Class PolynomialsLeastSquaresFitter


  • public class PolynomialsLeastSquaresFitter
    extends Object
    Derive coefficients of n-degree polynomial that minimizes least squares error of fit by using QR decomposition and back substitution.
    • Constructor Detail

      • PolynomialsLeastSquaresFitter

        public PolynomialsLeastSquaresFitter()
    • Method Detail

      • regress

        public LeastSquaresRegressionResult regress​(double[] xData,
                                                    double[] yData,
                                                    int degree)
        Given a set of data (X_i, Y_i) and degrees of a polynomial, determines optimal coefficients of the polynomial.
        Parameters:
        xData - X values of data
        yData - Y values of data
        degree - Degree of polynomial which fits the given data
        Returns:
        LeastSquaresRegressionResult Containing optimal coefficients of the polynomial and difference between yData[i] and f(xData[i]), where f() is the polynomial with the derived coefficients
      • regressVerbose

        public PolynomialsLeastSquaresFitterResult regressVerbose​(double[] xData,
                                                                  double[] yData,
                                                                  int degree,
                                                                  boolean normalize)
        Alternative regression method with different output.
        Parameters:
        xData - X values of data
        yData - Y values of data
        degree - Degree of polynomial which fits the given data
        normalize - Normalize xData by mean and standard deviation if normalize == true
        Returns:
        PolynomialsLeastSquaresRegressionResult containing coefficients, rMatrix, degrees of freedom, norm of residuals, and mean, standard deviation