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// FIXME - this example is not too good as that functionality is provided in the Eigen API
// additionally it's quite heavy. the CwiseUnaryOp example is better.
#include <Eigen/Core>
USING_PART_OF_NAMESPACE_EIGEN
using namespace std;
// define a custom template binary functor
template<typename Scalar> struct CwiseMinOp EIGEN_EMPTY_STRUCT {
Scalar operator()(const Scalar& a, const Scalar& b) const { return std::min(a,b); }
enum { Cost = Eigen::ConditionalJumpCost + Eigen::NumTraits<Scalar>::AddCost };
};
// define a custom binary operator between two matrices
template<typename Derived1, typename Derived2>
const Eigen::CwiseBinaryOp<CwiseMinOp<typename Derived1::Scalar>, Derived1, Derived2>
cwiseMin(const MatrixBase<Derived1> &mat1, const MatrixBase<Derived2> &mat2)
{
return Eigen::CwiseBinaryOp<CwiseMinOp<typename Derived1::Scalar>, Derived1, Derived2>(mat1, mat2);
}
int main(int, char**)
{
Matrix4d m1 = Matrix4d::random(), m2 = Matrix4d::random();
cout << cwiseMin(m1,m2) << endl; // use our new global operator
cout << m1.cwise<CwiseMinOp<double> >(m2) << endl; // directly use the generic expression member
cout << m1.cwise(m2, CwiseMinOp<double>()) << endl; // directly use the generic expression member (variant)
return 0;
}
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