Home » Developing U++ » Bazaar » Added Eigen
Added Eigen [message #32762] 
Tue, 07 June 2011 07:37 

koldo
Messages: 3129 Registered: August 2008

Ultimate Member 


Hello all
Eigen library has been included to Bazaar. See here for details.
It includes matrix algebra and math algorithms.
In addition to the Eigen package, there is an Eigen_demo package with many demos from very simple ones to nonlinear equations systems solving and optimization.
U++ Bazaar Eigen packages have been cooked by Honza (dolik.rce) and koldo (me).
Best regards
Iñaki





Re: Added Eigen [message #37031 is a reply to message #32762] 
Fri, 10 August 2012 08:42 

forlano
Messages: 1100 Registered: March 2006 Location: Italy

Experienced Contributor 


koldo wrote on Tue, 07 June 2011 07:37  Hello all
Eigen library has been included to Bazaar. See here for details.
It includes matrix algebra and math algorithms.
In addition to the Eigen package, there is an Eigen_demo package with many demos from very simple ones to nonlinear equations systems solving and optimization.
U++ Bazaar Eigen packages have been cooked by Honza (dolik.rce) and koldo (me).

Hello,
in the last week I needed a robust non linear fitting algorithm. I looked for it on the net and downloaded several. Unfortunately they do not compile on my windows machine or needed other libraries.
I was giving up when I realized it was already in my computer since one year... and the best one!
Thanks for providing this excellent package.
I have modified it for my need and of course it work as expected. Here my demo for a logistic fit:
#include <Core/Core.h>
using namespace Upp;
#include <plugin/Eigen/Eigen.h>
#include <plugin/Eigen/unsupported/Eigen/NonLinearOptimization>
using namespace Eigen;
// Generic functor
template<typename _Scalar, int nx = Dynamic, int ny = Dynamic>
struct Functor {
typedef _Scalar Scalar;
enum {
InputsAtCompileTime = nx,
ValuesAtCompileTime = ny
};
typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
const int m_inputs, m_values;
Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
int inputs() const {return m_inputs;}
int values() const {return m_values;}
// you should define that in the subclass :
virtual void operator() (const InputType& x, ValueType* v, JacobianType* _j=0) const {};
};
struct LogisticA_functor : Functor<double> {
LogisticA_functor() : Functor<double>(3,15) {}
static const double x[15];
static const double y[15];
int operator()(const VectorXd &b, VectorXd &fvec) const {
ASSERT(b.size()==3);
ASSERT(fvec.size()==15);
for(int i=0; i<15; i++)
fvec[i] = b[0] / (1.0 + b[1]*exp(1.0 * b[2] * x[i]))  y[i];
return 0;
}
};
const double LogisticA_functor::x[15] = {280.0, 240.0, 200.0, 160.0, 120.0, 80.0, 40.0, 0.0, 40.0, 80.0, 120.0, 160.0, 200.0, 240.0, 280.0};
const double LogisticA_functor::y[15] = {0.061276, 0.071429, 0.091574, 0.112821, 0.132959, 0.131597, 0.167887, 0.198380, 0.221380, 0.292292, 0.351831, 0.445803, 0.497754 , 0.609337,0.632353};
void NonLinearOptimization() {
VectorXd x(3);
x << 5., 5., 0.01; // Initial values
//first run
LogisticA_functor functor;
NumericalDiff<LogisticA_functor> numDiff(functor);
LevenbergMarquardt<NumericalDiff<LogisticA_functor> > lm(numDiff);
int ret = lm.minimize(x);
if (ret == LevenbergMarquardtSpace::ImproperInputParameters 
ret == LevenbergMarquardtSpace::TooManyFunctionEvaluation)
Cout() << "\nNo convergence!: " << ret;
else {
for (int i=0; i<3; i++) Cout() << "Parameter: "<< i << " = " << x[i] << "\n";
}
//second run with new data of different length and same curve to fit
//
// x[13] = {280.0, 240.0, 200.0, 160.0, 120.0, 80.0, 40.0, 0.0, 40.0, 80.0, 120.0, 160.0, 200.0};
// y[13] = {0.061276, 0.071429, 0.091574, 0.112821, 0.132959, 0.131597, 0.167887, 0.198380, 0.221380, 0.292292, 0.351831, 0.445803, 0.497754};
// ..... ??? .....
}
CONSOLE_APP_MAIN
{ NonLinearOptimization();
}
Now, and here comes the problems, I would like to use the same curve, with the same number of parameters, but with a NEW dataset [X,Y] of different size.
I can duplicate the code (new functor and new drive) and it should work. But I need to do it up to 16 different dataset and my way is very, very silly.
It should be a way to modify the template of the functor to permit to feed at request a data set [X,Y] of N values.
Unfortunately the template structure and the operator() scary me and I do not know where to put my hands.
Can I ask a more easy way to drive the same functor with different dataset leaving unchanged the fitting curve?
Thanks a lot for your patience.
Luigi



Re: Added Eigen [message #37033 is a reply to message #37031] 
Fri, 10 August 2012 12:45 
Sender Ghost
Messages: 301 Registered: November 2008

Senior Member 


Hello, Luigi.
forlano wrote on Fri, 10 August 2012 08:42  Now, and here comes the problems, I would like to use the same curve, with the same number of parameters, but with a NEW dataset [X,Y] of different size.

Following is a possible implementation:
Toggle Spoiler
#include <Core/Core.h>
using namespace Upp;
#include <plugin/Eigen/Eigen.h>
#include <plugin/Eigen/unsupported/Eigen/NonLinearOptimization>
using namespace Eigen;
// Generic functor
template<typename _Scalar, int nx = Dynamic, int ny = Dynamic>
struct Functor {
typedef _Scalar Scalar;
enum {
InputsAtCompileTime = nx,
ValuesAtCompileTime = ny
};
typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
int m_inputs, m_values;
void SetInputsCount(int count) { m_inputs = count; }
void SetValuesCount(int count) { m_values = count; }
Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
int inputs() const {return m_inputs;}
int values() const {return m_values;}
// you should define that in the subclass :
virtual void operator() (const InputType& x, ValueType* v, JacobianType* _j=0) const {};
};
class LogisticA_functor : public Functor<double> {
protected:
double *vx;
double *vy;
public:
void Set(double *x, double *y) { vx = x; vy = y; }
int operator()(const VectorXd &b, VectorXd &fvec) const {
ASSERT(b.size()==m_inputs);
ASSERT(fvec.size()==m_values);
for(int i=0; i<m_values; i++)
fvec[i] = b[0] / (1.0 + b[1]*exp(1.0 * b[2] * vx[i]))  vy[i];
return 0;
}
};
void DoLevenbergMarquardt(LogisticA_functor& functor, VectorXd& x, double *j, double *k, int inputs, int values)
{
functor.Set(j, k);
functor.SetInputsCount(inputs);
functor.SetValuesCount(values);
NumericalDiff<LogisticA_functor> numDiff(functor);
LevenbergMarquardt<NumericalDiff<LogisticA_functor> > lm(numDiff);
int ret = lm.minimize(x);
if (ret == LevenbergMarquardtSpace::ImproperInputParameters 
ret == LevenbergMarquardtSpace::TooManyFunctionEvaluation)
Cout() << "\nNo convergence!: " << ret << '\n';
else
for (int i = 0; i < inputs; ++i)
Cout() << "Parameter: " << i << " = " << x[i] << '\n';
}
void NonLinearOptimization() {
const int inputs = 3;
VectorXd x(inputs);
x << 5., 5., 0.01; // Initial values
LogisticA_functor functor;
Cout() << "First run\n";
double jx[13] = {280.0, 240.0, 200.0, 160.0, 120.0, 80.0, 40.0, 0.0, 40.0, 80.0, 120.0, 160.0, 200.0},
jy[13] = {0.061276, 0.071429, 0.091574, 0.112821, 0.132959, 0.131597, 0.167887, 0.198380, 0.221380, 0.292292, 0.351831, 0.445803, 0.497754};
VectorXd j = x;
DoLevenbergMarquardt(functor, j, jx, jy, inputs, 13);
Cout() << "Second run with new data of different length and same curve to fit\n";
double kx[15] = {280.0, 240.0, 200.0, 160.0, 120.0, 80.0, 40.0, 0.0, 40.0, 80.0, 120.0, 160.0, 200.0, 240.0, 280.0},
ky[15] = {0.061276, 0.071429, 0.091574, 0.112821, 0.132959, 0.131597, 0.167887, 0.198380, 0.221380, 0.292292, 0.351831, 0.445803, 0.497754 , 0.609337,0.632353};
VectorXd k = x;
DoLevenbergMarquardt(functor, k, kx, ky, inputs, 15);
}
CONSOLE_APP_MAIN
{
NonLinearOptimization();
}









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