(j).rows() == _meas.dim);
// store minimum ordered node
if (first_ordered_node_ > ordered_node)
first_ordered_node_ = ordered_node;
}
// error
b_.tail(_meas.dim) = _meas.error;
time_managing_ += ((double) clock() - t_managing_) / CLOCKS_PER_SEC;
}
void add_state_unit(const int node_dim, const int node_idx)
{
t_managing_ = clock();
n_nodes_++;
nodes_.push_back(node(node_idx, node_dim, x_incr_.size(), n_nodes_-1));
// Resize accumulated permutations
augment_permutation(acc_node_permutation_, n_nodes_);
// Resize state
x_incr_.conservativeResize(x_incr_.size() + node_dim);
// Resize problem
A_.conservativeResize(A_.rows(), A_.cols() + node_dim);
R_.conservativeResize(R_.cols() + node_dim, R_.cols() + node_dim);
//A_nodes_.conservativeResize(n_measurements, n_nodes); // not necessary
time_managing_ += ((double) clock() - t_managing_) / CLOCKS_PER_SEC;
}
VectorXd bbivP0(MatrixXd A, List prior, VectorXd phi) {
const int n=A.rows(); //, rho_MH=as<int>(prior["rho_MH"]);
#ifdef DEBUG_NEAL8
Rcout << "\npre:" << phi;
#endif
/* this Gibbs conditional is WRONG; see semi block below
RowVectorXd mu=phi;
mu.conservativeResize(2);
MatrixXd D(n, 2);
for(int i=0; i<n; ++i) D.row(i) = eps.row(i)-mu;
MatrixXd DtD=D.transpose()*D;
double rho=phi[2], zeta=corrMH(DtD(0, 0)+DtD(1, 1), DtD(0, 1), n, JKB(rho));
rho=JKB_inverse(zeta);
*/
MatrixXd eps=A.block(0, 0, n, 2); // semi
const double rho=phi[2], zeta=JKB(rho); // semi
Vector2d mean_eps=mean(eps).transpose(), mu0=as< Map<VectorXd> >(prior["mu0"]);
Matrix2d T0=as< Map<MatrixXd> >(prior["T0"]), Tj, Sigma, Tprec;
Tj << 1., -rho, -rho, 1.;
Tj *= (double(n)/(1.-pow(rho, 2.)));
Tprec=T0+Tj;
Sigma=Tprec.inverse();
Vector2d mean_mu=Sigma*(T0*mu0+Tj*mean_eps);
phi=rnorm2d(mean_mu, Sigma);
phi.conservativeResize(3);
/* semi block begins */
const double beta=as<double>(prior["beta"]);
const VectorXd t=A.col(2), y=A.col(3),
gamma=as< Map<VectorXd> >(prior["gamma"]),
delta=as< Map<VectorXd> >(prior["delta"]),
eta =as< Map<VectorXd> >(prior["eta"]);
const int p=gamma.size(), q=delta.size();
MatrixXd D(n, 2), X=A.block(0, 4, n, p), Z=A.block(0, p+4, n, q);
D.col(0).setConstant(phi[0]);
D.col(1).setConstant(phi[1]);
D.col(0) += (X*gamma + Z*delta); //mutmargin
D.col(1) += (beta*D.col(0)+X*eta);//muymargin
D.col(0) = t - D.col(0); //tdev=t-mutmargin
D.col(1) = y - D.col(1) - beta*D.col(0);//ycondtdev=y-muymargin-b*tdev
Matrix2d SSCP=D.transpose()*D;
phi[2]=corrMN(SSCP(0, 0)+SSCP(1, 1), SSCP(0, 1), n);
// #ifdef CORRBETAMH
// phi[2]=corrBetaMH(SSCP(0, 0)+SSCP(1, 1), SSCP(0, 1), n, rho);
// #else
// phi[2]=JKB_inverse(corrMH(SSCP(0, 0)+SSCP(1, 1), SSCP(0, 1), n, zeta, rho_MH));
// #endif
/* semi block ends */
#ifdef DEBUG_NEAL8
Rcout << "\npost:" << phi << "\nbeta:" << beta
<< "\ngamma:" << gamma << "\ndelta:" << delta << "\neta:" << eta
<< "\nn:" << n << "\nA:\n" << A << '\n';
#endif
return phi;
}
void SOGP::train(const VectorXd &state, const VectorXd &output) {
//check if we have initialised the system
if (!this->initialized) {
throw OTLException("SOGP not yet initialised");
}
double kstar = this->kernel->eval(state);
//change the output format if this is a classification problem
VectorXd mod_output;
if (this->problem_type == SOGP::CLASSIFICATION) {
mod_output = VectorXd::Zero(this->output_dim);
for (unsigned int i=0; i<this->output_dim; i++) {
mod_output(i) = -1;
}
mod_output(output(0)) = 1;
} else {
mod_output = output;
}
//we are just starting.
if (this->current_size == 0) {
this->alpha.block(0,0,1, this->output_dim) = (mod_output.array() / (kstar + this->noise)).transpose();
this->C.block(0,0,1,1) = VectorXd::Ones(1)*-1/(kstar + this->noise);
this->Q.block(0,0,1,1) = VectorXd::Ones(1)*1/(kstar);
this->basis_vectors.push_back(state);
this->current_size++;
return;
}
//Test if this is a "novel" state
VectorXd k;
this->kernel->eval(state, this->basis_vectors, k);
//cache Ck
VectorXd Ck = this->C.block(0,0, this->current_size, this->current_size)*k;
VectorXd m = k.transpose()*this->alpha.block(0,0,this->current_size, this->output_dim);
double s2 = kstar + (k.dot(Ck));
if (s2 < 1e-12) {
//std::cout << "s2: " << s2 << std::endl;
s2 = 1e-12;
}
double r = 0.0;
VectorXd q;
if (this->problem_type == SOGP::REGRESSION) {
r = -1.0/(s2 + this->noise);
q = (mod_output - m)*(-r);
} else if (this->problem_type == SOGP::CLASSIFICATION) {
double sx2 = this->noise + s2;
double sx = sqrt(sx2);
VectorXd z = VectorXd(this->output_dim);
VectorXd Erfz = VectorXd(this->output_dim);
for (unsigned int i=0; i<this->output_dim; i++) {
z(i) = mod_output(i) * m(i) / sx;
Erfz(i) = stdnormcdf(z(i));
//dErfz(i) = 1.0/sqrt(2*M_PI)*exp(-(z(i)*z(i))/2.0);
//dErfz2(i) = dErfz(i)*(-z(i));
}
/*
TO CONNTINUE
Erfz = Erf(z);
dErfz = 1.0/sqrt(2*pi)*exp(-(z.^2)/2);
dErfz2 = dErfz.*(-z);
q = y/sx * (dErfz/Erfz);
r = (1/sx2)*(dErfz2/dErfz - (dErfz/Erfz)^2);
*/
} else {
throw OTL::OTLException("Whoops! My problem type is wrong. How did this happen?");
}
VectorXd ehat = this->Q.block(0,0, this->current_size, this->current_size)*k;
double gamma = kstar - k.dot(ehat);
double eta = 1.0/(1.0 + gamma*r);
if (gamma < 1e-12) {
gamma = 0.0;
}
if (gamma >= this->epsilon*kstar) {
//perform a full update
VectorXd s = Ck;
s.conservativeResize(this->current_size + 1);
s(this->current_size) = 1;
//update Q (inverse of C)
ehat.conservativeResize(this->current_size+1);
ehat(this->current_size) = -1;
MatrixXd diffQ = Q.block(0,0,this->current_size+1, this->current_size+1)
+ (ehat*ehat.transpose())*(1.0/gamma);
Q.block(0,0,this->current_size+1, this->current_size+1) = diffQ;
//update alpha
MatrixXd diffAlpha = alpha.block(0,0, this->current_size+1, this->output_dim)
+ (s*q.transpose());
alpha.block(0,0, this->current_size+1, this->output_dim) = diffAlpha;
//update C
MatrixXd diffC = C.block(0,0, this->current_size+1, this->current_size+1)
+ r*(s*s.transpose());
C.block(0,0, this->current_size+1, this->current_size+1) = diffC;
//add to basis vectors
this->basis_vectors.push_back(state);
//increment current size
this->current_size++;
} else {
//perform a sparse update
VectorXd s = Ck + ehat;
//update alpha
MatrixXd diffAlpha = alpha.block(0,0, this->current_size, this->output_dim)
+ s*((q*eta).transpose());
alpha.block(0,0, this->current_size, this->output_dim) = diffAlpha;
//update C
MatrixXd diffC = C.block(0,0, this->current_size, this->current_size) +
r*eta*(s*s.transpose());
C.block(0,0, this->current_size, this->current_size) = diffC;
}
//check if we need to reduce size
if (this->basis_vectors.size() > this->capacity) {
//std::cout << "Reduction!" << std::endl;
double min_val = (alpha.row(0)).squaredNorm()/(Q(0,0) + C(0,0));
unsigned int min_index = 0;
for (unsigned int i=1; i<this->basis_vectors.size(); i++) {
double scorei = (alpha.row(i)).squaredNorm()/(Q(i,i) + C(i,i));
if (scorei < min_val) {
min_val = scorei;
min_index = i;
}
}
this->reduceBasisVectorSet(min_index);
}
return;
}
MatrixXd MNEInverseOperator::cluster_kernel(const AnnotationSet &p_AnnotationSet, qint32 p_iClusterSize, MatrixXd& p_D) const
{
MatrixXd p_outMT = this->m_K.transpose();
QList<MNEClusterInfo> t_qListMNEClusterInfo;
MNEClusterInfo t_MNEClusterInfo;
t_qListMNEClusterInfo.append(t_MNEClusterInfo);
t_qListMNEClusterInfo.append(t_MNEClusterInfo);
//
// Check consisty
//
if(this->isFixedOrient())
{
printf("Error: Fixed orientation not implemented jet!\n");
return p_outMT;
}
// qDebug() << "p_outMT" << p_outMT.rows() << "x" << p_outMT.cols();
// MatrixXd t_G_Whitened(0,0);
// bool t_bUseWhitened = false;
// //
// //Whiten gain matrix before clustering -> cause diffenerent units Magnetometer, Gradiometer and EEG
// //
// if(!p_pNoise_cov.isEmpty() && !p_pInfo.isEmpty())
// {
// FiffInfo p_outFwdInfo;
// FiffCov p_outNoiseCov;
// MatrixXd p_outWhitener;
// qint32 p_outNumNonZero;
// //do whitening with noise cov
// this->prepare_forward(p_pInfo, p_pNoise_cov, false, p_outFwdInfo, t_G_Whitened, p_outNoiseCov, p_outWhitener, p_outNumNonZero);
// printf("\tWhitening the forward solution.\n");
// t_G_Whitened = p_outWhitener*t_G_Whitened;
// t_bUseWhitened = true;
// }
//
// Assemble input data
//
qint32 count;
qint32 offset;
MatrixXd t_MT_new;
for(qint32 h = 0; h < this->src.size(); ++h )
{
count = 0;
offset = 0;
// Offset for continuous indexing;
if(h > 0)
for(qint32 j = 0; j < h; ++j)
offset += this->src[j].nuse;
if(h == 0)
printf("Cluster Left Hemisphere\n");
else
printf("Cluster Right Hemisphere\n");
Colortable t_CurrentColorTable = p_AnnotationSet[h].getColortable();
VectorXi label_ids = t_CurrentColorTable.getLabelIds();
// Get label ids for every vertex
VectorXi vertno_labeled = VectorXi::Zero(this->src[h].vertno.rows());
//ToDo make this more universal -> using Label instead of annotations - obsolete when using Labels
for(qint32 i = 0; i < vertno_labeled.rows(); ++i)
vertno_labeled[i] = p_AnnotationSet[h].getLabelIds()[this->src[h].vertno[i]];
//Qt Concurrent List
QList<RegionMT> m_qListRegionMTIn;
//
// Generate cluster input data
//
for (qint32 i = 0; i < label_ids.rows(); ++i)
{
if (label_ids[i] != 0)
{
QString curr_name = t_CurrentColorTable.struct_names[i];//obj.label2AtlasName(label(i));
printf("\tCluster %d / %li %s...", i+1, label_ids.rows(), curr_name.toUtf8().constData());
//
// Get source space indeces
//
VectorXi idcs = VectorXi::Zero(vertno_labeled.rows());
qint32 c = 0;
//Select ROIs //change this use label info with a hash tabel
for(qint32 j = 0; j < vertno_labeled.rows(); ++j)
{
if(vertno_labeled[j] == label_ids[i])
{
idcs[c] = j;
++c;
}
}
idcs.conservativeResize(c);
//get selected MT
MatrixXd t_MT(p_outMT.rows(), idcs.rows()*3);
for(qint32 j = 0; j < idcs.rows(); ++j)
t_MT.block(0, j*3, t_MT.rows(), 3) = p_outMT.block(0, (idcs[j]+offset)*3, t_MT.rows(), 3);
qint32 nSens = t_MT.rows();
qint32 nSources = t_MT.cols()/3;
if (nSources > 0)
{
RegionMT t_sensMT;
t_sensMT.idcs = idcs;
t_sensMT.iLabelIdxIn = i;
t_sensMT.nClusters = ceil((double)nSources/(double)p_iClusterSize);
t_sensMT.matRoiMTOrig = t_MT;
printf("%d Cluster(s)... ", t_sensMT.nClusters);
// Reshape Input data -> sources rows; sensors columns
t_sensMT.matRoiMT = MatrixXd(t_MT.cols()/3, 3*nSens);
for(qint32 j = 0; j < nSens; ++j)
for(qint32 k = 0; k < t_sensMT.matRoiMT.rows(); ++k)
t_sensMT.matRoiMT.block(k,j*3,1,3) = t_MT.block(j,k*3,1,3);
m_qListRegionMTIn.append(t_sensMT);
printf("[added]\n");
}
else
{
printf("failed! Label contains no sources.\n");
}
}
}
//
// Calculate clusters
//
printf("Clustering... ");
QFuture< RegionMTOut > res;
res = QtConcurrent::mapped(m_qListRegionMTIn, &RegionMT::cluster);
res.waitForFinished();
//
// Assign results
//
MatrixXd t_MT_partial;
qint32 nClusters;
qint32 nSens;
QList<RegionMT>::const_iterator itIn;
itIn = m_qListRegionMTIn.begin();
QFuture<RegionMTOut>::const_iterator itOut;
for (itOut = res.constBegin(); itOut != res.constEnd(); ++itOut)
{
nClusters = itOut->ctrs.rows();
nSens = itOut->ctrs.cols()/3;
t_MT_partial = MatrixXd::Zero(nSens, nClusters*3);
// std::cout << "Number of Clusters: " << nClusters << " x " << nSens << std::endl;//itOut->iLabelIdcsOut << std::endl;
//
// Assign the centroid for each cluster to the partial G
//
//ToDo change this use indeces found with whitened data
for(qint32 j = 0; j < nSens; ++j)
for(qint32 k = 0; k < nClusters; ++k)
t_MT_partial.block(j, k*3, 1, 3) = itOut->ctrs.block(k,j*3,1,3);
//
// Get cluster indizes and its distances to the centroid
//
for(qint32 j = 0; j < nClusters; ++j)
{
VectorXi clusterIdcs = VectorXi::Zero(itOut->roiIdx.rows());
VectorXd clusterDistance = VectorXd::Zero(itOut->roiIdx.rows());
qint32 nClusterIdcs = 0;
for(qint32 k = 0; k < itOut->roiIdx.rows(); ++k)
{
if(itOut->roiIdx[k] == j)
{
clusterIdcs[nClusterIdcs] = itIn->idcs[k];
// qint32 offset = h == 0 ? 0 : this->src[0].nuse;
// Q_UNUSED(offset)
clusterDistance[nClusterIdcs] = itOut->D(k,j);
++nClusterIdcs;
}
}
clusterIdcs.conservativeResize(nClusterIdcs);
clusterDistance.conservativeResize(nClusterIdcs);
VectorXi clusterVertnos = VectorXi::Zero(clusterIdcs.size());
for(qint32 k = 0; k < clusterVertnos.size(); ++k)
clusterVertnos(k) = this->src[h].vertno[clusterIdcs(k)];
t_qListMNEClusterInfo[h].clusterVertnos.append(clusterVertnos);
}
//
// Assign partial G to new LeadField
//
if(t_MT_partial.rows() > 0 && t_MT_partial.cols() > 0)
{
t_MT_new.conservativeResize(t_MT_partial.rows(), t_MT_new.cols() + t_MT_partial.cols());
t_MT_new.block(0, t_MT_new.cols() - t_MT_partial.cols(), t_MT_new.rows(), t_MT_partial.cols()) = t_MT_partial;
// Map the centroids to the closest rr
for(qint32 k = 0; k < nClusters; ++k)
{
qint32 j = 0;
double sqec = sqrt((itIn->matRoiMTOrig.block(0, j*3, itIn->matRoiMTOrig.rows(), 3) - t_MT_partial.block(0, k*3, t_MT_partial.rows(), 3)).array().pow(2).sum());
double sqec_min = sqec;
qint32 j_min = 0;
// MatrixXd matGainDiff;
for(qint32 j = 1; j < itIn->idcs.rows(); ++j)
{
sqec = sqrt((itIn->matRoiMTOrig.block(0, j*3, itIn->matRoiMTOrig.rows(), 3) - t_MT_partial.block(0, k*3, t_MT_partial.rows(), 3)).array().pow(2).sum());
if(sqec < sqec_min)
{
sqec_min = sqec;
j_min = j;
// matGainDiff = itIn->matRoiGOrig.block(0, j*3, itIn->matRoiGOrig.rows(), 3) - t_G_partial.block(0, k*3, t_G_partial.rows(), 3);
}
}
// qListGainDist.append(matGainDiff);
// Take the closest coordinates
// qint32 sel_idx = itIn->idcs[j_min];
// Q_UNUSED(sel_idx)
// //vertices
// std::cout << this->src[h].vertno[sel_idx] << ", ";
++count;
}
}
++itIn;
}
printf("[done]\n");
}
//
// Cluster operator D (sources x clusters)
//
qint32 totalNumOfClust = 0;
for (qint32 h = 0; h < 2; ++h)
totalNumOfClust += t_qListMNEClusterInfo[h].clusterVertnos.size();
if(this->isFixedOrient())
p_D = MatrixXd::Zero(p_outMT.cols(), totalNumOfClust);
else
p_D = MatrixXd::Zero(p_outMT.cols(), totalNumOfClust*3);
QList<VectorXi> t_vertnos = this->src.get_vertno();
// qDebug() << "Size: " << t_vertnos[0].size() << t_vertnos[1].size();
// qDebug() << "this->sol->data.cols(): " << this->sol->data.cols();
qint32 currentCluster = 0;
for (qint32 h = 0; h < 2; ++h)
{
int hemiOffset = h == 0 ? 0 : t_vertnos[0].size();
for(qint32 i = 0; i < t_qListMNEClusterInfo[h].clusterVertnos.size(); ++i)
{
VectorXi idx_sel;
MNEMath::intersect(t_vertnos[h], t_qListMNEClusterInfo[h].clusterVertnos[i], idx_sel);
// std::cout << "\nVertnos:\n" << t_vertnos[h] << std::endl;
// std::cout << "clusterVertnos[i]:\n" << t_qListMNEClusterInfo[h].clusterVertnos[i] << std::endl;
idx_sel.array() += hemiOffset;
// std::cout << "idx_sel]:\n" << idx_sel << std::endl;
double selectWeight = 1.0/idx_sel.size();
if(this->isFixedOrient())
{
for(qint32 j = 0; j < idx_sel.size(); ++j)
p_D.col(currentCluster)[idx_sel(j)] = selectWeight;
}
else
{
qint32 clustOffset = currentCluster*3;
for(qint32 j = 0; j < idx_sel.size(); ++j)
{
qint32 idx_sel_Offset = idx_sel(j)*3;
//x
p_D(idx_sel_Offset,clustOffset) = selectWeight;
//y
p_D(idx_sel_Offset+1, clustOffset+1) = selectWeight;
//z
p_D(idx_sel_Offset+2, clustOffset+2) = selectWeight;
}
}
++currentCluster;
}
}
// std::cout << "D:\n" << D.row(0) << std::endl << D.row(1) << std::endl << D.row(2) << std::endl << D.row(3) << std::endl << D.row(4) << std::endl << D.row(5) << std::endl;
//
// Put it all together
//
p_outMT = t_MT_new;
return p_outMT;
}
