void omxCallGREMLFitFunction(omxFitFunction *oo, int want, FitContext *fc){
if (want & (FF_COMPUTE_PREOPTIMIZE)) return;
//Recompute Expectation:
omxExpectation* expectation = oo->expectation;
omxExpectationCompute(expectation, NULL);
omxGREMLFitState *gff = (omxGREMLFitState*)oo->argStruct; //<--Cast generic omxFitFunction to omxGREMLFitState
//Ensure that the pointer in the GREML fitfunction is directed at the right FreeVarGroup
//(not necessary for most compute plans):
if(fc && gff->varGroup != fc->varGroup){
gff->buildParamMap(fc->varGroup);
}
//Declare local variables used in more than one scope in this function:
const double Scale = fabs(Global->llScale); //<--absolute value of loglikelihood scale
const double NATLOG_2PI = 1.837877066409345483560659472811; //<--log(2*pi)
int i;
Eigen::Map< Eigen::MatrixXd > Eigy(omxMatrixDataColumnMajor(gff->y), gff->y->cols, 1);
Eigen::Map< Eigen::MatrixXd > Vinv(omxMatrixDataColumnMajor(gff->invcov), gff->invcov->rows, gff->invcov->cols);
EigenMatrixAdaptor EigX(gff->X);
Eigen::MatrixXd P, Py;
P.setZero(gff->invcov->rows, gff->invcov->cols);
double logdetV=0, logdetquadX=0;
if(want & (FF_COMPUTE_FIT | FF_COMPUTE_GRADIENT | FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)){
if(gff->usingGREMLExpectation){
omxGREMLExpectation* oge = (omxGREMLExpectation*)(expectation->argStruct);
//Check that factorizations of V and the quadratic form in X succeeded:
if(oge->cholV_fail_om->data[0]){
oo->matrix->data[0] = NA_REAL;
if (fc) fc->recordIterationError("expected covariance matrix is non-positive-definite");
return;
}
if(oge->cholquadX_fail){
oo->matrix->data[0] = NA_REAL;
if (fc) fc->recordIterationError("Cholesky factorization failed; possibly, the matrix of covariates is rank-deficient");
return;
}
//Log determinant of V:
logdetV = oge->logdetV_om->data[0];
//Log determinant of quadX:
for(i=0; i < gff->X->cols; i++){
logdetquadX += log(oge->cholquadX_vectorD[i]);
}
logdetquadX *= 2;
gff->REMLcorrection = Scale*0.5*logdetquadX;
//Finish computing fit (negative loglikelihood):
P.triangularView<Eigen::Lower>() = Vinv.selfadjointView<Eigen::Lower>() *
(Eigen::MatrixXd::Identity(Vinv.rows(), Vinv.cols()) -
(EigX * oge->quadXinv.selfadjointView<Eigen::Lower>() * oge->XtVinv)); //Vinv * (I-Hatmat)
Py = P.selfadjointView<Eigen::Lower>() * Eigy;
oo->matrix->data[0] = gff->REMLcorrection + Scale*0.5*( (((double)gff->y->cols) * NATLOG_2PI) + logdetV + ( Eigy.transpose() * Py )(0,0));
gff->nll = oo->matrix->data[0];
}
else{ //If not using GREML expectation, deal with means and cov in a general way to compute fit...
//Declare locals:
EigenMatrixAdaptor yhat(gff->means);
EigenMatrixAdaptor EigV(gff->cov);
double logdetV=0, logdetquadX=0;
Eigen::MatrixXd Vinv, quadX;
Eigen::LLT< Eigen::MatrixXd > cholV(gff->cov->rows);
Eigen::LLT< Eigen::MatrixXd > cholquadX(gff->X->cols);
Eigen::VectorXd cholV_vectorD, cholquadX_vectorD;
//Cholesky factorization of V:
cholV.compute(EigV);
if(cholV.info() != Eigen::Success){
omxRaiseErrorf("expected covariance matrix is non-positive-definite");
oo->matrix->data[0] = NA_REAL;
return;
}
//Log determinant of V:
cholV_vectorD = (( Eigen::MatrixXd )(cholV.matrixL())).diagonal();
for(i=0; i < gff->X->rows; i++){
logdetV += log(cholV_vectorD[i]);
}
logdetV *= 2;
Vinv = cholV.solve(Eigen::MatrixXd::Identity( EigV.rows(), EigV.cols() )); //<-- V inverse
quadX = EigX.transpose() * Vinv * EigX; //<--Quadratic form in X
cholquadX.compute(quadX); //<--Cholesky factorization of quadX
if(cholquadX.info() != Eigen::Success){
omxRaiseErrorf("Cholesky factorization failed; possibly, the matrix of covariates is rank-deficient");
oo->matrix->data[0] = NA_REAL;
return;
}
cholquadX_vectorD = (( Eigen::MatrixXd )(cholquadX.matrixL())).diagonal();
for(i=0; i < gff->X->cols; i++){
logdetquadX += log(cholquadX_vectorD[i]);
}
logdetquadX *= 2;
gff->REMLcorrection = Scale*0.5*logdetquadX;
//Finish computing fit:
oo->matrix->data[0] = gff->REMLcorrection + Scale*0.5*( ((double)gff->y->rows * NATLOG_2PI) + logdetV +
( Eigy.transpose() * Vinv * (Eigy - yhat) )(0,0));
gff->nll = oo->matrix->data[0];
return;
}
}
if(want & (FF_COMPUTE_GRADIENT | FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)){
//This part requires GREML expectation:
omxGREMLExpectation* oge = (omxGREMLExpectation*)(expectation->argStruct);
//Declare local variables for this scope:
int numChildren = fc->childList.size();
int __attribute__((unused)) parallelism = (numChildren == 0) ? 1 : numChildren;
fc->grad.resize(gff->dVlength); //<--Resize gradient in FitContext
//Set up new HessianBlock:
HessianBlock *hb = new HessianBlock;
if(want & (FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)){
hb->vars.resize(gff->dVlength);
hb->mat.resize(gff->dVlength, gff->dVlength);
}
//Begin looping thru free parameters:
#pragma omp parallel for num_threads(parallelism)
for(i=0; i < gff->dVlength; i++){
//Declare locals within parallelized region:
int j=0, t1=0, t2=0;
Eigen::MatrixXd PdV_dtheta1;
Eigen::MatrixXd dV_dtheta1(Eigy.rows(), Eigy.rows()); //<--Derivative of V w/r/t parameter i.
Eigen::MatrixXd dV_dtheta2(Eigy.rows(), Eigy.rows()); //<--Derivative of V w/r/t parameter j.
t1 = gff->gradMap[i]; //<--Parameter number for parameter i.
if(t1 < 0){continue;}
if(want & (FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)){hb->vars[i] = t1;}
if( oge->numcases2drop ){
dropCasesAndEigenize(gff->dV[i], dV_dtheta1, oge->numcases2drop, oge->dropcase, 1);
}
else{dV_dtheta1 = Eigen::Map< Eigen::MatrixXd >(omxMatrixDataColumnMajor(gff->dV[i]), gff->dV[i]->rows, gff->dV[i]->cols);}
//PdV_dtheta1 = P.selfadjointView<Eigen::Lower>() * dV_dtheta1.selfadjointView<Eigen::Lower>();
PdV_dtheta1 = P.selfadjointView<Eigen::Lower>();
PdV_dtheta1 = PdV_dtheta1 * dV_dtheta1.selfadjointView<Eigen::Lower>();
for(j=i; j < gff->dVlength; j++){
if(j==i){
gff->gradient(t1) = Scale*0.5*(PdV_dtheta1.trace() - (Eigy.transpose() * PdV_dtheta1 * Py)(0,0));
fc->grad(t1) += gff->gradient(t1);
if(want & (FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)){
gff->avgInfo(t1,t1) = Scale*0.5*(Eigy.transpose() * PdV_dtheta1 * PdV_dtheta1 * Py)(0,0);
}
}
else{if(want & (FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)){
t2 = gff->gradMap[j]; //<--Parameter number for parameter j.
if(t2 < 0){continue;}
if( oge->numcases2drop ){
dropCasesAndEigenize(gff->dV[j], dV_dtheta2, oge->numcases2drop, oge->dropcase, 1);
}
else{dV_dtheta2 = Eigen::Map< Eigen::MatrixXd >(omxMatrixDataColumnMajor(gff->dV[j]), gff->dV[j]->rows, gff->dV[j]->cols);}
gff->avgInfo(t1,t2) = Scale*0.5*(Eigy.transpose() * PdV_dtheta1 * P.selfadjointView<Eigen::Lower>() * dV_dtheta2.selfadjointView<Eigen::Lower>() * Py)(0,0);
gff->avgInfo(t2,t1) = gff->avgInfo(t1,t2);
}}}}
//Assign upper triangle elements of avgInfo to the HessianBlock:
if(want & (FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)){
for (size_t d1=0, h1=0; h1 < gff->dV.size(); ++h1) {
for (size_t d2=0, h2=0; h2 <= h1; ++h2) {
hb->mat(d2,d1) = gff->avgInfo(h2,h1);
++d2;
}
++d1;
}
fc->queue(hb);
}}
/* main routine */
int main(int argc, char *argv[]){
FILELog::ReportingLevel() = logINFO;
VARIABLES vars;
vars = commandline_input(argc, argv);
clock_t init, final;
int number_of_particles = vars.num;
double timestep = vars.dt;
bool use_T2 = vars.use_T2; // = false (no T2 decay), = true (T2 decay)
int num_of_repeat = vars.num_of_repeat; //Number of times to repeat simulation. For every repeat all data is flushed and we start the simulation again.
int number_of_timesteps; number_of_timesteps = (int)ceil(vars.gs/timestep);
double permeability = 0.0;
double radius = .00535;
double d = sqrt( 2.0*PI*radius*radius/( sqrt(3.0)*.79 ) );
//double lattice_size = 0.00601922026995422789215053328651;
double grad_duration = 4.5;
double D_extra = 2.5E-6;
double D_intra = 1.0E-6;
double T2_e = 200;
double T2_i = 200;
FILE_LOG(logINFO) << "f = " << 2.0*PI*radius*radius/(sqrt(3.0)*d*d) << std::endl;
Vector3 xhat(1.0,0.0,0.0);
Vector3 yhat(0.0,1.0,0.0);
Vector3 zhat(0.0,0.0,1.0);
Lattice<Cylinder_XY> lattice(D_extra, T2_e, permeability);
lattice.setLatticeVectors(d,2.0*d*0.86602540378443864676372317075294,d,xhat,yhat,zhat);
lattice.addBasis(Cylinder_XY(d/2.0, 0.0, radius, T2_i, D_intra, 1));
lattice.addBasis(Cylinder_XY(0.0, d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 2));
lattice.addBasis(Cylinder_XY(d/2.0, 2.0*d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 3));
lattice.addBasis(Cylinder_XY(d, d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 4));
double gspacings [] = { 8.0 , 10.5 , 14.0 , 18.5 , 24.5 , 32.5 , 42.5 , 56.5 };
double bvals [] = {108780, 154720, 219040, 301730, 411980, 558990, 742420, 1000000 };
double G [9];
for (int kk = 0; kk < num_of_repeat; kk++) {
vector<PGSE> measurements_x;
vector<PGSE> measurements_y;
vector<PGSE> measurements_z;
for (int i = 0; i < 8; i++){
double echo_time = 2.0*grad_duration + gspacings[i];
G[0] = 0.0;
G[i+1] = sqrt(bvals[i]/(GAMMA*GAMMA*grad_duration*grad_duration*(grad_duration + gspacings[i] - (grad_duration/3.0))));
measurements_x.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, xhat));
measurements_y.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, yhat));
measurements_z.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, zhat));
measurements_x.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, xhat));
measurements_y.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, yhat));
measurements_z.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, zhat));
}
vector<double> lnsignal(2);
vector<double> b(2);
cout << " trial = " << kk << endl;
Particles ensemble(number_of_particles,timestep, use_T2);
lattice.initializeUniformly(ensemble.getGenerator() , ensemble.getEnsemble() );
for (int k = 0; k < measurements_x.size();k++){
measurements_x[k].updatePhase(ensemble.getEnsemble(), 0.0);
measurements_y[k].updatePhase(ensemble.getEnsemble(), 0.0);
measurements_z[k].updatePhase(ensemble.getEnsemble(), 0.0);
}
for (int i = 1; i <= number_of_timesteps; i++){
ensemble.updateposition(lattice);
for (int k = 0; k < measurements_x.size();k++){
measurements_x[k].updatePhase(ensemble.getEnsemble(), i*timestep);
measurements_y[k].updatePhase(ensemble.getEnsemble(), i*timestep);
measurements_z[k].updatePhase(ensemble.getEnsemble(), i*timestep);
}
}
for (int i = 0; i < 8*2; i+=2){
double ADCx, ADCy, ADCz, diff_time;
lnsignal[0] = log(measurements_x[i].get_signal());
b[0] = measurements_x[i].get_b();
lnsignal[1] = log(measurements_x[i+1].get_signal());
b[1] = measurements_x[i+1].get_b();
ADCx = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << " ";
lnsignal[0] = log(measurements_y[i].get_signal());
b[0] = measurements_y[i].get_b();
lnsignal[1] = log(measurements_y[i+1].get_signal());
b[1] = measurements_y[i+1].get_b();
ADCy = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << " ";
lnsignal[0] = log(measurements_z[i].get_signal());
b[0] = measurements_z[i].get_b();
lnsignal[1] = log(measurements_z[i+1].get_signal());
b[1] = measurements_z[i+1].get_b();
ADCz = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << std::endl;
diff_time = measurements_x[i].get_DT();
std::cout << i << " " << diff_time << " " << ADCx << " " << ADCy << " " << ADCz << " " << b[1] << " " << G[1] << std::endl;
}
}
final= clock()-init; //final time - intial time
