[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra.
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| 3 | //
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| 4 | // Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
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| 5 | // Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
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| 6 | //
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| 7 | // This Source Code Form is subject to the terms of the Mozilla
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| 8 | // Public License v. 2.0. If a copy of the MPL was not distributed
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| 9 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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| 10 | #include <iostream>
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| 11 | #include <fstream>
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| 12 | #include <iomanip>
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| 13 |
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| 14 | #include "main.h"
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| 15 | #include <Eigen/LevenbergMarquardt>
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| 16 |
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| 17 | using namespace std;
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| 18 | using namespace Eigen;
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| 19 |
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| 20 | template <typename Scalar>
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| 21 | struct sparseGaussianTest : SparseFunctor<Scalar, int>
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| 22 | {
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| 23 | typedef Matrix<Scalar,Dynamic,1> VectorType;
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| 24 | typedef SparseFunctor<Scalar,int> Base;
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| 25 | typedef typename Base::JacobianType JacobianType;
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| 26 | sparseGaussianTest(int inputs, int values) : SparseFunctor<Scalar,int>(inputs,values)
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| 27 | { }
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| 28 |
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| 29 | VectorType model(const VectorType& uv, VectorType& x)
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| 30 | {
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| 31 | VectorType y; //Change this to use expression template
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| 32 | int m = Base::values();
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| 33 | int n = Base::inputs();
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| 34 | eigen_assert(uv.size()%2 == 0);
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| 35 | eigen_assert(uv.size() == n);
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| 36 | eigen_assert(x.size() == m);
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| 37 | y.setZero(m);
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| 38 | int half = n/2;
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| 39 | VectorBlock<const VectorType> u(uv, 0, half);
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| 40 | VectorBlock<const VectorType> v(uv, half, half);
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| 41 | Scalar coeff;
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| 42 | for (int j = 0; j < m; j++)
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| 43 | {
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| 44 | for (int i = 0; i < half; i++)
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| 45 | {
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| 46 | coeff = (x(j)-i)/v(i);
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| 47 | coeff *= coeff;
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| 48 | if (coeff < 1. && coeff > 0.)
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| 49 | y(j) += u(i)*std::pow((1-coeff), 2);
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| 50 | }
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| 51 | }
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| 52 | return y;
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| 53 | }
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| 54 | void initPoints(VectorType& uv_ref, VectorType& x)
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| 55 | {
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| 56 | m_x = x;
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| 57 | m_y = this->model(uv_ref,x);
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| 58 | }
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| 59 | int operator()(const VectorType& uv, VectorType& fvec)
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| 60 | {
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| 61 | int m = Base::values();
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| 62 | int n = Base::inputs();
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| 63 | eigen_assert(uv.size()%2 == 0);
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| 64 | eigen_assert(uv.size() == n);
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| 65 | int half = n/2;
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| 66 | VectorBlock<const VectorType> u(uv, 0, half);
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| 67 | VectorBlock<const VectorType> v(uv, half, half);
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| 68 | fvec = m_y;
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| 69 | Scalar coeff;
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| 70 | for (int j = 0; j < m; j++)
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| 71 | {
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| 72 | for (int i = 0; i < half; i++)
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| 73 | {
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| 74 | coeff = (m_x(j)-i)/v(i);
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| 75 | coeff *= coeff;
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| 76 | if (coeff < 1. && coeff > 0.)
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| 77 | fvec(j) -= u(i)*std::pow((1-coeff), 2);
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| 78 | }
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| 79 | }
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| 80 | return 0;
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| 81 | }
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| 82 |
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| 83 | int df(const VectorType& uv, JacobianType& fjac)
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| 84 | {
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| 85 | int m = Base::values();
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| 86 | int n = Base::inputs();
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| 87 | eigen_assert(n == uv.size());
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| 88 | eigen_assert(fjac.rows() == m);
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| 89 | eigen_assert(fjac.cols() == n);
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| 90 | int half = n/2;
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| 91 | VectorBlock<const VectorType> u(uv, 0, half);
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| 92 | VectorBlock<const VectorType> v(uv, half, half);
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| 93 | Scalar coeff;
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| 94 |
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| 95 | //Derivatives with respect to u
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| 96 | for (int col = 0; col < half; col++)
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| 97 | {
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| 98 | for (int row = 0; row < m; row++)
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| 99 | {
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| 100 | coeff = (m_x(row)-col)/v(col);
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| 101 | coeff = coeff*coeff;
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| 102 | if(coeff < 1. && coeff > 0.)
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| 103 | {
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| 104 | fjac.coeffRef(row,col) = -(1-coeff)*(1-coeff);
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| 105 | }
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| 106 | }
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| 107 | }
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| 108 | //Derivatives with respect to v
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| 109 | for (int col = 0; col < half; col++)
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| 110 | {
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| 111 | for (int row = 0; row < m; row++)
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| 112 | {
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| 113 | coeff = (m_x(row)-col)/v(col);
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| 114 | coeff = coeff*coeff;
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| 115 | if(coeff < 1. && coeff > 0.)
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| 116 | {
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| 117 | fjac.coeffRef(row,col+half) = -4 * (u(col)/v(col))*coeff*(1-coeff);
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| 118 | }
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| 119 | }
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| 120 | }
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| 121 | return 0;
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| 122 | }
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| 123 |
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| 124 | VectorType m_x, m_y; //Data points
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| 125 | };
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| 126 |
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| 127 |
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| 128 | template<typename T>
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| 129 | void test_sparseLM_T()
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| 130 | {
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| 131 | typedef Matrix<T,Dynamic,1> VectorType;
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| 132 |
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| 133 | int inputs = 10;
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| 134 | int values = 2000;
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| 135 | sparseGaussianTest<T> sparse_gaussian(inputs, values);
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| 136 | VectorType uv(inputs),uv_ref(inputs);
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| 137 | VectorType x(values);
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| 138 | // Generate the reference solution
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| 139 | uv_ref << -2, 1, 4 ,8, 6, 1.8, 1.2, 1.1, 1.9 , 3;
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| 140 | //Generate the reference data points
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| 141 | x.setRandom();
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| 142 | x = 10*x;
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| 143 | x.array() += 10;
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| 144 | sparse_gaussian.initPoints(uv_ref, x);
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| 145 |
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| 146 |
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| 147 | // Generate the initial parameters
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| 148 | VectorBlock<VectorType> u(uv, 0, inputs/2);
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| 149 | VectorBlock<VectorType> v(uv, inputs/2, inputs/2);
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| 150 | v.setOnes();
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| 151 | //Generate u or Solve for u from v
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| 152 | u.setOnes();
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| 153 |
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| 154 | // Solve the optimization problem
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| 155 | LevenbergMarquardt<sparseGaussianTest<T> > lm(sparse_gaussian);
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| 156 | int info;
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| 157 | // info = lm.minimize(uv);
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| 158 |
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| 159 | VERIFY_IS_EQUAL(info,1);
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| 160 | // Do a step by step solution and save the residual
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| 161 | int maxiter = 200;
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| 162 | int iter = 0;
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| 163 | MatrixXd Err(values, maxiter);
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| 164 | MatrixXd Mod(values, maxiter);
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| 165 | LevenbergMarquardtSpace::Status status;
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| 166 | status = lm.minimizeInit(uv);
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| 167 | if (status==LevenbergMarquardtSpace::ImproperInputParameters)
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| 168 | return ;
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| 169 |
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| 170 | }
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| 171 | void test_sparseLM()
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| 172 | {
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| 173 | CALL_SUBTEST_1(test_sparseLM_T<double>());
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| 174 |
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| 175 | // CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
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| 176 | }
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