本文整理汇总了C++中eigen::SparseMatrix::middleRows方法的典型用法代码示例。如果您正苦于以下问题：C++ SparseMatrix::middleRows方法的具体用法？C++ SparseMatrix::middleRows怎么用？C++ SparseMatrix::middleRows使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类eigen::SparseMatrix的用法示例。
在下文中一共展示了SparseMatrix::middleRows方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: predictLong_cpp
//.........这里部分代码省略.........
gig rgig;
rgig.seed(std::chrono::high_resolution_clock::now().time_since_epoch().count());
Eigen::VectorXd z;
z.setZero(Kobj->d);
Eigen::VectorXd b, Ysim;
b.setZero(Kobj->d);
std::vector<int> longInd;
for (int i=0; i< nindv; i++) longInd.push_back(i);
Eigen::SparseMatrix<double,0,int> K = Eigen::SparseMatrix<double,0,int>(Kobj->Q);
K *= sqrt(Kobj->tau);
std::vector< Eigen::MatrixXd > WVec(nindv);
std::vector< Eigen::MatrixXd > XVec(nindv);
for(int i = 0; i < nindv; i++ ) {
if(silent == 0){
Rcpp::Rcout << " Compute prediction for patient " << i << "\n";
}
XVec[i].resize(As_pred[i].rows(), nSim);
WVec[i].resize(As_pred[i].rows(), nSim);
Eigen::MatrixXd random_effect = mixobj->Br[i];
Eigen::MatrixXd fixed_effect = mixobj->Bf[i];
for(int ipred = 0; ipred < pred_ind[i].rows(); ipred++){
//if(silent == 0){
// Rcpp::Rcout << " location = " << ipred << ": "<< obs_ind[i](ipred,0)<< " " << obs_ind[i](ipred,1) << "\n";
// Rcpp::Rcout << " A size = " << As[i].rows() << " " << As[i].cols() << "\n";
// Rcpp::Rcout << " Y size = " << Ys[i].size() << ", re = " << random_effect.rows() << ", fe = "<< fixed_effect.size() << "\n";
//}
//extract data to use for prediction:
Eigen::SparseMatrix<double,0,int> A = As[i].middleRows(obs_ind[i](ipred,0),obs_ind[i](ipred,1));
Eigen::VectorXd Y = Ys[i].segment(obs_ind[i](ipred,0),obs_ind[i](ipred,1));
mixobj->Br[i] = random_effect.middleRows(obs_ind[i](ipred,0),obs_ind[i](ipred,1));
mixobj->Bf[i] = fixed_effect.middleRows(obs_ind[i](ipred,0),obs_ind[i](ipred,1));
//Rcpp::Rcout << "here1111\n";
for(int ii = 0; ii < nSim + nBurnin; ii ++){
//Rcpp::Rcout << "iter = " << ii << "\n";
Eigen::VectorXd res = Y;
//***************************************
//***************************************
// building the residuals and sampling
//***************************************
//***************************************
// removing fixed effect from Y
mixobj->remove_cov(i, res);
res -= A * process->Xs[i];
//***********************************
// mixobj sampling
//***********************************
//Rcpp::Rcout << "here2\n";
if(type_MeasurementError == "Normal")
mixobj->sampleU( i, res, 2 * log(errObj->sigma));
else
//errObj->Vs[i] = errObj->Vs[i].head(ipred+1);
mixobj->sampleU2( i,
res,
errObj->Vs[i].segment(obs_ind[i](ipred,0),obs_ind[i](ipred,1)).cwiseInverse(),
2 * log(errObj->sigma));
mixobj->remove_inter(i, res);
//***********************************