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/*
* Copyright (C) 2010 Learning Algorithms and Systems Laboratory, EPFL, Switzerland
* Author: Eric Sauser
* email: eric.sauser@a3.epf.ch
* website: lasa.epfl.ch
*
* Permission is granted to copy, distribute, and/or modify this program
* under the terms of the GNU General Public License, version 2 or any
* later version published by the Free Software Foundation.
*
* This program is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
* Public License for more details
*/
#ifndef REGRESSION_H
#define REGRESSION_H
#include "MathLibCommon.h"
#include "Matrix4.h"
#include "Matrix.h"
#include "Vector.h"
#ifdef USE_MATHLIB_NAMESPACE
namespace MathLib {
#endif
/**
* \class Regression
*
* \ingroup MathLib
*
* \brief A set of regression function
*
*/
class Regression
{
private:
bool Create(int matrixSize, int covarianceMatricesCount);
public:
/**
* \brief Do a Hermitte spline fit
*
* \param inData The input data series
* \param inTimeBase The time base of the input data
* \param outTimeBase The desired time base for the output data
* \param outData The result
* \return outData
*/
static Vector& HermitteSplineFit(const Vector& inData, const Vector& inTimeBase, const Vector& outTimeBase, Vector& outData);
/**
* \brief Do a Hermitte spline fit (multidimensional data at once)
*
* \param inData The input data series
* \param inTimeBase The time base of the input data
* \param outTimeBase The desired time base for the output data
* \param outData The result
* \return outData
*/
static Matrix& HermitteSplineFit(const Matrix& inData, const Vector& inTimeBase, const Vector& outTimeBase, Matrix& outData);
/**
* \brief Do a Hermitte spline fit assuming constant time steps
*
* \param inData The input data series
* \param nbSteps The desired number of output steps
* \param outData The result
* \return outData
*/
static Vector& HermitteSplineFit(const Vector& inData, int nbSteps, Vector& outData);
/**
* \brief Do a Hermitte spline fit assuming constant time steps (multidimensional data at once)
*
* \param inData The input data series
* \param nbSteps The desired number of output steps
* \param outData The result
* \return outData
*/
static Matrix& HermitteSplineFit(const Matrix& inData, int nbSteps, Matrix& outData);
static void SaveData(Matrix &data, const char fileName[]);
static bool LoadData(const char fileName[], Matrix &result);
Matrix *covarianceMatrices;
int covarianceMatricesCount;
Matrix covMatInRow;
Matrix smoothCovMatrix;
Regression() : covarianceMatrices(NULL) {}
~Regression() {Release();}
void Release();
/**
* \brief Do a locally weighted regression (LWR)
*
* Note: the resulting covariance matrices are stored in the class member covarianceMatrices.
*
* \param inData The input data multidimensional data (a data point is set in a row)
* \param outData The output data (the result). The columns indexed by xIndices should be filled with appropriate data.
* \param xIndices An array of indices denoting the columns of the data to be used for the regression
* \param xIndicesCount The size of xIndices
* \param yIndices An array of indices denoting the columns of the data to be regressed (guessed...)
* \param yIndicesCount The size of yIndices
* \param spam An array of sigmas
* \param covSquare Keep if to false...
*/
bool LocallyWeightedRegression(Matrix &inData, Matrix &outData, int xIndices[], int yIndices[], int xIndicesCount, int yIndicesCount, const Matrix &spam, bool covSquare = false);
/**
* \brief Do a gaussian product of two gaussian sets produced by LocallyWeightedRegression()
*
* \param data0 The data of the first gaussian set
* \param data1 The data of the second gaussian set
* \param yIndices An array of indices denoting the columns of the data to be regressed (guessed...)
* \param yIndicesCount The size of yIndices
* \param covarianceMatrices0 A pointer to the covariance matrices of the first gaussian set
* \param covarianceMatricesCount0 The size of the corresponding array
* \param covarianceMatrices1 A pointer to the covariance matrices of the second gaussian set
* \param covarianceMatricesCount1 The size of the corresponding array
* \param resultData The result
* \param resultC Don't know. Just pass something...
*/
bool GaussProduct(
Matrix &data0,
Matrix &data1,
const int yIndices[],
int yIndicesCount,
Matrix *covarianceMatrices0,
int covarianceMatricesCount0,
Matrix *covarianceMatrices1,
int covarianceMatricesCount1,
Matrix &resultData,
Vector &resultC,
bool keepAlignment = false, bool pdiff=false,
Matrix * eigDiff = NULL);
void SaveCovarianceMatrices(const char fileName[]);
void SmoothCovarianceMatrices(int window);
void SetSmoothCovarianceMatrix();
void SmoothCovarianceMatrix(REALTYPE alpha);
/**
* \brief Find the best plane passing through n points
*
* \param inData The input data series
* \param nbSteps The desired number of output steps
* \param outData The result
* \return outData
*/
static Matrix4& GetBestPlaneFromPoints(const Matrix &points, const Vector3& zero, const Vector3& firAxis, const Vector3& secAxis, Matrix4 &out);
};
#ifdef USE_MATHLIB_NAMESPACE
}
#endif
#endif
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