Example usage for org.apache.commons.math3.fitting PolynomialFitter fit

Introduction

In this page you can find the example usage for org.apache.commons.math3.fitting PolynomialFitter fit.

Prototype

public double[] fit(int maxEval, double[] guess) 

Source Link

Document

Get the coefficients of the polynomial fitting the weighted data points.

Usage

From source file:javatranslation.matlab.dfa.java

public static void dfafunction(double[] tau, double MinBox, double MaxBox, int DFAorder) {

    double[] incoef = null;
    double[] l = null;
    l = new double[50];
    incoef = new double[50];
    for (int i = 0; i < 50; i++) {
        l = logspace(MinBox, MaxBox);/*ww  w.  jav a 2s  .c  o m*/
        //System.out.println(l[i]);
        incoef[i] = Math.round(l[i]);
    }
    double xx = mean(tau);
    for (int i = 0; i < tau.length; i++) {
        tau[i] = tau[i] - xx;
    }
    double[] Y = cumsum(tau, dfa.mean(tau));
    double maxnumseg = incoef.length;
    double[] winlen = null;
    winlen = new double[50];
    for (int truta = 0; truta < 50; truta++) {
        winlen[truta] = incoef[truta];
    }
    Arrays.sort(winlen);
    ArrayUtils.reverse(winlen);

    double Ylength = Y.length;
    double F[][] = new double[(int) maxnumseg][1];
    for (int k = 0; k < maxnumseg; k++) {
        F[k][0] = 0;
    }
    double[] timevec = new double[50];
    for (int kk = 0; kk < maxnumseg; kk++) {
        timevec[kk] = winlen[kk];
        double numsegm = Math.floor(Ylength / winlen[kk]);
        double tempY[][] = new double[1][(int) numsegm];
        for (int k = 0; k < numsegm; k++) {
            tempY[0][k] = 0;
        }
        for (int zz = 0; zz < numsegm; zz++) {
            double overflowtest = zz * winlen[kk];
            if (overflowtest <= Ylength) {
                double[] tempvec = null;
                int ko = 0;
                for (double p = ((zz - 1) * winlen[kk] + 1); p <= (zz
                        * winlen[kk]); p = ((zz - 1) * winlen[kk] + 1) + 1) { // there are some errors in this loop
                    tempvec[ko] = Y[(int) p];
                    System.out.println(tempvec[(int) p]);
                    ko++;
                }
                //double temppol = polyfit(timevec,tempvec,DFAorder);
                MultivariateVectorOptimizer optimizer;
                optimizer = null;
                PolynomialFitter x = new PolynomialFitter(optimizer); // error here too
                double[] temppol = x.fit(DFAorder, timevec);

                double[] temppol2 = x.fit(DFAorder, tempvec);
                double[] arrayOfCoefficients = new double[temppol2.length];
                arrayOfCoefficients = temppol2;
                int len = arrayOfCoefficients.length;
                double retAnswer = 0;
                double ret = 0;
                for (int i = 0; i < len; i++) {
                    retAnswer = retAnswer + Math.pow(arrayOfCoefficients[i], i);
                }
                for (int i = 0; i < tempvec.length; i++) {
                    ret = tempvec[i] - (Math.pow(retAnswer, 2));
                }
                tempY[0][zz] = ((ret) / winlen[kk]);
            }
        }
        int k;
        double[] kopp = new double[(int) maxnumseg];
        for (k = 0; k < maxnumseg; k++) {
            kopp[k] = tempY[0][k];
        }
        double nonzerotempY = dfa.sum2(kopp);
        F[kk][0] = Math.sqrt(dfa.sum(kopp) / nonzerotempY);

    }
    double[] LF;
    LF = new double[(int) maxnumseg];
    double[] LN;
    LN = new double[(int) maxnumseg];
    for (int i = 0; i < maxnumseg; i++) {
        LF[i] = Math.log10(F[i][0]);
        LN[i] = Math.log10(winlen[i]);
    }
    double[][] XLN = new double[LN.length][LN.length];
    for (int i = 0; i < LN.length; i++) {
        XLN[i][0] = 1;
        XLN[i][1] = LN[i];
    }
    SimpleRegression x;
    x = new SimpleRegression();
    x.addObservations(XLN, LF); // and probably error in this one too
    RegressionResults b = x.regress();
    // System.out.println(b.getParameterEstimates());

    //double LF=Math.log10(F); % log fluctuations
    //double LN=Math.log10(winlen);
}

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