C++ VectorXd::row方法代碼示例

void test_tovector(quotek::data::records& recs) {

  Eigen::VectorXd v = recs.to_vector();

  assert( v.rows() == 1600  );
  assert(v.row(1599)[0] = 1599);

}

void ExperimentalTrajectory::addNewWaypoint(Eigen::VectorXd newWaypoint, double waypointTime)
{
    std::cout << "Adding waypoint at: " << waypointTime << " seconds..." << std::endl;

    if ( (newWaypoint.rows() == nDoF) && (waypointTime>=0.0) && (waypointTime<=kernelCenters.maxCoeff()) )
    {
        //Find out where the new T falls...
        for (int i=0; i<kernelCenters.size(); i++)
        {
            std::cout << "i = " << i << std::endl;
            if (kernelCenters(i)>waypointTime) {
                youngestWaypointIndex=i;
                std::cout << "youngestWaypointIndex" << youngestWaypointIndex << std::endl;
                i = kernelCenters.size();
            }
        }


        Eigen::MatrixXd newWaypointsMat = Eigen::MatrixXd::Zero(nDoF, nWaypoints+1);
        newWaypointsMat.leftCols(youngestWaypointIndex) = waypoints.leftCols(youngestWaypointIndex);
        newWaypointsMat.col(youngestWaypointIndex) = newWaypoint;
        newWaypointsMat.rightCols(nWaypoints - youngestWaypointIndex) = waypoints.rightCols(nWaypoints - youngestWaypointIndex);

        waypoints.resize(nDoF, nWaypoints+1);
        waypoints = newWaypointsMat;

        Eigen::VectorXd durationVectorTmp = Eigen::VectorXd::Zero(nWaypoints);

        std::cout << "\npointToPointDurationVector\n " << pointToPointDurationVector << std::endl;

        durationVectorTmp.head(youngestWaypointIndex-1) = pointToPointDurationVector.head(youngestWaypointIndex-1);
        durationVectorTmp.row(youngestWaypointIndex-1) << waypointTime - kernelCenters(youngestWaypointIndex-1);
        durationVectorTmp.tail(nWaypoints - youngestWaypointIndex) = pointToPointDurationVector.tail(nWaypoints - youngestWaypointIndex);

        pointToPointDurationVector.resize(durationVectorTmp.size());
        pointToPointDurationVector = durationVectorTmp;

        std::cout << "\npointToPointDurationVector\n " << pointToPointDurationVector << std::endl;


        reinitialize();


    }
    else{
        if (newWaypoint.rows() != nDoF){
            std::cout << "[ERROR] (ExperimentalTrajectory::addNewWaypoint): New waypoint is not the right size. Should have dim = " <<nDoF << "x1." << std::endl;
        }
        if ((waypointTime<=0.0) || (waypointTime>=kernelCenters.maxCoeff())){
            std::cout << "[ERROR] (ExperimentalTrajectory::addNewWaypoint): New waypoint time is out of time bounds. Should have fall between 0.0 and " << kernelCenters.maxCoeff() << " seconds." << std::endl;
        }
    }


}

//.........這裏部分代碼省略.........
    
    
//#ifdef TESTMAT
    //Lanczos_Vec = Test_Lanczos;
//    Eigen::Tridiagonalization<Eigen::MatrixXd> triOfHam(Test_Ham);
//#endif
    //cout << "Lanczos not norm: " << Lanczos_Vec << endl;
    Lanczos_Vec.normalize();
    //cout << "Lanczos norm: " << Lanczos_Vec << endl;

    K_Mat.push_back(Lanczos_Vec);//error here
    int it = 0;
    
    
    do
    {
        if(it == 0)
        {
            
            r_vec = Ham.Ham_Tot*K_Mat[it]; //how can I use ifdef here?
            
            //#ifdef TESTMAT
                       //r_vec = Test_Ham*K_Mat[it];
            //#endif
            
        }
        else
        {
            
            r_vec = Ham.Ham_Tot*K_Mat[it]-(beta*K_Mat[it-1]);
            
            //            #ifdef TESTMAT
              //          r_vec = Test_Ham*K_Mat[it]-(beta*K_Mat[it-1]);
            //            #endif
            
            //cout << "Beta is: " << beta << endl;
        }
        // cout << K_Mat[it] << " " << K_Mat[it].adjoint() << endl;
        alpha = K_Mat[it].dot( r_vec );//this is correct
        //cout << "alpha: " << alpha << endl;
        r_vec -= (alpha.real()*K_Mat[it]); //= r_vec changed to -=
        beta = r_vec.norm();
        //cout << "Beta: " << beta << endl;
        TriDiag(it,it) = alpha.real();
        TriDiag(it+1,it)=beta; //self adjoint eigensolver only uses lower triangle
        TriDiag(it,it+1)=beta;
        r_vec.normalize(); //this is the new Lanczos Vector
        K_Mat.push_back(r_vec);
        
        
        
        it++;
        
        if (it > 1)
        {
            Work_Mat = TriDiag.block(0,0,it,it);
            //cout <<"Work mat: \n" << Work_Mat << endl;
            Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> DiagMe(Work_Mat);
            Eval = DiagMe.eigenvalues();
            Evec_Mat = DiagMe.eigenvectors();
            //cout << "Eigenvalues on " << it << " iteration /n" << Eval << endl;
            if(it > 2)
            {
                double Eval_Dif = Eval_P(0) - Eval(0);
                
                if(Eval_Dif < .0000000000001)
                {
                    cout << "Dif of Eval is zero \n";
                    Converged = true;
                }
            }
            Eval_P = Eval;
        }
        if( (beta < .00000000000001) )
        {
            cout << "Beta is zero \n";
            Converged = true;
        }
        if(it == (tb.Tot_base-2)|| it == itmax)
        {
            cout << "Not Converged \n";
            Converged = true;
        }
        
        
        
    }while(!(Converged));
    
    //cout << "T mat: " << TriDiag << endl;
    cnt = it;
    Evec.resize(cnt);
    Evec = Evec_Mat.col(0);
    TriDiag.resize(10,10);
    //cout << "We have Eigenvectors \n" << Evec_Mat << endl;
    
    for(int i = 0; i < cnt; i++)
    {
        G_state.real() += K_Mat[i]*Evec.row(i);//Evec.row(i)**K_Mat[i]
    }
}