"required from here" 使用 Eigen 库时
"required from here" when using Eigen library
我正在使用 Eigen 库在 Eclipse C++ 中实现一些控制算法。例如，当我想计算矩阵的特征值时，我会收到 "required from here" 警告，作为代码行旁边的感叹号。我也不知道怎么解决。
这是我的 main.cpp 文件：
#include <iostream>
#include "System.h"
#include "ControllerCode2.h"
using namespace std;
int main(){
int n = 3; // # of states
int m = 1; // # of inputs
int l = 1; // # of outputs
MatrixXd A(n, n), B(n,m), C(l,n), D(l,m), Q(n,n), R(m,m), Qe(n,n), Re(m,m);
A << 0, 1, 0,
0, 0, 1,
0, -2, -3;
B << 0,
0,
1;
C << 1, 0, 0;
D << 0;
MatrixXd C_trans = C.transpose();
Q = C_trans * C;
R = MatrixXd::Identity(m, m); // initially
MatrixXd B_trans = B.transpose();
Qe = B * B_trans;
Re = MatrixXd::Identity(m, m); // initially
System sys = System(A, B, C, D);
sys.set_covariance_matrices(R, Q);
sys.set_noise_covariance_matrices(Re, Qe);
schur_eigen_test(sys);
return 5;
}
现在，ControllerCode2.cpp代码：
#include "ControllerCode2.h"
MatrixXd U11;
MatrixXd U21;
void schur_eigen_test( System G ){
/****** Constructing the Hamiltonian Matrix ******/
int n = G.A.rows();
MatrixXd H(2*n, 2*n); // the Hamiltonian matrix has the dimensions of 2n*2n where n is the number of states
H.block(0,0,n,n) = G.A;
H.block(0,n,n,n) = -1 * G.B * G.R.inverse() * G.B.transpose();
H.block(n,0,n,n) = -1 * G.Q;
H.block(n,n,n,n) = -1 * G.A.transpose();
/****** Performing a real Schur decomposition on the square Hamiltonian matrix ******/
RealSchur<MatrixXd> schur(H);
MatrixXd U = schur.matrixU(); //The orthogonal matrix U
MatrixXd T = schur.matrixT(); //The quasi-triangular matrix T
/****** Find the eigenvalues and eigenvectors of the Hamiltonian matrix ******/
EigenSolver<MatrixXd> H_eigen; // create an EigenSolver Matrix
H_eigen.compute(H, false); // compute the eigenvalues ./and eigenvectors of matrix H
MatrixXd H_eigenval = H_eigen.eigenvalues();
// //MatrixXd H_eigenvec = H_eigen.eigenvectors();
/****** Select the eigenvectors (U11, U21) corresponding to the stable (with -ve real part) eigenvalues ******/
U11 = U.block(0,0,n,n);
U21 = U.block(n,0,n,n);
/****** Calculate F ******/
MatrixXd F = -1 * G.R.inverse() * G.B.transpose() * U21 * U11.inverse(); // transposeInPlace or transpose??
//////// Extra: for output
cout << endl << "H = " << endl << H << endl;
cout << endl << "U schur(H) " << endl << U << endl;
cout << endl << "T schur(H) " << endl << T << endl;
cout << endl << "U*T*U.transpose() " << endl << U * T * U.transpose();
// cout << endl << "U.transpose() - U.inverse() " << endl << U.transpose() - U.inverse(); // = which proves that U is orthogonal, i.e. U.transpose() = U.inverse()
// EigenSolver<MatrixXd> H_eigen; // create an EigenSolver Matrix
// H_eigen.compute(H, false); // compute the eigenvalues and eigenvectors of matrix H
// MatrixXd H_eigenval = H_eigen.eigenvalues();
// cout << endl << "eigenvalues of H = " << endl << H_eigenval << endl;
}
我在这一行旁边收到警告：
MatrixXd H_eigenval = H_eigen.eigenvalues();
我的ControllerCode2.h代码：
#ifndef CONTROLLERCODE_H_
#define CONTROLLERCODE_H_
#include "System.h"
void schur_eigen_test( System );
//};
#endif /* CONTROLLERCODE_H_ */
System.h代码：
// include guard
#ifndef SYSTEM_H_
#define SYSTEM_H_
#include <Eigen/Dense>
#include <iostream>
#include <Eigen/Eigenvalues>
#include <iostream>
using namespace Eigen;
using namespace std;
using Eigen::MatrixXd;
class System {
public:
MatrixXd A;
MatrixXd B;
MatrixXd C;
MatrixXd D;
MatrixXd Q;
MatrixXd R;
MatrixXd Re;
MatrixXd Qe;
System(MatrixXd a, MatrixXd b, MatrixXd c, MatrixXd d){
A = a;
B = b;
C = c;
D = d;
//cout << A << endl << B << endl << C << endl;
};
void set_covariance_matrices(MatrixXd r, MatrixXd q){
R = r;
Q = q;
//cout << R << endl << Q << endl;
}
void set_noise_covariance_matrices(MatrixXd re, MatrixXd qe){
Re = re;
Qe = qe;
//cout << Re << endl << Qe << endl;
}
virtual ~System();
// this function receives the 4 state space matrices and returns one plant matrix G
MatrixXd setContSys(MatrixXd a, MatrixXd b, MatrixXd c, MatrixXd d);
};
#endif /* SYSTEM_H_ */
最后这是控制台中显示的结果：
15:12:51 **** Incremental Build of configuration Debug for project Controller ****
Info: Internal Builder is used for build
g++ "-IC:\Users\Alsharif\eigen" -O0 -g3 -Wall -c -fmessage-length=0 -o ControllerCode2.o "..\ControllerCode2.cpp"
In file included from C:\Users\Alsharif\eigen/Eigen/Core:285:0,
from C:\Users\Alsharif\eigen/Eigen/Dense:1,
from ..\System.h:12,
from ..\ControllerCode2.h:11,
from ..\ControllerCode2.cpp:8:
C:\Users\Alsharif\eigen/Eigen/src/Core/Matrix.h: In instantiation of 'Eigen::Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::Matrix(const Eigen::MatrixBase<OtherDerived>&) [with OtherDerived = Eigen::Matrix<std::complex<double>, -1, 1>; _Scalar = double; int _Rows = -1; int _Cols = -1; int _Options = 0; int _MaxRows = -1; int _MaxCols = -1]':
..\ControllerCode2.cpp:220:45: required from here
C:\Users\Alsharif\eigen/Eigen/src/Core/util/StaticAssert.h:115:9: error: 'YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY' is not a member of 'Eigen::internal::static_assertion<false>'
if (Eigen::internal::static_assertion<static_cast<bool>(CONDITION)>::MSG) {}
^
C:\Users\Alsharif\eigen/Eigen/src/Core/Matrix.h:326:7: note: in expansion of macro 'EIGEN_STATIC_ASSERT'
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
^
15:12:55 Build Finished (took 4s.567ms)
所以你得到了一个静态断言：YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY。
我不是 Eigen 方面的专家，但我看到 eigenvalues() returns const EigenvalueType& 是一种不同的矩阵类型，而不是您用来实例化 EigenSolver 的矩阵类型（即 MatrixXd你的情况）。
所以你应该使用正确的类型，或者明确地转换矩阵，这就是静态断言所说的。
我正在使用 Eigen 库在 Eclipse C++ 中实现一些控制算法。例如，当我想计算矩阵的特征值时，我会收到 "required from here" 警告，作为代码行旁边的感叹号。我也不知道怎么解决。
这是我的 main.cpp 文件：
#include <iostream>
#include "System.h"
#include "ControllerCode2.h"
using namespace std;
int main(){
int n = 3; // # of states
int m = 1; // # of inputs
int l = 1; // # of outputs
MatrixXd A(n, n), B(n,m), C(l,n), D(l,m), Q(n,n), R(m,m), Qe(n,n), Re(m,m);
A << 0, 1, 0,
0, 0, 1,
0, -2, -3;
B << 0,
0,
1;
C << 1, 0, 0;
D << 0;
MatrixXd C_trans = C.transpose();
Q = C_trans * C;
R = MatrixXd::Identity(m, m); // initially
MatrixXd B_trans = B.transpose();
Qe = B * B_trans;
Re = MatrixXd::Identity(m, m); // initially
System sys = System(A, B, C, D);
sys.set_covariance_matrices(R, Q);
sys.set_noise_covariance_matrices(Re, Qe);
schur_eigen_test(sys);
return 5;
}
现在，ControllerCode2.cpp代码：
#include "ControllerCode2.h"
MatrixXd U11;
MatrixXd U21;
void schur_eigen_test( System G ){
/****** Constructing the Hamiltonian Matrix ******/
int n = G.A.rows();
MatrixXd H(2*n, 2*n); // the Hamiltonian matrix has the dimensions of 2n*2n where n is the number of states
H.block(0,0,n,n) = G.A;
H.block(0,n,n,n) = -1 * G.B * G.R.inverse() * G.B.transpose();
H.block(n,0,n,n) = -1 * G.Q;
H.block(n,n,n,n) = -1 * G.A.transpose();
/****** Performing a real Schur decomposition on the square Hamiltonian matrix ******/
RealSchur<MatrixXd> schur(H);
MatrixXd U = schur.matrixU(); //The orthogonal matrix U
MatrixXd T = schur.matrixT(); //The quasi-triangular matrix T
/****** Find the eigenvalues and eigenvectors of the Hamiltonian matrix ******/
EigenSolver<MatrixXd> H_eigen; // create an EigenSolver Matrix
H_eigen.compute(H, false); // compute the eigenvalues ./and eigenvectors of matrix H
MatrixXd H_eigenval = H_eigen.eigenvalues();
// //MatrixXd H_eigenvec = H_eigen.eigenvectors();
/****** Select the eigenvectors (U11, U21) corresponding to the stable (with -ve real part) eigenvalues ******/
U11 = U.block(0,0,n,n);
U21 = U.block(n,0,n,n);
/****** Calculate F ******/
MatrixXd F = -1 * G.R.inverse() * G.B.transpose() * U21 * U11.inverse(); // transposeInPlace or transpose??
//////// Extra: for output
cout << endl << "H = " << endl << H << endl;
cout << endl << "U schur(H) " << endl << U << endl;
cout << endl << "T schur(H) " << endl << T << endl;
cout << endl << "U*T*U.transpose() " << endl << U * T * U.transpose();
// cout << endl << "U.transpose() - U.inverse() " << endl << U.transpose() - U.inverse(); // = which proves that U is orthogonal, i.e. U.transpose() = U.inverse()
// EigenSolver<MatrixXd> H_eigen; // create an EigenSolver Matrix
// H_eigen.compute(H, false); // compute the eigenvalues and eigenvectors of matrix H
// MatrixXd H_eigenval = H_eigen.eigenvalues();
// cout << endl << "eigenvalues of H = " << endl << H_eigenval << endl;
}
我在这一行旁边收到警告：
MatrixXd H_eigenval = H_eigen.eigenvalues();
我的ControllerCode2.h代码：
#ifndef CONTROLLERCODE_H_
#define CONTROLLERCODE_H_
#include "System.h"
void schur_eigen_test( System );
//};
#endif /* CONTROLLERCODE_H_ */
System.h代码：
// include guard
#ifndef SYSTEM_H_
#define SYSTEM_H_
#include <Eigen/Dense>
#include <iostream>
#include <Eigen/Eigenvalues>
#include <iostream>
using namespace Eigen;
using namespace std;
using Eigen::MatrixXd;
class System {
public:
MatrixXd A;
MatrixXd B;
MatrixXd C;
MatrixXd D;
MatrixXd Q;
MatrixXd R;
MatrixXd Re;
MatrixXd Qe;
System(MatrixXd a, MatrixXd b, MatrixXd c, MatrixXd d){
A = a;
B = b;
C = c;
D = d;
//cout << A << endl << B << endl << C << endl;
};
void set_covariance_matrices(MatrixXd r, MatrixXd q){
R = r;
Q = q;
//cout << R << endl << Q << endl;
}
void set_noise_covariance_matrices(MatrixXd re, MatrixXd qe){
Re = re;
Qe = qe;
//cout << Re << endl << Qe << endl;
}
virtual ~System();
// this function receives the 4 state space matrices and returns one plant matrix G
MatrixXd setContSys(MatrixXd a, MatrixXd b, MatrixXd c, MatrixXd d);
};
#endif /* SYSTEM_H_ */
最后这是控制台中显示的结果：
15:12:51 **** Incremental Build of configuration Debug for project Controller ****
Info: Internal Builder is used for build
g++ "-IC:\Users\Alsharif\eigen" -O0 -g3 -Wall -c -fmessage-length=0 -o ControllerCode2.o "..\ControllerCode2.cpp"
In file included from C:\Users\Alsharif\eigen/Eigen/Core:285:0,
from C:\Users\Alsharif\eigen/Eigen/Dense:1,
from ..\System.h:12,
from ..\ControllerCode2.h:11,
from ..\ControllerCode2.cpp:8:
C:\Users\Alsharif\eigen/Eigen/src/Core/Matrix.h: In instantiation of 'Eigen::Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::Matrix(const Eigen::MatrixBase<OtherDerived>&) [with OtherDerived = Eigen::Matrix<std::complex<double>, -1, 1>; _Scalar = double; int _Rows = -1; int _Cols = -1; int _Options = 0; int _MaxRows = -1; int _MaxCols = -1]':
..\ControllerCode2.cpp:220:45: required from here
C:\Users\Alsharif\eigen/Eigen/src/Core/util/StaticAssert.h:115:9: error: 'YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY' is not a member of 'Eigen::internal::static_assertion<false>'
if (Eigen::internal::static_assertion<static_cast<bool>(CONDITION)>::MSG) {}
^
C:\Users\Alsharif\eigen/Eigen/src/Core/Matrix.h:326:7: note: in expansion of macro 'EIGEN_STATIC_ASSERT'
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
^
15:12:55 Build Finished (took 4s.567ms)
所以你得到了一个静态断言：YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY。
我不是 Eigen 方面的专家，但我看到 eigenvalues() returns const EigenvalueType& 是一种不同的矩阵类型，而不是您用来实例化 EigenSolver 的矩阵类型（即 MatrixXd你的情况）。
所以你应该使用正确的类型，或者明确地转换矩阵，这就是静态断言所说的。