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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#include <Eigen/StdVector>
#include <unsupported/Eigen/BVH>
#include <iostream>
using namespace Eigen;
typedef AlignedBox<double, 2> Box2d;
namespace Eigen {
Box2d bounding_box(const Vector2d &v) { return Box2d(v, v); } //compute the bounding box of a single point
}
struct PointPointMinimizer //how to compute squared distances between points and rectangles
{
PointPointMinimizer() : calls(0) {}
typedef double Scalar;
double minimumOnVolumeVolume(const Box2d &r1, const Box2d &r2) { ++calls; return r1.squaredExteriorDistance(r2); }
double minimumOnVolumeObject(const Box2d &r, const Vector2d &v) { ++calls; return r.squaredExteriorDistance(v); }
double minimumOnObjectVolume(const Vector2d &v, const Box2d &r) { ++calls; return r.squaredExteriorDistance(v); }
double minimumOnObjectObject(const Vector2d &v1, const Vector2d &v2) { ++calls; return (v1 - v2).squaredNorm(); }
int calls;
};
int main()
{
typedef std::vector<Vector2d, aligned_allocator<Vector2d> > StdVectorOfVector2d;
StdVectorOfVector2d redPoints, bluePoints;
for(int i = 0; i < 100; ++i) { //initialize random set of red points and blue points
redPoints.push_back(Vector2d::Random());
bluePoints.push_back(Vector2d::Random());
}
PointPointMinimizer minimizer;
double minDistSq = std::numeric_limits<double>::max();
//brute force to find closest red-blue pair
for(int i = 0; i < (int)redPoints.size(); ++i)
for(int j = 0; j < (int)bluePoints.size(); ++j)
minDistSq = std::min(minDistSq, minimizer.minimumOnObjectObject(redPoints[i], bluePoints[j]));
std::cout << "Brute force distance = " << sqrt(minDistSq) << ", calls = " << minimizer.calls << std::endl;
//using BVH to find closest red-blue pair
minimizer.calls = 0;
KdBVH<double, 2, Vector2d> redTree(redPoints.begin(), redPoints.end()), blueTree(bluePoints.begin(), bluePoints.end()); //construct the trees
minDistSq = BVMinimize(redTree, blueTree, minimizer); //actual BVH minimization call
std::cout << "BVH distance = " << sqrt(minDistSq) << ", calls = " << minimizer.calls << std::endl;
return 0;
}

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FILE(GLOB examples_SRCS "*.cpp")
ADD_CUSTOM_TARGET(unsupported_examples)
INCLUDE_DIRECTORIES(../../../unsupported ../../../unsupported/test)
FOREACH(example_src ${examples_SRCS})
GET_FILENAME_COMPONENT(example ${example_src} NAME_WE)
ADD_EXECUTABLE(example_${example} ${example_src})
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
target_link_libraries(example_${example} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
endif()
ADD_CUSTOM_COMMAND(
TARGET example_${example}
POST_BUILD
COMMAND example_${example}
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${example}.out
)
ADD_DEPENDENCIES(unsupported_examples example_${example})
ENDFOREACH(example_src)

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#include <unsupported/Eigen/EulerAngles>
#include <iostream>
using namespace Eigen;
int main()
{
// A common Euler system by many armies around the world,
// where the first one is the azimuth(the angle from the north -
// the same angle that is show in compass)
// and the second one is elevation(the angle from the horizon)
// and the third one is roll(the angle between the horizontal body
// direction and the plane ground surface)
// Keep remembering we're using radian angles here!
typedef EulerSystem<-EULER_Z, EULER_Y, EULER_X> MyArmySystem;
typedef EulerAngles<double, MyArmySystem> MyArmyAngles;
MyArmyAngles vehicleAngles(
3.14/*PI*/ / 2, /* heading to east, notice that this angle is counter-clockwise */
-0.3, /* going down from a mountain */
0.1); /* slightly rolled to the right */
// Some Euler angles representation that our plane use.
EulerAnglesZYZd planeAngles(0.78474, 0.5271, -0.513794);
MyArmyAngles planeAnglesInMyArmyAngles = MyArmyAngles::FromRotation<true, false, false>(planeAngles);
std::cout << "vehicle angles(MyArmy): " << vehicleAngles << std::endl;
std::cout << "plane angles(ZYZ): " << planeAngles << std::endl;
std::cout << "plane angles(MyArmy): " << planeAnglesInMyArmyAngles << std::endl;
// Now lets rotate the plane a little bit
std::cout << "==========================================================\n";
std::cout << "rotating plane now!\n";
std::cout << "==========================================================\n";
Quaterniond planeRotated = AngleAxisd(-0.342, Vector3d::UnitY()) * planeAngles;
planeAngles = planeRotated;
planeAnglesInMyArmyAngles = MyArmyAngles::FromRotation<true, false, false>(planeRotated);
std::cout << "new plane angles(ZYZ): " << planeAngles << std::endl;
std::cout << "new plane angles(MyArmy): " << planeAnglesInMyArmyAngles << std::endl;
return 0;
}

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// To use the simple FFT implementation
// g++ -o demofft -I.. -Wall -O3 FFT.cpp
// To use the FFTW implementation
// g++ -o demofft -I.. -DUSE_FFTW -Wall -O3 FFT.cpp -lfftw3 -lfftw3f -lfftw3l
#ifdef USE_FFTW
#include <fftw3.h>
#endif
#include <vector>
#include <complex>
#include <algorithm>
#include <iterator>
#include <iostream>
#include <Eigen/Core>
#include <unsupported/Eigen/FFT>
using namespace std;
using namespace Eigen;
template <typename T>
T mag2(T a)
{
return a*a;
}
template <typename T>
T mag2(std::complex<T> a)
{
return norm(a);
}
template <typename T>
T mag2(const std::vector<T> & vec)
{
T out=0;
for (size_t k=0;k<vec.size();++k)
out += mag2(vec[k]);
return out;
}
template <typename T>
T mag2(const std::vector<std::complex<T> > & vec)
{
T out=0;
for (size_t k=0;k<vec.size();++k)
out += mag2(vec[k]);
return out;
}
template <typename T>
vector<T> operator-(const vector<T> & a,const vector<T> & b )
{
vector<T> c(a);
for (size_t k=0;k<b.size();++k)
c[k] -= b[k];
return c;
}
template <typename T>
void RandomFill(std::vector<T> & vec)
{
for (size_t k=0;k<vec.size();++k)
vec[k] = T( rand() )/T(RAND_MAX) - T(.5);
}
template <typename T>
void RandomFill(std::vector<std::complex<T> > & vec)
{
for (size_t k=0;k<vec.size();++k)
vec[k] = std::complex<T> ( T( rand() )/T(RAND_MAX) - T(.5), T( rand() )/T(RAND_MAX) - T(.5));
}
template <typename T_time,typename T_freq>
void fwd_inv(size_t nfft)
{
typedef typename NumTraits<T_freq>::Real Scalar;
vector<T_time> timebuf(nfft);
RandomFill(timebuf);
vector<T_freq> freqbuf;
static FFT<Scalar> fft;
fft.fwd(freqbuf,timebuf);
vector<T_time> timebuf2;
fft.inv(timebuf2,freqbuf);
T_time rmse = mag2(timebuf - timebuf2) / mag2(timebuf);
cout << "roundtrip rmse: " << rmse << endl;
}
template <typename T_scalar>
void two_demos(int nfft)
{
cout << " scalar ";
fwd_inv<T_scalar,std::complex<T_scalar> >(nfft);
cout << " complex ";
fwd_inv<std::complex<T_scalar>,std::complex<T_scalar> >(nfft);
}
void demo_all_types(int nfft)
{
cout << "nfft=" << nfft << endl;
cout << " float" << endl;
two_demos<float>(nfft);
cout << " double" << endl;
two_demos<double>(nfft);
cout << " long double" << endl;
two_demos<long double>(nfft);
}
int main()
{
demo_all_types( 2*3*4*5*7 );
demo_all_types( 2*9*16*25 );
demo_all_types( 1024 );
return 0;
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
const double pi = std::acos(-1.0);
MatrixXd A(3,3);
A << 0, -pi/4, 0,
pi/4, 0, 0,
0, 0, 0;
std::cout << "The matrix A is:\n" << A << "\n\n";
std::cout << "The matrix exponential of A is:\n" << A.exp() << "\n\n";
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
std::complex<double> expfn(std::complex<double> x, int)
{
return std::exp(x);
}
int main()
{
const double pi = std::acos(-1.0);
MatrixXd A(3,3);
A << 0, -pi/4, 0,
pi/4, 0, 0,
0, 0, 0;
std::cout << "The matrix A is:\n" << A << "\n\n";
std::cout << "The matrix exponential of A is:\n"
<< A.matrixFunction(expfn) << "\n\n";
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
using std::sqrt;
MatrixXd A(3,3);
A << 0.5*sqrt(2), -0.5*sqrt(2), 0,
0.5*sqrt(2), 0.5*sqrt(2), 0,
0, 0, 1;
std::cout << "The matrix A is:\n" << A << "\n\n";
std::cout << "The matrix logarithm of A is:\n" << A.log() << "\n";
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
const double pi = std::acos(-1.0);
Matrix3d A;
A << cos(1), -sin(1), 0,
sin(1), cos(1), 0,
0 , 0 , 1;
std::cout << "The matrix A is:\n" << A << "\n\n"
"The matrix power A^(pi/4) is:\n" << A.pow(pi/4) << std::endl;
return 0;
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
Matrix4cd A = Matrix4cd::Random();
MatrixPower<Matrix4cd> Apow(A);
std::cout << "The matrix A is:\n" << A << "\n\n"
"A^3.1 is:\n" << Apow(3.1) << "\n\n"
"A^3.3 is:\n" << Apow(3.3) << "\n\n"
"A^3.7 is:\n" << Apow(3.7) << "\n\n"
"A^3.9 is:\n" << Apow(3.9) << std::endl;
return 0;
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
MatrixXd A = MatrixXd::Random(3,3);
std::cout << "A = \n" << A << "\n\n";
MatrixXd sinA = A.sin();
std::cout << "sin(A) = \n" << sinA << "\n\n";
MatrixXd cosA = A.cos();
std::cout << "cos(A) = \n" << cosA << "\n\n";
// The matrix functions satisfy sin^2(A) + cos^2(A) = I,
// like the scalar functions.
std::cout << "sin^2(A) + cos^2(A) = \n" << sinA*sinA + cosA*cosA << "\n\n";
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
MatrixXf A = MatrixXf::Random(3,3);
std::cout << "A = \n" << A << "\n\n";
MatrixXf sinhA = A.sinh();
std::cout << "sinh(A) = \n" << sinhA << "\n\n";
MatrixXf coshA = A.cosh();
std::cout << "cosh(A) = \n" << coshA << "\n\n";
// The matrix functions satisfy cosh^2(A) - sinh^2(A) = I,
// like the scalar functions.
std::cout << "cosh^2(A) - sinh^2(A) = \n" << coshA*coshA - sinhA*sinhA << "\n\n";
}

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#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
const double pi = std::acos(-1.0);
MatrixXd A(2,2);
A << cos(pi/3), -sin(pi/3),
sin(pi/3), cos(pi/3);
std::cout << "The matrix A is:\n" << A << "\n\n";
std::cout << "The matrix square root of A is:\n" << A.sqrt() << "\n\n";
std::cout << "The square of the last matrix is:\n" << A.sqrt() * A.sqrt() << "\n";
}

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#include <unsupported/Eigen/Polynomials>
#include <vector>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
typedef Matrix<double,5,1> Vector5d;
Vector5d roots = Vector5d::Random();
cout << "Roots: " << roots.transpose() << endl;
Eigen::Matrix<double,6,1> polynomial;
roots_to_monicPolynomial( roots, polynomial );
PolynomialSolver<double,5> psolve( polynomial );
cout << "Complex roots: " << psolve.roots().transpose() << endl;
std::vector<double> realRoots;
psolve.realRoots( realRoots );
Map<Vector5d> mapRR( &realRoots[0] );
cout << "Real roots: " << mapRR.transpose() << endl;
cout << endl;
cout << "Illustration of the convergence problem with the QR algorithm: " << endl;
cout << "---------------------------------------------------------------" << endl;
Eigen::Matrix<float,7,1> hardCase_polynomial;
hardCase_polynomial <<
-0.957, 0.9219, 0.3516, 0.9453, -0.4023, -0.5508, -0.03125;
cout << "Hard case polynomial defined by floats: " << hardCase_polynomial.transpose() << endl;
PolynomialSolver<float,6> psolvef( hardCase_polynomial );
cout << "Complex roots: " << psolvef.roots().transpose() << endl;
Eigen::Matrix<float,6,1> evals;
for( int i=0; i<6; ++i ){ evals[i] = std::abs( poly_eval( hardCase_polynomial, psolvef.roots()[i] ) ); }
cout << "Norms of the evaluations of the polynomial at the roots: " << evals.transpose() << endl << endl;
cout << "Using double's almost always solves the problem for small degrees: " << endl;
cout << "-------------------------------------------------------------------" << endl;
PolynomialSolver<double,6> psolve6d( hardCase_polynomial.cast<double>() );
cout << "Complex roots: " << psolve6d.roots().transpose() << endl;
for( int i=0; i<6; ++i )
{
std::complex<float> castedRoot( psolve6d.roots()[i].real(), psolve6d.roots()[i].imag() );
evals[i] = std::abs( poly_eval( hardCase_polynomial, castedRoot ) );
}
cout << "Norms of the evaluations of the polynomial at the roots: " << evals.transpose() << endl << endl;
cout.precision(10);
cout << "The last root in float then in double: " << psolvef.roots()[5] << "\t" << psolve6d.roots()[5] << endl;
std::complex<float> castedRoot( psolve6d.roots()[5].real(), psolve6d.roots()[5].imag() );
cout << "Norm of the difference: " << std::abs( psolvef.roots()[5] - castedRoot ) << endl;
}

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#include <unsupported/Eigen/Polynomials>
#include <iostream>
using namespace Eigen;
using namespace std;
int main()
{
Vector4d roots = Vector4d::Random();
cout << "Roots: " << roots.transpose() << endl;
Eigen::Matrix<double,5,1> polynomial;
roots_to_monicPolynomial( roots, polynomial );
cout << "Polynomial: ";
for( int i=0; i<4; ++i ){ cout << polynomial[i] << ".x^" << i << "+ "; }
cout << polynomial[4] << ".x^4" << endl;
Vector4d evaluation;
for( int i=0; i<4; ++i ){
evaluation[i] = poly_eval( polynomial, roots[i] ); }
cout << "Evaluation of the polynomial at the roots: " << evaluation.transpose();
}