C++ 特征值中的自定义标量类型
我目前正在尝试设置一个自定义标量类型，以便与Egeng3库一起使用（atm它是一个围绕C++ 特征值中的自定义标量类型,c++,eigen,eigen3,C++,Eigen,Eigen3,我目前正在尝试设置一个自定义标量类型，以便与Egeng3库一起使用（atm它是一个围绕double的简单包装器）。我已经尽我所知遵循了这些原则，基本的事情都很顺利 我需要用我的自定义类型来解决矩阵的特征值问题，而这正是事情开始分裂的地方。我的编译器向我吐出以下错误消息： /Eigen3/Eigen/src/Householder/Householder.h:131:18: error: no viable overloaded '-=' this->row(0) -= tau *
double/Eigen3/Eigen/src/Householder/Householder.h:131:18: error: no viable overloaded '-='
this->row(0) -= tau * tmp;
~~~~~~~~~~~~ ^ ~~~~~~~~~
/Eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h:314:10: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Block<Eigen::Matrix<MyDouble, 2,
2, 0, 2, 2>, -1, -1, false> >::applyHouseholderOnTheLeft<Eigen::VectorBlock<Eigen::Block<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2>, 2, 1, true>, -1> >' requested here
.applyHouseholderOnTheLeft(matA.col(i).tail(remainingSize-1), h, &temp.coeffRef(0));
^
/Eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h:161:7: note: in instantiation of member function 'Eigen::HessenbergDecomposition<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2>
>::_compute' requested here
_compute(m_matrix, m_hCoeffs, m_temp);
^
/Eigen3/Eigen/src/Eigenvalues/./RealSchur.h:271:10: note: in instantiation of function template specialization 'Eigen::HessenbergDecomposition<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2>
>::compute<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<MyDouble, MyDouble>, const Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<MyDouble>, const
Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2> > > >' requested here
m_hess.compute(matrix.derived()/scale);
^
/Eigen3/Eigen/src/Eigenvalues/EigenSolver.h:389:15: note: in instantiation of function template specialization 'Eigen::RealSchur<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2>
>::compute<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2> >' requested here
m_realSchur.compute(matrix.derived(), computeEigenvectors);
^
/Eigen3/Eigen/src/Eigenvalues/EigenSolver.h:156:7: note: in instantiation of function template specialization 'Eigen::EigenSolver<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2>
>::compute<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2> >' requested here
compute(matrix.derived(), computeEigenvectors);
^
/home/adam/Documents/Git/spin_phonon_coupling/src/test.cpp:205:52: note: in instantiation of function template specialization 'Eigen::EigenSolver<Eigen::Matrix<MyDouble, 2, 2, 0, 2, 2> >::EigenSolver<Eigen::Matrix<MyDouble, 2, 2, 0, 2,
2> >' requested here
Eigen::EigenSolver<Eigen::Matrix<MyDouble, 2, 2>> solver(test);
^
/Eigen3/Eigen/src/Core/DenseBase.h:298:14: note: candidate template ignored: could not match 'EigenBase<type-parameter-0-0>' against 'MyDouble'
Derived& operator-=(const EigenBase<OtherDerived> &other);
^
/Eigen3/Eigen/src/Core/MatrixBase.h:161:14: note: candidate template ignored: could not match 'MatrixBase<type-parameter-0-0>' against 'MyDouble'
Derived& operator-=(const MatrixBase<OtherDerived>& other);
^
/Eigen3/Eigen/src/Core/MatrixBase.h:495:46: note: candidate template ignored: could not match 'ArrayBase<type-parameter-0-0>' against 'MyDouble'
template<typename OtherDerived> Derived& operator-=(const ArrayBase<OtherDerived>& )
Marc Glisse指出我的操作符重载看起来有点奇怪，所以我重写了它们，最初的问题就消失了。我不知道为什么会这样 然后我只遇到了一个问题，即Eigen的
isfinite#include <iostream>
#include <cmath>
#include <complex>
#include <Eigen/Dense>
class MyDouble {
public:
double value;
MyDouble() : value() {};
MyDouble(double val) : value(val) {};
template<typename T>
MyDouble& operator+=(T rhs) {
value = static_cast<double>(value + rhs);
return *this;
}
template<typename T>
MyDouble& operator-=(const T &rhs) {
value = static_cast<double>(value - rhs);
return *this;
}
template<typename T>
MyDouble& operator*=(T rhs) {
value = static_cast<double>(value * rhs);
return *this;
}
template<typename T>
MyDouble& operator/=(T rhs) {
value = static_cast<double>(value / rhs);
return *this;
}
MyDouble operator-() const {
return -value;
}
friend std::ostream& operator<<(std::ostream& out, const MyDouble& val) {
out << val.value << " m";
return out;
}
explicit operator double() {
return value;
}
};
#define OVERLOAD_OPERATOR(op,ret) ret operator op(const MyDouble &lhs, const MyDouble &rhs) { \
return lhs.value op rhs.value; \
}
OVERLOAD_OPERATOR(+, MyDouble)
OVERLOAD_OPERATOR(-, MyDouble)
OVERLOAD_OPERATOR(*, MyDouble)
OVERLOAD_OPERATOR(/, MyDouble)
OVERLOAD_OPERATOR(>, bool)
OVERLOAD_OPERATOR(<, bool)
OVERLOAD_OPERATOR(>=, bool)
OVERLOAD_OPERATOR(<=, bool)
OVERLOAD_OPERATOR(==, bool)
OVERLOAD_OPERATOR(!=, bool)
MyDouble sqrt(MyDouble val) {
return std::sqrt(val.value);
}
MyDouble abs(MyDouble val) {
return std::abs(val.value);
}
MyDouble abs2(MyDouble val) {
return val * val;
}
bool isfinite(const MyDouble &) { return true; }
namespace Eigen {
template<> struct NumTraits<MyDouble>
: NumTraits<double> // permits to get the epsilon, dummy_precision, lowest, highest functions
{
typedef MyDouble Real;
typedef MyDouble NonInteger;
typedef MyDouble Nested;
enum {
IsComplex = 0,
IsInteger = 0,
IsSigned = 1,
RequireInitialization = 0,
ReadCost = 1,
AddCost = 3,
MulCost = 3
};
};
template<typename BinaryOp>
struct ScalarBinaryOpTraits<MyDouble,double,BinaryOp> { typedef MyDouble ReturnType; };
template<typename BinaryOp>
struct ScalarBinaryOpTraits<double,MyDouble,BinaryOp> { typedef MyDouble ReturnType; };
}
int main() {
Eigen::Matrix<MyDouble, 2, 2> test;
test << 1, 2, 3, 4;
Eigen::Matrix<double, 2, 2> reference;
reference << 1, 2, 3, 4;
MyDouble a = 3;
a += 2;
a = 2 + a;
a = a + 2;
a -= 2;
a -= MyDouble(3);
a = a / a;
std::complex<MyDouble> complexTest(3,4);
complexTest *= 2;
Eigen::EigenSolver<Eigen::Matrix<MyDouble, 2, 2>> solver(test);
Eigen::EigenSolver<Eigen::Matrix<double, 2, 2>> refSolver(reference);
std::cout << "MyDouble:" << std::endl;
std::cout << test.trace() << std::endl;
std::cout << solver.eigenvalues() << std::endl;
std::cout << "\nRefernce:" << std::endl;
std::cout << reference.trace() << std::endl;
std::cout << refSolver.eigenvalues() << std::endl;
}
#包括
#包括
#包括
#包括
类MyDouble{
公众：
双重价值；
MyDouble（）：value（）{}；
MyDouble（double val）：值（val）{}；
模板
MyDouble运算符+=（T rhs）{
值=静态施法（值+rhs）；
归还*这个；
}
模板
MyDouble和运算符-=（常数T和rhs）{
值=静态施法（值-rhs）；
归还*这个；
}
模板
MyDouble运算符*=（T rhs）{
值=静态施法（值*rhs）；
归还*这个；
}
模板
MyDouble&运算符/=（T rhs）{
值=静态施法（值/rhs）；
归还*这个；
}
MyDouble运算符-（）常量{
返回值；
}
friend std:：ostream&operator您对运算符的显式专门化看起来很奇怪，被我尝试过的编译器拒绝了。friend MyDouble运算符+（MyDouble a，MyDouble b）{return a.value+b.value；}
更常见。此外，特征解算器
必须能够处理复杂的问题（在您的情况下，它将尝试实例化std:：complex
）@ggael我尝试实例化std:：complex
，这似乎完美无瑕。您是否考虑了特定的场景，这会有问题？只要MyDouble
只是double
的包装器，编写bool是有限的（const MyDouble&x）{return std:：isfinite（x.value）；}
isfinite
如果（且仅当）x
为无限或NaN，则应为false。
#include <iostream>
#include <cmath>
#include <complex>
#include <Eigen/Dense>
class MyDouble {
public:
double value;
MyDouble() : value() {};
MyDouble(double val) : value(val) {};
template<typename T>
MyDouble& operator+=(T rhs) {
value = static_cast<double>(value + rhs);
return *this;
}
template<typename T>
MyDouble& operator-=(const T &rhs) {
value = static_cast<double>(value - rhs);
return *this;
}
template<typename T>
MyDouble& operator*=(T rhs) {
value = static_cast<double>(value * rhs);
return *this;
}
template<typename T>
MyDouble& operator/=(T rhs) {
value = static_cast<double>(value / rhs);
return *this;
}
MyDouble operator-() const {
return -value;
}
friend std::ostream& operator<<(std::ostream& out, const MyDouble& val) {
out << val.value << " m";
return out;
}
explicit operator double() {
return value;
}
};
#define OVERLOAD_OPERATOR(op,ret) ret operator op(const MyDouble &lhs, const MyDouble &rhs) { \
return lhs.value op rhs.value; \
}
OVERLOAD_OPERATOR(+, MyDouble)
OVERLOAD_OPERATOR(-, MyDouble)
OVERLOAD_OPERATOR(*, MyDouble)
OVERLOAD_OPERATOR(/, MyDouble)
OVERLOAD_OPERATOR(>, bool)
OVERLOAD_OPERATOR(<, bool)
OVERLOAD_OPERATOR(>=, bool)
OVERLOAD_OPERATOR(<=, bool)
OVERLOAD_OPERATOR(==, bool)
OVERLOAD_OPERATOR(!=, bool)
MyDouble sqrt(MyDouble val) {
return std::sqrt(val.value);
}
MyDouble abs(MyDouble val) {
return std::abs(val.value);
}
MyDouble abs2(MyDouble val) {
return val * val;
}
bool isfinite(const MyDouble &) { return true; }
namespace Eigen {
template<> struct NumTraits<MyDouble>
: NumTraits<double> // permits to get the epsilon, dummy_precision, lowest, highest functions
{
typedef MyDouble Real;
typedef MyDouble NonInteger;
typedef MyDouble Nested;
enum {
IsComplex = 0,
IsInteger = 0,
IsSigned = 1,
RequireInitialization = 0,
ReadCost = 1,
AddCost = 3,
MulCost = 3
};
};
template<typename BinaryOp>
struct ScalarBinaryOpTraits<MyDouble,double,BinaryOp> { typedef MyDouble ReturnType; };
template<typename BinaryOp>
struct ScalarBinaryOpTraits<double,MyDouble,BinaryOp> { typedef MyDouble ReturnType; };
}
int main() {
Eigen::Matrix<MyDouble, 2, 2> test;
test << 1, 2, 3, 4;
Eigen::Matrix<double, 2, 2> reference;
reference << 1, 2, 3, 4;
MyDouble a = 3;
a += 2;
a = 2 + a;
a = a + 2;
a -= 2;
a -= MyDouble(3);
a = a / a;
std::complex<MyDouble> complexTest(3,4);
complexTest *= 2;
Eigen::EigenSolver<Eigen::Matrix<MyDouble, 2, 2>> solver(test);
Eigen::EigenSolver<Eigen::Matrix<double, 2, 2>> refSolver(reference);
std::cout << "MyDouble:" << std::endl;
std::cout << test.trace() << std::endl;
std::cout << solver.eigenvalues() << std::endl;
std::cout << "\nRefernce:" << std::endl;
std::cout << reference.trace() << std::endl;
std::cout << refSolver.eigenvalues() << std::endl;
}