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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include <iostream>
#include <fstream>
#include <iomanip>
#include "main.h"
#include <Eigen/LevenbergMarquardt>
using namespace std;
using namespace Eigen;
template <typename Scalar>
struct sparseGaussianTest : SparseFunctor<Scalar, int>
{
typedef Matrix<Scalar,Dynamic,1> VectorType;
typedef SparseFunctor<Scalar,int> Base;
typedef typename Base::JacobianType JacobianType;
sparseGaussianTest(int inputs, int values) : SparseFunctor<Scalar,int>(inputs,values)
{ }
VectorType model(const VectorType& uv, VectorType& x)
{
VectorType y; //Change this to use expression template
int m = Base::values();
int n = Base::inputs();
eigen_assert(uv.size()%2 == 0);
eigen_assert(uv.size() == n);
eigen_assert(x.size() == m);
y.setZero(m);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
Scalar coeff;
for (int j = 0; j < m; j++)
{
for (int i = 0; i < half; i++)
{
coeff = (x(j)-i)/v(i);
coeff *= coeff;
if (coeff < 1. && coeff > 0.)
y(j) += u(i)*std::pow((1-coeff), 2);
}
}
return y;
}
void initPoints(VectorType& uv_ref, VectorType& x)
{
m_x = x;
m_y = this->model(uv_ref,x);
}
int operator()(const VectorType& uv, VectorType& fvec)
{
int m = Base::values();
int n = Base::inputs();
eigen_assert(uv.size()%2 == 0);
eigen_assert(uv.size() == n);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
fvec = m_y;
Scalar coeff;
for (int j = 0; j < m; j++)
{
for (int i = 0; i < half; i++)
{
coeff = (m_x(j)-i)/v(i);
coeff *= coeff;
if (coeff < 1. && coeff > 0.)
fvec(j) -= u(i)*std::pow((1-coeff), 2);
}
}
return 0;
}
int df(const VectorType& uv, JacobianType& fjac)
{
int m = Base::values();
int n = Base::inputs();
eigen_assert(n == uv.size());
eigen_assert(fjac.rows() == m);
eigen_assert(fjac.cols() == n);
int half = n/2;
VectorBlock<const VectorType> u(uv, 0, half);
VectorBlock<const VectorType> v(uv, half, half);
Scalar coeff;
//Derivatives with respect to u
for (int col = 0; col < half; col++)
{
for (int row = 0; row < m; row++)
{
coeff = (m_x(row)-col)/v(col);
coeff = coeff*coeff;
if(coeff < 1. && coeff > 0.)
{
fjac.coeffRef(row,col) = -(1-coeff)*(1-coeff);
}
}
}
//Derivatives with respect to v
for (int col = 0; col < half; col++)
{
for (int row = 0; row < m; row++)
{
coeff = (m_x(row)-col)/v(col);
coeff = coeff*coeff;
if(coeff < 1. && coeff > 0.)
{
fjac.coeffRef(row,col+half) = -4 * (u(col)/v(col))*coeff*(1-coeff);
}
}
}
return 0;
}
VectorType m_x, m_y; //Data points
};
template<typename T>
void test_sparseLM_T()
{
typedef Matrix<T,Dynamic,1> VectorType;
int inputs = 10;
int values = 2000;
sparseGaussianTest<T> sparse_gaussian(inputs, values);
VectorType uv(inputs),uv_ref(inputs);
VectorType x(values);
// Generate the reference solution
uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3;
//Generate the reference data points
x.setRandom();
x = 10*x;
x.array() += 10;
sparse_gaussian.initPoints(uv_ref, x);
// Generate the initial parameters
VectorBlock<VectorType> u(uv, 0, inputs/2);
VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
v.setOnes();
//Generate u or Solve for u from v
u.setOnes();
// Solve the optimization problem
LevenbergMarquardt<sparseGaussianTest<T> > lm(sparse_gaussian);
int info;
// info = lm.minimize(uv);
VERIFY_IS_EQUAL(info,1);
// Do a step by step solution and save the residual
int maxiter = 200;
int iter = 0;
MatrixXd Err(values, maxiter);
MatrixXd Mod(values, maxiter);
LevenbergMarquardtSpace::Status status;
status = lm.minimizeInit(uv);
if (status==LevenbergMarquardtSpace::ImproperInputParameters)
return ;
}
void test_sparseLM()
{
CALL_SUBTEST_1(test_sparseLM_T<double>());
// CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
}
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