89 lines
4.0 KiB
C
89 lines
4.0 KiB
C
|
//--------------------------------------------------------------------------
|
|||
|
|
// Copyright(c) 2024, Yoshiya Usui
|
|||
|
|
//
|
|||
|
|
// Redistribution and use in source and binary forms, with or without
|
|||
|
|
// modification, are permitted provided that the following conditions are met :
|
|||
|
|
//
|
|||
|
|
// 1. Redistributions of source code must retain the above copyright notice, this
|
|||
|
|
// list of conditions and the following disclaimer.
|
|||
|
|
//
|
|||
|
|
// 2. 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.
|
|||
|
|
//
|
|||
|
|
// 3. Neither the name of the copyright holder nor the names of its
|
|||
|
|
// contributors may be used to endorse or promote products derived from
|
|||
|
|
// this software without specific prior written permission.
|
|||
|
|
//
|
|||
|
|
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|||
|
|
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|||
|
|
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|||
|
|
// DISCLAIMED.IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|||
|
|
// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|||
|
|
// DAMAGES(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|||
|
|
// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|||
|
|
// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|||
|
|
// OR TORT(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|||
|
|
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|||
|
|
//--------------------------------------------------------------------------
|
|||
|
|
#ifndef DBLDEF_ROBUST_PREWHITENING
|
|||
|
|
#define DBLDEF_ROBUST_PREWHITENING
|
|||
|
|
|
|||
|
|
#include "CommonParameters.h"
|
|||
|
|
#include "DoubleDenseSquareSymmetricPositiveDefiniteMatrix.h"
|
|||
|
|
|
|||
|
|
#include <vector>
|
|||
|
|
|
|||
|
|
// Class of robust prewhitening
|
|||
|
|
class RobustPrewhitening{
|
|||
|
|
|
|||
|
|
public:
|
|||
|
|
|
|||
|
|
// Default constructer
|
|||
|
|
RobustPrewhitening();
|
|||
|
|
|
|||
|
|
// Destructer
|
|||
|
|
~RobustPrewhitening();
|
|||
|
|
|
|||
|
|
// Return the the instance of the class
|
|||
|
|
static RobustPrewhitening* getInstance();
|
|||
|
|
|
|||
|
|
// Perform robust prewhitening
|
|||
|
|
void robustPrewhitening( std::vector<CommonParameters::DataFileSet>& dataFileSets, std::vector<double>* coeffsAROutput ) const;
|
|||
|
|
|
|||
|
|
// Perform prewhitening using user-defined AR coefficients
|
|||
|
|
void prewhiteningUsingUserDefinedARCoeffs( std::vector<CommonParameters::DataFileSet>& dataFileSets, std::vector<double>* coeffsAROutput ) const;
|
|||
|
|
|
|||
|
|
private:
|
|||
|
|
|
|||
|
|
// Copy constructer
|
|||
|
|
RobustPrewhitening(const RobustPrewhitening& rhs);
|
|||
|
|
|
|||
|
|
// Assignment operator
|
|||
|
|
RobustPrewhitening& operator=(const RobustPrewhitening& rhs);
|
|||
|
|
|
|||
|
|
// Calculate robust-filtered value
|
|||
|
|
void calculateRobustFilteredValue( const int iChan, const int numOfData, const int degreesOfAR, const double* const coeffsOfAR,
|
|||
|
|
const double sigma, const double* const yOrg, const double* const autoCovariance, double* yMod ) const;
|
|||
|
|
|
|||
|
|
// Calculate robust auto-covariance matrix
|
|||
|
|
void calculateRobustAutoCovarianceMatrix( const int degreesOfAR, const int numOfDataSets, const int* const numOfData,
|
|||
|
|
double** data, DoubleDenseSquareSymmetricMatrix& covarianceMatrix ) const;
|
|||
|
|
|
|||
|
|
// Calculate Mahalanobis distances
|
|||
|
|
// @note covariance matrix is factorized in this function
|
|||
|
|
//void calculateMD( const int degreesOfAR, const int numOfDataAll, const double* const dataAll,
|
|||
|
|
// DoubleDenseSquareSymmetricMatrix& covarianceMatrix, double* residualVector, double* MD ) const;
|
|||
|
|
void calculateMD( const int degreesOfAR, const int numOfDataSets, const int* const numOfData, double** data,
|
|||
|
|
DoubleDenseSquareSymmetricMatrix& covarianceMatrix, double* MD ) const;
|
|||
|
|
|
|||
|
|
// Calculate weight for state vector in robust filter
|
|||
|
|
double calculateWeightForStateVectorOfRobustFilter( const double val, const double paramA, const double paramB, const bool residualAssumedToBeZero ) const;
|
|||
|
|
|
|||
|
|
// Calculate weight for covariance matrix in robust filter
|
|||
|
|
double calculateWeightForCovarianceMatrixOfRobustFilter( const double val, const double paramA, const double paramB, const bool residualAssumedToBeZero ) const;
|
|||
|
|
};
|
|||
|
|
|
|||
|
|
|
|||
|
|
#endif
|