Examples of GaussianFittingFunction

de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction
Gaussian function for parameter fitting Based loosely on fgauss in the book "Numerical Recipies".
We did not bother to implement all optimizations at the benefit of having easier to use parameters. Instead of position, amplitude and width used in the book, we use the traditional Gaussian parameters mean, standard deviation and a linear scaling factor (which is mostly useful when combining multiple distributions) The cost are some additional computations such as a square root and probably a slight loss in precision. This could of course have been handled by an appropriate wrapper instead. Due to their license, we cannot use their code, but we have to implement the mathematics ourselves. We hope the loss in precision isn't big. They are also arranged differently: the book uses
```
 amplitude, position, width 
```
whereas we use
```
 mean, stddev, scaling 
```
But we're obviously using essentially the same mathematics. The function also can use a mixture of gaussians, just use an appropriate number of parameters (which obviously needs to be a multiple of 3) @author Erich Schubert

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction

    for(int i = 0; i < testx.length; i++) {
      s[i] = 1.0;
    }
    double[] params = { mean, stddevq, 1 };
    boolean[] dofit = { true, true, false };
    LevenbergMarquardtMethod fit = new LevenbergMarquardtMethod(new GaussianFittingFunction(), params, dofit, testx, testy, s);
    for(int i = 0; i < 50; i++) {
      fit.iterate();
    }
    double[] ps = fit.getParams();
    // compare results.

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction

    for(int i = 0; i < testx.length; i++) {
      s[i] = 1.0;
    }
    double[] params = { mean, stddevq, 1 };
    boolean[] dofit = { true, true, false };
    LevenbergMarquardtMethod fit = new LevenbergMarquardtMethod(new GaussianFittingFunction(), params, dofit, testx, testy, s);
    for(int i = 0; i < 50; i++) {
      fit.iterate();
    }
    double[] ps = fit.getParams();
    // compare results.

View Full Code Here

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction

    // " Amp: " + params[2]);
    return params;
  }


  private double[] run(double[] data, double[] params) {
    FittingFunction func = new GaussianFittingFunction();
    boolean[] dofit = { true, true, true };
    KernelDensityEstimator de = new KernelDensityEstimator(data, GaussianKernelDensityFunction.KERNEL);
    LevenbergMarquardtMethod fit = new LevenbergMarquardtMethod(func, params, dofit, data, de.getDensity(), de.getVariance());
    // for(int i = 0; i < 100; i++) {
    // fit.iterate();

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction

    for(int i = 0; i < testx.length; i++) {
      s[i] = 1.0;
    }
    double[] params = { mean, stddevq, 1 };
    boolean[] dofit = { true, true, false };
    LevenbergMarquardtMethod fit = new LevenbergMarquardtMethod(new GaussianFittingFunction(), params, dofit, testx, testy, s);
    for(int i = 0; i < 50; i++) {
      fit.iterate();
    }
    double[] ps = fit.getParams();
    // compare results.

View Full Code Here

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction

    for(int i = 0; i < testx.length; i++) {
      s[i] = 1.0;
    }
    double[] params = { mean, stddevq, 1 };
    boolean[] dofit = { true, true, false };
    LevenbergMarquardtMethod fit = new LevenbergMarquardtMethod(new GaussianFittingFunction(), params, dofit, testx, testy, s);
    for(int i = 0; i < 50; i++) {
      fit.iterate();
    }
    double[] ps = fit.getParams();
    // compare results.

View Full Code Here

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.fitting.GaussianFittingFunction

    // " Amp: " + params[2]);
    return params;
  }


  private double[] run(double[] data, double[] params) {
    FittingFunction func = new GaussianFittingFunction();
    boolean[] dofit = { true, true, true };
    KernelDensityEstimator de = new KernelDensityEstimator(data, GaussianKernelDensityFunction.KERNEL);
    LevenbergMarquardtMethod fit = new LevenbergMarquardtMethod(func, params, dofit, data, de.getDensity(), de.getVariance());
    // for(int i = 0; i < 100; i++) {
    // fit.iterate();

View Full Code Here

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