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Eigen_demo: Updated Eigen and added NonLinearOptimization()

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git-svn-id: svn://ultimatepp.org/upp/trunk@14445 f0d560ea-af0d-0410-9eb7-867de7ffcac7
This commit is contained in:
koldo 2020-05-09 17:24:34 +00:00
parent b72a51a9f2
commit 1f0379af11
2 changed files with 43 additions and 5 deletions
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14
reference/Eigen_demo/eigen_demo.cpp
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@ -61,6 +61,20 @@ CONSOLE_APP_MAIN
}
Cout() << "\n\nAssignment and resizing";
{
double _dat[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17};// Assignment from C vector
VectorXd dat = Map<VectorXd>(_dat, sizeof(_dat)/sizeof(double));
Cout() << "\nC array data is " << dat.transpose();
const int dec = 5;
VectorXd decimated = Map<VectorXd, 0, InnerStride<dec>>(dat.data(), 1+((dat.size()-1)/dec));
Cout() << "\nDecimated " << decimated.transpose();
VectorXd even = Map<VectorXd, 0, InnerStride<2>>(dat.data(), (dat.size()+1)/2);
Cout() << "\nEven " << even.transpose();
VectorXd odd = Map<VectorXd, 0, InnerStride<2>>(dat.data()+1, dat.size()/2);
Cout() << "\nOdd " << odd.transpose();
MatrixXf a(2,2);
Cout() << "\na is of size " << a.rows() << "x" << a.cols();
MatrixXf b(3,3);

34
reference/Eigen_demo/non-linear.cpp
View file

@ -23,13 +23,15 @@ const double Eckerle4_functor::x[35] = {400.0, 405.0, 410.0, 415.0, 420.0, 425.0
const double Eckerle4_functor::y[35] = {0.0001575, 0.0001699, 0.0002350, 0.0003102, 0.0004917, 0.0008710, 0.0017418, 0.0046400, 0.0065895, 0.0097302, 0.0149002, 0.0237310, 0.0401683, 0.0712559, 0.1264458, 0.2073413, 0.2902366, 0.3445623, 0.3698049, 0.3668534, 0.3106727, 0.2078154, 0.1164354, 0.0616764, 0.0337200, 0.0194023, 0.0117831, 0.0074357, 0.0022732, 0.0008800, 0.0004579, 0.0002345, 0.0001586, 0.0001143, 0.0000710 };
struct Thurber_functor : NonLinearOptimizationFunctor<double> {
static Vector<double> _x, _y;
static VectorXd _x, _y;
Thurber_functor() : NonLinearOptimizationFunctor() {
_x << -3.067E0 << -2.981E0 << -2.921E0 << -2.912E0 << -2.840E0 << -2.797E0 << -2.702E0 << -2.699E0 << -2.633E0 << -2.481E0 << -2.363E0 << -2.322E0 << -1.501E0 << -1.460E0 << -1.274E0 << -1.212E0 << -1.100E0 << -1.046E0 << -0.915E0 << -0.714E0 << -0.566E0 << -0.545E0 << -0.400E0 << -0.309E0 << -0.109E0 << -0.103E0 << 0.010E0 << 0.119E0 << 0.377E0 << 0.790E0 << 0.963E0 << 1.006E0 << 1.115E0 << 1.572E0 << 1.841E0 << 2.047E0 << 2.200E0;
_y << 80.574E0 << 84.248E0 << 87.264E0 << 87.195E0 << 89.076E0 << 89.608E0 << 89.868E0 << 90.101E0 << 92.405E0 << 95.854E0 << 100.696E0 << 101.060E0 << 401.672E0 << 390.724E0 << 567.534E0 << 635.316E0 << 733.054E0 << 759.087E0 << 894.206E0 << 990.785E0 << 1090.109E0 << 1080.914E0 << 1122.643E0 << 1178.351E0 << 1260.531E0 << 1273.514E0 << 1288.339E0 << 1327.543E0 << 1353.863E0 << 1414.509E0 << 1425.208E0 << 1421.384E0 << 1442.962E0 << 1464.350E0 << 1468.705E0 << 1447.894E0 << 1457.628E0;
double x[] = {-3.067, -2.981, -2.921, -2.912, -2.840, -2.797, -2.702, -2.699, -2.633, -2.481, -2.363, -2.322, -1.501, -1.460, -1.274, -1.212, -1.100, -1.046, -0.915, -0.714, -0.566, -0.545, -0.400, -0.309, -0.109, -0.103, 0.010, 0.119, 0.377, 0.790, 0.963, 1.006, 1.115, 1.572, 1.841, 2.047, 2.200};
double y[] = {80.574, 84.248, 87.264, 87.195, 89.076, 89.608, 89.868, 90.101, 92.405, 95.854, 100.696, 101.060, 401.672, 390.724, 567.534, 635.316, 733.054, 759.087, 894.206, 990.785, 1090.109, 1080.914, 1122.643, 1178.351, 1260.531, 1273.514, 1288.339, 1327.543, 1353.863, 1414.509, 1425.208, 1421.384, 1442.962, 1464.350, 1468.705, 1447.894, 1457.628};
_x = Map<VectorXd>(x, sizeof(x)/sizeof(double));
_y = Map<VectorXd>(y, sizeof(y)/sizeof(double));
unknowns = 7;
datasetLen = _x.GetCount();
datasetLen = _x.size();
}
int operator()(const VectorXd &b, VectorXd &fvec) const {
@ -43,7 +45,7 @@ struct Thurber_functor : NonLinearOptimizationFunctor<double> {
}
};
Vector<double> Thurber_functor::_x, Thurber_functor::_y;
VectorXd Thurber_functor::_x, Thurber_functor::_y;
void NonLinearOptimization() {
Cout() << "\n\nNon linear equations optimization using Levenberg Marquardt based on Minpack\n"
@ -73,6 +75,28 @@ void NonLinearOptimization() {
Cout() << "\nNon-linear function optimization is right!";
}
}
{
Cout() << "\n\nThis is a simpler way, using NonLinearOptimization()";
double x[] = {400.0, 405.0, 410.0, 415.0, 420.0, 425.0, 430.0, 435.0, 436.5, 438.0, 439.5, 441.0, 442.5, 444.0, 445.5, 447.0, 448.5, 450.0, 451.5, 453.0, 454.5, 456.0, 457.5, 459.0, 460.5, 462.0, 463.5, 465.0, 470.0, 475.0, 480.0, 485.0, 490.0, 495.0, 500.0};
double y[] = {0.0001575, 0.0001699, 0.0002350, 0.0003102, 0.0004917, 0.0008710, 0.0017418, 0.0046400, 0.0065895, 0.0097302, 0.0149002, 0.0237310, 0.0401683, 0.0712559, 0.1264458, 0.2073413, 0.2902366, 0.3445623, 0.3698049, 0.3668534, 0.3106727, 0.2078154, 0.1164354, 0.0616764, 0.0337200, 0.0194023, 0.0117831, 0.0074357, 0.0022732, 0.0008800, 0.0004579, 0.0002345, 0.0001586, 0.0001143, 0.0000710 };
int num = sizeof(x)/sizeof(double);
VectorXd coeff(3);
coeff << 1., 10., 500.;
if(!NonLinearOptimization<VectorXd>(coeff, num, [&](const VectorXd &b, VectorXd &err)->int {
for(int i = 0; i < num; i++)
err[i] = b[0]/b[1] * exp(-0.5*(x[i]-b[2])*(x[i]-b[2])/(b[1]*b[1])) - y[i];
return 0;
}))
Cout() << "\nNo convergence!";
else {
if (VerifyIsApprox(coeff[0], 1.5543827178) &&
VerifyIsApprox(coeff[1], 4.0888321754) &&
VerifyIsApprox(coeff[2], 4.5154121844E+02))
Cout() << "\nNon-linear function optimization is right!";
else
Cout() << "\nBad results??";
}
}
{
Cout() << "\n\nThurber equation\nSee: http://www.itl.nist.gov/div898/strd/nls/data/thurber.shtml\n";