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(double[] guess) 

Source Link

Document

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

Usage

From source file:edu.uchc.octane.CalibrationDialogAstigmatism.java

double[] parabolicFit(double[] sigma) {

    PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());

    for (int i = 0; i < sigma.length; i++) {

        fitter.addObservedPoint((i - sigma.length / 2) * sliceSpacing_, sigma[i]);

    }//  w  w w.j  a  va 2  s . c o  m

    double[] guess = new double[3];
    guess[0] = sigma[(int) (sigma.length / 2)];
    guess[2] = (sigma[0] - guess[0]) / (sliceSpacing_ * sliceSpacing_ * sigma.length * sigma.length / 4);

    double[] results = fitter.fit(guess);

    return results;
}

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From source file:uk.ac.diamond.scisoft.analysis.optimize.ApachePolynomial.java

/**
 * Fit a polynomial to given x and y data
 * @param x/*www.  j  a  v  a  2  s .  c  om*/
 * @param y
 * @param guess initial values for coefficients (given in increasing degree of the terms)
 * @return coefficients of fitted polynomial
 */
public static double[] polynomialFit(Dataset x, Dataset y, double... guess) {
    PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());

    for (int i = 0; i < y.getSize(); i++) {
        fitter.addObservedPoint(1, x.getDouble(i), y.getDouble(i));
    }

    return fitter.fit(guess);
}

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From source file:uk.ac.diamond.scisoft.analysis.optimize.ApachePolynomial.java

/**
 * Calculate a polynomial filtered dataset using a specified window size and polynomial order.
 * /* w  w  w.j ava 2 s .  co  m*/
 * @param x The abscissa (generally energy/wavelength etc)
 * @param y The ordinate to be smoothed
 * @param windowSize The size of window used in the smoothing
 * @param polyOrder The order of the polynomial fitted to the window
 * @return result The smoothed data set
 * @throws Exception 
 */
public static Dataset getPolynomialSmoothed(final Dataset x, final Dataset y, int windowSize, int polyOrder)
        throws Exception {
    // Could probably do with more sanity check on relative size of
    // window vs polynomial but doesn't seem to trip up
    // So we'll see how it goes...

    // integer divide window size so window is symmetric around point
    int window = windowSize / 2;

    PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
    double dx = x.getDouble(1) - x.getDouble(0); // change in x for edge extrapolation
    int xs = x.getSize();
    Dataset result = DatasetFactory.zeros(y);
    double[] guess = new double[polyOrder + 1];

    for (int idx = 0; idx < xs; idx++) {
        fitter.clearObservations();

        // Deal with edge cases:
        // In both cases extend x edge by dx required for window size
        // Pad y with first or last value
        for (int idw = -window; idw < window + 1; idw++) {
            if (idx + idw < 0) {
                fitter.addObservedPoint(1, x.getDouble(0) + (dx * (idx + idw)), y.getDouble(0));
            } else if ((idx + idw) > (xs - 1)) {
                fitter.addObservedPoint(1, x.getDouble(xs - 1) + dx * (idx + idw - (xs - 1)),
                        y.getDouble(xs - 1));
            } else {
                fitter.addObservedPoint(1, x.getDouble(idx + idw), y.getDouble(idx + idw));
            }
        }

        PolynomialFunction fitted = new PolynomialFunction(fitter.fit(guess));
        result.set(fitted.value(x.getDouble(idx)), idx);
    }

    return result;
}

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