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/*
* Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
* Copyright (C) 2007-2012 Institut Mines Telecom / Telecom Bretagne
*
* 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 otbGaussianModelComponent_hxx
#define otbGaussianModelComponent_hxx
#include <iostream>
#include "itkNumericTraits.h"
#include "otbMacro.h"
#include "otbGaussianModelComponent.h"
namespace otb
{
namespace Statistics
{
template <class TSample>
GaussianModelComponent<TSample>::GaussianModelComponent()
{
m_CovarianceEstimator = nullptr;
m_GaussianMembershipFunction = nullptr;
}
template <class TSample>
void GaussianModelComponent<TSample>::PrintSelf(std::ostream& os, itk::Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Mean Estimator: " << m_CovarianceEstimator << std::endl;
os << indent << "Covariance Estimator: " << m_CovarianceEstimator << std::endl;
os << indent << "GaussianMembershipFunction: " << m_GaussianMembershipFunction << std::endl;
}
template <class TSample>
void GaussianModelComponent<TSample>::ShowParameters(std::ostream& os, itk::Indent indent) const
{
unsigned int i, j;
os << indent << "Gaussian model component : \n";
os << indent << "Mean : ";
for (i = 0; i < m_Mean.Size(); ++i)
os << m_Mean[i] << "\t";
os << "\n" << indent << "Covariance : ";
for (i = 0; i < m_Mean.Size(); ++i)
{
for (j = 0; j < m_Mean.Size(); ++j)
os << m_Covariance(i, j) << "\t";
os << "\n" << indent << " ";
}
os << "\n";
}
template <class TSample>
void GaussianModelComponent<TSample>::SetSample(const TSample* sample)
{
Superclass::SetSample(sample);
const MeasurementVectorSizeType measurementVectorLength = sample->GetMeasurementVectorSize();
this->m_Parameters.SetSize(measurementVectorLength * (1 + measurementVectorLength));
// Set the size of the mean vector
m_Mean.SetSize(measurementVectorLength);
// Set the parameters of the mean (internally) and the covariance estimator
m_Covariance.SetSize(measurementVectorLength, measurementVectorLength);
m_CovarianceEstimator = CovarianceEstimatorType::New();
m_CovarianceEstimator->SetInput(sample);
m_CovarianceEstimator->Update();
m_GaussianMembershipFunction = NativeMembershipFunctionType::New();
this->m_PdfFunction = (MembershipFunctionType*)m_GaussianMembershipFunction;
m_GaussianMembershipFunction->SetMeasurementVectorSize(measurementVectorLength);
this->SetPdfMembershipFunction((MembershipFunctionType*)m_GaussianMembershipFunction.GetPointer());
}
template <class TSample>
void GaussianModelComponent<TSample>::SetParameters(const ParametersType& parameters)
{
Superclass::SetParameters(parameters);
unsigned int paramIndex = 0;
unsigned int i, j;
MeasurementVectorSizeType measurementVectorSize = this->GetSample()->GetMeasurementVectorSize();
m_Mean.SetSize(measurementVectorSize);
for (i = 0; i < measurementVectorSize; i++)
{
m_Mean[i] = parameters[paramIndex];
paramIndex++;
}
m_Covariance.SetSize(measurementVectorSize, measurementVectorSize);
for (i = 0; i < measurementVectorSize; i++)
for (j = 0; j < measurementVectorSize; j++)
{
m_Covariance(i, j) = parameters[paramIndex];
paramIndex++;
}
this->m_GaussianMembershipFunction->SetMean(m_Mean);
this->m_GaussianMembershipFunction->SetCovariance(&m_Covariance);
}
template <class TSample>
void GaussianModelComponent<TSample>::GenerateData()
{
if (this->IsSampleModified() == 0)
return;
MeasurementVectorSizeType measurementVectorSize = this->GetSample()->GetMeasurementVectorSize();
unsigned int i, j;
int paramIndex = 0;
// Get the mean using the convariance estimator (computed internally)
typename CovarianceEstimatorType::MeasurementVectorType meanOutput = m_CovarianceEstimator->GetMean();
for (i = 0; i < measurementVectorSize; i++)
{
m_Mean.SetElement(i, meanOutput.GetElement(i));
this->m_Parameters[paramIndex] = meanOutput.GetElement(i);
++paramIndex;
}
// Get the covariance matrix and fill the parameters vector
const typename CovarianceEstimatorType::MatrixType covariance = m_CovarianceEstimator->GetCovarianceMatrix();
for (i = 0; i < measurementVectorSize; i++)
for (j = 0; j < measurementVectorSize; j++)
{
this->m_Parameters[paramIndex] = covariance.GetVnlMatrix().get(i, j);
m_Covariance(i, j) = covariance.GetVnlMatrix().get(i, j);
paramIndex++;
}
this->m_GaussianMembershipFunction->SetMean(meanOutput);
this->m_GaussianMembershipFunction->SetCovariance(m_Covariance);
Superclass::GenerateData();
}
} // end of namespace Statistics
} // end of namesapce otb
#endif
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