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Add tests for dense and sparse levenberg-Marquardt
This commit is contained in:
Desire NUENTSA
parent
2aba6645f4
commit
cc0fef9807
@ -220,6 +220,10 @@ ei_add_test(conjugate_gradient)
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ei_add_test(bicgstab)
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ei_add_test(sparselu)
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ei_add_test(levenberg_marquardt)
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# ei_add_test(denseLM)
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if(UMFPACK_FOUND)
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ei_add_test(umfpack_support "" "${UMFPACK_ALL_LIBS}")
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endif()
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@ -244,6 +248,10 @@ if(SPQR_FOUND AND CHOLMOD_FOUND)
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ei_add_test(spqr_support "" "${SPQR_ALL_LIBS}")
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endif()
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# if(SPQR_FOUND AND CHOLMOD_FOUND)
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# ei_add_test(sparseLM "" "${SPQR_ALL_LIBS}")
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# endif()
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string(TOLOWER "${CMAKE_CXX_COMPILER}" cmake_cxx_compiler_tolower)
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if(cmake_cxx_compiler_tolower MATCHES "qcc")
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set(CXX_IS_QCC "ON")
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190
test/denseLM.cpp
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190
test/denseLM.cpp
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@ -0,0 +1,190 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
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// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include <iostream>
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#include <fstream>
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#include <iomanip>
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#include "main.h"
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#include <Eigen/LevenbergMarquardt>
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using namespace std;
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using namespace Eigen;
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template<typename Scalar>
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struct DenseLM : DenseFunctor<Scalar>
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{
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typedef DenseFunctor<Scalar> Base;
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typedef typename Base::JacobianType JacobianType;
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typedef Matrix<Scalar,Dynamic,1> VectorType;
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DenseLM(int n, int m) : DenseFunctor<Scalar>(n,m)
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{ }
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VectorType model(const VectorType& uv, VectorType& x)
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{
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VectorType y; // Should change to use expression template
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int m = Base::values();
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int n = Base::inputs();
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eigen_assert(uv.size()%2 == 0);
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eigen_assert(uv.size() == n);
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eigen_assert(x.size() == m);
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y.setZero(m);
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int half = n/2;
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VectorBlock<const VectorType> u(uv, 0, half);
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VectorBlock<const VectorType> v(uv, half, half);
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for (int j = 0; j < m; j++)
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{
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for (int i = 0; i < half; i++)
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y(j) += u(i)*std::exp(-(x(j)-i)*(x(j)-i)/(v(i)*v(i)));
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}
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return y;
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}
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void initPoints(VectorType& uv_ref, VectorType& x)
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{
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m_x = x;
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m_y = this->model(uv_ref, x);
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}
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int operator()(const VectorType& uv, VectorType& fvec)
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{
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int m = Base::values();
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int n = Base::inputs();
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eigen_assert(uv.size()%2 == 0);
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eigen_assert(uv.size() == n);
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eigen_assert(fvec.size() == m);
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int half = n/2;
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VectorBlock<const VectorType> u(uv, 0, half);
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VectorBlock<const VectorType> v(uv, half, half);
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for (int j = 0; j < m; j++)
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{
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fvec(j) = m_y(j);
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for (int i = 0; i < half; i++)
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{
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fvec(j) -= u(i) *std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
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}
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}
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return 0;
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}
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int df(const VectorType& uv, JacobianType& fjac)
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{
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int m = Base::values();
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int n = Base::inputs();
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eigen_assert(n == uv.size());
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eigen_assert(fjac.rows() == m);
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eigen_assert(fjac.cols() == n);
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int half = n/2;
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VectorBlock<const VectorType> u(uv, 0, half);
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VectorBlock<const VectorType> v(uv, half, half);
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for (int j = 0; j < m; j++)
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{
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for (int i = 0; i < half; i++)
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{
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fjac.coeffRef(j,i) = -std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
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fjac.coeffRef(j,i+half) = -2.*u(i)*(m_x(j)-i)*(m_x(j)-i)/(std::pow(v(i),3)) * std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
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}
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}
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return 0;
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}
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VectorType m_x, m_y; //Data Points
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};
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template<typename FunctorType, typename VectorType>
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int test_minimizeLM(FunctorType& functor, VectorType& uv)
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{
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LevenbergMarquardt<FunctorType> lm(functor);
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LevenbergMarquardtSpace::Status info;
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info = lm.minimize(uv);
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VERIFY_IS_EQUAL(info, 1);
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//FIXME Check other parameters
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return info;
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}
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template<typename FunctorType, typename VectorType>
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int test_lmder(FunctorType& functor, VectorType& uv)
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{
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typedef typename VectorType::Scalar Scalar;
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LevenbergMarquardtSpace::Status info;
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LevenbergMarquardt<FunctorType> lm(functor);
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info = lm.lmder1(uv);
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VERIFY_IS_EQUAL(info, 1);
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//FIXME Check other parameters
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return info;
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}
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template<typename FunctorType, typename VectorType>
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int test_minimizeSteps(FunctorType& functor, VectorType& uv)
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{
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LevenbergMarquardtSpace::Status info;
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LevenbergMarquardt<FunctorType> lm(functor);
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info = lm.minimizeInit(uv);
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if (info==LevenbergMarquardtSpace::ImproperInputParameters)
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return info;
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do
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{
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info = lm.minimizeOneStep(uv);
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} while (info==LevenbergMarquardtSpace::Running);
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VERIFY_IS_EQUAL(info, 1);
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//FIXME Check other parameters
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return info;
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}
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template<typename T>
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void test_denseLM_T()
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{
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typedef Matrix<T,Dynamic,1> VectorType;
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int inputs = 10;
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int values = 1000;
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DenseLM<T> dense_gaussian(inputs, values);
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VectorType uv(inputs),uv_ref(inputs);
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VectorType x(values);
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// Generate the reference solution
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uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3;
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//Generate the reference data points
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x.setRandom();
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x = 10*x;
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x.array() += 10;
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dense_gaussian.initPoints(uv_ref, x);
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// Generate the initial parameters
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VectorBlock<VectorType> u(uv, 0, inputs/2);
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VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
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// Solve the optimization problem
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//Solve in one go
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u.setOnes(); v.setOnes();
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test_minimizeLM(dense_gaussian, uv);
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//Solve until the machine precision
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u.setOnes(); v.setOnes();
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test_lmder(dense_gaussian, uv);
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// Solve step by step
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v.setOnes(); u.setOnes();
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test_minimizeSteps(dense_gaussian, uv);
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}
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void test_denseLM()
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{
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CALL_SUBTEST_2(test_denseLM_T<double>());
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// CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
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}
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1447
test/levenberg_marquardt.cpp
Normal file
1447
test/levenberg_marquardt.cpp
Normal file
File diff suppressed because it is too large
Load Diff
176
test/sparseLM.cpp
Normal file
176
test/sparseLM.cpp
Normal file
@ -0,0 +1,176 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
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// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include <iostream>
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#include <fstream>
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#include <iomanip>
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#include "main.h"
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#include <Eigen/LevenbergMarquardt>
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using namespace std;
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using namespace Eigen;
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template <typename Scalar>
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struct sparseGaussianTest : SparseFunctor<Scalar, int>
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{
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typedef Matrix<Scalar,Dynamic,1> VectorType;
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typedef SparseFunctor<Scalar,int> Base;
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typedef typename Base::JacobianType JacobianType;
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sparseGaussianTest(int inputs, int values) : SparseFunctor<Scalar,int>(inputs,values)
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{ }
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VectorType model(const VectorType& uv, VectorType& x)
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{
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VectorType y; //Change this to use expression template
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int m = Base::values();
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int n = Base::inputs();
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eigen_assert(uv.size()%2 == 0);
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eigen_assert(uv.size() == n);
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eigen_assert(x.size() == m);
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y.setZero(m);
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int half = n/2;
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VectorBlock<const VectorType> u(uv, 0, half);
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VectorBlock<const VectorType> v(uv, half, half);
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Scalar coeff;
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for (int j = 0; j < m; j++)
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{
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for (int i = 0; i < half; i++)
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{
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coeff = (x(j)-i)/v(i);
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coeff *= coeff;
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if (coeff < 1. && coeff > 0.)
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y(j) += u(i)*std::pow((1-coeff), 2);
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}
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}
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return y;
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}
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void initPoints(VectorType& uv_ref, VectorType& x)
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{
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m_x = x;
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m_y = this->model(uv_ref,x);
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}
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int operator()(const VectorType& uv, VectorType& fvec)
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{
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int m = Base::values();
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int n = Base::inputs();
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eigen_assert(uv.size()%2 == 0);
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eigen_assert(uv.size() == n);
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int half = n/2;
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VectorBlock<const VectorType> u(uv, 0, half);
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VectorBlock<const VectorType> v(uv, half, half);
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fvec = m_y;
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Scalar coeff;
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for (int j = 0; j < m; j++)
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{
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for (int i = 0; i < half; i++)
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{
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coeff = (m_x(j)-i)/v(i);
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coeff *= coeff;
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if (coeff < 1. && coeff > 0.)
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fvec(j) -= u(i)*std::pow((1-coeff), 2);
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}
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}
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return 0;
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}
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int df(const VectorType& uv, JacobianType& fjac)
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{
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int m = Base::values();
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int n = Base::inputs();
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eigen_assert(n == uv.size());
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eigen_assert(fjac.rows() == m);
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eigen_assert(fjac.cols() == n);
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int half = n/2;
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VectorBlock<const VectorType> u(uv, 0, half);
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VectorBlock<const VectorType> v(uv, half, half);
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Scalar coeff;
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//Derivatives with respect to u
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for (int col = 0; col < half; col++)
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{
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for (int row = 0; row < m; row++)
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{
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coeff = (m_x(row)-col)/v(col);
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coeff = coeff*coeff;
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if(coeff < 1. && coeff > 0.)
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{
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fjac.coeffRef(row,col) = -(1-coeff)*(1-coeff);
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}
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}
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}
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//Derivatives with respect to v
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for (int col = 0; col < half; col++)
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{
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for (int row = 0; row < m; row++)
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{
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coeff = (m_x(row)-col)/v(col);
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coeff = coeff*coeff;
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if(coeff < 1. && coeff > 0.)
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{
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fjac.coeffRef(row,col+half) = -4 * (u(col)/v(col))*coeff*(1-coeff);
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}
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}
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}
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return 0;
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}
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VectorType m_x, m_y; //Data points
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};
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template<typename T>
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void test_sparseLM_T()
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{
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typedef Matrix<T,Dynamic,1> VectorType;
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int inputs = 10;
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int values = 2000;
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sparseGaussianTest<T> sparse_gaussian(inputs, values);
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VectorType uv(inputs),uv_ref(inputs);
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VectorType x(values);
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// Generate the reference solution
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uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3;
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//Generate the reference data points
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x.setRandom();
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x = 10*x;
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x.array() += 10;
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sparse_gaussian.initPoints(uv_ref, x);
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// Generate the initial parameters
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VectorBlock<VectorType> u(uv, 0, inputs/2);
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VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
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v.setOnes();
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//Generate u or Solve for u from v
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u.setOnes();
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// Solve the optimization problem
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LevenbergMarquardt<sparseGaussianTest<T> > lm(sparse_gaussian);
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int info;
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// info = lm.minimize(uv);
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VERIFY_IS_EQUAL(info,1);
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// Do a step by step solution and save the residual
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int maxiter = 200;
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int iter = 0;
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MatrixXd Err(values, maxiter);
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MatrixXd Mod(values, maxiter);
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LevenbergMarquardtSpace::Status status;
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status = lm.minimizeInit(uv);
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if (status==LevenbergMarquardtSpace::ImproperInputParameters)
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return ;
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}
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void test_sparseLM()
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{
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CALL_SUBTEST_1(test_sparseLM_T<double>());
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// CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
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}
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