1// Ceres Solver - A fast non-linear least squares minimizer
2// Copyright 2012 Google Inc. All rights reserved.
3// http://code.google.com/p/ceres-solver/
4//
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28//
29// Author: strandmark@google.com (Petter Strandmark)
30//
31// Class for loading the data required for descibing a Fields of Experts (FoE)
32// model.
33
34#include "fields_of_experts.h"
35
36#include <fstream>
37#include <cmath>
38
39#include "pgm_image.h"
40
41namespace ceres {
42namespace examples {
43
44FieldsOfExpertsCost::FieldsOfExpertsCost(const std::vector<double>& filter)
45    : filter_(filter) {
46  set_num_residuals(1);
47  for (int i = 0; i < filter_.size(); ++i) {
48    mutable_parameter_block_sizes()->push_back(1);
49  }
50}
51
52// This is a dot product between a the scalar parameters and a vector of filter
53// coefficients.
54bool FieldsOfExpertsCost::Evaluate(double const* const* parameters,
55                                   double* residuals,
56                                   double** jacobians) const {
57  int num_variables = filter_.size();
58  residuals[0] = 0;
59  for (int i = 0; i < num_variables; ++i) {
60    residuals[0] += filter_[i] * parameters[i][0];
61  }
62
63  if (jacobians != NULL) {
64    for (int i = 0; i < num_variables; ++i) {
65      if (jacobians[i] != NULL) {
66        jacobians[i][0] = filter_[i];
67      }
68    }
69  }
70
71  return true;
72}
73
74// This loss function builds the FoE terms and is equal to
75//
76//   f(x) = alpha_i * log(1 + (1/2)s)
77//
78void FieldsOfExpertsLoss::Evaluate(double sq_norm, double rho[3]) const {
79  const double c = 0.5;
80  const double sum = 1.0 + sq_norm * c;
81  const double inv = 1.0 / sum;
82  // 'sum' and 'inv' are always positive, assuming that 's' is.
83  rho[0] = alpha_ *  log(sum);
84  rho[1] = alpha_ * c * inv;
85  rho[2] = - alpha_ * c * c * inv * inv;
86}
87
88FieldsOfExperts::FieldsOfExperts()
89    :  size_(0), num_filters_(0) {
90}
91
92bool FieldsOfExperts::LoadFromFile(const std::string& filename) {
93  std::ifstream foe_file(filename.c_str());
94  foe_file >> size_;
95  foe_file >> num_filters_;
96  if (size_ < 0 || num_filters_ < 0) {
97    return false;
98  }
99  const int num_variables = NumVariables();
100
101  x_delta_indices_.resize(num_variables);
102  for (int i = 0; i < num_variables; ++i) {
103    foe_file >> x_delta_indices_[i];
104  }
105
106  y_delta_indices_.resize(NumVariables());
107  for (int i = 0; i < num_variables; ++i) {
108    foe_file >> y_delta_indices_[i];
109  }
110
111  alpha_.resize(num_filters_);
112  for (int i = 0; i < num_filters_; ++i) {
113    foe_file >> alpha_[i];
114  }
115
116  filters_.resize(num_filters_);
117  for (int i = 0; i < num_filters_; ++i) {
118    filters_[i].resize(num_variables);
119    for (int j = 0; j < num_variables; ++j) {
120      foe_file >> filters_[i][j];
121    }
122  }
123
124  // If any read failed, return failure.
125  if (!foe_file) {
126    size_ = 0;
127    return false;
128  }
129
130  // There cannot be anything else in the file. Try reading another number and
131  // return failure if that succeeded.
132  double temp;
133  foe_file >> temp;
134  if (foe_file) {
135    size_ = 0;
136    return false;
137  }
138
139  return true;
140}
141
142ceres::CostFunction* FieldsOfExperts::NewCostFunction(int alpha_index) const {
143  return new FieldsOfExpertsCost(filters_[alpha_index]);
144}
145
146ceres::LossFunction* FieldsOfExperts::NewLossFunction(int alpha_index) const {
147  return new FieldsOfExpertsLoss(alpha_[alpha_index]);
148}
149
150
151}  // namespace examples
152}  // namespace ceres
153