34 void SolveTransformed(
const Eigen::MatrixXd& inputCoordinates, Eigen::MatrixXd& outputCoordinates)
const;
37 Eigen::MatrixXd outputCoordinates);
41 Eigen::VectorXd mPolynomialCoeffs;
42 std::vector<std::pair<double, double>> mBoundaryConditions;
Definition: PolynomialLeastSquaresFitting.h:8
void AddBoundaryCondition(std::pair< double, double > boundaryCondition)
Adds a pair of x and y coordinates that should be matched by the polynomial.
Definition: PolynomialLeastSquaresFitting.cpp:8
Eigen::VectorXd GetPolynomialCoefficients() const
Gets the calculated polynomial coefficients.
Definition: PolynomialLeastSquaresFitting.cpp:70
stores the support points
Definition: SupportPoints.h:10
int dimOutput
Definition: NeuralNetwork.py:15
void BuildDerived()
determine regression parameters
Definition: PolynomialLeastSquaresFitting.cpp:22
void SolveTransformed(const Eigen::MatrixXd &inputCoordinates, Eigen::MatrixXd &outputCoordinates) const
calculate approximation (in transformed space)
Definition: PolynomialLeastSquaresFitting.cpp:82
Definition: Exception.h:6
int dimInput
Definition: NeuralNetwork.py:14
void SetSupportPoints(int dimInput, int dimOutput, Eigen::MatrixXd inputCoordinates, Eigen::MatrixXd outputCoordinates)
Definition: PolynomialLeastSquaresFitting.cpp:108
void SetDegree(int degree)
Sets the degree of the polynomial.
Definition: PolynomialLeastSquaresFitting.cpp:75