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/*
* Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef otbPCAModel_hxx
#define otbPCAModel_hxx
#include "otbPCAModel.h"
#include <fstream>
#include "itkMacro.h"
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
#if (defined (__GNUC__) && (__GNUC__ >= 9)) || (defined (__clang__) && (__clang_major__ >= 10))
#pragma GCC diagnostic ignored "-Wdeprecated-copy"
#endif
#pragma GCC diagnostic ignored "-Wshadow"
#pragma GCC diagnostic ignored "-Wunused-parameter"
#pragma GCC diagnostic ignored "-Woverloaded-virtual"
#endif
#include "otbSharkUtils.h"
// include train function
#include <shark/ObjectiveFunctions/ErrorFunction.h>
#include <shark/Algorithms/GradientDescent/Rprop.h> // the RProp optimization algorithm
#include <shark/ObjectiveFunctions/Loss/SquaredLoss.h> // squared loss used for regression
#include <shark/ObjectiveFunctions/Regularizer.h> //L2 regulariziation
#include <shark/ObjectiveFunctions/ErrorFunction.h>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
namespace otb
{
template <class TInputValue>
PCAModel<TInputValue>::PCAModel()
{
this->m_IsDoPredictBatchMultiThreaded = true;
this->m_Dimension = 0;
}
template <class TInputValue>
PCAModel<TInputValue>::~PCAModel()
{
}
template <class TInputValue>
void PCAModel<TInputValue>::Train()
{
std::vector<shark::RealVector> features;
Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange(features);
m_PCA.setData(inputSamples);
m_PCA.encoder(m_Encoder, this->m_Dimension);
m_PCA.decoder(m_Decoder, this->m_Dimension);
}
template <class TInputValue>
bool PCAModel<TInputValue>::CanReadFile(const std::string& filename)
{
try
{
this->Load(filename);
m_Encoder.name();
}
catch (...)
{
return false;
}
return true;
}
template <class TInputValue>
bool PCAModel<TInputValue>::CanWriteFile(const std::string& /*filename*/)
{
return true;
}
template <class TInputValue>
void PCAModel<TInputValue>::Save(const std::string& filename, const std::string& /*name*/)
{
std::ofstream ofs(filename);
ofs << "pca" << std::endl; // first line
shark::TextOutArchive oa(ofs);
m_Encoder.write(oa);
ofs.close();
if (this->m_WriteEigenvectors == true) // output the map vectors in a txt file
{
std::ofstream otxt(filename + ".txt");
otxt << "Eigenvectors : " << m_PCA.eigenvectors() << std::endl;
otxt << "Eigenvalues : " << m_PCA.eigenvalues() << std::endl;
std::vector<shark::RealVector> features;
shark::SquaredLoss<shark::RealVector> loss;
Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange(features);
otxt << "Reconstruction error : " << loss.eval(inputSamples, m_Decoder(m_Encoder(inputSamples))) << std::endl;
otxt.close();
}
}
template <class TInputValue>
void PCAModel<TInputValue>::Load(const std::string& filename, const std::string& /*name*/)
{
std::ifstream ifs(filename);
char encoder[256];
ifs.getline(encoder, 256);
std::string encoderstr(encoder);
if (encoderstr != "pca")
{
itkExceptionMacro(<< "Error opening " << filename.c_str());
}
shark::TextInArchive ia(ifs);
m_Encoder.read(ia);
ifs.close();
if (this->m_Dimension == 0)
{
this->m_Dimension = m_Encoder.outputShape()[0];
}
auto eigenvectors = m_Encoder.matrix();
eigenvectors.resize(this->m_Dimension, m_Encoder.inputShape()[0]);
m_Encoder.setStructure(eigenvectors, m_Encoder.offset());
}
template <class TInputValue>
typename PCAModel<TInputValue>::TargetSampleType PCAModel<TInputValue>::DoPredict(const InputSampleType& value, ConfidenceValueType* /*quality*/,
ProbaSampleType* /*proba*/) const
{
shark::RealVector samples(value.Size());
for (size_t i = 0; i < value.Size(); i++)
{
samples[i] = value[i];
}
std::vector<shark::RealVector> features;
features.push_back(samples);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
data = m_Encoder(data);
TargetSampleType target;
target.SetSize(this->m_Dimension);
for (unsigned int a = 0; a < this->m_Dimension; ++a)
{
target[a] = data.element(0)[a];
}
return target;
}
template <class TInputValue>
void PCAModel<TInputValue>::DoPredictBatch(const InputListSampleType* input, const unsigned int& startIndex, const unsigned int& size,
TargetListSampleType* targets, ConfidenceListSampleType* /*quality*/, ProbaListSampleType* /*proba*/) const
{
std::vector<shark::RealVector> features;
Shark::ListSampleRangeToSharkVector(input, features, startIndex, size);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
TargetSampleType target;
data = m_Encoder(data);
unsigned int id = startIndex;
target.SetSize(this->m_Dimension);
for (const auto& p : data.elements())
{
for (unsigned int a = 0; a < this->m_Dimension; ++a)
{
target[a] = p[a];
}
targets->SetMeasurementVector(id, target);
++id;
}
}
} // namespace otb
#endif
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