Disabled external gits

This commit is contained in:
2022-04-07 18:46:57 +02:00
parent 88cb3426ad
commit 15e7120d6d
5316 changed files with 4563444 additions and 6 deletions

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EXCLUDE copyright
EXCLUDE license

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file(GLOB examples_SRCS "*.cpp")
foreach(example_src ${examples_SRCS})
get_filename_component(example ${example_src} NAME_WE)
add_executable(${example} ${example_src})
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
target_link_libraries(${example} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
endif()
add_custom_command(
TARGET ${example}
POST_BUILD
COMMAND ${example}
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${example}.out
)
add_dependencies(all_examples ${example})
endforeach(example_src)
check_cxx_compiler_flag("-std=c++11" EIGEN_COMPILER_SUPPORT_CPP11)
if(EIGEN_COMPILER_SUPPORT_CPP11)
ei_add_target_property(nullary_indexing COMPILE_FLAGS "-std=c++11")
endif()

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#include <Eigen/Core>
#include <iostream>
class MyVectorType : public Eigen::VectorXd
{
public:
MyVectorType(void):Eigen::VectorXd() {}
// This constructor allows you to construct MyVectorType from Eigen expressions
template<typename OtherDerived>
MyVectorType(const Eigen::MatrixBase<OtherDerived>& other)
: Eigen::VectorXd(other)
{ }
// This method allows you to assign Eigen expressions to MyVectorType
template<typename OtherDerived>
MyVectorType& operator=(const Eigen::MatrixBase <OtherDerived>& other)
{
this->Eigen::VectorXd::operator=(other);
return *this;
}
};
int main()
{
MyVectorType v = MyVectorType::Ones(4);
v(2) += 10;
v = 2 * v;
std::cout << v.transpose() << std::endl;
}

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#include <Eigen/Core>
#include <unsupported/Eigen/SpecialFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
Array4d v(-0.5,2,0,-7);
std::cout << v.erf() << std::endl;
}

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#include <Eigen/Core>
#include <unsupported/Eigen/SpecialFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
Array4d v(-0.5,2,0,-7);
std::cout << v.erfc() << std::endl;
}

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#include <Eigen/Core>
#include <unsupported/Eigen/SpecialFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
Array4d v(0.5,10,0,-1);
std::cout << v.lgamma() << std::endl;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
int main(void)
{
int const N = 5;
MatrixXi A(N,N);
A.setRandom();
cout << "A =\n" << A << '\n' << endl;
cout << "A(1..3,:) =\n" << A.middleCols(1,3) << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
int main(void)
{
int const N = 5;
MatrixXi A(N,N);
A.setRandom();
cout << "A =\n" << A << '\n' << endl;
cout << "A(2..3,:) =\n" << A.middleRows(2,2) << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
int main(void)
{
int const N = 5;
MatrixXi A(N,N);
A.setRandom();
cout << "A =\n" << A << '\n' << endl;
cout << "A(:,1..3) =\n" << A.middleCols<3>(1) << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
int main(void)
{
int const N = 5;
MatrixXi A(N,N);
A.setRandom();
cout << "A =\n" << A << '\n' << endl;
cout << "A(1..3,:) =\n" << A.middleRows<3>(1) << endl;
return 0;
}

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#include <iostream>
#include <Eigen/Dense>
using Eigen::MatrixXd;
int main()
{
MatrixXd m(2,2);
m(0,0) = 3;
m(1,0) = 2.5;
m(0,1) = -1;
m(1,1) = m(1,0) + m(0,1);
std::cout << m << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
using namespace std;
int main()
{
MatrixXd m = MatrixXd::Random(3,3);
m = (m + MatrixXd::Constant(3,3,1.2)) * 50;
cout << "m =" << endl << m << endl;
VectorXd v(3);
v << 1, 2, 3;
cout << "m * v =" << endl << m * v << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
using namespace std;
int main()
{
Matrix3d m = Matrix3d::Random();
m = (m + Matrix3d::Constant(1.2)) * 50;
cout << "m =" << endl << m << endl;
Vector3d v(1,2,3);
cout << "m * v =" << endl << m * v << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
template <typename Derived1, typename Derived2>
void copyUpperTriangularPart(MatrixBase<Derived1>& dst, const MatrixBase<Derived2>& src)
{
/* Note the 'template' keywords in the following line! */
dst.template triangularView<Upper>() = src.template triangularView<Upper>();
}
int main()
{
MatrixXi m1 = MatrixXi::Ones(5,5);
MatrixXi m2 = MatrixXi::Random(4,4);
std::cout << "m2 before copy:" << std::endl;
std::cout << m2 << std::endl << std::endl;
copyUpperTriangularPart(m2, m1.topLeftCorner(4,4));
std::cout << "m2 after copy:" << std::endl;
std::cout << m2 << std::endl << std::endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
void copyUpperTriangularPart(MatrixXf& dst, const MatrixXf& src)
{
dst.triangularView<Upper>() = src.triangularView<Upper>();
}
int main()
{
MatrixXf m1 = MatrixXf::Ones(4,4);
MatrixXf m2 = MatrixXf::Random(4,4);
std::cout << "m2 before copy:" << std::endl;
std::cout << m2 << std::endl << std::endl;
copyUpperTriangularPart(m2, m1);
std::cout << "m2 after copy:" << std::endl;
std::cout << m2 << std::endl << std::endl;
}

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#include <iostream>
struct init {
init() { std::cout << "[" << "init" << "]" << std::endl; }
};
init init_obj;
// [init]
#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
MatrixXd A(2,2);
A << 2, -1, 1, 3;
cout << "Here is the input matrix A before decomposition:\n" << A << endl;
cout << "[init]" << endl;
cout << "[declaration]" << endl;
PartialPivLU<Ref<MatrixXd> > lu(A);
cout << "Here is the input matrix A after decomposition:\n" << A << endl;
cout << "[declaration]" << endl;
cout << "[matrixLU]" << endl;
cout << "Here is the matrix storing the L and U factors:\n" << lu.matrixLU() << endl;
cout << "[matrixLU]" << endl;
cout << "[solve]" << endl;
MatrixXd A0(2,2); A0 << 2, -1, 1, 3;
VectorXd b(2); b << 1, 2;
VectorXd x = lu.solve(b);
cout << "Residual: " << (A0 * x - b).norm() << endl;
cout << "[solve]" << endl;
cout << "[modifyA]" << endl;
A << 3, 4, -2, 1;
x = lu.solve(b);
cout << "Residual: " << (A0 * x - b).norm() << endl;
cout << "[modifyA]" << endl;
cout << "[recompute]" << endl;
A0 = A; // save A
lu.compute(A);
x = lu.solve(b);
cout << "Residual: " << (A0 * x - b).norm() << endl;
cout << "[recompute]" << endl;
cout << "[recompute_bis0]" << endl;
MatrixXd A1(2,2);
A1 << 5,-2,3,4;
lu.compute(A1);
cout << "Here is the input matrix A1 after decomposition:\n" << A1 << endl;
cout << "[recompute_bis0]" << endl;
cout << "[recompute_bis1]" << endl;
x = lu.solve(b);
cout << "Residual: " << (A1 * x - b).norm() << endl;
cout << "[recompute_bis1]" << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Matrix2f A, b;
LLT<Matrix2f> llt;
A << 2, -1, -1, 3;
b << 1, 2, 3, 1;
cout << "Here is the matrix A:\n" << A << endl;
cout << "Here is the right hand side b:\n" << b << endl;
cout << "Computing LLT decomposition..." << endl;
llt.compute(A);
cout << "The solution is:\n" << llt.solve(b) << endl;
A(1,1)++;
cout << "The matrix A is now:\n" << A << endl;
cout << "Computing LLT decomposition..." << endl;
llt.compute(A);
cout << "The solution is now:\n" << llt.solve(b) << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
MatrixXd A = MatrixXd::Random(100,100);
MatrixXd b = MatrixXd::Random(100,50);
MatrixXd x = A.fullPivLu().solve(b);
double relative_error = (A*x - b).norm() / b.norm(); // norm() is L2 norm
cout << "The relative error is:\n" << relative_error << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Matrix3f A;
Vector3f b;
A << 1,2,3, 4,5,6, 7,8,10;
b << 3, 3, 4;
cout << "Here is the matrix A:\n" << A << endl;
cout << "Here is the vector b:\n" << b << endl;
Vector3f x = A.colPivHouseholderQr().solve(b);
cout << "The solution is:\n" << x << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Matrix2f A, b;
A << 2, -1, -1, 3;
b << 1, 2, 3, 1;
cout << "Here is the matrix A:\n" << A << endl;
cout << "Here is the right hand side b:\n" << b << endl;
Matrix2f x = A.ldlt().solve(b);
cout << "The solution is:\n" << x << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Matrix3f A;
A << 1, 2, 1,
2, 1, 0,
-1, 1, 2;
cout << "Here is the matrix A:\n" << A << endl;
cout << "The determinant of A is " << A.determinant() << endl;
cout << "The inverse of A is:\n" << A.inverse() << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Matrix3f A;
A << 1, 2, 5,
2, 1, 4,
3, 0, 3;
cout << "Here is the matrix A:\n" << A << endl;
FullPivLU<Matrix3f> lu_decomp(A);
cout << "The rank of A is " << lu_decomp.rank() << endl;
cout << "Here is a matrix whose columns form a basis of the null-space of A:\n"
<< lu_decomp.kernel() << endl;
cout << "Here is a matrix whose columns form a basis of the column-space of A:\n"
<< lu_decomp.image(A) << endl; // yes, have to pass the original A
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
MatrixXf A = MatrixXf::Random(3, 2);
cout << "Here is the matrix A:\n" << A << endl;
VectorXf b = VectorXf::Random(3);
cout << "Here is the right hand side b:\n" << b << endl;
cout << "The least-squares solution is:\n"
<< A.bdcSvd(ComputeThinU | ComputeThinV).solve(b) << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Matrix2f A;
A << 1, 2, 2, 3;
cout << "Here is the matrix A:\n" << A << endl;
SelfAdjointEigenSolver<Matrix2f> eigensolver(A);
if (eigensolver.info() != Success) abort();
cout << "The eigenvalues of A are:\n" << eigensolver.eigenvalues() << endl;
cout << "Here's a matrix whose columns are eigenvectors of A \n"
<< "corresponding to these eigenvalues:\n"
<< eigensolver.eigenvectors() << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Matrix2d A;
A << 2, 1,
2, 0.9999999999;
FullPivLU<Matrix2d> lu(A);
cout << "By default, the rank of A is found to be " << lu.rank() << endl;
lu.setThreshold(1e-5);
cout << "With threshold 1e-5, the rank of A is found to be " << lu.rank() << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
ArrayXXf m(2,2);
// assign some values coefficient by coefficient
m(0,0) = 1.0; m(0,1) = 2.0;
m(1,0) = 3.0; m(1,1) = m(0,1) + m(1,0);
// print values to standard output
cout << m << endl << endl;
// using the comma-initializer is also allowed
m << 1.0,2.0,
3.0,4.0;
// print values to standard output
cout << m << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
ArrayXXf a(3,3);
ArrayXXf b(3,3);
a << 1,2,3,
4,5,6,
7,8,9;
b << 1,2,3,
1,2,3,
1,2,3;
// Adding two arrays
cout << "a + b = " << endl << a + b << endl << endl;
// Subtracting a scalar from an array
cout << "a - 2 = " << endl << a - 2 << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
ArrayXf a = ArrayXf::Random(5);
a *= 2;
cout << "a =" << endl
<< a << endl;
cout << "a.abs() =" << endl
<< a.abs() << endl;
cout << "a.abs().sqrt() =" << endl
<< a.abs().sqrt() << endl;
cout << "a.min(a.abs().sqrt()) =" << endl
<< a.min(a.abs().sqrt()) << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
MatrixXf m(2,2);
MatrixXf n(2,2);
MatrixXf result(2,2);
m << 1,2,
3,4;
n << 5,6,
7,8;
result = (m.array() + 4).matrix() * m;
cout << "-- Combination 1: --" << endl << result << endl << endl;
result = (m.array() * n.array()).matrix() * m;
cout << "-- Combination 2: --" << endl << result << endl << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
MatrixXf m(2,2);
MatrixXf n(2,2);
MatrixXf result(2,2);
m << 1,2,
3,4;
n << 5,6,
7,8;
result = m * n;
cout << "-- Matrix m*n: --" << endl << result << endl << endl;
result = m.array() * n.array();
cout << "-- Array m*n: --" << endl << result << endl << endl;
result = m.cwiseProduct(n);
cout << "-- With cwiseProduct: --" << endl << result << endl << endl;
result = m.array() + 4;
cout << "-- Array m + 4: --" << endl << result << endl << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
ArrayXXf a(2,2);
ArrayXXf b(2,2);
a << 1,2,
3,4;
b << 5,6,
7,8;
cout << "a * b = " << endl << a * b << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace std;
using namespace Eigen;
int main()
{
Array22f m;
m << 1,2,
3,4;
Array44f a = Array44f::Constant(0.6);
cout << "Here is the array a:" << endl << a << endl << endl;
a.block<2,2>(1,1) = m;
cout << "Here is now a with m copied into its central 2x2 block:" << endl << a << endl << endl;
a.block(0,0,2,3) = a.block(2,1,2,3);
cout << "Here is now a with bottom-right 2x3 block copied into top-left 2x3 block:" << endl << a << endl << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace std;
int main()
{
Eigen::MatrixXf m(3,3);
m << 1,2,3,
4,5,6,
7,8,9;
cout << "Here is the matrix m:" << endl << m << endl;
cout << "2nd Row: " << m.row(1) << endl;
m.col(2) += 3 * m.col(0);
cout << "After adding 3 times the first column into the third column, the matrix m is:\n";
cout << m << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace std;
int main()
{
Eigen::Matrix4f m;
m << 1, 2, 3, 4,
5, 6, 7, 8,
9, 10,11,12,
13,14,15,16;
cout << "m.leftCols(2) =" << endl << m.leftCols(2) << endl << endl;
cout << "m.bottomRows<2>() =" << endl << m.bottomRows<2>() << endl << endl;
m.topLeftCorner(1,3) = m.bottomRightCorner(3,1).transpose();
cout << "After assignment, m = " << endl << m << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace std;
int main()
{
Eigen::MatrixXf m(4,4);
m << 1, 2, 3, 4,
5, 6, 7, 8,
9,10,11,12,
13,14,15,16;
cout << "Block in the middle" << endl;
cout << m.block<2,2>(1,1) << endl << endl;
for (int i = 1; i <= 3; ++i)
{
cout << "Block of size " << i << "x" << i << endl;
cout << m.block(0,0,i,i) << endl << endl;
}
}

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#include <Eigen/Dense>
#include <iostream>
using namespace std;
int main()
{
Eigen::ArrayXf v(6);
v << 1, 2, 3, 4, 5, 6;
cout << "v.head(3) =" << endl << v.head(3) << endl << endl;
cout << "v.tail<3>() = " << endl << v.tail<3>() << endl << endl;
v.segment(1,4) *= 2;
cout << "after 'v.segment(1,4) *= 2', v =" << endl << v << endl;
}

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#include <Eigen/Core>
#include <Eigen/LU>
#include <iostream>
using namespace std;
using namespace Eigen;
int main()
{
Matrix3f A;
Vector3f b;
A << 1,2,3, 4,5,6, 7,8,10;
b << 3, 3, 4;
cout << "Here is the matrix A:" << endl << A << endl;
cout << "Here is the vector b:" << endl << b << endl;
Vector3f x = A.lu().solve(b);
cout << "The solution is:" << endl << x << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Eigen::MatrixXf m(2,4);
Eigen::VectorXf v(2);
m << 1, 23, 6, 9,
3, 11, 7, 2;
v << 2,
3;
MatrixXf::Index index;
// find nearest neighbour
(m.colwise() - v).colwise().squaredNorm().minCoeff(&index);
cout << "Nearest neighbour is column " << index << ":" << endl;
cout << m.col(index) << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
int main()
{
Eigen::MatrixXf mat(2,4);
Eigen::VectorXf v(2);
mat << 1, 2, 6, 9,
3, 1, 7, 2;
v << 0,
1;
//add v to each column of m
mat.colwise() += v;
std::cout << "Broadcasting result: " << std::endl;
std::cout << mat << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
int main()
{
Eigen::MatrixXf mat(2,4);
Eigen::VectorXf v(4);
mat << 1, 2, 6, 9,
3, 1, 7, 2;
v << 0,1,2,3;
//add v to each row of m
mat.rowwise() += v.transpose();
std::cout << "Broadcasting result: " << std::endl;
std::cout << mat << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
int main()
{
Eigen::MatrixXf mat(2,4);
mat << 1, 2, 6, 9,
3, 1, 7, 2;
std::cout << "Column's maximum: " << std::endl
<< mat.colwise().maxCoeff() << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
MatrixXf mat(2,4);
mat << 1, 2, 6, 9,
3, 1, 7, 2;
MatrixXf::Index maxIndex;
float maxNorm = mat.colwise().sum().maxCoeff(&maxIndex);
std::cout << "Maximum sum at position " << maxIndex << std::endl;
std::cout << "The corresponding vector is: " << std::endl;
std::cout << mat.col( maxIndex ) << std::endl;
std::cout << "And its sum is is: " << maxNorm << std::endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace std;
using namespace Eigen;
int main()
{
ArrayXXf a(2,2);
a << 1,2,
3,4;
cout << "(a > 0).all() = " << (a > 0).all() << endl;
cout << "(a > 0).any() = " << (a > 0).any() << endl;
cout << "(a > 0).count() = " << (a > 0).count() << endl;
cout << endl;
cout << "(a > 2).all() = " << (a > 2).all() << endl;
cout << "(a > 2).any() = " << (a > 2).any() << endl;
cout << "(a > 2).count() = " << (a > 2).count() << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace std;
using namespace Eigen;
int main()
{
VectorXf v(2);
MatrixXf m(2,2), n(2,2);
v << -1,
2;
m << 1,-2,
-3,4;
cout << "v.squaredNorm() = " << v.squaredNorm() << endl;
cout << "v.norm() = " << v.norm() << endl;
cout << "v.lpNorm<1>() = " << v.lpNorm<1>() << endl;
cout << "v.lpNorm<Infinity>() = " << v.lpNorm<Infinity>() << endl;
cout << endl;
cout << "m.squaredNorm() = " << m.squaredNorm() << endl;
cout << "m.norm() = " << m.norm() << endl;
cout << "m.lpNorm<1>() = " << m.lpNorm<1>() << endl;
cout << "m.lpNorm<Infinity>() = " << m.lpNorm<Infinity>() << endl;
}

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#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
MatrixXf m(2,2);
m << 1,-2,
-3,4;
cout << "1-norm(m) = " << m.cwiseAbs().colwise().sum().maxCoeff()
<< " == " << m.colwise().lpNorm<1>().maxCoeff() << endl;
cout << "infty-norm(m) = " << m.cwiseAbs().rowwise().sum().maxCoeff()
<< " == " << m.rowwise().lpNorm<1>().maxCoeff() << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
int main()
{
Eigen::MatrixXf mat(2,4);
mat << 1, 2, 6, 9,
3, 1, 7, 2;
std::cout << "Row's maximum: " << std::endl
<< mat.rowwise().maxCoeff() << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
using namespace Eigen;
int main()
{
Eigen::MatrixXf m(2,2);
m << 1, 2,
3, 4;
//get location of maximum
MatrixXf::Index maxRow, maxCol;
float max = m.maxCoeff(&maxRow, &maxCol);
//get location of minimum
MatrixXf::Index minRow, minCol;
float min = m.minCoeff(&minRow, &minCol);
cout << "Max: " << max << ", at: " <<
maxRow << "," << maxCol << endl;
cout << "Min: " << min << ", at: " <<
minRow << "," << minCol << endl;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
int main()
{
for (int size=1; size<=4; ++size)
{
MatrixXi m(size,size+1); // a (size)x(size+1)-matrix of int's
for (int j=0; j<m.cols(); ++j) // loop over columns
for (int i=0; i<m.rows(); ++i) // loop over rows
m(i,j) = i+j*size; // to access matrix coefficients,
// use operator()(int,int)
std::cout << m << "\n\n";
}
VectorXf v(4); // a vector of 4 float's
// to access vector coefficients, use either operator () or operator []
v[0] = 1; v[1] = 2; v(2) = 3; v(3) = 4;
std::cout << "\nv:\n" << v << std::endl;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
int main()
{
Matrix3f m3;
m3 << 1, 2, 3, 4, 5, 6, 7, 8, 9;
Matrix4f m4 = Matrix4f::Identity();
Vector4i v4(1, 2, 3, 4);
std::cout << "m3\n" << m3 << "\nm4:\n"
<< m4 << "\nv4:\n" << v4 << std::endl;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
template<typename Derived>
Eigen::Block<Derived>
topLeftCorner(MatrixBase<Derived>& m, int rows, int cols)
{
return Eigen::Block<Derived>(m.derived(), 0, 0, rows, cols);
}
template<typename Derived>
const Eigen::Block<const Derived>
topLeftCorner(const MatrixBase<Derived>& m, int rows, int cols)
{
return Eigen::Block<const Derived>(m.derived(), 0, 0, rows, cols);
}
int main(int, char**)
{
Matrix4d m = Matrix4d::Identity();
cout << topLeftCorner(4*m, 2, 3) << endl; // calls the const version
topLeftCorner(m, 2, 3) *= 5; // calls the non-const version
cout << "Now the matrix m is:" << endl << m << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
// define a custom template binary functor
template<typename Scalar> struct MakeComplexOp {
EIGEN_EMPTY_STRUCT_CTOR(MakeComplexOp)
typedef complex<Scalar> result_type;
complex<Scalar> operator()(const Scalar& a, const Scalar& b) const { return complex<Scalar>(a,b); }
};
int main(int, char**)
{
Matrix4d m1 = Matrix4d::Random(), m2 = Matrix4d::Random();
cout << m1.binaryExpr(m2, MakeComplexOp<double>()) << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
// define a custom template unary functor
template<typename Scalar>
struct CwiseClampOp {
CwiseClampOp(const Scalar& inf, const Scalar& sup) : m_inf(inf), m_sup(sup) {}
const Scalar operator()(const Scalar& x) const { return x<m_inf ? m_inf : (x>m_sup ? m_sup : x); }
Scalar m_inf, m_sup;
};
int main(int, char**)
{
Matrix4d m1 = Matrix4d::Random();
cout << m1 << endl << "becomes: " << endl << m1.unaryExpr(CwiseClampOp<double>(-0.5,0.5)) << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
// define function to be applied coefficient-wise
double ramp(double x)
{
if (x > 0)
return x;
else
return 0;
}
int main(int, char**)
{
Matrix4d m1 = Matrix4d::Random();
cout << m1 << endl << "becomes: " << endl << m1.unaryExpr(ptr_fun(ramp)) << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
template<typename Derived>
Eigen::Block<Derived, 2, 2>
topLeft2x2Corner(MatrixBase<Derived>& m)
{
return Eigen::Block<Derived, 2, 2>(m.derived(), 0, 0);
}
template<typename Derived>
const Eigen::Block<const Derived, 2, 2>
topLeft2x2Corner(const MatrixBase<Derived>& m)
{
return Eigen::Block<const Derived, 2, 2>(m.derived(), 0, 0);
}
int main(int, char**)
{
Matrix3d m = Matrix3d::Identity();
cout << topLeft2x2Corner(4*m) << endl; // calls the const version
topLeft2x2Corner(m) *= 2; // calls the non-const version
cout << "Now the matrix m is:" << endl << m << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
template<typename Derived>
Eigen::VectorBlock<Derived, 2>
firstTwo(MatrixBase<Derived>& v)
{
return Eigen::VectorBlock<Derived, 2>(v.derived(), 0);
}
template<typename Derived>
const Eigen::VectorBlock<const Derived, 2>
firstTwo(const MatrixBase<Derived>& v)
{
return Eigen::VectorBlock<const Derived, 2>(v.derived(), 0);
}
int main(int, char**)
{
Matrix<int,1,6> v; v << 1,2,3,4,5,6;
cout << firstTwo(4*v) << endl; // calls the const version
firstTwo(v) *= 2; // calls the non-const version
cout << "Now the vector v is:" << endl << v << endl;
return 0;
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
template<typename Derived>
Eigen::VectorBlock<Derived>
segmentFromRange(MatrixBase<Derived>& v, int start, int end)
{
return Eigen::VectorBlock<Derived>(v.derived(), start, end-start);
}
template<typename Derived>
const Eigen::VectorBlock<const Derived>
segmentFromRange(const MatrixBase<Derived>& v, int start, int end)
{
return Eigen::VectorBlock<const Derived>(v.derived(), start, end-start);
}
int main(int, char**)
{
Matrix<int,1,6> v; v << 1,2,3,4,5,6;
cout << segmentFromRange(2*v, 2, 4) << endl; // calls the const version
segmentFromRange(v, 1, 3) *= 5; // calls the non-const version
cout << "Now the vector v is:" << endl << v << endl;
return 0;
}

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#include <iostream>
#include <Eigen/Core>
using namespace Eigen;
template <typename Derived>
void print_size(const EigenBase<Derived>& b)
{
std::cout << "size (rows, cols): " << b.size() << " (" << b.rows()
<< ", " << b.cols() << ")" << std::endl;
}
int main()
{
Vector3f v;
print_size(v);
// v.asDiagonal() returns a 3x3 diagonal matrix pseudo-expression
print_size(v.asDiagonal());
}

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#include <iostream>
#include <Eigen/SVD>
using namespace Eigen;
using namespace std;
float inv_cond(const Ref<const MatrixXf>& a)
{
const VectorXf sing_vals = a.jacobiSvd().singularValues();
return sing_vals(sing_vals.size()-1) / sing_vals(0);
}
int main()
{
Matrix4f m = Matrix4f::Random();
cout << "matrix m:" << endl << m << endl << endl;
cout << "inv_cond(m): " << inv_cond(m) << endl;
cout << "inv_cond(m(1:3,1:3)): " << inv_cond(m.topLeftCorner(3,3)) << endl;
cout << "inv_cond(m+I): " << inv_cond(m+Matrix4f::Identity()) << endl;
}

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/*
This program is presented in several fragments in the doc page.
Every fragment is in its own file; this file simply combines them.
*/
#include "make_circulant.cpp.preamble"
#include "make_circulant.cpp.traits"
#include "make_circulant.cpp.expression"
#include "make_circulant.cpp.evaluator"
#include "make_circulant.cpp.entry"
#include "make_circulant.cpp.main"

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template <class ArgType>
Circulant<ArgType> makeCirculant(const Eigen::MatrixBase<ArgType>& arg)
{
return Circulant<ArgType>(arg.derived());
}

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namespace Eigen {
namespace internal {
template<typename ArgType>
struct evaluator<Circulant<ArgType> >
: evaluator_base<Circulant<ArgType> >
{
typedef Circulant<ArgType> XprType;
typedef typename nested_eval<ArgType, XprType::ColsAtCompileTime>::type ArgTypeNested;
typedef typename remove_all<ArgTypeNested>::type ArgTypeNestedCleaned;
typedef typename XprType::CoeffReturnType CoeffReturnType;
enum {
CoeffReadCost = evaluator<ArgTypeNestedCleaned>::CoeffReadCost,
Flags = Eigen::ColMajor
};
evaluator(const XprType& xpr)
: m_argImpl(xpr.m_arg), m_rows(xpr.rows())
{ }
CoeffReturnType coeff(Index row, Index col) const
{
Index index = row - col;
if (index < 0) index += m_rows;
return m_argImpl.coeff(index);
}
evaluator<ArgTypeNestedCleaned> m_argImpl;
const Index m_rows;
};
}
}

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template <class ArgType>
class Circulant : public Eigen::MatrixBase<Circulant<ArgType> >
{
public:
Circulant(const ArgType& arg)
: m_arg(arg)
{
EIGEN_STATIC_ASSERT(ArgType::ColsAtCompileTime == 1,
YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX);
}
typedef typename Eigen::internal::ref_selector<Circulant>::type Nested;
typedef Eigen::Index Index;
Index rows() const { return m_arg.rows(); }
Index cols() const { return m_arg.rows(); }
typedef typename Eigen::internal::ref_selector<ArgType>::type ArgTypeNested;
ArgTypeNested m_arg;
};

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int main()
{
Eigen::VectorXd vec(4);
vec << 1, 2, 4, 8;
Eigen::MatrixXd mat;
mat = makeCirculant(vec);
std::cout << mat << std::endl;
}

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#include <Eigen/Core>
#include <iostream>
template <class ArgType> class Circulant;

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namespace Eigen {
namespace internal {
template <class ArgType>
struct traits<Circulant<ArgType> >
{
typedef Eigen::Dense StorageKind;
typedef Eigen::MatrixXpr XprKind;
typedef typename ArgType::StorageIndex StorageIndex;
typedef typename ArgType::Scalar Scalar;
enum {
Flags = Eigen::ColMajor,
RowsAtCompileTime = ArgType::RowsAtCompileTime,
ColsAtCompileTime = ArgType::RowsAtCompileTime,
MaxRowsAtCompileTime = ArgType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = ArgType::MaxRowsAtCompileTime
};
};
}
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
// [circulant_func]
template<class ArgType>
class circulant_functor {
const ArgType &m_vec;
public:
circulant_functor(const ArgType& arg) : m_vec(arg) {}
const typename ArgType::Scalar& operator() (Index row, Index col) const {
Index index = row - col;
if (index < 0) index += m_vec.size();
return m_vec(index);
}
};
// [circulant_func]
// [square]
template<class ArgType>
struct circulant_helper {
typedef Matrix<typename ArgType::Scalar,
ArgType::SizeAtCompileTime,
ArgType::SizeAtCompileTime,
ColMajor,
ArgType::MaxSizeAtCompileTime,
ArgType::MaxSizeAtCompileTime> MatrixType;
};
// [square]
// [makeCirculant]
template <class ArgType>
CwiseNullaryOp<circulant_functor<ArgType>, typename circulant_helper<ArgType>::MatrixType>
makeCirculant(const Eigen::MatrixBase<ArgType>& arg)
{
typedef typename circulant_helper<ArgType>::MatrixType MatrixType;
return MatrixType::NullaryExpr(arg.size(), arg.size(), circulant_functor<ArgType>(arg.derived()));
}
// [makeCirculant]
// [main]
int main()
{
Eigen::VectorXd vec(4);
vec << 1, 2, 4, 8;
Eigen::MatrixXd mat;
mat = makeCirculant(vec);
std::cout << mat << std::endl;
}
// [main]

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#include <iostream>
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/IterativeLinearSolvers>
#include <unsupported/Eigen/IterativeSolvers>
class MatrixReplacement;
using Eigen::SparseMatrix;
namespace Eigen {
namespace internal {
// MatrixReplacement looks-like a SparseMatrix, so let's inherits its traits:
template<>
struct traits<MatrixReplacement> : public Eigen::internal::traits<Eigen::SparseMatrix<double> >
{};
}
}
// Example of a matrix-free wrapper from a user type to Eigen's compatible type
// For the sake of simplicity, this example simply wrap a Eigen::SparseMatrix.
class MatrixReplacement : public Eigen::EigenBase<MatrixReplacement> {
public:
// Required typedefs, constants, and method:
typedef double Scalar;
typedef double RealScalar;
typedef int StorageIndex;
enum {
ColsAtCompileTime = Eigen::Dynamic,
MaxColsAtCompileTime = Eigen::Dynamic,
IsRowMajor = false
};
Index rows() const { return mp_mat->rows(); }
Index cols() const { return mp_mat->cols(); }
template<typename Rhs>
Eigen::Product<MatrixReplacement,Rhs,Eigen::AliasFreeProduct> operator*(const Eigen::MatrixBase<Rhs>& x) const {
return Eigen::Product<MatrixReplacement,Rhs,Eigen::AliasFreeProduct>(*this, x.derived());
}
// Custom API:
MatrixReplacement() : mp_mat(0) {}
void attachMyMatrix(const SparseMatrix<double> &mat) {
mp_mat = &mat;
}
const SparseMatrix<double> my_matrix() const { return *mp_mat; }
private:
const SparseMatrix<double> *mp_mat;
};
// Implementation of MatrixReplacement * Eigen::DenseVector though a specialization of internal::generic_product_impl:
namespace Eigen {
namespace internal {
template<typename Rhs>
struct generic_product_impl<MatrixReplacement, Rhs, SparseShape, DenseShape, GemvProduct> // GEMV stands for matrix-vector
: generic_product_impl_base<MatrixReplacement,Rhs,generic_product_impl<MatrixReplacement,Rhs> >
{
typedef typename Product<MatrixReplacement,Rhs>::Scalar Scalar;
template<typename Dest>
static void scaleAndAddTo(Dest& dst, const MatrixReplacement& lhs, const Rhs& rhs, const Scalar& alpha)
{
// This method should implement "dst += alpha * lhs * rhs" inplace,
// however, for iterative solvers, alpha is always equal to 1, so let's not bother about it.
assert(alpha==Scalar(1) && "scaling is not implemented");
EIGEN_ONLY_USED_FOR_DEBUG(alpha);
// Here we could simply call dst.noalias() += lhs.my_matrix() * rhs,
// but let's do something fancier (and less efficient):
for(Index i=0; i<lhs.cols(); ++i)
dst += rhs(i) * lhs.my_matrix().col(i);
}
};
}
}
int main()
{
int n = 10;
Eigen::SparseMatrix<double> S = Eigen::MatrixXd::Random(n,n).sparseView(0.5,1);
S = S.transpose()*S;
MatrixReplacement A;
A.attachMyMatrix(S);
Eigen::VectorXd b(n), x;
b.setRandom();
// Solve Ax = b using various iterative solver with matrix-free version:
{
Eigen::ConjugateGradient<MatrixReplacement, Eigen::Lower|Eigen::Upper, Eigen::IdentityPreconditioner> cg;
cg.compute(A);
x = cg.solve(b);
std::cout << "CG: #iterations: " << cg.iterations() << ", estimated error: " << cg.error() << std::endl;
}
{
Eigen::BiCGSTAB<MatrixReplacement, Eigen::IdentityPreconditioner> bicg;
bicg.compute(A);
x = bicg.solve(b);
std::cout << "BiCGSTAB: #iterations: " << bicg.iterations() << ", estimated error: " << bicg.error() << std::endl;
}
{
Eigen::GMRES<MatrixReplacement, Eigen::IdentityPreconditioner> gmres;
gmres.compute(A);
x = gmres.solve(b);
std::cout << "GMRES: #iterations: " << gmres.iterations() << ", estimated error: " << gmres.error() << std::endl;
}
{
Eigen::DGMRES<MatrixReplacement, Eigen::IdentityPreconditioner> gmres;
gmres.compute(A);
x = gmres.solve(b);
std::cout << "DGMRES: #iterations: " << gmres.iterations() << ", estimated error: " << gmres.error() << std::endl;
}
{
Eigen::MINRES<MatrixReplacement, Eigen::Lower|Eigen::Upper, Eigen::IdentityPreconditioner> minres;
minres.compute(A);
x = minres.solve(b);
std::cout << "MINRES: #iterations: " << minres.iterations() << ", estimated error: " << minres.error() << std::endl;
}
}

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#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
// [functor]
template<class ArgType, class RowIndexType, class ColIndexType>
class indexing_functor {
const ArgType &m_arg;
const RowIndexType &m_rowIndices;
const ColIndexType &m_colIndices;
public:
typedef Matrix<typename ArgType::Scalar,
RowIndexType::SizeAtCompileTime,
ColIndexType::SizeAtCompileTime,
ArgType::Flags&RowMajorBit?RowMajor:ColMajor,
RowIndexType::MaxSizeAtCompileTime,
ColIndexType::MaxSizeAtCompileTime> MatrixType;
indexing_functor(const ArgType& arg, const RowIndexType& row_indices, const ColIndexType& col_indices)
: m_arg(arg), m_rowIndices(row_indices), m_colIndices(col_indices)
{}
const typename ArgType::Scalar& operator() (Index row, Index col) const {
return m_arg(m_rowIndices[row], m_colIndices[col]);
}
};
// [functor]
// [function]
template <class ArgType, class RowIndexType, class ColIndexType>
CwiseNullaryOp<indexing_functor<ArgType,RowIndexType,ColIndexType>, typename indexing_functor<ArgType,RowIndexType,ColIndexType>::MatrixType>
indexing(const Eigen::MatrixBase<ArgType>& arg, const RowIndexType& row_indices, const ColIndexType& col_indices)
{
typedef indexing_functor<ArgType,RowIndexType,ColIndexType> Func;
typedef typename Func::MatrixType MatrixType;
return MatrixType::NullaryExpr(row_indices.size(), col_indices.size(), Func(arg.derived(), row_indices, col_indices));
}
// [function]
int main()
{
std::cout << "[main1]\n";
Eigen::MatrixXi A = Eigen::MatrixXi::Random(4,4);
Array3i ri(1,2,1);
ArrayXi ci(6); ci << 3,2,1,0,0,2;
Eigen::MatrixXi B = indexing(A, ri, ci);
std::cout << "A =" << std::endl;
std::cout << A << std::endl << std::endl;
std::cout << "A([" << ri.transpose() << "], [" << ci.transpose() << "]) =" << std::endl;
std::cout << B << std::endl;
std::cout << "[main1]\n";
std::cout << "[main2]\n";
B = indexing(A, ri+1, ci);
std::cout << "A(ri+1,ci) =" << std::endl;
std::cout << B << std::endl << std::endl;
#if __cplusplus >= 201103L
B = indexing(A, ArrayXi::LinSpaced(13,0,12).unaryExpr([](int x){return x%4;}), ArrayXi::LinSpaced(4,0,3));
std::cout << "A(ArrayXi::LinSpaced(13,0,12).unaryExpr([](int x){return x%4;}), ArrayXi::LinSpaced(4,0,3)) =" << std::endl;
std::cout << B << std::endl << std::endl;
#endif
std::cout << "[main2]\n";
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
Matrix2d a;
a << 1, 2,
3, 4;
MatrixXd b(2,2);
b << 2, 3,
1, 4;
std::cout << "a + b =\n" << a + b << std::endl;
std::cout << "a - b =\n" << a - b << std::endl;
std::cout << "Doing a += b;" << std::endl;
a += b;
std::cout << "Now a =\n" << a << std::endl;
Vector3d v(1,2,3);
Vector3d w(1,0,0);
std::cout << "-v + w - v =\n" << -v + w - v << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
using namespace std;
int main()
{
Vector3d v(1,2,3);
Vector3d w(0,1,2);
cout << "Dot product: " << v.dot(w) << endl;
double dp = v.adjoint()*w; // automatic conversion of the inner product to a scalar
cout << "Dot product via a matrix product: " << dp << endl;
cout << "Cross product:\n" << v.cross(w) << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
Matrix2d mat;
mat << 1, 2,
3, 4;
Vector2d u(-1,1), v(2,0);
std::cout << "Here is mat*mat:\n" << mat*mat << std::endl;
std::cout << "Here is mat*u:\n" << mat*u << std::endl;
std::cout << "Here is u^T*mat:\n" << u.transpose()*mat << std::endl;
std::cout << "Here is u^T*v:\n" << u.transpose()*v << std::endl;
std::cout << "Here is u*v^T:\n" << u*v.transpose() << std::endl;
std::cout << "Let's multiply mat by itself" << std::endl;
mat = mat*mat;
std::cout << "Now mat is mat:\n" << mat << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace std;
int main()
{
Eigen::Matrix2d mat;
mat << 1, 2,
3, 4;
cout << "Here is mat.sum(): " << mat.sum() << endl;
cout << "Here is mat.prod(): " << mat.prod() << endl;
cout << "Here is mat.mean(): " << mat.mean() << endl;
cout << "Here is mat.minCoeff(): " << mat.minCoeff() << endl;
cout << "Here is mat.maxCoeff(): " << mat.maxCoeff() << endl;
cout << "Here is mat.trace(): " << mat.trace() << endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
Matrix2d a;
a << 1, 2,
3, 4;
Vector3d v(1,2,3);
std::cout << "a * 2.5 =\n" << a * 2.5 << std::endl;
std::cout << "0.1 * v =\n" << 0.1 * v << std::endl;
std::cout << "Doing v *= 2;" << std::endl;
v *= 2;
std::cout << "Now v =\n" << v << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
MatrixXd m(2,2);
m(0,0) = 3;
m(1,0) = 2.5;
m(0,1) = -1;
m(1,1) = m(1,0) + m(0,1);
std::cout << "Here is the matrix m:\n" << m << std::endl;
VectorXd v(2);
v(0) = 4;
v(1) = v(0) - 1;
std::cout << "Here is the vector v:\n" << v << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
MatrixXd m(2,5);
m.resize(4,3);
std::cout << "The matrix m is of size "
<< m.rows() << "x" << m.cols() << std::endl;
std::cout << "It has " << m.size() << " coefficients" << std::endl;
VectorXd v(2);
v.resize(5);
std::cout << "The vector v is of size " << v.size() << std::endl;
std::cout << "As a matrix, v is of size "
<< v.rows() << "x" << v.cols() << std::endl;
}

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#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
Matrix4d m;
m.resize(4,4); // no operation
std::cout << "The matrix m is of size "
<< m.rows() << "x" << m.cols() << std::endl;
}