Code-Bin #1085 - Classification Sampling

Classification Sampling
Posted by: Simon | February 1, 2011 @ 12:06pm

Other Code

public double[][] sampleGibbsClassificationsIG(double[] observations, double[][] parameters) {

Random ran = new Random();
        double[][] observationClass = new double[observations.length][2];
        Double[][] probabilities = new Double[parameters.length][2];
        double[] IGparams = new double[2];
        Double constant = 0.0;
        double random = 0.0;

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

random = ran.nextDouble();
            observationClass[i][0] = observations[i];

for (int k = 0; k < parameters.length; k++) {

IGparams[0] = parameters[k][0];
                IGparams[1] = parameters[k][1];
                probabilities[k][0] = (Double) (Distribution.computeIGDensityGeneral(observations[i], IGparams) * parameters[k][2]);
                constant += probabilities[k][0];
                //System.out.println("Probabilities: " + probabilities[k][0]);
            }
            //Comparator sorts the fields in descending order
            Arrays.sort(probabilities, new DoubleArrayComparator(0));

//Look now if the argumentum minimum over the sequence of the sum of the probabilities ist smaller than the random draw.
            for (int k = 0; k < parameters.length; k++) {

if (k == 0) {
                    probabilities[k][1] = probabilities[k][0] / constant;
                } else {
                    probabilities[k][1] = probabilities[k - 1][1] + probabilities[k][0]/constant;
                }
                //System.out.println("Probability: " + probabilities[k][1] + " random number: " + random);
                if(probabilities[k][1].compareTo((Double) random) >= 0) {
                    observationClass[i][1] = k;
                    break;
                }
                else continue;

}

constant = 0.0;
        }

return observationClass;

}

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