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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// POSSIBILITY OF SUCH DAMAGE.
//
// Author: strandmark@google.com (Petter Strandmark)
//
// Class for loading the data required for describing a Fields of Experts (FoE)
// model.
#include "fields_of_experts.h"
#include <cmath>
#include <fstream>
#include "pgm_image.h"
namespace ceres::examples {
FieldsOfExpertsCost::FieldsOfExpertsCost(const std::vector<double>& filter)
: filter_(filter) {
set_num_residuals(1);
for (int i = 0; i < filter_.size(); ++i) {
mutable_parameter_block_sizes()->push_back(1);
}
}
// This is a dot product between a the scalar parameters and a vector of filter
// coefficients.
bool FieldsOfExpertsCost::Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const {
int num_variables = filter_.size();
residuals[0] = 0;
for (int i = 0; i < num_variables; ++i) {
residuals[0] += filter_[i] * parameters[i][0];
}
if (jacobians != nullptr) {
for (int i = 0; i < num_variables; ++i) {
if (jacobians[i] != nullptr) {
jacobians[i][0] = filter_[i];
}
}
}
return true;
}
// This loss function builds the FoE terms and is equal to
//
// f(x) = alpha_i * log(1 + (1/2)s)
//
void FieldsOfExpertsLoss::Evaluate(double sq_norm, double rho[3]) const {
const double c = 0.5;
const double sum = 1.0 + sq_norm * c;
const double inv = 1.0 / sum;
// 'sum' and 'inv' are always positive, assuming that 's' is.
rho[0] = alpha_ * log(sum);
rho[1] = alpha_ * c * inv;
rho[2] = -alpha_ * c * c * inv * inv;
}
FieldsOfExperts::FieldsOfExperts() : size_(0), num_filters_(0) {}
bool FieldsOfExperts::LoadFromFile(const std::string& filename) {
std::ifstream foe_file(filename.c_str());
foe_file >> size_;
foe_file >> num_filters_;
if (size_ < 0 || num_filters_ < 0) {
return false;
}
const int num_variables = NumVariables();
x_delta_indices_.resize(num_variables);
for (int i = 0; i < num_variables; ++i) {
foe_file >> x_delta_indices_[i];
}
y_delta_indices_.resize(NumVariables());
for (int i = 0; i < num_variables; ++i) {
foe_file >> y_delta_indices_[i];
}
alpha_.resize(num_filters_);
for (int i = 0; i < num_filters_; ++i) {
foe_file >> alpha_[i];
}
filters_.resize(num_filters_);
for (int i = 0; i < num_filters_; ++i) {
filters_[i].resize(num_variables);
for (int j = 0; j < num_variables; ++j) {
foe_file >> filters_[i][j];
}
}
// If any read failed, return failure.
if (!foe_file) {
size_ = 0;
return false;
}
// There cannot be anything else in the file. Try reading another number and
// return failure if that succeeded.
double temp;
foe_file >> temp;
if (foe_file) {
size_ = 0;
return false;
}
return true;
}
ceres::CostFunction* FieldsOfExperts::NewCostFunction(int alpha_index) const {
return new FieldsOfExpertsCost(filters_[alpha_index]);
}
ceres::LossFunction* FieldsOfExperts::NewLossFunction(int alpha_index) const {
return new FieldsOfExpertsLoss(alpha_[alpha_index]);
}
} // namespace ceres::examples